The Obesity Paradox in Cancer: Current Knowledge and ......Apr 10, 2018 · The obesity paradox...
Transcript of The Obesity Paradox in Cancer: Current Knowledge and ......Apr 10, 2018 · The obesity paradox...
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The Obesity Paradox in Cancer: Current Knowledge and Current Gaps
Kaiser Permanente, Division of Research Oakland, California, USA
Bette Caan, DrPH
Tuesday, April 10, 2018
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Cohort Studies Demonstrating Poorer Survival with Weight loss In Non Metastatic Breast Cancer
Total Mortality
%weight change n = 1,436 (292 events)
HR (95%CI)
Post-diagnosis weight change
>5% loss 5.29 (3.48–8.09)
±5% maintain 1.00
5–10% gain 1.09 (0.51–2.18)
>10% gain 2.67 (1.37–5.05)
Weight change (kg)
Total mortality
No. of subjects
No. of event HR 95% CI
Pre-diagnosis to 18 monthspost-diagnosis
Kg n=291
<−1 93 2.41 1.62, 3.58
−1 ~ 1 35 1.00
1 ~ 5 94 1.89 1.27, 2.82
≥5 69 1.71 1.12, 2.60
Chen X. et al. Breast Cancer Res Treat. 2010 Aug; 122(3): 823–833.
Bradshaw P. et al.. Epidemiology. 2012 Mar; 23(2): 320–327.
Caan B et al.. Cancer Causes Control. 2008 Dec;19(10):1319-28.
% Weight change
Total Mortality n = 1,689 (160 events)# Events HR 95% CI
pre-diagnosis to study entry
Weight loss
Loss 5–10% 15 1.1 (0.6, 1.9)
Loss ≥ 10% 23 2.1 (1.3, 3.4)
Weight stable
Within 5% Referent
Weight gain
Gain 5–10% 32 1.2 (0.8, 1.9)Gain ≥ 10% 20 0.7 (0.4, 1.2)
Test for trend p = 0.08
Bradshaw, 2012, Long Island BCChen, 2010, Shanghai BCCaan, 2008, LACE
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Risk of reduced survival is a function of body mass index (BMI) and percent weight loss: lessons from the cachexia world
Martin et al. J Clin Oncol. 2014 Nov 24.
BSurvival, Months Diagnostic
Criteria: Cancer weight loss
Locally advanced or metastatic tumors
A
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Recent literature on the Obesity Paradox in Cancer
2016
2018
2017
2017
2014
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No Standard Definition of the Obesity Paradox
Overweight/obesity is related to increased incident disease. Once people get the disease overweight / obesity is related to better survival.
.
The obesity paradox occurs where the risk of mortality is significantly reduced for BMI values above the referent (normal BMI category), where an increased risk is expected. At very high BMI values, risk either returns to unity or is increased.
As cancer incidence linearly increases with each BMI category above normal, if the BMI relationship with survival doesn’t (can be flat or decreased) , then that is called a paradox. Sometimes referred to as the BMI paradox or the overweight paradox.
Simple
More refined
Most broad
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Cancers where some paradoxical relationship with BMI has been
demonstrated
ColorectalProstate
Thyroid
Leukemia
Lymphoma
Gastric Renal
LungMelanoma
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Possible Explanations : The Obesity Paradox
Associations are false and reflect methodological issues; reverse causation, confounding , collider/selection bias?
BMI has significant measurement error with regard to adiposity
Muscle, which increases with adiposity, may play an important protective role
Associations are true and plausible; excess energy reserves are important in cancer survivors
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Current Questions
What is the relationship of BMI and cancer survival? It depends and varies by stage, sex, cancer, treatment and overweight/obesity level
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Jonathan M Kocarnik, Andrew T Chan, Martha L Slattery, John D Potter, et. al. Relationship of pre-diagnostic body mass index with survival after colorectal cancer: Stage-specific associations. Int J Cancer. 2016 September 1; 139(5): 1065–1072. doi:10.1002/ijc.30163.
