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Figure.
BMI at Diagnosis and Overall Mortality
BMI at Diagnosis and Overall Mortality

Adjusted for age, sex, race, stage, site, grade, chemotherapy, radiation, prediagnosis BMI, smoking, and physical activity; P < .001 (test for nonlinearity). BMI indicates body mass index.

Table 1.  
Selected Characteristics According to Categories of BMI at Diagnosis in 3408 Patients in the Kaiser Permanente Northern California Population
Selected Characteristics According to Categories of BMI at Diagnosis in 3408 Patients in the Kaiser Permanente Northern California Population
Table 2.  
Hazard Ratios of At-Diagnosis BMI and Mortality in 3408 Patients
Hazard Ratios of At-Diagnosis BMI and Mortality in 3408 Patients
Table 3.  
Postdiagnosis BMI and Mortality Outcomes in 3157 Patients
Postdiagnosis BMI and Mortality Outcomes in 3157 Patients
Table 4.  
Stratified Analyses of Weight and All-Cause Mortality in 3408 Patients
Stratified Analyses of Weight and All-Cause Mortality in 3408 Patients
1.
Aleksandrova  K, Pischon  T, Buijsse  B,  et al.  Adult weight change and risk of colorectal cancer in the European Prospective Investigation into Cancer and Nutrition.  Eur J Cancer. 2013;49(16):3526-3536.PubMedGoogle ScholarCrossref
2.
Keimling  M, Renehan  AG, Behrens  G,  et al.  Comparison of associations of body mass index, abdominal adiposity, and risk of colorectal cancer in a large prospective cohort study.  Cancer Epidemiol Biomarkers Prev. 2013;22(8):1383-1394.PubMedGoogle ScholarCrossref
3.
Renehan  AG, Flood  A, Adams  KF,  et al.  Body mass index at different adult ages, weight change, and colorectal cancer risk in the National Institutes of Health-AARP Cohort.  Am J Epidemiol. 2012;176(12):1130-1140.PubMedGoogle ScholarCrossref
4.
Li  H, Yang  G, Xiang  YB,  et al.  Body weight, fat distribution and colorectal cancer risk: a report from cohort studies of 134255 Chinese men and women.  Int J Obes (Lond). 2013;37(6):783-789.PubMedGoogle ScholarCrossref
5.
Odegaard  AO, Koh  WP, Yu  MC, Yuan  JM.  Body mass index and risk of colorectal cancer in Chinese Singaporeans: the Singapore Chinese Health Study.  Cancer. 2011;117(16):3841-3849.PubMedGoogle ScholarCrossref
6.
Laake  I, Thune  I, Selmer  R, Tretli  S, Slattery  ML, Veierød  MB.  A prospective study of body mass index, weight change, and risk of cancer in the proximal and distal colon.  Cancer Epidemiol Biomarkers Prev. 2010;19(6):1511-1522.PubMedGoogle ScholarCrossref
7.
Campbell  PT, Cotterchio  M, Dicks  E, Parfrey  P, Gallinger  S, McLaughlin  JR.  Excess body weight and colorectal cancer risk in Canada: associations in subgroups of clinically defined familial risk of cancer.  Cancer Epidemiol Biomarkers Prev. 2007;16(9):1735-1744.PubMedGoogle ScholarCrossref
8.
Lin  J, Zhang  SM, Cook  NR, Rexrode  KM, Lee  IM, Buring  JE.  Body mass index and risk of colorectal cancer in women (United States).  Cancer Causes Control. 2004;15(6):581-589.PubMedGoogle ScholarCrossref
9.
Caan  BJ, Coates  AO, Slattery  ML, Potter  JD, Quesenberry  CP  Jr, Edwards  SM.  Body size and the risk of colon cancer in a large case-control study.  Int J Obes Relat Metab Disord. 1998;22(2):178-184.PubMedGoogle ScholarCrossref
10.
Sinicrope  FA, Foster  NR, Yothers  G,  et al; Adjuvant Colon Cancer Endpoints (ACCENT) Group.  Body mass index at diagnosis and survival among colon cancer patients enrolled in clinical trials of adjuvant chemotherapy.  Cancer. 2013;119(8):1528-1536.PubMedGoogle ScholarCrossref
11.
Meyerhardt  JA, Niedzwiecki  D, Hollis  D,  et al; Cancer and Leukemia Group B 89803.  Impact of body mass index and weight change after treatment on cancer recurrence and survival in patients with stage III colon cancer: findings from Cancer and Leukemia Group B 89803.  J Clin Oncol. 2008;26(25):4109-4115.PubMedGoogle ScholarCrossref
12.
Dignam  JJ, Polite  BN, Yothers  G,  et al.  Body mass index and outcomes in patients who receive adjuvant chemotherapy for colon cancer.  J Natl Cancer Inst. 2006;98(22):1647-1654.PubMedGoogle ScholarCrossref
13.
Prizment  AE, Flood  A, Anderson  KE, Folsom  AR.  Survival of women with colon cancer in relation to precancer anthropometric characteristics: the Iowa Women’s Health Study.  Cancer Epidemiol Biomarkers Prev. 2010;19(9):2229-2237.PubMedGoogle ScholarCrossref
14.
Sinicrope  FA, Foster  NR, Sargent  DJ, O’Connell  MJ, Rankin  C.  Obesity is an independent prognostic variable in colon cancer survivors.  Clin Cancer Res. 2010;16(6):1884-1893.PubMedGoogle ScholarCrossref
15.
Doria-Rose  VP, Newcomb  PA, Morimoto  LM, Hampton  JM, Trentham-Dietz  A.  Body mass index and the risk of death following the diagnosis of colorectal cancer in postmenopausal women (United States).  Cancer Causes Control. 2006;17(1):63-70.PubMedGoogle ScholarCrossref
16.
Murphy  TK, Calle  EE, Rodriguez  C, Kahn  HS, Thun  MJ.  Body mass index and colon cancer mortality in a large prospective study.  Am J Epidemiol. 2000;152(9):847-854.PubMedGoogle ScholarCrossref
17.
Alipour  S, Kennecke  HF, Woods  R,  et al.  Body mass index and body surface area and their associations with outcomes in stage II and III colon cancer.  J Gastrointest Cancer. 2013;44(2):203-210.PubMedGoogle ScholarCrossref
18.
Schlesinger  S, Siegert  S, Koch  M,  et al.  Postdiagnosis body mass index and risk of mortality in colorectal cancer survivors: a prospective study and meta-analysis.  Cancer Causes Control. 2014;25(10):1407-1418.PubMedGoogle ScholarCrossref
19.
Pearl  J.  Causal diagrams for empirical research.  Biometrika. 1995;82:669-710.Google ScholarCrossref
20.
Hernán  MA, Hernández-Díaz  S, Robins  JM.  A structural approach to selection bias.  Epidemiology. 2004;15(5):615-625.PubMedGoogle ScholarCrossref
21.
Banack  HR, Kaufman  JS.  The obesity paradox: understanding the effect of obesity on mortality among individuals with cardiovascular disease.  Prev Med. 2014;62:96-102.PubMedGoogle ScholarCrossref
22.
Flegal  KM, Graubard  BI, Williamson  DF, Cooper  RS.  Reverse causation and illness-related weight loss in observational studies of body weight and mortality.  Am J Epidemiol. 2011;173(1):1-9.PubMedGoogle ScholarCrossref
23.
Pearl  J.  An introduction to causal inference.  Int J Biostat. 2010;6(2):7.PubMedGoogle ScholarCrossref
24.
Gordon  NP.  Characteristics of Adult Health Plan Members in Kaiser Permanente’s Northern California Region, as Estimated from the 2011 Member Health Survey. Oakland, CA: Division of Research, Kaiser Permanente Medical Care Program; 2013.
25.
Classification  BMI. World Health Organization; 2014. http://apps.who.int/bmi/index.jsp?introPage=intro_3.html. Accessed April 7, 2014.
26.
