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A Global Overview of Precision Medicine in Type 2 Diabetes · standing of diabetes and ultimately...
12
A Global Overview of Precision Medicine in Type 2 Diabetes Hugo Fitipaldi, 1 Mark I. McCarthy, 2,3 Jose C. Florez, 4,5,6 and Paul W. Franks 1,2,7,8 Diabetes 2018;67:1911–1922 | https://doi.org/10.2337/dbi17-0045 The detailed characterization of human biology and behaviors is now possible at scale owing to innovations in biomarkers, bioimaging, and wearable technologies; “big data” from electronic medical records, health insur- ance databases, and other platforms becoming increas- ingly accessible; and rapidly evolving computational power and bioinformatics methods. Collectively, these advances are creating unprecedented opportunities to better un- derstand diabetes and many other complex traits. Identi- fying hidden structures within these complex data sets and linking these structures to outcome data may yield unique insights into the risk factors and natural history of diabetes, which in turn may help optimize the prevention and man- agement of the disease. This emerging area is broadly termed “precision medicine.” In this Perspective, we give an overview of the evidence and barriers to the develop- ment and implementation of precision medicine in type 2 diabetes. We also discuss recently presented paradigms through which complex data might enhance our under- standing of diabetes and ultimately our ability to tackle the disease more effectively than ever before. The etiology, clinical presentation, and consequences of type 2 diabetes can vary greatly from one patient to the next, complicating the prevention and management of the disease. The cardinal feature of type 2 diabetes is chron- ically elevated blood glucose concentrations; however, to categorize it as “type 2 diabetes,” the clinician must exclude autoimmunity, pregnancy, pancreatic disease or injury, and rare syndromic or genetic forms of diabetes. In addition to assessing the signs and symptoms of diabetes, the clinician uses high-level data about the patient, such as their age, family history, ethnicity, mental health, medi- cations, biochemical profile, lifestyle, and body weight, to understand the nature of the disease and in turn to help optimize treatment. The fact that type 2 diabetes is di- agnosed on the basis of exclusion speaks to the lack of mechanistic understanding we have of the disease and the absence of assays that detect the underlying defect(s) rather than its biochemical consequence (elevated glucose). In Western health care systems, diabetes treatment follows an algorithmic sequence that starts with lifestyle modification + metformin (absent contraindications) and later may progress to other drugs and/or insulin, while monitoring tolerability and glycemic goals to determine if the next step in the sequence should be taken. Lifestyle modification alone is usually unsuccessful and even with combined drug therapy about one-third of patients with diabetes in the U.S. eventually require exogenous insulin (1). The emergence of type 2 diabetes as one of the major causes of morbidity and premature mortality is closely linked to widespread adoption of obesogenic (Western- ized) lifestyles. This relationship is likely to be causal, as aggressively intervening on lifestyle factors that promote and/or help maintain weight loss delays the onset of diabetes in high-risk adults (2–5) and about half of patients undergoing medium-term hypocaloric diet inter- ventions leading to major weight loss ($15 kg) achieve medication-free diabetes remission within a year (6). Diabetes remission is also common in patients who 1 Genetic and Molecular Epidemiology Unit, Department of Clinical Sciences Malmö, Lund University Diabetes Centre, Skåne University Hospital, Malmö, Sweden 2 Oxford Centre for Diabetes, Endocrinology and Metabolism, University of Oxford, Oxford, U.K. 3 Wellcome Trust Centre for Human Genetics, University of Oxford, Oxford, U.K. 4 Diabetes Unit and Center for Genomic Medicine, Massachusetts General Hospital, Boston, MA 5 Programs in Metabolism and Medical and Population Genetics, Broad Institute, Cambridge, MA 6 Department of Medicine, Harvard Medical School, Boston, MA 7 Department of Nutrition, Harvard T.H. Chan School of Public Health, Boston, MA 8 Department of Public Health and Clinical Medicine, Umeå University, Umeå, Sweden Corresponding author: Paul W. Franks, [email protected]. Received 30 April 2018 and accepted 7 July 2018. © 2018 by the American Diabetes Association. Readers may use this article as long as the work is properly cited, the use is educational and not for profit, and the work is not altered. More information is available at http://www.diabetesjournals .org/content/license. Diabetes Volume 67, October 2018 1911 PERSPECTIVES IN DIABETES
Transcript of A Global Overview of Precision Medicine in Type 2 Diabetes · standing of diabetes and ultimately...
A Global Overview of Precision Medicinein Type 2 DiabetesHugo Fitipaldi1 Mark I McCarthy23 Jose C Florez456 and Paul W Franks1278
Diabetes 2018671911ndash1922 | httpsdoiorg102337dbi17-0045
The detailed characterization of human biology andbehaviors is now possible at scale owing to innovationsin biomarkers bioimaging and wearable technologiesldquobig datardquo from electronic medical records health insur-ance databases and other platforms becoming increas-ingly accessible and rapidly evolving computational powerand bioinformatics methods Collectively these advancesare creating unprecedented opportunities to better un-derstand diabetes and many other complex traits Identi-fying hidden structureswithin these complex data sets andlinking these structures to outcome data may yield uniqueinsights into the risk factors andnatural history of diabeteswhich in turn may help optimize the prevention and man-agement of the disease This emerging area is broadlytermed ldquoprecision medicinerdquo In this Perspective we givean overview of the evidence and barriers to the develop-ment and implementation of precision medicine in type 2diabetes We also discuss recently presented paradigmsthrough which complex data might enhance our under-standing of diabetes and ultimately our ability to tackle thedisease more effectively than ever before
The etiology clinical presentation and consequences oftype 2 diabetes can vary greatly from one patient to thenext complicating the prevention and management of thedisease The cardinal feature of type 2 diabetes is chron-ically elevated blood glucose concentrations however tocategorize it as ldquotype 2 diabetesrdquo the clinician must excludeautoimmunity pregnancy pancreatic disease or injuryand rare syndromic or genetic forms of diabetes In
addition to assessing the signs and symptoms of diabetesthe clinician uses high-level data about the patient such astheir age family history ethnicity mental health medi-cations biochemical profile lifestyle and body weight tounderstand the nature of the disease and in turn to helpoptimize treatment The fact that type 2 diabetes is di-agnosed on the basis of exclusion speaks to the lack ofmechanistic understanding we have of the disease andthe absence of assays that detect the underlying defect(s)rather than its biochemical consequence (elevated glucose)
In Western health care systems diabetes treatmentfollows an algorithmic sequence that starts with lifestylemodification + metformin (absent contraindications) andlater may progress to other drugs andor insulin whilemonitoring tolerability and glycemic goals to determine ifthe next step in the sequence should be taken Lifestylemodification alone is usually unsuccessful and even withcombined drug therapy about one-third of patients withdiabetes in the US eventually require exogenous insulin (1)
The emergence of type 2 diabetes as one of the majorcauses of morbidity and premature mortality is closelylinked to widespread adoption of obesogenic (Western-ized) lifestyles This relationship is likely to be causal asaggressively intervening on lifestyle factors that promoteandor help maintain weight loss delays the onset ofdiabetes in high-risk adults (2ndash5) and about half ofpatients undergoing medium-term hypocaloric diet inter-ventions leading to major weight loss ($15 kg) achievemedication-free diabetes remission within a year (6)Diabetes remission is also common in patients who
1Genetic and Molecular Epidemiology Unit Department of Clinical SciencesMalmouml Lund University Diabetes Centre Skaringne University Hospital MalmoumlSweden2Oxford Centre for Diabetes Endocrinology and Metabolism University of OxfordOxford UK3Wellcome Trust Centre for Human Genetics University of Oxford Oxford UK4Diabetes Unit and Center for Genomic Medicine Massachusetts General HospitalBoston MA5Programs in Metabolism and Medical and Population Genetics Broad InstituteCambridge MA6Department of Medicine Harvard Medical School Boston MA
7Department of Nutrition Harvard TH Chan School of Public Health Boston MA8Department of Public Health and Clinical Medicine Umearing University UmearingSweden
Corresponding author Paul W Franks paulfranksmedluse
Received 30 April 2018 and accepted 7 July 2018
copy 2018 by the American Diabetes Association Readers may use this article aslong as the work is properly cited the use is educational and not for profit and thework is not altered More information is available at httpwwwdiabetesjournalsorgcontentlicense
Diabetes Volume 67 October 2018 1911
PERSPECTIVES
INDIA
BETES
experience major weight loss with bariatric surgery (7)The population-scale decline in rates of obesity diabetesand cardiovascular disease during economic crises thathave forced entire populations to increase physical activitylevels and reduce caloric intake reinforces the role ofnegative energy-balance in diabetes prevention (8)
The proven benefits of lifestyle modification in diabetesprevention and the effectiveness of several diabetes drugsare counterbalanced by the continuing emergence of type 2diabetes as one of the most prevalent and burdensomediseases globally (9) In the US for example diabetes wasthe seventh leading cause of death in 2010 accounting forat least 10ndash15 of deaths in people aged $20 years withadult death rates 50 higher in people with diabetesthan in those without (1) The economic burden of di-abetes is also formidable with diabetes costing the USeconomy about US$245 billion in 2012 with the averageannual medical costs per patient with diabetes more thandouble those for people without the disease (1)
The rising prevalence of diabetes on a population scaleand the insufficiency of established therapeutic optionsjustify the search for orthogonal diabetes prevention andtreatment strategies to complement existing approachesIdeally these new approaches would be better tailored tothe individual for enhanced tolerability and effectiveness
This drive to develop precision medicine for diabetesis predicated on the major technological advances seenin recent years that include high-resolution lsquoomic assayswearable devices that monitor behaviors and exposuresand digital imaging technologies The intelligent inte-gration of the data these technologies yield can providedetailed digital impressions of a personrsquos physical condi-tion their past exposure and ongoing susceptibility tocertain risk factors and how they might respond to specificantidiabetes therapies as well as help track disease pro-gression All of this has the potential to substantially im-prove the prediction prevention and treatment of type 2diabetes and there are as discussed later many majorinitiatives under way with this objective However as alsodiscussed there is much to resolve before precision di-abetes medicine becomes common practice
HOW PRECISION DIABETES MEDICINE MIGHTWORK
The foundation of precision medicine in type 2 diabeteslike all other complex diseases is population geneticsthe catalyst for which was the sequencing of the humangenome a US$3 billion initiative that completed its workin 2003 (10) The availability of human genome sequencesprovided a framework that facilitated the design of mas-sively parallel chip-based genotyping arrays These tech-nologies have since been used to characterize commongenomic variation in millions of people worldwide whichin turn has helped identify hundreds of variants associ-ated with type 2 diabetes and related traits throughgenome-wide association studies (GWAS) (11) The devel-opment of biotechnologies designed for high-resolution
characterization of other types of biological variants (tran-scripts proteins epigenetic marks metabolites micro-biota etc) has followed further expanding our abilityto map the paths that link a personrsquos biological idiosyn-crasies to disease susceptibility Although this work hasprimarily involved hypothesis-free association studies inlarge cohort collections and thus the results are highlydescriptive and of minimal relevance to the individualpatient it has provided substrate for functional studiesrevealing novel aspects of disease biology and therapeutictargets that may seed individualized therapies
It is generally accepted that precision medicine in-formed by diverse sources of big data will improve pre-vention and treatment of common multifactorial diseasessuch as type 2 diabetes but there are few examples to dateThe common variants shown by GWAS to be associatedwith type 2 diabetes have at best modest effect sizes andthe consensus view has historically been that even incombination their predictive value is limited particularlywhen compared with the performance of classic risk fac-tors such as age BMI and blood glucose (12) In the pastyear however there has been a reinvigoration of interestin the translational potential offered by genetic risk scores(13) Recent expanded GWAS data sets have yielded locithat explain a sizable proportion (50) of diabetes heri-tability and highlight that there are millions of people(in the UK or US) who on the basis of genetic evidencealone have very high (50) lifetime risks of type 2 di-abetes (14) This raises the prospect that the rollout ofmedical genotyping and sequencing will provide clinicallyactionable information on diabetes risk particularly ifgenetic information is combined with other relevant clin-ical or exposure data
So far however the clinical application of genetics indiabetes remains limited to rare monogenic subtypesModels for the personalization of type 2 diabetes care(especially in relation to exploiting the marked clinicalheterogeneity within type 2 diabetes) have often extrap-olated from this experience to invoke quasi-Mendelianscenarios characterized by distinctive subtypes of type 2diabetes each of which has the potential to be mappedto a specific remedial therapy or intervention In realityeffective strategies in this area must address the multi-factorial etiology of type 2 diabetes and a continuousspectrum of predisposition mediated in most individualsthrough joint effects across multiple pathways
One recently described conceptualization of the path-ophysiological architecture of type 2 diabetes predisposi-tion (termed the ldquopalette modelrdquo) focuses attention insteadon the intermediary processes contributing to type 2 di-abetes risk (15) The most obvious of these include obesityfat distribution islet development and function and in-sulin sensitivity but other as-yet poorly characterizedcontributors are likely Each of these processes is itselfunder multifactorial (genetic and nongenetic) control andindividual ldquoloadingsrdquo across them combinatorially influ-ence both diabetes risk and the phenotype of any diabetes
1912 Precision Medicine in Type 2 Diabetes Diabetes Volume 67 October 2018
that results Several recent studies (16ndash19) have addedempirical support for this approach demonstrating forexample differential relationships between process-specific risk scores and the risk of complications such asdiabetic kidney disease and coronary artery disease
The palette model captures the range of diabetes sub-types (from monogenic via hybrid forms [eg latent au-toimmune diabetes in adults] through to the clinicalheterogeneity evident within type 2 diabetes) as a contin-uum consistent with the genetic architecture of diabetesand real-world clinical observation (2021) The modelsuggests that for many people with type 2 diabetes theircondition is the consequence not of a major defect ina single process but in the confluence of suboptimalperformance across several processes contributing in par-allel This is what one would predict given the evidencethat type 2 diabetes risk can be subtly modulated bycommon variants acting through any one of several dis-tinct mechanisms (energy balance adipocyte differentia-tion incretin signaling insulin secretion etc) and by thewide range of therapeutic interventions that have theproven capacity to ameliorate to some degree at leastthe diabetic state (eg insulin sensitizers b-cell secre-tagogues calorie restriction exercise and metabolicsurgery)
This model provides a framework for understanding themechanistic basis of heterogeneity and its clinical conse-quences It also allows for the existence of cases at theextremes of the distribution for which a targeted inter-vention might be particularly effective Genetic risk scoresthat capture each of these processes may help to teaseapart heterogeneity in phenotype progression and ther-apeutic response They may allow identification within theoverall population of subsets of selected individuals inwhom the profile of genetic predisposition is dominatedby defects in a single pathway facilitating personalizedmechanism-specific interventions
Nevertheless for multifactorial diseases like type 2 di-abetes where individual predisposition is influenced asmuch by nongenetic as genetic factors there is an absolutelimit to the clinical precision that genetics alone can pro-vide For precision medicine to flourish this geneticinformation will need to be integrated with relevantmeasures of those aspects of the external and internalenvironment (the latter term for example encompassingthe gut microbiome) that also influence type 2 diabetespredisposition phenotype and progression This informa-tion would ideally be supported by pertinent biomarkerand clinical readouts generating integrated profiles thatmay provide the predictive accuracy needed for clinicalutility
Along these lines Ahlqvist et al (20) used data from8980 Swedish patients with newly diagnosed diabetes (ofany type) and applied machine learning algorithms toderive a data-driven approach to classifying diabetes sub-types The authors proposed five subtypes determined byvariations across six variables (glutamate decarboxylase
antibodies age at diagnosis BMI HbA1c and HOMA2estimates of b-cell function and insulin resistance) Therelationship of subtype and incident events (use of anti-diabetes medication achievement of treatment goals anddiabetes complications) were assessed and replicated inindependent cohorts Each cluster was characterized bycertain phenotypic features assessed soon after diagnosisThese include early-onset severe autoimmune diabetes(67 of patients) severe insulin-deficient diabetes (175of patients) severe insulin-resistant diabetes (153 ofpatients) mild obesity-related diabetes (216 of patients)and mild age-related diabetes (391 of patients) Rates ofdiabetic renal disease and coronary disease were highest insevere insulin-resistant diabetes although rates of retinop-athy were lowest for this group (retinopathies were morefrequent in severe insulin-deficient diabetes) Severe insulin-resistant diabetes also progressedmost rapidly to oral diabetesmedications (other than metformin) and was the slowestto reach the HbA1c treatment goal69 (52 mmolmol)Both severe autoimmune diabetes and severe insulin-deficient diabetes progressed to sustained insulin use con-siderably more quickly than the remaining clusters Bycontrast mild age-related diabetes and mild obesity-relateddiabetes were characterized by reasonably favorable profiles
This initial step demonstrated that one can deriveclusters from existing big data that make eminent phys-iological sense One limitation of this approach though incontrast with a genetically driven strategy resides in theuse of phenotypic criteria for clustering that are ascer-tained at disease onset and are not necessarily generaliz-able to the many people one would want to stratify (withnormoglycemia or with advanced diabetes) One shouldalso remain cognizant that a clinician may not be able tounequivocally place an individual patient into a givencluster thus the implementation and clinical utility ofthis approach remain to be demonstrated
It will be essential to understand how the relationshipsbetween these genetic and physiological approaches tostratification might be combined and in doing so howmuch value is added from a clinical perspective A crucialmissing piece is evidence that by clustering patients in thisway disease course diabetes complications or treatmentresponse can be predicted with sufficient accuracy to be ofclinical value This determination requires the explicitassessment of predictive accuracy and reclassificationSimilarly it is not known whether treatment that is guidedby cluster identity will be more or less effective thancurrent approaches for which intervention studies willbe needed Moreover because clustering techniques typ-ically require the dichotomization of continuous exposurevariables which usually results in loss of power the pre-dictive accuracy of algorithms that do not require this typeof data transformation is likely to be superior
Family history of diabetes is a strong predictor ofdisease and is frequently included in diabetes predictionalgorithms For information on family history to enhancethe predictive ability of the phenotypic clusters proposed
diabetesdiabetesjournalsorg Fitipaldi and Associates 1913
by Ahlqvist et al (20) it would require that some clustersbe more driven by familial factors than others This maywell be true and future studies exploring the genetic basisof these clusters may help test this hypothesis Howeverfamily history is the consequence of both genetic andenvironmental influences (including developmental expo-sures) and understanding why family history affects dis-ease progression differentially across clusters if indeed itdoes would likely be intensely scrutinized owing to theetiological insights this might reveal
OVERVIEW OF MAJOR PRECISION MEDICINEINITIATIVES
After the first draft sequence of the human genome waspublished in 2001 (10) methods and technologies wererapidly developed that enabled large-scale quantification ofhuman genetic variation and subsequently variation acrossother biological strata As investigators began using thesetechnologies global research consortia grew through whichnovel biomarkers for type 2 diabetes were discovered Therapid growth of large data sets and ad hoc collaborativenetworks as well as clear evidence that this new approachto biomarker discovery worked laid the foundations forlarge precision medicine initiatives Although some initia-tives have built new cohorts from scratch most haveleveraged existing data and biomaterials already stored inbiobanks (Table 1 and Fig 1)
In 2008 a joint undertaking between the EuropeanCommission European academic institutions and theEuropean Federation of Pharmaceutical Industries andAssociations was launched This euro56 billion programcalled the Innovative Medicines Initiative (IMI) comprises100 projects focused on the major diseases affectingEuropean citizens Of these several focus on precisionmedicine in type 2 diabetes SUrrogate markers for Micro-and Macro-vascular hard endpoints for Innovative dia-betes Tools (SUMMIT) (diabetes complications) DiabetesResearch on Patient Stratification (DIRECT) (drug responseand glycemic deterioration before and after the onset oftype 2 diabetes) (22) Risk Assessment and ProgreSsiOn ofDIabetes (RHAPSODY) (glycemic deterioration before andafter the onset of type 2 diabetes) and Biomarker En-terprise to Attack Diabetic Kidney Disease (BEAt-DKD)(diabetic kidney disease) A common objective is the discov-ery and validation of biomarkers that facilitate the strati-fication of patient populations into subgroups that mightbe treated more effectively than without biomarker strat-ification In some instances the focus is also on diagnos-tic reclassification Unlike most other precision medicineinitiatives those within the IMI focus on the integration ofmultiple biomarkers such as genotypes transcripts pro-teins metabolites and metagenomics sequences
The UK Biobank cohort (established 2005) includesaround 500000 adults the vast majority of whom haveprovided nonfasting blood samples and self-reported dataon lifestyle health and well-being and have been geno-typed using genome-wide arrays and targeted metabolomic
analyses are also under way In subcohorts more detailedphenotyping has been undertaken such as MRI scans andphysical activity monitoring Research conducted using UKBiobank data for example has shown that genetic variantsassociated with obesity are likely to influence susceptibilityto a range of modifiable lifestyle exposures (23ndash26) whichmay have relevance to diabetes Nevertheless the very smallmagnitude of these effects and susceptibility to confounding(25) precludes the immediate translation of these findingsinto clinical practice Alternatively as UK Biobank accruesincident events of diabetes complications the database islikely to become a powerful resource through which theprognostic value of stratifying populations into subgroupsor determining how environmental risk factors differentiallyaffect disease susceptibility by subgroup can be assessed
Plans to implement precision medicine into US med-ical practice accelerated in 2015 with the announcement ofthe Precision Medicine Initiative (PMI) for which US$215million was initially budgeted by the federal government(2728) In the short term the PMI is focused on cancerwhereas in the longer term all areas of health and healthcare will be studied with specific emphasis placed on thediscovery of predictive biomarkers for type 2 diabetes (28)A major feature of the PMI is a cohort of 1 million peopleand a research program called the All of Us Research Pro-gram The cohort supported by a US$55 million budget isbuilt around nationwide recruitment sites under the bannerof the PMI Cohort Program Managed access for accreditedresearchers to biobanked data and samples is intendedto facilitate several major goals including those related togenendashenvironment interactions pharmacogenomics riskstratification mobile health technologies health empower-ment and innovative clinical trials (httpsallofusnihgov)
Several other major US initiatives predate the PMI andhave diabetes and its risk factors at their core Theseinclude the Million Veteran Program (MVP) and theAccelerating Medicines Partnership in Type 2 Diabetes(AMP T2D) The MVP uses genomics and other healthdata obtained through electronic medical records andfollow-up surveys in about 600000 military veteransaged 50ndash69 years (29) The AMP T2D seeks to gathergenomic and metagenomic data and to create an analyticalengine that can be used to mine the genetic basis todiabetes and related traits while safeguarding the confi-dentiality of the results Data access is a central pillar ofAMP T2D and through the T2D Knowledge Portal (wwwtype2diabetesgeneticsorg) the consortium has assembledgenetic and phenotypic data from participants with andwithout type 2 diabetes frommultiple populations creatinga central repository for such data (wwwtype2diabetesgeneticsorginformationalabout) The ability of these initiatives tocontribute to precision diabetes medicine is likely to beenhanced by their partnerships with industry which arenecessary to translate research into therapeutics ap-proved by the US Food and Drug Administration (FDA)
The Nordic countries have a long history of biobank-ing and registry-based research and have contributed
1914 Precision Medicine in Type 2 Diabetes Diabetes Volume 67 October 2018
Tab
le1mdashGlobalp
recisionmed
icineinitiatives
ofrelevance
totyp
e2diab
etes
Nam
eRegion
Start
date
Funding
InitiativePop
ulation(in
thousands)
Australian
Precision
Med
icineInitiative
Australia
2016A$25
million
Pub
licndashp
rivatemdash
Chinese
Precision
Med
icineInitiative
China
2016US$9
billion
Pub
licndashp
rivate2
InnovativeMed
icinesInitiative
(diab
etesprojects
only)Europ
eanUnion
2008euro200
million
Pub
licndashp
rivate100
Nord
icPrecision
Med
icineInitiative
Nord
ic(Denm
arkEstonia
FinlandIceland
Norw
ayand
Swed
en)2015
mdashPub
licndashp
rivate1000
Estonian
Personalised
Med
icinePilot
Project
(Nord
icPrecision
Med
icineInitiative)
Nord
ic(Estonia)
2015euro33
million
Governm
ental100
FinnGen
(Nord
icPrecision
Med
icineInitiative)
Nord
ic(Finland)
2017euro60
million
Pub
licndashp
rivate500
Genom
icAggregation
Project
inSwed
en(Nord
icPrecision
Med
icineInitiative)
Nord
ic(Swed
en)2015
mdashPub
lic160
Saud
iHum
anGenom
eProject
Saud
iArab
ia2013
US$40
million
Governm
ental100
Genom
icsEngland
UK
2012US$523
million
Pub
licndashp
rivate75
UK
Biob
ankUK
2005US$122
million
Pub
licndashp
rivate500
Project
Baseline
US
2017US$41
million
Pub
licndashp
rivate10
USPrecision
Med
icineInitiative
US
2015US$215
million
Pub
licndashp
rivate1000
Accelerating
Med
icinesPartnership
US
2014US$528
million
Pub
licndashp
rivate150
Million
Veteran
Program
US
2011US$116
million
Pub
licndashp
rivate612
diabetesdiabetesjournalsorg Fitipaldi and Associates 1915
substantially to discoveries in diabetes genetics over thepast decade In 2015 the Nordic Precision MedicineInitiative (NPMI) was formed to bring together genetic andother biomedical data from 1 million Nordic citizensSeveral national precision and genomic medicine projectsare under way in Estonia for example the EstonianGenome Center at the University of Tartu is curatinga population-based biobank suitable for precision medicineresearch in diabetes and other complex diseases The aimis to promote and advance the development of geneticresearch and the implementation of genomic data intoclinical practice to improve public health The Estoniangovernment recently launched the Estonian PersonalisedMedicine Pilot Project (EPMPP) (2015ndash2018) which seeksto implement personalized medicine on a national scaleAccordingly the government has provided US$59 millionto support genotyping in 100000 Estonians In Finlandthe FinnGen project was launched in 2017 with theobjective to link genomic data with digital health caredata for 500000 Finnish citizens (10 of the countryrsquospopulation) through a publicndashprivate partnership In SwedenGAPS (Genomic Aggregation Project in Sweden) hasassimilated160000 genotyped samples with corresponding
phenotype data covering a wide range of diseases includingdiabetes (30) and GMS (Genomic Medicine Sweden) isspearheading clinical genomics on a national scale In Icelandthe company deCODE genetics has established a genetic andphenotype database that encompasses the entire population(n = 334000) which fuels genetics research and drug targetvalidation for a wide range of diseases including diabetes
Several other countries have launched large-scale pro-grams of research on genomic and precision medicinemany with clear paths to clinical application althoughoften only in relation to rare pediatric diseases In SaudiArabia the very high prevalence of type 2 diabetes (20)led the Saudi government and King Abdulaziz City forScience and Technology to initiate the Saudi HumanGenome Project (SHGP) which seeks to sequence thegenomes of 100000 Saudi citizens in order to identifythe genetic basis of monogenic and complex diseases liketype 2 diabetes In China which has become a formidableforce in decoding genomes (31) the government an-nounced in 2016 a bold initiative to become a globalsuperpower in precision medicine with a US$9 billion15-year precision medicine initiative (32) The Chineseinitiative has three core objectives focused on recruiting
Figure 1mdashGlobal precision medicine initiatives of relevance to type 2 diabetes
1916 Precision Medicine in Type 2 Diabetes Diabetes Volume 67 October 2018
1) millions of participants from the seven main regions ofChina to form a nationally representative cohort 2) eightdisease-specific cohorts (cardiovascular cerebrovascularrespiratory metabolic neurological psychosomatic im-mune system disorders and seven common malignanttumors) totaling 700000 participants and 3) a clinicalcohort (N = 50000 patients with 50 rare diseases) (33)
CLINICAL TRANSLATION
The practice of precision diabetes medicine is currentlyconfined to rare monogenic forms of the disease Never-theless individual variants present only in specific pop-ulation isolates have been discovered that may have clinicalvalue in the prevention or treatment of type 2 diabetesThese include a TBC1D4 nonsense variant (pArg684ter)of relatively high prevalence in Greenlandic Inuit (minorallele frequency 17) and other circumpolar Inuit popu-lations that predisposes a substantially increased risk ofdiabetes (odds ratio 10 in those homozygous for thevariant) (34) The mechanism of action involves the mus-cle-selective loss of the long TBC1D4 isoform and dimin-ished GLUT4-mediated cellular glucose uptake in responseto insulin A thymine for cytosine substitution that causesa premature stop at codon 363 of TBC1D4 has also beendiscovered in people with acanthosis nigricans the muta-tion causes postprandial hyperinsulinemia as a possibleconsequence of a specific muscle and adipose tissue insulinresistance (35)
In Latinos a low frequency (minor allele frequency =21 in people with type 2 diabetes) missense variant (pE508K) at HNF1A conveys a type 2 diabetes odds ratio5(36)HNF1A also harbors mutations that cause rare mono-genic diabetes that can be successfully treated with sulfo-nylureas (discussed later) suggesting this is true for thenontrivial number of Latino individuals with type 2 di-abetes who carry pE508K Despite the relatively largeeffects these variants convey whether they will proveuseful in the context of precision medicine is unknown asno formal assessment of clinical utility or cost-effectivenesshas been reported
Metformin was discovered in the 1960s and is currentlythe frontline drug for early-stage diabetes treatment al-though its mechanisms of action remain unclear (37) Thereis genetic evidence for metformin intolerance (38) althoughthis has not been widely replicated By contrast variants atATM (39) and SLC2A2 (encoding GLUT2) (40) have beenassociated at a genome-wide level of statistical significancewith metformin response Although the effects conveyed bythese variants are too small to guide patient-level treatmentdecisions these findings may prove valuable in determiningmetforminrsquos mechanisms of action
Many major precision medicine statements includingthe executive summary of the US PMI (28) and a NationalResearch Council report focused on precision medicine andnew disease taxonomies (41) make clear that research andpractice in precision medicine should consider the majorimpact of lifestyle in disease etiology prevention and
treatment The incorporation of lifestyle into precisiondiabetes medicine is adequately justified by its provenbenefits in diabetes prevention and treatment Yet thereis good evidence that people benefit to varying degreesfrom diet and exercise and that personal biology underliesthis (42) Thus if precision medicine programs are to ad-equately consider the impact of lifestyle and other envi-ronmental exposures it seems logical that emphasis shouldbe placed on improving the precision of not only drugs butalso this type of antidiabetes therapy too
One compelling example of how glycemic response tofood can be predicted using an individualrsquos biomarkerscomes from a study of 800 young adults whose gut meta-genomic sequences were ascertained and diet and bloodglucose variation (from continuous glucose monitors) weremonitored for a week (43) Each participant received onestandardized meal (50 g carbohydrate) daily The authorsobserved that although participants elicited different post-prandial glycemic responses the responses to the samefood were consistent within individuals Using machinelearning algorithms these data were used to predict eachparticipantrsquos postprandial glycemic response to a givenfood They subsequently administered tailored diet inter-ventions to modulate glucose levels providing proof ofconcept that personalized diets guided by biomarker pro-filing can reduce blood glucose variability Notwithstand-ing the importance of this work from a basic scienceperspective to determine its value for precision medicinerequires knowing if minimizing variations in glucose inhealthy adults is clinically relevant and whether the ap-proach to predicting glycemic response to food also worksin people with diabetes To date neither of these impor-tant questions is addressed in the published literature
Despite lifestylersquos proven efficacy in diabetes preven-tion the resources available for research on lifestylemedicine are massively outweighed by investments inpharmacotherapy In 2013 for example US$35 billionwas spent by pharmaceutical companies on research anddevelopment (44) Although there are no comparablestatistics for research in lifestyle medicine funding islikely to be orders of magnitude less as most of this comesfrom the public purse The marketing of foods and bev-erages on the other hand is a huge industry whose mes-sages often counter public health efforts to promotehealthy lifestyle On an international scale hundreds ofbillions of US dollars are spent on food marketing annu-ally one of the biggest spenders is Unilever which spent US$8 billion on brand and marketing investments in2017 alone (45) Marketing junk foods and beverages ishandsomely resourced competing with public health ini-tiatives The major food companies in the UK for exam-ple have invested roughly US$200 million each year injunk food advertising which contrasts the US$72 millionspent on Change4Life the UK governmentrsquos flagshiphealthy eating campaign (46)
Notwithstanding the relative dearth of funding forlifestyle medicine some key initiatives exist One of the
diabetesdiabetesjournalsorg Fitipaldi and Associates 1917
largest of these is the National Institutes of HealthrsquosCommon Fund initiative on the Molecular Transducersof Physical Activity in Humans (httpcommonfundnihgovMolecularTransducers) which seeks to determineldquooptimal physical activity recommendations for peopleat various stages of liferdquo and develop ldquoprecisely targeted[exercise] regimens for individuals with particular healthneedsrdquo (47) Elsewhere the Food4Me study a research pro-gram funded by the European Union explored the role ofgenetics in the personalization of diet data from the studysuggest that personalized diet interventions supported bygenetic data improve consumption of healthy foods (48) andhelp sustain healthy food choices (49) Interestingly mostloci selected for this intervention (variants at MTHFR FTOTCF7L2 APOE and FADS1) lack evidence of individual-levelclinical relevance (50) suggesting that the success of theFood4Me intervention was not dependent on the quality ofthe genetic information provided to participants
Although most if not all precision medicine initiativesare predicated on the assumption that their findings willhelp improve peoplersquos health the emphasis of these ini-tiatives is almost always on the generation of new knowl-edge MVP for example seeks ldquoto improve understandingof how health is affected by genetic characteristics behav-iors and environmental factorsrdquo (29) Although this objec-tive will undoubtedly be achieved how this information willeventually be used to optimize prevention and treatment
of diabetes and other diseases is unclear Indeed few pre-cision medicine initiatives focused on complex diseasesprovide a clear plan for clinical translation (Fig 2)
POTENTIAL BOTTLENECKS ON THE PATH TOPRECISION MEDICINE
Cost-effectivenessThe global precision medicine market was valued atUS$436 billion in 2016 and is predicted to triple in valuewithin the next decade (51) The economic dynamicsassociated with precision medicine differ from those ofconventional medicines as the nature of the approachmeans that precision medicines are likely to be approvedfor use only in specific subpopulations limiting the num-ber of patients eligible for treatment with a given drug Bycontrast the effectiveness of precision medicines shouldbe higher for patients within these subpopulationsthan for conventional medicines and side effects may befewer and less severe Owing to the smaller market sharefor a given precision medicine competition to producecheaper drugs between manufacturers may be less than forconventional drugs and the drive to develop off-patentbiosimilars (ie drugs with active properties identical orsimilar to previously licensed drugs) is likely to differcompared with conventional medicines depending onthe extent to which first-in-class drugs are undercut bycheaper alternatives Collectively these factors will impact
Figure 2mdashThe path to precision medicine in type 2 diabetes HEA health economic assessment
1918 Precision Medicine in Type 2 Diabetes Diabetes Volume 67 October 2018
the cost-effectiveness of precision medicines in type 2 di-abetes (52)
Thus far very few cost-effectiveness analyses in pre-cision diabetes medicine have been published despitelong-standing recognition that cost-effectiveness is a keyrequirement for clinical translation (53) The rare exam-ples of such studies have focused exclusively on rare mono-genic forms of diabetes neonatal diabetes (54) andmaturity-onset diabetes of the young (MODY) (5556)
Neonatal diabetes is diagnosed in infancy and affectsabout 1400000 live births and roughly half (1252000in people aged 20 years) develop permanent neonataldiabetes (54) Of the latter group up to 70 are estimatedto carry mutations in the genes encoding the ATP-sensitivepotassium channel (KCNJ11 and ABCC8) These mutationsblock the closing of the KATP channels which preventsb-cell depolarization and corresponding insulin secretion(57) However treatment with sulfonylureas rectifies thisdefect in 90 of cases allowing the discontinuation ofexogenous insulin (58) The earliest costndasheffect analysisin precision medicine focused on sulfonylurea therapy inpermanent neonatal diabetes (54) The authors estimatedthat genetic testing in permanent neonatal diabetes wouldconvey significant quality-of-life benefits at 10 years (032quality-adjusted life-years US$12528 saved) increasingto 070 at 30 years (US$30437 saved)
A range of single gene defects cause MODY each ofwhich are used to diagnose one of five forms of the diseaseIn MODY1 and MODY3 insulin secretion is impaired Thedisease occurs early in life and is often misdiagnosed astype 1 diabetes and exogenous insulin therapy is thusprescribed However treatment with sulfonylureas enablessecretion of endogenously produced insulin Switchingfrom insulin to sulfonylureas is preferable for a numberof reasons including those related to safety and burdenMODY2 is caused by mutations in GCK which lead tochronically elevated but nonprogressive blood glucoseconcentrations that do not typically require treatmentthus diagnosing MODY2 also conveys reduced costs andburdens associated with unnecessary treatment The firstcost-effectiveness analysis focused on MODY1ndash3 in USadults (aged 25ndash40 years) with diagnosed type 2 diabetes(56) based many of its assumptions on the findings ofthe UK Prospective Diabetes Study (UKPDS) (59) It alsoassumed a 2 frequency of MODY among people withdiabetes and set genetic screening costs at US$2580Under these assumptions genetic screening for MODYwould not be cost-effective The second study focused onlyonMODY1 screening and found that genetic screening wasnot cost-effective given the diabetes population frequencyof MODY1 mutations is unlikely to exceed 2 Sub-stantial reductions in genetic sequencing costs may how-ever render screening cost-effective (55)
Regulatory ConsensusMore than half of the most clinically impactful drugs de-veloped recently were discovered through academia-led
research (60) emphasizing the huge value of publicndashprivate partnerships Many of the large precision diabetesmedicine consortia involve both academia and industrywithin which unsurprising emphasis is placed on the dis-covery and validation of biomarkers that expedite drugdevelopment This expectation is backed up by the provenability of genetics to fast-track drug development bypinpointing high-value drug targets helping halve drugfailures and markedly reducing their costs (61) In drugrepositioning genetics has also proven its worth by re-vealing previously unknown conditions that a drug can beused to treat and by helping predict drug side effects
To obtain FDA (or European Medicines Agency [EMA])approval for biomarkers in medical products and de-vices involves a comprehensive qualification process (seehttpswwwfdagovDrugsDevelopmentApprovalProcessDrugDevelopmentToolsQualificationProgramBiomarker-QualificationProgramucm535395htm) within which ex-ists the requirement for a statement termed a ldquocontext ofuserdquo The context of use statement requires a clear anddetailed description of the intended role of the biomarkerin drug development as well as risks and benefits TheFoundation for the National Institutes of Health recentlypublished the Framework for Defining Evidentiary Criteriafor Biomarker Qualification (62) which provides an ex-panded outline of the biomarker classifications definedby the FDA
The process of biomarker qualification in drug devel-opment requires stringent adherence to the relevant reg-ulatory agenciesrsquo approvals processes In some studies thatseek to discover biomarkers for diabetes drug develop-ment the process is well rehearsed and aligning biomarkerdiscovery and validation strategies with regulatory expect-ations is relatively straightforward however it does re-quire that precision medicine consortia are well versedin these processes Biomarker discovery in prediabetes ismore challenging though as prediabetes is not a distinctdisease state and although some people with prediabetesdevelop diabetes and its complications a large proportionremains healthy for many years (63) Nevertheless a sub-group of those with prediabetes will progress to diabetesand identifying these people before b-cell function issubstantially diminished would open the door for severalpowerful therapeutics that are ineffective once endoge-nous insulin production has faded However becauseneither the FDA nor EMA currently recognizes an inter-mediate end point for disease progression in prediabetesdrug trials gaining regulatory approval for new prediabe-tes biomarkers requires that researchers foster close andfrequent communications with the regulatory agencies toensure candidate biomarkers have the possibility to pro-gress through the regulatory process
Legal Social and EthicalBalancing the interests of society and its citizens the risksand benefits legislation and individual freedom as wellas the risk of exasperating disparities is central to many
diabetesdiabetesjournalsorg Fitipaldi and Associates 1919
commentaries on the legal social and ethical aspects ofprecision medicine
Precision medicine research is heavily dependent onbiobanked data and tissues Often the original informedconsents were obtained many years before modern lsquoomicstechnologies existed at a time when many research ques-tions germane to precision medicine were inconceivableOften the informed consents were broad and ethicscommittees have concluded that these modern researchquestions are within the scope of the consents As newever larger and interconnected cohorts are being formedthe desire many researchers share for broad consentingandor to bank data or samples for unspecified futureresearch must be placed in context with several importantethical realities the need to maintain participantsrsquo confi-dentiality appropriately handle incidental findings deter-mine who owns the samples and data consider culturalsensitivities return results to participants engage societyand ensure unused material is properly disposed (64)
Much as precision medicine was inconceivable whenmany existing cohorts were formed it is impossible toaccurately predict how contemporary cohorts will be usedin the future Furthermore many precision medicineinitiatives involve the hybridization of academia healthcare and industry which may challenge some of the morealtruistic motivations of participants in academia-led bio-medical research Moreover much of the highly sensitivedata collated by these initiatives will eventually be madeavailable through managed-access portals to a wide rangeof end users who were not part of the data collectionprocess Thus much of this highly sensitive data will bewidely disseminated and used by distant end users toaddress a plethora of diverse research questions thatcannot yet be conceived factors that need to be consideredduring the informed consent process These are challengesfor historical biobanks as well as new precision medicinecohorts that consent only at enrollment To help overcomethis a process called dynamic consent has been developedwhich allows consents to be progressively updated asprojects evolve but requires fluid communication withparticipants throughout the life course of a study (65)
A major concern about genetic data are its linkage todatabases that enables inferences about a personrsquos socialcognitive moral cultural health or sexual identity whichparticipants had not consented to or for such inferencesto be made about family members without their consent(64) Indeed ensuring genomics data are used in a wayconsistent with appropriate ethical standards is a corefeature of most current health data protection legislation(66) Notwithstanding these risks there are major benefitsassociated with big data which include the discovery ofnovel disease mechanisms and therapeutic targets manyof which only become visible when data sets scale tomassive proportions Thus rather than constrain thegrowth and responsible utilization of big data emphasis isbeing placed on implementing legislation that protectsprivacy and helps prevent data breaches while facilitating
the safe and responsible storage transfer and utilizationof data In the European Union for example the Gen-eral Data Protection Regulation was implemented in May2018 to harmonize data privacy laws across Europe A keyfeature of this legislation as it relates to precision medicineresearch is that the analysis of sensitive data requiresexplicit opt-in consent whereas for nonsensitive dataunambiguous (implied) consent is sufficient
SUMMARY AND CONCLUSIONS
Diabetes medicine is likely to evolve dramatically in thecoming years ideally because evidence emerges from themajor precision medicine initiatives to support its utility inpreventing or treating diabetes but no doubt also becauseof the major commercial interest in seeing precision med-icine grow One of the possible outcomes will very likely bethe replacement of current classifications of diabetes withempirically derived subclassifications that can be coupledwith treatment regimens which compared with con-ventional medicines are more effective less costly andconvey fewer unnecessary side effects As genetic data-bases grow genotypes will be increasingly used to discovernew drug targets At this time the clinical application ofgenetics is confined to the diagnosis of and therapeuticguidance for rare forms of diabetes as well as the pre-diction of adverse drug reactions The ways in whichgenetics may or may not guide prevention and therapy incommon forms of diabetes is unclear However becausecharacterizing variation in a patientrsquos nuclear genomeis inexpensive and easily achieved and DNA variation re-mains constant across the life course it is likely that geneticsequences will feature in most patientsrsquo clinical recordsGiven this genetic data will likely be deployed in diverseways in the diabetes clinic of the future The roles otherlsquoomics technologies digital imaging devices and wearableswill play in precision diabetes medicine are harder to fore-cast as research in these areas is less advanced and the costof obtaining some of these data will likely remain highrelative to genetics Finally although many precision med-icine initiatives focus predominantly on pharmacotherapyoptimizing lifestyle (and possibly surgical) interventionsusing biotechnologies also has great potential for improvingtype 2 diabetes prevention and treatment
Funding and Duality of Interest The work of HF and PWF relatedto the manuscript was supported by the European Commission (CoG-2015_681742_NASCENT) Swedish Research Council (Distinguished YoungResearchers Award in Medicine) Swedish Heart-Lung Foundation Novo NordiskFoundation European Foundation for the Study of Diabetes and the SwedishFoundation for Strategic Research (Industrial Research Center in PrecisionMedicine) (all grants to PWF) PWF is a consultant for and has stock optionsin Zoe Global and has received research funding from Boehringer Ingelheim EliLilly Janssen Novo Nordisk Sanofi and Servier MIM is a National Institute forHealth Research (NIHR) Senior Investigator and a Wellcome Trust Senior In-vestigator The views expressed in this article are those of the author(s) and notnecessarily those of the National Health Service NIHR or the Department ofHealth Work described in this review was principally supported by the Wellcome