BMI and Survival Varies by Stage : Hazard Ratios for Colorectal Cancer-Specific Mortality,
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BMI at diagnosis and ovarian cancer-specific mortality varies by stage (KP-ROCS study, 2000–2014)
British Journal of Cancer (2017) 117, 282–289 | doi: 10.1038/bjc.2017.162
BMI at diagnosis and ovarian cancer-specific mortality by stage (KP-ROCS study, 2000–2014)
HR’s - overweight and obesity Class I never elevated (mostly HR’s < 1.00)
BMI > 35kg/m2 elevated in stage I, II; null in Stage III; protective in Stage IV
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COLORECTAL:
1 2 3 4 5
Follow-Up (years)
Sur
viva
l Pro
bab
p = .1
NHL: CHOP
1 2 3 4 5
Follow-Up (years)
p = .47
RENAL: a-IFN
1 2 3 4 5
Follow-Up (years)
p = .9
AML: ara-C/DNR
1 2 3 4 5
Follow-Up (years)
p = .5
NSCLC: Cisplati
1 2 3 4 5
Follow-Up (years)
p = .6
BREAST: CAFx6
1 2 3 4 5
Follow-Up (years)
Sur
viva
l Pro
babi
lity
p = .29
OVARIAN: Pacl
1 2 3 4 5
Follow-Up (years)
p = .38
BREAST: AC + P
1 2 3 4 5
Follow-Up (years)
p = .16
BREAST: CMFx
1 2 3 4 5
Follow-Up (years)
p = .42
Kaplan Meier survival curves, by BMI, varies by cancer & treatment : Either Protective or No association
22 SWOG trials, 14 cancer/treatment combinations
Greenlee, Unger, et al., CEBP January 2017
BLADDER: BCG
1 2 3 4 5
Follow-Up (years)
Sur
viva
l Pro
babi
lity
p = .02
SARCOMA-GIST
1 2 3 4 5
Follow-Up (years)
p = .006
NSCLC: Carbop
1 2 3 4 5
Follow-Up (years)
p = .0
PROSTATE: AD
1 2 3 4 5
Follow-Up (years)
p = .01
PROSTATE: Doc
1 2 3 4 5
Follow-Up (years)
p = .1
ty
BMI ≥25 kg/m2
at time of diagnosis
Protective
BMI <25 kg/m2
at time of diagnosis
No association
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Greenlee, Unger, et al., CEBP 2017
Men
Association of BMI and Overall Survival, Based on BMI>25 kg/m2 Cutpoint; Differences by Sex
BMI>25 kg/m2 harmfulBMI>25 kg/m2 protective
Women
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In metastatic melanoma obesity is associated with improved OS in patients on targeted and immune therapy but not chemotherapy
Mostly driven by effects in males , not females
Jennifer L. McQuade, Carrie R. Daniel, Kenneth R. Hess, et al. The association of BMI and outcomes in metastatic melanoma: A retrospective, multicohort analysis of patients treated with targeted therapy, immunotherapy, or chemotherapy. Lancet Oncol. 2018 March ; 19(3): 310–322.
All Patients
Males
Females
Therapy BMI>30 BMI<25Events (n)/patients (N)
Average Adjusted HR95% (CI)
0.25 0.50 0.75 1.0 1.5 2.0
Favors BMI>30 Favors BMI<25
Targeted therapy 89/213 165/307Immune therapy 87/171 115/170Chemotherapy 104/129 176/220
Targeted therapyImmune therapyChemotherapy
Targeted therapyImmune therapyChemotherapy
Targeted therapy 52/126 97/149Immune therapy 57/112 72/98Chemotherapy 57/67 93/108
Targeted therapy 50/87 101/158Immune therapy 47/59 52/72Chemotherapy 54/62 89/112
0.60 (0.45-0.79)0.64 (0.47-0.86)1.03 (0.80-1.34)
0.51 (0.34-0.76)0.55 (0.32-0.93)1.18 (0.71-1.97)
0.82 (0.53-1.26)0.90 (0.54-1.50)0.93 (0.64-1.35)
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Kaplan-Meier curves for survival outcome for each body mass index (BMI) category
Breast Cancer Res. 2013 Nov 6;15(6):R105 Obesity and survival in operable breast cancer patients treated with adjuvant anthracyclines and taxanes according to pathological subtypes: a pooled analysis.