Winter  JE, MacInnis  RJ, Wattanapenpaiboon  N, Nowson  CA.  BMI and all-cause mortality in older adults: a meta-analysis.  Am J Clin Nutr. 2014;99(4):875-890.PubMedGoogle ScholarCrossref
27.
Greenland  S, Pearl  J, Robins  JM.  Causal diagrams for epidemiologic research.  Epidemiology. 1999;10(1):37-48.PubMedGoogle ScholarCrossref
28.
Durrleman  S, Simon  R.  Flexible regression models with cubic splines.  Stat Med. 1989;8(5):551-561.PubMedGoogle ScholarCrossref
29.
Pelser  C, Arem  H, Pfeiffer  RM,  et al.  Prediagnostic lifestyle factors and survival after colon and rectal cancer diagnosis in the National Institutes of Health (NIH)-AARP Diet and Health Study.  Cancer. 2014;120(10):1540-1547.PubMedGoogle ScholarCrossref
30.
Campbell  PT, Newton  CC, Dehal  AN, Jacobs  EJ, Patel  AV, Gapstur  SM.  Impact of body mass index on survival after colorectal cancer diagnosis: the Cancer Prevention Study-II Nutrition Cohort.  J Clin Oncol. 2012;30(1):42-52.PubMedGoogle ScholarCrossref
31.
Meyerhardt  JA, Tepper  JE, Niedzwiecki  D,  et al.  Impact of body mass index on outcomes and treatment-related toxicity in patients with stage II and III rectal cancer: findings from Intergroup Trial 0114.  J Clin Oncol. 2004;22(4):648-657.PubMedGoogle ScholarCrossref
32.
Kuiper  JG, Phipps  AI, Neuhouser  ML,  et al.  Recreational physical activity, body mass index, and survival in women with colorectal cancer.  Cancer Causes Control. 2012;23(12):1939-1948.PubMedGoogle ScholarCrossref
33.
Baade  PD, Meng  X, Youl  PH, Aitken  JF, Dunn  J, Chambers  SK.  The impact of body mass index and physical activity on mortality among patients with colorectal cancer in Queensland, Australia.  Cancer Epidemiol Biomarkers Prev. 2011;20(7):1410-1420.PubMedGoogle ScholarCrossref
34.
Lajous  M, Bijon  A, Fagherazzi  G,  et al.  Body mass index, diabetes, and mortality in French women: explaining away a “paradox”.  Epidemiology. 2014;25(1):10-14.PubMedGoogle ScholarCrossref
35.
Carnethon  MR, De Chavez  PJ, Biggs  ML,  et al.  Association of weight status with mortality in adults with incident diabetes.  JAMA. 2012;308(6):581-590.PubMedGoogle ScholarCrossref
36.
Casas-Vara  A, Santolaria  F, Fernández-Bereciartúa  A, González-Reimers  E, García-Ochoa  A, Martínez-Riera  A.  The obesity paradox in elderly patients with heart failure: analysis of nutritional status.  Nutrition. 2012;28(6):616-622.PubMedGoogle ScholarCrossref
37.
Doehner  W, Erdmann  E, Cairns  R,  et al.  Inverse relation of body weight and weight change with mortality and morbidity in patients with type 2 diabetes and cardiovascular co-morbidity: an analysis of the PROactive study population.  Int J Cardiol. 2012;162(1):20-26.PubMedGoogle ScholarCrossref
38.
Kim  BJ, Lee  SH, Jung  KH, Yu  KH, Lee  BC, Roh  JK; For Korean Stroke Registry investigators.  Dynamics of obesity paradox after stroke, related to time from onset, age, and causes of death.  Neurology. 2012;79(9):856-863.PubMedGoogle ScholarCrossref
39.
Lancefield  T, Clark  DJ, Andrianopoulos  N,  et al; MIG (Melbourne Interventional Group) Registry.  Is there an obesity paradox after percutaneous coronary intervention in the contemporary era? An analysis from a multicenter Australian registry.  JACC Cardiovasc Interv. 2010;3(6):660-668.PubMedGoogle ScholarCrossref
40.
Lavie  CJ, De Schutter  A, Patel  D, Artham  SM, Milani  RV.  Body composition and coronary heart disease mortality--an obesity or a lean paradox?  Mayo Clin Proc. 2011;86(9):857-864.PubMedGoogle ScholarCrossref
41.
Tseng  CH.  Obesity paradox: differential effects on cancer and noncancer mortality in patients with type 2 diabetes mellitus.  Atherosclerosis. 2013;226(1):186-192.PubMedGoogle ScholarCrossref
42.
Uretsky  S, Messerli  FH, Bangalore  S,  et al.  Obesity paradox in patients with hypertension and coronary artery disease.  Am J Med. 2007;120(10):863-870.PubMedGoogle ScholarCrossref
43.
Angerås  O, Albertsson  P, Karason  K,  et al.  Evidence for obesity paradox in patients with acute coronary syndromes: a report from the Swedish Coronary Angiography and Angioplasty Registry.  Eur Heart J. 2013;34(5):345-353.PubMedGoogle ScholarCrossref
44.
Blum  A, Simsolo  C, Sirchan  R, Haiek  S.  “Obesity paradox” in chronic obstructive pulmonary disease.  Isr Med Assoc J. 2011;13(11):672-675.PubMedGoogle Scholar
45.
Jackson  RS, Black  JH  III, Lum  YW,  et al.  Class I obesity is paradoxically associated with decreased risk of postoperative stroke after carotid endarterectomy.  J Vasc Surg. 2012;55(5):1306-1312.PubMedGoogle ScholarCrossref
46.
Jialin  W, Yi  Z, Weijie  Y.  Relationship between body mass index and mortality in hemodialysis patients: a meta-analysis.  Nephron Clin Pract. 2012;121(3-4):c102-c111.PubMedGoogle ScholarCrossref
47.
Komukai  K, Minai  K, Arase  S,  et al.  Impact of body mass index on clinical outcome in patients hospitalized with congestive heart failure.  Circ J. 2012;76(1):145-151.PubMedGoogle ScholarCrossref
48.
Stein  PD, Matta  F, Goldman  J.  Obesity and pulmonary embolism: the mounting evidence of risk and the mortality paradox.  Thromb Res. 2011;128(6):518-523.PubMedGoogle ScholarCrossref
49.
Schenkeveld  L, Magro  M, Oemrawsingh  RM,  et al.  The influence of optimal medical treatment on the ‘obesity paradox’, body mass index and long-term mortality in patients treated with percutaneous coronary intervention: a prospective cohort study.  BMJ Open. 2012;2:e000535.PubMedGoogle ScholarCrossref
50.
Biasucci  LM, Graziani  F, Rizzello  V,  et al.  Paradoxical preservation of vascular function in severe obesity.  Am J Med. 2010;123(8):727-734.PubMedGoogle ScholarCrossref
51.
Graziani  F, Biasucci  LM, Cialdella  P,  et al.  Thromboxane production in morbidly obese subjects.  Am J Cardiol. 2011;107(11):1656-1661.PubMedGoogle ScholarCrossref
52.
Lund  LH, Williams  JJ, Freda  P, LaManca  JJ, LeJemtel  TH, Mancini  DM.  Ghrelin resistance occurs in severe heart failure and resolves after heart transplantation.  Eur J Heart Fail. 2009;11(8):789-794.PubMedGoogle ScholarCrossref
53.
Feldman  AM, Combes  A, Wagner  D,  et al.  The role of tumor necrosis factor in the pathophysiology of heart failure.  J Am Coll Cardiol. 2000;35(3):537-544.PubMedGoogle ScholarCrossref
54.
McTiernan  CF, Feldman  AM.  The role of tumor necrosis factor alpha in the pathophysiology of congestive heart failure.  Curr Cardiol Rep. 2000;2(3):189-197.PubMedGoogle ScholarCrossref
55.
Glymour  MM, Vittinghoff  E.  Commentary: selection bias as an explanation for the obesity paradox: just because it’s possible doesn’t mean it’s plausible.  Epidemiology. 2014;25(1):4-6.PubMedGoogle ScholarCrossref
Original Investigation
September 2016

