Biliary tract cancers are rare but highly fatal with poorly understood etiology. Identifying potentially modifiable risk factors for these cancers is essential for prevention. Here we estimated the relationship between adiposity and cancer across the biliary tract, including cancers of the gallbladder (GBC), intrahepatic bile ducts (IHBDC), extrahepatic bile ducts (EHBDC), and the ampulla of Vater (AVC). We pooled data from 27 prospective cohorts with over 2.7 million adults. Adiposity was measured using baseline body mass index (BMI), waist circumference, hip circumference, waist-to-hip, and waist-to-height ratios. HRs and 95% confidence intervals (95% CI) were estimated using Cox proportional hazards models adjusted for sex, education, race, smoking, and alcohol consumption with age as the time metric and the baseline hazard stratified by study. During 37,883,648 person-years of follow-up, 1,343 GBC cases, 1,194 EHBDC cases, 784 IHBDC cases, and 623 AVC cases occurred. For each 5 kg/m2 increase in BMI, there were risk increases for GBC (HR = 1.27; 95% CI, 1.19–1.36), IHBDC (HR = 1.32; 95% CI, 1.21–1.45), and EHBDC (HR = 1.13; 95% CI, 1.03–1.23), but not AVC (HR = 0.99; 95% CI, 0.88–1.11). Increasing waist circumference, hip circumference, waist-to-hip ratio, and waist-to-height ratio were associated with GBC and IHBDC but not EHBDC or AVC. These results indicate that adult adiposity is associated with an increased risk of biliary tract cancer, particularly GBC and IHBDC. Moreover, they provide evidence for recommending weight maintenance programs to reduce the risk of developing these cancers.

Significance:

These findings identify a correlation between adiposity and biliary tract cancers, indicating that weight management programs may help minimize the risk of these diseases.

Biliary tract cancers (BTC) include cancers of the gallbladder (GBC), intrahepatic bile duct (IHBDC), extrahepatic bile duct (EHBDC), and the ampulla of Vater (AVC). Worldwide BTCs account for 3% of all adult cancers with marked variations in incidence by geography and ethnicity (1). The highest rates of BTC are seen among women in Latin America, South Asia, and Eastern Europe, whereas rates for men are also elevated in China and Japan (2, 3). In most countries, 5-year survival is less than 20% (2), largely attributable to lack of early signs or symptoms of disease until the cancers are well advanced (4). Thus, understanding the etiology of BTCs and identifying potentially modifiable risk factors are critical for primary prevention.

Globally, the prevalence of obesity has been steadily rising, increasing 3-fold among men, and more than doubling among women between 1975 and 2014 (5). If this trajectory persists, global obesity prevalence will be 18% among men and over 21% among women by 2025 (6). These trends in adiposity are translating into increasing incidence of obesity-related diseases among children and adolescents, and likely elevate the risk of developing several obesity-related cancers later in adulthood. Anthropometric parameters, such as body mass index (BMI) and waist circumference, have been associated with higher all-cause and cancer-specific mortality rates across racial and ethnic groups (7–11). Furthermore, obesity is associated with increasing years of life lost, and being overweight is also associated with increased cancer risk (12, 13).

Previous research suggests that overweight and obesity are associated with GBC (14), but the literature on obesity and other BTC sites has been less consistent. Studies of obesity and cholangiocarcinoma have found either an increased risk (15, 16) or no association (17, 18). Similar inconclusive results were obtained when IHBDC has been analyzed separately from EHBDC (17, 19, 20), although a recent meta-analysis found strong associations between obesity and IHBDC (21). The rarity of BTCs often results in studies that are insufficiently powered, highlighting the need for large consortium pooling projects. We examined associations between different anthropometric measures of adiposity and cancer across the biliary tract in the largest study to date involving prospective data from 27 cohorts with over 2.7 million individuals from North America, Europe, Asia, and Australia.

Study population

Data were analyzed from the Biliary Tract Cancers Pooling Project (BiTCaPP), which consists of 27 studies, including 22 prospective cohort studies, and observational follow-up of participants enrolled in 4 randomized controlled prevention trials, and 1 cancer screening trial (Table 1), from North America, Europe, Asia, and Australia. BiTCaPP was determined to be exempt from Institutional Review Board review by the NCI's Office of Human Subjects Research. All component cohort studies within BiTCaPP received Institutional Review Board approval at their respective institutions.

Table 1.