Breast cancer survival
Overall survival Recurrence-free survival
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BMI and ALL Cause MortalityCalifornia Breast Cancer Consortium
All-cause mortality
Prediagnosis BMI category No.No. of Events HR 95% CI
Underweight 213 63 1.47 1.14, 1.91
Normal weight 5,332 1,130 1.00 Referent
Overweight 3,401 820 0.98 0.89, 1.07
Obese 1,576 467 1.09 0.97, 1.22
Severely obese 553 162 1.10 0.93, 1.31
Morbidly obese 276 102 1.41 1.14, 1.75
P for trend = 0.04P for linearity = 0.004
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BMI Increases Risk of CRC Mortality Outcomes
Author Years N Outcome Hazard Ratio (95% CI) or P value(compared to normal weight)
Meyerhardt 2003 3,759 OS 1.11 (0.96-1.29) for >30 BMI v 21-25
Meyerhardt 2004 1,688 OS 1.09 (0.90-1.33) for >30 BMI v 21-25
Dignam 2006 4,288 CRCOS
1.27 (1.05-1.53) for >35 BMI v 21-251.28 (1.04-1.57) for >35 BMI v 21-25
Meyerhardt 2008 1,053 CRCOS
1.24 (0.84-1.83) for >35 BMI v 21-250.87 (0.54-1.42) for >35 BMI v 21-25
Sinicrope 2010 4,381 OS 1.19 (0.98-1.45) for >35 BMI v 20-25
Baade 2011 2,561 CRCOS
1.34 (0.70-2.58) for >30 BMI v 18.5-250.78 (0.59-1.03) for >35 BMI v 18.5-25
Chin 2012 2,765 CRCOS
1.06 (0.80-1.41) for >30 BMI v 18.5-250.94 (0.68-1.01) for >30 BMI v 18.5-25
Kuiper 2012 676 CRCOS
0.95 (0.49-1.85) for >30 BMI v 18.5-251.09 (0.65-1.83) for >30 BMI v 18.5-25
Campbell 2013 2,303 CRCOS
1.14 (0.81-1.60) for >30 BMI v 18.5-250.95 (0.75-1.17) for >30 BMI v 18.5-25
Sinicrope 2013 25291 OS 1.11 (1.00-1.23) for >35 BMI v 20-25
Risks don’t increase until
BMI > 35
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“The assumption that ideal weight range is the same for all individuals under all conditions is
biologically challenging”
One size or one weight range does not fit All
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Current Questions
What is the relationship of BMI and cancer survival? It depends and varies by stage, sex, cancer treatment and overweight/obesity levelIs there measurement error in BMI and does examining muscle help, in part to explain the obesity paradox?
YES
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Mourtzakis et al. Appl Physiol Nutr Metab. 2009 Oct;34(5):950-6. Prado et al. Curr Opin Support Palliat Care 2009 Dec;3(4):269-75.
L3
L3
Red – Skeletal muscleGreen – Intermuscular adipose tissueYellow – Visceral adipose tissueTeal – Subcutaneous adipose tissue
Slice-O-Matic software to assess muscle and fat (Hounsfield units)
Computerized tomography: an opportunistic methodusing clinical scans
11
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Association Between Whole Body Tissue Volume (L) and Single Abdominal Surface Area (cm2)
Ross & Janssen. CT and MRI. In: Human Body Composition, 2005:p.89
L4-L5(Image 24)
Image ~46Image 1
AT area 5 cm below L4-L5 (cm2) SM area 5 cm above L4-L5 (cm2)
Shen et al. J Appl Physiol 97:2333-2338.
Adipose Tissue Skeletal MuscleAT V
olum
e (L
)
SM
Vol
ume
(L)
12
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Visceral Adiposity Index: 127 cm2/m2
Is BMI a good measure of adiposity and does it measure all of the important body size variables?