Analysis of Body Mass Index and Mortality in Patients With Colorectal Cancer Using Causal Diagrams

Author Affiliations
  • 1Division of Research, Kaiser Permanente Oakland, California
  • 2Dana Farber Cancer Institute, Boston, Massachusetts
  • 3Department of Agricultural, Food and Nutritional Sciences, University of Alberta, Edmonton, Alberta, Canada
 

Copyright 2016 American Medical Association. All Rights Reserved. Applicable FARS/DFARS Restrictions Apply to Government Use.

JAMA Oncol. 2016;2(9):1137-1145. doi:10.1001/jamaoncol.2016.0732
Abstract

Importance  Physicians and investigators have sought to determine the relationship between body mass index (BMI [calculated as weight in kilograms divided by height in meters squared]) and colorectal cancer (CRC) outcomes, but methodologic limitations including sampling selection bias, reverse causality, and collider bias have prevented the ability to draw definitive conclusions.

Objective  To evaluate the association of BMI at the time of, and following, colorectal cancer (CRC) diagnosis with mortality in a complete population using causal diagrams.

Design, Setting, and Participants  This retrospective observational study with prospectively collected data included a cohort of 3408 men and women, ages 18 to 80 years, from the Kaiser Permanente Northern California population, who were diagnosed with stage I to III CRC between 2006 and 2011 and who also had surgery.

Exposures  Body mass index at diagnosis and 15 months following diagnosis.

Main Outcomes and Measures  Hazard ratios (HRs) for all-cause mortality and CRC-specific mortality compared with normal-weight patients, adjusted for sociodemographics, disease severity, treatment, and prediagnosis BMI.

Results  This study investigated a cohort of 3408 men and women ages 18 to 80 years diagnosed with stage I to III CRC between 2006 and 2011 who also had surgery. At-diagnosis BMI was associated with all-cause mortality in a nonlinear fashion, with patients who were underweight (BMI <18.5; HR, 2.65; 95% CI, 1.63-4.31) and patients who were class II or III obese (BMI ≥35; HR, 1.33; 95% CI, 0.89-1.98) exhibiting elevated mortality risks, compared with patients who were low-normal weight (BMI 18.5 to <23). In contrast, patients who were high-normal weight (BMI 23 to <25; HR, 0.77; 95% CI, 0.56-1.06), low-overweight (BMI 25 to <28; HR, 0.75; 95% CI, 0.55-1.04), and high-overweight (BMI 28 to <30; HR, 0.52; 95% CI, 0.35-0.77) had lower mortality risks, and patients who were class I obese (BMI 30 to <35) showed no difference in risk. Spline analysis confirmed a U-shaped relationship in participants with lowest mortality at a BMI of 28. Associations with CRC-specific mortality were similar. Associations of postdiagnosis BMI and mortality were also similar, but patients who were class I obese had significantly lower all-cause and cancer-specific mortality risks.

Conclusions and Relevance  In this study, body mass index at the time of diagnosis and following diagnosis of CRC was associated with mortality risk. Though evidence shows that exercise in patients with cancer should be encouraged, findings suggest that recommendations for weight loss in the immediate postdiagnosis period among patients with CRC who are overweight may be unwarranted.

Introduction

Body mass index (BMI [calculated as weight in kilograms divided by height in meters squared]) is positively associated with the risk of colorectal cancer (CRC).1-9 For that reason, investigators have hypothesized that being overweight or obese leads to worse CRC prognosis. However, though previous studies have shown an apparently elevated mortality risk in patients who are class II or III obese (BMI ≥35),10-12 associations of class I obesity (BMI 30 to <35) and survival are mixed; some studies show a higher mortality risk6,10,13-16 and others show no higher17 mortality risk or a possible lower18 mortality risk, depending in part on when BMI is measured relative to diagnosis. Patients who have CRC and are overweight or obese have shown lower mortality risks11,18 compared with patients who are normal weight or underweight when BMI is measured following diagnosis, but concerns are that the obesity paradox could reflect sample selection bias, reverse causality, and/or collider bias.19-21 We sought to employ methods to overcome these concerns in our evaluation of the effect of BMI on postdiagnosis mortality.