Summary of study characteristics contributing to the BiTCaPPa

Study (acronym)Study populationFollow-up periodBaseline sample, N (%)Total person-timeGBC cases, N (%)IHBDC cases, N (%)bEHBDC cases, N (%)AVC cases, N (%)
AgHealth USA 1993–2013 69,422 (2.6) 1,129,815 18 (1.3) 15 (1.9) 14 (1.5) 10 (1.6) 
AHS-2 USA 2002–2015 93,264 (3.4) 971,114 15 (1.1) 9 (1.1) 11 (1.0) 6 (1.0) 
ATBC Finland 1985–2010 29,101 (1.1) 443,724 17 (1.3) 38 (4.6) 42 (3.5) 16 (2.6) 
BCDDP USA 1980–1998 42,874 (1.6) 329,205 8 (0.6) 4 (0.5) 7 (0.6) 8 (1.3) 
COSM Sweden 1998–2008 43,430 (1.6) 404,822 11 (0.8) 6 (0.7) 13 (1.1) 3 (0.5) 
CPS-II NC USA 1992–2011 152,771 (5.6) 2,055,047 69 (5.1) 54 (6.9) 53 (4.4) 34 (5.4) 
EPIC Europe 1992–2010 485,465 (17.9) 6,762,629 132 (9.8) 118 (15.1) 109 (9.1) 85 (13.6) 
HPFS USA 1986–2012 50,178 (1.9) 931,984 11 (0.8) 14 (1.8) 22 (1.8) 10 (1.6) 
IWHS USA 1986–2013 37,977 (1.4) 723,154 69 (5.1) 14 (1.8) 30 (2.5) 12 (1.9) 
JPHC I Japan 1990–2011 98,031 (3.6) 1,673,119 167 (12.4) 120 (15.3) 196 (16.4) 38 (6.1) 
JPHC II  1993–2011       
MCCS Australia 1990–2009 39,961 (1.5) 679021 35 (2.6) 20 (2.6) 22 (1.8) 6 (1.0) 
MEC USA 1993–2010 185,398 (6.9) 2,670,391 109 (8.1) 59 (7.5) 117 (9.8) 63 (10.1) 
NHS USA 1980–2012 95,609 (3.5) 2,382,004 52 (3.9) 16 (2.0) 31 (2.6) 18 (2.9) 
NIH-AARP USA 1995–2011 542,356 (20.0) 6,867,629 210 (15.6) 132 (16.8) 216 (18.8) 148 (23.8) 
NYUWHS USA 1985–2007 13,299 (0.5) 271,155 6 (0.5) 6 (0.7) 9 (0.8) 
PHS USA 1982–2009 28,419 (1.1) 517,908 7 (0.5) 9 (1.2) 9 (0.8) 7 (1.1) 
  1997–2009       
PLCO USA 1993–2009 146,688 (5.4) 1,624,424 45 (3.4) 20 (2.6) 49 (4.1) 34 (5.5) 
RERF Japan 1950–2005 47,953 (1.8) 1,099,161 140 (10.4) N/A 112 (9.4) 23 (3.7) 
REVEAL Taiwan 1991–2012 23,640 (0.9) 447, 458 7 (0.5) 44 (5.6) 12 (1.0) 8 (1.3) 
SCHS Singapore 1993–2008 61,251 (2.3) 739,282 29 (2.2) 26 (3.3) 14 (1.2) 22 (3.5) 
SCS China 1986–2012 18,075 (0.7) 339,282 15 (1.1) 13 (1.7) 21 (1.8) 15 (2.4) 
Sisters USA 2003–2012 47,782 (1.8) 395,658 4 (0.3) 4 (0.5) 4 (0.6) 
SMC Sweden 1997–2008 36,393 (1.3) 352,405 42 (3.1) 3 (0.4) 6 (0.5) 1 (0.2) 
VITAL USA 2000–2009 73,301 (2.7) 540,831 16 (1.2) 15 (1.9) 15 (1.3) 5 (0.8) 
WHI USA 1993–2014 159,801 (5.9) 2,024,899 87 (6.5) 19 (2.4) 41 (3.4) 35 (5.6) 
WHS USA 1992–2010 38,974 (1.4) 573,970 10 (0.7) 6 (0.8) 1 (0.1) 11 (1.8) 
WLHS Norway, 1991–2011 45,429 (1.7) 933,557 12 (0.9) 6 (0.8) 4 (0.4) 1 (0.2) 
 Sweden 2003–2011       
Total   2,707,448 37,883,648 1,343 784 1,194 623 
Study (acronym)Study populationFollow-up periodBaseline sample, N (%)Total person-timeGBC cases, N (%)IHBDC cases, N (%)bEHBDC cases, N (%)AVC cases, N (%)
AgHealth USA 1993–2013 69,422 (2.6) 1,129,815 18 (1.3) 15 (1.9) 14 (1.5) 10 (1.6) 
AHS-2 USA 2002–2015 93,264 (3.4) 971,114 15 (1.1) 9 (1.1) 11 (1.0) 6 (1.0) 
ATBC Finland 1985–2010 29,101 (1.1) 443,724 17 (1.3) 38 (4.6) 42 (3.5) 16 (2.6) 
BCDDP USA 1980–1998 42,874 (1.6) 329,205 8 (0.6) 4 (0.5) 7 (0.6) 8 (1.3) 
COSM Sweden 1998–2008 43,430 (1.6) 404,822 11 (0.8) 6 (0.7) 13 (1.1) 3 (0.5) 
CPS-II NC USA 1992–2011 152,771 (5.6) 2,055,047 69 (5.1) 54 (6.9) 53 (4.4) 34 (5.4) 
EPIC Europe 1992–2010 485,465 (17.9) 6,762,629 132 (9.8) 118 (15.1) 109 (9.1) 85 (13.6) 
HPFS USA 1986–2012 50,178 (1.9) 931,984 11 (0.8) 14 (1.8) 22 (1.8) 10 (1.6) 
IWHS USA 1986–2013 37,977 (1.4) 723,154 69 (5.1) 14 (1.8) 30 (2.5) 12 (1.9) 
JPHC I Japan 1990–2011 98,031 (3.6) 1,673,119 167 (12.4) 120 (15.3) 196 (16.4) 38 (6.1) 
JPHC II  1993–2011       
MCCS Australia 1990–2009 39,961 (1.5) 679021 35 (2.6) 20 (2.6) 22 (1.8) 6 (1.0) 
MEC USA 1993–2010 185,398 (6.9) 2,670,391 109 (8.1) 59 (7.5) 117 (9.8) 63 (10.1) 
NHS USA 1980–2012 95,609 (3.5) 2,382,004 52 (3.9) 16 (2.0) 31 (2.6) 18 (2.9) 
NIH-AARP USA 1995–2011 542,356 (20.0) 6,867,629 210 (15.6) 132 (16.8) 216 (18.8) 148 (23.8) 
NYUWHS USA 1985–2007 13,299 (0.5) 271,155 6 (0.5) 6 (0.7) 9 (0.8) 
PHS USA 1982–2009 28,419 (1.1) 517,908 7 (0.5) 9 (1.2) 9 (0.8) 7 (1.1) 
  1997–2009       
PLCO USA 1993–2009 146,688 (5.4) 1,624,424 45 (3.4) 20 (2.6) 49 (4.1) 34 (5.5) 
RERF Japan 1950–2005 47,953 (1.8) 1,099,161 140 (10.4) N/A 112 (9.4) 23 (3.7) 
REVEAL Taiwan 1991–2012 23,640 (0.9) 447, 458 7 (0.5) 44 (5.6) 12 (1.0) 8 (1.3) 
SCHS Singapore 1993–2008 61,251 (2.3) 739,282 29 (2.2) 26 (3.3) 14 (1.2) 22 (3.5) 
SCS China 1986–2012 18,075 (0.7) 339,282 15 (1.1) 13 (1.7) 21 (1.8) 15 (2.4) 
Sisters USA 2003–2012 47,782 (1.8) 395,658 4 (0.3) 4 (0.5) 4 (0.6) 
SMC Sweden 1997–2008 36,393 (1.3) 352,405 42 (3.1) 3 (0.4) 6 (0.5) 1 (0.2) 
VITAL USA 2000–2009 73,301 (2.7) 540,831 16 (1.2) 15 (1.9) 15 (1.3) 5 (0.8) 
WHI USA 1993–2014 159,801 (5.9) 2,024,899 87 (6.5) 19 (2.4) 41 (3.4) 35 (5.6) 
WHS USA 1992–2010 38,974 (1.4) 573,970 10 (0.7) 6 (0.8) 1 (0.1) 11 (1.8) 
WLHS Norway, 1991–2011 45,429 (1.7) 933,557 12 (0.9) 6 (0.8) 4 (0.4) 1 (0.2) 
 Sweden 2003–2011       
Total   2,707,448 37,883,648 1,343 784 1,194 623 

aATBC, PHS, WHI, and WHS are randomized controlled trials and PLCO is a screening trial. The remaining studies included in BiTCaPP are prospective cohort studies.

bIntrahepatic bile duct cancer cases not reported by RERF.

Outcomes

Incident BTC for each anatomical site was defined by the International Classification of Diseases codes and/or medical and death record text and classified as primary GBC, IHBDC, EHBDC, and AVC (Supplementary Table S1). A diagnosis of BTC was verified by linkage to local, state, or national cancer registries [Agricultural Health Study (AgHealth), Adventist Health Study 2 (AHS-2), the Alpha-Tocopherol, Beta-Carotene Cancer Prevention Study (ATBC), Cohort of Swedish Men (COSM), Iowa Women's Health Study (IWHS), Melbourne Collaborative Cohort Study (MCCS), Multiethnic Cohort Study (MEC), NIH-American Association of Retired Persons Diet and Health Study (NIH-AARP), Radiation Effects Research Foundation Life Span Study (RERF), Singapore Chinese Health Study (SCHS), Swedish Mammography Cohort (SMC), and VITamins and Lifestyle Study (VITAL)]; medical record, pathology report, or death certificate [Health Professionals Follow-Up Study (HPFS), Nurses’ Health Study (NHS), Prostate, Lung, Colorectal and Ovarian Cancer Screening Trial (PLCO), Physicians’ Health Study (PHS), Women's Health Initiative (WHI), Women's Health Study (WHS), and Women's Lifestyle and Health Study (WLHS)]; or a combination of methods [Breast Cancer Detection Demonstration Project (BCDDP), Cancer Prevention Study-II Nutrition Cohort (CPS-II NC), European Prospective Investigation into Cancer and Nutrition (EPIC), Japan Public Health Center Study (JPHC), New York University Women's Health Study (NYUWHS), Risk Evaluation of Viral Load Elevation and Associated Liver Disease/Cancer-Hepatitis B Virus Study (REVEAL), Shanghai Cohort Study (SCS), and the Sister Study (Sisters)].