BMI 40.2 kg/m2 BMI 28.1 kg/m2
Body Mass Index30-kg/m2
Skeletal Muscle Index: 69 cm2/m2
Visceral Adiposity Index: 33 cm2/m2
Skeletal Muscle Index: 36 cm2/m2
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BMI Versus Body Composition
Elizabeth M. Cespedes Feliciano, Candyce H. Kroenke, and Bette J. Caan (2018) The Obesity Paradox in Cancer: How Important Is Muscle? Annu. Rev. Nutr. 2018. 38:X–X. (in Press)
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Skeletal Muscle is Important
6
Major cause of whole-body metabolicimpairment, in turn responsible for negative outcomes
Muscle depletion in cancer is not just mechanical function loss
Muscle secrete hundreds of myokine peptides that influence insulin sensitivity, inflammation, immune function, adipose tissue oxidation and whole-body metabolism and regulates anabolism and catabolism
Skeletal muscle is the most abundant tissue in the body (~40% of body mass)
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Mechanisms leading to muscle loss in Cancer
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2009 First paper using CT scans to measure Sarcopenia
Clin Cancer Res. 2009 Nov 15;15(22):6973-9. doi: 10.1158/1078-0432.CCR-09-1525. Epub 20094
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PubMed Search through 2017: “sarcopenia + cancer”
Rapidly emerging area of cancer study, especially in: Lung RenalColorectal
RenalGastricProstate
PancreasLiverOvary
5
200+
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Liver, Gastric and Pancreatic Cancer Sarcopenia and Mortality
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Liver CancerStudy N
Gastric CancerStudy N
Pancreatic CancerStudy N
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Colorectal and Breast CancerObesity Related Cancer
Colorectal - Sarcopenia Study N
Breast - Sarcopenia Study N
Rier (2016)
Del Fabbro (2012)
Caan (2018)
Schachar (2016)
Prado (2009)
Villasenor (2012)
Deluche (2018)
166
67
3,241
40
55
471
119
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23
Sarcopenia leads to 44% increase in overall mortality7843 patients from 38 studies were included
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B(Breast)-SCANS
PopulationNon Metastatic II-III Breast CancerDiagnosed 2000-2013Total (N=3283)KP Cancer Registry (N=2467)Dana Farber Cancer Institute (N=816)
GoalUsing diagnostic CT examine muscle mass area, muscle radiodensity, and visceral and subcutaneous adipose tissue
OutcomesOverall mortality
Data AnalysesKM Curves, Cox proportional hazards models, Restricted cubic splines
C(Colorectal)-SCANS
PopulationNon Metastatic I-III CRC Cancer w/surgical resectionDiagnosed 2006-2011KP Cancer Registry (N=3262)
GoalUsing diagnostic CT examine muscle mass area, muscle radiodensity, and visceral and subcutaneous adipose tissue
OutcomesOverall and colorectal specific mortality
Data AnalysesKM Curves, Cox proportional hazards models, Restricted cubic splines
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Effect of Body Composition Phenotypes on Overall Mortality – CRC and Breast
Colorectal (CSCANS) Breast (CSCANS)
At Risk #Events HR (95% CI) At Risk #Events HR (95% CI)Body composition phenotypes
Normal ( neither) 1251 239 Referent 1255 199 Referent
Low muscle (alone) 925 272 1.33 (1.10, 1.61) 934 197 1.24 (1.01, 1.53)
High adiposity( alone) 925 221 1.21 (1.01, 1.46) 936 193 1.22 (1.00, 1.50)
Low muscle and high adiposity 161 56 1.40 (1.03, 1.90) 158 41 1.43 (1.01, 2.03)
25
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Skeletal Muscle and Adiposity and BMI
Elizabeth M. Cespedes Feliciano, Candyce H. Kroenke, and Bette J. Caan (2018) The Obesity Paradox in Cancer: How Important Is Muscle? Annu. Rev. Nutr. 2018. 38:X–X. (in Press)
Both Skeletal Muscle and Adiposity increase as BMI increases , potentially counteracting effects of each other
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Body composition and the paradoxical relationship of BMI to mortality
Elizabeth M. Cespedes Feliciano, Candyce H. Kroenke, and Bette J. Caan (2018) The Obesity Paradox in Cancer: How Important Is Muscle? Annu. Rev. Nutr. 2018. 38:X–X. (in Press)
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Muscle Loss and Cancer: Multiple Hits Hypothesis
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Current Questions
What is the relationship of BMI and cancer survival? It depends and varies by stage, sex, cancer treatment and overweight/obesity level
Can the Obesity Paradox be explained away completely by methodological issues? Probably not
Is there measurement error in BMI and does examining muscle help, in part to explain the obesity paradox?
YES
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Methodological biases put forth to explain the obesity paradox
Reverse Causality
Confounding
Collider Bias(a type of selection bias)
If you condition on the disease and obesity is related to incident disease- the non-obese (thinner) who get the disease may have other risk factors that both cause the disease but are also more strongly related to worse survival-so it appears thinner have increased mortality
E.g. Smoking. Smokers tend to be thinner but have higher mortality so it looks like being thinner is related to worse mortality
Sicker people lose weight and so it appears that the thinner people have increased mortality.