First, many studies of weight and CRC survival are conducted in samples recruited after diagnosis, causing concerns that only the healthiest may enroll. Such sampling selection bias could produce an obesity paradox if the sickest patients who are overweight are less likely to enroll than the sickest patients who are normal weight or if those patients die prior to enrollment. Second, patients who are underweight or normal weight may have higher mortality compared with patients who are overweight because they may include patients who are cachectic nearing death (ie, reverse causality).22 A third concern is collider bias20; in the presence of an unmeasured risk factor for CRC diagnosis (eFigure 1 in the Supplement), selecting a population based on a CRC diagnosis could introduce a spurious association between an exposure (eg, postdiagnosis BMI) and an outcome (eg, death) that could reverse the direction of association, making a harmful exposure appear protective.21 In other words, collider bias may occur if being overweight or obese leads to higher disease incidence, but unmeasured risk factors occurring disproportionately in patients who are normal-weight are more strongly related to mortality than being overweight or obese. In this case, spuriousness can be avoided with adjustment for prediagnosis BMI (eFigure 1 in the Supplement).

Using electronic medical record (EMR) data collected as a part of routine clinical care within the Kaiser Permanente Northern California (KPNC) population, we examined the effect of BMI at diagnosis, and following diagnosis, on all-cause mortality and CRC-specific mortality in KPNC patients diagnosed with CRC between 2006 and 2011 using several strategies to overcome bias. These included the use of data from a complete population of patients with CRC with longitudinally collected data, examination of associations in patients with stage I disease not expected to be at imminent risk of death, stratification by weight loss status, and the use of causal diagrams to identify sufficient adjustment sets23 of covariates. Related to this, we were able to adjust for prospectively collected data on prediagnosis BMI, a unique feature of our data.

We hypothesized higher mortality risks in patients with CRC who were underweight or were class II or III obese and a lower mortality risk in patients who were overweight, compared with patients of normal weight.

Box Section Ref ID

Key Points

  • Question What is the effect of body mass index (BMI) on colorectal cancer (CRC) mortality?

  • Findings In a complete population of patients with CRC with prospectively collected data, BMI at (and following) diagnosis of CRC was associated with all-cause and disease-specific mortality in a nonlinear fashion with the highest risks occurring in patients who were underweight, low-normal weight, and class II or III obese. By contrast, patients who were overweight consistently had the lowest mortality risk.

  • Meaning Despite scientific evidence showing that exercise in patients with cancer should be encouraged, findings suggest that recommendations for weight loss in the immediate postdiagnosis period in patients with CRC who are overweight may be unwarranted.

Methods
Study Population

The study population consisted of all patients ages 18 to 80 years from KPNC diagnosed between 2006 to 2011 with stage I to III invasive CRC whose cancer was confirmed by computed tomography, who received surgery, and for whom an electronic weight and height were available at diagnosis. Case ascertainment began in 2006, 1 year after weights routinely became available in the EMR, to enable inclusion of prediagnosis weight in analyses. Approximately one-third of the California population are Kaiser Permanente members; members represent the underlying population except at socioeconomic extremes.24 Approximately 5% of patients were missing at-diagnosis BMI data in a year. Overall, 33 patients (1.0%) were lost to follow-up; 1693 (49.7%) of study participants were women and 1715 (50.3%) were men. A waiver of written informed consent was obtained and the study was approved by the KPNC institutional review board.

Data Collection
Body Mass Index

Height and weight were measured by a medical assistant at each medical visit. Body mass index was computed as weight in kilograms divided by height in meters squared. Patients were included in analyses if they had a BMI recorded less than 6 months from a CRC diagnosis and prior to surgery. Body mass index closest to the diagnosis date (median [range], 0.0 [−5.3 to 6.0] months) was used in analyses of at-diagnosis BMI; BMI measured 9 or more months before CRC diagnosis (median [range], −12.5 [−9.0 to −68.9]) was used to assess prediagnosis BMI; and BMI measured approximately 15 months following diagnosis (median [range], 14.7 [9.0-27.0] months), posttreatment, was used to assess postdiagnosis BMI.

We initially categorized BMI using World Health Organization (WHO) categories.25 However, the optimal weight for patients with CRC is unknown and large BMI categories may obscure risk in the case of U-shaped or J-shaped relationships, so we categorized BMI into finer categories (<18.5, 18.5 to <23, 23 to <25, 25 to <28, 28 to <30, 30 to <35, and ≥35) consistent with those used in a recent meta-analysis of weight and mortality26 and to distinguish risks for low-normal weight (18.5 to <23) and high-normal weight (23 to <25), as well as low-overweight (25 to <28) and high-overweight (28 to <30).

Clinical Variables and End Points

The KPNC Cancer Registry and EMR data were reviewed for information on prognostic factors, including disease stage, tumor characteristics, surgical procedures, and treatment (ie, chemotherapy, radiation therapy). Data on overall and CRC-specific mortality were obtained from the KPNC computerized mortality file that is comprised of data from the California State Department of Vital Statistics, US Social Security Administration, and KPNC utilization data sources. Colorectal cancer death was attributed to persons if CRC was listed as a cause of death on the death certificate.

Other Covariate Data

Electronic medical record data were accessed for information on numerous potential confounders including demographics (self-reported race is included in the EMR) and smoking status. The Charlson index was used to measure (any vs no) comorbidity. Physical activity data (minutes per week of moderate or vigorous activity) were available for 763 patient (22.4% of the population).

Statistical Analysis

We used analysis of covariance to examine linear covariates by categories of at-diagnosis BMI adjusted for age. For categorical variables, we examined covariate distributions by categories of at-diagnosis BMI.

We used Cox proportional hazards regression models to examine associations between BMI at the time of diagnosis, BMI following diagnosis, and all-cause and CRC-specific mortality. For analyses of at-diagnosis BMI, time was computed from time of diagnosis. In analyses of postdiagnosis BMI, time was computed from the time of the follow-up BMI measure to time of event or study end.

To address possible reverse causality, we evaluated associations in patients with stage I cancer. Typically, researchers eliminate deaths occurring early after measurement of a risk factor. Though this can lead to collider bias,20 this strategy is commonly used so we nonetheless conducted sensitivity analyses eliminating deaths occurring within the first 6 months and the first year to facilitate comparison with other studies.

Potential confounding variables in models were selected based on subject matter expertise encoded in directed acyclic graphs,27 diagrams that help elucidate the causal structure relating variables under study. We compared models controlling for age, race, and sex, with those adjusted additionally for stage, grade, cancer site (colon; distal or proximal, and rectal), smoking status, and physical activity. To address concerns about the potential for collider bias because of the restriction of analyses to diagnosed CRC patients (eFigure 1 in the Supplement), we adjusted for prediagnosis BMI when evaluating the effects of both at-diagnosis and postdiagnosis BMI. Adjustment for chemotherapy and radiation were not suggested by the directed acyclic graph in the analysis of at-diagnosis BMI based on the time order of covariates. We nonetheless included these variables in models based on convention; adjustment had no substantive effect on associations. We considered adjustment for comorbidity but sought to avoid overadjustment because CRC and comorbidities have mechanisms in common related to BMI.

