The use of new omic technologies to understand the impact ... · Bone turnover markers - Blood,...
Transcript of The use of new omic technologies to understand the impact ... · Bone turnover markers - Blood,...
Paolo VineisImperial College London
And HuGeF Foundation Torino
The use of new omic technologies tounderstand the impact of socio-economic differentials and the
environment on ageing
25 November 2015
Socio-economic status and biomarkers
Hypothalamic-pituitary-adrenal axis Cortisol - Saliva, urineDehydroepiandrosterone sulfate - Blood
Sympathetic neuro-hormonal system Norepinephrine/Epinephrine - UrineAlpha-amylase - Saliva
Parasympathetic neuro-hormonal system Heart rate variability - Pulse rate recordingInflammatory/Immune system C-reactive protein- Blood
Erythrocyte sedimentation rate- BloodInterleukins- BloodLymphocyte number and function- BloodCirculating serum albumin - Blood, saliva
Cardiovascular Diastolic/systolic blood pressureResting heart rate
Glucose metabolism Fasting glucose- BloodGlycosylated hemoglobin- BloodFasting insulin- Blood
Lipid metabolism Cholesterol and lipoprotein fractions - BloodBMI, waist to hip ratioTotal body fat - DXA scan
Hematological Serum hemoglobin- BloodClotting factors and clotting time - Blood
Renal Creatinine - Serum or 24h urineUrine albumin leakage - UrineCystatin C - Serum or dried blood spot
Hepatic Circulating serum albumin - Blood, salivaReproductive Serum testosterone/estradiol- Blood
Follicle-stimulating hormone - BloodPulmonary Arterial oxygen saturation - Pulse oximeter
Peak expiratory flow - SpirometerBone Bone density - DXA scan
Bone turnover markers - Blood, fasting urineMuscle Skeletal muscle mass - DXA scan, body impedance
Grip strength - Dynamometer
Source: Wolfe B, Evans W,Seeman T. The biologicalconsequences of healthinequalities (2012). RusselSage Foundation, New York
SES and immune system biomarkers
Alley et al. Socioeconomic status and C-reactive protein levels in the US population: NHANES IV. Brain Behav Immun. 2006Sep;20(5):498-504
NHANES IV
Epigenetics – DNA methylation
Epigenetic modifications
Functionally relevant modifications to the genome that do notinvolve a change in the nucleotide sequence. Examples of suchmodifications are DNA methylation and histone modification,both of which serve to regulate gene expression without alteringthe underlying DNA sequence.
Gene expression
Phenotype
Dominance rank and expression level of pro-inflammatory genes (macaques)
Tung et al. Social environment is associated with gene regulatory variation in the rhesus macaque immune system.Proc Natl Acad Sci U S A. 2012 Apr 24;109(17):6490-5.
SES and DNA methylation – EPIC Turin
• Selection of candidate genes based on literaturereview: NR3C1, IL1A, CCL2, CXCL2, CCL20,GPR132, ADM, OLR1, CREBZF, TNFRSF11A, PTGS2,CXCR2, NFATC1, SAT2, MTHFR, AHRR, IGF2
• A total of 599 CpG sites were examined.
• Several indicators of socioeconomic status acrossthe lifecourse
• Adjustment for potential confounding fromlifestyle factors
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Mean methylation difference (low vs high SES)
Lifecourse SES trajectories
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Mean methylation difference (low vs high SES)
Father's occupational position
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Mean methylation difference (low vs high SES)
Household's highest occupational position
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Indicators of socioeconomic statusare associated with DNAmethylation of candidate genes.The graphs represent the plot ofbeta coefficients and p-values fromlinear regression of CpG sites onsocioeconomic indicators,adjusted for age, sex, season ofblood collection and diseasestatus. The red line represents thecorrected overall critical p-valueafter a multiple-test procedure(FDR). Data points on or above thered line correspond to rejected nullhypotheses (p-values thatremained significant after multiple-testing). For household’s highestoccupational position (B)26 datapoints are above the red line; forlifecourse socioeconomictrajectory (C), 7 data points.
Stringhini et al, InternationalJournal of Epidemiology 2015
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Lifecourse SES trajectory
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Preliminary evidence: application of Horvath model of ageing(biological clock based on methylation) to EPIC-Italy data.