1920 Precision Medicine in Type 2 Diabetes Diabetes Volume 67 October 2018
Trust (098381 203141 106130) and by the National Institutes of Health (U01-DK105535) MIM serves on advisory panels for Pfizer Novo Nordisk and ZoeGlobal has received honoraria from Pfizer Novo Nordisk and Eli Lilly hasstock options in Zoe Global and has received research funding from AbbVieAstraZeneca Boehringer Ingelheim Eli Lilly Janssen Merck Novo Nordisk PfizerRoche Sanofi Servier and Takeda JCF is a Massachusetts General HospitalResearch Scholar and is supported by National Institutes of Health National Instituteof Diabetes and Digestive and Kidney Diseases grants R01 DK072041 U01DK105554 R01 DK105154 and K24 DK110550 and National Institute of GeneralMedical Sciences grant R01 GM117163 MIM JCF and PWF are membersof the American Diabetes Association Precision Medicine in Diabetes TaskforceThe views expressed in this article are those of the authors and do not necessarilyreflect those of the American Diabetes Association or the taskforce No otherpotential conflicts of interest relevant to this article were reported
References1 Centers for Disease Control and Prevention National Diabetes StatisticsReport Estimates of Diabetes and Its Burden in the United States 2014 AtlantaGA US Department of Health and Human Services 20142 Knowler WC Fowler SE Hamman RF et al Diabetes Prevention ProgramResearch Group 10-year follow-up of diabetes incidence and weight loss in theDiabetes Prevention Program Outcomes Study Lancet 20093741677ndash16863 Li G Zhang P Wang J et al The long-term effect of lifestyle interventions toprevent diabetes in the China Da Qing Diabetes Prevention Study a 20-yearfollow-up study Lancet 20083711783ndash17894 Tuomilehto J Lindstroumlm J Eriksson JG et al Finnish Diabetes PreventionStudy Group Prevention of type 2 diabetes mellitus by changes in lifestyleamong subjects with impaired glucose tolerance N Engl J Med 20013441343ndash13505 Ramachandran A Snehalatha C Mary S Mukesh B Bhaskar AD Vijay VIndian Diabetes Prevention Programme (IDPP) The Indian Diabetes PreventionProgramme shows that lifestyle modification and metformin prevent type 2 di-abetes in Asian Indian subjects with impaired glucose tolerance (IDPP-1) Dia-betologia 200649289ndash2976 Lean ME Leslie WS Barnes AC et al Primary care-led weight managementfor remission of type 2 diabetes (DiRECT) an open-label cluster-randomised trialLancet 2018391541ndash5517 Polyzogopoulou EV Kalfarentzos F Vagenakis AG Alexandrides TK Res-toration of euglycemia and normal acute insulin response to glucose in obesesubjects with type 2 diabetes following bariatric surgery Diabetes 2003521098ndash11038 Franco M Orduntildeez P Caballero B et al Impact of energy intake physicalactivity and population-wide weight loss on cardiovascular disease and diabetesmortality in Cuba 1980-2005 Am J Epidemiol 20071661374ndash13809 Cho NH Shaw JE Karuranga S et al IDF Diabetes Atlas global estimates ofdiabetes prevalence for 2017 and projections for 2045 Diabetes Res Clin Pract2018138271ndash28110 Lander ES Linton LM Birren B et al International Human Genome Se-quencing Consortium Initial sequencing and analysis of the human genome[published correction appears in Nature 2001411720] Nature 2001409860ndash92111 Visscher PM Wray NR Zhang Q et al 10 Years of GWAS discovery biologyfunction and translation Am J Hum Genet 20171015ndash2212 Merino J Udler MS Leong A Meigs JB A decade of genetic and metab-olomic contributions to type 2 diabetes risk prediction Curr Diab Rep 20171713513 Knowles JW Ashley EA Cardiovascular disease the rise of the genetic riskscore PLoS Med 201815e100254614 Mahajan A Taliun D Thurner M et al Fine-mapping of an expanded set oftype 2 diabetes loci to single-variant resolution using high-density imputation andislet-specific epigenome maps [preprint article online] 2018 Available fromhttpswwwbiorxivorgcontentearly20180109245506 Accessed 30 January2018
15 McCarthy MI Painting a new picture of personalised medicine for diabetesDiabetologia 201760793ndash79916 Scott RA Scott LJ Maumlgi R et al DIAbetes Genetics Replication AndMeta-analysis (DIAGRAM) Consortium An expanded genome-wide asso-ciation study of type 2 diabetes in Europeans Diabetes 2017662888ndash290217 Mahajan A Wessel J Willems SM et al ExomeBP Consortium MAGICConsortium GIANT Consortium Refining the accuracy of validated target iden-tification through coding variant fine-mapping in type 2 diabetes Nat Genet 201850559ndash57118 Udler MS Kim J von Grotthuss M et al Clustering of type 2 diabetes geneticloci by multi-trait associations identifies disease mechanisms and subtypes[preprint article online] 2018 Available from httpswwwbiorxivorgcontentearly20180510319509 Accessed 30 March 201819 van Zuydam NR Ahlqvist E Sandholm N et al A genome-wide associationstudy of diabetic kidney disease in subjects with type 2 diabetes Diabetes 2018671414ndash142720 Ahlqvist E Storm P Kaumlraumljaumlmaumlki A et al Novel subgroups of adult-onsetdiabetes and their association with outcomes a data-driven cluster analysis of sixvariables Lancet Diabetes Endocrinol 20186361ndash36921 Fuchsberger C Flannick J Teslovich TM et al The genetic architecture oftype 2 diabetes Nature 201653641ndash4722 Koivula RW Heggie A Barnett A et al DIRECT Consortium Discovery ofbiomarkers for glycaemic deterioration before and after the onset of type 2 di-abetes rationale and design of the epidemiological studies within the IMI DIRECTConsortium Diabetologia 2014571132ndash114223 Rask-Andersen M Karlsson T Ek WE Johansson Aring Gene-environmentinteraction study for BMI reveals interactions between genetic factors and physicalactivity alcohol consumption and socioeconomic status PLoS Genet 201713e100697724 Celis-Morales CA Lyall DM Gray SR et al Dietary fat and total energy intakemodifies the association of genetic profile risk score on obesity evidence from48 170 UK Biobank participants Int J Obes 2017411761ndash176825 Tyrrell J Wood AR Ames RM et al Gene-obesogenic environment inter-actions in the UK Biobank study Int J Epidemiol 201746559ndash57526 Young AI Wauthier F Donnelly P Multiple novel gene-by-environment in-teractions modify the effect of FTO variants on body mass index Nat Commun201671272427 Reardon S Giant study poses DNA data-sharing dilemma Nature 201552516ndash1728 Hudson K Lifton R Patrick-Lake B The Precision Medicine Initiative CohortProgram ndash Building a Research Foundation for 21st Century Medicine BethesdaMD National Institutes of Health 201529 Gaziano JM Concato J Brophy M et al Million Veteran Program a mega-biobank to study genetic influences on health and disease J Clin Epidemiol 201670214ndash22330 Bergen SE Sullivan PF National-scale precision medicine for psychiatricdisorders in Sweden Am J Med Genet B Neuropsychiatr Genet 7 July 2017 [Epubahead of print] DOI 101002ajmgb3256231 Cyranoski D Chinarsquos bid to be a DNA superpower Nature 2016534462ndash46332 Cyranoski D China embraces precision medicine on a massive scale Nature20165299ndash1033 Bu D Peng S Luo H et al Precision medicine and cancer immunology inChina From big data to knowledge in precision medicine Science 2018 (Suppl)S35ndashS3834 Moltke I Grarup N Joslashrgensen ME et al A common Greenlandic TBC1D4variant confers muscle insulin resistance and type 2 diabetes Nature 2014512190ndash19335 Dash S Sano H Rochford JJ et al A truncation mutation in TBC1D4 ina family with acanthosis nigricans and postprandial hyperinsulinemia Proc NatlAcad Sci U S A 20091069350ndash9355
diabetesdiabetesjournalsorg Fitipaldi and Associates 1921
36 Estrada K Aukrust I Bjoslashrkhaug L et al SIGMA Type 2 Diabetes ConsortiumAssociation of a low-frequency variant in HNF1A with type 2 diabetes in a Latinopopulation [published correction appears in JAMA 20143121932] JAMA 2014
3112305ndash231437 Marshall SM 60 years of metformin use a glance at the past and a look tothe future Diabetologia 2017601561ndash156538 Pearson ER Pharmacogenetics and target identification in diabetes CurrOpin Genet Dev 20185068ndash7339 Zhou K Bellenguez C Spencer CC et al GoDARTS and UKPDS Diabetes
Pharmacogenetics Study Group Wellcome Trust Case Control Consortium 2MAGIC investigators Common variants near ATM are associated with glycemicresponse to metformin in type 2 diabetes Nat Genet 201143117ndash12040 Zhou K Yee SW Seiser EL et al MetGen Investigators DPP InvestigatorsACCORD Investigators Variation in the glucose transporter gene SLC2A2 isassociated with glycemic response to metformin Nat Genet 2016481055ndash
105941 National Research Council Committee on a Framework for Developing a NewTaxonomy of Disease Toward Precision Medicine Building a Knowledge Networkfor Bomedical Research and a New Taxonomy of Disease Washington DCNational Academies Press 201142 Franks PW Poveda A Lifestyle and precision diabetes medicine will ge-
nomics help optimise the prediction prevention and treatment of type 2 diabetesthrough lifestyle therapy Diabetologia 201760784ndash79243 Zeevi D Korem T Zmora N et al Personalized nutrition by prediction of
glycemic responses Cell 20151631079ndash109444 Swanson A Big pharmaceutical companies are spending far more onmarketing than research The Washington Post 11 February 2015 Available fromhttpswwwwashingtonpostcomnewswonkwp20150211big-pharmaceutical-companies-are-spending-far-more-on-marketing-than-researchnoredirect=onamputm_term=787d031b9b7c Accessed 20 February 201845 Unilever Advertising amp Marketing Unilever 2018 Available from httpwwwunilevercomsustainable-livingwhat-matters-to-youadvertising-and-marketinghtml Accessed 31 March 201846 OrsquoDowd A Spending on junk food advertising is nearly 30 times whatgovernment spends on promoting healthy eating BMJ 2017359j467747 National Institutes of Health Molecular Transducers of Physical Activity in
Humans Frequently Asked Questions (FAQs) [Internet] 2018 Available from httpscommonfundnihgovMolecularTransducersFAQs Accessed 20 March 201848 Livingstone KM Celis-Morales C Navas-Carretero S et al Food4Me Study
Effect of an Internet-based personalized nutrition randomized trial on dietarychanges associated with the Mediterranean diet the Food4Me Study Am J ClinNutr 2016104288ndash29749 Celis-Morales C Livingstone KM Marsaux CF et al Food4Me Study Effectof personalized nutrition on health-related behaviour change evidence from theFood4Me European randomized controlled trial Int J Epidemiol 201746578ndash
588
50 Celis-Morales C Livingstone KM Marsaux CF et al Design and baselinecharacteristics of the Food4Me study a web-based randomised controlled trial ofpersonalised nutrition in seven European countries Genes Nutr 20151045051 BIS Research Global precision medicine market - analysis and forecast2017-2026 2017 Available from httpsbisresearchcomindustry-reportglobal-precision-medicine-market-2026html Accessed 31 January 201852 Stern AD Alexander BM Chandra A How economics can shape precisionmedicines Science 20173551131ndash113353 Smith RJ Nathan DM Arslanian SA Groop L Rizza RA Rotter JI In-dividualizing therapies in type 2 diabetes mellitus based on patient characteristicswhat we know and what we need to know J Clin Endocrinol Metab 2010951566ndash157454 Greeley SA John PM Winn AN et al The cost-effectiveness of personalizedgenetic medicine the case of genetic testing in neonatal diabetes Diabetes Care201134622ndash62755 Nguyen HV Finkelstein EA Mital S Gardner DS Incremental cost-effectivenessof algorithm-driven genetic testing versus no testing for maturity onset diabetes of theyoung (MODY) in Singapore J Med Genet 201754747ndash75356 Naylor RN John PM Winn AN et al Cost-effectiveness of MODY genetictesting translating genomic advances into practical health applications DiabetesCare 201437202ndash20957 Gloyn AL Pearson ER Antcliff JF et al Activating mutations in the geneencoding the ATP-sensitive potassium-channel subunit Kir62 and permanentneonatal diabetes N Engl J Med 20043501838ndash184958 Pearson ER Flechtner I Njoslashlstad PR et al Neonatal Diabetes InternationalCollaborative Group Switching from insulin to oral sulfonylureas in patients withdiabetes due to Kir62 mutations N Engl J Med 2006355467ndash47759 UK Prospective Diabetes Study (UKPDS) UK Prospective Diabetes Study(UKPDS) VIII Study design progress and performance Diabetologia 199134877ndash89060 Kesselheim AS Tan YT Avorn J The roles of academia rare diseases andrepurposing in the development of the most transformative drugs Health Aff(Millwood) 201534286ndash29361 Nelson MR Tipney H Painter JL et al The support of human geneticevidence for approved drug indications Nat Genet 201547856ndash86062 FNIH Evidentiary Criteria Writing Group Framework for Defining EvidentiaryCriteria for Biomarker Qualification Bethesda MD National Library of Medicine201663 Tabaacutek AG Herder C Rathmann W Brunner EJ Kivimaumlki M Prediabetesa high-risk state for diabetes development Lancet 20123792279ndash229064 Nicol D Bubela T Chalmers D et al Precision medicine drowning ina regulatory soup J Law Biosci 20163281ndash30365 Kaye J Whitley EA Lund D Morrison M Teare H Melham K Dynamicconsent a patient interface for twenty-first century research networks Eur J HumGenet 201523141ndash14666 Kinkorovaacute J Biobanks in the era of personalized medicine objectiveschallenges and innovation Overview EPMA J 201674
1922 Precision Medicine in Type 2 Diabetes Diabetes Volume 67 October 2018
experience major weight loss with bariatric surgery (7)The population-scale decline in rates of obesity diabetesand cardiovascular disease during economic crises thathave forced entire populations to increase physical activitylevels and reduce caloric intake reinforces the role ofnegative energy-balance in diabetes prevention (8)
The proven benefits of lifestyle modification in diabetesprevention and the effectiveness of several diabetes drugsare counterbalanced by the continuing emergence of type 2diabetes as one of the most prevalent and burdensomediseases globally (9) In the US for example diabetes wasthe seventh leading cause of death in 2010 accounting forat least 10ndash15 of deaths in people aged $20 years withadult death rates 50 higher in people with diabetesthan in those without (1) The economic burden of di-abetes is also formidable with diabetes costing the USeconomy about US$245 billion in 2012 with the averageannual medical costs per patient with diabetes more thandouble those for people without the disease (1)
The rising prevalence of diabetes on a population scaleand the insufficiency of established therapeutic optionsjustify the search for orthogonal diabetes prevention andtreatment strategies to complement existing approachesIdeally these new approaches would be better tailored tothe individual for enhanced tolerability and effectiveness
This drive to develop precision medicine for diabetesis predicated on the major technological advances seenin recent years that include high-resolution lsquoomic assayswearable devices that monitor behaviors and exposuresand digital imaging technologies The intelligent inte-gration of the data these technologies yield can providedetailed digital impressions of a personrsquos physical condi-tion their past exposure and ongoing susceptibility tocertain risk factors and how they might respond to specificantidiabetes therapies as well as help track disease pro-gression All of this has the potential to substantially im-prove the prediction prevention and treatment of type 2diabetes and there are as discussed later many majorinitiatives under way with this objective However as alsodiscussed there is much to resolve before precision di-abetes medicine becomes common practice
HOW PRECISION DIABETES MEDICINE MIGHTWORK
The foundation of precision medicine in type 2 diabeteslike all other complex diseases is population geneticsthe catalyst for which was the sequencing of the humangenome a US$3 billion initiative that completed its workin 2003 (10) The availability of human genome sequencesprovided a framework that facilitated the design of mas-sively parallel chip-based genotyping arrays These tech-nologies have since been used to characterize commongenomic variation in millions of people worldwide whichin turn has helped identify hundreds of variants associ-ated with type 2 diabetes and related traits throughgenome-wide association studies (GWAS) (11) The devel-opment of biotechnologies designed for high-resolution
characterization of other types of biological variants (tran-scripts proteins epigenetic marks metabolites micro-biota etc) has followed further expanding our abilityto map the paths that link a personrsquos biological idiosyn-crasies to disease susceptibility Although this work hasprimarily involved hypothesis-free association studies inlarge cohort collections and thus the results are highlydescriptive and of minimal relevance to the individualpatient it has provided substrate for functional studiesrevealing novel aspects of disease biology and therapeutictargets that may seed individualized therapies
It is generally accepted that precision medicine in-formed by diverse sources of big data will improve pre-vention and treatment of common multifactorial diseasessuch as type 2 diabetes but there are few examples to dateThe common variants shown by GWAS to be associatedwith type 2 diabetes have at best modest effect sizes andthe consensus view has historically been that even incombination their predictive value is limited particularlywhen compared with the performance of classic risk fac-tors such as age BMI and blood glucose (12) In the pastyear however there has been a reinvigoration of interestin the translational potential offered by genetic risk scores(13) Recent expanded GWAS data sets have yielded locithat explain a sizable proportion (50) of diabetes heri-tability and highlight that there are millions of people(in the UK or US) who on the basis of genetic evidencealone have very high (50) lifetime risks of type 2 di-abetes (14) This raises the prospect that the rollout ofmedical genotyping and sequencing will provide clinicallyactionable information on diabetes risk particularly ifgenetic information is combined with other relevant clin-ical or exposure data
So far however the clinical application of genetics indiabetes remains limited to rare monogenic subtypesModels for the personalization of type 2 diabetes care(especially in relation to exploiting the marked clinicalheterogeneity within type 2 diabetes) have often extrap-olated from this experience to invoke quasi-Mendelianscenarios characterized by distinctive subtypes of type 2diabetes each of which has the potential to be mappedto a specific remedial therapy or intervention In realityeffective strategies in this area must address the multi-factorial etiology of type 2 diabetes and a continuousspectrum of predisposition mediated in most individualsthrough joint effects across multiple pathways
One recently described conceptualization of the path-ophysiological architecture of type 2 diabetes predisposi-tion (termed the ldquopalette modelrdquo) focuses attention insteadon the intermediary processes contributing to type 2 di-abetes risk (15) The most obvious of these include obesityfat distribution islet development and function and in-sulin sensitivity but other as-yet poorly characterizedcontributors are likely Each of these processes is itselfunder multifactorial (genetic and nongenetic) control andindividual ldquoloadingsrdquo across them combinatorially influ-ence both diabetes risk and the phenotype of any diabetes
1912 Precision Medicine in Type 2 Diabetes Diabetes Volume 67 October 2018
that results Several recent studies (16ndash19) have addedempirical support for this approach demonstrating forexample differential relationships between process-specific risk scores and the risk of complications such asdiabetic kidney disease and coronary artery disease
The palette model captures the range of diabetes sub-types (from monogenic via hybrid forms [eg latent au-toimmune diabetes in adults] through to the clinicalheterogeneity evident within type 2 diabetes) as a contin-uum consistent with the genetic architecture of diabetesand real-world clinical observation (2021) The modelsuggests that for many people with type 2 diabetes theircondition is the consequence not of a major defect ina single process but in the confluence of suboptimalperformance across several processes contributing in par-allel This is what one would predict given the evidencethat type 2 diabetes risk can be subtly modulated bycommon variants acting through any one of several dis-tinct mechanisms (energy balance adipocyte differentia-tion incretin signaling insulin secretion etc) and by thewide range of therapeutic interventions that have theproven capacity to ameliorate to some degree at leastthe diabetic state (eg insulin sensitizers b-cell secre-tagogues calorie restriction exercise and metabolicsurgery)
This model provides a framework for understanding themechanistic basis of heterogeneity and its clinical conse-quences It also allows for the existence of cases at theextremes of the distribution for which a targeted inter-vention might be particularly effective Genetic risk scoresthat capture each of these processes may help to teaseapart heterogeneity in phenotype progression and ther-apeutic response They may allow identification within theoverall population of subsets of selected individuals inwhom the profile of genetic predisposition is dominatedby defects in a single pathway facilitating personalizedmechanism-specific interventions
Nevertheless for multifactorial diseases like type 2 di-abetes where individual predisposition is influenced asmuch by nongenetic as genetic factors there is an absolutelimit to the clinical precision that genetics alone can pro-vide For precision medicine to flourish this geneticinformation will need to be integrated with relevantmeasures of those aspects of the external and internalenvironment (the latter term for example encompassingthe gut microbiome) that also influence type 2 diabetespredisposition phenotype and progression This informa-tion would ideally be supported by pertinent biomarkerand clinical readouts generating integrated profiles thatmay provide the predictive accuracy needed for clinicalutility
Along these lines Ahlqvist et al (20) used data from8980 Swedish patients with newly diagnosed diabetes (ofany type) and applied machine learning algorithms toderive a data-driven approach to classifying diabetes sub-types The authors proposed five subtypes determined byvariations across six variables (glutamate decarboxylase
antibodies age at diagnosis BMI HbA1c and HOMA2estimates of b-cell function and insulin resistance) Therelationship of subtype and incident events (use of anti-diabetes medication achievement of treatment goals anddiabetes complications) were assessed and replicated inindependent cohorts Each cluster was characterized bycertain phenotypic features assessed soon after diagnosisThese include early-onset severe autoimmune diabetes(67 of patients) severe insulin-deficient diabetes (175of patients) severe insulin-resistant diabetes (153 ofpatients) mild obesity-related diabetes (216 of patients)and mild age-related diabetes (391 of patients) Rates ofdiabetic renal disease and coronary disease were highest insevere insulin-resistant diabetes although rates of retinop-athy were lowest for this group (retinopathies were morefrequent in severe insulin-deficient diabetes) Severe insulin-resistant diabetes also progressedmost rapidly to oral diabetesmedications (other than metformin) and was the slowestto reach the HbA1c treatment goal69 (52 mmolmol)Both severe autoimmune diabetes and severe insulin-deficient diabetes progressed to sustained insulin use con-siderably more quickly than the remaining clusters Bycontrast mild age-related diabetes and mild obesity-relateddiabetes were characterized by reasonably favorable profiles
This initial step demonstrated that one can deriveclusters from existing big data that make eminent phys-iological sense One limitation of this approach though incontrast with a genetically driven strategy resides in theuse of phenotypic criteria for clustering that are ascer-tained at disease onset and are not necessarily generaliz-able to the many people one would want to stratify (withnormoglycemia or with advanced diabetes) One shouldalso remain cognizant that a clinician may not be able tounequivocally place an individual patient into a givencluster thus the implementation and clinical utility ofthis approach remain to be demonstrated
It will be essential to understand how the relationshipsbetween these genetic and physiological approaches tostratification might be combined and in doing so howmuch value is added from a clinical perspective A crucialmissing piece is evidence that by clustering patients in thisway disease course diabetes complications or treatmentresponse can be predicted with sufficient accuracy to be ofclinical value This determination requires the explicitassessment of predictive accuracy and reclassificationSimilarly it is not known whether treatment that is guidedby cluster identity will be more or less effective thancurrent approaches for which intervention studies willbe needed Moreover because clustering techniques typ-ically require the dichotomization of continuous exposurevariables which usually results in loss of power the pre-dictive accuracy of algorithms that do not require this typeof data transformation is likely to be superior
Family history of diabetes is a strong predictor ofdisease and is frequently included in diabetes predictionalgorithms For information on family history to enhancethe predictive ability of the phenotypic clusters proposed
diabetesdiabetesjournalsorg Fitipaldi and Associates 1913
by Ahlqvist et al (20) it would require that some clustersbe more driven by familial factors than others This maywell be true and future studies exploring the genetic basisof these clusters may help test this hypothesis Howeverfamily history is the consequence of both genetic andenvironmental influences (including developmental expo-sures) and understanding why family history affects dis-ease progression differentially across clusters if indeed itdoes would likely be intensely scrutinized owing to theetiological insights this might reveal
OVERVIEW OF MAJOR PRECISION MEDICINEINITIATIVES
After the first draft sequence of the human genome waspublished in 2001 (10) methods and technologies wererapidly developed that enabled large-scale quantification ofhuman genetic variation and subsequently variation acrossother biological strata As investigators began using thesetechnologies global research consortia grew through whichnovel biomarkers for type 2 diabetes were discovered Therapid growth of large data sets and ad hoc collaborativenetworks as well as clear evidence that this new approachto biomarker discovery worked laid the foundations forlarge precision medicine initiatives Although some initia-tives have built new cohorts from scratch most haveleveraged existing data and biomaterials already stored inbiobanks (Table 1 and Fig 1)
In 2008 a joint undertaking between the EuropeanCommission European academic institutions and theEuropean Federation of Pharmaceutical Industries andAssociations was launched This euro56 billion programcalled the Innovative Medicines Initiative (IMI) comprises100 projects focused on the major diseases affectingEuropean citizens Of these several focus on precisionmedicine in type 2 diabetes SUrrogate markers for Micro-and Macro-vascular hard endpoints for Innovative dia-betes Tools (SUMMIT) (diabetes complications) DiabetesResearch on Patient Stratification (DIRECT) (drug responseand glycemic deterioration before and after the onset oftype 2 diabetes) (22) Risk Assessment and ProgreSsiOn ofDIabetes (RHAPSODY) (glycemic deterioration before andafter the onset of type 2 diabetes) and Biomarker En-terprise to Attack Diabetic Kidney Disease (BEAt-DKD)(diabetic kidney disease) A common objective is the discov-ery and validation of biomarkers that facilitate the strati-fication of patient populations into subgroups that mightbe treated more effectively than without biomarker strat-ification In some instances the focus is also on diagnos-tic reclassification Unlike most other precision medicineinitiatives those within the IMI focus on the integration ofmultiple biomarkers such as genotypes transcripts pro-teins metabolites and metagenomics sequences
The UK Biobank cohort (established 2005) includesaround 500000 adults the vast majority of whom haveprovided nonfasting blood samples and self-reported dataon lifestyle health and well-being and have been geno-typed using genome-wide arrays and targeted metabolomic
analyses are also under way In subcohorts more detailedphenotyping has been undertaken such as MRI scans andphysical activity monitoring Research conducted using UKBiobank data for example has shown that genetic variantsassociated with obesity are likely to influence susceptibilityto a range of modifiable lifestyle exposures (23ndash26) whichmay have relevance to diabetes Nevertheless the very smallmagnitude of these effects and susceptibility to confounding(25) precludes the immediate translation of these findingsinto clinical practice Alternatively as UK Biobank accruesincident events of diabetes complications the database islikely to become a powerful resource through which theprognostic value of stratifying populations into subgroupsor determining how environmental risk factors differentiallyaffect disease susceptibility by subgroup can be assessed
Plans to implement precision medicine into US med-ical practice accelerated in 2015 with the announcement ofthe Precision Medicine Initiative (PMI) for which US$215million was initially budgeted by the federal government(2728) In the short term the PMI is focused on cancerwhereas in the longer term all areas of health and healthcare will be studied with specific emphasis placed on thediscovery of predictive biomarkers for type 2 diabetes (28)A major feature of the PMI is a cohort of 1 million peopleand a research program called the All of Us Research Pro-gram The cohort supported by a US$55 million budget isbuilt around nationwide recruitment sites under the bannerof the PMI Cohort Program Managed access for accreditedresearchers to biobanked data and samples is intendedto facilitate several major goals including those related togenendashenvironment interactions pharmacogenomics riskstratification mobile health technologies health empower-ment and innovative clinical trials (httpsallofusnihgov)
Several other major US initiatives predate the PMI andhave diabetes and its risk factors at their core Theseinclude the Million Veteran Program (MVP) and theAccelerating Medicines Partnership in Type 2 Diabetes(AMP T2D) The MVP uses genomics and other healthdata obtained through electronic medical records andfollow-up surveys in about 600000 military veteransaged 50ndash69 years (29) The AMP T2D seeks to gathergenomic and metagenomic data and to create an analyticalengine that can be used to mine the genetic basis todiabetes and related traits while safeguarding the confi-dentiality of the results Data access is a central pillar ofAMP T2D and through the T2D Knowledge Portal (wwwtype2diabetesgeneticsorg) the consortium has assembledgenetic and phenotypic data from participants with andwithout type 2 diabetes frommultiple populations creatinga central repository for such data (wwwtype2diabetesgeneticsorginformationalabout) The ability of these initiatives tocontribute to precision diabetes medicine is likely to beenhanced by their partnerships with industry which arenecessary to translate research into therapeutics ap-proved by the US Food and Drug Administration (FDA)
The Nordic countries have a long history of biobank-ing and registry-based research and have contributed
1914 Precision Medicine in Type 2 Diabetes Diabetes Volume 67 October 2018
Tab
le1mdashGlobalp
recisionmed
icineinitiatives
ofrelevance
totyp
e2diab
etes
Nam
eRegion
Start
date
Funding
InitiativePop
ulation(in
thousands)
Australian
Precision
Med
icineInitiative
Australia
2016A$25
million
Pub
licndashp
rivatemdash
Chinese
Precision
Med
icineInitiative
China
2016US$9
billion
Pub
licndashp
rivate2
InnovativeMed
icinesInitiative
(diab
etesprojects
only)Europ
eanUnion
2008euro200
million
Pub
licndashp
rivate100
Nord
icPrecision
Med
icineInitiative
Nord
ic(Denm
arkEstonia
FinlandIceland
Norw
ayand
Swed
en)2015
mdashPub
licndashp
rivate1000
Estonian
Personalised
Med
icinePilot
Project
(Nord
icPrecision
Med
icineInitiative)
Nord
ic(Estonia)
2015euro33
million
Governm
ental100
FinnGen
(Nord
icPrecision
Med
icineInitiative)
Nord
ic(Finland)
2017euro60
million
Pub
licndashp
rivate500
Genom
icAggregation
Project
inSwed
en(Nord
icPrecision
Med
icineInitiative)
Nord
ic(Swed
en)2015
mdashPub
lic160
Saud
iHum
anGenom
eProject
Saud
iArab
ia2013
US$40
million
Governm
ental100
Genom
icsEngland
UK
2012US$523
million
Pub
licndashp
rivate75
UK
Biob
ankUK
2005US$122
million
Pub
licndashp
rivate500
Project
Baseline
US
2017US$41
million
Pub
licndashp
rivate10
USPrecision
Med
icineInitiative
US
2015US$215
million
Pub
licndashp
rivate1000
Accelerating
Med
icinesPartnership
US
2014US$528
million
Pub
licndashp
rivate150
Million
Veteran
Program
US
2011US$116
million
Pub
licndashp
rivate612
diabetesdiabetesjournalsorg Fitipaldi and Associates 1915
substantially to discoveries in diabetes genetics over thepast decade In 2015 the Nordic Precision MedicineInitiative (NPMI) was formed to bring together genetic andother biomedical data from 1 million Nordic citizensSeveral national precision and genomic medicine projectsare under way in Estonia for example the EstonianGenome Center at the University of Tartu is curatinga population-based biobank suitable for precision medicineresearch in diabetes and other complex diseases The aimis to promote and advance the development of geneticresearch and the implementation of genomic data intoclinical practice to improve public health The Estoniangovernment recently launched the Estonian PersonalisedMedicine Pilot Project (EPMPP) (2015ndash2018) which seeksto implement personalized medicine on a national scaleAccordingly the government has provided US$59 millionto support genotyping in 100000 Estonians In Finlandthe FinnGen project was launched in 2017 with theobjective to link genomic data with digital health caredata for 500000 Finnish citizens (10 of the countryrsquospopulation) through a publicndashprivate partnership In SwedenGAPS (Genomic Aggregation Project in Sweden) hasassimilated160000 genotyped samples with corresponding
phenotype data covering a wide range of diseases includingdiabetes (30) and GMS (Genomic Medicine Sweden) isspearheading clinical genomics on a national scale In Icelandthe company deCODE genetics has established a genetic andphenotype database that encompasses the entire population(n = 334000) which fuels genetics research and drug targetvalidation for a wide range of diseases including diabetes
Several other countries have launched large-scale pro-grams of research on genomic and precision medicinemany with clear paths to clinical application althoughoften only in relation to rare pediatric diseases In SaudiArabia the very high prevalence of type 2 diabetes (20)led the Saudi government and King Abdulaziz City forScience and Technology to initiate the Saudi HumanGenome Project (SHGP) which seeks to sequence thegenomes of 100000 Saudi citizens in order to identifythe genetic basis of monogenic and complex diseases liketype 2 diabetes In China which has become a formidableforce in decoding genomes (31) the government an-nounced in 2016 a bold initiative to become a globalsuperpower in precision medicine with a US$9 billion15-year precision medicine initiative (32) The Chineseinitiative has three core objectives focused on recruiting
Figure 1mdashGlobal precision medicine initiatives of relevance to type 2 diabetes
1916 Precision Medicine in Type 2 Diabetes Diabetes Volume 67 October 2018
1) millions of participants from the seven main regions ofChina to form a nationally representative cohort 2) eightdisease-specific cohorts (cardiovascular cerebrovascularrespiratory metabolic neurological psychosomatic im-mune system disorders and seven common malignanttumors) totaling 700000 participants and 3) a clinicalcohort (N = 50000 patients with 50 rare diseases) (33)
CLINICAL TRANSLATION
The practice of precision diabetes medicine is currentlyconfined to rare monogenic forms of the disease Never-theless individual variants present only in specific pop-ulation isolates have been discovered that may have clinicalvalue in the prevention or treatment of type 2 diabetesThese include a TBC1D4 nonsense variant (pArg684ter)of relatively high prevalence in Greenlandic Inuit (minorallele frequency 17) and other circumpolar Inuit popu-lations that predisposes a substantially increased risk ofdiabetes (odds ratio 10 in those homozygous for thevariant) (34) The mechanism of action involves the mus-cle-selective loss of the long TBC1D4 isoform and dimin-ished GLUT4-mediated cellular glucose uptake in responseto insulin A thymine for cytosine substitution that causesa premature stop at codon 363 of TBC1D4 has also beendiscovered in people with acanthosis nigricans the muta-tion causes postprandial hyperinsulinemia as a possibleconsequence of a specific muscle and adipose tissue insulinresistance (35)
In Latinos a low frequency (minor allele frequency =21 in people with type 2 diabetes) missense variant (pE508K) at HNF1A conveys a type 2 diabetes odds ratio5(36)HNF1A also harbors mutations that cause rare mono-genic diabetes that can be successfully treated with sulfo-nylureas (discussed later) suggesting this is true for thenontrivial number of Latino individuals with type 2 di-abetes who carry pE508K Despite the relatively largeeffects these variants convey whether they will proveuseful in the context of precision medicine is unknown asno formal assessment of clinical utility or cost-effectivenesshas been reported
Metformin was discovered in the 1960s and is currentlythe frontline drug for early-stage diabetes treatment al-though its mechanisms of action remain unclear (37) Thereis genetic evidence for metformin intolerance (38) althoughthis has not been widely replicated By contrast variants atATM (39) and SLC2A2 (encoding GLUT2) (40) have beenassociated at a genome-wide level of statistical significancewith metformin response Although the effects conveyed bythese variants are too small to guide patient-level treatmentdecisions these findings may prove valuable in determiningmetforminrsquos mechanisms of action
Many major precision medicine statements includingthe executive summary of the US PMI (28) and a NationalResearch Council report focused on precision medicine andnew disease taxonomies (41) make clear that research andpractice in precision medicine should consider the majorimpact of lifestyle in disease etiology prevention and
treatment The incorporation of lifestyle into precisiondiabetes medicine is adequately justified by its provenbenefits in diabetes prevention and treatment Yet thereis good evidence that people benefit to varying degreesfrom diet and exercise and that personal biology underliesthis (42) Thus if precision medicine programs are to ad-equately consider the impact of lifestyle and other envi-ronmental exposures it seems logical that emphasis shouldbe placed on improving the precision of not only drugs butalso this type of antidiabetes therapy too
One compelling example of how glycemic response tofood can be predicted using an individualrsquos biomarkerscomes from a study of 800 young adults whose gut meta-genomic sequences were ascertained and diet and bloodglucose variation (from continuous glucose monitors) weremonitored for a week (43) Each participant received onestandardized meal (50 g carbohydrate) daily The authorsobserved that although participants elicited different post-prandial glycemic responses the responses to the samefood were consistent within individuals Using machinelearning algorithms these data were used to predict eachparticipantrsquos postprandial glycemic response to a givenfood They subsequently administered tailored diet inter-ventions to modulate glucose levels providing proof ofconcept that personalized diets guided by biomarker pro-filing can reduce blood glucose variability Notwithstand-ing the importance of this work from a basic scienceperspective to determine its value for precision medicinerequires knowing if minimizing variations in glucose inhealthy adults is clinically relevant and whether the ap-proach to predicting glycemic response to food also worksin people with diabetes To date neither of these impor-tant questions is addressed in the published literature
Despite lifestylersquos proven efficacy in diabetes preven-tion the resources available for research on lifestylemedicine are massively outweighed by investments inpharmacotherapy In 2013 for example US$35 billionwas spent by pharmaceutical companies on research anddevelopment (44) Although there are no comparablestatistics for research in lifestyle medicine funding islikely to be orders of magnitude less as most of this comesfrom the public purse The marketing of foods and bev-erages on the other hand is a huge industry whose mes-sages often counter public health efforts to promotehealthy lifestyle On an international scale hundreds ofbillions of US dollars are spent on food marketing annu-ally one of the biggest spenders is Unilever which spent US$8 billion on brand and marketing investments in2017 alone (45) Marketing junk foods and beverages ishandsomely resourced competing with public health ini-tiatives The major food companies in the UK for exam-ple have invested roughly US$200 million each year injunk food advertising which contrasts the US$72 millionspent on Change4Life the UK governmentrsquos flagshiphealthy eating campaign (46)
Notwithstanding the relative dearth of funding forlifestyle medicine some key initiatives exist One of the
diabetesdiabetesjournalsorg Fitipaldi and Associates 1917
largest of these is the National Institutes of HealthrsquosCommon Fund initiative on the Molecular Transducersof Physical Activity in Humans (httpcommonfundnihgovMolecularTransducers) which seeks to determineldquooptimal physical activity recommendations for peopleat various stages of liferdquo and develop ldquoprecisely targeted[exercise] regimens for individuals with particular healthneedsrdquo (47) Elsewhere the Food4Me study a research pro-gram funded by the European Union explored the role ofgenetics in the personalization of diet data from the studysuggest that personalized diet interventions supported bygenetic data improve consumption of healthy foods (48) andhelp sustain healthy food choices (49) Interestingly mostloci selected for this intervention (variants at MTHFR FTOTCF7L2 APOE and FADS1) lack evidence of individual-levelclinical relevance (50) suggesting that the success of theFood4Me intervention was not dependent on the quality ofthe genetic information provided to participants
Although most if not all precision medicine initiativesare predicated on the assumption that their findings willhelp improve peoplersquos health the emphasis of these ini-tiatives is almost always on the generation of new knowl-edge MVP for example seeks ldquoto improve understandingof how health is affected by genetic characteristics behav-iors and environmental factorsrdquo (29) Although this objec-tive will undoubtedly be achieved how this information willeventually be used to optimize prevention and treatment
of diabetes and other diseases is unclear Indeed few pre-cision medicine initiatives focused on complex diseasesprovide a clear plan for clinical translation (Fig 2)
POTENTIAL BOTTLENECKS ON THE PATH TOPRECISION MEDICINE
Cost-effectivenessThe global precision medicine market was valued atUS$436 billion in 2016 and is predicted to triple in valuewithin the next decade (51) The economic dynamicsassociated with precision medicine differ from those ofconventional medicines as the nature of the approachmeans that precision medicines are likely to be approvedfor use only in specific subpopulations limiting the num-ber of patients eligible for treatment with a given drug Bycontrast the effectiveness of precision medicines shouldbe higher for patients within these subpopulationsthan for conventional medicines and side effects may befewer and less severe Owing to the smaller market sharefor a given precision medicine competition to producecheaper drugs between manufacturers may be less than forconventional drugs and the drive to develop off-patentbiosimilars (ie drugs with active properties identical orsimilar to previously licensed drugs) is likely to differcompared with conventional medicines depending onthe extent to which first-in-class drugs are undercut bycheaper alternatives Collectively these factors will impact
Figure 2mdashThe path to precision medicine in type 2 diabetes HEA health economic assessment
1918 Precision Medicine in Type 2 Diabetes Diabetes Volume 67 October 2018
the cost-effectiveness of precision medicines in type 2 di-abetes (52)
Thus far very few cost-effectiveness analyses in pre-cision diabetes medicine have been published despitelong-standing recognition that cost-effectiveness is a keyrequirement for clinical translation (53) The rare exam-ples of such studies have focused exclusively on rare mono-genic forms of diabetes neonatal diabetes (54) andmaturity-onset diabetes of the young (MODY) (5556)
Neonatal diabetes is diagnosed in infancy and affectsabout 1400000 live births and roughly half (1252000in people aged 20 years) develop permanent neonataldiabetes (54) Of the latter group up to 70 are estimatedto carry mutations in the genes encoding the ATP-sensitivepotassium channel (KCNJ11 and ABCC8) These mutationsblock the closing of the KATP channels which preventsb-cell depolarization and corresponding insulin secretion(57) However treatment with sulfonylureas rectifies thisdefect in 90 of cases allowing the discontinuation ofexogenous insulin (58) The earliest costndasheffect analysisin precision medicine focused on sulfonylurea therapy inpermanent neonatal diabetes (54) The authors estimatedthat genetic testing in permanent neonatal diabetes wouldconvey significant quality-of-life benefits at 10 years (032quality-adjusted life-years US$12528 saved) increasingto 070 at 30 years (US$30437 saved)
A range of single gene defects cause MODY each ofwhich are used to diagnose one of five forms of the diseaseIn MODY1 and MODY3 insulin secretion is impaired Thedisease occurs early in life and is often misdiagnosed astype 1 diabetes and exogenous insulin therapy is thusprescribed However treatment with sulfonylureas enablessecretion of endogenously produced insulin Switchingfrom insulin to sulfonylureas is preferable for a numberof reasons including those related to safety and burdenMODY2 is caused by mutations in GCK which lead tochronically elevated but nonprogressive blood glucoseconcentrations that do not typically require treatmentthus diagnosing MODY2 also conveys reduced costs andburdens associated with unnecessary treatment The firstcost-effectiveness analysis focused on MODY1ndash3 in USadults (aged 25ndash40 years) with diagnosed type 2 diabetes(56) based many of its assumptions on the findings ofthe UK Prospective Diabetes Study (UKPDS) (59) It alsoassumed a 2 frequency of MODY among people withdiabetes and set genetic screening costs at US$2580Under these assumptions genetic screening for MODYwould not be cost-effective The second study focused onlyonMODY1 screening and found that genetic screening wasnot cost-effective given the diabetes population frequencyof MODY1 mutations is unlikely to exceed 2 Sub-stantial reductions in genetic sequencing costs may how-ever render screening cost-effective (55)
Regulatory ConsensusMore than half of the most clinically impactful drugs de-veloped recently were discovered through academia-led
research (60) emphasizing the huge value of publicndashprivate partnerships Many of the large precision diabetesmedicine consortia involve both academia and industrywithin which unsurprising emphasis is placed on the dis-covery and validation of biomarkers that expedite drugdevelopment This expectation is backed up by the provenability of genetics to fast-track drug development bypinpointing high-value drug targets helping halve drugfailures and markedly reducing their costs (61) In drugrepositioning genetics has also proven its worth by re-vealing previously unknown conditions that a drug can beused to treat and by helping predict drug side effects
To obtain FDA (or European Medicines Agency [EMA])approval for biomarkers in medical products and de-vices involves a comprehensive qualification process (seehttpswwwfdagovDrugsDevelopmentApprovalProcessDrugDevelopmentToolsQualificationProgramBiomarker-QualificationProgramucm535395htm) within which ex-ists the requirement for a statement termed a ldquocontext ofuserdquo The context of use statement requires a clear anddetailed description of the intended role of the biomarkerin drug development as well as risks and benefits TheFoundation for the National Institutes of Health recentlypublished the Framework for Defining Evidentiary Criteriafor Biomarker Qualification (62) which provides an ex-panded outline of the biomarker classifications definedby the FDA
The process of biomarker qualification in drug devel-opment requires stringent adherence to the relevant reg-ulatory agenciesrsquo approvals processes In some studies thatseek to discover biomarkers for diabetes drug develop-ment the process is well rehearsed and aligning biomarkerdiscovery and validation strategies with regulatory expect-ations is relatively straightforward however it does re-quire that precision medicine consortia are well versedin these processes Biomarker discovery in prediabetes ismore challenging though as prediabetes is not a distinctdisease state and although some people with prediabetesdevelop diabetes and its complications a large proportionremains healthy for many years (63) Nevertheless a sub-group of those with prediabetes will progress to diabetesand identifying these people before b-cell function issubstantially diminished would open the door for severalpowerful therapeutics that are ineffective once endoge-nous insulin production has faded However becauseneither the FDA nor EMA currently recognizes an inter-mediate end point for disease progression in prediabetesdrug trials gaining regulatory approval for new prediabe-tes biomarkers requires that researchers foster close andfrequent communications with the regulatory agencies toensure candidate biomarkers have the possibility to pro-gress through the regulatory process
Legal Social and EthicalBalancing the interests of society and its citizens the risksand benefits legislation and individual freedom as wellas the risk of exasperating disparities is central to many
diabetesdiabetesjournalsorg Fitipaldi and Associates 1919
commentaries on the legal social and ethical aspects ofprecision medicine
Precision medicine research is heavily dependent onbiobanked data and tissues Often the original informedconsents were obtained many years before modern lsquoomicstechnologies existed at a time when many research ques-tions germane to precision medicine were inconceivableOften the informed consents were broad and ethicscommittees have concluded that these modern researchquestions are within the scope of the consents As newever larger and interconnected cohorts are being formedthe desire many researchers share for broad consentingandor to bank data or samples for unspecified futureresearch must be placed in context with several importantethical realities the need to maintain participantsrsquo confi-dentiality appropriately handle incidental findings deter-mine who owns the samples and data consider culturalsensitivities return results to participants engage societyand ensure unused material is properly disposed (64)