We used both standard WHO25 and expanded BMI categories. We also examined possible nonlinear relationships between BMI and survival nonparametrically and by sex with restricted cubic splines,28 a technique enabling specification of a relationship between 2 variables when the function is nonlinear. Tests for nonlinearity used the likelihood ratio test, comparing the model with the linear term with one with linear and cubic spline terms.

Finally, we conducted analyses of at-diagnosis BMI and outcomes, stratified in separate analyses by sex, age (<65 vs ≥65 years), race, stage, comorbidity, treatment status, CRC site, and weight loss status between diagnosis and postdiagnosis. Heterogeneity in associations in stratified analyses was examined via introduction of cross-product terms for BMI categories and stratification variables in regression models and evaluation of significance with likelihood ratio χ2 tests. We conducted sensitivity analyses restricting to the population with complete information on prediagnosis BMI. We also conducted tests of proportionality with variable by time interactions. Tests of statistical significance were 2-sided. A P value of ≤.05 indicates significant results.

Results

Of the 3408 CRC patients, 617 died (411 from CRC), with a median (range) follow-up of 4.5 (0.0-8.7) years. For analyses of postdiagnosis BMI and mortality (n = 3157), 482 patients died (317 from CRC), and median (range) follow-up was 3.5 (0.0-7.9) years.

Baseline Characteristics

Examining covariates, age was inversely associated with at-diagnosis BMI. Women were more likely to be underweight, normal weight, and class II or III obese, compared with men. Asians were more likely to be underweight or normal weight; whites, blacks, and Hispanics were more likely to be class II or III obese. Patients with stages II and III cancers were more likely to be underweight or normal weight compared with patients with stage I cancer. However, those with stage III cancer were more likely to be obese than those with earlier stage cancer. Predictably, levels of smoking and physical activity were higher in patients who were underweight or normal weight. Distal cancers were more common among patients who were obese; those with a BMI less than 25 were more likely to have proximal colon or rectal cancers. Patients who were underweight and class II or III obese were less likely to receive chemotherapy (Table 1).

BMI and All-Cause Mortality

Using WHO criteria, in models adjusted for age, sex, and race, at-diagnosis BMI was associated with all-cause mortality in a nonlinear fashion. Specifically, patients who were underweight (BMI <18.5) and patients who were class II or III obese (BMI ≥35) exhibited elevated mortality risks, and overweight (BMI 25 to <30) patients showed reduced mortality risks, compared with patients who were normal weight (BMI 18.5 to <25). Mortality risks of patients who were class I obese and normal weight were similar. In multivariable-adjusted analyses, associations were similar, though the association for overall mortality in patients who were overweight was somewhat attenuated (Table 2). After comorbidity adjustment, the association between class II and III obesity and all-cause mortality was no longer significant (hazard ratio [HR], 1.27; 95% CI, 0.88-1.84).

Using expanded BMI categories, in models adjusted for age, sex, and race, at-diagnosis BMI was also associated with all-cause mortality in a nonlinear fashion. Multivariable-adjusted results were qualitatively similar (Table 2). Patients who were underweight (BMI <18.5; HR, 2.65; 95% CI,1.63-4.31) or class II or III obese (BMI ≥35; HR, 1.33; 95% CI, 0.89-1.98) exhibited elevated mortality risks; patients who were high-normal weight (BMI 23 to <25; HR, 0.77; 95% CI, 0.56-1.06), low-overweight (BMI 25 to <28; HR, 0.75; 95% CI, 0.55-1.04), or high-overweight (BMI 28 to <30; HR, 0.52; 95% CI, 0.35-0.77) showed reduced mortality risks; and patients who were class I obese showed no difference in risk, compared with patients who were low-normal weight. Mortality risk did not differ comparing patients who were high-normal weight and low-overweight (Wald χ2 = 0.03; P = .87).

In spline analyses of all-cause mortality, risk was lowest in patients with a BMI of 28 (Figure) in both men (eFigure 2 in the Supplement) and women (eFigure 3 in the Supplement). Tests for linear trend suggested linear relationships of BMI with mortality hazard overall (P = .01) and in women (P = .01) but not in men (P = .21). In all cases, spline analyses provided evidence of a nonlinear component (P < .001 for nonlinear association [men, P < .001; women, P = .02]).

Patterns of associations of postdiagnosis BMI and all-cause mortality were similar to results for at-diagnosis BMI and all-cause mortality (Table 3). However, patients who were class I obese had a lower mortality risk compared with patients who were low-normal weight, and patients who were class II or III obese showed no higher mortality risk compared with patients who were low-normal weight.

BMI and CRC-Specific Mortality

Associations of both at-diagnosis and postdiagnosis BMI with CRC-specific mortality were similar to those for analyses of all-cause mortality. However, in postdiagnosis analyses, patients who were class I obese had a significantly lower mortality risk than patients who were low-normal weight, and patients who were class II or III obese showed no higher mortality risk, and a possibly lower mortality risk, compared with patients who were low-normal weight (Tables 2 and 3).

Stratified Analyses

In stratified analyses, there were few differences by age, race, sex, stage, comorbidity, treatment, or weight loss status (Table 4) (eTable in the Supplement). However, there was strong evidence of statistical interaction by primary tumor site. The nonlinear pattern of BMI and mortality was apparent in all sites, but elevated risks for patients who were underweight and class II and III obese were most evident in proximal cases, whereas reduced risks for patients who were overweight and class I obese were most evident in distal and rectal cancers cases (P = .005 for interaction) (Table 4).

In sensitivity analyses excluding early deaths, we noted little qualitative change or attenuation in associations for at-diagnosis weight and mortality. Associations for patients who were underweight and mortality in postdiagnosis analyses were somewhat attenuated when we excluded first year deaths, but other associations were similar (data not shown). Results in sensitivity analyses were also similar (data not shown). With stratification on grade, proportional hazards assumptions were met.

Discussion

Consistent with hypotheses, patients with CRC who were underweight or class II or III obese at diagnosis had worse overall prognosis than patients who were normal weight. By contrast, neither all-cause nor disease-specific mortality was elevated in patients with CRC who were class II or III obese more than 1 year after diagnosis. Patients who were overweight consistently had the best prognosis, with the lowest risks of all-cause and CRC-specific mortality, though the mortality risks of patients with CRC who were low-overweight and high-normal weight did not differ. We were able to overcome limitations of previous studies with the availability of a complete population of patients with CRC, prospectively measured prediagnosis weight data, and methods that may improve causal claims regarding the effects of BMI on postdiagnosis mortality for patients with CRC. These findings, in the largest cohort of CRC patients to date with data on weight prior to diagnosis, at the time of diagnosis, and following diagnosis, provide support that being overweight does not confer an increased mortality risk and may support an obesity paradox in patients with CRC.