A «socio-molecular» study fromexisting cohorts:
LIFEPATH
Section Title
Enter text here
www.environment-health.ac.uk
Participant organisation name Country
Imperial College London - P Vineis (Coordinator), M Ezzati, P Elliott, M Chadeau-Hyam, ACVergnaud
UK
University College London - M Kivimaki, M Marmot UK
Lausanne University - S Stringhini, M Bochud Switzerland
INSERM Toulouse - M Kelly, T Lang, C Delpierre France
Erasmus University, Rotterdam - J Mackenbach Netherlands
London School of Economics - M Avendano-Pabon UK
Columbia University, New York - S Galea, P Muennig USA
Finnish Institute of Occupational Health, Helsinki - H Alenius, D Greco Finland
HuGeF Foundation, Torino - GL Severi, S Polidoro Italy
INSERM Paris - M Goldberg, F Clavel France
Porto University - H Barros Portugal
Cancer Council Victoria - G Giles Australia
ESRI, Dublin - R Layte Ireland
University of Torino - G Costa, A D’Errico Italy
Zadig (SME) - R Satolli, L Carra Italy
We use the revised Strachan-Sheikh (2004) model of life-course functioning (Kuh D 2007;Blane et al, 2013), to describe ageing across the life-course. This model presents ageing
as a phenomenon with two broad stages across life: build-up & decline.
Objectives:
To show that healthy ageing is an achievable goal for society, as itis already experienced by individuals of high socio-economicstatus (SES).
To improve the understanding of the mechanisms through whichhealthy ageing pathways diverge by SES, by investigating life-course biological pathways using omic technologies.
To examine the consequences of the current economic recessionon health and the biology of ageing (and the consequent increasein social inequalities).
To provide updated, relevant and innovative evidence for healthyageing policies (particularly “health in all policies”)
These objectives will be accomplished by using different data sources:
1. Europe-wide and national surveys (updated to 2010), including EU-27.
2. Longitudinal cohorts (across Europe) with intense phenotyping andrepeat biological samples (total population >33,000).
3. Other large cohorts with biological samples (total population >202,000and a large registry dataset with over a million individuals with very richinformation on work trajectories and health.
4. A randomized experiment on conditional cash transfer for povertyreduction in New York City.
Data will be harmonized and integrated to conceptualize healthy ageing as acomposite outcome at different stages of life, resulting from life-courseenvironmental, behavioural and social determinants.
Early life Geography Available markers
Young Finns North (Finland) 2,300 IM
Generacao 21 South (Portugal) 4,500 IM
EPITEEN South (Portugal) 2,900 IM
Late life
Whitehall II North (UK) 6,600 IM, 10,000 metabolomics
TILDA North (Ireland) 5,800 IM
Airwave North (UK) 35,000 IM, 3,000 metabolomics
Skipogh Centre (Switzerland) 250 methylome and transcriptome, 1,100 IM
Colaus Centre (Switzerland) 6,300 IM
EPIC Italy South (Italy) Methylome>1,000
E3N South (France) Metabolome 1,600
Constances South (France) 35,000 IM
EPIPORTO South (Portugal) 2,500 IM
MCCS Australia Methylome 3,000, IM 500
Markers already measured or whose measurement is funded/on-going, bygeographical location of the cohorts and life stage. IM=inflammation markers.
A new paradigm for the study of environmental causes ofdisease: the EXPOSOME
Relationships between macro-environment and micro-environment
S.M. Rappaport and M.T. Smith, Science, 2010: 330, 460-461
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IARC: Exposome-Explorer
PBDEs
PAHs
PCDDs
PCBs
PCDFs
FATTY ACIDS
CAROTENOIDS
POLYPHENOLS
Pesticides
350 environmental
pollutants
147 dietarycompounds
- Chemistry- Cohorts where measured- Biospecimens- Analytical methods- Concentrations- State of validation- Correlations with exposures- Confunding factors- Available on-line- Linked to other databases
All biomarkers- 497 biomarkers- 10,480 concentration values
Biomarkers for environmentalpollutants- 350 biomarkers- 7,342 concentration values- 265 publications analyzed
365 concentrationvalues
Thank you