Exclusion criteria

BiTCaPP is comprised of 2,847,787 individuals. Those under the age of 18 (n = 132), missing age at baseline or exit (n = 5,048), with prior cancer diagnoses at baseline (n = 61,356), with incident cancers categorized as being at other/other unknown sites or overlapping lesion of biliary tract (n = 323), and unknown BTC status (n = 9). We also excluded those with missing height or weight (n = 73,471) data, which represents 2.6% of the total study population. Data from the remaining 2,707,448 individuals comprised the analytic dataset.

Exposures

BMI was calculated from weight in kilograms divided by squared height in meters. Baseline height and weight were directly measured by study staff in 7 of the cohorts and self-reported in the other 20 studies (see Supplementary Table S2, for details on data collection for anthropometric and other measures by study). BMI was categorized according to the WHO International Classification for Western adults as follows: underweight (15.0–<18.5 kg/m2), normal weight (18.5–<25 kg/m2), overweight (25–<30 kg/m2), and obese (≥30 kg/m2). Obesity was further classified as: classes I (30–<35 kg/m2), II (35–<40 kg/m2), and III (≥40 kg/m2; ref. 22). BMI for JPHC I and II, RERF, REVEAL, SCHS, and SCS was classified according to WHO recommendations for Asian adults as follows: underweight (15.0–<18.5 kg/m2), normal weight (18.5–<23 kg/m2), overweight (23–<27.5 kg/m2), obese (≥27.5 kg/m2), with obesity further categorized as: classes I (27.5–<32.5 kg/m2), II (32.5–<37.5 kg/m2), and III (≥37.5 kg/m2; ref. 23). Self-reported weight at ages 18 or 20 was available from 7 studies and was divided by baseline adult height (in m2) to calculate young adult BMI (categorized as above). Adult weight change was calculated by subtracting young adult BMI from baseline adult BMI. Weight change was categorized as: adult weight loss (change of <2 kg/m2), stable weight (2–<2 kg/m2), and weight gains (2–<5 kg/m2, 5–<10 kg/m2, or ≥10 kg/m2; ref. 24). Adult and young adult BMI were also modeled continuously per 5 kg/m2 increase after confirming the linear relationship between BMI and development of a BTC. When analyzed continuously, individuals with a BMI <15.0 or >60 kg/m2 were excluded from analysis (n = 52,410). When analyzed categorically, the BMI categories included all individuals with nonmissing values. Analyses excluding extreme values from BMI categories showed no difference in the estimates.

Baseline waist circumference and hip circumference were measured in centimeters directly by study staff (n = 5 studies) or using participant self-measurements (n = 9 studies). Individuals with waist or hip circumferences <45 or >190 cm were excluded from the analysis (n = 35,252 and 31,427, respectively). Waist-to-hip ratio was calculated by dividing waist circumference by hip circumference, and waist-to-height ratio was calculated by dividing waist circumference by height (both in cm). We modeled waist and hip circumferences (both per 5 cm increase) and waist-to-hip and waist-to-height ratios (both per 0.1-unit increase) as continuous variables.

Information on sex, race (white, black, Asian/Pacific Islander, and other), education level (some college, high school graduate or GED, or less than high school graduate), smoking (ever/never), and alcohol consumption (ever/never) were collected by self-report at baseline (25). Because of a high number of missing values, education was set at “some college” for all missing in the cohorts made up of health professionals (HPFS, NHS, and PHS), and missing race set to “white” in HPFS. History of gallstone information was captured by 18 studies, and cholecystectomy was collected by 9 studies.

Statistical analyses

Participant baseline demographic characteristics and history of relevant medical conditions were summarized using descriptive statistics. We evaluated associations between anthropometric measures and incident BTC using Cox proportional hazards regression models with age as the time scale and left truncation at baseline to estimate site-specific HRs and 95% confidence intervals (95% CI). Confounding was assessed using directed acyclic graphs to identify the minimally sufficient set of covariates for control (26). All models of adult adiposity were adjusted for sex, education level, race, smoking, and alcohol consumption with baseline hazard stratified by study. We tested for linear trends across BMI categories with the Wald test with 1 degree of freedom. Models examining weight change from young to middle adulthood were additionally adjusted for young adult BMI.

To examine whether the association between continuous BMI and incidence of BTC differed by cancer subtype, we used Cox proportional hazards regression models with a duplication method, testing for heterogeneity using the Wald test (27). To assess statistical heterogeneity of results between the studies, we performed a random-effects meta-analysis using Cochrane I2 (28). Study-specific models were adjusted for sex, race, education level, smoking, and alcohol, where appropriate. The proportional hazards assumption was assessed visually by plotting the scaled Schoenfeld residuals against time and by testing for independence between the residuals and time in the models. The proportional hazards assumption was met for all models.

In the subset of cohorts (n = 19) with information on prior gallstone diagnoses, we compared the estimated risk with and without adjustment for gallstones to assess whether the effect of adiposity on BTC is mediated by gallstones, the major risk factor for BTCs (17). GBC analyses were repeated for the subset of studies (n = 9) that collected cholecystectomy history, comparing the estimated risk of GBC when restricted to individuals with a gallbladder.

To test for effect measure modification, an interaction term for BMI by sex was included in all models. As there was no evidence of effect measure modification by sex, this term was omitted from final models. We repeated the main analyses stratified by Western and Asian countries as the etiology of BTCs may differ between these areas. As a sensitivity analysis, we re-ran the analyses excluding events within that occurred within 1 year of exposure assessment to account for possible reverse causation. We saw no difference in the associations in this analysis. For example, the HR for GBC in obese versus normal-weight individuals was 1.71 (95% CI, 1.40–2.08) with the 1-year lag, compared with 1.72 (95% CI, 1.41–2.08) without the lag. We also assessed associations between continuous BMI and BTC in the same participants who provided young adult BMI. Because of concerns about residual confounding by smoking and alcohol, we also conducted a sensitivity analysis for continuous BMI restricted to the studies that collected smoking in packyears and drinking in drinks per day.

Statistical tests were 2-sided with a type I error rate of α = 0.05. Stata (v.14) software was used for meta-analyses; R Studio (v.3.5.0) was used to test the proportional hazards assumptions; and SAS (v.9.4) software was used for pooled and study-specific association estimates.

In this analysis, 1,343 GBC cases, 1,194 EHBDC cases, 784 IHBDC cases, and 623 AVC cases occurred during 37,883,648 person-years of follow-up time as shown in Table 1. Participant characteristics are presented by cohort in Table 2. The median age at baseline was 59 years (SD = 10), 59% were women, 80% were white, 59% had some college education, 52% were ever smokers, and 57% were ever drinkers. The mean BMI for each of the cohorts ranged from 21.9 kg/m2 (SD = 3) in RERF to 29.9 kg/m2 (SD = 7) in AHS-2. Within the cohorts that collected relevant medical history, 9% of participants had gallstones and 11% of participants reported a cholecystectomy. Anthropometric characteristics at baseline are presented by cohort and sex in Supplementary Table S3.