Previous investigators have generally shown that patients with CRC who are underweight or class II or III obese have higher mortality risks when BMI is assessed prior to diagnosis13,29,30 or near the time of diagnosis10-12,14,31. Risks for patients who are underweight are higher than for other patients. Our findings are generally consistent with these studies, though we found elevated mortality risks for underweight patients at all time points in contrast with previous findings showing diminished effects in postdiagnosis analyses. Consistent with our findings, several (though not all) previous studies have shown lower overall and/or disease-specific mortality risks in patients with CRC who are overweight18,30,32,33 and even mildly obese33 when BMI has been evaluated after diagnosis. However, results from these analyses have been a source of concern among researchers interested in causal methods who have argued that inverse associations are due to collider bias.21,34

With sufficient covariate adjustment, including prediagnosis BMI, we found robust evidence that in patients with CRC, prognosis is best in those who are overweight at and after the time of diagnosis. In this study, patients with CRC who were overweight had a lower mortality risk among those with stage I disease, whether or not we eliminated early mortality, and among those with or without weight loss, suggesting results were not attributable to reverse causality. Our strong inverse findings for patients who were overweight and both all-cause and disease-specific mortality are novel, obtained using improved methodologies.

A lack of association of patients who were overweight around the time of diagnosis and outcomes in previous studies may be due to small sample sizes11,17,32 or broad categorization of weight groups. Most studies have defined normal weight as a BMI between 18.5 and 25, though some omit patients of lower-normal weight in this category.10,11,14,15 Our findings, including a nonsignificant inverse association in patients who were overweight using similar categorizations, but strong inverse findings in all other analyses, suggest that traditional BMI categories are insufficiently granular to understand mortality risk in patients with CRC because the nature of the relationship appears convincingly nonlinear. Intriguingly, some studies10,12 with long follow-up (median ≥8 years) have shown weak, significant, or borderline significant positive findings of overweight and mortality. However, hazard ratios of approximately 1.0 suggest that these estimates are influenced by the tail at the high end of the BMI distribution or that they might represent time-averaged effects of being overweight and/or obese on patient outcomes with increasing risks further from diagnosis. In fact, eliminating the top percentile of the distributions eliminated the linear association (data not shown). Long-term studies of BMI and CRC survival, with regularly updated BMI measures, are needed.

By examining associations in a population unaffected by sample selection bias and using causal methods to address confounding, we provide support that results are biologically meaningful. In the context of disease, being overweight may confer survival benefits,35-48 attributed to better nutritional status,36 more optimal medical treatment,49 greater endothelial progenitor cells,50 lower thromboxane production,51 higher ghrelin sensitivity,52 and lower concentrations of tumor necrosis factor-α.53,54 Patients with CRC with extra weight may have greater muscle and fat mass enabling them to cope with the metabolic demands of tumor progression and treatment.49-54 However, though mortality was lowest in patients who were overweight, risk was similarly low in patients who were high-normal weight, suggesting that a BMI in this range may also minimize the risks entailed in managing disease.

A strength of this study was the ability to examine prospectively measured weight at the time of diagnosis and following diagnosis with outcomes adjusted for prediagnosis BMI. Other study strengths include a large sample size, data on treatment and comorbidities, weights and height measured by a medical assistant, and follow-up to 8.7 years. Study limitations include lack of physical activity data for the full cohort. However, adjustment for this variable did not affect results in the subset with these data (data not shown). As with all observational studies, residual confounding is possible. We were unable to control for detailed treatment information. However, Glymour and Vittinghoff55 found that the level of unmeasured confounding would have to be very large to explain the obesity paradox, and covariate adjustment in our study had little influence on associations.

Conclusions

Extremes in weight at diagnosis were associated with elevated all-cause and CRC-specific mortality in a large population of patients with stage I to III CRC. By contrast, patients with CRC who were overweight had the best prognosis. Studies are needed to understand the mechanisms underlying these results, particularly studies of body composition. Though strong scientific evidence shows that exercise in patients with cancer should be encouraged, findings suggest that recommendations for weight loss in the immediate postdiagnosis period in patients with CRC who are overweight may be unwarranted.

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Article Information

Corresponding Author: Candyce H. Kroenke, ScD, Division of Research, Kaiser Permanente, 2000 Broadway, Fifth Floor, Oakland, CA 94612 (Candyce.h.kroenke@kp.org).

Accepted for Publication: March 1, 2016.

Published Online: May 19, 2016. doi:10.1001/jamaoncol.2016.0732

Author Contributions: Dr Kroenke and Ms Weltzien from Kaiser Permanente, who each contributed to data analyses, had full access to all of the data in the study and take responsibility for the integrity of the data and the accuracy of the data analysis.

Study concept and design: Kroenke, Meyerhardt, Prado, Caan.

Acquisition, analysis, or interpretation of data: Kroenke, Neugebauer, Prado, Weltzien, Kwan, Xiao, Caan.

Drafting of the manuscript: Kroenke, Caan.

Critical revision of the manuscript for important intellectual content: Kroenke, Neugebauer, Meyerhardt, Prado, Weltzien, Kwan, Xiao, Caan.

Statistical analysis: Kroenke, Neugebauer, Weltzien, Kwan, Caan.

Obtained funding: Caan.

Administrative, technical, or material support: Prado, Xiao.

Study supervision: Caan.

Conflict of Interest Disclosures: None reported.

Funding/Support: This work was supported by the National Cancer Institute at the National Institutes of Health (grant No. 5R01CA175011).

Role of the Funder/Sponsor: The National Cancer Institute at the National Institutes of Health had no role in the design and conduct of the study; collection, management, analysis, and interpretation of the data; preparation, review, or approval of the manuscript; and decision to submit the manuscript for publication.