Much as precision medicine was inconceivable whenmany existing cohorts were formed it is impossible toaccurately predict how contemporary cohorts will be usedin the future Furthermore many precision medicineinitiatives involve the hybridization of academia healthcare and industry which may challenge some of the morealtruistic motivations of participants in academia-led bio-medical research Moreover much of the highly sensitivedata collated by these initiatives will eventually be madeavailable through managed-access portals to a wide rangeof end users who were not part of the data collectionprocess Thus much of this highly sensitive data will bewidely disseminated and used by distant end users toaddress a plethora of diverse research questions thatcannot yet be conceived factors that need to be consideredduring the informed consent process These are challengesfor historical biobanks as well as new precision medicinecohorts that consent only at enrollment To help overcomethis a process called dynamic consent has been developedwhich allows consents to be progressively updated asprojects evolve but requires fluid communication withparticipants throughout the life course of a study (65)
A major concern about genetic data are its linkage todatabases that enables inferences about a personrsquos socialcognitive moral cultural health or sexual identity whichparticipants had not consented to or for such inferencesto be made about family members without their consent(64) Indeed ensuring genomics data are used in a wayconsistent with appropriate ethical standards is a corefeature of most current health data protection legislation(66) Notwithstanding these risks there are major benefitsassociated with big data which include the discovery ofnovel disease mechanisms and therapeutic targets manyof which only become visible when data sets scale tomassive proportions Thus rather than constrain thegrowth and responsible utilization of big data emphasis isbeing placed on implementing legislation that protectsprivacy and helps prevent data breaches while facilitating
the safe and responsible storage transfer and utilizationof data In the European Union for example the Gen-eral Data Protection Regulation was implemented in May2018 to harmonize data privacy laws across Europe A keyfeature of this legislation as it relates to precision medicineresearch is that the analysis of sensitive data requiresexplicit opt-in consent whereas for nonsensitive dataunambiguous (implied) consent is sufficient
SUMMARY AND CONCLUSIONS
Diabetes medicine is likely to evolve dramatically in thecoming years ideally because evidence emerges from themajor precision medicine initiatives to support its utility inpreventing or treating diabetes but no doubt also becauseof the major commercial interest in seeing precision med-icine grow One of the possible outcomes will very likely bethe replacement of current classifications of diabetes withempirically derived subclassifications that can be coupledwith treatment regimens which compared with con-ventional medicines are more effective less costly andconvey fewer unnecessary side effects As genetic data-bases grow genotypes will be increasingly used to discovernew drug targets At this time the clinical application ofgenetics is confined to the diagnosis of and therapeuticguidance for rare forms of diabetes as well as the pre-diction of adverse drug reactions The ways in whichgenetics may or may not guide prevention and therapy incommon forms of diabetes is unclear However becausecharacterizing variation in a patientrsquos nuclear genomeis inexpensive and easily achieved and DNA variation re-mains constant across the life course it is likely that geneticsequences will feature in most patientsrsquo clinical recordsGiven this genetic data will likely be deployed in diverseways in the diabetes clinic of the future The roles otherlsquoomics technologies digital imaging devices and wearableswill play in precision diabetes medicine are harder to fore-cast as research in these areas is less advanced and the costof obtaining some of these data will likely remain highrelative to genetics Finally although many precision med-icine initiatives focus predominantly on pharmacotherapyoptimizing lifestyle (and possibly surgical) interventionsusing biotechnologies also has great potential for improvingtype 2 diabetes prevention and treatment
Funding and Duality of Interest The work of HF and PWF relatedto the manuscript was supported by the European Commission (CoG-2015_681742_NASCENT) Swedish Research Council (Distinguished YoungResearchers Award in Medicine) Swedish Heart-Lung Foundation Novo NordiskFoundation European Foundation for the Study of Diabetes and the SwedishFoundation for Strategic Research (Industrial Research Center in PrecisionMedicine) (all grants to PWF) PWF is a consultant for and has stock optionsin Zoe Global and has received research funding from Boehringer Ingelheim EliLilly Janssen Novo Nordisk Sanofi and Servier MIM is a National Institute forHealth Research (NIHR) Senior Investigator and a Wellcome Trust Senior In-vestigator The views expressed in this article are those of the author(s) and notnecessarily those of the National Health Service NIHR or the Department ofHealth Work described in this review was principally supported by the Wellcome
1920 Precision Medicine in Type 2 Diabetes Diabetes Volume 67 October 2018
Trust (098381 203141 106130) and by the National Institutes of Health (U01-DK105535) MIM serves on advisory panels for Pfizer Novo Nordisk and ZoeGlobal has received honoraria from Pfizer Novo Nordisk and Eli Lilly hasstock options in Zoe Global and has received research funding from AbbVieAstraZeneca Boehringer Ingelheim Eli Lilly Janssen Merck Novo Nordisk PfizerRoche Sanofi Servier and Takeda JCF is a Massachusetts General HospitalResearch Scholar and is supported by National Institutes of Health National Instituteof Diabetes and Digestive and Kidney Diseases grants R01 DK072041 U01DK105554 R01 DK105154 and K24 DK110550 and National Institute of GeneralMedical Sciences grant R01 GM117163 MIM JCF and PWF are membersof the American Diabetes Association Precision Medicine in Diabetes TaskforceThe views expressed in this article are those of the authors and do not necessarilyreflect those of the American Diabetes Association or the taskforce No otherpotential conflicts of interest relevant to this article were reported
References1 Centers for Disease Control and Prevention National Diabetes StatisticsReport Estimates of Diabetes and Its Burden in the United States 2014 AtlantaGA US Department of Health and Human Services 20142 Knowler WC Fowler SE Hamman RF et al Diabetes Prevention ProgramResearch Group 10-year follow-up of diabetes incidence and weight loss in theDiabetes Prevention Program Outcomes Study Lancet 20093741677ndash16863 Li G Zhang P Wang J et al The long-term effect of lifestyle interventions toprevent diabetes in the China Da Qing Diabetes Prevention Study a 20-yearfollow-up study Lancet 20083711783ndash17894 Tuomilehto J Lindstroumlm J Eriksson JG et al Finnish Diabetes PreventionStudy Group Prevention of type 2 diabetes mellitus by changes in lifestyleamong subjects with impaired glucose tolerance N Engl J Med 20013441343ndash13505 Ramachandran A Snehalatha C Mary S Mukesh B Bhaskar AD Vijay VIndian Diabetes Prevention Programme (IDPP) The Indian Diabetes PreventionProgramme shows that lifestyle modification and metformin prevent type 2 di-abetes in Asian Indian subjects with impaired glucose tolerance (IDPP-1) Dia-betologia 200649289ndash2976 Lean ME Leslie WS Barnes AC et al Primary care-led weight managementfor remission of type 2 diabetes (DiRECT) an open-label cluster-randomised trialLancet 2018391541ndash5517 Polyzogopoulou EV Kalfarentzos F Vagenakis AG Alexandrides TK Res-toration of euglycemia and normal acute insulin response to glucose in obesesubjects with type 2 diabetes following bariatric surgery Diabetes 2003521098ndash11038 Franco M Orduntildeez P Caballero B et al Impact of energy intake physicalactivity and population-wide weight loss on cardiovascular disease and diabetesmortality in Cuba 1980-2005 Am J Epidemiol 20071661374ndash13809 Cho NH Shaw JE Karuranga S et al IDF Diabetes Atlas global estimates ofdiabetes prevalence for 2017 and projections for 2045 Diabetes Res Clin Pract2018138271ndash28110 Lander ES Linton LM Birren B et al International Human Genome Se-quencing Consortium Initial sequencing and analysis of the human genome[published correction appears in Nature 2001411720] Nature 2001409860ndash92111 Visscher PM Wray NR Zhang Q et al 10 Years of GWAS discovery biologyfunction and translation Am J Hum Genet 20171015ndash2212 Merino J Udler MS Leong A Meigs JB A decade of genetic and metab-olomic contributions to type 2 diabetes risk prediction Curr Diab Rep 20171713513 Knowles JW Ashley EA Cardiovascular disease the rise of the genetic riskscore PLoS Med 201815e100254614 Mahajan A Taliun D Thurner M et al Fine-mapping of an expanded set oftype 2 diabetes loci to single-variant resolution using high-density imputation andislet-specific epigenome maps [preprint article online] 2018 Available fromhttpswwwbiorxivorgcontentearly20180109245506 Accessed 30 January2018
15 McCarthy MI Painting a new picture of personalised medicine for diabetesDiabetologia 201760793ndash79916 Scott RA Scott LJ Maumlgi R et al DIAbetes Genetics Replication AndMeta-analysis (DIAGRAM) Consortium An expanded genome-wide asso-ciation study of type 2 diabetes in Europeans Diabetes 2017662888ndash290217 Mahajan A Wessel J Willems SM et al ExomeBP Consortium MAGICConsortium GIANT Consortium Refining the accuracy of validated target iden-tification through coding variant fine-mapping in type 2 diabetes Nat Genet 201850559ndash57118 Udler MS Kim J von Grotthuss M et al Clustering of type 2 diabetes geneticloci by multi-trait associations identifies disease mechanisms and subtypes[preprint article online] 2018 Available from httpswwwbiorxivorgcontentearly20180510319509 Accessed 30 March 201819 van Zuydam NR Ahlqvist E Sandholm N et al A genome-wide associationstudy of diabetic kidney disease in subjects with type 2 diabetes Diabetes 2018671414ndash142720 Ahlqvist E Storm P Kaumlraumljaumlmaumlki A et al Novel subgroups of adult-onsetdiabetes and their association with outcomes a data-driven cluster analysis of sixvariables Lancet Diabetes Endocrinol 20186361ndash36921 Fuchsberger C Flannick J Teslovich TM et al The genetic architecture oftype 2 diabetes Nature 201653641ndash4722 Koivula RW Heggie A Barnett A et al DIRECT Consortium Discovery ofbiomarkers for glycaemic deterioration before and after the onset of type 2 di-abetes rationale and design of the epidemiological studies within the IMI DIRECTConsortium Diabetologia 2014571132ndash114223 Rask-Andersen M Karlsson T Ek WE Johansson Aring Gene-environmentinteraction study for BMI reveals interactions between genetic factors and physicalactivity alcohol consumption and socioeconomic status PLoS Genet 201713e100697724 Celis-Morales CA Lyall DM Gray SR et al Dietary fat and total energy intakemodifies the association of genetic profile risk score on obesity evidence from48 170 UK Biobank participants Int J Obes 2017411761ndash176825 Tyrrell J Wood AR Ames RM et al Gene-obesogenic environment inter-actions in the UK Biobank study Int J Epidemiol 201746559ndash57526 Young AI Wauthier F Donnelly P Multiple novel gene-by-environment in-teractions modify the effect of FTO variants on body mass index Nat Commun201671272427 Reardon S Giant study poses DNA data-sharing dilemma Nature 201552516ndash1728 Hudson K Lifton R Patrick-Lake B The Precision Medicine Initiative CohortProgram ndash Building a Research Foundation for 21st Century Medicine BethesdaMD National Institutes of Health 201529 Gaziano JM Concato J Brophy M et al Million Veteran Program a mega-biobank to study genetic influences on health and disease J Clin Epidemiol 201670214ndash22330 Bergen SE Sullivan PF National-scale precision medicine for psychiatricdisorders in Sweden Am J Med Genet B Neuropsychiatr Genet 7 July 2017 [Epubahead of print] DOI 101002ajmgb3256231 Cyranoski D Chinarsquos bid to be a DNA superpower Nature 2016534462ndash46332 Cyranoski D China embraces precision medicine on a massive scale Nature20165299ndash1033 Bu D Peng S Luo H et al Precision medicine and cancer immunology inChina From big data to knowledge in precision medicine Science 2018 (Suppl)S35ndashS3834 Moltke I Grarup N Joslashrgensen ME et al A common Greenlandic TBC1D4variant confers muscle insulin resistance and type 2 diabetes Nature 2014512190ndash19335 Dash S Sano H Rochford JJ et al A truncation mutation in TBC1D4 ina family with acanthosis nigricans and postprandial hyperinsulinemia Proc NatlAcad Sci U S A 20091069350ndash9355
diabetesdiabetesjournalsorg Fitipaldi and Associates 1921
36 Estrada K Aukrust I Bjoslashrkhaug L et al SIGMA Type 2 Diabetes ConsortiumAssociation of a low-frequency variant in HNF1A with type 2 diabetes in a Latinopopulation [published correction appears in JAMA 20143121932] JAMA 2014
3112305ndash231437 Marshall SM 60 years of metformin use a glance at the past and a look tothe future Diabetologia 2017601561ndash156538 Pearson ER Pharmacogenetics and target identification in diabetes CurrOpin Genet Dev 20185068ndash7339 Zhou K Bellenguez C Spencer CC et al GoDARTS and UKPDS Diabetes
Pharmacogenetics Study Group Wellcome Trust Case Control Consortium 2MAGIC investigators Common variants near ATM are associated with glycemicresponse to metformin in type 2 diabetes Nat Genet 201143117ndash12040 Zhou K Yee SW Seiser EL et al MetGen Investigators DPP InvestigatorsACCORD Investigators Variation in the glucose transporter gene SLC2A2 isassociated with glycemic response to metformin Nat Genet 2016481055ndash
105941 National Research Council Committee on a Framework for Developing a NewTaxonomy of Disease Toward Precision Medicine Building a Knowledge Networkfor Bomedical Research and a New Taxonomy of Disease Washington DCNational Academies Press 201142 Franks PW Poveda A Lifestyle and precision diabetes medicine will ge-
nomics help optimise the prediction prevention and treatment of type 2 diabetesthrough lifestyle therapy Diabetologia 201760784ndash79243 Zeevi D Korem T Zmora N et al Personalized nutrition by prediction of
glycemic responses Cell 20151631079ndash109444 Swanson A Big pharmaceutical companies are spending far more onmarketing than research The Washington Post 11 February 2015 Available fromhttpswwwwashingtonpostcomnewswonkwp20150211big-pharmaceutical-companies-are-spending-far-more-on-marketing-than-researchnoredirect=onamputm_term=787d031b9b7c Accessed 20 February 201845 Unilever Advertising amp Marketing Unilever 2018 Available from httpwwwunilevercomsustainable-livingwhat-matters-to-youadvertising-and-marketinghtml Accessed 31 March 201846 OrsquoDowd A Spending on junk food advertising is nearly 30 times whatgovernment spends on promoting healthy eating BMJ 2017359j467747 National Institutes of Health Molecular Transducers of Physical Activity in
Humans Frequently Asked Questions (FAQs) [Internet] 2018 Available from httpscommonfundnihgovMolecularTransducersFAQs Accessed 20 March 201848 Livingstone KM Celis-Morales C Navas-Carretero S et al Food4Me Study
Effect of an Internet-based personalized nutrition randomized trial on dietarychanges associated with the Mediterranean diet the Food4Me Study Am J ClinNutr 2016104288ndash29749 Celis-Morales C Livingstone KM Marsaux CF et al Food4Me Study Effectof personalized nutrition on health-related behaviour change evidence from theFood4Me European randomized controlled trial Int J Epidemiol 201746578ndash
588
50 Celis-Morales C Livingstone KM Marsaux CF et al Design and baselinecharacteristics of the Food4Me study a web-based randomised controlled trial ofpersonalised nutrition in seven European countries Genes Nutr 20151045051 BIS Research Global precision medicine market - analysis and forecast2017-2026 2017 Available from httpsbisresearchcomindustry-reportglobal-precision-medicine-market-2026html Accessed 31 January 201852 Stern AD Alexander BM Chandra A How economics can shape precisionmedicines Science 20173551131ndash113353 Smith RJ Nathan DM Arslanian SA Groop L Rizza RA Rotter JI In-dividualizing therapies in type 2 diabetes mellitus based on patient characteristicswhat we know and what we need to know J Clin Endocrinol Metab 2010951566ndash157454 Greeley SA John PM Winn AN et al The cost-effectiveness of personalizedgenetic medicine the case of genetic testing in neonatal diabetes Diabetes Care201134622ndash62755 Nguyen HV Finkelstein EA Mital S Gardner DS Incremental cost-effectivenessof algorithm-driven genetic testing versus no testing for maturity onset diabetes of theyoung (MODY) in Singapore J Med Genet 201754747ndash75356 Naylor RN John PM Winn AN et al Cost-effectiveness of MODY genetictesting translating genomic advances into practical health applications DiabetesCare 201437202ndash20957 Gloyn AL Pearson ER Antcliff JF et al Activating mutations in the geneencoding the ATP-sensitive potassium-channel subunit Kir62 and permanentneonatal diabetes N Engl J Med 20043501838ndash184958 Pearson ER Flechtner I Njoslashlstad PR et al Neonatal Diabetes InternationalCollaborative Group Switching from insulin to oral sulfonylureas in patients withdiabetes due to Kir62 mutations N Engl J Med 2006355467ndash47759 UK Prospective Diabetes Study (UKPDS) UK Prospective Diabetes Study(UKPDS) VIII Study design progress and performance Diabetologia 199134877ndash89060 Kesselheim AS Tan YT Avorn J The roles of academia rare diseases andrepurposing in the development of the most transformative drugs Health Aff(Millwood) 201534286ndash29361 Nelson MR Tipney H Painter JL et al The support of human geneticevidence for approved drug indications Nat Genet 201547856ndash86062 FNIH Evidentiary Criteria Writing Group Framework for Defining EvidentiaryCriteria for Biomarker Qualification Bethesda MD National Library of Medicine201663 Tabaacutek AG Herder C Rathmann W Brunner EJ Kivimaumlki M Prediabetesa high-risk state for diabetes development Lancet 20123792279ndash229064 Nicol D Bubela T Chalmers D et al Precision medicine drowning ina regulatory soup J Law Biosci 20163281ndash30365 Kaye J Whitley EA Lund D Morrison M Teare H Melham K Dynamicconsent a patient interface for twenty-first century research networks Eur J HumGenet 201523141ndash14666 Kinkorovaacute J Biobanks in the era of personalized medicine objectiveschallenges and innovation Overview EPMA J 201674
1922 Precision Medicine in Type 2 Diabetes Diabetes Volume 67 October 2018
that results Several recent studies (16ndash19) have addedempirical support for this approach demonstrating forexample differential relationships between process-specific risk scores and the risk of complications such asdiabetic kidney disease and coronary artery disease
The palette model captures the range of diabetes sub-types (from monogenic via hybrid forms [eg latent au-toimmune diabetes in adults] through to the clinicalheterogeneity evident within type 2 diabetes) as a contin-uum consistent with the genetic architecture of diabetesand real-world clinical observation (2021) The modelsuggests that for many people with type 2 diabetes theircondition is the consequence not of a major defect ina single process but in the confluence of suboptimalperformance across several processes contributing in par-allel This is what one would predict given the evidencethat type 2 diabetes risk can be subtly modulated bycommon variants acting through any one of several dis-tinct mechanisms (energy balance adipocyte differentia-tion incretin signaling insulin secretion etc) and by thewide range of therapeutic interventions that have theproven capacity to ameliorate to some degree at leastthe diabetic state (eg insulin sensitizers b-cell secre-tagogues calorie restriction exercise and metabolicsurgery)
This model provides a framework for understanding themechanistic basis of heterogeneity and its clinical conse-quences It also allows for the existence of cases at theextremes of the distribution for which a targeted inter-vention might be particularly effective Genetic risk scoresthat capture each of these processes may help to teaseapart heterogeneity in phenotype progression and ther-apeutic response They may allow identification within theoverall population of subsets of selected individuals inwhom the profile of genetic predisposition is dominatedby defects in a single pathway facilitating personalizedmechanism-specific interventions
Nevertheless for multifactorial diseases like type 2 di-abetes where individual predisposition is influenced asmuch by nongenetic as genetic factors there is an absolutelimit to the clinical precision that genetics alone can pro-vide For precision medicine to flourish this geneticinformation will need to be integrated with relevantmeasures of those aspects of the external and internalenvironment (the latter term for example encompassingthe gut microbiome) that also influence type 2 diabetespredisposition phenotype and progression This informa-tion would ideally be supported by pertinent biomarkerand clinical readouts generating integrated profiles thatmay provide the predictive accuracy needed for clinicalutility
Along these lines Ahlqvist et al (20) used data from8980 Swedish patients with newly diagnosed diabetes (ofany type) and applied machine learning algorithms toderive a data-driven approach to classifying diabetes sub-types The authors proposed five subtypes determined byvariations across six variables (glutamate decarboxylase
antibodies age at diagnosis BMI HbA1c and HOMA2estimates of b-cell function and insulin resistance) Therelationship of subtype and incident events (use of anti-diabetes medication achievement of treatment goals anddiabetes complications) were assessed and replicated inindependent cohorts Each cluster was characterized bycertain phenotypic features assessed soon after diagnosisThese include early-onset severe autoimmune diabetes(67 of patients) severe insulin-deficient diabetes (175of patients) severe insulin-resistant diabetes (153 ofpatients) mild obesity-related diabetes (216 of patients)and mild age-related diabetes (391 of patients) Rates ofdiabetic renal disease and coronary disease were highest insevere insulin-resistant diabetes although rates of retinop-athy were lowest for this group (retinopathies were morefrequent in severe insulin-deficient diabetes) Severe insulin-resistant diabetes also progressedmost rapidly to oral diabetesmedications (other than metformin) and was the slowestto reach the HbA1c treatment goal69 (52 mmolmol)Both severe autoimmune diabetes and severe insulin-deficient diabetes progressed to sustained insulin use con-siderably more quickly than the remaining clusters Bycontrast mild age-related diabetes and mild obesity-relateddiabetes were characterized by reasonably favorable profiles
This initial step demonstrated that one can deriveclusters from existing big data that make eminent phys-iological sense One limitation of this approach though incontrast with a genetically driven strategy resides in theuse of phenotypic criteria for clustering that are ascer-tained at disease onset and are not necessarily generaliz-able to the many people one would want to stratify (withnormoglycemia or with advanced diabetes) One shouldalso remain cognizant that a clinician may not be able tounequivocally place an individual patient into a givencluster thus the implementation and clinical utility ofthis approach remain to be demonstrated
It will be essential to understand how the relationshipsbetween these genetic and physiological approaches tostratification might be combined and in doing so howmuch value is added from a clinical perspective A crucialmissing piece is evidence that by clustering patients in thisway disease course diabetes complications or treatmentresponse can be predicted with sufficient accuracy to be ofclinical value This determination requires the explicitassessment of predictive accuracy and reclassificationSimilarly it is not known whether treatment that is guidedby cluster identity will be more or less effective thancurrent approaches for which intervention studies willbe needed Moreover because clustering techniques typ-ically require the dichotomization of continuous exposurevariables which usually results in loss of power the pre-dictive accuracy of algorithms that do not require this typeof data transformation is likely to be superior
Family history of diabetes is a strong predictor ofdisease and is frequently included in diabetes predictionalgorithms For information on family history to enhancethe predictive ability of the phenotypic clusters proposed
diabetesdiabetesjournalsorg Fitipaldi and Associates 1913
by Ahlqvist et al (20) it would require that some clustersbe more driven by familial factors than others This maywell be true and future studies exploring the genetic basisof these clusters may help test this hypothesis Howeverfamily history is the consequence of both genetic andenvironmental influences (including developmental expo-sures) and understanding why family history affects dis-ease progression differentially across clusters if indeed itdoes would likely be intensely scrutinized owing to theetiological insights this might reveal
OVERVIEW OF MAJOR PRECISION MEDICINEINITIATIVES
After the first draft sequence of the human genome waspublished in 2001 (10) methods and technologies wererapidly developed that enabled large-scale quantification ofhuman genetic variation and subsequently variation acrossother biological strata As investigators began using thesetechnologies global research consortia grew through whichnovel biomarkers for type 2 diabetes were discovered Therapid growth of large data sets and ad hoc collaborativenetworks as well as clear evidence that this new approachto biomarker discovery worked laid the foundations forlarge precision medicine initiatives Although some initia-tives have built new cohorts from scratch most haveleveraged existing data and biomaterials already stored inbiobanks (Table 1 and Fig 1)
In 2008 a joint undertaking between the EuropeanCommission European academic institutions and theEuropean Federation of Pharmaceutical Industries andAssociations was launched This euro56 billion programcalled the Innovative Medicines Initiative (IMI) comprises100 projects focused on the major diseases affectingEuropean citizens Of these several focus on precisionmedicine in type 2 diabetes SUrrogate markers for Micro-and Macro-vascular hard endpoints for Innovative dia-betes Tools (SUMMIT) (diabetes complications) DiabetesResearch on Patient Stratification (DIRECT) (drug responseand glycemic deterioration before and after the onset oftype 2 diabetes) (22) Risk Assessment and ProgreSsiOn ofDIabetes (RHAPSODY) (glycemic deterioration before andafter the onset of type 2 diabetes) and Biomarker En-terprise to Attack Diabetic Kidney Disease (BEAt-DKD)(diabetic kidney disease) A common objective is the discov-ery and validation of biomarkers that facilitate the strati-fication of patient populations into subgroups that mightbe treated more effectively than without biomarker strat-ification In some instances the focus is also on diagnos-tic reclassification Unlike most other precision medicineinitiatives those within the IMI focus on the integration ofmultiple biomarkers such as genotypes transcripts pro-teins metabolites and metagenomics sequences
The UK Biobank cohort (established 2005) includesaround 500000 adults the vast majority of whom haveprovided nonfasting blood samples and self-reported dataon lifestyle health and well-being and have been geno-typed using genome-wide arrays and targeted metabolomic
analyses are also under way In subcohorts more detailedphenotyping has been undertaken such as MRI scans andphysical activity monitoring Research conducted using UKBiobank data for example has shown that genetic variantsassociated with obesity are likely to influence susceptibilityto a range of modifiable lifestyle exposures (23ndash26) whichmay have relevance to diabetes Nevertheless the very smallmagnitude of these effects and susceptibility to confounding(25) precludes the immediate translation of these findingsinto clinical practice Alternatively as UK Biobank accruesincident events of diabetes complications the database islikely to become a powerful resource through which theprognostic value of stratifying populations into subgroupsor determining how environmental risk factors differentiallyaffect disease susceptibility by subgroup can be assessed
Plans to implement precision medicine into US med-ical practice accelerated in 2015 with the announcement ofthe Precision Medicine Initiative (PMI) for which US$215million was initially budgeted by the federal government(2728) In the short term the PMI is focused on cancerwhereas in the longer term all areas of health and healthcare will be studied with specific emphasis placed on thediscovery of predictive biomarkers for type 2 diabetes (28)A major feature of the PMI is a cohort of 1 million peopleand a research program called the All of Us Research Pro-gram The cohort supported by a US$55 million budget isbuilt around nationwide recruitment sites under the bannerof the PMI Cohort Program Managed access for accreditedresearchers to biobanked data and samples is intendedto facilitate several major goals including those related togenendashenvironment interactions pharmacogenomics riskstratification mobile health technologies health empower-ment and innovative clinical trials (httpsallofusnihgov)
Several other major US initiatives predate the PMI andhave diabetes and its risk factors at their core Theseinclude the Million Veteran Program (MVP) and theAccelerating Medicines Partnership in Type 2 Diabetes(AMP T2D) The MVP uses genomics and other healthdata obtained through electronic medical records andfollow-up surveys in about 600000 military veteransaged 50ndash69 years (29) The AMP T2D seeks to gathergenomic and metagenomic data and to create an analyticalengine that can be used to mine the genetic basis todiabetes and related traits while safeguarding the confi-dentiality of the results Data access is a central pillar ofAMP T2D and through the T2D Knowledge Portal (wwwtype2diabetesgeneticsorg) the consortium has assembledgenetic and phenotypic data from participants with andwithout type 2 diabetes frommultiple populations creatinga central repository for such data (wwwtype2diabetesgeneticsorginformationalabout) The ability of these initiatives tocontribute to precision diabetes medicine is likely to beenhanced by their partnerships with industry which arenecessary to translate research into therapeutics ap-proved by the US Food and Drug Administration (FDA)
The Nordic countries have a long history of biobank-ing and registry-based research and have contributed
1914 Precision Medicine in Type 2 Diabetes Diabetes Volume 67 October 2018
Tab
le1mdashGlobalp
recisionmed
icineinitiatives
ofrelevance
totyp
e2diab
etes
Nam
eRegion
Start
date
Funding
InitiativePop
ulation(in
thousands)
Australian
Precision
Med
icineInitiative
Australia
2016A$25
million
Pub
licndashp
rivatemdash
Chinese
Precision
Med
icineInitiative
China
2016US$9
billion
Pub
licndashp
rivate2
InnovativeMed
icinesInitiative
(diab
etesprojects
only)Europ
eanUnion
2008euro200
million
Pub
licndashp
rivate100
Nord
icPrecision
Med
icineInitiative
Nord
ic(Denm
arkEstonia
FinlandIceland
Norw
ayand
Swed
en)2015
mdashPub
licndashp
rivate1000
Estonian
Personalised
Med
icinePilot
Project
(Nord
icPrecision
Med
icineInitiative)
Nord
ic(Estonia)
2015euro33
million
Governm
ental100
FinnGen
(Nord
icPrecision
Med
icineInitiative)
Nord
ic(Finland)
2017euro60
million
Pub
licndashp
rivate500
Genom
icAggregation
Project
inSwed
en(Nord
icPrecision
Med
icineInitiative)
Nord
ic(Swed
en)2015
mdashPub
lic160
Saud
iHum
anGenom
eProject
Saud
iArab
ia2013
US$40
million
Governm
ental100
Genom
icsEngland
UK
2012US$523
million
Pub
licndashp
rivate75
UK
Biob
ankUK
2005US$122
million
Pub
licndashp
rivate500
Project
Baseline
US
2017US$41
million
Pub
licndashp
rivate10
USPrecision
Med
icineInitiative
US
2015US$215
million
Pub
licndashp
rivate1000
Accelerating
Med
icinesPartnership
US
2014US$528
million
Pub
licndashp
rivate150
Million
Veteran
Program
US
2011US$116
million
Pub
licndashp
rivate612
diabetesdiabetesjournalsorg Fitipaldi and Associates 1915
substantially to discoveries in diabetes genetics over thepast decade In 2015 the Nordic Precision MedicineInitiative (NPMI) was formed to bring together genetic andother biomedical data from 1 million Nordic citizensSeveral national precision and genomic medicine projectsare under way in Estonia for example the EstonianGenome Center at the University of Tartu is curatinga population-based biobank suitable for precision medicineresearch in diabetes and other complex diseases The aimis to promote and advance the development of geneticresearch and the implementation of genomic data intoclinical practice to improve public health The Estoniangovernment recently launched the Estonian PersonalisedMedicine Pilot Project (EPMPP) (2015ndash2018) which seeksto implement personalized medicine on a national scaleAccordingly the government has provided US$59 millionto support genotyping in 100000 Estonians In Finlandthe FinnGen project was launched in 2017 with theobjective to link genomic data with digital health caredata for 500000 Finnish citizens (10 of the countryrsquospopulation) through a publicndashprivate partnership In SwedenGAPS (Genomic Aggregation Project in Sweden) hasassimilated160000 genotyped samples with corresponding
phenotype data covering a wide range of diseases includingdiabetes (30) and GMS (Genomic Medicine Sweden) isspearheading clinical genomics on a national scale In Icelandthe company deCODE genetics has established a genetic andphenotype database that encompasses the entire population(n = 334000) which fuels genetics research and drug targetvalidation for a wide range of diseases including diabetes
Several other countries have launched large-scale pro-grams of research on genomic and precision medicinemany with clear paths to clinical application althoughoften only in relation to rare pediatric diseases In SaudiArabia the very high prevalence of type 2 diabetes (20)led the Saudi government and King Abdulaziz City forScience and Technology to initiate the Saudi HumanGenome Project (SHGP) which seeks to sequence thegenomes of 100000 Saudi citizens in order to identifythe genetic basis of monogenic and complex diseases liketype 2 diabetes In China which has become a formidableforce in decoding genomes (31) the government an-nounced in 2016 a bold initiative to become a globalsuperpower in precision medicine with a US$9 billion15-year precision medicine initiative (32) The Chineseinitiative has three core objectives focused on recruiting
Figure 1mdashGlobal precision medicine initiatives of relevance to type 2 diabetes
1916 Precision Medicine in Type 2 Diabetes Diabetes Volume 67 October 2018
1) millions of participants from the seven main regions ofChina to form a nationally representative cohort 2) eightdisease-specific cohorts (cardiovascular cerebrovascularrespiratory metabolic neurological psychosomatic im-mune system disorders and seven common malignanttumors) totaling 700000 participants and 3) a clinicalcohort (N = 50000 patients with 50 rare diseases) (33)
CLINICAL TRANSLATION
The practice of precision diabetes medicine is currentlyconfined to rare monogenic forms of the disease Never-theless individual variants present only in specific pop-ulation isolates have been discovered that may have clinicalvalue in the prevention or treatment of type 2 diabetesThese include a TBC1D4 nonsense variant (pArg684ter)of relatively high prevalence in Greenlandic Inuit (minorallele frequency 17) and other circumpolar Inuit popu-lations that predisposes a substantially increased risk ofdiabetes (odds ratio 10 in those homozygous for thevariant) (34) The mechanism of action involves the mus-cle-selective loss of the long TBC1D4 isoform and dimin-ished GLUT4-mediated cellular glucose uptake in responseto insulin A thymine for cytosine substitution that causesa premature stop at codon 363 of TBC1D4 has also beendiscovered in people with acanthosis nigricans the muta-tion causes postprandial hyperinsulinemia as a possibleconsequence of a specific muscle and adipose tissue insulinresistance (35)
In Latinos a low frequency (minor allele frequency =21 in people with type 2 diabetes) missense variant (pE508K) at HNF1A conveys a type 2 diabetes odds ratio5(36)HNF1A also harbors mutations that cause rare mono-genic diabetes that can be successfully treated with sulfo-nylureas (discussed later) suggesting this is true for thenontrivial number of Latino individuals with type 2 di-abetes who carry pE508K Despite the relatively largeeffects these variants convey whether they will proveuseful in the context of precision medicine is unknown asno formal assessment of clinical utility or cost-effectivenesshas been reported
Metformin was discovered in the 1960s and is currentlythe frontline drug for early-stage diabetes treatment al-though its mechanisms of action remain unclear (37) Thereis genetic evidence for metformin intolerance (38) althoughthis has not been widely replicated By contrast variants atATM (39) and SLC2A2 (encoding GLUT2) (40) have beenassociated at a genome-wide level of statistical significancewith metformin response Although the effects conveyed bythese variants are too small to guide patient-level treatmentdecisions these findings may prove valuable in determiningmetforminrsquos mechanisms of action
Many major precision medicine statements includingthe executive summary of the US PMI (28) and a NationalResearch Council report focused on precision medicine andnew disease taxonomies (41) make clear that research andpractice in precision medicine should consider the majorimpact of lifestyle in disease etiology prevention and
treatment The incorporation of lifestyle into precisiondiabetes medicine is adequately justified by its provenbenefits in diabetes prevention and treatment Yet thereis good evidence that people benefit to varying degreesfrom diet and exercise and that personal biology underliesthis (42) Thus if precision medicine programs are to ad-equately consider the impact of lifestyle and other envi-ronmental exposures it seems logical that emphasis shouldbe placed on improving the precision of not only drugs butalso this type of antidiabetes therapy too
One compelling example of how glycemic response tofood can be predicted using an individualrsquos biomarkerscomes from a study of 800 young adults whose gut meta-genomic sequences were ascertained and diet and bloodglucose variation (from continuous glucose monitors) weremonitored for a week (43) Each participant received onestandardized meal (50 g carbohydrate) daily The authorsobserved that although participants elicited different post-prandial glycemic responses the responses to the samefood were consistent within individuals Using machinelearning algorithms these data were used to predict eachparticipantrsquos postprandial glycemic response to a givenfood They subsequently administered tailored diet inter-ventions to modulate glucose levels providing proof ofconcept that personalized diets guided by biomarker pro-filing can reduce blood glucose variability Notwithstand-ing the importance of this work from a basic scienceperspective to determine its value for precision medicinerequires knowing if minimizing variations in glucose inhealthy adults is clinically relevant and whether the ap-proach to predicting glycemic response to food also worksin people with diabetes To date neither of these impor-tant questions is addressed in the published literature
Despite lifestylersquos proven efficacy in diabetes preven-tion the resources available for research on lifestylemedicine are massively outweighed by investments inpharmacotherapy In 2013 for example US$35 billionwas spent by pharmaceutical companies on research anddevelopment (44) Although there are no comparablestatistics for research in lifestyle medicine funding islikely to be orders of magnitude less as most of this comesfrom the public purse The marketing of foods and bev-erages on the other hand is a huge industry whose mes-sages often counter public health efforts to promotehealthy lifestyle On an international scale hundreds ofbillions of US dollars are spent on food marketing annu-ally one of the biggest spenders is Unilever which spent US$8 billion on brand and marketing investments in2017 alone (45) Marketing junk foods and beverages ishandsomely resourced competing with public health ini-tiatives The major food companies in the UK for exam-ple have invested roughly US$200 million each year injunk food advertising which contrasts the US$72 millionspent on Change4Life the UK governmentrsquos flagshiphealthy eating campaign (46)
Notwithstanding the relative dearth of funding forlifestyle medicine some key initiatives exist One of the
diabetesdiabetesjournalsorg Fitipaldi and Associates 1917
largest of these is the National Institutes of HealthrsquosCommon Fund initiative on the Molecular Transducersof Physical Activity in Humans (httpcommonfundnihgovMolecularTransducers) which seeks to determineldquooptimal physical activity recommendations for peopleat various stages of liferdquo and develop ldquoprecisely targeted[exercise] regimens for individuals with particular healthneedsrdquo (47) Elsewhere the Food4Me study a research pro-gram funded by the European Union explored the role ofgenetics in the personalization of diet data from the studysuggest that personalized diet interventions supported bygenetic data improve consumption of healthy foods (48) andhelp sustain healthy food choices (49) Interestingly mostloci selected for this intervention (variants at MTHFR FTOTCF7L2 APOE and FADS1) lack evidence of individual-levelclinical relevance (50) suggesting that the success of theFood4Me intervention was not dependent on the quality ofthe genetic information provided to participants
Although most if not all precision medicine initiativesare predicated on the assumption that their findings willhelp improve peoplersquos health the emphasis of these ini-tiatives is almost always on the generation of new knowl-edge MVP for example seeks ldquoto improve understandingof how health is affected by genetic characteristics behav-iors and environmental factorsrdquo (29) Although this objec-tive will undoubtedly be achieved how this information willeventually be used to optimize prevention and treatment
of diabetes and other diseases is unclear Indeed few pre-cision medicine initiatives focused on complex diseasesprovide a clear plan for clinical translation (Fig 2)
POTENTIAL BOTTLENECKS ON THE PATH TOPRECISION MEDICINE
Cost-effectivenessThe global precision medicine market was valued atUS$436 billion in 2016 and is predicted to triple in valuewithin the next decade (51) The economic dynamicsassociated with precision medicine differ from those ofconventional medicines as the nature of the approachmeans that precision medicines are likely to be approvedfor use only in specific subpopulations limiting the num-ber of patients eligible for treatment with a given drug Bycontrast the effectiveness of precision medicines shouldbe higher for patients within these subpopulationsthan for conventional medicines and side effects may befewer and less severe Owing to the smaller market sharefor a given precision medicine competition to producecheaper drugs between manufacturers may be less than forconventional drugs and the drive to develop off-patentbiosimilars (ie drugs with active properties identical orsimilar to previously licensed drugs) is likely to differcompared with conventional medicines depending onthe extent to which first-in-class drugs are undercut bycheaper alternatives Collectively these factors will impact
Figure 2mdashThe path to precision medicine in type 2 diabetes HEA health economic assessment
1918 Precision Medicine in Type 2 Diabetes Diabetes Volume 67 October 2018
the cost-effectiveness of precision medicines in type 2 di-abetes (52)
Thus far very few cost-effectiveness analyses in pre-cision diabetes medicine have been published despitelong-standing recognition that cost-effectiveness is a keyrequirement for clinical translation (53) The rare exam-ples of such studies have focused exclusively on rare mono-genic forms of diabetes neonatal diabetes (54) andmaturity-onset diabetes of the young (MODY) (5556)
Neonatal diabetes is diagnosed in infancy and affectsabout 1400000 live births and roughly half (1252000in people aged 20 years) develop permanent neonataldiabetes (54) Of the latter group up to 70 are estimatedto carry mutations in the genes encoding the ATP-sensitivepotassium channel (KCNJ11 and ABCC8) These mutationsblock the closing of the KATP channels which preventsb-cell depolarization and corresponding insulin secretion(57) However treatment with sulfonylureas rectifies thisdefect in 90 of cases allowing the discontinuation ofexogenous insulin (58) The earliest costndasheffect analysisin precision medicine focused on sulfonylurea therapy inpermanent neonatal diabetes (54) The authors estimatedthat genetic testing in permanent neonatal diabetes wouldconvey significant quality-of-life benefits at 10 years (032quality-adjusted life-years US$12528 saved) increasingto 070 at 30 years (US$30437 saved)
A range of single gene defects cause MODY each ofwhich are used to diagnose one of five forms of the diseaseIn MODY1 and MODY3 insulin secretion is impaired Thedisease occurs early in life and is often misdiagnosed astype 1 diabetes and exogenous insulin therapy is thusprescribed However treatment with sulfonylureas enablessecretion of endogenously produced insulin Switchingfrom insulin to sulfonylureas is preferable for a numberof reasons including those related to safety and burdenMODY2 is caused by mutations in GCK which lead tochronically elevated but nonprogressive blood glucoseconcentrations that do not typically require treatmentthus diagnosing MODY2 also conveys reduced costs andburdens associated with unnecessary treatment The firstcost-effectiveness analysis focused on MODY1ndash3 in USadults (aged 25ndash40 years) with diagnosed type 2 diabetes(56) based many of its assumptions on the findings ofthe UK Prospective Diabetes Study (UKPDS) (59) It alsoassumed a 2 frequency of MODY among people withdiabetes and set genetic screening costs at US$2580Under these assumptions genetic screening for MODYwould not be cost-effective The second study focused onlyonMODY1 screening and found that genetic screening wasnot cost-effective given the diabetes population frequencyof MODY1 mutations is unlikely to exceed 2 Sub-stantial reductions in genetic sequencing costs may how-ever render screening cost-effective (55)
Regulatory ConsensusMore than half of the most clinically impactful drugs de-veloped recently were discovered through academia-led
research (60) emphasizing the huge value of publicndashprivate partnerships Many of the large precision diabetesmedicine consortia involve both academia and industrywithin which unsurprising emphasis is placed on the dis-covery and validation of biomarkers that expedite drugdevelopment This expectation is backed up by the provenability of genetics to fast-track drug development bypinpointing high-value drug targets helping halve drugfailures and markedly reducing their costs (61) In drugrepositioning genetics has also proven its worth by re-vealing previously unknown conditions that a drug can beused to treat and by helping predict drug side effects
To obtain FDA (or European Medicines Agency [EMA])approval for biomarkers in medical products and de-vices involves a comprehensive qualification process (seehttpswwwfdagovDrugsDevelopmentApprovalProcessDrugDevelopmentToolsQualificationProgramBiomarker-QualificationProgramucm535395htm) within which ex-ists the requirement for a statement termed a ldquocontext ofuserdquo The context of use statement requires a clear anddetailed description of the intended role of the biomarkerin drug development as well as risks and benefits TheFoundation for the National Institutes of Health recentlypublished the Framework for Defining Evidentiary Criteriafor Biomarker Qualification (62) which provides an ex-panded outline of the biomarker classifications definedby the FDA
The process of biomarker qualification in drug devel-opment requires stringent adherence to the relevant reg-ulatory agenciesrsquo approvals processes In some studies thatseek to discover biomarkers for diabetes drug develop-ment the process is well rehearsed and aligning biomarkerdiscovery and validation strategies with regulatory expect-ations is relatively straightforward however it does re-quire that precision medicine consortia are well versedin these processes Biomarker discovery in prediabetes ismore challenging though as prediabetes is not a distinctdisease state and although some people with prediabetesdevelop diabetes and its complications a large proportionremains healthy for many years (63) Nevertheless a sub-group of those with prediabetes will progress to diabetesand identifying these people before b-cell function issubstantially diminished would open the door for severalpowerful therapeutics that are ineffective once endoge-nous insulin production has faded However becauseneither the FDA nor EMA currently recognizes an inter-mediate end point for disease progression in prediabetesdrug trials gaining regulatory approval for new prediabe-tes biomarkers requires that researchers foster close andfrequent communications with the regulatory agencies toensure candidate biomarkers have the possibility to pro-gress through the regulatory process
Legal Social and EthicalBalancing the interests of society and its citizens the risksand benefits legislation and individual freedom as wellas the risk of exasperating disparities is central to many
diabetesdiabetesjournalsorg Fitipaldi and Associates 1919
commentaries on the legal social and ethical aspects ofprecision medicine
Precision medicine research is heavily dependent onbiobanked data and tissues Often the original informedconsents were obtained many years before modern lsquoomicstechnologies existed at a time when many research ques-tions germane to precision medicine were inconceivableOften the informed consents were broad and ethicscommittees have concluded that these modern researchquestions are within the scope of the consents As newever larger and interconnected cohorts are being formedthe desire many researchers share for broad consentingandor to bank data or samples for unspecified futureresearch must be placed in context with several importantethical realities the need to maintain participantsrsquo confi-dentiality appropriately handle incidental findings deter-mine who owns the samples and data consider culturalsensitivities return results to participants engage societyand ensure unused material is properly disposed (64)
Much as precision medicine was inconceivable whenmany existing cohorts were formed it is impossible toaccurately predict how contemporary cohorts will be usedin the future Furthermore many precision medicineinitiatives involve the hybridization of academia healthcare and industry which may challenge some of the morealtruistic motivations of participants in academia-led bio-medical research Moreover much of the highly sensitivedata collated by these initiatives will eventually be madeavailable through managed-access portals to a wide rangeof end users who were not part of the data collectionprocess Thus much of this highly sensitive data will bewidely disseminated and used by distant end users toaddress a plethora of diverse research questions thatcannot yet be conceived factors that need to be consideredduring the informed consent process These are challengesfor historical biobanks as well as new precision medicinecohorts that consent only at enrollment To help overcomethis a process called dynamic consent has been developedwhich allows consents to be progressively updated asprojects evolve but requires fluid communication withparticipants throughout the life course of a study (65)
A major concern about genetic data are its linkage todatabases that enables inferences about a personrsquos socialcognitive moral cultural health or sexual identity whichparticipants had not consented to or for such inferencesto be made about family members without their consent(64) Indeed ensuring genomics data are used in a wayconsistent with appropriate ethical standards is a corefeature of most current health data protection legislation(66) Notwithstanding these risks there are major benefitsassociated with big data which include the discovery ofnovel disease mechanisms and therapeutic targets manyof which only become visible when data sets scale tomassive proportions Thus rather than constrain thegrowth and responsible utilization of big data emphasis isbeing placed on implementing legislation that protectsprivacy and helps prevent data breaches while facilitating
the safe and responsible storage transfer and utilizationof data In the European Union for example the Gen-eral Data Protection Regulation was implemented in May2018 to harmonize data privacy laws across Europe A keyfeature of this legislation as it relates to precision medicineresearch is that the analysis of sensitive data requiresexplicit opt-in consent whereas for nonsensitive dataunambiguous (implied) consent is sufficient