Table 2.

Summary of participant characteristics by cohort included in the BiTCaPP

Race/ethnicityb, %
StudyWomena, %Age mean (SD)WhiteBlackAsian/Pacific islanderOtherSome collegec, %BMI mean (SD)Gallstonesd, %Cholecyst-ectomye, %Ever smokerf, %Ever drinkerg, %
AgHealth 42 47 (13) 98 <1 <1 48 26.7 (5) N/A N/A 39 18 
AHS-2 65 58 (14) 68 28 78 29.9 (7) N/A 20 36 
ATBC 57 (5) 100 26.3 (4) 100 79 
BCDDP 100 62 (8) 90 46 25.1 (5) N/A N/A 43 44 
COSM 61 (10) 100 17 25.9 (3) 11 N/A 64 80 
CPS-II NC 53 63 (6) 98 <1 <1 68 26.0 (4) 12 13 56 61 
EPIC 70 51 (10) 100 48 25.4 (4) N/A 50 84 
HPFS 54 (10) 97 <1 100 25.5 (3) N/A 54 77 
IWHS 100 62 (4) 99 <1 <1 <1 39 25.9 (5) N/A N/A 34 12 
JPHC 52 53 (8) 100 12 23.5 (3) N/A 40 43 
MCCS 59 55 (9) 100 25 26.9 (4) 43 71 
MEC 54 60 (9) 25 17 36 23 26 26.5 (5) 56 25 
NIH-AARP 40 62 (5) 95 <1 74 27.1 (5) 10 14 64 48 
NHS 100 47 (7) 94 100 24.4 (5) 56 81 
NYUWHS 100 51 (9) 84 12 69 24.9 (5) N/A 53 19 
PHS 55 (10) 94 100 24.9 (3) N/A 47 100 
PLCO 51 63 (5) 89 70 27.3 (5) 12 N/A 54 72 
RERF 60 52 (14) 100 13 21.9 (3) N/A N/A 45 47 
REVEAL 50 47 (10) 100 24.0 (3) N/A 29 11 
SCHS 55 56 (8) 100 28 23.1 (3) N/A N/A 31 19 
SCS 56 (6) 100 72 22.2 (3) N/A N/A 57 43 
Sisters 100 55 (9) 87 85 27.8 (6) 15 13 43 37 
SMC 100 62 (9) 100 19 25.3 (4) 20 N/A 46 36 
VITAL 51 61 (7) 94 80 27.2 (5) N/A N/A 53 34 
WHI 100 63 (7) 83 77 27.9 (6) 16 13 49 58 
WHS 100 55 (7) 96 <1 100 25.8 (5) 10 N/A 49 20 
WLHS 100 40 (6) 100 41 23.5 (4) N/A N/A 59 86 
Total 59 57 (10) 80 13 59 26.2 (5) 11 52 57 
Race/ethnicityb, %
StudyWomena, %Age mean (SD)WhiteBlackAsian/Pacific islanderOtherSome collegec, %BMI mean (SD)Gallstonesd, %Cholecyst-ectomye, %Ever smokerf, %Ever drinkerg, %
AgHealth 42 47 (13) 98 <1 <1 48 26.7 (5) N/A N/A 39 18 
AHS-2 65 58 (14) 68 28 78 29.9 (7) N/A 20 36 
ATBC 57 (5) 100 26.3 (4) 100 79 
BCDDP 100 62 (8) 90 46 25.1 (5) N/A N/A 43 44 
COSM 61 (10) 100 17 25.9 (3) 11 N/A 64 80 
CPS-II NC 53 63 (6) 98 <1 <1 68 26.0 (4) 12 13 56 61 
EPIC 70 51 (10) 100 48 25.4 (4) N/A 50 84 
HPFS 54 (10) 97 <1 100 25.5 (3) N/A 54 77 
IWHS 100 62 (4) 99 <1 <1 <1 39 25.9 (5) N/A N/A 34 12 
JPHC 52 53 (8) 100 12 23.5 (3) N/A 40 43 
MCCS 59 55 (9) 100 25 26.9 (4) 43 71 
MEC 54 60 (9) 25 17 36 23 26 26.5 (5) 56 25 
NIH-AARP 40 62 (5) 95 <1 74 27.1 (5) 10 14 64 48 
NHS 100 47 (7) 94 100 24.4 (5) 56 81 
NYUWHS 100 51 (9) 84 12 69 24.9 (5) N/A 53 19 
PHS 55 (10) 94 100 24.9 (3) N/A 47 100 
PLCO 51 63 (5) 89 70 27.3 (5) 12 N/A 54 72 
RERF 60 52 (14) 100 13 21.9 (3) N/A N/A 45 47 
REVEAL 50 47 (10) 100 24.0 (3) N/A 29 11 
SCHS 55 56 (8) 100 28 23.1 (3) N/A N/A 31 19 
SCS 56 (6) 100 72 22.2 (3) N/A N/A 57 43 
Sisters 100 55 (9) 87 85 27.8 (6) 15 13 43 37 
SMC 100 62 (9) 100 19 25.3 (4) 20 N/A 46 36 
VITAL 51 61 (7) 94 80 27.2 (5) N/A N/A 53 34 
WHI 100 63 (7) 83 77 27.9 (6) 16 13 49 58 
WHS 100 55 (7) 96 <1 100 25.8 (5) 10 N/A 49 20 
WLHS 100 40 (6) 100 41 23.5 (4) N/A N/A 59 86 
Total 59 57 (10) 80 13 59 26.2 (5) 11 52 57 

Abbreviation: N/A, data not available.

Variables are missing for the following numbers of participants out of the studies reporting these variables:

aSex: 35.

bRace: 31,185.

cEducation: 115,157.

dHistory of gallstones: 684,518.

eCholecystectomy: 1,722,714.

fEver smoker: 46,979.

gEver drinker: 741,107.

Associations of adiposity with BTC risk from the pooled analysis are shown in Table 3. An increase in BMI was associated with increased risk for each anatomic-specific cancers except AVC. For each 5 kg/m2 increase in BMI, there was an increased risk of GBC (HR = 1.27; 95% CI, 1.19–1.36), IHBDC (HR = 1.32; 95% CI, 1.21–1.45), and EHBDC (HR = 1.13; 95% CI, 1.03–1.23). These associations differed by anatomic site when GBC was compared with AVC and EHBDC (Pheterogeneity = 0.0005 and 0.04, respectively), but not when compared with IHBDC (P = 0.89). There was no evidence that these associations differed by sex or of between-study heterogeneity for adult BMI (I2: 0% for all cancer sites; Supplementary Fig. S1A–S1D). These associations did not change substantially when adjusting for smoking packyears and drinks per day among the subset of studies that collected these measures.

Table 3.

