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

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

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

Recent literature on the Obesity Paradox in Cancer

2016

2018

2017

2017

2014

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

Cancers where some paradoxical relationship with BMI has been

demonstrated

ColorectalProstate

Thyroid

Leukemia

Lymphoma

Gastric Renal

LungMelanoma

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

Current Questions

What is the relationship of BMI and cancer survival? It depends and varies by stage, sex, cancer, treatment and overweight/obesity level

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,

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

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

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

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)

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

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

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

“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

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

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

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

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

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)

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)

Mechanisms leading to muscle loss in Cancer

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

PubMed Search through 2017: “sarcopenia + cancer”

Rapidly emerging area of cancer study, especially in: Lung RenalColorectal

RenalGastricProstate

PancreasLiverOvary

5

200+

Liver, Gastric and Pancreatic Cancer Sarcopenia and Mortality

13

Liver CancerStudy N

Gastric CancerStudy N

Pancreatic CancerStudy N

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

23

Sarcopenia leads to 44% increase in overall mortality7843 patients from 38 studies were included

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

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

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

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)

Muscle Loss and Cancer: Multiple Hits Hypothesis

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

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.

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

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)

38

39

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

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

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

23

B-Scans :Breast Cancer Restricted Spline Curves for Subcutaneous and Visceral Fat

CSCANS: Colorectal Restricted Spline Curves for Subcutaneous and Visceral Fat

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?

The term, “obesity paradox”

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