SUMMARY AND CONCLUSIONS
Diabetes medicine is likely to evolve dramatically in thecoming years ideally because evidence emerges from themajor precision medicine initiatives to support its utility inpreventing or treating diabetes but no doubt also becauseof the major commercial interest in seeing precision med-icine grow One of the possible outcomes will very likely bethe replacement of current classifications of diabetes withempirically derived subclassifications that can be coupledwith treatment regimens which compared with con-ventional medicines are more effective less costly andconvey fewer unnecessary side effects As genetic data-bases grow genotypes will be increasingly used to discovernew drug targets At this time the clinical application ofgenetics is confined to the diagnosis of and therapeuticguidance for rare forms of diabetes as well as the pre-diction of adverse drug reactions The ways in whichgenetics may or may not guide prevention and therapy incommon forms of diabetes is unclear However becausecharacterizing variation in a patientrsquos nuclear genomeis inexpensive and easily achieved and DNA variation re-mains constant across the life course it is likely that geneticsequences will feature in most patientsrsquo clinical recordsGiven this genetic data will likely be deployed in diverseways in the diabetes clinic of the future The roles otherlsquoomics technologies digital imaging devices and wearableswill play in precision diabetes medicine are harder to fore-cast as research in these areas is less advanced and the costof obtaining some of these data will likely remain highrelative to genetics Finally although many precision med-icine initiatives focus predominantly on pharmacotherapyoptimizing lifestyle (and possibly surgical) interventionsusing biotechnologies also has great potential for improvingtype 2 diabetes prevention and treatment
Funding and Duality of Interest The work of HF and PWF relatedto the manuscript was supported by the European Commission (CoG-2015_681742_NASCENT) Swedish Research Council (Distinguished YoungResearchers Award in Medicine) Swedish Heart-Lung Foundation Novo NordiskFoundation European Foundation for the Study of Diabetes and the SwedishFoundation for Strategic Research (Industrial Research Center in PrecisionMedicine) (all grants to PWF) PWF is a consultant for and has stock optionsin Zoe Global and has received research funding from Boehringer Ingelheim EliLilly Janssen Novo Nordisk Sanofi and Servier MIM is a National Institute forHealth Research (NIHR) Senior Investigator and a Wellcome Trust Senior In-vestigator The views expressed in this article are those of the author(s) and notnecessarily those of the National Health Service NIHR or the Department ofHealth Work described in this review was principally supported by the Wellcome
1920 Precision Medicine in Type 2 Diabetes Diabetes Volume 67 October 2018
Trust (098381 203141 106130) and by the National Institutes of Health (U01-DK105535) MIM serves on advisory panels for Pfizer Novo Nordisk and ZoeGlobal has received honoraria from Pfizer Novo Nordisk and Eli Lilly hasstock options in Zoe Global and has received research funding from AbbVieAstraZeneca Boehringer Ingelheim Eli Lilly Janssen Merck Novo Nordisk PfizerRoche Sanofi Servier and Takeda JCF is a Massachusetts General HospitalResearch Scholar and is supported by National Institutes of Health National Instituteof Diabetes and Digestive and Kidney Diseases grants R01 DK072041 U01DK105554 R01 DK105154 and K24 DK110550 and National Institute of GeneralMedical Sciences grant R01 GM117163 MIM JCF and PWF are membersof the American Diabetes Association Precision Medicine in Diabetes TaskforceThe views expressed in this article are those of the authors and do not necessarilyreflect those of the American Diabetes Association or the taskforce No otherpotential conflicts of interest relevant to this article were reported
References1 Centers for Disease Control and Prevention National Diabetes StatisticsReport Estimates of Diabetes and Its Burden in the United States 2014 AtlantaGA US Department of Health and Human Services 20142 Knowler WC Fowler SE Hamman RF et al Diabetes Prevention ProgramResearch Group 10-year follow-up of diabetes incidence and weight loss in theDiabetes Prevention Program Outcomes Study Lancet 20093741677ndash16863 Li G Zhang P Wang J et al The long-term effect of lifestyle interventions toprevent diabetes in the China Da Qing Diabetes Prevention Study a 20-yearfollow-up study Lancet 20083711783ndash17894 Tuomilehto J Lindstroumlm J Eriksson JG et al Finnish Diabetes PreventionStudy Group Prevention of type 2 diabetes mellitus by changes in lifestyleamong subjects with impaired glucose tolerance N Engl J Med 20013441343ndash13505 Ramachandran A Snehalatha C Mary S Mukesh B Bhaskar AD Vijay VIndian Diabetes Prevention Programme (IDPP) The Indian Diabetes PreventionProgramme shows that lifestyle modification and metformin prevent type 2 di-abetes in Asian Indian subjects with impaired glucose tolerance (IDPP-1) Dia-betologia 200649289ndash2976 Lean ME Leslie WS Barnes AC et al Primary care-led weight managementfor remission of type 2 diabetes (DiRECT) an open-label cluster-randomised trialLancet 2018391541ndash5517 Polyzogopoulou EV Kalfarentzos F Vagenakis AG Alexandrides TK Res-toration of euglycemia and normal acute insulin response to glucose in obesesubjects with type 2 diabetes following bariatric surgery Diabetes 2003521098ndash11038 Franco M Orduntildeez P Caballero B et al Impact of energy intake physicalactivity and population-wide weight loss on cardiovascular disease and diabetesmortality in Cuba 1980-2005 Am J Epidemiol 20071661374ndash13809 Cho NH Shaw JE Karuranga S et al IDF Diabetes Atlas global estimates ofdiabetes prevalence for 2017 and projections for 2045 Diabetes Res Clin Pract2018138271ndash28110 Lander ES Linton LM Birren B et al International Human Genome Se-quencing Consortium Initial sequencing and analysis of the human genome[published correction appears in Nature 2001411720] Nature 2001409860ndash92111 Visscher PM Wray NR Zhang Q et al 10 Years of GWAS discovery biologyfunction and translation Am J Hum Genet 20171015ndash2212 Merino J Udler MS Leong A Meigs JB A decade of genetic and metab-olomic contributions to type 2 diabetes risk prediction Curr Diab Rep 20171713513 Knowles JW Ashley EA Cardiovascular disease the rise of the genetic riskscore PLoS Med 201815e100254614 Mahajan A Taliun D Thurner M et al Fine-mapping of an expanded set oftype 2 diabetes loci to single-variant resolution using high-density imputation andislet-specific epigenome maps [preprint article online] 2018 Available fromhttpswwwbiorxivorgcontentearly20180109245506 Accessed 30 January2018
15 McCarthy MI Painting a new picture of personalised medicine for diabetesDiabetologia 201760793ndash79916 Scott RA Scott LJ Maumlgi R et al DIAbetes Genetics Replication AndMeta-analysis (DIAGRAM) Consortium An expanded genome-wide asso-ciation study of type 2 diabetes in Europeans Diabetes 2017662888ndash290217 Mahajan A Wessel J Willems SM et al ExomeBP Consortium MAGICConsortium GIANT Consortium Refining the accuracy of validated target iden-tification through coding variant fine-mapping in type 2 diabetes Nat Genet 201850559ndash57118 Udler MS Kim J von Grotthuss M et al Clustering of type 2 diabetes geneticloci by multi-trait associations identifies disease mechanisms and subtypes[preprint article online] 2018 Available from httpswwwbiorxivorgcontentearly20180510319509 Accessed 30 March 201819 van Zuydam NR Ahlqvist E Sandholm N et al A genome-wide associationstudy of diabetic kidney disease in subjects with type 2 diabetes Diabetes 2018671414ndash142720 Ahlqvist E Storm P Kaumlraumljaumlmaumlki A et al Novel subgroups of adult-onsetdiabetes and their association with outcomes a data-driven cluster analysis of sixvariables Lancet Diabetes Endocrinol 20186361ndash36921 Fuchsberger C Flannick J Teslovich TM et al The genetic architecture oftype 2 diabetes Nature 201653641ndash4722 Koivula RW Heggie A Barnett A et al DIRECT Consortium Discovery ofbiomarkers for glycaemic deterioration before and after the onset of type 2 di-abetes rationale and design of the epidemiological studies within the IMI DIRECTConsortium Diabetologia 2014571132ndash114223 Rask-Andersen M Karlsson T Ek WE Johansson Aring Gene-environmentinteraction study for BMI reveals interactions between genetic factors and physicalactivity alcohol consumption and socioeconomic status PLoS Genet 201713e100697724 Celis-Morales CA Lyall DM Gray SR et al Dietary fat and total energy intakemodifies the association of genetic profile risk score on obesity evidence from48 170 UK Biobank participants Int J Obes 2017411761ndash176825 Tyrrell J Wood AR Ames RM et al Gene-obesogenic environment inter-actions in the UK Biobank study Int J Epidemiol 201746559ndash57526 Young AI Wauthier F Donnelly P Multiple novel gene-by-environment in-teractions modify the effect of FTO variants on body mass index Nat Commun201671272427 Reardon S Giant study poses DNA data-sharing dilemma Nature 201552516ndash1728 Hudson K Lifton R Patrick-Lake B The Precision Medicine Initiative CohortProgram ndash Building a Research Foundation for 21st Century Medicine BethesdaMD National Institutes of Health 201529 Gaziano JM Concato J Brophy M et al Million Veteran Program a mega-biobank to study genetic influences on health and disease J Clin Epidemiol 201670214ndash22330 Bergen SE Sullivan PF National-scale precision medicine for psychiatricdisorders in Sweden Am J Med Genet B Neuropsychiatr Genet 7 July 2017 [Epubahead of print] DOI 101002ajmgb3256231 Cyranoski D Chinarsquos bid to be a DNA superpower Nature 2016534462ndash46332 Cyranoski D China embraces precision medicine on a massive scale Nature20165299ndash1033 Bu D Peng S Luo H et al Precision medicine and cancer immunology inChina From big data to knowledge in precision medicine Science 2018 (Suppl)S35ndashS3834 Moltke I Grarup N Joslashrgensen ME et al A common Greenlandic TBC1D4variant confers muscle insulin resistance and type 2 diabetes Nature 2014512190ndash19335 Dash S Sano H Rochford JJ et al A truncation mutation in TBC1D4 ina family with acanthosis nigricans and postprandial hyperinsulinemia Proc NatlAcad Sci U S A 20091069350ndash9355
diabetesdiabetesjournalsorg Fitipaldi and Associates 1921
36 Estrada K Aukrust I Bjoslashrkhaug L et al SIGMA Type 2 Diabetes ConsortiumAssociation of a low-frequency variant in HNF1A with type 2 diabetes in a Latinopopulation [published correction appears in JAMA 20143121932] JAMA 2014
3112305ndash231437 Marshall SM 60 years of metformin use a glance at the past and a look tothe future Diabetologia 2017601561ndash156538 Pearson ER Pharmacogenetics and target identification in diabetes CurrOpin Genet Dev 20185068ndash7339 Zhou K Bellenguez C Spencer CC et al GoDARTS and UKPDS Diabetes
Pharmacogenetics Study Group Wellcome Trust Case Control Consortium 2MAGIC investigators Common variants near ATM are associated with glycemicresponse to metformin in type 2 diabetes Nat Genet 201143117ndash12040 Zhou K Yee SW Seiser EL et al MetGen Investigators DPP InvestigatorsACCORD Investigators Variation in the glucose transporter gene SLC2A2 isassociated with glycemic response to metformin Nat Genet 2016481055ndash
105941 National Research Council Committee on a Framework for Developing a NewTaxonomy of Disease Toward Precision Medicine Building a Knowledge Networkfor Bomedical Research and a New Taxonomy of Disease Washington DCNational Academies Press 201142 Franks PW Poveda A Lifestyle and precision diabetes medicine will ge-
nomics help optimise the prediction prevention and treatment of type 2 diabetesthrough lifestyle therapy Diabetologia 201760784ndash79243 Zeevi D Korem T Zmora N et al Personalized nutrition by prediction of
glycemic responses Cell 20151631079ndash109444 Swanson A Big pharmaceutical companies are spending far more onmarketing than research The Washington Post 11 February 2015 Available fromhttpswwwwashingtonpostcomnewswonkwp20150211big-pharmaceutical-companies-are-spending-far-more-on-marketing-than-researchnoredirect=onamputm_term=787d031b9b7c Accessed 20 February 201845 Unilever Advertising amp Marketing Unilever 2018 Available from httpwwwunilevercomsustainable-livingwhat-matters-to-youadvertising-and-marketinghtml Accessed 31 March 201846 OrsquoDowd A Spending on junk food advertising is nearly 30 times whatgovernment spends on promoting healthy eating BMJ 2017359j467747 National Institutes of Health Molecular Transducers of Physical Activity in
Humans Frequently Asked Questions (FAQs) [Internet] 2018 Available from httpscommonfundnihgovMolecularTransducersFAQs Accessed 20 March 201848 Livingstone KM Celis-Morales C Navas-Carretero S et al Food4Me Study
Effect of an Internet-based personalized nutrition randomized trial on dietarychanges associated with the Mediterranean diet the Food4Me Study Am J ClinNutr 2016104288ndash29749 Celis-Morales C Livingstone KM Marsaux CF et al Food4Me Study Effectof personalized nutrition on health-related behaviour change evidence from theFood4Me European randomized controlled trial Int J Epidemiol 201746578ndash
588
50 Celis-Morales C Livingstone KM Marsaux CF et al Design and baselinecharacteristics of the Food4Me study a web-based randomised controlled trial ofpersonalised nutrition in seven European countries Genes Nutr 20151045051 BIS Research Global precision medicine market - analysis and forecast2017-2026 2017 Available from httpsbisresearchcomindustry-reportglobal-precision-medicine-market-2026html Accessed 31 January 201852 Stern AD Alexander BM Chandra A How economics can shape precisionmedicines Science 20173551131ndash113353 Smith RJ Nathan DM Arslanian SA Groop L Rizza RA Rotter JI In-dividualizing therapies in type 2 diabetes mellitus based on patient characteristicswhat we know and what we need to know J Clin Endocrinol Metab 2010951566ndash157454 Greeley SA John PM Winn AN et al The cost-effectiveness of personalizedgenetic medicine the case of genetic testing in neonatal diabetes Diabetes Care201134622ndash62755 Nguyen HV Finkelstein EA Mital S Gardner DS Incremental cost-effectivenessof algorithm-driven genetic testing versus no testing for maturity onset diabetes of theyoung (MODY) in Singapore J Med Genet 201754747ndash75356 Naylor RN John PM Winn AN et al Cost-effectiveness of MODY genetictesting translating genomic advances into practical health applications DiabetesCare 201437202ndash20957 Gloyn AL Pearson ER Antcliff JF et al Activating mutations in the geneencoding the ATP-sensitive potassium-channel subunit Kir62 and permanentneonatal diabetes N Engl J Med 20043501838ndash184958 Pearson ER Flechtner I Njoslashlstad PR et al Neonatal Diabetes InternationalCollaborative Group Switching from insulin to oral sulfonylureas in patients withdiabetes due to Kir62 mutations N Engl J Med 2006355467ndash47759 UK Prospective Diabetes Study (UKPDS) UK Prospective Diabetes Study(UKPDS) VIII Study design progress and performance Diabetologia 199134877ndash89060 Kesselheim AS Tan YT Avorn J The roles of academia rare diseases andrepurposing in the development of the most transformative drugs Health Aff(Millwood) 201534286ndash29361 Nelson MR Tipney H Painter JL et al The support of human geneticevidence for approved drug indications Nat Genet 201547856ndash86062 FNIH Evidentiary Criteria Writing Group Framework for Defining EvidentiaryCriteria for Biomarker Qualification Bethesda MD National Library of Medicine201663 Tabaacutek AG Herder C Rathmann W Brunner EJ Kivimaumlki M Prediabetesa high-risk state for diabetes development Lancet 20123792279ndash229064 Nicol D Bubela T Chalmers D et al Precision medicine drowning ina regulatory soup J Law Biosci 20163281ndash30365 Kaye J Whitley EA Lund D Morrison M Teare H Melham K Dynamicconsent a patient interface for twenty-first century research networks Eur J HumGenet 201523141ndash14666 Kinkorovaacute J Biobanks in the era of personalized medicine objectiveschallenges and innovation Overview EPMA J 201674
1922 Precision Medicine in Type 2 Diabetes Diabetes Volume 67 October 2018
by Ahlqvist et al (20) it would require that some clustersbe more driven by familial factors than others This maywell be true and future studies exploring the genetic basisof these clusters may help test this hypothesis Howeverfamily history is the consequence of both genetic andenvironmental influences (including developmental expo-sures) and understanding why family history affects dis-ease progression differentially across clusters if indeed itdoes would likely be intensely scrutinized owing to theetiological insights this might reveal
OVERVIEW OF MAJOR PRECISION MEDICINEINITIATIVES
After the first draft sequence of the human genome waspublished in 2001 (10) methods and technologies wererapidly developed that enabled large-scale quantification ofhuman genetic variation and subsequently variation acrossother biological strata As investigators began using thesetechnologies global research consortia grew through whichnovel biomarkers for type 2 diabetes were discovered Therapid growth of large data sets and ad hoc collaborativenetworks as well as clear evidence that this new approachto biomarker discovery worked laid the foundations forlarge precision medicine initiatives Although some initia-tives have built new cohorts from scratch most haveleveraged existing data and biomaterials already stored inbiobanks (Table 1 and Fig 1)
In 2008 a joint undertaking between the EuropeanCommission European academic institutions and theEuropean Federation of Pharmaceutical Industries andAssociations was launched This euro56 billion programcalled the Innovative Medicines Initiative (IMI) comprises100 projects focused on the major diseases affectingEuropean citizens Of these several focus on precisionmedicine in type 2 diabetes SUrrogate markers for Micro-and Macro-vascular hard endpoints for Innovative dia-betes Tools (SUMMIT) (diabetes complications) DiabetesResearch on Patient Stratification (DIRECT) (drug responseand glycemic deterioration before and after the onset oftype 2 diabetes) (22) Risk Assessment and ProgreSsiOn ofDIabetes (RHAPSODY) (glycemic deterioration before andafter the onset of type 2 diabetes) and Biomarker En-terprise to Attack Diabetic Kidney Disease (BEAt-DKD)(diabetic kidney disease) A common objective is the discov-ery and validation of biomarkers that facilitate the strati-fication of patient populations into subgroups that mightbe treated more effectively than without biomarker strat-ification In some instances the focus is also on diagnos-tic reclassification Unlike most other precision medicineinitiatives those within the IMI focus on the integration ofmultiple biomarkers such as genotypes transcripts pro-teins metabolites and metagenomics sequences
The UK Biobank cohort (established 2005) includesaround 500000 adults the vast majority of whom haveprovided nonfasting blood samples and self-reported dataon lifestyle health and well-being and have been geno-typed using genome-wide arrays and targeted metabolomic
analyses are also under way In subcohorts more detailedphenotyping has been undertaken such as MRI scans andphysical activity monitoring Research conducted using UKBiobank data for example has shown that genetic variantsassociated with obesity are likely to influence susceptibilityto a range of modifiable lifestyle exposures (23ndash26) whichmay have relevance to diabetes Nevertheless the very smallmagnitude of these effects and susceptibility to confounding(25) precludes the immediate translation of these findingsinto clinical practice Alternatively as UK Biobank accruesincident events of diabetes complications the database islikely to become a powerful resource through which theprognostic value of stratifying populations into subgroupsor determining how environmental risk factors differentiallyaffect disease susceptibility by subgroup can be assessed
Plans to implement precision medicine into US med-ical practice accelerated in 2015 with the announcement ofthe Precision Medicine Initiative (PMI) for which US$215million was initially budgeted by the federal government(2728) In the short term the PMI is focused on cancerwhereas in the longer term all areas of health and healthcare will be studied with specific emphasis placed on thediscovery of predictive biomarkers for type 2 diabetes (28)A major feature of the PMI is a cohort of 1 million peopleand a research program called the All of Us Research Pro-gram The cohort supported by a US$55 million budget isbuilt around nationwide recruitment sites under the bannerof the PMI Cohort Program Managed access for accreditedresearchers to biobanked data and samples is intendedto facilitate several major goals including those related togenendashenvironment interactions pharmacogenomics riskstratification mobile health technologies health empower-ment and innovative clinical trials (httpsallofusnihgov)
Several other major US initiatives predate the PMI andhave diabetes and its risk factors at their core Theseinclude the Million Veteran Program (MVP) and theAccelerating Medicines Partnership in Type 2 Diabetes(AMP T2D) The MVP uses genomics and other healthdata obtained through electronic medical records andfollow-up surveys in about 600000 military veteransaged 50ndash69 years (29) The AMP T2D seeks to gathergenomic and metagenomic data and to create an analyticalengine that can be used to mine the genetic basis todiabetes and related traits while safeguarding the confi-dentiality of the results Data access is a central pillar ofAMP T2D and through the T2D Knowledge Portal (wwwtype2diabetesgeneticsorg) the consortium has assembledgenetic and phenotypic data from participants with andwithout type 2 diabetes frommultiple populations creatinga central repository for such data (wwwtype2diabetesgeneticsorginformationalabout) The ability of these initiatives tocontribute to precision diabetes medicine is likely to beenhanced by their partnerships with industry which arenecessary to translate research into therapeutics ap-proved by the US Food and Drug Administration (FDA)
The Nordic countries have a long history of biobank-ing and registry-based research and have contributed
1914 Precision Medicine in Type 2 Diabetes Diabetes Volume 67 October 2018
Tab
le1mdashGlobalp
recisionmed
icineinitiatives
ofrelevance
totyp
e2diab
etes
Nam
eRegion
Start
date
Funding
InitiativePop
ulation(in
thousands)
Australian
Precision
Med
icineInitiative
Australia
2016A$25
million
Pub
licndashp
rivatemdash
Chinese
Precision
Med
icineInitiative
China
2016US$9
billion
Pub
licndashp
rivate2
InnovativeMed
icinesInitiative
(diab
etesprojects
only)Europ
eanUnion
2008euro200
million
Pub
licndashp
rivate100
Nord
icPrecision
Med
icineInitiative
Nord
ic(Denm
arkEstonia
FinlandIceland
Norw
ayand
Swed
en)2015
mdashPub
licndashp
rivate1000
Estonian
Personalised
Med
icinePilot
Project
(Nord
icPrecision
Med
icineInitiative)
Nord
ic(Estonia)
2015euro33
million
Governm
ental100
FinnGen
(Nord
icPrecision
Med
icineInitiative)
Nord
ic(Finland)
2017euro60
million
Pub
licndashp
rivate500
Genom
icAggregation
Project
inSwed
en(Nord
icPrecision
Med
icineInitiative)
Nord
ic(Swed
en)2015
mdashPub
lic160
Saud
iHum
anGenom
eProject
Saud
iArab
ia2013
US$40
million
Governm
ental100
Genom
icsEngland
UK
2012US$523
million
Pub
licndashp
rivate75
UK
Biob
ankUK
2005US$122
million
Pub
licndashp
rivate500
Project
Baseline
US
2017US$41
million
Pub
licndashp
rivate10
USPrecision
Med
icineInitiative
US
2015US$215
million
Pub
licndashp
rivate1000
Accelerating
Med
icinesPartnership
US
2014US$528
million
Pub
licndashp
rivate150
Million
Veteran
Program
US
2011US$116
million
Pub
licndashp
rivate612
diabetesdiabetesjournalsorg Fitipaldi and Associates 1915
substantially to discoveries in diabetes genetics over thepast decade In 2015 the Nordic Precision MedicineInitiative (NPMI) was formed to bring together genetic andother biomedical data from 1 million Nordic citizensSeveral national precision and genomic medicine projectsare under way in Estonia for example the EstonianGenome Center at the University of Tartu is curatinga population-based biobank suitable for precision medicineresearch in diabetes and other complex diseases The aimis to promote and advance the development of geneticresearch and the implementation of genomic data intoclinical practice to improve public health The Estoniangovernment recently launched the Estonian PersonalisedMedicine Pilot Project (EPMPP) (2015ndash2018) which seeksto implement personalized medicine on a national scaleAccordingly the government has provided US$59 millionto support genotyping in 100000 Estonians In Finlandthe FinnGen project was launched in 2017 with theobjective to link genomic data with digital health caredata for 500000 Finnish citizens (10 of the countryrsquospopulation) through a publicndashprivate partnership In SwedenGAPS (Genomic Aggregation Project in Sweden) hasassimilated160000 genotyped samples with corresponding
phenotype data covering a wide range of diseases includingdiabetes (30) and GMS (Genomic Medicine Sweden) isspearheading clinical genomics on a national scale In Icelandthe company deCODE genetics has established a genetic andphenotype database that encompasses the entire population(n = 334000) which fuels genetics research and drug targetvalidation for a wide range of diseases including diabetes
Several other countries have launched large-scale pro-grams of research on genomic and precision medicinemany with clear paths to clinical application althoughoften only in relation to rare pediatric diseases In SaudiArabia the very high prevalence of type 2 diabetes (20)led the Saudi government and King Abdulaziz City forScience and Technology to initiate the Saudi HumanGenome Project (SHGP) which seeks to sequence thegenomes of 100000 Saudi citizens in order to identifythe genetic basis of monogenic and complex diseases liketype 2 diabetes In China which has become a formidableforce in decoding genomes (31) the government an-nounced in 2016 a bold initiative to become a globalsuperpower in precision medicine with a US$9 billion15-year precision medicine initiative (32) The Chineseinitiative has three core objectives focused on recruiting
Figure 1mdashGlobal precision medicine initiatives of relevance to type 2 diabetes
1916 Precision Medicine in Type 2 Diabetes Diabetes Volume 67 October 2018
1) millions of participants from the seven main regions ofChina to form a nationally representative cohort 2) eightdisease-specific cohorts (cardiovascular cerebrovascularrespiratory metabolic neurological psychosomatic im-mune system disorders and seven common malignanttumors) totaling 700000 participants and 3) a clinicalcohort (N = 50000 patients with 50 rare diseases) (33)
CLINICAL TRANSLATION
The practice of precision diabetes medicine is currentlyconfined to rare monogenic forms of the disease Never-theless individual variants present only in specific pop-ulation isolates have been discovered that may have clinicalvalue in the prevention or treatment of type 2 diabetesThese include a TBC1D4 nonsense variant (pArg684ter)of relatively high prevalence in Greenlandic Inuit (minorallele frequency 17) and other circumpolar Inuit popu-lations that predisposes a substantially increased risk ofdiabetes (odds ratio 10 in those homozygous for thevariant) (34) The mechanism of action involves the mus-cle-selective loss of the long TBC1D4 isoform and dimin-ished GLUT4-mediated cellular glucose uptake in responseto insulin A thymine for cytosine substitution that causesa premature stop at codon 363 of TBC1D4 has also beendiscovered in people with acanthosis nigricans the muta-tion causes postprandial hyperinsulinemia as a possibleconsequence of a specific muscle and adipose tissue insulinresistance (35)
In Latinos a low frequency (minor allele frequency =21 in people with type 2 diabetes) missense variant (pE508K) at HNF1A conveys a type 2 diabetes odds ratio5(36)HNF1A also harbors mutations that cause rare mono-genic diabetes that can be successfully treated with sulfo-nylureas (discussed later) suggesting this is true for thenontrivial number of Latino individuals with type 2 di-abetes who carry pE508K Despite the relatively largeeffects these variants convey whether they will proveuseful in the context of precision medicine is unknown asno formal assessment of clinical utility or cost-effectivenesshas been reported
Metformin was discovered in the 1960s and is currentlythe frontline drug for early-stage diabetes treatment al-though its mechanisms of action remain unclear (37) Thereis genetic evidence for metformin intolerance (38) althoughthis has not been widely replicated By contrast variants atATM (39) and SLC2A2 (encoding GLUT2) (40) have beenassociated at a genome-wide level of statistical significancewith metformin response Although the effects conveyed bythese variants are too small to guide patient-level treatmentdecisions these findings may prove valuable in determiningmetforminrsquos mechanisms of action
Many major precision medicine statements includingthe executive summary of the US PMI (28) and a NationalResearch Council report focused on precision medicine andnew disease taxonomies (41) make clear that research andpractice in precision medicine should consider the majorimpact of lifestyle in disease etiology prevention and
treatment The incorporation of lifestyle into precisiondiabetes medicine is adequately justified by its provenbenefits in diabetes prevention and treatment Yet thereis good evidence that people benefit to varying degreesfrom diet and exercise and that personal biology underliesthis (42) Thus if precision medicine programs are to ad-equately consider the impact of lifestyle and other envi-ronmental exposures it seems logical that emphasis shouldbe placed on improving the precision of not only drugs butalso this type of antidiabetes therapy too
One compelling example of how glycemic response tofood can be predicted using an individualrsquos biomarkerscomes from a study of 800 young adults whose gut meta-genomic sequences were ascertained and diet and bloodglucose variation (from continuous glucose monitors) weremonitored for a week (43) Each participant received onestandardized meal (50 g carbohydrate) daily The authorsobserved that although participants elicited different post-prandial glycemic responses the responses to the samefood were consistent within individuals Using machinelearning algorithms these data were used to predict eachparticipantrsquos postprandial glycemic response to a givenfood They subsequently administered tailored diet inter-ventions to modulate glucose levels providing proof ofconcept that personalized diets guided by biomarker pro-filing can reduce blood glucose variability Notwithstand-ing the importance of this work from a basic scienceperspective to determine its value for precision medicinerequires knowing if minimizing variations in glucose inhealthy adults is clinically relevant and whether the ap-proach to predicting glycemic response to food also worksin people with diabetes To date neither of these impor-tant questions is addressed in the published literature
Despite lifestylersquos proven efficacy in diabetes preven-tion the resources available for research on lifestylemedicine are massively outweighed by investments inpharmacotherapy In 2013 for example US$35 billionwas spent by pharmaceutical companies on research anddevelopment (44) Although there are no comparablestatistics for research in lifestyle medicine funding islikely to be orders of magnitude less as most of this comesfrom the public purse The marketing of foods and bev-erages on the other hand is a huge industry whose mes-sages often counter public health efforts to promotehealthy lifestyle On an international scale hundreds ofbillions of US dollars are spent on food marketing annu-ally one of the biggest spenders is Unilever which spent US$8 billion on brand and marketing investments in2017 alone (45) Marketing junk foods and beverages ishandsomely resourced competing with public health ini-tiatives The major food companies in the UK for exam-ple have invested roughly US$200 million each year injunk food advertising which contrasts the US$72 millionspent on Change4Life the UK governmentrsquos flagshiphealthy eating campaign (46)
Notwithstanding the relative dearth of funding forlifestyle medicine some key initiatives exist One of the
diabetesdiabetesjournalsorg Fitipaldi and Associates 1917
largest of these is the National Institutes of HealthrsquosCommon Fund initiative on the Molecular Transducersof Physical Activity in Humans (httpcommonfundnihgovMolecularTransducers) which seeks to determineldquooptimal physical activity recommendations for peopleat various stages of liferdquo and develop ldquoprecisely targeted[exercise] regimens for individuals with particular healthneedsrdquo (47) Elsewhere the Food4Me study a research pro-gram funded by the European Union explored the role ofgenetics in the personalization of diet data from the studysuggest that personalized diet interventions supported bygenetic data improve consumption of healthy foods (48) andhelp sustain healthy food choices (49) Interestingly mostloci selected for this intervention (variants at MTHFR FTOTCF7L2 APOE and FADS1) lack evidence of individual-levelclinical relevance (50) suggesting that the success of theFood4Me intervention was not dependent on the quality ofthe genetic information provided to participants
Although most if not all precision medicine initiativesare predicated on the assumption that their findings willhelp improve peoplersquos health the emphasis of these ini-tiatives is almost always on the generation of new knowl-edge MVP for example seeks ldquoto improve understandingof how health is affected by genetic characteristics behav-iors and environmental factorsrdquo (29) Although this objec-tive will undoubtedly be achieved how this information willeventually be used to optimize prevention and treatment
of diabetes and other diseases is unclear Indeed few pre-cision medicine initiatives focused on complex diseasesprovide a clear plan for clinical translation (Fig 2)
POTENTIAL BOTTLENECKS ON THE PATH TOPRECISION MEDICINE
Cost-effectivenessThe global precision medicine market was valued atUS$436 billion in 2016 and is predicted to triple in valuewithin the next decade (51) The economic dynamicsassociated with precision medicine differ from those ofconventional medicines as the nature of the approachmeans that precision medicines are likely to be approvedfor use only in specific subpopulations limiting the num-ber of patients eligible for treatment with a given drug Bycontrast the effectiveness of precision medicines shouldbe higher for patients within these subpopulationsthan for conventional medicines and side effects may befewer and less severe Owing to the smaller market sharefor a given precision medicine competition to producecheaper drugs between manufacturers may be less than forconventional drugs and the drive to develop off-patentbiosimilars (ie drugs with active properties identical orsimilar to previously licensed drugs) is likely to differcompared with conventional medicines depending onthe extent to which first-in-class drugs are undercut bycheaper alternatives Collectively these factors will impact
Figure 2mdashThe path to precision medicine in type 2 diabetes HEA health economic assessment
1918 Precision Medicine in Type 2 Diabetes Diabetes Volume 67 October 2018
the cost-effectiveness of precision medicines in type 2 di-abetes (52)
Thus far very few cost-effectiveness analyses in pre-cision diabetes medicine have been published despitelong-standing recognition that cost-effectiveness is a keyrequirement for clinical translation (53) The rare exam-ples of such studies have focused exclusively on rare mono-genic forms of diabetes neonatal diabetes (54) andmaturity-onset diabetes of the young (MODY) (5556)
Neonatal diabetes is diagnosed in infancy and affectsabout 1400000 live births and roughly half (1252000in people aged 20 years) develop permanent neonataldiabetes (54) Of the latter group up to 70 are estimatedto carry mutations in the genes encoding the ATP-sensitivepotassium channel (KCNJ11 and ABCC8) These mutationsblock the closing of the KATP channels which preventsb-cell depolarization and corresponding insulin secretion(57) However treatment with sulfonylureas rectifies thisdefect in 90 of cases allowing the discontinuation ofexogenous insulin (58) The earliest costndasheffect analysisin precision medicine focused on sulfonylurea therapy inpermanent neonatal diabetes (54) The authors estimatedthat genetic testing in permanent neonatal diabetes wouldconvey significant quality-of-life benefits at 10 years (032quality-adjusted life-years US$12528 saved) increasingto 070 at 30 years (US$30437 saved)
A range of single gene defects cause MODY each ofwhich are used to diagnose one of five forms of the diseaseIn MODY1 and MODY3 insulin secretion is impaired Thedisease occurs early in life and is often misdiagnosed astype 1 diabetes and exogenous insulin therapy is thusprescribed However treatment with sulfonylureas enablessecretion of endogenously produced insulin Switchingfrom insulin to sulfonylureas is preferable for a numberof reasons including those related to safety and burdenMODY2 is caused by mutations in GCK which lead tochronically elevated but nonprogressive blood glucoseconcentrations that do not typically require treatmentthus diagnosing MODY2 also conveys reduced costs andburdens associated with unnecessary treatment The firstcost-effectiveness analysis focused on MODY1ndash3 in USadults (aged 25ndash40 years) with diagnosed type 2 diabetes(56) based many of its assumptions on the findings ofthe UK Prospective Diabetes Study (UKPDS) (59) It alsoassumed a 2 frequency of MODY among people withdiabetes and set genetic screening costs at US$2580Under these assumptions genetic screening for MODYwould not be cost-effective The second study focused onlyonMODY1 screening and found that genetic screening wasnot cost-effective given the diabetes population frequencyof MODY1 mutations is unlikely to exceed 2 Sub-stantial reductions in genetic sequencing costs may how-ever render screening cost-effective (55)
Regulatory ConsensusMore than half of the most clinically impactful drugs de-veloped recently were discovered through academia-led
research (60) emphasizing the huge value of publicndashprivate partnerships Many of the large precision diabetesmedicine consortia involve both academia and industrywithin which unsurprising emphasis is placed on the dis-covery and validation of biomarkers that expedite drugdevelopment This expectation is backed up by the provenability of genetics to fast-track drug development bypinpointing high-value drug targets helping halve drugfailures and markedly reducing their costs (61) In drugrepositioning genetics has also proven its worth by re-vealing previously unknown conditions that a drug can beused to treat and by helping predict drug side effects
To obtain FDA (or European Medicines Agency [EMA])approval for biomarkers in medical products and de-vices involves a comprehensive qualification process (seehttpswwwfdagovDrugsDevelopmentApprovalProcessDrugDevelopmentToolsQualificationProgramBiomarker-QualificationProgramucm535395htm) within which ex-ists the requirement for a statement termed a ldquocontext ofuserdquo The context of use statement requires a clear anddetailed description of the intended role of the biomarkerin drug development as well as risks and benefits TheFoundation for the National Institutes of Health recentlypublished the Framework for Defining Evidentiary Criteriafor Biomarker Qualification (62) which provides an ex-panded outline of the biomarker classifications definedby the FDA
The process of biomarker qualification in drug devel-opment requires stringent adherence to the relevant reg-ulatory agenciesrsquo approvals processes In some studies thatseek to discover biomarkers for diabetes drug develop-ment the process is well rehearsed and aligning biomarkerdiscovery and validation strategies with regulatory expect-ations is relatively straightforward however it does re-quire that precision medicine consortia are well versedin these processes Biomarker discovery in prediabetes ismore challenging though as prediabetes is not a distinctdisease state and although some people with prediabetesdevelop diabetes and its complications a large proportionremains healthy for many years (63) Nevertheless a sub-group of those with prediabetes will progress to diabetesand identifying these people before b-cell function issubstantially diminished would open the door for severalpowerful therapeutics that are ineffective once endoge-nous insulin production has faded However becauseneither the FDA nor EMA currently recognizes an inter-mediate end point for disease progression in prediabetesdrug trials gaining regulatory approval for new prediabe-tes biomarkers requires that researchers foster close andfrequent communications with the regulatory agencies toensure candidate biomarkers have the possibility to pro-gress through the regulatory process
Legal Social and EthicalBalancing the interests of society and its citizens the risksand benefits legislation and individual freedom as wellas the risk of exasperating disparities is central to many
diabetesdiabetesjournalsorg Fitipaldi and Associates 1919
commentaries on the legal social and ethical aspects ofprecision medicine
Precision medicine research is heavily dependent onbiobanked data and tissues Often the original informedconsents were obtained many years before modern lsquoomicstechnologies existed at a time when many research ques-tions germane to precision medicine were inconceivableOften the informed consents were broad and ethicscommittees have concluded that these modern researchquestions are within the scope of the consents As newever larger and interconnected cohorts are being formedthe desire many researchers share for broad consentingandor to bank data or samples for unspecified futureresearch must be placed in context with several importantethical realities the need to maintain participantsrsquo confi-dentiality appropriately handle incidental findings deter-mine who owns the samples and data consider culturalsensitivities return results to participants engage societyand ensure unused material is properly disposed (64)
Much as precision medicine was inconceivable whenmany existing cohorts were formed it is impossible toaccurately predict how contemporary cohorts will be usedin the future Furthermore many precision medicineinitiatives involve the hybridization of academia healthcare and industry which may challenge some of the morealtruistic motivations of participants in academia-led bio-medical research Moreover much of the highly sensitivedata collated by these initiatives will eventually be madeavailable through managed-access portals to a wide rangeof end users who were not part of the data collectionprocess Thus much of this highly sensitive data will bewidely disseminated and used by distant end users toaddress a plethora of diverse research questions thatcannot yet be conceived factors that need to be consideredduring the informed consent process These are challengesfor historical biobanks as well as new precision medicinecohorts that consent only at enrollment To help overcomethis a process called dynamic consent has been developedwhich allows consents to be progressively updated asprojects evolve but requires fluid communication withparticipants throughout the life course of a study (65)
A major concern about genetic data are its linkage todatabases that enables inferences about a personrsquos socialcognitive moral cultural health or sexual identity whichparticipants had not consented to or for such inferencesto be made about family members without their consent(64) Indeed ensuring genomics data are used in a wayconsistent with appropriate ethical standards is a corefeature of most current health data protection legislation(66) Notwithstanding these risks there are major benefitsassociated with big data which include the discovery ofnovel disease mechanisms and therapeutic targets manyof which only become visible when data sets scale tomassive proportions Thus rather than constrain thegrowth and responsible utilization of big data emphasis isbeing placed on implementing legislation that protectsprivacy and helps prevent data breaches while facilitating
the safe and responsible storage transfer and utilizationof data In the European Union for example the Gen-eral Data Protection Regulation was implemented in May2018 to harmonize data privacy laws across Europe A keyfeature of this legislation as it relates to precision medicineresearch is that the analysis of sensitive data requiresexplicit opt-in consent whereas for nonsensitive dataunambiguous (implied) consent is sufficient
SUMMARY AND CONCLUSIONS
Diabetes medicine is likely to evolve dramatically in thecoming years ideally because evidence emerges from themajor precision medicine initiatives to support its utility inpreventing or treating diabetes but no doubt also becauseof the major commercial interest in seeing precision med-icine grow One of the possible outcomes will very likely bethe replacement of current classifications of diabetes withempirically derived subclassifications that can be coupledwith treatment regimens which compared with con-ventional medicines are more effective less costly andconvey fewer unnecessary side effects As genetic data-bases grow genotypes will be increasingly used to discovernew drug targets At this time the clinical application ofgenetics is confined to the diagnosis of and therapeuticguidance for rare forms of diabetes as well as the pre-diction of adverse drug reactions The ways in whichgenetics may or may not guide prevention and therapy incommon forms of diabetes is unclear However becausecharacterizing variation in a patientrsquos nuclear genomeis inexpensive and easily achieved and DNA variation re-mains constant across the life course it is likely that geneticsequences will feature in most patientsrsquo clinical recordsGiven this genetic data will likely be deployed in diverseways in the diabetes clinic of the future The roles otherlsquoomics technologies digital imaging devices and wearableswill play in precision diabetes medicine are harder to fore-cast as research in these areas is less advanced and the costof obtaining some of these data will likely remain highrelative to genetics Finally although many precision med-icine initiatives focus predominantly on pharmacotherapyoptimizing lifestyle (and possibly surgical) interventionsusing biotechnologies also has great potential for improvingtype 2 diabetes prevention and treatment
Funding and Duality of Interest The work of HF and PWF relatedto the manuscript was supported by the European Commission (CoG-2015_681742_NASCENT) Swedish Research Council (Distinguished YoungResearchers Award in Medicine) Swedish Heart-Lung Foundation Novo NordiskFoundation European Foundation for the Study of Diabetes and the SwedishFoundation for Strategic Research (Industrial Research Center in PrecisionMedicine) (all grants to PWF) PWF is a consultant for and has stock optionsin Zoe Global and has received research funding from Boehringer Ingelheim EliLilly Janssen Novo Nordisk Sanofi and Servier MIM is a National Institute forHealth Research (NIHR) Senior Investigator and a Wellcome Trust Senior In-vestigator The views expressed in this article are those of the author(s) and notnecessarily those of the National Health Service NIHR or the Department ofHealth Work described in this review was principally supported by the Wellcome
1920 Precision Medicine in Type 2 Diabetes Diabetes Volume 67 October 2018
Trust (098381 203141 106130) and by the National Institutes of Health (U01-DK105535) MIM serves on advisory panels for Pfizer Novo Nordisk and ZoeGlobal has received honoraria from Pfizer Novo Nordisk and Eli Lilly hasstock options in Zoe Global and has received research funding from AbbVieAstraZeneca Boehringer Ingelheim Eli Lilly Janssen Merck Novo Nordisk PfizerRoche Sanofi Servier and Takeda JCF is a Massachusetts General HospitalResearch Scholar and is supported by National Institutes of Health National Instituteof Diabetes and Digestive and Kidney Diseases grants R01 DK072041 U01DK105554 R01 DK105154 and K24 DK110550 and National Institute of GeneralMedical Sciences grant R01 GM117163 MIM JCF and PWF are membersof the American Diabetes Association Precision Medicine in Diabetes TaskforceThe views expressed in this article are those of the authors and do not necessarilyreflect those of the American Diabetes Association or the taskforce No otherpotential conflicts of interest relevant to this article were reported
References1 Centers for Disease Control and Prevention National Diabetes StatisticsReport Estimates of Diabetes and Its Burden in the United States 2014 AtlantaGA US Department of Health and Human Services 20142 Knowler WC Fowler SE Hamman RF et al Diabetes Prevention ProgramResearch Group 10-year follow-up of diabetes incidence and weight loss in theDiabetes Prevention Program Outcomes Study Lancet 20093741677ndash16863 Li G Zhang P Wang J et al The long-term effect of lifestyle interventions toprevent diabetes in the China Da Qing Diabetes Prevention Study a 20-yearfollow-up study Lancet 20083711783ndash17894 Tuomilehto J Lindstroumlm J Eriksson JG et al Finnish Diabetes PreventionStudy Group Prevention of type 2 diabetes mellitus by changes in lifestyleamong subjects with impaired glucose tolerance N Engl J Med 20013441343ndash13505 Ramachandran A Snehalatha C Mary S Mukesh B Bhaskar AD Vijay VIndian Diabetes Prevention Programme (IDPP) The Indian Diabetes PreventionProgramme shows that lifestyle modification and metformin prevent type 2 di-abetes in Asian Indian subjects with impaired glucose tolerance (IDPP-1) Dia-betologia 200649289ndash2976 Lean ME Leslie WS Barnes AC et al Primary care-led weight managementfor remission of type 2 diabetes (DiRECT) an open-label cluster-randomised trialLancet 2018391541ndash5517 Polyzogopoulou EV Kalfarentzos F Vagenakis AG Alexandrides TK Res-toration of euglycemia and normal acute insulin response to glucose in obesesubjects with type 2 diabetes following bariatric surgery Diabetes 2003521098ndash11038 Franco M Orduntildeez P Caballero B et al Impact of energy intake physicalactivity and population-wide weight loss on cardiovascular disease and diabetesmortality in Cuba 1980-2005 Am J Epidemiol 20071661374ndash13809 Cho NH Shaw JE Karuranga S et al IDF Diabetes Atlas global estimates ofdiabetes prevalence for 2017 and projections for 2045 Diabetes Res Clin Pract2018138271ndash28110 Lander ES Linton LM Birren B et al International Human Genome Se-quencing Consortium Initial sequencing and analysis of the human genome[published correction appears in Nature 2001411720] Nature 2001409860ndash92111 Visscher PM Wray NR Zhang Q et al 10 Years of GWAS discovery biologyfunction and translation Am J Hum Genet 20171015ndash2212 Merino J Udler MS Leong A Meigs JB A decade of genetic and metab-olomic contributions to type 2 diabetes risk prediction Curr Diab Rep 20171713513 Knowles JW Ashley EA Cardiovascular disease the rise of the genetic riskscore PLoS Med 201815e100254614 Mahajan A Taliun D Thurner M et al Fine-mapping of an expanded set oftype 2 diabetes loci to single-variant resolution using high-density imputation andislet-specific epigenome maps [preprint article online] 2018 Available fromhttpswwwbiorxivorgcontentearly20180109245506 Accessed 30 January2018