Associations between anthropometric characteristics and BTCs in the BiTCaPPa

CharacteristicNoncasesbGBC casesGBC HR (95% CI)IHBDC casesIHBDC HR (95% CI)EHBDC casesEHBDC HR (95% CI)AVC casesAVC HR (95% CI)
BMIc (kg/m2): per 5 kg/m2 2,383,716 1,101 1.27 (1.19–1.36) 624 1.32 (1.21–1.45) 937 1.13 (1.03–1.23) 538 0.99 (0.88–1.11) 
Pheterogeneity (compared with AVC)d   0.0005  0.001  0.308   
Pheterogeneity (compared with IHBDC)d   0.886    0.009   
Pheterogeneity (compared with EHBDC)d   0.004       
BMI category (kg/m2)e 
 Underweight 39,400 29 0.98 (0.60–1.60) 0.91 (0.43–1.95) 15 0.92 (0.53–1.59) 14 1.25 (0.65–2.39) 
 Normal 1,036,939 402 1.00 (reference) 215 1.00 (reference) 352 1.00 (reference) 205 1.00 (reference) 
 Overweight 912,487 431 1.31 (1.11–1.54) 266 1.34 (1.09–1.64) 407 1.14 (0.96–1.35) 232 1.09 (0.87–1.35) 
 Obese 442,878 261 1.72 (1.41–2.08) 144 2.06 (1.62–2.61) 175 1.33 (1.06–1.65) 92 1.08 (0.81–1.35) 
 Obese I 338,889 181 1.62 (1.31–2.00) 110 2.03 (1.57–2.61) 131 1.31 (1.03–1.67) 73 1.07 (0.78–1.48) 
 Obese II 97,456 50 1.54 (1.06–2.22) 24 2.17 (1.39–3.38) 31 1.27 (0.81–2.00) 17 1.33 (0.78–2.28) 
 Obese III 45,101 30 3.32 (2.15–4.80) 10 2.16 (1.05–4.45) 13 1.67 (0.88–3.18) 0.49 (0.12–1.94) 
Ptrendf   <0.0001  <0.0001  0.008  0.78 
 Waist circumferenceg (cm): per 5 cm 1,127,471 434 1.15 (1.11–1.20) 245 1.09 (1.04–1.16) 327 1.03 (0.98–1.09) 223 1.03 (0.97–1.10) 
 Hip circumferenceh(cm): per 5 cm 1,011,338 398 1.16 (1.11–1.22) 216 1.13 (1.05–1.21) 298 1.02 (0.95–1.09) 198 1.02 (0.94–1.11) 
 Waist-to-hip ratiog,h: per 0.1 1,037,853 445 1.11 (1.06–1.17) 229 1.09 (1.01–1.18) 323 1.04 (0.93–1.16) 207 0.99 (0.80–1.22) 
 Waist-to-height ratiog: per 0.1 1,104,877 418 1.47 (1.33–1.63) 242 1.27 (1.07–1.51) 317 1.08 (0.94–1.25) 216 1.07 (0.87–1.32) 
CharacteristicNoncasesbGBC casesGBC HR (95% CI)IHBDC casesIHBDC HR (95% CI)EHBDC casesEHBDC HR (95% CI)AVC casesAVC HR (95% CI)
BMIc (kg/m2): per 5 kg/m2 2,383,716 1,101 1.27 (1.19–1.36) 624 1.32 (1.21–1.45) 937 1.13 (1.03–1.23) 538 0.99 (0.88–1.11) 
Pheterogeneity (compared with AVC)d   0.0005  0.001  0.308   
Pheterogeneity (compared with IHBDC)d   0.886    0.009   
Pheterogeneity (compared with EHBDC)d   0.004       
BMI category (kg/m2)e 
 Underweight 39,400 29 0.98 (0.60–1.60) 0.91 (0.43–1.95) 15 0.92 (0.53–1.59) 14 1.25 (0.65–2.39) 
 Normal 1,036,939 402 1.00 (reference) 215 1.00 (reference) 352 1.00 (reference) 205 1.00 (reference) 
 Overweight 912,487 431 1.31 (1.11–1.54) 266 1.34 (1.09–1.64) 407 1.14 (0.96–1.35) 232 1.09 (0.87–1.35) 
 Obese 442,878 261 1.72 (1.41–2.08) 144 2.06 (1.62–2.61) 175 1.33 (1.06–1.65) 92 1.08 (0.81–1.35) 
 Obese I 338,889 181 1.62 (1.31–2.00) 110 2.03 (1.57–2.61) 131 1.31 (1.03–1.67) 73 1.07 (0.78–1.48) 
 Obese II 97,456 50 1.54 (1.06–2.22) 24 2.17 (1.39–3.38) 31 1.27 (0.81–2.00) 17 1.33 (0.78–2.28) 
 Obese III 45,101 30 3.32 (2.15–4.80) 10 2.16 (1.05–4.45) 13 1.67 (0.88–3.18) 0.49 (0.12–1.94) 
Ptrendf   <0.0001  <0.0001  0.008  0.78 
 Waist circumferenceg (cm): per 5 cm 1,127,471 434 1.15 (1.11–1.20) 245 1.09 (1.04–1.16) 327 1.03 (0.98–1.09) 223 1.03 (0.97–1.10) 
 Hip circumferenceh(cm): per 5 cm 1,011,338 398 1.16 (1.11–1.22) 216 1.13 (1.05–1.21) 298 1.02 (0.95–1.09) 198 1.02 (0.94–1.11) 
 Waist-to-hip ratiog,h: per 0.1 1,037,853 445 1.11 (1.06–1.17) 229 1.09 (1.01–1.18) 323 1.04 (0.93–1.16) 207 0.99 (0.80–1.22) 
 Waist-to-height ratiog: per 0.1 1,104,877 418 1.47 (1.33–1.63) 242 1.27 (1.07–1.51) 317 1.08 (0.94–1.25) 216 1.07 (0.87–1.32) 

aAll models use age as the time scale and were adjusted for sex, race (white, black, Asian/Pacific Islander, other), education (<high school graduate, high school graduate, some college/post-high school training), smoking (ever vs. never), and alcohol consumption (ever vs. never) and the baseline hazard is stratified by study.

bNoncases: The same noncase group was used for all analyses, except for analyses of intrahepatic bile duct cancer. RERF did not provide information on intrahepatic bile duct cancer diagnoses, so this study was excluded from these analyses.

cExcluded those with a BMI less than 15 kg/m2 or greater than 60 kg/m2.

dThe Wald test was used to test for the heterogeneity of the associations between continuous BMI and BTC subtypes.

eThe BMI categories for Western adults were defined as follows: underweight (15.0–<18.5 kg/m2), normal weight (18.5–<25 kg/m2), overweight (25–<30 kg/m2), and obese (≥30 kg/m2), obese class I (30–<35 kg/m2), obese class II (35–<40 kg/m2), and obese class III (≥40 kg/m2). The BMI categories for Asian adults were defined as follows: underweight (15.0–<18.5 kg/m2), normal weight (18.5–<23 kg/m2), overweight (23–<27.5 kg/m2), obese (≥27.5 kg/m2), obese class I (27.5–<32.5 kg/m2), obese class II (32.5–<37.5 kg/m2), and obese class III (≥37.5 kg/m2).

fThe Wald test was used to test for a linear trend across categories of BMI and BTC sites.

gRestricted to cohorts that collected waist circumference at baseline (BCDDP, COSM, CPS-II NC, EPIC, IWHS, MCCS, NIH-AARP, NHS, NYUWHS, REVEAL, Sister, SMC, WHI, and WLHS).

hRestricted to cohorts that collected hip circumference at baseline (BCDDP, COSM, EPIC, IWHS, MCCS, NIH-AARP, NHS, NYUWHS, REVEAL, Sister, SMC, WHI, and WLHS).