References
1.
Aleksandrova  K, Pischon  T, Buijsse  B,  et al.  Adult weight change and risk of colorectal cancer in the European Prospective Investigation into Cancer and Nutrition.  Eur J Cancer. 2013;49(16):3526-3536.PubMedGoogle ScholarCrossref
2.
Keimling  M, Renehan  AG, Behrens  G,  et al.  Comparison of associations of body mass index, abdominal adiposity, and risk of colorectal cancer in a large prospective cohort study.  Cancer Epidemiol Biomarkers Prev. 2013;22(8):1383-1394.PubMedGoogle ScholarCrossref
3.
Renehan  AG, Flood  A, Adams  KF,  et al.  Body mass index at different adult ages, weight change, and colorectal cancer risk in the National Institutes of Health-AARP Cohort.  Am J Epidemiol. 2012;176(12):1130-1140.PubMedGoogle ScholarCrossref
4.
Li  H, Yang  G, Xiang  YB,  et al.  Body weight, fat distribution and colorectal cancer risk: a report from cohort studies of 134255 Chinese men and women.  Int J Obes (Lond). 2013;37(6):783-789.PubMedGoogle ScholarCrossref
5.
Odegaard  AO, Koh  WP, Yu  MC, Yuan  JM.  Body mass index and risk of colorectal cancer in Chinese Singaporeans: the Singapore Chinese Health Study.  Cancer. 2011;117(16):3841-3849.PubMedGoogle ScholarCrossref
6.
Laake  I, Thune  I, Selmer  R, Tretli  S, Slattery  ML, Veierød  MB.  A prospective study of body mass index, weight change, and risk of cancer in the proximal and distal colon.  Cancer Epidemiol Biomarkers Prev. 2010;19(6):1511-1522.PubMedGoogle ScholarCrossref
7.
Campbell  PT, Cotterchio  M, Dicks  E, Parfrey  P, Gallinger  S, McLaughlin  JR.  Excess body weight and colorectal cancer risk in Canada: associations in subgroups of clinically defined familial risk of cancer.  Cancer Epidemiol Biomarkers Prev. 2007;16(9):1735-1744.PubMedGoogle ScholarCrossref
8.
Lin  J, Zhang  SM, Cook  NR, Rexrode  KM, Lee  IM, Buring  JE.  Body mass index and risk of colorectal cancer in women (United States).  Cancer Causes Control. 2004;15(6):581-589.PubMedGoogle ScholarCrossref
9.
Caan  BJ, Coates  AO, Slattery  ML, Potter  JD, Quesenberry  CP  Jr, Edwards  SM.  Body size and the risk of colon cancer in a large case-control study.  Int J Obes Relat Metab Disord. 1998;22(2):178-184.PubMedGoogle ScholarCrossref
10.
Sinicrope  FA, Foster  NR, Yothers  G,  et al; Adjuvant Colon Cancer Endpoints (ACCENT) Group.  Body mass index at diagnosis and survival among colon cancer patients enrolled in clinical trials of adjuvant chemotherapy.  Cancer. 2013;119(8):1528-1536.PubMedGoogle ScholarCrossref
11.
Meyerhardt  JA, Niedzwiecki  D, Hollis  D,  et al; Cancer and Leukemia Group B 89803.  Impact of body mass index and weight change after treatment on cancer recurrence and survival in patients with stage III colon cancer: findings from Cancer and Leukemia Group B 89803.  J Clin Oncol. 2008;26(25):4109-4115.PubMedGoogle ScholarCrossref
12.
Dignam  JJ, Polite  BN, Yothers  G,  et al.  Body mass index and outcomes in patients who receive adjuvant chemotherapy for colon cancer.  J Natl Cancer Inst. 2006;98(22):1647-1654.PubMedGoogle ScholarCrossref
13.
Prizment  AE, Flood  A, Anderson  KE, Folsom  AR.  Survival of women with colon cancer in relation to precancer anthropometric characteristics: the Iowa Women’s Health Study.  Cancer Epidemiol Biomarkers Prev. 2010;19(9):2229-2237.PubMedGoogle ScholarCrossref
14.
Sinicrope  FA, Foster  NR, Sargent  DJ, O’Connell  MJ, Rankin  C.  Obesity is an independent prognostic variable in colon cancer survivors.  Clin Cancer Res. 2010;16(6):1884-1893.PubMedGoogle ScholarCrossref
15.
Doria-Rose  VP, Newcomb  PA, Morimoto  LM, Hampton  JM, Trentham-Dietz  A.  Body mass index and the risk of death following the diagnosis of colorectal cancer in postmenopausal women (United States).  Cancer Causes Control. 2006;17(1):63-70.PubMedGoogle ScholarCrossref
16.
Murphy  TK, Calle  EE, Rodriguez  C, Kahn  HS, Thun  MJ.  Body mass index and colon cancer mortality in a large prospective study.  Am J Epidemiol. 2000;152(9):847-854.PubMedGoogle ScholarCrossref
17.
Alipour  S, Kennecke  HF, Woods  R,  et al.  Body mass index and body surface area and their associations with outcomes in stage II and III colon cancer.  J Gastrointest Cancer. 2013;44(2):203-210.PubMedGoogle ScholarCrossref
18.
Schlesinger  S, Siegert  S, Koch  M,  et al.  Postdiagnosis body mass index and risk of mortality in colorectal cancer survivors: a prospective study and meta-analysis.  Cancer Causes Control. 2014;25(10):1407-1418.PubMedGoogle ScholarCrossref
19.
Pearl  J.  Causal diagrams for empirical research.  Biometrika. 1995;82:669-710.Google ScholarCrossref
20.
Hernán  MA, Hernández-Díaz  S, Robins  JM.  A structural approach to selection bias.  Epidemiology. 2004;15(5):615-625.PubMedGoogle ScholarCrossref
21.
Banack  HR, Kaufman  JS.  The obesity paradox: understanding the effect of obesity on mortality among individuals with cardiovascular disease.  Prev Med. 2014;62:96-102.PubMedGoogle ScholarCrossref
22.
Flegal  KM, Graubard  BI, Williamson  DF, Cooper  RS.  Reverse causation and illness-related weight loss in observational studies of body weight and mortality.  Am J Epidemiol. 2011;173(1):1-9.PubMedGoogle ScholarCrossref
23.
Pearl  J.  An introduction to causal inference.  Int J Biostat. 2010;6(2):7.PubMedGoogle ScholarCrossref
24.
Gordon  NP.  Characteristics of Adult Health Plan Members in Kaiser Permanente’s Northern California Region, as Estimated from the 2011 Member Health Survey. Oakland, CA: Division of Research, Kaiser Permanente Medical Care Program; 2013.
25.
Classification  BMI. World Health Organization; 2014. http://apps.who.int/bmi/index.jsp?introPage=intro_3.html. Accessed April 7, 2014.
26.
Winter  JE, MacInnis  RJ, Wattanapenpaiboon  N, Nowson  CA.  BMI and all-cause mortality in older adults: a meta-analysis.  Am J Clin Nutr. 2014;99(4):875-890.PubMedGoogle ScholarCrossref
27.
Greenland  S, Pearl  J, Robins  JM.  Causal diagrams for epidemiologic research.  Epidemiology. 1999;10(1):37-48.PubMedGoogle ScholarCrossref
28.
Durrleman  S, Simon  R.  Flexible regression models with cubic splines.  Stat Med. 1989;8(5):551-561.PubMedGoogle ScholarCrossref
29.