15 McCarthy MI Painting a new picture of personalised medicine for diabetesDiabetologia 201760793ndash79916 Scott RA Scott LJ Maumlgi R et al DIAbetes Genetics Replication AndMeta-analysis (DIAGRAM) Consortium An expanded genome-wide asso-ciation study of type 2 diabetes in Europeans Diabetes 2017662888ndash290217 Mahajan A Wessel J Willems SM et al ExomeBP Consortium MAGICConsortium GIANT Consortium Refining the accuracy of validated target iden-tification through coding variant fine-mapping in type 2 diabetes Nat Genet 201850559ndash57118 Udler MS Kim J von Grotthuss M et al Clustering of type 2 diabetes geneticloci by multi-trait associations identifies disease mechanisms and subtypes[preprint article online] 2018 Available from httpswwwbiorxivorgcontentearly20180510319509 Accessed 30 March 201819 van Zuydam NR Ahlqvist E Sandholm N et al A genome-wide associationstudy of diabetic kidney disease in subjects with type 2 diabetes Diabetes 2018671414ndash142720 Ahlqvist E Storm P Kaumlraumljaumlmaumlki A et al Novel subgroups of adult-onsetdiabetes and their association with outcomes a data-driven cluster analysis of sixvariables Lancet Diabetes Endocrinol 20186361ndash36921 Fuchsberger C Flannick J Teslovich TM et al The genetic architecture oftype 2 diabetes Nature 201653641ndash4722 Koivula RW Heggie A Barnett A et al DIRECT Consortium Discovery ofbiomarkers for glycaemic deterioration before and after the onset of type 2 di-abetes rationale and design of the epidemiological studies within the IMI DIRECTConsortium Diabetologia 2014571132ndash114223 Rask-Andersen M Karlsson T Ek WE Johansson Aring Gene-environmentinteraction study for BMI reveals interactions between genetic factors and physicalactivity alcohol consumption and socioeconomic status PLoS Genet 201713e100697724 Celis-Morales CA Lyall DM Gray SR et al Dietary fat and total energy intakemodifies the association of genetic profile risk score on obesity evidence from48 170 UK Biobank participants Int J Obes 2017411761ndash176825 Tyrrell J Wood AR Ames RM et al Gene-obesogenic environment inter-actions in the UK Biobank study Int J Epidemiol 201746559ndash57526 Young AI Wauthier F Donnelly P Multiple novel gene-by-environment in-teractions modify the effect of FTO variants on body mass index Nat Commun201671272427 Reardon S Giant study poses DNA data-sharing dilemma Nature 201552516ndash1728 Hudson K Lifton R Patrick-Lake B The Precision Medicine Initiative CohortProgram ndash Building a Research Foundation for 21st Century Medicine BethesdaMD National Institutes of Health 201529 Gaziano JM Concato J Brophy M et al Million Veteran Program a mega-biobank to study genetic influences on health and disease J Clin Epidemiol 201670214ndash22330 Bergen SE Sullivan PF National-scale precision medicine for psychiatricdisorders in Sweden Am J Med Genet B Neuropsychiatr Genet 7 July 2017 [Epubahead of print] DOI 101002ajmgb3256231 Cyranoski D Chinarsquos bid to be a DNA superpower Nature 2016534462ndash46332 Cyranoski D China embraces precision medicine on a massive scale Nature20165299ndash1033 Bu D Peng S Luo H et al Precision medicine and cancer immunology inChina From big data to knowledge in precision medicine Science 2018 (Suppl)S35ndashS3834 Moltke I Grarup N Joslashrgensen ME et al A common Greenlandic TBC1D4variant confers muscle insulin resistance and type 2 diabetes Nature 2014512190ndash19335 Dash S Sano H Rochford JJ et al A truncation mutation in TBC1D4 ina family with acanthosis nigricans and postprandial hyperinsulinemia Proc NatlAcad Sci U S A 20091069350ndash9355
diabetesdiabetesjournalsorg Fitipaldi and Associates 1921
36 Estrada K Aukrust I Bjoslashrkhaug L et al SIGMA Type 2 Diabetes ConsortiumAssociation of a low-frequency variant in HNF1A with type 2 diabetes in a Latinopopulation [published correction appears in JAMA 20143121932] JAMA 2014
3112305ndash231437 Marshall SM 60 years of metformin use a glance at the past and a look tothe future Diabetologia 2017601561ndash156538 Pearson ER Pharmacogenetics and target identification in diabetes CurrOpin Genet Dev 20185068ndash7339 Zhou K Bellenguez C Spencer CC et al GoDARTS and UKPDS Diabetes
Pharmacogenetics Study Group Wellcome Trust Case Control Consortium 2MAGIC investigators Common variants near ATM are associated with glycemicresponse to metformin in type 2 diabetes Nat Genet 201143117ndash12040 Zhou K Yee SW Seiser EL et al MetGen Investigators DPP InvestigatorsACCORD Investigators Variation in the glucose transporter gene SLC2A2 isassociated with glycemic response to metformin Nat Genet 2016481055ndash
105941 National Research Council Committee on a Framework for Developing a NewTaxonomy of Disease Toward Precision Medicine Building a Knowledge Networkfor Bomedical Research and a New Taxonomy of Disease Washington DCNational Academies Press 201142 Franks PW Poveda A Lifestyle and precision diabetes medicine will ge-
nomics help optimise the prediction prevention and treatment of type 2 diabetesthrough lifestyle therapy Diabetologia 201760784ndash79243 Zeevi D Korem T Zmora N et al Personalized nutrition by prediction of
glycemic responses Cell 20151631079ndash109444 Swanson A Big pharmaceutical companies are spending far more onmarketing than research The Washington Post 11 February 2015 Available fromhttpswwwwashingtonpostcomnewswonkwp20150211big-pharmaceutical-companies-are-spending-far-more-on-marketing-than-researchnoredirect=onamputm_term=787d031b9b7c Accessed 20 February 201845 Unilever Advertising amp Marketing Unilever 2018 Available from httpwwwunilevercomsustainable-livingwhat-matters-to-youadvertising-and-marketinghtml Accessed 31 March 201846 OrsquoDowd A Spending on junk food advertising is nearly 30 times whatgovernment spends on promoting healthy eating BMJ 2017359j467747 National Institutes of Health Molecular Transducers of Physical Activity in
Humans Frequently Asked Questions (FAQs) [Internet] 2018 Available from httpscommonfundnihgovMolecularTransducersFAQs Accessed 20 March 201848 Livingstone KM Celis-Morales C Navas-Carretero S et al Food4Me Study
Effect of an Internet-based personalized nutrition randomized trial on dietarychanges associated with the Mediterranean diet the Food4Me Study Am J ClinNutr 2016104288ndash29749 Celis-Morales C Livingstone KM Marsaux CF et al Food4Me Study Effectof personalized nutrition on health-related behaviour change evidence from theFood4Me European randomized controlled trial Int J Epidemiol 201746578ndash
588
50 Celis-Morales C Livingstone KM Marsaux CF et al Design and baselinecharacteristics of the Food4Me study a web-based randomised controlled trial ofpersonalised nutrition in seven European countries Genes Nutr 20151045051 BIS Research Global precision medicine market - analysis and forecast2017-2026 2017 Available from httpsbisresearchcomindustry-reportglobal-precision-medicine-market-2026html Accessed 31 January 201852 Stern AD Alexander BM Chandra A How economics can shape precisionmedicines Science 20173551131ndash113353 Smith RJ Nathan DM Arslanian SA Groop L Rizza RA Rotter JI In-dividualizing therapies in type 2 diabetes mellitus based on patient characteristicswhat we know and what we need to know J Clin Endocrinol Metab 2010951566ndash157454 Greeley SA John PM Winn AN et al The cost-effectiveness of personalizedgenetic medicine the case of genetic testing in neonatal diabetes Diabetes Care201134622ndash62755 Nguyen HV Finkelstein EA Mital S Gardner DS Incremental cost-effectivenessof algorithm-driven genetic testing versus no testing for maturity onset diabetes of theyoung (MODY) in Singapore J Med Genet 201754747ndash75356 Naylor RN John PM Winn AN et al Cost-effectiveness of MODY genetictesting translating genomic advances into practical health applications DiabetesCare 201437202ndash20957 Gloyn AL Pearson ER Antcliff JF et al Activating mutations in the geneencoding the ATP-sensitive potassium-channel subunit Kir62 and permanentneonatal diabetes N Engl J Med 20043501838ndash184958 Pearson ER Flechtner I Njoslashlstad PR et al Neonatal Diabetes InternationalCollaborative Group Switching from insulin to oral sulfonylureas in patients withdiabetes due to Kir62 mutations N Engl J Med 2006355467ndash47759 UK Prospective Diabetes Study (UKPDS) UK Prospective Diabetes Study(UKPDS) VIII Study design progress and performance Diabetologia 199134877ndash89060 Kesselheim AS Tan YT Avorn J The roles of academia rare diseases andrepurposing in the development of the most transformative drugs Health Aff(Millwood) 201534286ndash29361 Nelson MR Tipney H Painter JL et al The support of human geneticevidence for approved drug indications Nat Genet 201547856ndash86062 FNIH Evidentiary Criteria Writing Group Framework for Defining EvidentiaryCriteria for Biomarker Qualification Bethesda MD National Library of Medicine201663 Tabaacutek AG Herder C Rathmann W Brunner EJ Kivimaumlki M Prediabetesa high-risk state for diabetes development Lancet 20123792279ndash229064 Nicol D Bubela T Chalmers D et al Precision medicine drowning ina regulatory soup J Law Biosci 20163281ndash30365 Kaye J Whitley EA Lund D Morrison M Teare H Melham K Dynamicconsent a patient interface for twenty-first century research networks Eur J HumGenet 201523141ndash14666 Kinkorovaacute J Biobanks in the era of personalized medicine objectiveschallenges and innovation Overview EPMA J 201674
1922 Precision Medicine in Type 2 Diabetes Diabetes Volume 67 October 2018
Tab
le1mdashGlobalp
recisionmed
icineinitiatives
ofrelevance
totyp
e2diab
etes
Nam
eRegion
Start
date
Funding
InitiativePop
ulation(in
thousands)
Australian
Precision
Med
icineInitiative
Australia
2016A$25
million
Pub
licndashp
rivatemdash
Chinese
Precision
Med
icineInitiative
China
2016US$9
billion
Pub
licndashp
rivate2
InnovativeMed
icinesInitiative
(diab
etesprojects
only)Europ
eanUnion
2008euro200
million
Pub
licndashp
rivate100
Nord
icPrecision
Med
icineInitiative
Nord
ic(Denm
arkEstonia
FinlandIceland
Norw
ayand
Swed
en)2015
mdashPub
licndashp
rivate1000
Estonian
Personalised
Med
icinePilot
Project
(Nord
icPrecision
Med
icineInitiative)
Nord
ic(Estonia)
2015euro33
million
Governm
ental100
FinnGen
(Nord
icPrecision
Med
icineInitiative)
Nord
ic(Finland)
2017euro60
million
Pub
licndashp
rivate500
Genom
icAggregation
Project
inSwed
en(Nord
icPrecision
Med
icineInitiative)
Nord
ic(Swed
en)2015
mdashPub
lic160
Saud
iHum
anGenom
eProject
Saud
iArab
ia2013
US$40
million
Governm
ental100
Genom
icsEngland
UK
2012US$523
million
Pub
licndashp
rivate75
UK
Biob
ankUK
2005US$122
million
Pub
licndashp
rivate500
Project
Baseline
US
2017US$41
million
Pub
licndashp
rivate10
USPrecision
Med
icineInitiative
US
2015US$215
million
Pub
licndashp
rivate1000
Accelerating
Med
icinesPartnership
US
2014US$528
million
Pub
licndashp
rivate150
Million
Veteran
Program
US
2011US$116
million
Pub
licndashp
rivate612
diabetesdiabetesjournalsorg Fitipaldi and Associates 1915
substantially to discoveries in diabetes genetics over thepast decade In 2015 the Nordic Precision MedicineInitiative (NPMI) was formed to bring together genetic andother biomedical data from 1 million Nordic citizensSeveral national precision and genomic medicine projectsare under way in Estonia for example the EstonianGenome Center at the University of Tartu is curatinga population-based biobank suitable for precision medicineresearch in diabetes and other complex diseases The aimis to promote and advance the development of geneticresearch and the implementation of genomic data intoclinical practice to improve public health The Estoniangovernment recently launched the Estonian PersonalisedMedicine Pilot Project (EPMPP) (2015ndash2018) which seeksto implement personalized medicine on a national scaleAccordingly the government has provided US$59 millionto support genotyping in 100000 Estonians In Finlandthe FinnGen project was launched in 2017 with theobjective to link genomic data with digital health caredata for 500000 Finnish citizens (10 of the countryrsquospopulation) through a publicndashprivate partnership In SwedenGAPS (Genomic Aggregation Project in Sweden) hasassimilated160000 genotyped samples with corresponding
phenotype data covering a wide range of diseases includingdiabetes (30) and GMS (Genomic Medicine Sweden) isspearheading clinical genomics on a national scale In Icelandthe company deCODE genetics has established a genetic andphenotype database that encompasses the entire population(n = 334000) which fuels genetics research and drug targetvalidation for a wide range of diseases including diabetes
Several other countries have launched large-scale pro-grams of research on genomic and precision medicinemany with clear paths to clinical application althoughoften only in relation to rare pediatric diseases In SaudiArabia the very high prevalence of type 2 diabetes (20)led the Saudi government and King Abdulaziz City forScience and Technology to initiate the Saudi HumanGenome Project (SHGP) which seeks to sequence thegenomes of 100000 Saudi citizens in order to identifythe genetic basis of monogenic and complex diseases liketype 2 diabetes In China which has become a formidableforce in decoding genomes (31) the government an-nounced in 2016 a bold initiative to become a globalsuperpower in precision medicine with a US$9 billion15-year precision medicine initiative (32) The Chineseinitiative has three core objectives focused on recruiting
Figure 1mdashGlobal precision medicine initiatives of relevance to type 2 diabetes
1916 Precision Medicine in Type 2 Diabetes Diabetes Volume 67 October 2018
1) millions of participants from the seven main regions ofChina to form a nationally representative cohort 2) eightdisease-specific cohorts (cardiovascular cerebrovascularrespiratory metabolic neurological psychosomatic im-mune system disorders and seven common malignanttumors) totaling 700000 participants and 3) a clinicalcohort (N = 50000 patients with 50 rare diseases) (33)
CLINICAL TRANSLATION
The practice of precision diabetes medicine is currentlyconfined to rare monogenic forms of the disease Never-theless individual variants present only in specific pop-ulation isolates have been discovered that may have clinicalvalue in the prevention or treatment of type 2 diabetesThese include a TBC1D4 nonsense variant (pArg684ter)of relatively high prevalence in Greenlandic Inuit (minorallele frequency 17) and other circumpolar Inuit popu-lations that predisposes a substantially increased risk ofdiabetes (odds ratio 10 in those homozygous for thevariant) (34) The mechanism of action involves the mus-cle-selective loss of the long TBC1D4 isoform and dimin-ished GLUT4-mediated cellular glucose uptake in responseto insulin A thymine for cytosine substitution that causesa premature stop at codon 363 of TBC1D4 has also beendiscovered in people with acanthosis nigricans the muta-tion causes postprandial hyperinsulinemia as a possibleconsequence of a specific muscle and adipose tissue insulinresistance (35)
In Latinos a low frequency (minor allele frequency =21 in people with type 2 diabetes) missense variant (pE508K) at HNF1A conveys a type 2 diabetes odds ratio5(36)HNF1A also harbors mutations that cause rare mono-genic diabetes that can be successfully treated with sulfo-nylureas (discussed later) suggesting this is true for thenontrivial number of Latino individuals with type 2 di-abetes who carry pE508K Despite the relatively largeeffects these variants convey whether they will proveuseful in the context of precision medicine is unknown asno formal assessment of clinical utility or cost-effectivenesshas been reported
Metformin was discovered in the 1960s and is currentlythe frontline drug for early-stage diabetes treatment al-though its mechanisms of action remain unclear (37) Thereis genetic evidence for metformin intolerance (38) althoughthis has not been widely replicated By contrast variants atATM (39) and SLC2A2 (encoding GLUT2) (40) have beenassociated at a genome-wide level of statistical significancewith metformin response Although the effects conveyed bythese variants are too small to guide patient-level treatmentdecisions these findings may prove valuable in determiningmetforminrsquos mechanisms of action
Many major precision medicine statements includingthe executive summary of the US PMI (28) and a NationalResearch Council report focused on precision medicine andnew disease taxonomies (41) make clear that research andpractice in precision medicine should consider the majorimpact of lifestyle in disease etiology prevention and
treatment The incorporation of lifestyle into precisiondiabetes medicine is adequately justified by its provenbenefits in diabetes prevention and treatment Yet thereis good evidence that people benefit to varying degreesfrom diet and exercise and that personal biology underliesthis (42) Thus if precision medicine programs are to ad-equately consider the impact of lifestyle and other envi-ronmental exposures it seems logical that emphasis shouldbe placed on improving the precision of not only drugs butalso this type of antidiabetes therapy too
One compelling example of how glycemic response tofood can be predicted using an individualrsquos biomarkerscomes from a study of 800 young adults whose gut meta-genomic sequences were ascertained and diet and bloodglucose variation (from continuous glucose monitors) weremonitored for a week (43) Each participant received onestandardized meal (50 g carbohydrate) daily The authorsobserved that although participants elicited different post-prandial glycemic responses the responses to the samefood were consistent within individuals Using machinelearning algorithms these data were used to predict eachparticipantrsquos postprandial glycemic response to a givenfood They subsequently administered tailored diet inter-ventions to modulate glucose levels providing proof ofconcept that personalized diets guided by biomarker pro-filing can reduce blood glucose variability Notwithstand-ing the importance of this work from a basic scienceperspective to determine its value for precision medicinerequires knowing if minimizing variations in glucose inhealthy adults is clinically relevant and whether the ap-proach to predicting glycemic response to food also worksin people with diabetes To date neither of these impor-tant questions is addressed in the published literature
Despite lifestylersquos proven efficacy in diabetes preven-tion the resources available for research on lifestylemedicine are massively outweighed by investments inpharmacotherapy In 2013 for example US$35 billionwas spent by pharmaceutical companies on research anddevelopment (44) Although there are no comparablestatistics for research in lifestyle medicine funding islikely to be orders of magnitude less as most of this comesfrom the public purse The marketing of foods and bev-erages on the other hand is a huge industry whose mes-sages often counter public health efforts to promotehealthy lifestyle On an international scale hundreds ofbillions of US dollars are spent on food marketing annu-ally one of the biggest spenders is Unilever which spent US$8 billion on brand and marketing investments in2017 alone (45) Marketing junk foods and beverages ishandsomely resourced competing with public health ini-tiatives The major food companies in the UK for exam-ple have invested roughly US$200 million each year injunk food advertising which contrasts the US$72 millionspent on Change4Life the UK governmentrsquos flagshiphealthy eating campaign (46)
Notwithstanding the relative dearth of funding forlifestyle medicine some key initiatives exist One of the
diabetesdiabetesjournalsorg Fitipaldi and Associates 1917
largest of these is the National Institutes of HealthrsquosCommon Fund initiative on the Molecular Transducersof Physical Activity in Humans (httpcommonfundnihgovMolecularTransducers) which seeks to determineldquooptimal physical activity recommendations for peopleat various stages of liferdquo and develop ldquoprecisely targeted[exercise] regimens for individuals with particular healthneedsrdquo (47) Elsewhere the Food4Me study a research pro-gram funded by the European Union explored the role ofgenetics in the personalization of diet data from the studysuggest that personalized diet interventions supported bygenetic data improve consumption of healthy foods (48) andhelp sustain healthy food choices (49) Interestingly mostloci selected for this intervention (variants at MTHFR FTOTCF7L2 APOE and FADS1) lack evidence of individual-levelclinical relevance (50) suggesting that the success of theFood4Me intervention was not dependent on the quality ofthe genetic information provided to participants
Although most if not all precision medicine initiativesare predicated on the assumption that their findings willhelp improve peoplersquos health the emphasis of these ini-tiatives is almost always on the generation of new knowl-edge MVP for example seeks ldquoto improve understandingof how health is affected by genetic characteristics behav-iors and environmental factorsrdquo (29) Although this objec-tive will undoubtedly be achieved how this information willeventually be used to optimize prevention and treatment
of diabetes and other diseases is unclear Indeed few pre-cision medicine initiatives focused on complex diseasesprovide a clear plan for clinical translation (Fig 2)
POTENTIAL BOTTLENECKS ON THE PATH TOPRECISION MEDICINE
Cost-effectivenessThe global precision medicine market was valued atUS$436 billion in 2016 and is predicted to triple in valuewithin the next decade (51) The economic dynamicsassociated with precision medicine differ from those ofconventional medicines as the nature of the approachmeans that precision medicines are likely to be approvedfor use only in specific subpopulations limiting the num-ber of patients eligible for treatment with a given drug Bycontrast the effectiveness of precision medicines shouldbe higher for patients within these subpopulationsthan for conventional medicines and side effects may befewer and less severe Owing to the smaller market sharefor a given precision medicine competition to producecheaper drugs between manufacturers may be less than forconventional drugs and the drive to develop off-patentbiosimilars (ie drugs with active properties identical orsimilar to previously licensed drugs) is likely to differcompared with conventional medicines depending onthe extent to which first-in-class drugs are undercut bycheaper alternatives Collectively these factors will impact
Figure 2mdashThe path to precision medicine in type 2 diabetes HEA health economic assessment
1918 Precision Medicine in Type 2 Diabetes Diabetes Volume 67 October 2018
the cost-effectiveness of precision medicines in type 2 di-abetes (52)
Thus far very few cost-effectiveness analyses in pre-cision diabetes medicine have been published despitelong-standing recognition that cost-effectiveness is a keyrequirement for clinical translation (53) The rare exam-ples of such studies have focused exclusively on rare mono-genic forms of diabetes neonatal diabetes (54) andmaturity-onset diabetes of the young (MODY) (5556)
Neonatal diabetes is diagnosed in infancy and affectsabout 1400000 live births and roughly half (1252000in people aged 20 years) develop permanent neonataldiabetes (54) Of the latter group up to 70 are estimatedto carry mutations in the genes encoding the ATP-sensitivepotassium channel (KCNJ11 and ABCC8) These mutationsblock the closing of the KATP channels which preventsb-cell depolarization and corresponding insulin secretion(57) However treatment with sulfonylureas rectifies thisdefect in 90 of cases allowing the discontinuation ofexogenous insulin (58) The earliest costndasheffect analysisin precision medicine focused on sulfonylurea therapy inpermanent neonatal diabetes (54) The authors estimatedthat genetic testing in permanent neonatal diabetes wouldconvey significant quality-of-life benefits at 10 years (032quality-adjusted life-years US$12528 saved) increasingto 070 at 30 years (US$30437 saved)
A range of single gene defects cause MODY each ofwhich are used to diagnose one of five forms of the diseaseIn MODY1 and MODY3 insulin secretion is impaired Thedisease occurs early in life and is often misdiagnosed astype 1 diabetes and exogenous insulin therapy is thusprescribed However treatment with sulfonylureas enablessecretion of endogenously produced insulin Switchingfrom insulin to sulfonylureas is preferable for a numberof reasons including those related to safety and burdenMODY2 is caused by mutations in GCK which lead tochronically elevated but nonprogressive blood glucoseconcentrations that do not typically require treatmentthus diagnosing MODY2 also conveys reduced costs andburdens associated with unnecessary treatment The firstcost-effectiveness analysis focused on MODY1ndash3 in USadults (aged 25ndash40 years) with diagnosed type 2 diabetes(56) based many of its assumptions on the findings ofthe UK Prospective Diabetes Study (UKPDS) (59) It alsoassumed a 2 frequency of MODY among people withdiabetes and set genetic screening costs at US$2580Under these assumptions genetic screening for MODYwould not be cost-effective The second study focused onlyonMODY1 screening and found that genetic screening wasnot cost-effective given the diabetes population frequencyof MODY1 mutations is unlikely to exceed 2 Sub-stantial reductions in genetic sequencing costs may how-ever render screening cost-effective (55)
Regulatory ConsensusMore than half of the most clinically impactful drugs de-veloped recently were discovered through academia-led
research (60) emphasizing the huge value of publicndashprivate partnerships Many of the large precision diabetesmedicine consortia involve both academia and industrywithin which unsurprising emphasis is placed on the dis-covery and validation of biomarkers that expedite drugdevelopment This expectation is backed up by the provenability of genetics to fast-track drug development bypinpointing high-value drug targets helping halve drugfailures and markedly reducing their costs (61) In drugrepositioning genetics has also proven its worth by re-vealing previously unknown conditions that a drug can beused to treat and by helping predict drug side effects
To obtain FDA (or European Medicines Agency [EMA])approval for biomarkers in medical products and de-vices involves a comprehensive qualification process (seehttpswwwfdagovDrugsDevelopmentApprovalProcessDrugDevelopmentToolsQualificationProgramBiomarker-QualificationProgramucm535395htm) within which ex-ists the requirement for a statement termed a ldquocontext ofuserdquo The context of use statement requires a clear anddetailed description of the intended role of the biomarkerin drug development as well as risks and benefits TheFoundation for the National Institutes of Health recentlypublished the Framework for Defining Evidentiary Criteriafor Biomarker Qualification (62) which provides an ex-panded outline of the biomarker classifications definedby the FDA
The process of biomarker qualification in drug devel-opment requires stringent adherence to the relevant reg-ulatory agenciesrsquo approvals processes In some studies thatseek to discover biomarkers for diabetes drug develop-ment the process is well rehearsed and aligning biomarkerdiscovery and validation strategies with regulatory expect-ations is relatively straightforward however it does re-quire that precision medicine consortia are well versedin these processes Biomarker discovery in prediabetes ismore challenging though as prediabetes is not a distinctdisease state and although some people with prediabetesdevelop diabetes and its complications a large proportionremains healthy for many years (63) Nevertheless a sub-group of those with prediabetes will progress to diabetesand identifying these people before b-cell function issubstantially diminished would open the door for severalpowerful therapeutics that are ineffective once endoge-nous insulin production has faded However becauseneither the FDA nor EMA currently recognizes an inter-mediate end point for disease progression in prediabetesdrug trials gaining regulatory approval for new prediabe-tes biomarkers requires that researchers foster close andfrequent communications with the regulatory agencies toensure candidate biomarkers have the possibility to pro-gress through the regulatory process
Legal Social and EthicalBalancing the interests of society and its citizens the risksand benefits legislation and individual freedom as wellas the risk of exasperating disparities is central to many
diabetesdiabetesjournalsorg Fitipaldi and Associates 1919
commentaries on the legal social and ethical aspects ofprecision medicine
Precision medicine research is heavily dependent onbiobanked data and tissues Often the original informedconsents were obtained many years before modern lsquoomicstechnologies existed at a time when many research ques-tions germane to precision medicine were inconceivableOften the informed consents were broad and ethicscommittees have concluded that these modern researchquestions are within the scope of the consents As newever larger and interconnected cohorts are being formedthe desire many researchers share for broad consentingandor to bank data or samples for unspecified futureresearch must be placed in context with several importantethical realities the need to maintain participantsrsquo confi-dentiality appropriately handle incidental findings deter-mine who owns the samples and data consider culturalsensitivities return results to participants engage societyand ensure unused material is properly disposed (64)
Much as precision medicine was inconceivable whenmany existing cohorts were formed it is impossible toaccurately predict how contemporary cohorts will be usedin the future Furthermore many precision medicineinitiatives involve the hybridization of academia healthcare and industry which may challenge some of the morealtruistic motivations of participants in academia-led bio-medical research Moreover much of the highly sensitivedata collated by these initiatives will eventually be madeavailable through managed-access portals to a wide rangeof end users who were not part of the data collectionprocess Thus much of this highly sensitive data will bewidely disseminated and used by distant end users toaddress a plethora of diverse research questions thatcannot yet be conceived factors that need to be consideredduring the informed consent process These are challengesfor historical biobanks as well as new precision medicinecohorts that consent only at enrollment To help overcomethis a process called dynamic consent has been developedwhich allows consents to be progressively updated asprojects evolve but requires fluid communication withparticipants throughout the life course of a study (65)
A major concern about genetic data are its linkage todatabases that enables inferences about a personrsquos socialcognitive moral cultural health or sexual identity whichparticipants had not consented to or for such inferencesto be made about family members without their consent(64) Indeed ensuring genomics data are used in a wayconsistent with appropriate ethical standards is a corefeature of most current health data protection legislation(66) Notwithstanding these risks there are major benefitsassociated with big data which include the discovery ofnovel disease mechanisms and therapeutic targets manyof which only become visible when data sets scale tomassive proportions Thus rather than constrain thegrowth and responsible utilization of big data emphasis isbeing placed on implementing legislation that protectsprivacy and helps prevent data breaches while facilitating
the safe and responsible storage transfer and utilizationof data In the European Union for example the Gen-eral Data Protection Regulation was implemented in May2018 to harmonize data privacy laws across Europe A keyfeature of this legislation as it relates to precision medicineresearch is that the analysis of sensitive data requiresexplicit opt-in consent whereas for nonsensitive dataunambiguous (implied) consent is sufficient
SUMMARY AND CONCLUSIONS
Diabetes medicine is likely to evolve dramatically in thecoming years ideally because evidence emerges from themajor precision medicine initiatives to support its utility inpreventing or treating diabetes but no doubt also becauseof the major commercial interest in seeing precision med-icine grow One of the possible outcomes will very likely bethe replacement of current classifications of diabetes withempirically derived subclassifications that can be coupledwith treatment regimens which compared with con-ventional medicines are more effective less costly andconvey fewer unnecessary side effects As genetic data-bases grow genotypes will be increasingly used to discovernew drug targets At this time the clinical application ofgenetics is confined to the diagnosis of and therapeuticguidance for rare forms of diabetes as well as the pre-diction of adverse drug reactions The ways in whichgenetics may or may not guide prevention and therapy incommon forms of diabetes is unclear However becausecharacterizing variation in a patientrsquos nuclear genomeis inexpensive and easily achieved and DNA variation re-mains constant across the life course it is likely that geneticsequences will feature in most patientsrsquo clinical recordsGiven this genetic data will likely be deployed in diverseways in the diabetes clinic of the future The roles otherlsquoomics technologies digital imaging devices and wearableswill play in precision diabetes medicine are harder to fore-cast as research in these areas is less advanced and the costof obtaining some of these data will likely remain highrelative to genetics Finally although many precision med-icine initiatives focus predominantly on pharmacotherapyoptimizing lifestyle (and possibly surgical) interventionsusing biotechnologies also has great potential for improvingtype 2 diabetes prevention and treatment
Funding and Duality of Interest The work of HF and PWF relatedto the manuscript was supported by the European Commission (CoG-2015_681742_NASCENT) Swedish Research Council (Distinguished YoungResearchers Award in Medicine) Swedish Heart-Lung Foundation Novo NordiskFoundation European Foundation for the Study of Diabetes and the SwedishFoundation for Strategic Research (Industrial Research Center in PrecisionMedicine) (all grants to PWF) PWF is a consultant for and has stock optionsin Zoe Global and has received research funding from Boehringer Ingelheim EliLilly Janssen Novo Nordisk Sanofi and Servier MIM is a National Institute forHealth Research (NIHR) Senior Investigator and a Wellcome Trust Senior In-vestigator The views expressed in this article are those of the author(s) and notnecessarily those of the National Health Service NIHR or the Department ofHealth Work described in this review was principally supported by the Wellcome
1920 Precision Medicine in Type 2 Diabetes Diabetes Volume 67 October 2018
Trust (098381 203141 106130) and by the National Institutes of Health (U01-DK105535) MIM serves on advisory panels for Pfizer Novo Nordisk and ZoeGlobal has received honoraria from Pfizer Novo Nordisk and Eli Lilly hasstock options in Zoe Global and has received research funding from AbbVieAstraZeneca Boehringer Ingelheim Eli Lilly Janssen Merck Novo Nordisk PfizerRoche Sanofi Servier and Takeda JCF is a Massachusetts General HospitalResearch Scholar and is supported by National Institutes of Health National Instituteof Diabetes and Digestive and Kidney Diseases grants R01 DK072041 U01DK105554 R01 DK105154 and K24 DK110550 and National Institute of GeneralMedical Sciences grant R01 GM117163 MIM JCF and PWF are membersof the American Diabetes Association Precision Medicine in Diabetes TaskforceThe views expressed in this article are those of the authors and do not necessarilyreflect those of the American Diabetes Association or the taskforce No otherpotential conflicts of interest relevant to this article were reported
References1 Centers for Disease Control and Prevention National Diabetes StatisticsReport Estimates of Diabetes and Its Burden in the United States 2014 AtlantaGA US Department of Health and Human Services 20142 Knowler WC Fowler SE Hamman RF et al Diabetes Prevention ProgramResearch Group 10-year follow-up of diabetes incidence and weight loss in theDiabetes Prevention Program Outcomes Study Lancet 20093741677ndash16863 Li G Zhang P Wang J et al The long-term effect of lifestyle interventions toprevent diabetes in the China Da Qing Diabetes Prevention Study a 20-yearfollow-up study Lancet 20083711783ndash17894 Tuomilehto J Lindstroumlm J Eriksson JG et al Finnish Diabetes PreventionStudy Group Prevention of type 2 diabetes mellitus by changes in lifestyleamong subjects with impaired glucose tolerance N Engl J Med 20013441343ndash13505 Ramachandran A Snehalatha C Mary S Mukesh B Bhaskar AD Vijay VIndian Diabetes Prevention Programme (IDPP) The Indian Diabetes PreventionProgramme shows that lifestyle modification and metformin prevent type 2 di-abetes in Asian Indian subjects with impaired glucose tolerance (IDPP-1) Dia-betologia 200649289ndash2976 Lean ME Leslie WS Barnes AC et al Primary care-led weight managementfor remission of type 2 diabetes (DiRECT) an open-label cluster-randomised trialLancet 2018391541ndash5517 Polyzogopoulou EV Kalfarentzos F Vagenakis AG Alexandrides TK Res-toration of euglycemia and normal acute insulin response to glucose in obesesubjects with type 2 diabetes following bariatric surgery Diabetes 2003521098ndash11038 Franco M Orduntildeez P Caballero B et al Impact of energy intake physicalactivity and population-wide weight loss on cardiovascular disease and diabetesmortality in Cuba 1980-2005 Am J Epidemiol 20071661374ndash13809 Cho NH Shaw JE Karuranga S et al IDF Diabetes Atlas global estimates ofdiabetes prevalence for 2017 and projections for 2045 Diabetes Res Clin Pract2018138271ndash28110 Lander ES Linton LM Birren B et al International Human Genome Se-quencing Consortium Initial sequencing and analysis of the human genome[published correction appears in Nature 2001411720] Nature 2001409860ndash92111 Visscher PM Wray NR Zhang Q et al 10 Years of GWAS discovery biologyfunction and translation Am J Hum Genet 20171015ndash2212 Merino J Udler MS Leong A Meigs JB A decade of genetic and metab-olomic contributions to type 2 diabetes risk prediction Curr Diab Rep 20171713513 Knowles JW Ashley EA Cardiovascular disease the rise of the genetic riskscore PLoS Med 201815e100254614 Mahajan A Taliun D Thurner M et al Fine-mapping of an expanded set oftype 2 diabetes loci to single-variant resolution using high-density imputation andislet-specific epigenome maps [preprint article online] 2018 Available fromhttpswwwbiorxivorgcontentearly20180109245506 Accessed 30 January2018
15 McCarthy MI Painting a new picture of personalised medicine for diabetesDiabetologia 201760793ndash79916 Scott RA Scott LJ Maumlgi R et al DIAbetes Genetics Replication AndMeta-analysis (DIAGRAM) Consortium An expanded genome-wide asso-ciation study of type 2 diabetes in Europeans Diabetes 2017662888ndash290217 Mahajan A Wessel J Willems SM et al ExomeBP Consortium MAGICConsortium GIANT Consortium Refining the accuracy of validated target iden-tification through coding variant fine-mapping in type 2 diabetes Nat Genet 201850559ndash57118 Udler MS Kim J von Grotthuss M et al Clustering of type 2 diabetes geneticloci by multi-trait associations identifies disease mechanisms and subtypes[preprint article online] 2018 Available from httpswwwbiorxivorgcontentearly20180510319509 Accessed 30 March 201819 van Zuydam NR Ahlqvist E Sandholm N et al A genome-wide associationstudy of diabetic kidney disease in subjects with type 2 diabetes Diabetes 2018671414ndash142720 Ahlqvist E Storm P Kaumlraumljaumlmaumlki A et al Novel subgroups of adult-onsetdiabetes and their association with outcomes a data-driven cluster analysis of sixvariables Lancet Diabetes Endocrinol 20186361ndash36921 Fuchsberger C Flannick J Teslovich TM et al The genetic architecture oftype 2 diabetes Nature 201653641ndash4722 Koivula RW Heggie A Barnett A et al DIRECT Consortium Discovery ofbiomarkers for glycaemic deterioration before and after the onset of type 2 di-abetes rationale and design of the epidemiological studies within the IMI DIRECTConsortium Diabetologia 2014571132ndash114223 Rask-Andersen M Karlsson T Ek WE Johansson Aring Gene-environmentinteraction study for BMI reveals interactions between genetic factors and physicalactivity alcohol consumption and socioeconomic status PLoS Genet 201713e100697724 Celis-Morales CA Lyall DM Gray SR et al Dietary fat and total energy intakemodifies the association of genetic profile risk score on obesity evidence from48 170 UK Biobank participants Int J Obes 2017411761ndash176825 Tyrrell J Wood AR Ames RM et al Gene-obesogenic environment inter-actions in the UK Biobank study Int J Epidemiol 201746559ndash57526 Young AI Wauthier F Donnelly P Multiple novel gene-by-environment in-teractions modify the effect of FTO variants on body mass index Nat Commun201671272427 Reardon S Giant study poses DNA data-sharing dilemma Nature 201552516ndash1728 Hudson K Lifton R Patrick-Lake B The Precision Medicine Initiative CohortProgram ndash Building a Research Foundation for 21st Century Medicine BethesdaMD National Institutes of Health 201529 Gaziano JM Concato J Brophy M et al Million Veteran Program a mega-biobank to study genetic influences on health and disease J Clin Epidemiol 201670214ndash22330 Bergen SE Sullivan PF National-scale precision medicine for psychiatricdisorders in Sweden Am J Med Genet B Neuropsychiatr Genet 7 July 2017 [Epubahead of print] DOI 101002ajmgb3256231 Cyranoski D Chinarsquos bid to be a DNA superpower Nature 2016534462ndash46332 Cyranoski D China embraces precision medicine on a massive scale Nature20165299ndash1033 Bu D Peng S Luo H et al Precision medicine and cancer immunology inChina From big data to knowledge in precision medicine Science 2018 (Suppl)S35ndashS3834 Moltke I Grarup N Joslashrgensen ME et al A common Greenlandic TBC1D4variant confers muscle insulin resistance and type 2 diabetes Nature 2014512190ndash19335 Dash S Sano H Rochford JJ et al A truncation mutation in TBC1D4 ina family with acanthosis nigricans and postprandial hyperinsulinemia Proc NatlAcad Sci U S A 20091069350ndash9355
diabetesdiabetesjournalsorg Fitipaldi and Associates 1921
36 Estrada K Aukrust I Bjoslashrkhaug L et al SIGMA Type 2 Diabetes ConsortiumAssociation of a low-frequency variant in HNF1A with type 2 diabetes in a Latinopopulation [published correction appears in JAMA 20143121932] JAMA 2014
3112305ndash231437 Marshall SM 60 years of metformin use a glance at the past and a look tothe future Diabetologia 2017601561ndash156538 Pearson ER Pharmacogenetics and target identification in diabetes CurrOpin Genet Dev 20185068ndash7339 Zhou K Bellenguez C Spencer CC et al GoDARTS and UKPDS Diabetes
Pharmacogenetics Study Group Wellcome Trust Case Control Consortium 2MAGIC investigators Common variants near ATM are associated with glycemicresponse to metformin in type 2 diabetes Nat Genet 201143117ndash12040 Zhou K Yee SW Seiser EL et al MetGen Investigators DPP InvestigatorsACCORD Investigators Variation in the glucose transporter gene SLC2A2 isassociated with glycemic response to metformin Nat Genet 2016481055ndash
105941 National Research Council Committee on a Framework for Developing a NewTaxonomy of Disease Toward Precision Medicine Building a Knowledge Networkfor Bomedical Research and a New Taxonomy of Disease Washington DCNational Academies Press 201142 Franks PW Poveda A Lifestyle and precision diabetes medicine will ge-
nomics help optimise the prediction prevention and treatment of type 2 diabetesthrough lifestyle therapy Diabetologia 201760784ndash79243 Zeevi D Korem T Zmora N et al Personalized nutrition by prediction of
glycemic responses Cell 20151631079ndash109444 Swanson A Big pharmaceutical companies are spending far more onmarketing than research The Washington Post 11 February 2015 Available fromhttpswwwwashingtonpostcomnewswonkwp20150211big-pharmaceutical-companies-are-spending-far-more-on-marketing-than-researchnoredirect=onamputm_term=787d031b9b7c Accessed 20 February 201845 Unilever Advertising amp Marketing Unilever 2018 Available from httpwwwunilevercomsustainable-livingwhat-matters-to-youadvertising-and-marketinghtml Accessed 31 March 201846 OrsquoDowd A Spending on junk food advertising is nearly 30 times whatgovernment spends on promoting healthy eating BMJ 2017359j467747 National Institutes of Health Molecular Transducers of Physical Activity in
Humans Frequently Asked Questions (FAQs) [Internet] 2018 Available from httpscommonfundnihgovMolecularTransducersFAQs Accessed 20 March 201848 Livingstone KM Celis-Morales C Navas-Carretero S et al Food4Me Study
Effect of an Internet-based personalized nutrition randomized trial on dietarychanges associated with the Mediterranean diet the Food4Me Study Am J ClinNutr 2016104288ndash29749 Celis-Morales C Livingstone KM Marsaux CF et al Food4Me Study Effectof personalized nutrition on health-related behaviour change evidence from theFood4Me European randomized controlled trial Int J Epidemiol 201746578ndash
588
50 Celis-Morales C Livingstone KM Marsaux CF et al Design and baselinecharacteristics of the Food4Me study a web-based randomised controlled trial ofpersonalised nutrition in seven European countries Genes Nutr 20151045051 BIS Research Global precision medicine market - analysis and forecast2017-2026 2017 Available from httpsbisresearchcomindustry-reportglobal-precision-medicine-market-2026html Accessed 31 January 201852 Stern AD Alexander BM Chandra A How economics can shape precisionmedicines Science 20173551131ndash113353 Smith RJ Nathan DM Arslanian SA Groop L Rizza RA Rotter JI In-dividualizing therapies in type 2 diabetes mellitus based on patient characteristicswhat we know and what we need to know J Clin Endocrinol Metab 2010951566ndash157454 Greeley SA John PM Winn AN et al The cost-effectiveness of personalizedgenetic medicine the case of genetic testing in neonatal diabetes Diabetes Care201134622ndash62755 Nguyen HV Finkelstein EA Mital S Gardner DS Incremental cost-effectivenessof algorithm-driven genetic testing versus no testing for maturity onset diabetes of theyoung (MODY) in Singapore J Med Genet 201754747ndash75356 Naylor RN John PM Winn AN et al Cost-effectiveness of MODY genetictesting translating genomic advances into practical health applications DiabetesCare 201437202ndash20957 Gloyn AL Pearson ER Antcliff JF et al Activating mutations in the geneencoding the ATP-sensitive potassium-channel subunit Kir62 and permanentneonatal diabetes N Engl J Med 20043501838ndash184958 Pearson ER Flechtner I Njoslashlstad PR et al Neonatal Diabetes InternationalCollaborative Group Switching from insulin to oral sulfonylureas in patients withdiabetes due to Kir62 mutations N Engl J Med 2006355467ndash47759 UK Prospective Diabetes Study (UKPDS) UK Prospective Diabetes Study(UKPDS) VIII Study design progress and performance Diabetologia 199134877ndash89060 Kesselheim AS Tan YT Avorn J The roles of academia rare diseases andrepurposing in the development of the most transformative drugs Health Aff(Millwood) 201534286ndash29361 Nelson MR Tipney H Painter JL et al The support of human geneticevidence for approved drug indications Nat Genet 201547856ndash86062 FNIH Evidentiary Criteria Writing Group Framework for Defining EvidentiaryCriteria for Biomarker Qualification Bethesda MD National Library of Medicine201663 Tabaacutek AG Herder C Rathmann W Brunner EJ Kivimaumlki M Prediabetesa high-risk state for diabetes development Lancet 20123792279ndash229064 Nicol D Bubela T Chalmers D et al Precision medicine drowning ina regulatory soup J Law Biosci 20163281ndash30365 Kaye J Whitley EA Lund D Morrison M Teare H Melham K Dynamicconsent a patient interface for twenty-first century research networks Eur J HumGenet 201523141ndash14666 Kinkorovaacute J Biobanks in the era of personalized medicine objectiveschallenges and innovation Overview EPMA J 201674
1922 Precision Medicine in Type 2 Diabetes Diabetes Volume 67 October 2018
substantially to discoveries in diabetes genetics over thepast decade In 2015 the Nordic Precision MedicineInitiative (NPMI) was formed to bring together genetic andother biomedical data from 1 million Nordic citizensSeveral national precision and genomic medicine projectsare under way in Estonia for example the EstonianGenome Center at the University of Tartu is curatinga population-based biobank suitable for precision medicineresearch in diabetes and other complex diseases The aimis to promote and advance the development of geneticresearch and the implementation of genomic data intoclinical practice to improve public health The Estoniangovernment recently launched the Estonian PersonalisedMedicine Pilot Project (EPMPP) (2015ndash2018) which seeksto implement personalized medicine on a national scaleAccordingly the government has provided US$59 millionto support genotyping in 100000 Estonians In Finlandthe FinnGen project was launched in 2017 with theobjective to link genomic data with digital health caredata for 500000 Finnish citizens (10 of the countryrsquospopulation) through a publicndashprivate partnership In SwedenGAPS (Genomic Aggregation Project in Sweden) hasassimilated160000 genotyped samples with corresponding
phenotype data covering a wide range of diseases includingdiabetes (30) and GMS (Genomic Medicine Sweden) isspearheading clinical genomics on a national scale In Icelandthe company deCODE genetics has established a genetic andphenotype database that encompasses the entire population(n = 334000) which fuels genetics research and drug targetvalidation for a wide range of diseases including diabetes
Several other countries have launched large-scale pro-grams of research on genomic and precision medicinemany with clear paths to clinical application althoughoften only in relation to rare pediatric diseases In SaudiArabia the very high prevalence of type 2 diabetes (20)led the Saudi government and King Abdulaziz City forScience and Technology to initiate the Saudi HumanGenome Project (SHGP) which seeks to sequence thegenomes of 100000 Saudi citizens in order to identifythe genetic basis of monogenic and complex diseases liketype 2 diabetes In China which has become a formidableforce in decoding genomes (31) the government an-nounced in 2016 a bold initiative to become a globalsuperpower in precision medicine with a US$9 billion15-year precision medicine initiative (32) The Chineseinitiative has three core objectives focused on recruiting
Figure 1mdashGlobal precision medicine initiatives of relevance to type 2 diabetes
1916 Precision Medicine in Type 2 Diabetes Diabetes Volume 67 October 2018
1) millions of participants from the seven main regions ofChina to form a nationally representative cohort 2) eightdisease-specific cohorts (cardiovascular cerebrovascularrespiratory metabolic neurological psychosomatic im-mune system disorders and seven common malignanttumors) totaling 700000 participants and 3) a clinicalcohort (N = 50000 patients with 50 rare diseases) (33)
CLINICAL TRANSLATION
The practice of precision diabetes medicine is currentlyconfined to rare monogenic forms of the disease Never-theless individual variants present only in specific pop-ulation isolates have been discovered that may have clinicalvalue in the prevention or treatment of type 2 diabetesThese include a TBC1D4 nonsense variant (pArg684ter)of relatively high prevalence in Greenlandic Inuit (minorallele frequency 17) and other circumpolar Inuit popu-lations that predisposes a substantially increased risk ofdiabetes (odds ratio 10 in those homozygous for thevariant) (34) The mechanism of action involves the mus-cle-selective loss of the long TBC1D4 isoform and dimin-ished GLUT4-mediated cellular glucose uptake in responseto insulin A thymine for cytosine substitution that causesa premature stop at codon 363 of TBC1D4 has also beendiscovered in people with acanthosis nigricans the muta-tion causes postprandial hyperinsulinemia as a possibleconsequence of a specific muscle and adipose tissue insulinresistance (35)
In Latinos a low frequency (minor allele frequency =21 in people with type 2 diabetes) missense variant (pE508K) at HNF1A conveys a type 2 diabetes odds ratio5(36)HNF1A also harbors mutations that cause rare mono-genic diabetes that can be successfully treated with sulfo-nylureas (discussed later) suggesting this is true for thenontrivial number of Latino individuals with type 2 di-abetes who carry pE508K Despite the relatively largeeffects these variants convey whether they will proveuseful in the context of precision medicine is unknown asno formal assessment of clinical utility or cost-effectivenesshas been reported
Metformin was discovered in the 1960s and is currentlythe frontline drug for early-stage diabetes treatment al-though its mechanisms of action remain unclear (37) Thereis genetic evidence for metformin intolerance (38) althoughthis has not been widely replicated By contrast variants atATM (39) and SLC2A2 (encoding GLUT2) (40) have beenassociated at a genome-wide level of statistical significancewith metformin response Although the effects conveyed bythese variants are too small to guide patient-level treatmentdecisions these findings may prove valuable in determiningmetforminrsquos mechanisms of action
Many major precision medicine statements includingthe executive summary of the US PMI (28) and a NationalResearch Council report focused on precision medicine andnew disease taxonomies (41) make clear that research andpractice in precision medicine should consider the majorimpact of lifestyle in disease etiology prevention and
treatment The incorporation of lifestyle into precisiondiabetes medicine is adequately justified by its provenbenefits in diabetes prevention and treatment Yet thereis good evidence that people benefit to varying degreesfrom diet and exercise and that personal biology underliesthis (42) Thus if precision medicine programs are to ad-equately consider the impact of lifestyle and other envi-ronmental exposures it seems logical that emphasis shouldbe placed on improving the precision of not only drugs butalso this type of antidiabetes therapy too