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Kroenke et al. JAMA Oncology 2016 May 19
Adjusted for age, race, grade, stage, treatment, pre-diagnosis BMI, cancer site, smoking and physical activity
One way to deal with collider bias is to adjust for pre-diagnosis BMI
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Addressing Confounding by Smoking: Association of BMI and Mortality in Non-metastatic Colorectal Cancer and Breast Cancer
Normal Weight (18.5-<25)
Overweight (25-<30)
Obese(>=30)
HR (95% CI) HR (95% CI) HR (95% CI)ColonFull cohort (N=3175) Reference 0.81 (0.67, 0.96) 1.08 (0.90, 1.29)
To address residual confounding:
Ever smokers (N=1691) Reference 0.76 (0.60, 0.95) 1.07 (0.85, 1.34)
Never smokers(N= 1481) Reference 0.87 (0.65, 1.17) 1.03 (0.76, 1.41)
BreastFull cohort (N=3192) Reference 0.93 (0.76, 1.12) 1.11 (0.92, 1.35)
To address residual confounding:
Ever smokers (N=805) Reference 1.03 (0.77, 1.38) 1.02 (0.76, 1.38)
Never smokers(N= 1356) Reference 0.87 (0.67, 1.13) 1.21 (0.94, 1.56)
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Normal Weight (18.5-<25)
Overweight (25-<30)
Obese(>=30)
HR (95% CI) HR (95% CI) HR (95% CI)ColonFull cohort (N=3175) Reference 0.81 (0.67, 0.96) 1.08 (0.90, 1.29)
To address reverse causality:
Weight stable/gain*(those with major weight loss dropped) N=2589
Reference 0.89 (0.71, 1.12) 1.11 (0.89, 1.38)
Breast
Full cohort (N=1885) Reference 0.85 (0.66, 1.09) 1.03 (0.81, 1.32)To address reverse causality:
Weight stable/gain*(those with major weight loss dropped) N=1634
Reference 0.81 (0.61, 1.08) 0.98 (0.75, 1.27)
*Excluded those who dropped a BMI category: from Overweight/Obese to Normal, or from Obese to Overweight.
Addressing Reverse Causality: Association of BMI and Mortality in Non-metastatic Colorectal Cancer and Breast Cancer
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Current Questions
Is there measurement error in BMI and does examining muscle help, in part to explain the obesity paradox?
YES
Is there a difference between types of adiposity on cancer survival?
What is the relationship of BMI and cancer survival? It depends and varies by stage, sex, cancer treatment and overweight/obesity level
Can the Obesity Paradox be explained away completely by methodological issues? Probably not
Maybe
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Antoun, Sami, et al. High subcutaneous adipose tissue predicts the prognosis in metastatic castration-resistant prostate cancer patients in post chemotherapy setting. European Journal of Cancer 51.17 (2015): 2570-2577.
Kaplan–Meier survival curves in patients with high vs low subcutaneous adiposity.
Kaplan–Meier estimates of overall survival stratified on subcutaneous adipose tissue index (SAT): yellow line: less than median value, blue line: greater than or equal to the median value.
Ebadi, M. et al. Subcutaneous adiposity is an independent predictor of mortality in cancer patients. British Journal of Cancer (2017) 117, 148–155. doi:10.1038/bjc.2017.149 www.bjcancer.com Published online 6 June 2017
Some Evidence of Protective Effect of Subcutaneous Adiposity on Survival
ProstateMultiple Cancers
High subcutaneous
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B-Scans :Breast Cancer Restricted Spline Curves for Subcutaneous and Visceral Fat
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CSCANS: Colorectal Restricted Spline Curves for Subcutaneous and Visceral Fat
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Current GAPS in our understandingMoving the Science Forward
Is muscle loss in cancer patients reversible? How do we best go about maximizing patients’ anabolic potential?
How do muscle and adipose tissue interact to increase or decrease mortality in cancer?
Is there cross talk between the tumor and its surrounding adipose tissue so that relationships are cancer specific?
Is obesity associated with indolent tumor subtypes?
Are moderate levels of adipose tissue protective? Does it differ between visceral and subcutaneous adipose tissue?
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The term, “obesity paradox”
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AcknowledgementsKaiser Permanente Northern California Stacey AlexeeffElizabeth Cespedes Feliciano Adrienne Castillo Candyce Kroenke Erin Weltzien Adam Boroian Dan Fernandez Marilyn Kwan Valerie Lee Kathleen AlbersCharles Quesenberry
University of California, Berkeley Patrick Bradshaw
National Cancer InstituteJoanne Elena
University of AlbertaCarla Prado Jingjie XiaoTaiwo OlobatuyiSherin Fernandes
Jeff MeyerhardtWendy ChenJustin BrownCatherine Marinac
Dana Farber Cancer Institute
University of WaterlooMarina Mourtzakis
Cornell University School of Medicine Andrew Danneberg
Rambam Health Care Campus, Haifa, Israel.Shlomit Strulov Shachar
Memorial Sloane Kettering Cancer CenterHelena Furberg