Pelser  C, Arem  H, Pfeiffer  RM,  et al.  Prediagnostic lifestyle factors and survival after colon and rectal cancer diagnosis in the National Institutes of Health (NIH)-AARP Diet and Health Study.  Cancer. 2014;120(10):1540-1547.PubMedGoogle ScholarCrossref
30.
Campbell  PT, Newton  CC, Dehal  AN, Jacobs  EJ, Patel  AV, Gapstur  SM.  Impact of body mass index on survival after colorectal cancer diagnosis: the Cancer Prevention Study-II Nutrition Cohort.  J Clin Oncol. 2012;30(1):42-52.PubMedGoogle ScholarCrossref
31.
Meyerhardt  JA, Tepper  JE, Niedzwiecki  D,  et al.  Impact of body mass index on outcomes and treatment-related toxicity in patients with stage II and III rectal cancer: findings from Intergroup Trial 0114.  J Clin Oncol. 2004;22(4):648-657.PubMedGoogle ScholarCrossref
32.
Kuiper  JG, Phipps  AI, Neuhouser  ML,  et al.  Recreational physical activity, body mass index, and survival in women with colorectal cancer.  Cancer Causes Control. 2012;23(12):1939-1948.PubMedGoogle ScholarCrossref
33.
Baade  PD, Meng  X, Youl  PH, Aitken  JF, Dunn  J, Chambers  SK.  The impact of body mass index and physical activity on mortality among patients with colorectal cancer in Queensland, Australia.  Cancer Epidemiol Biomarkers Prev. 2011;20(7):1410-1420.PubMedGoogle ScholarCrossref
34.
Lajous  M, Bijon  A, Fagherazzi  G,  et al.  Body mass index, diabetes, and mortality in French women: explaining away a “paradox”.  Epidemiology. 2014;25(1):10-14.PubMedGoogle ScholarCrossref
35.
Carnethon  MR, De Chavez  PJ, Biggs  ML,  et al.  Association of weight status with mortality in adults with incident diabetes.  JAMA. 2012;308(6):581-590.PubMedGoogle ScholarCrossref
36.
Casas-Vara  A, Santolaria  F, Fernández-Bereciartúa  A, González-Reimers  E, García-Ochoa  A, Martínez-Riera  A.  The obesity paradox in elderly patients with heart failure: analysis of nutritional status.  Nutrition. 2012;28(6):616-622.PubMedGoogle ScholarCrossref
37.
Doehner  W, Erdmann  E, Cairns  R,  et al.  Inverse relation of body weight and weight change with mortality and morbidity in patients with type 2 diabetes and cardiovascular co-morbidity: an analysis of the PROactive study population.  Int J Cardiol. 2012;162(1):20-26.PubMedGoogle ScholarCrossref
38.
Kim  BJ, Lee  SH, Jung  KH, Yu  KH, Lee  BC, Roh  JK; For Korean Stroke Registry investigators.  Dynamics of obesity paradox after stroke, related to time from onset, age, and causes of death.  Neurology. 2012;79(9):856-863.PubMedGoogle ScholarCrossref
39.
Lancefield  T, Clark  DJ, Andrianopoulos  N,  et al; MIG (Melbourne Interventional Group) Registry.  Is there an obesity paradox after percutaneous coronary intervention in the contemporary era? An analysis from a multicenter Australian registry.  JACC Cardiovasc Interv. 2010;3(6):660-668.PubMedGoogle ScholarCrossref
40.
Lavie  CJ, De Schutter  A, Patel  D, Artham  SM, Milani  RV.  Body composition and coronary heart disease mortality--an obesity or a lean paradox?  Mayo Clin Proc. 2011;86(9):857-864.PubMedGoogle ScholarCrossref
41.
Tseng  CH.  Obesity paradox: differential effects on cancer and noncancer mortality in patients with type 2 diabetes mellitus.  Atherosclerosis. 2013;226(1):186-192.PubMedGoogle ScholarCrossref
42.
Uretsky  S, Messerli  FH, Bangalore  S,  et al.  Obesity paradox in patients with hypertension and coronary artery disease.  Am J Med. 2007;120(10):863-870.PubMedGoogle ScholarCrossref
43.
Angerås  O, Albertsson  P, Karason  K,  et al.  Evidence for obesity paradox in patients with acute coronary syndromes: a report from the Swedish Coronary Angiography and Angioplasty Registry.  Eur Heart J. 2013;34(5):345-353.PubMedGoogle ScholarCrossref
44.
Blum  A, Simsolo  C, Sirchan  R, Haiek  S.  “Obesity paradox” in chronic obstructive pulmonary disease.  Isr Med Assoc J. 2011;13(11):672-675.PubMedGoogle Scholar
45.
Jackson  RS, Black  JH  III, Lum  YW,  et al.  Class I obesity is paradoxically associated with decreased risk of postoperative stroke after carotid endarterectomy.  J Vasc Surg. 2012;55(5):1306-1312.PubMedGoogle ScholarCrossref
46.
Jialin  W, Yi  Z, Weijie  Y.  Relationship between body mass index and mortality in hemodialysis patients: a meta-analysis.  Nephron Clin Pract. 2012;121(3-4):c102-c111.PubMedGoogle ScholarCrossref
47.
Komukai  K, Minai  K, Arase  S,  et al.  Impact of body mass index on clinical outcome in patients hospitalized with congestive heart failure.  Circ J. 2012;76(1):145-151.PubMedGoogle ScholarCrossref
48.
Stein  PD, Matta  F, Goldman  J.  Obesity and pulmonary embolism: the mounting evidence of risk and the mortality paradox.  Thromb Res. 2011;128(6):518-523.PubMedGoogle ScholarCrossref
49.
Schenkeveld  L, Magro  M, Oemrawsingh  RM,  et al.  The influence of optimal medical treatment on the ‘obesity paradox’, body mass index and long-term mortality in patients treated with percutaneous coronary intervention: a prospective cohort study.  BMJ Open. 2012;2:e000535.PubMedGoogle ScholarCrossref
50.
Biasucci  LM, Graziani  F, Rizzello  V,  et al.  Paradoxical preservation of vascular function in severe obesity.  Am J Med. 2010;123(8):727-734.PubMedGoogle ScholarCrossref
51.
Graziani  F, Biasucci  LM, Cialdella  P,  et al.  Thromboxane production in morbidly obese subjects.  Am J Cardiol. 2011;107(11):1656-1661.PubMedGoogle ScholarCrossref
52.
Lund  LH, Williams  JJ, Freda  P, LaManca  JJ, LeJemtel  TH, Mancini  DM.  Ghrelin resistance occurs in severe heart failure and resolves after heart transplantation.  Eur J Heart Fail. 2009;11(8):789-794.PubMedGoogle ScholarCrossref
53.
Feldman  AM, Combes  A, Wagner  D,  et al.  The role of tumor necrosis factor in the pathophysiology of heart failure.  J Am Coll Cardiol. 2000;35(3):537-544.PubMedGoogle ScholarCrossref
54.
McTiernan  CF, Feldman  AM.  The role of tumor necrosis factor alpha in the pathophysiology of congestive heart failure.  Curr Cardiol Rep. 2000;2(3):189-197.PubMedGoogle ScholarCrossref
55.
Glymour  MM, Vittinghoff  E.  Commentary: selection bias as an explanation for the obesity paradox: just because it’s possible doesn’t mean it’s plausible.  Epidemiology. 2014;25(1):4-6.PubMedGoogle ScholarCrossref
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