When BMI was categorized according to WHO classifications, compared with normal weight, those in the overweight and obese classes had an increased risk of GBC (HR for overweight = 1.31; 95% CI, 1.11–1.54 and HR for obese = 1.72; 95% CI, 1.41–2.08) and IHBDC (HR for overweight = 1.34; 95% CI, 1.09–1.64) and HR for obese = 2.06; 95% CI, 1.62–2.61). Increasing BMI category was also associated with an increased risk in EHBDC (HR for overweight = 1.14; 95% CI, 0.96–1.35 and HR for obese = 1.33; 95% CI, 1.06–1.65, compared with normal weight). The risk of cancer was particularly pronounced in individuals in the highest obesity category (obese III) compared with normal weight for GBC (HR = 3.32; 95% CI, 2.15–4.80) and IHBDC (HR = 2.16; 95% CI, 1.05–4.45). There was no evidence of an association between BMI classification and AVC.

Waist and hip circumference, waist-to-hip ratio, and waist-to-height ratios were associated with increased risk of developing GBC and IHBDC, but not EHBDC or AVC (Table 3). The risk of GBC increased 15%, and the risk of IHBDC increased 9% for each 5 cm increase in waist circumference. A 5 cm increase in hip circumference conferred a 16% and 13% increased risk for GBC and IHBDC, respectively. Waist-to-hip ratio was associated with the risk of GBC (HR per 0.1 unit = 1.11; 95% CI, 1.06–1.17) and IHBDC (HR per 0.1 unit = 1.09; 95% CI, 1.01–1.18). Stronger associations were seen with increases in waist-to-height ratios than in waist-to-hip ratios for GBC (HR per 0.1 unit = 1.47; 95% CI, 1.33–1.63) and IHBDC (HR per 0.1 unit = 1.27; 95% CI, 1.07–1.51).

Seven studies collected from participants their recalled weight at ages 18 or 20. Among these participants, increasing BMI in young adulthood was associated with increased risk later in adult life of GBC (HR per 5 kg/m2 = 1.26; 95% CI, 1.04–1.52), IHBDC (HR per 5 kg/m2 = 1.34; 95% CI, 1.03–1.73), and AVC (HR per 5 kg/m2 = 1.33; 95% CI, 1.01–1.75), but not EHBDC (HR per 5 kg/m2 = 0.87; 95% CI, 0.68–1.12; Supplementary Table S4). Adult weight gain (≥10 kg/m2) compared with maintaining stable weight was associated with increased risk of developing GBC later in life (HR = 1.77; 95% CI, 1.16–2.70]) and EHBDC (HR = 2.04; 95% CI, 1.17–3.55]). Being overweight or obese as a young adult compared with normal weight was associated with an increased risk of AVC later in life (HR for overweight = 1.88; 95% CI, 1.10–3.21 and HR obese = 2.07; 95% CI, 0.65–6.60). The results of the main analysis were similar when we repeated the analysis of adult adiposity restricting to participants who provided data on young adult BMI.

The associations between adiposity and BTCs did not change substantially when models were additionally adjusted for gallstones (Supplementary Table S5). However, when restricting the analysis of GBC to those without a history of cholecystectomy, the associations with cancer in the obese classes I, II, and III compared with normal weight were stronger than the associations where cholecystectomy was ignored (Supplementary Table S6). For example, compared with normal weight, the HR for obese class III was 3.45 (95% CI, 1.95–6.11) when restricted to those without cholecystectomy and 2.57 (95% CI, 1.58–4.19) when not restricting inclusion in GBC analysis based on history of cholecystectomy. There were also consistently stronger associations between waist and hip circumferences, waist-to-hip ratio, and waist-to-height ratio with GBC when restricting the analysis to those without a history of cholecystectomy.

When stratifying the results by region (Supplementary Table S7), the association between BMI and GBC remained for individuals in Western countries (BMI continuous HR = 1.29; 95% CI, 1.20–1.39]), but this association was not observed in Asian countries (BMI continuous HR = 1.00; 95% CI, 0.79–1.28).

In the largest prospective study of BTCs by site to date, we found that adult adiposity was associated with increased risk of GBC and IHBDC, and to a lesser extent EHBDC, but not AVC. Compared with being in the normal weight range, being in the overweight and obese classifications was associated with a 31% and 72% increased risk of GBC, respectively, and with a 34% and 106% increased risk of IHBDC. Being in the obese category was associated with a 33% increased risk of EHBDC. These results imply that interventions to maintain a healthy weight in adulthood may be beneficial in the prevention of BTCs. The robustness of our findings is strengthened by the consistency across multiple measures of adiposity, and several sensitivity analyses.

Obesity may increase risk of carcinogenesis of the biliary tract through disruption in the metabolism of hormones and inflammatory mediators, such as insulin and cytokines (17). Obesity may also affect cancer risk indirectly by increasing the risk for gallstones (29). However, these effects appear to vary across the biliary tract. The overweight and obese categories were strongly associated with GBC and IHBDC, whereas obesity was more modestly associated with EHBDC, and BMI category was not associated with AVC. A prior case–control study found that increasing BMI over time was associated with GBC, but not EHBDC or AVC; however power was limited to assess risk at these sites (17). In addition, strong associations with GBC and IHBDC risk were seen across multiple measures of adult adiposity, whereas adiposity was not consistently associated with EHBDC or AVC risk in this study and in other studies (17, 30). These findings suggest that the etiologic mechanism by which adiposity causes cancer differs by biliary tract site.

The lack of an association between AVC and adult adiposity may be due in part because the ampulla may be epidemiologically more similar to duodenal cancers for which there is mixed evidence for the effect of obesity on cancer development (31). Excess adipose tissue can contribute to low-grade systemic inflammation, and thus, a condition like nonalcoholic fatty liver disease might be expected to have the strongest effect on cancer development within the intrahepatic bile ducts and the gallbladder little to no impact on AVC (21, 32). However, we did see an association between young adult BMI, but not adult weight gain, and increased risk AVC. Yet, the small number of AVC cases that reported their young adult weight makes it difficult to draw conclusions from these data.

Gallstones are a major risk factor for GBC and, to a lesser extent, other BTCs. In a previous study from Shanghai, China, 80% of gallbladder, 59% of bile duct, and 42% of AVCs were attributed to gallstones (17). Given that obesity increases the risk of gallstones (3), it is unclear whether the association between obesity and BTCs is due to the increased risk of gallstones, and to what extent it is independent of gallstones. That our risk estimates were not substantially different from the main analyses when we adjusted for gallstones, suggests that BMI increases the risk of BTCs through mechanisms other than gallstones. Further, our finding that increasing BMI was not associated with GBC risk in Asia suggest that the etiology of GBC may differ between Asian and Western countries. For example, compared with Western countries, the prevalence of gallstones is lower in Asian countries where pigment stones associated with infection have historically been more common than cholesterol gallstones. The lack of association between obesity and GBC in Asian countries may suggest that the association in Western countries is related to cholesterol gallstones, highlighting the importance of gallstones in gallbladder carcinogenesis (3). However, it is important to note that gallstone data were based on self-report at baseline; many people with gallstones are unaware that they have them (3), and some individuals who reported no gallstones may have developed them later on. Ideally, an analysis of BMI within a cohort of individuals with gallstones is needed to better assess the mechanisms by which obesity increases the risk of BTCs, especially GBC, independently of gallstones.