One compelling example of how glycemic response tofood can be predicted using an individualrsquos biomarkerscomes from a study of 800 young adults whose gut meta-genomic sequences were ascertained and diet and bloodglucose variation (from continuous glucose monitors) weremonitored for a week (43) Each participant received onestandardized meal (50 g carbohydrate) daily The authorsobserved that although participants elicited different post-prandial glycemic responses the responses to the samefood were consistent within individuals Using machinelearning algorithms these data were used to predict eachparticipantrsquos postprandial glycemic response to a givenfood They subsequently administered tailored diet inter-ventions to modulate glucose levels providing proof ofconcept that personalized diets guided by biomarker pro-filing can reduce blood glucose variability Notwithstand-ing the importance of this work from a basic scienceperspective to determine its value for precision medicinerequires knowing if minimizing variations in glucose inhealthy adults is clinically relevant and whether the ap-proach to predicting glycemic response to food also worksin people with diabetes To date neither of these impor-tant questions is addressed in the published literature
Despite lifestylersquos proven efficacy in diabetes preven-tion the resources available for research on lifestylemedicine are massively outweighed by investments inpharmacotherapy In 2013 for example US$35 billionwas spent by pharmaceutical companies on research anddevelopment (44) Although there are no comparablestatistics for research in lifestyle medicine funding islikely to be orders of magnitude less as most of this comesfrom the public purse The marketing of foods and bev-erages on the other hand is a huge industry whose mes-sages often counter public health efforts to promotehealthy lifestyle On an international scale hundreds ofbillions of US dollars are spent on food marketing annu-ally one of the biggest spenders is Unilever which spent US$8 billion on brand and marketing investments in2017 alone (45) Marketing junk foods and beverages ishandsomely resourced competing with public health ini-tiatives The major food companies in the UK for exam-ple have invested roughly US$200 million each year injunk food advertising which contrasts the US$72 millionspent on Change4Life the UK governmentrsquos flagshiphealthy eating campaign (46)
Notwithstanding the relative dearth of funding forlifestyle medicine some key initiatives exist One of the
diabetesdiabetesjournalsorg Fitipaldi and Associates 1917
largest of these is the National Institutes of HealthrsquosCommon Fund initiative on the Molecular Transducersof Physical Activity in Humans (httpcommonfundnihgovMolecularTransducers) which seeks to determineldquooptimal physical activity recommendations for peopleat various stages of liferdquo and develop ldquoprecisely targeted[exercise] regimens for individuals with particular healthneedsrdquo (47) Elsewhere the Food4Me study a research pro-gram funded by the European Union explored the role ofgenetics in the personalization of diet data from the studysuggest that personalized diet interventions supported bygenetic data improve consumption of healthy foods (48) andhelp sustain healthy food choices (49) Interestingly mostloci selected for this intervention (variants at MTHFR FTOTCF7L2 APOE and FADS1) lack evidence of individual-levelclinical relevance (50) suggesting that the success of theFood4Me intervention was not dependent on the quality ofthe genetic information provided to participants
Although most if not all precision medicine initiativesare predicated on the assumption that their findings willhelp improve peoplersquos health the emphasis of these ini-tiatives is almost always on the generation of new knowl-edge MVP for example seeks ldquoto improve understandingof how health is affected by genetic characteristics behav-iors and environmental factorsrdquo (29) Although this objec-tive will undoubtedly be achieved how this information willeventually be used to optimize prevention and treatment
of diabetes and other diseases is unclear Indeed few pre-cision medicine initiatives focused on complex diseasesprovide a clear plan for clinical translation (Fig 2)
POTENTIAL BOTTLENECKS ON THE PATH TOPRECISION MEDICINE
Cost-effectivenessThe global precision medicine market was valued atUS$436 billion in 2016 and is predicted to triple in valuewithin the next decade (51) The economic dynamicsassociated with precision medicine differ from those ofconventional medicines as the nature of the approachmeans that precision medicines are likely to be approvedfor use only in specific subpopulations limiting the num-ber of patients eligible for treatment with a given drug Bycontrast the effectiveness of precision medicines shouldbe higher for patients within these subpopulationsthan for conventional medicines and side effects may befewer and less severe Owing to the smaller market sharefor a given precision medicine competition to producecheaper drugs between manufacturers may be less than forconventional drugs and the drive to develop off-patentbiosimilars (ie drugs with active properties identical orsimilar to previously licensed drugs) is likely to differcompared with conventional medicines depending onthe extent to which first-in-class drugs are undercut bycheaper alternatives Collectively these factors will impact
Figure 2mdashThe path to precision medicine in type 2 diabetes HEA health economic assessment
1918 Precision Medicine in Type 2 Diabetes Diabetes Volume 67 October 2018
the cost-effectiveness of precision medicines in type 2 di-abetes (52)
Thus far very few cost-effectiveness analyses in pre-cision diabetes medicine have been published despitelong-standing recognition that cost-effectiveness is a keyrequirement for clinical translation (53) The rare exam-ples of such studies have focused exclusively on rare mono-genic forms of diabetes neonatal diabetes (54) andmaturity-onset diabetes of the young (MODY) (5556)
Neonatal diabetes is diagnosed in infancy and affectsabout 1400000 live births and roughly half (1252000in people aged 20 years) develop permanent neonataldiabetes (54) Of the latter group up to 70 are estimatedto carry mutations in the genes encoding the ATP-sensitivepotassium channel (KCNJ11 and ABCC8) These mutationsblock the closing of the KATP channels which preventsb-cell depolarization and corresponding insulin secretion(57) However treatment with sulfonylureas rectifies thisdefect in 90 of cases allowing the discontinuation ofexogenous insulin (58) The earliest costndasheffect analysisin precision medicine focused on sulfonylurea therapy inpermanent neonatal diabetes (54) The authors estimatedthat genetic testing in permanent neonatal diabetes wouldconvey significant quality-of-life benefits at 10 years (032quality-adjusted life-years US$12528 saved) increasingto 070 at 30 years (US$30437 saved)
A range of single gene defects cause MODY each ofwhich are used to diagnose one of five forms of the diseaseIn MODY1 and MODY3 insulin secretion is impaired Thedisease occurs early in life and is often misdiagnosed astype 1 diabetes and exogenous insulin therapy is thusprescribed However treatment with sulfonylureas enablessecretion of endogenously produced insulin Switchingfrom insulin to sulfonylureas is preferable for a numberof reasons including those related to safety and burdenMODY2 is caused by mutations in GCK which lead tochronically elevated but nonprogressive blood glucoseconcentrations that do not typically require treatmentthus diagnosing MODY2 also conveys reduced costs andburdens associated with unnecessary treatment The firstcost-effectiveness analysis focused on MODY1ndash3 in USadults (aged 25ndash40 years) with diagnosed type 2 diabetes(56) based many of its assumptions on the findings ofthe UK Prospective Diabetes Study (UKPDS) (59) It alsoassumed a 2 frequency of MODY among people withdiabetes and set genetic screening costs at US$2580Under these assumptions genetic screening for MODYwould not be cost-effective The second study focused onlyonMODY1 screening and found that genetic screening wasnot cost-effective given the diabetes population frequencyof MODY1 mutations is unlikely to exceed 2 Sub-stantial reductions in genetic sequencing costs may how-ever render screening cost-effective (55)
Regulatory ConsensusMore than half of the most clinically impactful drugs de-veloped recently were discovered through academia-led
research (60) emphasizing the huge value of publicndashprivate partnerships Many of the large precision diabetesmedicine consortia involve both academia and industrywithin which unsurprising emphasis is placed on the dis-covery and validation of biomarkers that expedite drugdevelopment This expectation is backed up by the provenability of genetics to fast-track drug development bypinpointing high-value drug targets helping halve drugfailures and markedly reducing their costs (61) In drugrepositioning genetics has also proven its worth by re-vealing previously unknown conditions that a drug can beused to treat and by helping predict drug side effects
To obtain FDA (or European Medicines Agency [EMA])approval for biomarkers in medical products and de-vices involves a comprehensive qualification process (seehttpswwwfdagovDrugsDevelopmentApprovalProcessDrugDevelopmentToolsQualificationProgramBiomarker-QualificationProgramucm535395htm) within which ex-ists the requirement for a statement termed a ldquocontext ofuserdquo The context of use statement requires a clear anddetailed description of the intended role of the biomarkerin drug development as well as risks and benefits TheFoundation for the National Institutes of Health recentlypublished the Framework for Defining Evidentiary Criteriafor Biomarker Qualification (62) which provides an ex-panded outline of the biomarker classifications definedby the FDA
The process of biomarker qualification in drug devel-opment requires stringent adherence to the relevant reg-ulatory agenciesrsquo approvals processes In some studies thatseek to discover biomarkers for diabetes drug develop-ment the process is well rehearsed and aligning biomarkerdiscovery and validation strategies with regulatory expect-ations is relatively straightforward however it does re-quire that precision medicine consortia are well versedin these processes Biomarker discovery in prediabetes ismore challenging though as prediabetes is not a distinctdisease state and although some people with prediabetesdevelop diabetes and its complications a large proportionremains healthy for many years (63) Nevertheless a sub-group of those with prediabetes will progress to diabetesand identifying these people before b-cell function issubstantially diminished would open the door for severalpowerful therapeutics that are ineffective once endoge-nous insulin production has faded However becauseneither the FDA nor EMA currently recognizes an inter-mediate end point for disease progression in prediabetesdrug trials gaining regulatory approval for new prediabe-tes biomarkers requires that researchers foster close andfrequent communications with the regulatory agencies toensure candidate biomarkers have the possibility to pro-gress through the regulatory process
Legal Social and EthicalBalancing the interests of society and its citizens the risksand benefits legislation and individual freedom as wellas the risk of exasperating disparities is central to many
diabetesdiabetesjournalsorg Fitipaldi and Associates 1919
commentaries on the legal social and ethical aspects ofprecision medicine
Precision medicine research is heavily dependent onbiobanked data and tissues Often the original informedconsents were obtained many years before modern lsquoomicstechnologies existed at a time when many research ques-tions germane to precision medicine were inconceivableOften the informed consents were broad and ethicscommittees have concluded that these modern researchquestions are within the scope of the consents As newever larger and interconnected cohorts are being formedthe desire many researchers share for broad consentingandor to bank data or samples for unspecified futureresearch must be placed in context with several importantethical realities the need to maintain participantsrsquo confi-dentiality appropriately handle incidental findings deter-mine who owns the samples and data consider culturalsensitivities return results to participants engage societyand ensure unused material is properly disposed (64)
Much as precision medicine was inconceivable whenmany existing cohorts were formed it is impossible toaccurately predict how contemporary cohorts will be usedin the future Furthermore many precision medicineinitiatives involve the hybridization of academia healthcare and industry which may challenge some of the morealtruistic motivations of participants in academia-led bio-medical research Moreover much of the highly sensitivedata collated by these initiatives will eventually be madeavailable through managed-access portals to a wide rangeof end users who were not part of the data collectionprocess Thus much of this highly sensitive data will bewidely disseminated and used by distant end users toaddress a plethora of diverse research questions thatcannot yet be conceived factors that need to be consideredduring the informed consent process These are challengesfor historical biobanks as well as new precision medicinecohorts that consent only at enrollment To help overcomethis a process called dynamic consent has been developedwhich allows consents to be progressively updated asprojects evolve but requires fluid communication withparticipants throughout the life course of a study (65)
A major concern about genetic data are its linkage todatabases that enables inferences about a personrsquos socialcognitive moral cultural health or sexual identity whichparticipants had not consented to or for such inferencesto be made about family members without their consent(64) Indeed ensuring genomics data are used in a wayconsistent with appropriate ethical standards is a corefeature of most current health data protection legislation(66) Notwithstanding these risks there are major benefitsassociated with big data which include the discovery ofnovel disease mechanisms and therapeutic targets manyof which only become visible when data sets scale tomassive proportions Thus rather than constrain thegrowth and responsible utilization of big data emphasis isbeing placed on implementing legislation that protectsprivacy and helps prevent data breaches while facilitating
the safe and responsible storage transfer and utilizationof data In the European Union for example the Gen-eral Data Protection Regulation was implemented in May2018 to harmonize data privacy laws across Europe A keyfeature of this legislation as it relates to precision medicineresearch is that the analysis of sensitive data requiresexplicit opt-in consent whereas for nonsensitive dataunambiguous (implied) consent is sufficient
SUMMARY AND CONCLUSIONS
Diabetes medicine is likely to evolve dramatically in thecoming years ideally because evidence emerges from themajor precision medicine initiatives to support its utility inpreventing or treating diabetes but no doubt also becauseof the major commercial interest in seeing precision med-icine grow One of the possible outcomes will very likely bethe replacement of current classifications of diabetes withempirically derived subclassifications that can be coupledwith treatment regimens which compared with con-ventional medicines are more effective less costly andconvey fewer unnecessary side effects As genetic data-bases grow genotypes will be increasingly used to discovernew drug targets At this time the clinical application ofgenetics is confined to the diagnosis of and therapeuticguidance for rare forms of diabetes as well as the pre-diction of adverse drug reactions The ways in whichgenetics may or may not guide prevention and therapy incommon forms of diabetes is unclear However becausecharacterizing variation in a patientrsquos nuclear genomeis inexpensive and easily achieved and DNA variation re-mains constant across the life course it is likely that geneticsequences will feature in most patientsrsquo clinical recordsGiven this genetic data will likely be deployed in diverseways in the diabetes clinic of the future The roles otherlsquoomics technologies digital imaging devices and wearableswill play in precision diabetes medicine are harder to fore-cast as research in these areas is less advanced and the costof obtaining some of these data will likely remain highrelative to genetics Finally although many precision med-icine initiatives focus predominantly on pharmacotherapyoptimizing lifestyle (and possibly surgical) interventionsusing biotechnologies also has great potential for improvingtype 2 diabetes prevention and treatment
Funding and Duality of Interest The work of HF and PWF relatedto the manuscript was supported by the European Commission (CoG-2015_681742_NASCENT) Swedish Research Council (Distinguished YoungResearchers Award in Medicine) Swedish Heart-Lung Foundation Novo NordiskFoundation European Foundation for the Study of Diabetes and the SwedishFoundation for Strategic Research (Industrial Research Center in PrecisionMedicine) (all grants to PWF) PWF is a consultant for and has stock optionsin Zoe Global and has received research funding from Boehringer Ingelheim EliLilly Janssen Novo Nordisk Sanofi and Servier MIM is a National Institute forHealth Research (NIHR) Senior Investigator and a Wellcome Trust Senior In-vestigator The views expressed in this article are those of the author(s) and notnecessarily those of the National Health Service NIHR or the Department ofHealth Work described in this review was principally supported by the Wellcome
1920 Precision Medicine in Type 2 Diabetes Diabetes Volume 67 October 2018
Trust (098381 203141 106130) and by the National Institutes of Health (U01-DK105535) MIM serves on advisory panels for Pfizer Novo Nordisk and ZoeGlobal has received honoraria from Pfizer Novo Nordisk and Eli Lilly hasstock options in Zoe Global and has received research funding from AbbVieAstraZeneca Boehringer Ingelheim Eli Lilly Janssen Merck Novo Nordisk PfizerRoche Sanofi Servier and Takeda JCF is a Massachusetts General HospitalResearch Scholar and is supported by National Institutes of Health National Instituteof Diabetes and Digestive and Kidney Diseases grants R01 DK072041 U01DK105554 R01 DK105154 and K24 DK110550 and National Institute of GeneralMedical Sciences grant R01 GM117163 MIM JCF and PWF are membersof the American Diabetes Association Precision Medicine in Diabetes TaskforceThe views expressed in this article are those of the authors and do not necessarilyreflect those of the American Diabetes Association or the taskforce No otherpotential conflicts of interest relevant to this article were reported
References1 Centers for Disease Control and Prevention National Diabetes StatisticsReport Estimates of Diabetes and Its Burden in the United States 2014 AtlantaGA US Department of Health and Human Services 20142 Knowler WC Fowler SE Hamman RF et al Diabetes Prevention ProgramResearch Group 10-year follow-up of diabetes incidence and weight loss in theDiabetes Prevention Program Outcomes Study Lancet 20093741677ndash16863 Li G Zhang P Wang J et al The long-term effect of lifestyle interventions toprevent diabetes in the China Da Qing Diabetes Prevention Study a 20-yearfollow-up study Lancet 20083711783ndash17894 Tuomilehto J Lindstroumlm J Eriksson JG et al Finnish Diabetes PreventionStudy Group Prevention of type 2 diabetes mellitus by changes in lifestyleamong subjects with impaired glucose tolerance N Engl J Med 20013441343ndash13505 Ramachandran A Snehalatha C Mary S Mukesh B Bhaskar AD Vijay VIndian Diabetes Prevention Programme (IDPP) The Indian Diabetes PreventionProgramme shows that lifestyle modification and metformin prevent type 2 di-abetes in Asian Indian subjects with impaired glucose tolerance (IDPP-1) Dia-betologia 200649289ndash2976 Lean ME Leslie WS Barnes AC et al Primary care-led weight managementfor remission of type 2 diabetes (DiRECT) an open-label cluster-randomised trialLancet 2018391541ndash5517 Polyzogopoulou EV Kalfarentzos F Vagenakis AG Alexandrides TK Res-toration of euglycemia and normal acute insulin response to glucose in obesesubjects with type 2 diabetes following bariatric surgery Diabetes 2003521098ndash11038 Franco M Orduntildeez P Caballero B et al Impact of energy intake physicalactivity and population-wide weight loss on cardiovascular disease and diabetesmortality in Cuba 1980-2005 Am J Epidemiol 20071661374ndash13809 Cho NH Shaw JE Karuranga S et al IDF Diabetes Atlas global estimates ofdiabetes prevalence for 2017 and projections for 2045 Diabetes Res Clin Pract2018138271ndash28110 Lander ES Linton LM Birren B et al International Human Genome Se-quencing Consortium Initial sequencing and analysis of the human genome[published correction appears in Nature 2001411720] Nature 2001409860ndash92111 Visscher PM Wray NR Zhang Q et al 10 Years of GWAS discovery biologyfunction and translation Am J Hum Genet 20171015ndash2212 Merino J Udler MS Leong A Meigs JB A decade of genetic and metab-olomic contributions to type 2 diabetes risk prediction Curr Diab Rep 20171713513 Knowles JW Ashley EA Cardiovascular disease the rise of the genetic riskscore PLoS Med 201815e100254614 Mahajan A Taliun D Thurner M et al Fine-mapping of an expanded set oftype 2 diabetes loci to single-variant resolution using high-density imputation andislet-specific epigenome maps [preprint article online] 2018 Available fromhttpswwwbiorxivorgcontentearly20180109245506 Accessed 30 January2018
15 McCarthy MI Painting a new picture of personalised medicine for diabetesDiabetologia 201760793ndash79916 Scott RA Scott LJ Maumlgi R et al DIAbetes Genetics Replication AndMeta-analysis (DIAGRAM) Consortium An expanded genome-wide asso-ciation study of type 2 diabetes in Europeans Diabetes 2017662888ndash290217 Mahajan A Wessel J Willems SM et al ExomeBP Consortium MAGICConsortium GIANT Consortium Refining the accuracy of validated target iden-tification through coding variant fine-mapping in type 2 diabetes Nat Genet 201850559ndash57118 Udler MS Kim J von Grotthuss M et al Clustering of type 2 diabetes geneticloci by multi-trait associations identifies disease mechanisms and subtypes[preprint article online] 2018 Available from httpswwwbiorxivorgcontentearly20180510319509 Accessed 30 March 201819 van Zuydam NR Ahlqvist E Sandholm N et al A genome-wide associationstudy of diabetic kidney disease in subjects with type 2 diabetes Diabetes 2018671414ndash142720 Ahlqvist E Storm P Kaumlraumljaumlmaumlki A et al Novel subgroups of adult-onsetdiabetes and their association with outcomes a data-driven cluster analysis of sixvariables Lancet Diabetes Endocrinol 20186361ndash36921 Fuchsberger C Flannick J Teslovich TM et al The genetic architecture oftype 2 diabetes Nature 201653641ndash4722 Koivula RW Heggie A Barnett A et al DIRECT Consortium Discovery ofbiomarkers for glycaemic deterioration before and after the onset of type 2 di-abetes rationale and design of the epidemiological studies within the IMI DIRECTConsortium Diabetologia 2014571132ndash114223 Rask-Andersen M Karlsson T Ek WE Johansson Aring Gene-environmentinteraction study for BMI reveals interactions between genetic factors and physicalactivity alcohol consumption and socioeconomic status PLoS Genet 201713e100697724 Celis-Morales CA Lyall DM Gray SR et al Dietary fat and total energy intakemodifies the association of genetic profile risk score on obesity evidence from48 170 UK Biobank participants Int J Obes 2017411761ndash176825 Tyrrell J Wood AR Ames RM et al Gene-obesogenic environment inter-actions in the UK Biobank study Int J Epidemiol 201746559ndash57526 Young AI Wauthier F Donnelly P Multiple novel gene-by-environment in-teractions modify the effect of FTO variants on body mass index Nat Commun201671272427 Reardon S Giant study poses DNA data-sharing dilemma Nature 201552516ndash1728 Hudson K Lifton R Patrick-Lake B The Precision Medicine Initiative CohortProgram ndash Building a Research Foundation for 21st Century Medicine BethesdaMD National Institutes of Health 201529 Gaziano JM Concato J Brophy M et al Million Veteran Program a mega-biobank to study genetic influences on health and disease J Clin Epidemiol 201670214ndash22330 Bergen SE Sullivan PF National-scale precision medicine for psychiatricdisorders in Sweden Am J Med Genet B Neuropsychiatr Genet 7 July 2017 [Epubahead of print] DOI 101002ajmgb3256231 Cyranoski D Chinarsquos bid to be a DNA superpower Nature 2016534462ndash46332 Cyranoski D China embraces precision medicine on a massive scale Nature20165299ndash1033 Bu D Peng S Luo H et al Precision medicine and cancer immunology inChina From big data to knowledge in precision medicine Science 2018 (Suppl)S35ndashS3834 Moltke I Grarup N Joslashrgensen ME et al A common Greenlandic TBC1D4variant confers muscle insulin resistance and type 2 diabetes Nature 2014512190ndash19335 Dash S Sano H Rochford JJ et al A truncation mutation in TBC1D4 ina family with acanthosis nigricans and postprandial hyperinsulinemia Proc NatlAcad Sci U S A 20091069350ndash9355
diabetesdiabetesjournalsorg Fitipaldi and Associates 1921
36 Estrada K Aukrust I Bjoslashrkhaug L et al SIGMA Type 2 Diabetes ConsortiumAssociation of a low-frequency variant in HNF1A with type 2 diabetes in a Latinopopulation [published correction appears in JAMA 20143121932] JAMA 2014
3112305ndash231437 Marshall SM 60 years of metformin use a glance at the past and a look tothe future Diabetologia 2017601561ndash156538 Pearson ER Pharmacogenetics and target identification in diabetes CurrOpin Genet Dev 20185068ndash7339 Zhou K Bellenguez C Spencer CC et al GoDARTS and UKPDS Diabetes
Pharmacogenetics Study Group Wellcome Trust Case Control Consortium 2MAGIC investigators Common variants near ATM are associated with glycemicresponse to metformin in type 2 diabetes Nat Genet 201143117ndash12040 Zhou K Yee SW Seiser EL et al MetGen Investigators DPP InvestigatorsACCORD Investigators Variation in the glucose transporter gene SLC2A2 isassociated with glycemic response to metformin Nat Genet 2016481055ndash
105941 National Research Council Committee on a Framework for Developing a NewTaxonomy of Disease Toward Precision Medicine Building a Knowledge Networkfor Bomedical Research and a New Taxonomy of Disease Washington DCNational Academies Press 201142 Franks PW Poveda A Lifestyle and precision diabetes medicine will ge-
nomics help optimise the prediction prevention and treatment of type 2 diabetesthrough lifestyle therapy Diabetologia 201760784ndash79243 Zeevi D Korem T Zmora N et al Personalized nutrition by prediction of
glycemic responses Cell 20151631079ndash109444 Swanson A Big pharmaceutical companies are spending far more onmarketing than research The Washington Post 11 February 2015 Available fromhttpswwwwashingtonpostcomnewswonkwp20150211big-pharmaceutical-companies-are-spending-far-more-on-marketing-than-researchnoredirect=onamputm_term=787d031b9b7c Accessed 20 February 201845 Unilever Advertising amp Marketing Unilever 2018 Available from httpwwwunilevercomsustainable-livingwhat-matters-to-youadvertising-and-marketinghtml Accessed 31 March 201846 OrsquoDowd A Spending on junk food advertising is nearly 30 times whatgovernment spends on promoting healthy eating BMJ 2017359j467747 National Institutes of Health Molecular Transducers of Physical Activity in
Humans Frequently Asked Questions (FAQs) [Internet] 2018 Available from httpscommonfundnihgovMolecularTransducersFAQs Accessed 20 March 201848 Livingstone KM Celis-Morales C Navas-Carretero S et al Food4Me Study
Effect of an Internet-based personalized nutrition randomized trial on dietarychanges associated with the Mediterranean diet the Food4Me Study Am J ClinNutr 2016104288ndash29749 Celis-Morales C Livingstone KM Marsaux CF et al Food4Me Study Effectof personalized nutrition on health-related behaviour change evidence from theFood4Me European randomized controlled trial Int J Epidemiol 201746578ndash
588
50 Celis-Morales C Livingstone KM Marsaux CF et al Design and baselinecharacteristics of the Food4Me study a web-based randomised controlled trial ofpersonalised nutrition in seven European countries Genes Nutr 20151045051 BIS Research Global precision medicine market - analysis and forecast2017-2026 2017 Available from httpsbisresearchcomindustry-reportglobal-precision-medicine-market-2026html Accessed 31 January 201852 Stern AD Alexander BM Chandra A How economics can shape precisionmedicines Science 20173551131ndash113353 Smith RJ Nathan DM Arslanian SA Groop L Rizza RA Rotter JI In-dividualizing therapies in type 2 diabetes mellitus based on patient characteristicswhat we know and what we need to know J Clin Endocrinol Metab 2010951566ndash157454 Greeley SA John PM Winn AN et al The cost-effectiveness of personalizedgenetic medicine the case of genetic testing in neonatal diabetes Diabetes Care201134622ndash62755 Nguyen HV Finkelstein EA Mital S Gardner DS Incremental cost-effectivenessof algorithm-driven genetic testing versus no testing for maturity onset diabetes of theyoung (MODY) in Singapore J Med Genet 201754747ndash75356 Naylor RN John PM Winn AN et al Cost-effectiveness of MODY genetictesting translating genomic advances into practical health applications DiabetesCare 201437202ndash20957 Gloyn AL Pearson ER Antcliff JF et al Activating mutations in the geneencoding the ATP-sensitive potassium-channel subunit Kir62 and permanentneonatal diabetes N Engl J Med 20043501838ndash184958 Pearson ER Flechtner I Njoslashlstad PR et al Neonatal Diabetes InternationalCollaborative Group Switching from insulin to oral sulfonylureas in patients withdiabetes due to Kir62 mutations N Engl J Med 2006355467ndash47759 UK Prospective Diabetes Study (UKPDS) UK Prospective Diabetes Study(UKPDS) VIII Study design progress and performance Diabetologia 199134877ndash89060 Kesselheim AS Tan YT Avorn J The roles of academia rare diseases andrepurposing in the development of the most transformative drugs Health Aff(Millwood) 201534286ndash29361 Nelson MR Tipney H Painter JL et al The support of human geneticevidence for approved drug indications Nat Genet 201547856ndash86062 FNIH Evidentiary Criteria Writing Group Framework for Defining EvidentiaryCriteria for Biomarker Qualification Bethesda MD National Library of Medicine201663 Tabaacutek AG Herder C Rathmann W Brunner EJ Kivimaumlki M Prediabetesa high-risk state for diabetes development Lancet 20123792279ndash229064 Nicol D Bubela T Chalmers D et al Precision medicine drowning ina regulatory soup J Law Biosci 20163281ndash30365 Kaye J Whitley EA Lund D Morrison M Teare H Melham K Dynamicconsent a patient interface for twenty-first century research networks Eur J HumGenet 201523141ndash14666 Kinkorovaacute J Biobanks in the era of personalized medicine objectiveschallenges and innovation Overview EPMA J 201674
1922 Precision Medicine in Type 2 Diabetes Diabetes Volume 67 October 2018
1) millions of participants from the seven main regions ofChina to form a nationally representative cohort 2) eightdisease-specific cohorts (cardiovascular cerebrovascularrespiratory metabolic neurological psychosomatic im-mune system disorders and seven common malignanttumors) totaling 700000 participants and 3) a clinicalcohort (N = 50000 patients with 50 rare diseases) (33)
CLINICAL TRANSLATION
The practice of precision diabetes medicine is currentlyconfined to rare monogenic forms of the disease Never-theless individual variants present only in specific pop-ulation isolates have been discovered that may have clinicalvalue in the prevention or treatment of type 2 diabetesThese include a TBC1D4 nonsense variant (pArg684ter)of relatively high prevalence in Greenlandic Inuit (minorallele frequency 17) and other circumpolar Inuit popu-lations that predisposes a substantially increased risk ofdiabetes (odds ratio 10 in those homozygous for thevariant) (34) The mechanism of action involves the mus-cle-selective loss of the long TBC1D4 isoform and dimin-ished GLUT4-mediated cellular glucose uptake in responseto insulin A thymine for cytosine substitution that causesa premature stop at codon 363 of TBC1D4 has also beendiscovered in people with acanthosis nigricans the muta-tion causes postprandial hyperinsulinemia as a possibleconsequence of a specific muscle and adipose tissue insulinresistance (35)
In Latinos a low frequency (minor allele frequency =21 in people with type 2 diabetes) missense variant (pE508K) at HNF1A conveys a type 2 diabetes odds ratio5(36)HNF1A also harbors mutations that cause rare mono-genic diabetes that can be successfully treated with sulfo-nylureas (discussed later) suggesting this is true for thenontrivial number of Latino individuals with type 2 di-abetes who carry pE508K Despite the relatively largeeffects these variants convey whether they will proveuseful in the context of precision medicine is unknown asno formal assessment of clinical utility or cost-effectivenesshas been reported
Metformin was discovered in the 1960s and is currentlythe frontline drug for early-stage diabetes treatment al-though its mechanisms of action remain unclear (37) Thereis genetic evidence for metformin intolerance (38) althoughthis has not been widely replicated By contrast variants atATM (39) and SLC2A2 (encoding GLUT2) (40) have beenassociated at a genome-wide level of statistical significancewith metformin response Although the effects conveyed bythese variants are too small to guide patient-level treatmentdecisions these findings may prove valuable in determiningmetforminrsquos mechanisms of action
Many major precision medicine statements includingthe executive summary of the US PMI (28) and a NationalResearch Council report focused on precision medicine andnew disease taxonomies (41) make clear that research andpractice in precision medicine should consider the majorimpact of lifestyle in disease etiology prevention and
treatment The incorporation of lifestyle into precisiondiabetes medicine is adequately justified by its provenbenefits in diabetes prevention and treatment Yet thereis good evidence that people benefit to varying degreesfrom diet and exercise and that personal biology underliesthis (42) Thus if precision medicine programs are to ad-equately consider the impact of lifestyle and other envi-ronmental exposures it seems logical that emphasis shouldbe placed on improving the precision of not only drugs butalso this type of antidiabetes therapy too
One compelling example of how glycemic response tofood can be predicted using an individualrsquos biomarkerscomes from a study of 800 young adults whose gut meta-genomic sequences were ascertained and diet and bloodglucose variation (from continuous glucose monitors) weremonitored for a week (43) Each participant received onestandardized meal (50 g carbohydrate) daily The authorsobserved that although participants elicited different post-prandial glycemic responses the responses to the samefood were consistent within individuals Using machinelearning algorithms these data were used to predict eachparticipantrsquos postprandial glycemic response to a givenfood They subsequently administered tailored diet inter-ventions to modulate glucose levels providing proof ofconcept that personalized diets guided by biomarker pro-filing can reduce blood glucose variability Notwithstand-ing the importance of this work from a basic scienceperspective to determine its value for precision medicinerequires knowing if minimizing variations in glucose inhealthy adults is clinically relevant and whether the ap-proach to predicting glycemic response to food also worksin people with diabetes To date neither of these impor-tant questions is addressed in the published literature
Despite lifestylersquos proven efficacy in diabetes preven-tion the resources available for research on lifestylemedicine are massively outweighed by investments inpharmacotherapy In 2013 for example US$35 billionwas spent by pharmaceutical companies on research anddevelopment (44) Although there are no comparablestatistics for research in lifestyle medicine funding islikely to be orders of magnitude less as most of this comesfrom the public purse The marketing of foods and bev-erages on the other hand is a huge industry whose mes-sages often counter public health efforts to promotehealthy lifestyle On an international scale hundreds ofbillions of US dollars are spent on food marketing annu-ally one of the biggest spenders is Unilever which spent US$8 billion on brand and marketing investments in2017 alone (45) Marketing junk foods and beverages ishandsomely resourced competing with public health ini-tiatives The major food companies in the UK for exam-ple have invested roughly US$200 million each year injunk food advertising which contrasts the US$72 millionspent on Change4Life the UK governmentrsquos flagshiphealthy eating campaign (46)
Notwithstanding the relative dearth of funding forlifestyle medicine some key initiatives exist One of the
diabetesdiabetesjournalsorg Fitipaldi and Associates 1917
largest of these is the National Institutes of HealthrsquosCommon Fund initiative on the Molecular Transducersof Physical Activity in Humans (httpcommonfundnihgovMolecularTransducers) which seeks to determineldquooptimal physical activity recommendations for peopleat various stages of liferdquo and develop ldquoprecisely targeted[exercise] regimens for individuals with particular healthneedsrdquo (47) Elsewhere the Food4Me study a research pro-gram funded by the European Union explored the role ofgenetics in the personalization of diet data from the studysuggest that personalized diet interventions supported bygenetic data improve consumption of healthy foods (48) andhelp sustain healthy food choices (49) Interestingly mostloci selected for this intervention (variants at MTHFR FTOTCF7L2 APOE and FADS1) lack evidence of individual-levelclinical relevance (50) suggesting that the success of theFood4Me intervention was not dependent on the quality ofthe genetic information provided to participants
Although most if not all precision medicine initiativesare predicated on the assumption that their findings willhelp improve peoplersquos health the emphasis of these ini-tiatives is almost always on the generation of new knowl-edge MVP for example seeks ldquoto improve understandingof how health is affected by genetic characteristics behav-iors and environmental factorsrdquo (29) Although this objec-tive will undoubtedly be achieved how this information willeventually be used to optimize prevention and treatment
of diabetes and other diseases is unclear Indeed few pre-cision medicine initiatives focused on complex diseasesprovide a clear plan for clinical translation (Fig 2)
POTENTIAL BOTTLENECKS ON THE PATH TOPRECISION MEDICINE
Cost-effectivenessThe global precision medicine market was valued atUS$436 billion in 2016 and is predicted to triple in valuewithin the next decade (51) The economic dynamicsassociated with precision medicine differ from those ofconventional medicines as the nature of the approachmeans that precision medicines are likely to be approvedfor use only in specific subpopulations limiting the num-ber of patients eligible for treatment with a given drug Bycontrast the effectiveness of precision medicines shouldbe higher for patients within these subpopulationsthan for conventional medicines and side effects may befewer and less severe Owing to the smaller market sharefor a given precision medicine competition to producecheaper drugs between manufacturers may be less than forconventional drugs and the drive to develop off-patentbiosimilars (ie drugs with active properties identical orsimilar to previously licensed drugs) is likely to differcompared with conventional medicines depending onthe extent to which first-in-class drugs are undercut bycheaper alternatives Collectively these factors will impact
Figure 2mdashThe path to precision medicine in type 2 diabetes HEA health economic assessment
1918 Precision Medicine in Type 2 Diabetes Diabetes Volume 67 October 2018
the cost-effectiveness of precision medicines in type 2 di-abetes (52)
Thus far very few cost-effectiveness analyses in pre-cision diabetes medicine have been published despitelong-standing recognition that cost-effectiveness is a keyrequirement for clinical translation (53) The rare exam-ples of such studies have focused exclusively on rare mono-genic forms of diabetes neonatal diabetes (54) andmaturity-onset diabetes of the young (MODY) (5556)
Neonatal diabetes is diagnosed in infancy and affectsabout 1400000 live births and roughly half (1252000in people aged 20 years) develop permanent neonataldiabetes (54) Of the latter group up to 70 are estimatedto carry mutations in the genes encoding the ATP-sensitivepotassium channel (KCNJ11 and ABCC8) These mutationsblock the closing of the KATP channels which preventsb-cell depolarization and corresponding insulin secretion(57) However treatment with sulfonylureas rectifies thisdefect in 90 of cases allowing the discontinuation ofexogenous insulin (58) The earliest costndasheffect analysisin precision medicine focused on sulfonylurea therapy inpermanent neonatal diabetes (54) The authors estimatedthat genetic testing in permanent neonatal diabetes wouldconvey significant quality-of-life benefits at 10 years (032quality-adjusted life-years US$12528 saved) increasingto 070 at 30 years (US$30437 saved)
A range of single gene defects cause MODY each ofwhich are used to diagnose one of five forms of the diseaseIn MODY1 and MODY3 insulin secretion is impaired Thedisease occurs early in life and is often misdiagnosed astype 1 diabetes and exogenous insulin therapy is thusprescribed However treatment with sulfonylureas enablessecretion of endogenously produced insulin Switchingfrom insulin to sulfonylureas is preferable for a numberof reasons including those related to safety and burdenMODY2 is caused by mutations in GCK which lead tochronically elevated but nonprogressive blood glucoseconcentrations that do not typically require treatmentthus diagnosing MODY2 also conveys reduced costs andburdens associated with unnecessary treatment The firstcost-effectiveness analysis focused on MODY1ndash3 in USadults (aged 25ndash40 years) with diagnosed type 2 diabetes(56) based many of its assumptions on the findings ofthe UK Prospective Diabetes Study (UKPDS) (59) It alsoassumed a 2 frequency of MODY among people withdiabetes and set genetic screening costs at US$2580Under these assumptions genetic screening for MODYwould not be cost-effective The second study focused onlyonMODY1 screening and found that genetic screening wasnot cost-effective given the diabetes population frequencyof MODY1 mutations is unlikely to exceed 2 Sub-stantial reductions in genetic sequencing costs may how-ever render screening cost-effective (55)
Regulatory ConsensusMore than half of the most clinically impactful drugs de-veloped recently were discovered through academia-led
research (60) emphasizing the huge value of publicndashprivate partnerships Many of the large precision diabetesmedicine consortia involve both academia and industrywithin which unsurprising emphasis is placed on the dis-covery and validation of biomarkers that expedite drugdevelopment This expectation is backed up by the provenability of genetics to fast-track drug development bypinpointing high-value drug targets helping halve drugfailures and markedly reducing their costs (61) In drugrepositioning genetics has also proven its worth by re-vealing previously unknown conditions that a drug can beused to treat and by helping predict drug side effects
To obtain FDA (or European Medicines Agency [EMA])approval for biomarkers in medical products and de-vices involves a comprehensive qualification process (seehttpswwwfdagovDrugsDevelopmentApprovalProcessDrugDevelopmentToolsQualificationProgramBiomarker-QualificationProgramucm535395htm) within which ex-ists the requirement for a statement termed a ldquocontext ofuserdquo The context of use statement requires a clear anddetailed description of the intended role of the biomarkerin drug development as well as risks and benefits TheFoundation for the National Institutes of Health recentlypublished the Framework for Defining Evidentiary Criteriafor Biomarker Qualification (62) which provides an ex-panded outline of the biomarker classifications definedby the FDA
The process of biomarker qualification in drug devel-opment requires stringent adherence to the relevant reg-ulatory agenciesrsquo approvals processes In some studies thatseek to discover biomarkers for diabetes drug develop-ment the process is well rehearsed and aligning biomarkerdiscovery and validation strategies with regulatory expect-ations is relatively straightforward however it does re-quire that precision medicine consortia are well versedin these processes Biomarker discovery in prediabetes ismore challenging though as prediabetes is not a distinctdisease state and although some people with prediabetesdevelop diabetes and its complications a large proportionremains healthy for many years (63) Nevertheless a sub-group of those with prediabetes will progress to diabetesand identifying these people before b-cell function issubstantially diminished would open the door for severalpowerful therapeutics that are ineffective once endoge-nous insulin production has faded However becauseneither the FDA nor EMA currently recognizes an inter-mediate end point for disease progression in prediabetesdrug trials gaining regulatory approval for new prediabe-tes biomarkers requires that researchers foster close andfrequent communications with the regulatory agencies toensure candidate biomarkers have the possibility to pro-gress through the regulatory process
Legal Social and EthicalBalancing the interests of society and its citizens the risksand benefits legislation and individual freedom as wellas the risk of exasperating disparities is central to many
diabetesdiabetesjournalsorg Fitipaldi and Associates 1919
commentaries on the legal social and ethical aspects ofprecision medicine
Precision medicine research is heavily dependent onbiobanked data and tissues Often the original informedconsents were obtained many years before modern lsquoomicstechnologies existed at a time when many research ques-tions germane to precision medicine were inconceivableOften the informed consents were broad and ethicscommittees have concluded that these modern researchquestions are within the scope of the consents As newever larger and interconnected cohorts are being formedthe desire many researchers share for broad consentingandor to bank data or samples for unspecified futureresearch must be placed in context with several importantethical realities the need to maintain participantsrsquo confi-dentiality appropriately handle incidental findings deter-mine who owns the samples and data consider culturalsensitivities return results to participants engage societyand ensure unused material is properly disposed (64)
Much as precision medicine was inconceivable whenmany existing cohorts were formed it is impossible toaccurately predict how contemporary cohorts will be usedin the future Furthermore many precision medicineinitiatives involve the hybridization of academia healthcare and industry which may challenge some of the morealtruistic motivations of participants in academia-led bio-medical research Moreover much of the highly sensitivedata collated by these initiatives will eventually be madeavailable through managed-access portals to a wide rangeof end users who were not part of the data collectionprocess Thus much of this highly sensitive data will bewidely disseminated and used by distant end users toaddress a plethora of diverse research questions thatcannot yet be conceived factors that need to be consideredduring the informed consent process These are challengesfor historical biobanks as well as new precision medicinecohorts that consent only at enrollment To help overcomethis a process called dynamic consent has been developedwhich allows consents to be progressively updated asprojects evolve but requires fluid communication withparticipants throughout the life course of a study (65)
A major concern about genetic data are its linkage todatabases that enables inferences about a personrsquos socialcognitive moral cultural health or sexual identity whichparticipants had not consented to or for such inferencesto be made about family members without their consent(64) Indeed ensuring genomics data are used in a wayconsistent with appropriate ethical standards is a corefeature of most current health data protection legislation(66) Notwithstanding these risks there are major benefitsassociated with big data which include the discovery ofnovel disease mechanisms and therapeutic targets manyof which only become visible when data sets scale tomassive proportions Thus rather than constrain thegrowth and responsible utilization of big data emphasis isbeing placed on implementing legislation that protectsprivacy and helps prevent data breaches while facilitating
the safe and responsible storage transfer and utilizationof data In the European Union for example the Gen-eral Data Protection Regulation was implemented in May2018 to harmonize data privacy laws across Europe A keyfeature of this legislation as it relates to precision medicineresearch is that the analysis of sensitive data requiresexplicit opt-in consent whereas for nonsensitive dataunambiguous (implied) consent is sufficient
SUMMARY AND CONCLUSIONS
Diabetes medicine is likely to evolve dramatically in thecoming years ideally because evidence emerges from themajor precision medicine initiatives to support its utility inpreventing or treating diabetes but no doubt also becauseof the major commercial interest in seeing precision med-icine grow One of the possible outcomes will very likely bethe replacement of current classifications of diabetes withempirically derived subclassifications that can be coupledwith treatment regimens which compared with con-ventional medicines are more effective less costly andconvey fewer unnecessary side effects As genetic data-bases grow genotypes will be increasingly used to discovernew drug targets At this time the clinical application ofgenetics is confined to the diagnosis of and therapeuticguidance for rare forms of diabetes as well as the pre-diction of adverse drug reactions The ways in whichgenetics may or may not guide prevention and therapy incommon forms of diabetes is unclear However becausecharacterizing variation in a patientrsquos nuclear genomeis inexpensive and easily achieved and DNA variation re-mains constant across the life course it is likely that geneticsequences will feature in most patientsrsquo clinical recordsGiven this genetic data will likely be deployed in diverseways in the diabetes clinic of the future The roles otherlsquoomics technologies digital imaging devices and wearableswill play in precision diabetes medicine are harder to fore-cast as research in these areas is less advanced and the costof obtaining some of these data will likely remain highrelative to genetics Finally although many precision med-icine initiatives focus predominantly on pharmacotherapyoptimizing lifestyle (and possibly surgical) interventionsusing biotechnologies also has great potential for improvingtype 2 diabetes prevention and treatment
Funding and Duality of Interest The work of HF and PWF relatedto the manuscript was supported by the European Commission (CoG-2015_681742_NASCENT) Swedish Research Council (Distinguished YoungResearchers Award in Medicine) Swedish Heart-Lung Foundation Novo NordiskFoundation European Foundation for the Study of Diabetes and the SwedishFoundation for Strategic Research (Industrial Research Center in PrecisionMedicine) (all grants to PWF) PWF is a consultant for and has stock optionsin Zoe Global and has received research funding from Boehringer Ingelheim EliLilly Janssen Novo Nordisk Sanofi and Servier MIM is a National Institute forHealth Research (NIHR) Senior Investigator and a Wellcome Trust Senior In-vestigator The views expressed in this article are those of the author(s) and notnecessarily those of the National Health Service NIHR or the Department ofHealth Work described in this review was principally supported by the Wellcome
1920 Precision Medicine in Type 2 Diabetes Diabetes Volume 67 October 2018