Our study expands on previously pooled analyses, which also found that adiposity was associated with GBC and IHBDC risk (14, 21, 33). Most research on adiposity has focused on BMI and do not always included measures abdominal obesity, such as waist circumference, hip circumference, waist-to-hip ratio, and waist-to-height ratio. Abdominal fat, independent of BMI, has been associated with the risk of cancers of the colon and rectum (34), endometrium (35), and esophagus (36). Our analysis not only confirmed that central adiposity increases the risk of GBC, but also identified differences in the risk of cancer across the biliary tract. We found that GBC and IHBDC were more strongly associated with waist-to-height ratio than waist circumference, hip circumference, or waist-to-hip ratio, similar to a study of hepatocellular carcinoma, which found waist-to-height ratio, compared with waist-to-hip ratio, had a stronger association with cancer risk (37). The association between waist-to-height ratio was weaker for EHBDC. In addition, we saw no evidence that the risk of any of the BTCs differed by sex, although other studies have (38, 39). Our study was largely comprised of postmenopausal women and is consistent with previous research that shows the sex disparity for BTCs narrows in older ages (14, 21, 40).

This study has several strengths, including its prospective design and sample size; with nearly 4,000 BTC cases, we were able to analyze associations by anatomic site within the biliary tract. BiTCaPP also includes one of the largest collections of AVCs, a relatively unstudied cancer. Our results indicate that obesity may not be a risk factor for the development of AVC, which has also been suggested by smaller studies (17, 30). Such site-specific analysis is important given that BTC risk factors vary by site, resulting in differential clinical health management. By pooling data across existing cohorts, we were able to study a rare cancer in these relatively lower risk populations. We were also able to account for 2 important factors that have largely been ignored in previous studies: gallstones and cholecystectomy. Our analysis of associations with GBC restricted to people not reporting a history of cholecystectomy are particularly important as they allowed us to assess associations between measures of adiposity and GBC among only those people who were truly at risk of developing GBC. Additionally, this pooling project is not subject to publication bias where null findings are often excluded from analysis, as is often the case in publication-based meta-analyses (41).

This study also has some limitations. Seven of the 27 studies collected anthropometric measures by trained study staff and 20 relied on participant self-report. Women tend to underreport their weight and men are more likely to overreport their height (42). Thus, bias towards lower BMI when measured by self-report can occur. However, as this bias is nondifferential by case status, it likely skews our associations between the obese weight category and BTCs towards the null. We also lacked data on physical activity and had a large amount of missing data on important covariates, which may have resulted incomplete adjustment for confounding. Our analysis on young adult BMI should also be interpreted with caution because these variables were not collected by all the cohort studies and the number of events, especially for AVC, was small.

In conclusion, findings from this pooled analysis of 27 prospective studies support the hypothesis that adiposity is associated with an increased risk of BTC, particularly GBC and IHBDC. The differences found in the associations with several measures of adiposity across the biliary tract point to a unique etiology for cancer at these sites. These results provide evidence for recommending weight maintenance programs to patients, especially those at high risk for BTCs (e.g., those with a family history or a history of gallstones). Further research is warranted to see if these relationships between adult adiposity and cancer risk across the biliary tract persist in higher-risk geographic regions, such as Latin America and South Asia.

No potential conflicts of interest were disclosed.

Where authors are identified as personnel of the International Agency for Research on Cancer/World Health Organization, the authors alone are responsible for the views expressed in this article and they do not necessarily represent the decisions, policy or views of the International Agency for Research on Cancer/World Health Organization.

Conception and design: A.L. Van Dyke, J.L. Petrick, H.-O. Adami, P. Hartge, S.F. Knutsen, S.C. Larsson, E.E. McGee, R.L. Milne, U. Peters, A. Wolk, X. Zhang, P.T. Campbell, J. Koshiol

Development of methodology: S.S. Jackson, A.L. Van Dyke, B. Zhu, E.J. Grant, P. Hartge, P.T. Campbell

Acquisition of data (provided animals, acquired and managed patients, provided facilities, etc.): A.L. Van Dyke, H.-O. Adami, D. Albanes, G. Andreotti, L.E. Beane Freeman, A. Berrington de González, J.E. Buring, A.T. Chan, G.E. Fraser, N.D. Freedman, Y.-T. Gao, S.M. Gapstur, J.M. Gaziano, G.G. Giles, E.J. Grant, F. Grodstein, P. Hartge, M. Jenab, C.M. Kitahara, S.F. Knutsen, W.-P. Koh, S.C. Larsson, I.-M. Lee, E.E. McGee, R.L. Milne, K.R. Monroe, M.L. Neuhouser, U. Peters, J.N. Poynter, M.P. Purdue, K. Robien, D.P. Sandler, N. Sawada, H.D. Sesso, R. Sinha, R. Wang, E. Weiderpass, S.J. Weinstein, E. White, A. Wolk, J.-M. Yuan, A. Zeleniuch-Jacquotte, X. Zhang, P.T. Campbell, J. Koshiol

Analysis and interpretation of data (e.g., statistical analysis, biostatistics, computational analysis): S.S. Jackson, A.L. Van Dyke, B. Zhu, R.M. Pfeiffer, J.L. Petrick, A.T. Chan, N.D. Freedman, Y.-T. Gao, M. Jenab, C.M. Kitahara, E.E. McGee, R.L. Milne, K.R. Monroe, M.P. Purdue, R.Z. Stolzenberg-Solomon, S. Tsugane, E. Weiderpass, E. White, A. Wolk, X. Zhang, P.T. Campbell, J. Koshiol

Writing, review, and/or revision of the manuscript: S.S. Jackson, A.L. Van Dyke, B. Zhu, R.M. Pfeiffer, J.L. Petrick, H.-O. Adami, D. Albanes, G. Andreotti, L.E. Beane Freeman, A. Berrington de González, J.E. Buring, A.T. Chan, Y. Chen, G.E. Fraser, N.D. Freedman, S.M. Gapstur, J.M. Gaziano, G.G. Giles, F. Grodstein, P. Hartge, M. Jenab, C.M. Kitahara, S.F. Knutsen, W.-P. Koh, S.C. Larsson, I.-M. Lee, L.M. Liao, J. Luo, E.E. McGee, R.L. Milne, K.R. Monroe, M.L. Neuhouser, K.M. O’Brien, U. Peters, J.N. Poynter, M.P. Purdue, K. Robien, D.P. Sandler, N. Sawada, H.D. Sesso, T.G. Simon, R. Sinha, R.Z. Stolzenberg-Solomon, S. Tsugane, E. Weiderpass, S.J. Weinstein, E. White, A. Wolk, J.-M. Yuan, A. Zeleniuch-Jacquotte, X. Zhang, K.A. McGlynn, P.T. Campbell, J. Koshiol

Administrative, technical, or material support (i.e., reporting or organizing data, constructing databases): S.S. Jackson, A.L. Van Dyke, G.E. Fraser, N.D. Freedman, W.-P. Koh, I.-M. Lee, L.M. Liao, E.E. McGee, K.R. Monroe, M.L. Neuhouser, K.M. O’Brien, C. Schairer, H.D. Sesso, S. Tsugane, R. Wang, S.J. Weinstein, X. Zhang, P.T. Campbell,

Study supervision: H.-O. Adami, D. Albanes, G.E. Fraser, Y.-T. Gao, U. Peters, J. Koshiol

Other (interpretation of data from RERF study): E.J. Grant

AgHealth: This study was funded by the Intramural Program of the NIH, NCI (Z01 P010119), and the National Institute of Environmental Health Sciences (Z01 ES 049030-11).