Trust (098381 203141 106130) and by the National Institutes of Health (U01-DK105535) MIM serves on advisory panels for Pfizer Novo Nordisk and ZoeGlobal has received honoraria from Pfizer Novo Nordisk and Eli Lilly hasstock options in Zoe Global and has received research funding from AbbVieAstraZeneca Boehringer Ingelheim Eli Lilly Janssen Merck Novo Nordisk PfizerRoche Sanofi Servier and Takeda JCF is a Massachusetts General HospitalResearch Scholar and is supported by National Institutes of Health National Instituteof Diabetes and Digestive and Kidney Diseases grants R01 DK072041 U01DK105554 R01 DK105154 and K24 DK110550 and National Institute of GeneralMedical Sciences grant R01 GM117163 MIM JCF and PWF are membersof the American Diabetes Association Precision Medicine in Diabetes TaskforceThe views expressed in this article are those of the authors and do not necessarilyreflect those of the American Diabetes Association or the taskforce No otherpotential conflicts of interest relevant to this article were reported
References1 Centers for Disease Control and Prevention National Diabetes StatisticsReport Estimates of Diabetes and Its Burden in the United States 2014 AtlantaGA US Department of Health and Human Services 20142 Knowler WC Fowler SE Hamman RF et al Diabetes Prevention ProgramResearch Group 10-year follow-up of diabetes incidence and weight loss in theDiabetes Prevention Program Outcomes Study Lancet 20093741677ndash16863 Li G Zhang P Wang J et al The long-term effect of lifestyle interventions toprevent diabetes in the China Da Qing Diabetes Prevention Study a 20-yearfollow-up study Lancet 20083711783ndash17894 Tuomilehto J Lindstroumlm J Eriksson JG et al Finnish Diabetes PreventionStudy Group Prevention of type 2 diabetes mellitus by changes in lifestyleamong subjects with impaired glucose tolerance N Engl J Med 20013441343ndash13505 Ramachandran A Snehalatha C Mary S Mukesh B Bhaskar AD Vijay VIndian Diabetes Prevention Programme (IDPP) The Indian Diabetes PreventionProgramme shows that lifestyle modification and metformin prevent type 2 di-abetes in Asian Indian subjects with impaired glucose tolerance (IDPP-1) Dia-betologia 200649289ndash2976 Lean ME Leslie WS Barnes AC et al Primary care-led weight managementfor remission of type 2 diabetes (DiRECT) an open-label cluster-randomised trialLancet 2018391541ndash5517 Polyzogopoulou EV Kalfarentzos F Vagenakis AG Alexandrides TK Res-toration of euglycemia and normal acute insulin response to glucose in obesesubjects with type 2 diabetes following bariatric surgery Diabetes 2003521098ndash11038 Franco M Orduntildeez P Caballero B et al Impact of energy intake physicalactivity and population-wide weight loss on cardiovascular disease and diabetesmortality in Cuba 1980-2005 Am J Epidemiol 20071661374ndash13809 Cho NH Shaw JE Karuranga S et al IDF Diabetes Atlas global estimates ofdiabetes prevalence for 2017 and projections for 2045 Diabetes Res Clin Pract2018138271ndash28110 Lander ES Linton LM Birren B et al International Human Genome Se-quencing Consortium Initial sequencing and analysis of the human genome[published correction appears in Nature 2001411720] Nature 2001409860ndash92111 Visscher PM Wray NR Zhang Q et al 10 Years of GWAS discovery biologyfunction and translation Am J Hum Genet 20171015ndash2212 Merino J Udler MS Leong A Meigs JB A decade of genetic and metab-olomic contributions to type 2 diabetes risk prediction Curr Diab Rep 20171713513 Knowles JW Ashley EA Cardiovascular disease the rise of the genetic riskscore PLoS Med 201815e100254614 Mahajan A Taliun D Thurner M et al Fine-mapping of an expanded set oftype 2 diabetes loci to single-variant resolution using high-density imputation andislet-specific epigenome maps [preprint article online] 2018 Available fromhttpswwwbiorxivorgcontentearly20180109245506 Accessed 30 January2018
15 McCarthy MI Painting a new picture of personalised medicine for diabetesDiabetologia 201760793ndash79916 Scott RA Scott LJ Maumlgi R et al DIAbetes Genetics Replication AndMeta-analysis (DIAGRAM) Consortium An expanded genome-wide asso-ciation study of type 2 diabetes in Europeans Diabetes 2017662888ndash290217 Mahajan A Wessel J Willems SM et al ExomeBP Consortium MAGICConsortium GIANT Consortium Refining the accuracy of validated target iden-tification through coding variant fine-mapping in type 2 diabetes Nat Genet 201850559ndash57118 Udler MS Kim J von Grotthuss M et al Clustering of type 2 diabetes geneticloci by multi-trait associations identifies disease mechanisms and subtypes[preprint article online] 2018 Available from httpswwwbiorxivorgcontentearly20180510319509 Accessed 30 March 201819 van Zuydam NR Ahlqvist E Sandholm N et al A genome-wide associationstudy of diabetic kidney disease in subjects with type 2 diabetes Diabetes 2018671414ndash142720 Ahlqvist E Storm P Kaumlraumljaumlmaumlki A et al Novel subgroups of adult-onsetdiabetes and their association with outcomes a data-driven cluster analysis of sixvariables Lancet Diabetes Endocrinol 20186361ndash36921 Fuchsberger C Flannick J Teslovich TM et al The genetic architecture oftype 2 diabetes Nature 201653641ndash4722 Koivula RW Heggie A Barnett A et al DIRECT Consortium Discovery ofbiomarkers for glycaemic deterioration before and after the onset of type 2 di-abetes rationale and design of the epidemiological studies within the IMI DIRECTConsortium Diabetologia 2014571132ndash114223 Rask-Andersen M Karlsson T Ek WE Johansson Aring Gene-environmentinteraction study for BMI reveals interactions between genetic factors and physicalactivity alcohol consumption and socioeconomic status PLoS Genet 201713e100697724 Celis-Morales CA Lyall DM Gray SR et al Dietary fat and total energy intakemodifies the association of genetic profile risk score on obesity evidence from48 170 UK Biobank participants Int J Obes 2017411761ndash176825 Tyrrell J Wood AR Ames RM et al Gene-obesogenic environment inter-actions in the UK Biobank study Int J Epidemiol 201746559ndash57526 Young AI Wauthier F Donnelly P Multiple novel gene-by-environment in-teractions modify the effect of FTO variants on body mass index Nat Commun201671272427 Reardon S Giant study poses DNA data-sharing dilemma Nature 201552516ndash1728 Hudson K Lifton R Patrick-Lake B The Precision Medicine Initiative CohortProgram ndash Building a Research Foundation for 21st Century Medicine BethesdaMD National Institutes of Health 201529 Gaziano JM Concato J Brophy M et al Million Veteran Program a mega-biobank to study genetic influences on health and disease J Clin Epidemiol 201670214ndash22330 Bergen SE Sullivan PF National-scale precision medicine for psychiatricdisorders in Sweden Am J Med Genet B Neuropsychiatr Genet 7 July 2017 [Epubahead of print] DOI 101002ajmgb3256231 Cyranoski D Chinarsquos bid to be a DNA superpower Nature 2016534462ndash46332 Cyranoski D China embraces precision medicine on a massive scale Nature20165299ndash1033 Bu D Peng S Luo H et al Precision medicine and cancer immunology inChina From big data to knowledge in precision medicine Science 2018 (Suppl)S35ndashS3834 Moltke I Grarup N Joslashrgensen ME et al A common Greenlandic TBC1D4variant confers muscle insulin resistance and type 2 diabetes Nature 2014512190ndash19335 Dash S Sano H Rochford JJ et al A truncation mutation in TBC1D4 ina family with acanthosis nigricans and postprandial hyperinsulinemia Proc NatlAcad Sci U S A 20091069350ndash9355
diabetesdiabetesjournalsorg Fitipaldi and Associates 1921
36 Estrada K Aukrust I Bjoslashrkhaug L et al SIGMA Type 2 Diabetes ConsortiumAssociation of a low-frequency variant in HNF1A with type 2 diabetes in a Latinopopulation [published correction appears in JAMA 20143121932] JAMA 2014
3112305ndash231437 Marshall SM 60 years of metformin use a glance at the past and a look tothe future Diabetologia 2017601561ndash156538 Pearson ER Pharmacogenetics and target identification in diabetes CurrOpin Genet Dev 20185068ndash7339 Zhou K Bellenguez C Spencer CC et al GoDARTS and UKPDS Diabetes
Pharmacogenetics Study Group Wellcome Trust Case Control Consortium 2MAGIC investigators Common variants near ATM are associated with glycemicresponse to metformin in type 2 diabetes Nat Genet 201143117ndash12040 Zhou K Yee SW Seiser EL et al MetGen Investigators DPP InvestigatorsACCORD Investigators Variation in the glucose transporter gene SLC2A2 isassociated with glycemic response to metformin Nat Genet 2016481055ndash
105941 National Research Council Committee on a Framework for Developing a NewTaxonomy of Disease Toward Precision Medicine Building a Knowledge Networkfor Bomedical Research and a New Taxonomy of Disease Washington DCNational Academies Press 201142 Franks PW Poveda A Lifestyle and precision diabetes medicine will ge-
nomics help optimise the prediction prevention and treatment of type 2 diabetesthrough lifestyle therapy Diabetologia 201760784ndash79243 Zeevi D Korem T Zmora N et al Personalized nutrition by prediction of
glycemic responses Cell 20151631079ndash109444 Swanson A Big pharmaceutical companies are spending far more onmarketing than research The Washington Post 11 February 2015 Available fromhttpswwwwashingtonpostcomnewswonkwp20150211big-pharmaceutical-companies-are-spending-far-more-on-marketing-than-researchnoredirect=onamputm_term=787d031b9b7c Accessed 20 February 201845 Unilever Advertising amp Marketing Unilever 2018 Available from httpwwwunilevercomsustainable-livingwhat-matters-to-youadvertising-and-marketinghtml Accessed 31 March 201846 OrsquoDowd A Spending on junk food advertising is nearly 30 times whatgovernment spends on promoting healthy eating BMJ 2017359j467747 National Institutes of Health Molecular Transducers of Physical Activity in
Humans Frequently Asked Questions (FAQs) [Internet] 2018 Available from httpscommonfundnihgovMolecularTransducersFAQs Accessed 20 March 201848 Livingstone KM Celis-Morales C Navas-Carretero S et al Food4Me Study
Effect of an Internet-based personalized nutrition randomized trial on dietarychanges associated with the Mediterranean diet the Food4Me Study Am J ClinNutr 2016104288ndash29749 Celis-Morales C Livingstone KM Marsaux CF et al Food4Me Study Effectof personalized nutrition on health-related behaviour change evidence from theFood4Me European randomized controlled trial Int J Epidemiol 201746578ndash
588
50 Celis-Morales C Livingstone KM Marsaux CF et al Design and baselinecharacteristics of the Food4Me study a web-based randomised controlled trial ofpersonalised nutrition in seven European countries Genes Nutr 20151045051 BIS Research Global precision medicine market - analysis and forecast2017-2026 2017 Available from httpsbisresearchcomindustry-reportglobal-precision-medicine-market-2026html Accessed 31 January 201852 Stern AD Alexander BM Chandra A How economics can shape precisionmedicines Science 20173551131ndash113353 Smith RJ Nathan DM Arslanian SA Groop L Rizza RA Rotter JI In-dividualizing therapies in type 2 diabetes mellitus based on patient characteristicswhat we know and what we need to know J Clin Endocrinol Metab 2010951566ndash157454 Greeley SA John PM Winn AN et al The cost-effectiveness of personalizedgenetic medicine the case of genetic testing in neonatal diabetes Diabetes Care201134622ndash62755 Nguyen HV Finkelstein EA Mital S Gardner DS Incremental cost-effectivenessof algorithm-driven genetic testing versus no testing for maturity onset diabetes of theyoung (MODY) in Singapore J Med Genet 201754747ndash75356 Naylor RN John PM Winn AN et al Cost-effectiveness of MODY genetictesting translating genomic advances into practical health applications DiabetesCare 201437202ndash20957 Gloyn AL Pearson ER Antcliff JF et al Activating mutations in the geneencoding the ATP-sensitive potassium-channel subunit Kir62 and permanentneonatal diabetes N Engl J Med 20043501838ndash184958 Pearson ER Flechtner I Njoslashlstad PR et al Neonatal Diabetes InternationalCollaborative Group Switching from insulin to oral sulfonylureas in patients withdiabetes due to Kir62 mutations N Engl J Med 2006355467ndash47759 UK Prospective Diabetes Study (UKPDS) UK Prospective Diabetes Study(UKPDS) VIII Study design progress and performance Diabetologia 199134877ndash89060 Kesselheim AS Tan YT Avorn J The roles of academia rare diseases andrepurposing in the development of the most transformative drugs Health Aff(Millwood) 201534286ndash29361 Nelson MR Tipney H Painter JL et al The support of human geneticevidence for approved drug indications Nat Genet 201547856ndash86062 FNIH Evidentiary Criteria Writing Group Framework for Defining EvidentiaryCriteria for Biomarker Qualification Bethesda MD National Library of Medicine201663 Tabaacutek AG Herder C Rathmann W Brunner EJ Kivimaumlki M Prediabetesa high-risk state for diabetes development Lancet 20123792279ndash229064 Nicol D Bubela T Chalmers D et al Precision medicine drowning ina regulatory soup J Law Biosci 20163281ndash30365 Kaye J Whitley EA Lund D Morrison M Teare H Melham K Dynamicconsent a patient interface for twenty-first century research networks Eur J HumGenet 201523141ndash14666 Kinkorovaacute J Biobanks in the era of personalized medicine objectiveschallenges and innovation Overview EPMA J 201674
1922 Precision Medicine in Type 2 Diabetes Diabetes Volume 67 October 2018
largest of these is the National Institutes of HealthrsquosCommon Fund initiative on the Molecular Transducersof Physical Activity in Humans (httpcommonfundnihgovMolecularTransducers) which seeks to determineldquooptimal physical activity recommendations for peopleat various stages of liferdquo and develop ldquoprecisely targeted[exercise] regimens for individuals with particular healthneedsrdquo (47) Elsewhere the Food4Me study a research pro-gram funded by the European Union explored the role ofgenetics in the personalization of diet data from the studysuggest that personalized diet interventions supported bygenetic data improve consumption of healthy foods (48) andhelp sustain healthy food choices (49) Interestingly mostloci selected for this intervention (variants at MTHFR FTOTCF7L2 APOE and FADS1) lack evidence of individual-levelclinical relevance (50) suggesting that the success of theFood4Me intervention was not dependent on the quality ofthe genetic information provided to participants
Although most if not all precision medicine initiativesare predicated on the assumption that their findings willhelp improve peoplersquos health the emphasis of these ini-tiatives is almost always on the generation of new knowl-edge MVP for example seeks ldquoto improve understandingof how health is affected by genetic characteristics behav-iors and environmental factorsrdquo (29) Although this objec-tive will undoubtedly be achieved how this information willeventually be used to optimize prevention and treatment
of diabetes and other diseases is unclear Indeed few pre-cision medicine initiatives focused on complex diseasesprovide a clear plan for clinical translation (Fig 2)
POTENTIAL BOTTLENECKS ON THE PATH TOPRECISION MEDICINE
Cost-effectivenessThe global precision medicine market was valued atUS$436 billion in 2016 and is predicted to triple in valuewithin the next decade (51) The economic dynamicsassociated with precision medicine differ from those ofconventional medicines as the nature of the approachmeans that precision medicines are likely to be approvedfor use only in specific subpopulations limiting the num-ber of patients eligible for treatment with a given drug Bycontrast the effectiveness of precision medicines shouldbe higher for patients within these subpopulationsthan for conventional medicines and side effects may befewer and less severe Owing to the smaller market sharefor a given precision medicine competition to producecheaper drugs between manufacturers may be less than forconventional drugs and the drive to develop off-patentbiosimilars (ie drugs with active properties identical orsimilar to previously licensed drugs) is likely to differcompared with conventional medicines depending onthe extent to which first-in-class drugs are undercut bycheaper alternatives Collectively these factors will impact
Figure 2mdashThe path to precision medicine in type 2 diabetes HEA health economic assessment
1918 Precision Medicine in Type 2 Diabetes Diabetes Volume 67 October 2018
the cost-effectiveness of precision medicines in type 2 di-abetes (52)
Thus far very few cost-effectiveness analyses in pre-cision diabetes medicine have been published despitelong-standing recognition that cost-effectiveness is a keyrequirement for clinical translation (53) The rare exam-ples of such studies have focused exclusively on rare mono-genic forms of diabetes neonatal diabetes (54) andmaturity-onset diabetes of the young (MODY) (5556)
Neonatal diabetes is diagnosed in infancy and affectsabout 1400000 live births and roughly half (1252000in people aged 20 years) develop permanent neonataldiabetes (54) Of the latter group up to 70 are estimatedto carry mutations in the genes encoding the ATP-sensitivepotassium channel (KCNJ11 and ABCC8) These mutationsblock the closing of the KATP channels which preventsb-cell depolarization and corresponding insulin secretion(57) However treatment with sulfonylureas rectifies thisdefect in 90 of cases allowing the discontinuation ofexogenous insulin (58) The earliest costndasheffect analysisin precision medicine focused on sulfonylurea therapy inpermanent neonatal diabetes (54) The authors estimatedthat genetic testing in permanent neonatal diabetes wouldconvey significant quality-of-life benefits at 10 years (032quality-adjusted life-years US$12528 saved) increasingto 070 at 30 years (US$30437 saved)
A range of single gene defects cause MODY each ofwhich are used to diagnose one of five forms of the diseaseIn MODY1 and MODY3 insulin secretion is impaired Thedisease occurs early in life and is often misdiagnosed astype 1 diabetes and exogenous insulin therapy is thusprescribed However treatment with sulfonylureas enablessecretion of endogenously produced insulin Switchingfrom insulin to sulfonylureas is preferable for a numberof reasons including those related to safety and burdenMODY2 is caused by mutations in GCK which lead tochronically elevated but nonprogressive blood glucoseconcentrations that do not typically require treatmentthus diagnosing MODY2 also conveys reduced costs andburdens associated with unnecessary treatment The firstcost-effectiveness analysis focused on MODY1ndash3 in USadults (aged 25ndash40 years) with diagnosed type 2 diabetes(56) based many of its assumptions on the findings ofthe UK Prospective Diabetes Study (UKPDS) (59) It alsoassumed a 2 frequency of MODY among people withdiabetes and set genetic screening costs at US$2580Under these assumptions genetic screening for MODYwould not be cost-effective The second study focused onlyonMODY1 screening and found that genetic screening wasnot cost-effective given the diabetes population frequencyof MODY1 mutations is unlikely to exceed 2 Sub-stantial reductions in genetic sequencing costs may how-ever render screening cost-effective (55)
Regulatory ConsensusMore than half of the most clinically impactful drugs de-veloped recently were discovered through academia-led
research (60) emphasizing the huge value of publicndashprivate partnerships Many of the large precision diabetesmedicine consortia involve both academia and industrywithin which unsurprising emphasis is placed on the dis-covery and validation of biomarkers that expedite drugdevelopment This expectation is backed up by the provenability of genetics to fast-track drug development bypinpointing high-value drug targets helping halve drugfailures and markedly reducing their costs (61) In drugrepositioning genetics has also proven its worth by re-vealing previously unknown conditions that a drug can beused to treat and by helping predict drug side effects
To obtain FDA (or European Medicines Agency [EMA])approval for biomarkers in medical products and de-vices involves a comprehensive qualification process (seehttpswwwfdagovDrugsDevelopmentApprovalProcessDrugDevelopmentToolsQualificationProgramBiomarker-QualificationProgramucm535395htm) within which ex-ists the requirement for a statement termed a ldquocontext ofuserdquo The context of use statement requires a clear anddetailed description of the intended role of the biomarkerin drug development as well as risks and benefits TheFoundation for the National Institutes of Health recentlypublished the Framework for Defining Evidentiary Criteriafor Biomarker Qualification (62) which provides an ex-panded outline of the biomarker classifications definedby the FDA
The process of biomarker qualification in drug devel-opment requires stringent adherence to the relevant reg-ulatory agenciesrsquo approvals processes In some studies thatseek to discover biomarkers for diabetes drug develop-ment the process is well rehearsed and aligning biomarkerdiscovery and validation strategies with regulatory expect-ations is relatively straightforward however it does re-quire that precision medicine consortia are well versedin these processes Biomarker discovery in prediabetes ismore challenging though as prediabetes is not a distinctdisease state and although some people with prediabetesdevelop diabetes and its complications a large proportionremains healthy for many years (63) Nevertheless a sub-group of those with prediabetes will progress to diabetesand identifying these people before b-cell function issubstantially diminished would open the door for severalpowerful therapeutics that are ineffective once endoge-nous insulin production has faded However becauseneither the FDA nor EMA currently recognizes an inter-mediate end point for disease progression in prediabetesdrug trials gaining regulatory approval for new prediabe-tes biomarkers requires that researchers foster close andfrequent communications with the regulatory agencies toensure candidate biomarkers have the possibility to pro-gress through the regulatory process
Legal Social and EthicalBalancing the interests of society and its citizens the risksand benefits legislation and individual freedom as wellas the risk of exasperating disparities is central to many
diabetesdiabetesjournalsorg Fitipaldi and Associates 1919
commentaries on the legal social and ethical aspects ofprecision medicine
Precision medicine research is heavily dependent onbiobanked data and tissues Often the original informedconsents were obtained many years before modern lsquoomicstechnologies existed at a time when many research ques-tions germane to precision medicine were inconceivableOften the informed consents were broad and ethicscommittees have concluded that these modern researchquestions are within the scope of the consents As newever larger and interconnected cohorts are being formedthe desire many researchers share for broad consentingandor to bank data or samples for unspecified futureresearch must be placed in context with several importantethical realities the need to maintain participantsrsquo confi-dentiality appropriately handle incidental findings deter-mine who owns the samples and data consider culturalsensitivities return results to participants engage societyand ensure unused material is properly disposed (64)
Much as precision medicine was inconceivable whenmany existing cohorts were formed it is impossible toaccurately predict how contemporary cohorts will be usedin the future Furthermore many precision medicineinitiatives involve the hybridization of academia healthcare and industry which may challenge some of the morealtruistic motivations of participants in academia-led bio-medical research Moreover much of the highly sensitivedata collated by these initiatives will eventually be madeavailable through managed-access portals to a wide rangeof end users who were not part of the data collectionprocess Thus much of this highly sensitive data will bewidely disseminated and used by distant end users toaddress a plethora of diverse research questions thatcannot yet be conceived factors that need to be consideredduring the informed consent process These are challengesfor historical biobanks as well as new precision medicinecohorts that consent only at enrollment To help overcomethis a process called dynamic consent has been developedwhich allows consents to be progressively updated asprojects evolve but requires fluid communication withparticipants throughout the life course of a study (65)
A major concern about genetic data are its linkage todatabases that enables inferences about a personrsquos socialcognitive moral cultural health or sexual identity whichparticipants had not consented to or for such inferencesto be made about family members without their consent(64) Indeed ensuring genomics data are used in a wayconsistent with appropriate ethical standards is a corefeature of most current health data protection legislation(66) Notwithstanding these risks there are major benefitsassociated with big data which include the discovery ofnovel disease mechanisms and therapeutic targets manyof which only become visible when data sets scale tomassive proportions Thus rather than constrain thegrowth and responsible utilization of big data emphasis isbeing placed on implementing legislation that protectsprivacy and helps prevent data breaches while facilitating
the safe and responsible storage transfer and utilizationof data In the European Union for example the Gen-eral Data Protection Regulation was implemented in May2018 to harmonize data privacy laws across Europe A keyfeature of this legislation as it relates to precision medicineresearch is that the analysis of sensitive data requiresexplicit opt-in consent whereas for nonsensitive dataunambiguous (implied) consent is sufficient
SUMMARY AND CONCLUSIONS
Diabetes medicine is likely to evolve dramatically in thecoming years ideally because evidence emerges from themajor precision medicine initiatives to support its utility inpreventing or treating diabetes but no doubt also becauseof the major commercial interest in seeing precision med-icine grow One of the possible outcomes will very likely bethe replacement of current classifications of diabetes withempirically derived subclassifications that can be coupledwith treatment regimens which compared with con-ventional medicines are more effective less costly andconvey fewer unnecessary side effects As genetic data-bases grow genotypes will be increasingly used to discovernew drug targets At this time the clinical application ofgenetics is confined to the diagnosis of and therapeuticguidance for rare forms of diabetes as well as the pre-diction of adverse drug reactions The ways in whichgenetics may or may not guide prevention and therapy incommon forms of diabetes is unclear However becausecharacterizing variation in a patientrsquos nuclear genomeis inexpensive and easily achieved and DNA variation re-mains constant across the life course it is likely that geneticsequences will feature in most patientsrsquo clinical recordsGiven this genetic data will likely be deployed in diverseways in the diabetes clinic of the future The roles otherlsquoomics technologies digital imaging devices and wearableswill play in precision diabetes medicine are harder to fore-cast as research in these areas is less advanced and the costof obtaining some of these data will likely remain highrelative to genetics Finally although many precision med-icine initiatives focus predominantly on pharmacotherapyoptimizing lifestyle (and possibly surgical) interventionsusing biotechnologies also has great potential for improvingtype 2 diabetes prevention and treatment
Funding and Duality of Interest The work of HF and PWF relatedto the manuscript was supported by the European Commission (CoG-2015_681742_NASCENT) Swedish Research Council (Distinguished YoungResearchers Award in Medicine) Swedish Heart-Lung Foundation Novo NordiskFoundation European Foundation for the Study of Diabetes and the SwedishFoundation for Strategic Research (Industrial Research Center in PrecisionMedicine) (all grants to PWF) PWF is a consultant for and has stock optionsin Zoe Global and has received research funding from Boehringer Ingelheim EliLilly Janssen Novo Nordisk Sanofi and Servier MIM is a National Institute forHealth Research (NIHR) Senior Investigator and a Wellcome Trust Senior In-vestigator The views expressed in this article are those of the author(s) and notnecessarily those of the National Health Service NIHR or the Department ofHealth Work described in this review was principally supported by the Wellcome
1920 Precision Medicine in Type 2 Diabetes Diabetes Volume 67 October 2018
Trust (098381 203141 106130) and by the National Institutes of Health (U01-DK105535) MIM serves on advisory panels for Pfizer Novo Nordisk and ZoeGlobal has received honoraria from Pfizer Novo Nordisk and Eli Lilly hasstock options in Zoe Global and has received research funding from AbbVieAstraZeneca Boehringer Ingelheim Eli Lilly Janssen Merck Novo Nordisk PfizerRoche Sanofi Servier and Takeda JCF is a Massachusetts General HospitalResearch Scholar and is supported by National Institutes of Health National Instituteof Diabetes and Digestive and Kidney Diseases grants R01 DK072041 U01DK105554 R01 DK105154 and K24 DK110550 and National Institute of GeneralMedical Sciences grant R01 GM117163 MIM JCF and PWF are membersof the American Diabetes Association Precision Medicine in Diabetes TaskforceThe views expressed in this article are those of the authors and do not necessarilyreflect those of the American Diabetes Association or the taskforce No otherpotential conflicts of interest relevant to this article were reported
References1 Centers for Disease Control and Prevention National Diabetes StatisticsReport Estimates of Diabetes and Its Burden in the United States 2014 AtlantaGA US Department of Health and Human Services 20142 Knowler WC Fowler SE Hamman RF et al Diabetes Prevention ProgramResearch Group 10-year follow-up of diabetes incidence and weight loss in theDiabetes Prevention Program Outcomes Study Lancet 20093741677ndash16863 Li G Zhang P Wang J et al The long-term effect of lifestyle interventions toprevent diabetes in the China Da Qing Diabetes Prevention Study a 20-yearfollow-up study Lancet 20083711783ndash17894 Tuomilehto J Lindstroumlm J Eriksson JG et al Finnish Diabetes PreventionStudy Group Prevention of type 2 diabetes mellitus by changes in lifestyleamong subjects with impaired glucose tolerance N Engl J Med 20013441343ndash13505 Ramachandran A Snehalatha C Mary S Mukesh B Bhaskar AD Vijay VIndian Diabetes Prevention Programme (IDPP) The Indian Diabetes PreventionProgramme shows that lifestyle modification and metformin prevent type 2 di-abetes in Asian Indian subjects with impaired glucose tolerance (IDPP-1) Dia-betologia 200649289ndash2976 Lean ME Leslie WS Barnes AC et al Primary care-led weight managementfor remission of type 2 diabetes (DiRECT) an open-label cluster-randomised trialLancet 2018391541ndash5517 Polyzogopoulou EV Kalfarentzos F Vagenakis AG Alexandrides TK Res-toration of euglycemia and normal acute insulin response to glucose in obesesubjects with type 2 diabetes following bariatric surgery Diabetes 2003521098ndash11038 Franco M Orduntildeez P Caballero B et al Impact of energy intake physicalactivity and population-wide weight loss on cardiovascular disease and diabetesmortality in Cuba 1980-2005 Am J Epidemiol 20071661374ndash13809 Cho NH Shaw JE Karuranga S et al IDF Diabetes Atlas global estimates ofdiabetes prevalence for 2017 and projections for 2045 Diabetes Res Clin Pract2018138271ndash28110 Lander ES Linton LM Birren B et al International Human Genome Se-quencing Consortium Initial sequencing and analysis of the human genome[published correction appears in Nature 2001411720] Nature 2001409860ndash92111 Visscher PM Wray NR Zhang Q et al 10 Years of GWAS discovery biologyfunction and translation Am J Hum Genet 20171015ndash2212 Merino J Udler MS Leong A Meigs JB A decade of genetic and metab-olomic contributions to type 2 diabetes risk prediction Curr Diab Rep 20171713513 Knowles JW Ashley EA Cardiovascular disease the rise of the genetic riskscore PLoS Med 201815e100254614 Mahajan A Taliun D Thurner M et al Fine-mapping of an expanded set oftype 2 diabetes loci to single-variant resolution using high-density imputation andislet-specific epigenome maps [preprint article online] 2018 Available fromhttpswwwbiorxivorgcontentearly20180109245506 Accessed 30 January2018
15 McCarthy MI Painting a new picture of personalised medicine for diabetesDiabetologia 201760793ndash79916 Scott RA Scott LJ Maumlgi R et al DIAbetes Genetics Replication AndMeta-analysis (DIAGRAM) Consortium An expanded genome-wide asso-ciation study of type 2 diabetes in Europeans Diabetes 2017662888ndash290217 Mahajan A Wessel J Willems SM et al ExomeBP Consortium MAGICConsortium GIANT Consortium Refining the accuracy of validated target iden-tification through coding variant fine-mapping in type 2 diabetes Nat Genet 201850559ndash57118 Udler MS Kim J von Grotthuss M et al Clustering of type 2 diabetes geneticloci by multi-trait associations identifies disease mechanisms and subtypes[preprint article online] 2018 Available from httpswwwbiorxivorgcontentearly20180510319509 Accessed 30 March 201819 van Zuydam NR Ahlqvist E Sandholm N et al A genome-wide associationstudy of diabetic kidney disease in subjects with type 2 diabetes Diabetes 2018671414ndash142720 Ahlqvist E Storm P Kaumlraumljaumlmaumlki A et al Novel subgroups of adult-onsetdiabetes and their association with outcomes a data-driven cluster analysis of sixvariables Lancet Diabetes Endocrinol 20186361ndash36921 Fuchsberger C Flannick J Teslovich TM et al The genetic architecture oftype 2 diabetes Nature 201653641ndash4722 Koivula RW Heggie A Barnett A et al DIRECT Consortium Discovery ofbiomarkers for glycaemic deterioration before and after the onset of type 2 di-abetes rationale and design of the epidemiological studies within the IMI DIRECTConsortium Diabetologia 2014571132ndash114223 Rask-Andersen M Karlsson T Ek WE Johansson Aring Gene-environmentinteraction study for BMI reveals interactions between genetic factors and physicalactivity alcohol consumption and socioeconomic status PLoS Genet 201713e100697724 Celis-Morales CA Lyall DM Gray SR et al Dietary fat and total energy intakemodifies the association of genetic profile risk score on obesity evidence from48 170 UK Biobank participants Int J Obes 2017411761ndash176825 Tyrrell J Wood AR Ames RM et al Gene-obesogenic environment inter-actions in the UK Biobank study Int J Epidemiol 201746559ndash57526 Young AI Wauthier F Donnelly P Multiple novel gene-by-environment in-teractions modify the effect of FTO variants on body mass index Nat Commun201671272427 Reardon S Giant study poses DNA data-sharing dilemma Nature 201552516ndash1728 Hudson K Lifton R Patrick-Lake B The Precision Medicine Initiative CohortProgram ndash Building a Research Foundation for 21st Century Medicine BethesdaMD National Institutes of Health 201529 Gaziano JM Concato J Brophy M et al Million Veteran Program a mega-biobank to study genetic influences on health and disease J Clin Epidemiol 201670214ndash22330 Bergen SE Sullivan PF National-scale precision medicine for psychiatricdisorders in Sweden Am J Med Genet B Neuropsychiatr Genet 7 July 2017 [Epubahead of print] DOI 101002ajmgb3256231 Cyranoski D Chinarsquos bid to be a DNA superpower Nature 2016534462ndash46332 Cyranoski D China embraces precision medicine on a massive scale Nature20165299ndash1033 Bu D Peng S Luo H et al Precision medicine and cancer immunology inChina From big data to knowledge in precision medicine Science 2018 (Suppl)S35ndashS3834 Moltke I Grarup N Joslashrgensen ME et al A common Greenlandic TBC1D4variant confers muscle insulin resistance and type 2 diabetes Nature 2014512190ndash19335 Dash S Sano H Rochford JJ et al A truncation mutation in TBC1D4 ina family with acanthosis nigricans and postprandial hyperinsulinemia Proc NatlAcad Sci U S A 20091069350ndash9355
diabetesdiabetesjournalsorg Fitipaldi and Associates 1921
36 Estrada K Aukrust I Bjoslashrkhaug L et al SIGMA Type 2 Diabetes ConsortiumAssociation of a low-frequency variant in HNF1A with type 2 diabetes in a Latinopopulation [published correction appears in JAMA 20143121932] JAMA 2014
3112305ndash231437 Marshall SM 60 years of metformin use a glance at the past and a look tothe future Diabetologia 2017601561ndash156538 Pearson ER Pharmacogenetics and target identification in diabetes CurrOpin Genet Dev 20185068ndash7339 Zhou K Bellenguez C Spencer CC et al GoDARTS and UKPDS Diabetes
Pharmacogenetics Study Group Wellcome Trust Case Control Consortium 2MAGIC investigators Common variants near ATM are associated with glycemicresponse to metformin in type 2 diabetes Nat Genet 201143117ndash12040 Zhou K Yee SW Seiser EL et al MetGen Investigators DPP InvestigatorsACCORD Investigators Variation in the glucose transporter gene SLC2A2 isassociated with glycemic response to metformin Nat Genet 2016481055ndash
105941 National Research Council Committee on a Framework for Developing a NewTaxonomy of Disease Toward Precision Medicine Building a Knowledge Networkfor Bomedical Research and a New Taxonomy of Disease Washington DCNational Academies Press 201142 Franks PW Poveda A Lifestyle and precision diabetes medicine will ge-
nomics help optimise the prediction prevention and treatment of type 2 diabetesthrough lifestyle therapy Diabetologia 201760784ndash79243 Zeevi D Korem T Zmora N et al Personalized nutrition by prediction of
glycemic responses Cell 20151631079ndash109444 Swanson A Big pharmaceutical companies are spending far more onmarketing than research The Washington Post 11 February 2015 Available fromhttpswwwwashingtonpostcomnewswonkwp20150211big-pharmaceutical-companies-are-spending-far-more-on-marketing-than-researchnoredirect=onamputm_term=787d031b9b7c Accessed 20 February 201845 Unilever Advertising amp Marketing Unilever 2018 Available from httpwwwunilevercomsustainable-livingwhat-matters-to-youadvertising-and-marketinghtml Accessed 31 March 201846 OrsquoDowd A Spending on junk food advertising is nearly 30 times whatgovernment spends on promoting healthy eating BMJ 2017359j467747 National Institutes of Health Molecular Transducers of Physical Activity in
Humans Frequently Asked Questions (FAQs) [Internet] 2018 Available from httpscommonfundnihgovMolecularTransducersFAQs Accessed 20 March 201848 Livingstone KM Celis-Morales C Navas-Carretero S et al Food4Me Study
Effect of an Internet-based personalized nutrition randomized trial on dietarychanges associated with the Mediterranean diet the Food4Me Study Am J ClinNutr 2016104288ndash29749 Celis-Morales C Livingstone KM Marsaux CF et al Food4Me Study Effectof personalized nutrition on health-related behaviour change evidence from theFood4Me European randomized controlled trial Int J Epidemiol 201746578ndash
588
50 Celis-Morales C Livingstone KM Marsaux CF et al Design and baselinecharacteristics of the Food4Me study a web-based randomised controlled trial ofpersonalised nutrition in seven European countries Genes Nutr 20151045051 BIS Research Global precision medicine market - analysis and forecast2017-2026 2017 Available from httpsbisresearchcomindustry-reportglobal-precision-medicine-market-2026html Accessed 31 January 201852 Stern AD Alexander BM Chandra A How economics can shape precisionmedicines Science 20173551131ndash113353 Smith RJ Nathan DM Arslanian SA Groop L Rizza RA Rotter JI In-dividualizing therapies in type 2 diabetes mellitus based on patient characteristicswhat we know and what we need to know J Clin Endocrinol Metab 2010951566ndash157454 Greeley SA John PM Winn AN et al The cost-effectiveness of personalizedgenetic medicine the case of genetic testing in neonatal diabetes Diabetes Care201134622ndash62755 Nguyen HV Finkelstein EA Mital S Gardner DS Incremental cost-effectivenessof algorithm-driven genetic testing versus no testing for maturity onset diabetes of theyoung (MODY) in Singapore J Med Genet 201754747ndash75356 Naylor RN John PM Winn AN et al Cost-effectiveness of MODY genetictesting translating genomic advances into practical health applications DiabetesCare 201437202ndash20957 Gloyn AL Pearson ER Antcliff JF et al Activating mutations in the geneencoding the ATP-sensitive potassium-channel subunit Kir62 and permanentneonatal diabetes N Engl J Med 20043501838ndash184958 Pearson ER Flechtner I Njoslashlstad PR et al Neonatal Diabetes InternationalCollaborative Group Switching from insulin to oral sulfonylureas in patients withdiabetes due to Kir62 mutations N Engl J Med 2006355467ndash47759 UK Prospective Diabetes Study (UKPDS) UK Prospective Diabetes Study(UKPDS) VIII Study design progress and performance Diabetologia 199134877ndash89060 Kesselheim AS Tan YT Avorn J The roles of academia rare diseases andrepurposing in the development of the most transformative drugs Health Aff(Millwood) 201534286ndash29361 Nelson MR Tipney H Painter JL et al The support of human geneticevidence for approved drug indications Nat Genet 201547856ndash86062 FNIH Evidentiary Criteria Writing Group Framework for Defining EvidentiaryCriteria for Biomarker Qualification Bethesda MD National Library of Medicine201663 Tabaacutek AG Herder C Rathmann W Brunner EJ Kivimaumlki M Prediabetesa high-risk state for diabetes development Lancet 20123792279ndash229064 Nicol D Bubela T Chalmers D et al Precision medicine drowning ina regulatory soup J Law Biosci 20163281ndash30365 Kaye J Whitley EA Lund D Morrison M Teare H Melham K Dynamicconsent a patient interface for twenty-first century research networks Eur J HumGenet 201523141ndash14666 Kinkorovaacute J Biobanks in the era of personalized medicine objectiveschallenges and innovation Overview EPMA J 201674
1922 Precision Medicine in Type 2 Diabetes Diabetes Volume 67 October 2018
the cost-effectiveness of precision medicines in type 2 di-abetes (52)
Thus far very few cost-effectiveness analyses in pre-cision diabetes medicine have been published despitelong-standing recognition that cost-effectiveness is a keyrequirement for clinical translation (53) The rare exam-ples of such studies have focused exclusively on rare mono-genic forms of diabetes neonatal diabetes (54) andmaturity-onset diabetes of the young (MODY) (5556)
Neonatal diabetes is diagnosed in infancy and affectsabout 1400000 live births and roughly half (1252000in people aged 20 years) develop permanent neonataldiabetes (54) Of the latter group up to 70 are estimatedto carry mutations in the genes encoding the ATP-sensitivepotassium channel (KCNJ11 and ABCC8) These mutationsblock the closing of the KATP channels which preventsb-cell depolarization and corresponding insulin secretion(57) However treatment with sulfonylureas rectifies thisdefect in 90 of cases allowing the discontinuation ofexogenous insulin (58) The earliest costndasheffect analysisin precision medicine focused on sulfonylurea therapy inpermanent neonatal diabetes (54) The authors estimatedthat genetic testing in permanent neonatal diabetes wouldconvey significant quality-of-life benefits at 10 years (032quality-adjusted life-years US$12528 saved) increasingto 070 at 30 years (US$30437 saved)
A range of single gene defects cause MODY each ofwhich are used to diagnose one of five forms of the diseaseIn MODY1 and MODY3 insulin secretion is impaired Thedisease occurs early in life and is often misdiagnosed astype 1 diabetes and exogenous insulin therapy is thusprescribed However treatment with sulfonylureas enablessecretion of endogenously produced insulin Switchingfrom insulin to sulfonylureas is preferable for a numberof reasons including those related to safety and burdenMODY2 is caused by mutations in GCK which lead tochronically elevated but nonprogressive blood glucoseconcentrations that do not typically require treatmentthus diagnosing MODY2 also conveys reduced costs andburdens associated with unnecessary treatment The firstcost-effectiveness analysis focused on MODY1ndash3 in USadults (aged 25ndash40 years) with diagnosed type 2 diabetes(56) based many of its assumptions on the findings ofthe UK Prospective Diabetes Study (UKPDS) (59) It alsoassumed a 2 frequency of MODY among people withdiabetes and set genetic screening costs at US$2580Under these assumptions genetic screening for MODYwould not be cost-effective The second study focused onlyonMODY1 screening and found that genetic screening wasnot cost-effective given the diabetes population frequencyof MODY1 mutations is unlikely to exceed 2 Sub-stantial reductions in genetic sequencing costs may how-ever render screening cost-effective (55)
Regulatory ConsensusMore than half of the most clinically impactful drugs de-veloped recently were discovered through academia-led
research (60) emphasizing the huge value of publicndashprivate partnerships Many of the large precision diabetesmedicine consortia involve both academia and industrywithin which unsurprising emphasis is placed on the dis-covery and validation of biomarkers that expedite drugdevelopment This expectation is backed up by the provenability of genetics to fast-track drug development bypinpointing high-value drug targets helping halve drugfailures and markedly reducing their costs (61) In drugrepositioning genetics has also proven its worth by re-vealing previously unknown conditions that a drug can beused to treat and by helping predict drug side effects
To obtain FDA (or European Medicines Agency [EMA])approval for biomarkers in medical products and de-vices involves a comprehensive qualification process (seehttpswwwfdagovDrugsDevelopmentApprovalProcessDrugDevelopmentToolsQualificationProgramBiomarker-QualificationProgramucm535395htm) within which ex-ists the requirement for a statement termed a ldquocontext ofuserdquo The context of use statement requires a clear anddetailed description of the intended role of the biomarkerin drug development as well as risks and benefits TheFoundation for the National Institutes of Health recentlypublished the Framework for Defining Evidentiary Criteriafor Biomarker Qualification (62) which provides an ex-panded outline of the biomarker classifications definedby the FDA
The process of biomarker qualification in drug devel-opment requires stringent adherence to the relevant reg-ulatory agenciesrsquo approvals processes In some studies thatseek to discover biomarkers for diabetes drug develop-ment the process is well rehearsed and aligning biomarkerdiscovery and validation strategies with regulatory expect-ations is relatively straightforward however it does re-quire that precision medicine consortia are well versedin these processes Biomarker discovery in prediabetes ismore challenging though as prediabetes is not a distinctdisease state and although some people with prediabetesdevelop diabetes and its complications a large proportionremains healthy for many years (63) Nevertheless a sub-group of those with prediabetes will progress to diabetesand identifying these people before b-cell function issubstantially diminished would open the door for severalpowerful therapeutics that are ineffective once endoge-nous insulin production has faded However becauseneither the FDA nor EMA currently recognizes an inter-mediate end point for disease progression in prediabetesdrug trials gaining regulatory approval for new prediabe-tes biomarkers requires that researchers foster close andfrequent communications with the regulatory agencies toensure candidate biomarkers have the possibility to pro-gress through the regulatory process
Legal Social and EthicalBalancing the interests of society and its citizens the risksand benefits legislation and individual freedom as wellas the risk of exasperating disparities is central to many
diabetesdiabetesjournalsorg Fitipaldi and Associates 1919
commentaries on the legal social and ethical aspects ofprecision medicine
Precision medicine research is heavily dependent onbiobanked data and tissues Often the original informedconsents were obtained many years before modern lsquoomicstechnologies existed at a time when many research ques-tions germane to precision medicine were inconceivableOften the informed consents were broad and ethicscommittees have concluded that these modern researchquestions are within the scope of the consents As newever larger and interconnected cohorts are being formedthe desire many researchers share for broad consentingandor to bank data or samples for unspecified futureresearch must be placed in context with several importantethical realities the need to maintain participantsrsquo confi-dentiality appropriately handle incidental findings deter-mine who owns the samples and data consider culturalsensitivities return results to participants engage societyand ensure unused material is properly disposed (64)
Much as precision medicine was inconceivable whenmany existing cohorts were formed it is impossible toaccurately predict how contemporary cohorts will be usedin the future Furthermore many precision medicineinitiatives involve the hybridization of academia healthcare and industry which may challenge some of the morealtruistic motivations of participants in academia-led bio-medical research Moreover much of the highly sensitivedata collated by these initiatives will eventually be madeavailable through managed-access portals to a wide rangeof end users who were not part of the data collectionprocess Thus much of this highly sensitive data will bewidely disseminated and used by distant end users toaddress a plethora of diverse research questions thatcannot yet be conceived factors that need to be consideredduring the informed consent process These are challengesfor historical biobanks as well as new precision medicinecohorts that consent only at enrollment To help overcomethis a process called dynamic consent has been developedwhich allows consents to be progressively updated asprojects evolve but requires fluid communication withparticipants throughout the life course of a study (65)
A major concern about genetic data are its linkage todatabases that enables inferences about a personrsquos socialcognitive moral cultural health or sexual identity whichparticipants had not consented to or for such inferencesto be made about family members without their consent(64) Indeed ensuring genomics data are used in a wayconsistent with appropriate ethical standards is a corefeature of most current health data protection legislation(66) Notwithstanding these risks there are major benefitsassociated with big data which include the discovery ofnovel disease mechanisms and therapeutic targets manyof which only become visible when data sets scale tomassive proportions Thus rather than constrain thegrowth and responsible utilization of big data emphasis isbeing placed on implementing legislation that protectsprivacy and helps prevent data breaches while facilitating
the safe and responsible storage transfer and utilizationof data In the European Union for example the Gen-eral Data Protection Regulation was implemented in May2018 to harmonize data privacy laws across Europe A keyfeature of this legislation as it relates to precision medicineresearch is that the analysis of sensitive data requiresexplicit opt-in consent whereas for nonsensitive dataunambiguous (implied) consent is sufficient
SUMMARY AND CONCLUSIONS
Diabetes medicine is likely to evolve dramatically in thecoming years ideally because evidence emerges from themajor precision medicine initiatives to support its utility inpreventing or treating diabetes but no doubt also becauseof the major commercial interest in seeing precision med-icine grow One of the possible outcomes will very likely bethe replacement of current classifications of diabetes withempirically derived subclassifications that can be coupledwith treatment regimens which compared with con-ventional medicines are more effective less costly andconvey fewer unnecessary side effects As genetic data-bases grow genotypes will be increasingly used to discovernew drug targets At this time the clinical application ofgenetics is confined to the diagnosis of and therapeuticguidance for rare forms of diabetes as well as the pre-diction of adverse drug reactions The ways in whichgenetics may or may not guide prevention and therapy incommon forms of diabetes is unclear However becausecharacterizing variation in a patientrsquos nuclear genomeis inexpensive and easily achieved and DNA variation re-mains constant across the life course it is likely that geneticsequences will feature in most patientsrsquo clinical recordsGiven this genetic data will likely be deployed in diverseways in the diabetes clinic of the future The roles otherlsquoomics technologies digital imaging devices and wearableswill play in precision diabetes medicine are harder to fore-cast as research in these areas is less advanced and the costof obtaining some of these data will likely remain highrelative to genetics Finally although many precision med-icine initiatives focus predominantly on pharmacotherapyoptimizing lifestyle (and possibly surgical) interventionsusing biotechnologies also has great potential for improvingtype 2 diabetes prevention and treatment
Funding and Duality of Interest The work of HF and PWF relatedto the manuscript was supported by the European Commission (CoG-2015_681742_NASCENT) Swedish Research Council (Distinguished YoungResearchers Award in Medicine) Swedish Heart-Lung Foundation Novo NordiskFoundation European Foundation for the Study of Diabetes and the SwedishFoundation for Strategic Research (Industrial Research Center in PrecisionMedicine) (all grants to PWF) PWF is a consultant for and has stock optionsin Zoe Global and has received research funding from Boehringer Ingelheim EliLilly Janssen Novo Nordisk Sanofi and Servier MIM is a National Institute forHealth Research (NIHR) Senior Investigator and a Wellcome Trust Senior In-vestigator The views expressed in this article are those of the author(s) and notnecessarily those of the National Health Service NIHR or the Department ofHealth Work described in this review was principally supported by the Wellcome
1920 Precision Medicine in Type 2 Diabetes Diabetes Volume 67 October 2018
Trust (098381 203141 106130) and by the National Institutes of Health (U01-DK105535) MIM serves on advisory panels for Pfizer Novo Nordisk and ZoeGlobal has received honoraria from Pfizer Novo Nordisk and Eli Lilly hasstock options in Zoe Global and has received research funding from AbbVieAstraZeneca Boehringer Ingelheim Eli Lilly Janssen Merck Novo Nordisk PfizerRoche Sanofi Servier and Takeda JCF is a Massachusetts General HospitalResearch Scholar and is supported by National Institutes of Health National Instituteof Diabetes and Digestive and Kidney Diseases grants R01 DK072041 U01DK105554 R01 DK105154 and K24 DK110550 and National Institute of GeneralMedical Sciences grant R01 GM117163 MIM JCF and PWF are membersof the American Diabetes Association Precision Medicine in Diabetes TaskforceThe views expressed in this article are those of the authors and do not necessarilyreflect those of the American Diabetes Association or the taskforce No otherpotential conflicts of interest relevant to this article were reported
References1 Centers for Disease Control and Prevention National Diabetes StatisticsReport Estimates of Diabetes and Its Burden in the United States 2014 AtlantaGA US Department of Health and Human Services 20142 Knowler WC Fowler SE Hamman RF et al Diabetes Prevention ProgramResearch Group 10-year follow-up of diabetes incidence and weight loss in theDiabetes Prevention Program Outcomes Study Lancet 20093741677ndash16863 Li G Zhang P Wang J et al The long-term effect of lifestyle interventions toprevent diabetes in the China Da Qing Diabetes Prevention Study a 20-yearfollow-up study Lancet 20083711783ndash17894 Tuomilehto J Lindstroumlm J Eriksson JG et al Finnish Diabetes PreventionStudy Group Prevention of type 2 diabetes mellitus by changes in lifestyleamong subjects with impaired glucose tolerance N Engl J Med 20013441343ndash13505 Ramachandran A Snehalatha C Mary S Mukesh B Bhaskar AD Vijay VIndian Diabetes Prevention Programme (IDPP) The Indian Diabetes PreventionProgramme shows that lifestyle modification and metformin prevent type 2 di-abetes in Asian Indian subjects with impaired glucose tolerance (IDPP-1) Dia-betologia 200649289ndash2976 Lean ME Leslie WS Barnes AC et al Primary care-led weight managementfor remission of type 2 diabetes (DiRECT) an open-label cluster-randomised trialLancet 2018391541ndash5517 Polyzogopoulou EV Kalfarentzos F Vagenakis AG Alexandrides TK Res-toration of euglycemia and normal acute insulin response to glucose in obesesubjects with type 2 diabetes following bariatric surgery Diabetes 2003521098ndash11038 Franco M Orduntildeez P Caballero B et al Impact of energy intake physicalactivity and population-wide weight loss on cardiovascular disease and diabetesmortality in Cuba 1980-2005 Am J Epidemiol 20071661374ndash13809 Cho NH Shaw JE Karuranga S et al IDF Diabetes Atlas global estimates ofdiabetes prevalence for 2017 and projections for 2045 Diabetes Res Clin Pract2018138271ndash28110 Lander ES Linton LM Birren B et al International Human Genome Se-quencing Consortium Initial sequencing and analysis of the human genome[published correction appears in Nature 2001411720] Nature 2001409860ndash92111 Visscher PM Wray NR Zhang Q et al 10 Years of GWAS discovery biologyfunction and translation Am J Hum Genet 20171015ndash2212 Merino J Udler MS Leong A Meigs JB A decade of genetic and metab-olomic contributions to type 2 diabetes risk prediction Curr Diab Rep 20171713513 Knowles JW Ashley EA Cardiovascular disease the rise of the genetic riskscore PLoS Med 201815e100254614 Mahajan A Taliun D Thurner M et al Fine-mapping of an expanded set oftype 2 diabetes loci to single-variant resolution using high-density imputation andislet-specific epigenome maps [preprint article online] 2018 Available fromhttpswwwbiorxivorgcontentearly20180109245506 Accessed 30 January2018
15 McCarthy MI Painting a new picture of personalised medicine for diabetesDiabetologia 201760793ndash79916 Scott RA Scott LJ Maumlgi R et al DIAbetes Genetics Replication AndMeta-analysis (DIAGRAM) Consortium An expanded genome-wide asso-ciation study of type 2 diabetes in Europeans Diabetes 2017662888ndash290217 Mahajan A Wessel J Willems SM et al ExomeBP Consortium MAGICConsortium GIANT Consortium Refining the accuracy of validated target iden-tification through coding variant fine-mapping in type 2 diabetes Nat Genet 201850559ndash57118 Udler MS Kim J von Grotthuss M et al Clustering of type 2 diabetes geneticloci by multi-trait associations identifies disease mechanisms and subtypes[preprint article online] 2018 Available from httpswwwbiorxivorgcontentearly20180510319509 Accessed 30 March 201819 van Zuydam NR Ahlqvist E Sandholm N et al A genome-wide associationstudy of diabetic kidney disease in subjects with type 2 diabetes Diabetes 2018671414ndash142720 Ahlqvist E Storm P Kaumlraumljaumlmaumlki A et al Novel subgroups of adult-onsetdiabetes and their association with outcomes a data-driven cluster analysis of sixvariables Lancet Diabetes Endocrinol 20186361ndash36921 Fuchsberger C Flannick J Teslovich TM et al The genetic architecture oftype 2 diabetes Nature 201653641ndash4722 Koivula RW Heggie A Barnett A et al DIRECT Consortium Discovery ofbiomarkers for glycaemic deterioration before and after the onset of type 2 di-abetes rationale and design of the epidemiological studies within the IMI DIRECTConsortium Diabetologia 2014571132ndash114223 Rask-Andersen M Karlsson T Ek WE Johansson Aring Gene-environmentinteraction study for BMI reveals interactions between genetic factors and physicalactivity alcohol consumption and socioeconomic status PLoS Genet 201713e100697724 Celis-Morales CA Lyall DM Gray SR et al Dietary fat and total energy intakemodifies the association of genetic profile risk score on obesity evidence from48 170 UK Biobank participants Int J Obes 2017411761ndash176825 Tyrrell J Wood AR Ames RM et al Gene-obesogenic environment inter-actions in the UK Biobank study Int J Epidemiol 201746559ndash57526 Young AI Wauthier F Donnelly P Multiple novel gene-by-environment in-teractions modify the effect of FTO variants on body mass index Nat Commun201671272427 Reardon S Giant study poses DNA data-sharing dilemma Nature 201552516ndash1728 Hudson K Lifton R Patrick-Lake B The Precision Medicine Initiative CohortProgram ndash Building a Research Foundation for 21st Century Medicine BethesdaMD National Institutes of Health 201529 Gaziano JM Concato J Brophy M et al Million Veteran Program a mega-biobank to study genetic influences on health and disease J Clin Epidemiol 201670214ndash22330 Bergen SE Sullivan PF National-scale precision medicine for psychiatricdisorders in Sweden Am J Med Genet B Neuropsychiatr Genet 7 July 2017 [Epubahead of print] DOI 101002ajmgb3256231 Cyranoski D Chinarsquos bid to be a DNA superpower Nature 2016534462ndash46332 Cyranoski D China embraces precision medicine on a massive scale Nature20165299ndash1033 Bu D Peng S Luo H et al Precision medicine and cancer immunology inChina From big data to knowledge in precision medicine Science 2018 (Suppl)S35ndashS3834 Moltke I Grarup N Joslashrgensen ME et al A common Greenlandic TBC1D4variant confers muscle insulin resistance and type 2 diabetes Nature 2014512190ndash19335 Dash S Sano H Rochford JJ et al A truncation mutation in TBC1D4 ina family with acanthosis nigricans and postprandial hyperinsulinemia Proc NatlAcad Sci U S A 20091069350ndash9355
diabetesdiabetesjournalsorg Fitipaldi and Associates 1921
36 Estrada K Aukrust I Bjoslashrkhaug L et al SIGMA Type 2 Diabetes ConsortiumAssociation of a low-frequency variant in HNF1A with type 2 diabetes in a Latinopopulation [published correction appears in JAMA 20143121932] JAMA 2014
3112305ndash231437 Marshall SM 60 years of metformin use a glance at the past and a look tothe future Diabetologia 2017601561ndash156538 Pearson ER Pharmacogenetics and target identification in diabetes CurrOpin Genet Dev 20185068ndash7339 Zhou K Bellenguez C Spencer CC et al GoDARTS and UKPDS Diabetes
Pharmacogenetics Study Group Wellcome Trust Case Control Consortium 2MAGIC investigators Common variants near ATM are associated with glycemicresponse to metformin in type 2 diabetes Nat Genet 201143117ndash12040 Zhou K Yee SW Seiser EL et al MetGen Investigators DPP InvestigatorsACCORD Investigators Variation in the glucose transporter gene SLC2A2 isassociated with glycemic response to metformin Nat Genet 2016481055ndash
105941 National Research Council Committee on a Framework for Developing a NewTaxonomy of Disease Toward Precision Medicine Building a Knowledge Networkfor Bomedical Research and a New Taxonomy of Disease Washington DCNational Academies Press 201142 Franks PW Poveda A Lifestyle and precision diabetes medicine will ge-
nomics help optimise the prediction prevention and treatment of type 2 diabetesthrough lifestyle therapy Diabetologia 201760784ndash79243 Zeevi D Korem T Zmora N et al Personalized nutrition by prediction of
glycemic responses Cell 20151631079ndash109444 Swanson A Big pharmaceutical companies are spending far more onmarketing than research The Washington Post 11 February 2015 Available fromhttpswwwwashingtonpostcomnewswonkwp20150211big-pharmaceutical-companies-are-spending-far-more-on-marketing-than-researchnoredirect=onamputm_term=787d031b9b7c Accessed 20 February 201845 Unilever Advertising amp Marketing Unilever 2018 Available from httpwwwunilevercomsustainable-livingwhat-matters-to-youadvertising-and-marketinghtml Accessed 31 March 201846 OrsquoDowd A Spending on junk food advertising is nearly 30 times whatgovernment spends on promoting healthy eating BMJ 2017359j467747 National Institutes of Health Molecular Transducers of Physical Activity in
Humans Frequently Asked Questions (FAQs) [Internet] 2018 Available from httpscommonfundnihgovMolecularTransducersFAQs Accessed 20 March 201848 Livingstone KM Celis-Morales C Navas-Carretero S et al Food4Me Study
Effect of an Internet-based personalized nutrition randomized trial on dietarychanges associated with the Mediterranean diet the Food4Me Study Am J ClinNutr 2016104288ndash29749 Celis-Morales C Livingstone KM Marsaux CF et al Food4Me Study Effectof personalized nutrition on health-related behaviour change evidence from theFood4Me European randomized controlled trial Int J Epidemiol 201746578ndash
588
50 Celis-Morales C Livingstone KM Marsaux CF et al Design and baselinecharacteristics of the Food4Me study a web-based randomised controlled trial ofpersonalised nutrition in seven European countries Genes Nutr 20151045051 BIS Research Global precision medicine market - analysis and forecast2017-2026 2017 Available from httpsbisresearchcomindustry-reportglobal-precision-medicine-market-2026html Accessed 31 January 201852 Stern AD Alexander BM Chandra A How economics can shape precisionmedicines Science 20173551131ndash113353 Smith RJ Nathan DM Arslanian SA Groop L Rizza RA Rotter JI In-dividualizing therapies in type 2 diabetes mellitus based on patient characteristicswhat we know and what we need to know J Clin Endocrinol Metab 2010951566ndash157454 Greeley SA John PM Winn AN et al The cost-effectiveness of personalizedgenetic medicine the case of genetic testing in neonatal diabetes Diabetes Care201134622ndash62755 Nguyen HV Finkelstein EA Mital S Gardner DS Incremental cost-effectivenessof algorithm-driven genetic testing versus no testing for maturity onset diabetes of theyoung (MODY) in Singapore J Med Genet 201754747ndash75356 Naylor RN John PM Winn AN et al Cost-effectiveness of MODY genetictesting translating genomic advances into practical health applications DiabetesCare 201437202ndash20957 Gloyn AL Pearson ER Antcliff JF et al Activating mutations in the geneencoding the ATP-sensitive potassium-channel subunit Kir62 and permanentneonatal diabetes N Engl J Med 20043501838ndash184958 Pearson ER Flechtner I Njoslashlstad PR et al Neonatal Diabetes InternationalCollaborative Group Switching from insulin to oral sulfonylureas in patients withdiabetes due to Kir62 mutations N Engl J Med 2006355467ndash47759 UK Prospective Diabetes Study (UKPDS) UK Prospective Diabetes Study(UKPDS) VIII Study design progress and performance Diabetologia 199134877ndash89060 Kesselheim AS Tan YT Avorn J The roles of academia rare diseases andrepurposing in the development of the most transformative drugs Health Aff(Millwood) 201534286ndash29361 Nelson MR Tipney H Painter JL et al The support of human geneticevidence for approved drug indications Nat Genet 201547856ndash86062 FNIH Evidentiary Criteria Writing Group Framework for Defining EvidentiaryCriteria for Biomarker Qualification Bethesda MD National Library of Medicine201663 Tabaacutek AG Herder C Rathmann W Brunner EJ Kivimaumlki M Prediabetesa high-risk state for diabetes development Lancet 20123792279ndash229064 Nicol D Bubela T Chalmers D et al Precision medicine drowning ina regulatory soup J Law Biosci 20163281ndash30365 Kaye J Whitley EA Lund D Morrison M Teare H Melham K Dynamicconsent a patient interface for twenty-first century research networks Eur J HumGenet 201523141ndash14666 Kinkorovaacute J Biobanks in the era of personalized medicine objectiveschallenges and innovation Overview EPMA J 201674
1922 Precision Medicine in Type 2 Diabetes Diabetes Volume 67 October 2018
commentaries on the legal social and ethical aspects ofprecision medicine
Precision medicine research is heavily dependent onbiobanked data and tissues Often the original informedconsents were obtained many years before modern lsquoomicstechnologies existed at a time when many research ques-tions germane to precision medicine were inconceivableOften the informed consents were broad and ethicscommittees have concluded that these modern researchquestions are within the scope of the consents As newever larger and interconnected cohorts are being formedthe desire many researchers share for broad consentingandor to bank data or samples for unspecified futureresearch must be placed in context with several importantethical realities the need to maintain participantsrsquo confi-dentiality appropriately handle incidental findings deter-mine who owns the samples and data consider culturalsensitivities return results to participants engage societyand ensure unused material is properly disposed (64)
Much as precision medicine was inconceivable whenmany existing cohorts were formed it is impossible toaccurately predict how contemporary cohorts will be usedin the future Furthermore many precision medicineinitiatives involve the hybridization of academia healthcare and industry which may challenge some of the morealtruistic motivations of participants in academia-led bio-medical research Moreover much of the highly sensitivedata collated by these initiatives will eventually be madeavailable through managed-access portals to a wide rangeof end users who were not part of the data collectionprocess Thus much of this highly sensitive data will bewidely disseminated and used by distant end users toaddress a plethora of diverse research questions thatcannot yet be conceived factors that need to be consideredduring the informed consent process These are challengesfor historical biobanks as well as new precision medicinecohorts that consent only at enrollment To help overcomethis a process called dynamic consent has been developedwhich allows consents to be progressively updated asprojects evolve but requires fluid communication withparticipants throughout the life course of a study (65)
A major concern about genetic data are its linkage todatabases that enables inferences about a personrsquos socialcognitive moral cultural health or sexual identity whichparticipants had not consented to or for such inferencesto be made about family members without their consent(64) Indeed ensuring genomics data are used in a wayconsistent with appropriate ethical standards is a corefeature of most current health data protection legislation(66) Notwithstanding these risks there are major benefitsassociated with big data which include the discovery ofnovel disease mechanisms and therapeutic targets manyof which only become visible when data sets scale tomassive proportions Thus rather than constrain thegrowth and responsible utilization of big data emphasis isbeing placed on implementing legislation that protectsprivacy and helps prevent data breaches while facilitating
the safe and responsible storage transfer and utilizationof data In the European Union for example the Gen-eral Data Protection Regulation was implemented in May2018 to harmonize data privacy laws across Europe A keyfeature of this legislation as it relates to precision medicineresearch is that the analysis of sensitive data requiresexplicit opt-in consent whereas for nonsensitive dataunambiguous (implied) consent is sufficient
SUMMARY AND CONCLUSIONS
Diabetes medicine is likely to evolve dramatically in thecoming years ideally because evidence emerges from themajor precision medicine initiatives to support its utility inpreventing or treating diabetes but no doubt also becauseof the major commercial interest in seeing precision med-icine grow One of the possible outcomes will very likely bethe replacement of current classifications of diabetes withempirically derived subclassifications that can be coupledwith treatment regimens which compared with con-ventional medicines are more effective less costly andconvey fewer unnecessary side effects As genetic data-bases grow genotypes will be increasingly used to discovernew drug targets At this time the clinical application ofgenetics is confined to the diagnosis of and therapeuticguidance for rare forms of diabetes as well as the pre-diction of adverse drug reactions The ways in whichgenetics may or may not guide prevention and therapy incommon forms of diabetes is unclear However becausecharacterizing variation in a patientrsquos nuclear genomeis inexpensive and easily achieved and DNA variation re-mains constant across the life course it is likely that geneticsequences will feature in most patientsrsquo clinical recordsGiven this genetic data will likely be deployed in diverseways in the diabetes clinic of the future The roles otherlsquoomics technologies digital imaging devices and wearableswill play in precision diabetes medicine are harder to fore-cast as research in these areas is less advanced and the costof obtaining some of these data will likely remain highrelative to genetics Finally although many precision med-icine initiatives focus predominantly on pharmacotherapyoptimizing lifestyle (and possibly surgical) interventionsusing biotechnologies also has great potential for improvingtype 2 diabetes prevention and treatment
Funding and Duality of Interest The work of HF and PWF relatedto the manuscript was supported by the European Commission (CoG-2015_681742_NASCENT) Swedish Research Council (Distinguished YoungResearchers Award in Medicine) Swedish Heart-Lung Foundation Novo NordiskFoundation European Foundation for the Study of Diabetes and the SwedishFoundation for Strategic Research (Industrial Research Center in PrecisionMedicine) (all grants to PWF) PWF is a consultant for and has stock optionsin Zoe Global and has received research funding from Boehringer Ingelheim EliLilly Janssen Novo Nordisk Sanofi and Servier MIM is a National Institute forHealth Research (NIHR) Senior Investigator and a Wellcome Trust Senior In-vestigator The views expressed in this article are those of the author(s) and notnecessarily those of the National Health Service NIHR or the Department ofHealth Work described in this review was principally supported by the Wellcome
1920 Precision Medicine in Type 2 Diabetes Diabetes Volume 67 October 2018
Trust (098381 203141 106130) and by the National Institutes of Health (U01-DK105535) MIM serves on advisory panels for Pfizer Novo Nordisk and ZoeGlobal has received honoraria from Pfizer Novo Nordisk and Eli Lilly hasstock options in Zoe Global and has received research funding from AbbVieAstraZeneca Boehringer Ingelheim Eli Lilly Janssen Merck Novo Nordisk PfizerRoche Sanofi Servier and Takeda JCF is a Massachusetts General HospitalResearch Scholar and is supported by National Institutes of Health National Instituteof Diabetes and Digestive and Kidney Diseases grants R01 DK072041 U01DK105554 R01 DK105154 and K24 DK110550 and National Institute of GeneralMedical Sciences grant R01 GM117163 MIM JCF and PWF are membersof the American Diabetes Association Precision Medicine in Diabetes TaskforceThe views expressed in this article are those of the authors and do not necessarilyreflect those of the American Diabetes Association or the taskforce No otherpotential conflicts of interest relevant to this article were reported
References1 Centers for Disease Control and Prevention National Diabetes StatisticsReport Estimates of Diabetes and Its Burden in the United States 2014 AtlantaGA US Department of Health and Human Services 20142 Knowler WC Fowler SE Hamman RF et al Diabetes Prevention ProgramResearch Group 10-year follow-up of diabetes incidence and weight loss in theDiabetes Prevention Program Outcomes Study Lancet 20093741677ndash16863 Li G Zhang P Wang J et al The long-term effect of lifestyle interventions toprevent diabetes in the China Da Qing Diabetes Prevention Study a 20-yearfollow-up study Lancet 20083711783ndash17894 Tuomilehto J Lindstroumlm J Eriksson JG et al Finnish Diabetes PreventionStudy Group Prevention of type 2 diabetes mellitus by changes in lifestyleamong subjects with impaired glucose tolerance N Engl J Med 20013441343ndash13505 Ramachandran A Snehalatha C Mary S Mukesh B Bhaskar AD Vijay VIndian Diabetes Prevention Programme (IDPP) The Indian Diabetes PreventionProgramme shows that lifestyle modification and metformin prevent type 2 di-abetes in Asian Indian subjects with impaired glucose tolerance (IDPP-1) Dia-betologia 200649289ndash2976 Lean ME Leslie WS Barnes AC et al Primary care-led weight managementfor remission of type 2 diabetes (DiRECT) an open-label cluster-randomised trialLancet 2018391541ndash5517 Polyzogopoulou EV Kalfarentzos F Vagenakis AG Alexandrides TK Res-toration of euglycemia and normal acute insulin response to glucose in obesesubjects with type 2 diabetes following bariatric surgery Diabetes 2003521098ndash11038 Franco M Orduntildeez P Caballero B et al Impact of energy intake physicalactivity and population-wide weight loss on cardiovascular disease and diabetesmortality in Cuba 1980-2005 Am J Epidemiol 20071661374ndash13809 Cho NH Shaw JE Karuranga S et al IDF Diabetes Atlas global estimates ofdiabetes prevalence for 2017 and projections for 2045 Diabetes Res Clin Pract2018138271ndash28110 Lander ES Linton LM Birren B et al International Human Genome Se-quencing Consortium Initial sequencing and analysis of the human genome[published correction appears in Nature 2001411720] Nature 2001409860ndash92111 Visscher PM Wray NR Zhang Q et al 10 Years of GWAS discovery biologyfunction and translation Am J Hum Genet 20171015ndash2212 Merino J Udler MS Leong A Meigs JB A decade of genetic and metab-olomic contributions to type 2 diabetes risk prediction Curr Diab Rep 20171713513 Knowles JW Ashley EA Cardiovascular disease the rise of the genetic riskscore PLoS Med 201815e100254614 Mahajan A Taliun D Thurner M et al Fine-mapping of an expanded set oftype 2 diabetes loci to single-variant resolution using high-density imputation andislet-specific epigenome maps [preprint article online] 2018 Available fromhttpswwwbiorxivorgcontentearly20180109245506 Accessed 30 January2018
15 McCarthy MI Painting a new picture of personalised medicine for diabetesDiabetologia 201760793ndash79916 Scott RA Scott LJ Maumlgi R et al DIAbetes Genetics Replication AndMeta-analysis (DIAGRAM) Consortium An expanded genome-wide asso-ciation study of type 2 diabetes in Europeans Diabetes 2017662888ndash290217 Mahajan A Wessel J Willems SM et al ExomeBP Consortium MAGICConsortium GIANT Consortium Refining the accuracy of validated target iden-tification through coding variant fine-mapping in type 2 diabetes Nat Genet 201850559ndash57118 Udler MS Kim J von Grotthuss M et al Clustering of type 2 diabetes geneticloci by multi-trait associations identifies disease mechanisms and subtypes[preprint article online] 2018 Available from httpswwwbiorxivorgcontentearly20180510319509 Accessed 30 March 201819 van Zuydam NR Ahlqvist E Sandholm N et al A genome-wide associationstudy of diabetic kidney disease in subjects with type 2 diabetes Diabetes 2018671414ndash142720 Ahlqvist E Storm P Kaumlraumljaumlmaumlki A et al Novel subgroups of adult-onsetdiabetes and their association with outcomes a data-driven cluster analysis of sixvariables Lancet Diabetes Endocrinol 20186361ndash36921 Fuchsberger C Flannick J Teslovich TM et al The genetic architecture oftype 2 diabetes Nature 201653641ndash4722 Koivula RW Heggie A Barnett A et al DIRECT Consortium Discovery ofbiomarkers for glycaemic deterioration before and after the onset of type 2 di-abetes rationale and design of the epidemiological studies within the IMI DIRECTConsortium Diabetologia 2014571132ndash114223 Rask-Andersen M Karlsson T Ek WE Johansson Aring Gene-environmentinteraction study for BMI reveals interactions between genetic factors and physicalactivity alcohol consumption and socioeconomic status PLoS Genet 201713e100697724 Celis-Morales CA Lyall DM Gray SR et al Dietary fat and total energy intakemodifies the association of genetic profile risk score on obesity evidence from48 170 UK Biobank participants Int J Obes 2017411761ndash176825 Tyrrell J Wood AR Ames RM et al Gene-obesogenic environment inter-actions in the UK Biobank study Int J Epidemiol 201746559ndash57526 Young AI Wauthier F Donnelly P Multiple novel gene-by-environment in-teractions modify the effect of FTO variants on body mass index Nat Commun201671272427 Reardon S Giant study poses DNA data-sharing dilemma Nature 201552516ndash1728 Hudson K Lifton R Patrick-Lake B The Precision Medicine Initiative CohortProgram ndash Building a Research Foundation for 21st Century Medicine BethesdaMD National Institutes of Health 201529 Gaziano JM Concato J Brophy M et al Million Veteran Program a mega-biobank to study genetic influences on health and disease J Clin Epidemiol 201670214ndash22330 Bergen SE Sullivan PF National-scale precision medicine for psychiatricdisorders in Sweden Am J Med Genet B Neuropsychiatr Genet 7 July 2017 [Epubahead of print] DOI 101002ajmgb3256231 Cyranoski D Chinarsquos bid to be a DNA superpower Nature 2016534462ndash46332 Cyranoski D China embraces precision medicine on a massive scale Nature20165299ndash1033 Bu D Peng S Luo H et al Precision medicine and cancer immunology inChina From big data to knowledge in precision medicine Science 2018 (Suppl)S35ndashS3834 Moltke I Grarup N Joslashrgensen ME et al A common Greenlandic TBC1D4variant confers muscle insulin resistance and type 2 diabetes Nature 2014512190ndash19335 Dash S Sano H Rochford JJ et al A truncation mutation in TBC1D4 ina family with acanthosis nigricans and postprandial hyperinsulinemia Proc NatlAcad Sci U S A 20091069350ndash9355
diabetesdiabetesjournalsorg Fitipaldi and Associates 1921
36 Estrada K Aukrust I Bjoslashrkhaug L et al SIGMA Type 2 Diabetes ConsortiumAssociation of a low-frequency variant in HNF1A with type 2 diabetes in a Latinopopulation [published correction appears in JAMA 20143121932] JAMA 2014
3112305ndash231437 Marshall SM 60 years of metformin use a glance at the past and a look tothe future Diabetologia 2017601561ndash156538 Pearson ER Pharmacogenetics and target identification in diabetes CurrOpin Genet Dev 20185068ndash7339 Zhou K Bellenguez C Spencer CC et al GoDARTS and UKPDS Diabetes
Pharmacogenetics Study Group Wellcome Trust Case Control Consortium 2MAGIC investigators Common variants near ATM are associated with glycemicresponse to metformin in type 2 diabetes Nat Genet 201143117ndash12040 Zhou K Yee SW Seiser EL et al MetGen Investigators DPP InvestigatorsACCORD Investigators Variation in the glucose transporter gene SLC2A2 isassociated with glycemic response to metformin Nat Genet 2016481055ndash
105941 National Research Council Committee on a Framework for Developing a NewTaxonomy of Disease Toward Precision Medicine Building a Knowledge Networkfor Bomedical Research and a New Taxonomy of Disease Washington DCNational Academies Press 201142 Franks PW Poveda A Lifestyle and precision diabetes medicine will ge-
nomics help optimise the prediction prevention and treatment of type 2 diabetesthrough lifestyle therapy Diabetologia 201760784ndash79243 Zeevi D Korem T Zmora N et al Personalized nutrition by prediction of
glycemic responses Cell 20151631079ndash109444 Swanson A Big pharmaceutical companies are spending far more onmarketing than research The Washington Post 11 February 2015 Available fromhttpswwwwashingtonpostcomnewswonkwp20150211big-pharmaceutical-companies-are-spending-far-more-on-marketing-than-researchnoredirect=onamputm_term=787d031b9b7c Accessed 20 February 201845 Unilever Advertising amp Marketing Unilever 2018 Available from httpwwwunilevercomsustainable-livingwhat-matters-to-youadvertising-and-marketinghtml Accessed 31 March 201846 OrsquoDowd A Spending on junk food advertising is nearly 30 times whatgovernment spends on promoting healthy eating BMJ 2017359j467747 National Institutes of Health Molecular Transducers of Physical Activity in
Humans Frequently Asked Questions (FAQs) [Internet] 2018 Available from httpscommonfundnihgovMolecularTransducersFAQs Accessed 20 March 201848 Livingstone KM Celis-Morales C Navas-Carretero S et al Food4Me Study
Effect of an Internet-based personalized nutrition randomized trial on dietarychanges associated with the Mediterranean diet the Food4Me Study Am J ClinNutr 2016104288ndash29749 Celis-Morales C Livingstone KM Marsaux CF et al Food4Me Study Effectof personalized nutrition on health-related behaviour change evidence from theFood4Me European randomized controlled trial Int J Epidemiol 201746578ndash
588
50 Celis-Morales C Livingstone KM Marsaux CF et al Design and baselinecharacteristics of the Food4Me study a web-based randomised controlled trial ofpersonalised nutrition in seven European countries Genes Nutr 20151045051 BIS Research Global precision medicine market - analysis and forecast2017-2026 2017 Available from httpsbisresearchcomindustry-reportglobal-precision-medicine-market-2026html Accessed 31 January 201852 Stern AD Alexander BM Chandra A How economics can shape precisionmedicines Science 20173551131ndash113353 Smith RJ Nathan DM Arslanian SA Groop L Rizza RA Rotter JI In-dividualizing therapies in type 2 diabetes mellitus based on patient characteristicswhat we know and what we need to know J Clin Endocrinol Metab 2010951566ndash157454 Greeley SA John PM Winn AN et al The cost-effectiveness of personalizedgenetic medicine the case of genetic testing in neonatal diabetes Diabetes Care201134622ndash62755 Nguyen HV Finkelstein EA Mital S Gardner DS Incremental cost-effectivenessof algorithm-driven genetic testing versus no testing for maturity onset diabetes of theyoung (MODY) in Singapore J Med Genet 201754747ndash75356 Naylor RN John PM Winn AN et al Cost-effectiveness of MODY genetictesting translating genomic advances into practical health applications DiabetesCare 201437202ndash20957 Gloyn AL Pearson ER Antcliff JF et al Activating mutations in the geneencoding the ATP-sensitive potassium-channel subunit Kir62 and permanentneonatal diabetes N Engl J Med 20043501838ndash184958 Pearson ER Flechtner I Njoslashlstad PR et al Neonatal Diabetes InternationalCollaborative Group Switching from insulin to oral sulfonylureas in patients withdiabetes due to Kir62 mutations N Engl J Med 2006355467ndash47759 UK Prospective Diabetes Study (UKPDS) UK Prospective Diabetes Study(UKPDS) VIII Study design progress and performance Diabetologia 199134877ndash89060 Kesselheim AS Tan YT Avorn J The roles of academia rare diseases andrepurposing in the development of the most transformative drugs Health Aff(Millwood) 201534286ndash29361 Nelson MR Tipney H Painter JL et al The support of human geneticevidence for approved drug indications Nat Genet 201547856ndash86062 FNIH Evidentiary Criteria Writing Group Framework for Defining EvidentiaryCriteria for Biomarker Qualification Bethesda MD National Library of Medicine201663 Tabaacutek AG Herder C Rathmann W Brunner EJ Kivimaumlki M Prediabetesa high-risk state for diabetes development Lancet 20123792279ndash229064 Nicol D Bubela T Chalmers D et al Precision medicine drowning ina regulatory soup J Law Biosci 20163281ndash30365 Kaye J Whitley EA Lund D Morrison M Teare H Melham K Dynamicconsent a patient interface for twenty-first century research networks Eur J HumGenet 201523141ndash14666 Kinkorovaacute J Biobanks in the era of personalized medicine objectiveschallenges and innovation Overview EPMA J 201674
1922 Precision Medicine in Type 2 Diabetes Diabetes Volume 67 October 2018
Trust (098381 203141 106130) and by the National Institutes of Health (U01-DK105535) MIM serves on advisory panels for Pfizer Novo Nordisk and ZoeGlobal has received honoraria from Pfizer Novo Nordisk and Eli Lilly hasstock options in Zoe Global and has received research funding from AbbVieAstraZeneca Boehringer Ingelheim Eli Lilly Janssen Merck Novo Nordisk PfizerRoche Sanofi Servier and Takeda JCF is a Massachusetts General HospitalResearch Scholar and is supported by National Institutes of Health National Instituteof Diabetes and Digestive and Kidney Diseases grants R01 DK072041 U01DK105554 R01 DK105154 and K24 DK110550 and National Institute of GeneralMedical Sciences grant R01 GM117163 MIM JCF and PWF are membersof the American Diabetes Association Precision Medicine in Diabetes TaskforceThe views expressed in this article are those of the authors and do not necessarilyreflect those of the American Diabetes Association or the taskforce No otherpotential conflicts of interest relevant to this article were reported
References1 Centers for Disease Control and Prevention National Diabetes StatisticsReport Estimates of Diabetes and Its Burden in the United States 2014 AtlantaGA US Department of Health and Human Services 20142 Knowler WC Fowler SE Hamman RF et al Diabetes Prevention ProgramResearch Group 10-year follow-up of diabetes incidence and weight loss in theDiabetes Prevention Program Outcomes Study Lancet 20093741677ndash16863 Li G Zhang P Wang J et al The long-term effect of lifestyle interventions toprevent diabetes in the China Da Qing Diabetes Prevention Study a 20-yearfollow-up study Lancet 20083711783ndash17894 Tuomilehto J Lindstroumlm J Eriksson JG et al Finnish Diabetes PreventionStudy Group Prevention of type 2 diabetes mellitus by changes in lifestyleamong subjects with impaired glucose tolerance N Engl J Med 20013441343ndash13505 Ramachandran A Snehalatha C Mary S Mukesh B Bhaskar AD Vijay VIndian Diabetes Prevention Programme (IDPP) The Indian Diabetes PreventionProgramme shows that lifestyle modification and metformin prevent type 2 di-abetes in Asian Indian subjects with impaired glucose tolerance (IDPP-1) Dia-betologia 200649289ndash2976 Lean ME Leslie WS Barnes AC et al Primary care-led weight managementfor remission of type 2 diabetes (DiRECT) an open-label cluster-randomised trialLancet 2018391541ndash5517 Polyzogopoulou EV Kalfarentzos F Vagenakis AG Alexandrides TK Res-toration of euglycemia and normal acute insulin response to glucose in obesesubjects with type 2 diabetes following bariatric surgery Diabetes 2003521098ndash11038 Franco M Orduntildeez P Caballero B et al Impact of energy intake physicalactivity and population-wide weight loss on cardiovascular disease and diabetesmortality in Cuba 1980-2005 Am J Epidemiol 20071661374ndash13809 Cho NH Shaw JE Karuranga S et al IDF Diabetes Atlas global estimates ofdiabetes prevalence for 2017 and projections for 2045 Diabetes Res Clin Pract2018138271ndash28110 Lander ES Linton LM Birren B et al International Human Genome Se-quencing Consortium Initial sequencing and analysis of the human genome[published correction appears in Nature 2001411720] Nature 2001409860ndash92111 Visscher PM Wray NR Zhang Q et al 10 Years of GWAS discovery biologyfunction and translation Am J Hum Genet 20171015ndash2212 Merino J Udler MS Leong A Meigs JB A decade of genetic and metab-olomic contributions to type 2 diabetes risk prediction Curr Diab Rep 20171713513 Knowles JW Ashley EA Cardiovascular disease the rise of the genetic riskscore PLoS Med 201815e100254614 Mahajan A Taliun D Thurner M et al Fine-mapping of an expanded set oftype 2 diabetes loci to single-variant resolution using high-density imputation andislet-specific epigenome maps [preprint article online] 2018 Available fromhttpswwwbiorxivorgcontentearly20180109245506 Accessed 30 January2018
15 McCarthy MI Painting a new picture of personalised medicine for diabetesDiabetologia 201760793ndash79916 Scott RA Scott LJ Maumlgi R et al DIAbetes Genetics Replication AndMeta-analysis (DIAGRAM) Consortium An expanded genome-wide asso-ciation study of type 2 diabetes in Europeans Diabetes 2017662888ndash290217 Mahajan A Wessel J Willems SM et al ExomeBP Consortium MAGICConsortium GIANT Consortium Refining the accuracy of validated target iden-tification through coding variant fine-mapping in type 2 diabetes Nat Genet 201850559ndash57118 Udler MS Kim J von Grotthuss M et al Clustering of type 2 diabetes geneticloci by multi-trait associations identifies disease mechanisms and subtypes[preprint article online] 2018 Available from httpswwwbiorxivorgcontentearly20180510319509 Accessed 30 March 201819 van Zuydam NR Ahlqvist E Sandholm N et al A genome-wide associationstudy of diabetic kidney disease in subjects with type 2 diabetes Diabetes 2018671414ndash142720 Ahlqvist E Storm P Kaumlraumljaumlmaumlki A et al Novel subgroups of adult-onsetdiabetes and their association with outcomes a data-driven cluster analysis of sixvariables Lancet Diabetes Endocrinol 20186361ndash36921 Fuchsberger C Flannick J Teslovich TM et al The genetic architecture oftype 2 diabetes Nature 201653641ndash4722 Koivula RW Heggie A Barnett A et al DIRECT Consortium Discovery ofbiomarkers for glycaemic deterioration before and after the onset of type 2 di-abetes rationale and design of the epidemiological studies within the IMI DIRECTConsortium Diabetologia 2014571132ndash114223 Rask-Andersen M Karlsson T Ek WE Johansson Aring Gene-environmentinteraction study for BMI reveals interactions between genetic factors and physicalactivity alcohol consumption and socioeconomic status PLoS Genet 201713e100697724 Celis-Morales CA Lyall DM Gray SR et al Dietary fat and total energy intakemodifies the association of genetic profile risk score on obesity evidence from48 170 UK Biobank participants Int J Obes 2017411761ndash176825 Tyrrell J Wood AR Ames RM et al Gene-obesogenic environment inter-actions in the UK Biobank study Int J Epidemiol 201746559ndash57526 Young AI Wauthier F Donnelly P Multiple novel gene-by-environment in-teractions modify the effect of FTO variants on body mass index Nat Commun201671272427 Reardon S Giant study poses DNA data-sharing dilemma Nature 201552516ndash1728 Hudson K Lifton R Patrick-Lake B The Precision Medicine Initiative CohortProgram ndash Building a Research Foundation for 21st Century Medicine BethesdaMD National Institutes of Health 201529 Gaziano JM Concato J Brophy M et al Million Veteran Program a mega-biobank to study genetic influences on health and disease J Clin Epidemiol 201670214ndash22330 Bergen SE Sullivan PF National-scale precision medicine for psychiatricdisorders in Sweden Am J Med Genet B Neuropsychiatr Genet 7 July 2017 [Epubahead of print] DOI 101002ajmgb3256231 Cyranoski D Chinarsquos bid to be a DNA superpower Nature 2016534462ndash46332 Cyranoski D China embraces precision medicine on a massive scale Nature20165299ndash1033 Bu D Peng S Luo H et al Precision medicine and cancer immunology inChina From big data to knowledge in precision medicine Science 2018 (Suppl)S35ndashS3834 Moltke I Grarup N Joslashrgensen ME et al A common Greenlandic TBC1D4variant confers muscle insulin resistance and type 2 diabetes Nature 2014512190ndash19335 Dash S Sano H Rochford JJ et al A truncation mutation in TBC1D4 ina family with acanthosis nigricans and postprandial hyperinsulinemia Proc NatlAcad Sci U S A 20091069350ndash9355
diabetesdiabetesjournalsorg Fitipaldi and Associates 1921
36 Estrada K Aukrust I Bjoslashrkhaug L et al SIGMA Type 2 Diabetes ConsortiumAssociation of a low-frequency variant in HNF1A with type 2 diabetes in a Latinopopulation [published correction appears in JAMA 20143121932] JAMA 2014
3112305ndash231437 Marshall SM 60 years of metformin use a glance at the past and a look tothe future Diabetologia 2017601561ndash156538 Pearson ER Pharmacogenetics and target identification in diabetes CurrOpin Genet Dev 20185068ndash7339 Zhou K Bellenguez C Spencer CC et al GoDARTS and UKPDS Diabetes
Pharmacogenetics Study Group Wellcome Trust Case Control Consortium 2MAGIC investigators Common variants near ATM are associated with glycemicresponse to metformin in type 2 diabetes Nat Genet 201143117ndash12040 Zhou K Yee SW Seiser EL et al MetGen Investigators DPP InvestigatorsACCORD Investigators Variation in the glucose transporter gene SLC2A2 isassociated with glycemic response to metformin Nat Genet 2016481055ndash
105941 National Research Council Committee on a Framework for Developing a NewTaxonomy of Disease Toward Precision Medicine Building a Knowledge Networkfor Bomedical Research and a New Taxonomy of Disease Washington DCNational Academies Press 201142 Franks PW Poveda A Lifestyle and precision diabetes medicine will ge-
nomics help optimise the prediction prevention and treatment of type 2 diabetesthrough lifestyle therapy Diabetologia 201760784ndash79243 Zeevi D Korem T Zmora N et al Personalized nutrition by prediction of
glycemic responses Cell 20151631079ndash109444 Swanson A Big pharmaceutical companies are spending far more onmarketing than research The Washington Post 11 February 2015 Available fromhttpswwwwashingtonpostcomnewswonkwp20150211big-pharmaceutical-companies-are-spending-far-more-on-marketing-than-researchnoredirect=onamputm_term=787d031b9b7c Accessed 20 February 201845 Unilever Advertising amp Marketing Unilever 2018 Available from httpwwwunilevercomsustainable-livingwhat-matters-to-youadvertising-and-marketinghtml Accessed 31 March 201846 OrsquoDowd A Spending on junk food advertising is nearly 30 times whatgovernment spends on promoting healthy eating BMJ 2017359j467747 National Institutes of Health Molecular Transducers of Physical Activity in
Humans Frequently Asked Questions (FAQs) [Internet] 2018 Available from httpscommonfundnihgovMolecularTransducersFAQs Accessed 20 March 201848 Livingstone KM Celis-Morales C Navas-Carretero S et al Food4Me Study
Effect of an Internet-based personalized nutrition randomized trial on dietarychanges associated with the Mediterranean diet the Food4Me Study Am J ClinNutr 2016104288ndash29749 Celis-Morales C Livingstone KM Marsaux CF et al Food4Me Study Effectof personalized nutrition on health-related behaviour change evidence from theFood4Me European randomized controlled trial Int J Epidemiol 201746578ndash
588
50 Celis-Morales C Livingstone KM Marsaux CF et al Design and baselinecharacteristics of the Food4Me study a web-based randomised controlled trial ofpersonalised nutrition in seven European countries Genes Nutr 20151045051 BIS Research Global precision medicine market - analysis and forecast2017-2026 2017 Available from httpsbisresearchcomindustry-reportglobal-precision-medicine-market-2026html Accessed 31 January 201852 Stern AD Alexander BM Chandra A How economics can shape precisionmedicines Science 20173551131ndash113353 Smith RJ Nathan DM Arslanian SA Groop L Rizza RA Rotter JI In-dividualizing therapies in type 2 diabetes mellitus based on patient characteristicswhat we know and what we need to know J Clin Endocrinol Metab 2010951566ndash157454 Greeley SA John PM Winn AN et al The cost-effectiveness of personalizedgenetic medicine the case of genetic testing in neonatal diabetes Diabetes Care201134622ndash62755 Nguyen HV Finkelstein EA Mital S Gardner DS Incremental cost-effectivenessof algorithm-driven genetic testing versus no testing for maturity onset diabetes of theyoung (MODY) in Singapore J Med Genet 201754747ndash75356 Naylor RN John PM Winn AN et al Cost-effectiveness of MODY genetictesting translating genomic advances into practical health applications DiabetesCare 201437202ndash20957 Gloyn AL Pearson ER Antcliff JF et al Activating mutations in the geneencoding the ATP-sensitive potassium-channel subunit Kir62 and permanentneonatal diabetes N Engl J Med 20043501838ndash184958 Pearson ER Flechtner I Njoslashlstad PR et al Neonatal Diabetes InternationalCollaborative Group Switching from insulin to oral sulfonylureas in patients withdiabetes due to Kir62 mutations N Engl J Med 2006355467ndash47759 UK Prospective Diabetes Study (UKPDS) UK Prospective Diabetes Study(UKPDS) VIII Study design progress and performance Diabetologia 199134877ndash89060 Kesselheim AS Tan YT Avorn J The roles of academia rare diseases andrepurposing in the development of the most transformative drugs Health Aff(Millwood) 201534286ndash29361 Nelson MR Tipney H Painter JL et al The support of human geneticevidence for approved drug indications Nat Genet 201547856ndash86062 FNIH Evidentiary Criteria Writing Group Framework for Defining EvidentiaryCriteria for Biomarker Qualification Bethesda MD National Library of Medicine201663 Tabaacutek AG Herder C Rathmann W Brunner EJ Kivimaumlki M Prediabetesa high-risk state for diabetes development Lancet 20123792279ndash229064 Nicol D Bubela T Chalmers D et al Precision medicine drowning ina regulatory soup J Law Biosci 20163281ndash30365 Kaye J Whitley EA Lund D Morrison M Teare H Melham K Dynamicconsent a patient interface for twenty-first century research networks Eur J HumGenet 201523141ndash14666 Kinkorovaacute J Biobanks in the era of personalized medicine objectiveschallenges and innovation Overview EPMA J 201674
1922 Precision Medicine in Type 2 Diabetes Diabetes Volume 67 October 2018
36 Estrada K Aukrust I Bjoslashrkhaug L et al SIGMA Type 2 Diabetes ConsortiumAssociation of a low-frequency variant in HNF1A with type 2 diabetes in a Latinopopulation [published correction appears in JAMA 20143121932] JAMA 2014
3112305ndash231437 Marshall SM 60 years of metformin use a glance at the past and a look tothe future Diabetologia 2017601561ndash156538 Pearson ER Pharmacogenetics and target identification in diabetes CurrOpin Genet Dev 20185068ndash7339 Zhou K Bellenguez C Spencer CC et al GoDARTS and UKPDS Diabetes
Pharmacogenetics Study Group Wellcome Trust Case Control Consortium 2MAGIC investigators Common variants near ATM are associated with glycemicresponse to metformin in type 2 diabetes Nat Genet 201143117ndash12040 Zhou K Yee SW Seiser EL et al MetGen Investigators DPP InvestigatorsACCORD Investigators Variation in the glucose transporter gene SLC2A2 isassociated with glycemic response to metformin Nat Genet 2016481055ndash
105941 National Research Council Committee on a Framework for Developing a NewTaxonomy of Disease Toward Precision Medicine Building a Knowledge Networkfor Bomedical Research and a New Taxonomy of Disease Washington DCNational Academies Press 201142 Franks PW Poveda A Lifestyle and precision diabetes medicine will ge-
nomics help optimise the prediction prevention and treatment of type 2 diabetesthrough lifestyle therapy Diabetologia 201760784ndash79243 Zeevi D Korem T Zmora N et al Personalized nutrition by prediction of
glycemic responses Cell 20151631079ndash109444 Swanson A Big pharmaceutical companies are spending far more onmarketing than research The Washington Post 11 February 2015 Available fromhttpswwwwashingtonpostcomnewswonkwp20150211big-pharmaceutical-companies-are-spending-far-more-on-marketing-than-researchnoredirect=onamputm_term=787d031b9b7c Accessed 20 February 201845 Unilever Advertising amp Marketing Unilever 2018 Available from httpwwwunilevercomsustainable-livingwhat-matters-to-youadvertising-and-marketinghtml Accessed 31 March 201846 OrsquoDowd A Spending on junk food advertising is nearly 30 times whatgovernment spends on promoting healthy eating BMJ 2017359j467747 National Institutes of Health Molecular Transducers of Physical Activity in
Humans Frequently Asked Questions (FAQs) [Internet] 2018 Available from httpscommonfundnihgovMolecularTransducersFAQs Accessed 20 March 201848 Livingstone KM Celis-Morales C Navas-Carretero S et al Food4Me Study
Effect of an Internet-based personalized nutrition randomized trial on dietarychanges associated with the Mediterranean diet the Food4Me Study Am J ClinNutr 2016104288ndash29749 Celis-Morales C Livingstone KM Marsaux CF et al Food4Me Study Effectof personalized nutrition on health-related behaviour change evidence from theFood4Me European randomized controlled trial Int J Epidemiol 201746578ndash
588
50 Celis-Morales C Livingstone KM Marsaux CF et al Design and baselinecharacteristics of the Food4Me study a web-based randomised controlled trial ofpersonalised nutrition in seven European countries Genes Nutr 20151045051 BIS Research Global precision medicine market - analysis and forecast2017-2026 2017 Available from httpsbisresearchcomindustry-reportglobal-precision-medicine-market-2026html Accessed 31 January 201852 Stern AD Alexander BM Chandra A How economics can shape precisionmedicines Science 20173551131ndash113353 Smith RJ Nathan DM Arslanian SA Groop L Rizza RA Rotter JI In-dividualizing therapies in type 2 diabetes mellitus based on patient characteristicswhat we know and what we need to know J Clin Endocrinol Metab 2010951566ndash157454 Greeley SA John PM Winn AN et al The cost-effectiveness of personalizedgenetic medicine the case of genetic testing in neonatal diabetes Diabetes Care201134622ndash62755 Nguyen HV Finkelstein EA Mital S Gardner DS Incremental cost-effectivenessof algorithm-driven genetic testing versus no testing for maturity onset diabetes of theyoung (MODY) in Singapore J Med Genet 201754747ndash75356 Naylor RN John PM Winn AN et al Cost-effectiveness of MODY genetictesting translating genomic advances into practical health applications DiabetesCare 201437202ndash20957 Gloyn AL Pearson ER Antcliff JF et al Activating mutations in the geneencoding the ATP-sensitive potassium-channel subunit Kir62 and permanentneonatal diabetes N Engl J Med 20043501838ndash184958 Pearson ER Flechtner I Njoslashlstad PR et al Neonatal Diabetes InternationalCollaborative Group Switching from insulin to oral sulfonylureas in patients withdiabetes due to Kir62 mutations N Engl J Med 2006355467ndash47759 UK Prospective Diabetes Study (UKPDS) UK Prospective Diabetes Study(UKPDS) VIII Study design progress and performance Diabetologia 199134877ndash89060 Kesselheim AS Tan YT Avorn J The roles of academia rare diseases andrepurposing in the development of the most transformative drugs Health Aff(Millwood) 201534286ndash29361 Nelson MR Tipney H Painter JL et al The support of human geneticevidence for approved drug indications Nat Genet 201547856ndash86062 FNIH Evidentiary Criteria Writing Group Framework for Defining EvidentiaryCriteria for Biomarker Qualification Bethesda MD National Library of Medicine201663 Tabaacutek AG Herder C Rathmann W Brunner EJ Kivimaumlki M Prediabetesa high-risk state for diabetes development Lancet 20123792279ndash229064 Nicol D Bubela T Chalmers D et al Precision medicine drowning ina regulatory soup J Law Biosci 20163281ndash30365 Kaye J Whitley EA Lund D Morrison M Teare H Melham K Dynamicconsent a patient interface for twenty-first century research networks Eur J HumGenet 201523141ndash14666 Kinkorovaacute J Biobanks in the era of personalized medicine objectiveschallenges and innovation Overview EPMA J 201674
1922 Precision Medicine in Type 2 Diabetes Diabetes Volume 67 October 2018