AHS-2: Project support was obtained from NCI Grant No. 1U01CA152939.

ATBC: The ATBC Study is supported by the Intramural Research Program of the U.S. NCI, NIH, and by U.S. Public Health Service contract HHSN 261201500005C from the NCI, Department of Health and Human Services.

BCDDP: The BCDDP Follow-up Study was supported by the Intramural Research Program of the NHI, NCI.

COSM: This cohort is supported by the Swedish Research Council (Research Infrastructure SIMPLER), the Swedish Cancer Foundation, and by Strategic Funds from Karolinska Institutet, Stockholm, Sweden.

CPS-II NC: The American Cancer Society funds the creation, maintenance, and updating of the Cancer Prevention Study-II Nutrition Cohort.

EPIC: The coordination of EPIC is financially supported by the European Commission (DG-SANCO) and the International Agency for Research on Cancer. The national cohorts are supported by Danish Cancer Society, Denmark; Ligue Contre le Cancer, France; Institut Gustave Roussy, France; Mutuelle Generale de l'Education Nationale, France; Institut National de la Sante et de la Recherche Medicale, France; Deutsche Krebshilfe, Germany, Deutsches Krebsforschungszentrum and Federal Ministry of Education and Research, Germany; Hellenic Health Foundation, Greece; Italian Association for Research on Cancer; National Research Council, Italy; Dutch Ministry of Public Health, Welfare and Sports, the Netherlands; Netherlands Cancer Registry, the Netherlands; LK Research Funds, the Netherlands; Dutch Prevention Funds, the Netherlands; Dutch ZON (Zorg Onderzoek Nederland), the Netherlands; World Cancer Research Fund, London, UK; Statistics Netherlands, the Netherlands; European Research Council, Norway; Health Research Fund, Regional Governments of Andalucia, Asturias, Basque Country, Murcia (project no. 6236) and Navarra, ISCIII RETIC (RD06/0020/0091), Spain; Swedish Cancer Society, Sweden; Swedish Scientific Council, Sweden; Regional Government of Skane and Vasterbotten, Sweden; Cancer Research United Kingdom; Medical Research Council, United Kingdom; Stroke Association, United Kingdom, British Heart Foundation, United Kingdom; Department of Health, Food Standards Agency, United Kingdom; and Wellcome Trust; United Kingdom. We thank Bertrand Hemon for his precious help with the EPIC database. The principle investigators and funders corresponding to each of the EPIC centers that contributed cases were Kim Overvad, Anne Tjonneland (Denmark); Francoise Clavel-Chapelon (France); Heiner Boeing, Rudolf Kaaks (Germany); Antonia Trichopoulou (Greece); Vittorio Krogh, Domenico Palli, Paolo Vineis, Salvatore Panico, Rosario Tumino (Italy); Eiliv Lund (Norway); Antonio Agudo, Maria Jose Sanchez, J.Ramón Quirós, Carmen Navarro, Aurelio Barricarte, Miren Dorronsoro (Spain); Mattias Johansson, Jonas Manjer (Sweden); H. Bas Bueno-de-Mesquita, Petra H. Peeters (The Netherlands); Timothy Key, Nick Wareham (UK); The coordination of European Prospective Investigation into Cancer and Nutrition is financially supported by the European Commission (DG-SANCO) and the International Agency for Research on Cancer. The national cohorts are supported by the French NCI (L'Institut National du Cancer; INCA); Ligue contre le Cancer, Institut Gustave Roussy, Mutuelle Générale de l'Education Nationale, Institut National de la Santé et de la Recherche Médicale (INSERM) (France); German Cancer Aid; German Cancer Research Center (DKFZ); German Federal Ministry of Education and Research; Danish Cancer Society; Health Research Fund (FIS) of the Spanish Ministry of Health (RTICC (DR06/0020/0091); the participating regional governments from Asturias, Andalucía, Murcia, Navarra and Vasco Country and the Catalan Institute of Oncology of Spain; Cancer Research UK; Medical Research Council, UK; the Stroke Association, UK; British Heart Foundation; Department of Health, UK; Food Standards Agency, UK; the Wellcome Trust, UK; the Hellenic Health Foundation; Italian Association for Research on Cancer; Compagnia San Paolo, Italy; Dutch Ministry of Public Health, Welfare and Sports; Dutch Ministry of Health; Dutch Prevention Funds; LK Research Funds; Dutch ZON (Zorg Onderzoek Nederland); World Cancer Research Fund (WCRF); Statistics Netherlands (The Netherlands); Swedish Cancer Society; Swedish Scientific Council; Regional Government of Skane, Sweden; Nordforsk—Centre of Excellence programme.

HPFS: This work was supported by grants from the NCI (UM1 CA167552, P01 CA55075), the Entertainment Industry Foundation, and the National Colorectal Cancer Research Alliance. HPFS would like to thank the participants and staff of the HPFS for their valuable contributions as well as the following state cancer registries for their help: AL, AZ, AR, CA, CO, CT, DE, FL, GA, ID, IL, IN, IA, KY, LA, ME, MD, MA, MI, NE, NH, NJ, NY, NC, ND, OH, OK, OR, PA, RI, SC, TN, TX, VA, WA, WY. The authors assume full responsibility for analyses and interpretation of these data.

IWHS: IWHS was funded by a grant from the NCI (R01 CA39742).

JPHC: This work was supported by the National Cancer Center Research and Development Fund (since 2011) and a grant-in-aid from Cancer Research (1989–2010) from the Ministry of Health, Labor, and Welfare of Japan.

MCCS: MCCS receives core funding from Cancer Council Victoria and is additionally supported by grants from the Australian NHMRC (209057, 251533, 396414, and 504715).

MEC: This work was supported by the NIH (P01 CA33619 and U01 CA164973).

NHS: Data used in this study was supported by an infrastructure grant (UM1 CA186107) and a program project grant that funds cancer research (P01 CA87969). NHS would like to thank the participants and staff of the NHS for their valuable contributions as well as the following state cancer registries for their help: AL, AZ, AR, CA, CO, CT, DE, FL, GA, ID, IL, IN, IA, KY, LA, ME, MD, MA, MI, NE, NH, NJ, NY, NC, ND, OH, OK, OR, PA, RI, SC, TN, TX, VA, WA, WY. The authors assume full responsibility for analyses and interpretation of these data.

NIH-AARP: This research was supported [in part] by the Intramural Research Program of the NIH, NCI. Cancer incidence data from the Atlanta metropolitan area were collected by the Georgia Center for Cancer Statistics, Department of Epidemiology, Rollins School of Public Health, Emory University, Atlanta, GA. Cancer incidence data from California were collected by the California Cancer Registry, California Department of Public Health's Cancer Surveillance and Research Branch, Sacramento, CA. Cancer incidence data from the Detroit metropolitan area were collected by the Michigan Cancer Surveillance Program, Community Health Administration, Lansing, MI. The Florida cancer incidence data used in this report were collected by the Florida Cancer Data System (Miami, FL) under contract with the Florida Department of Health, Tallahassee, FL. The views expressed herein are solely those of the authors and do not necessarily reflect those of the FCDC or FDOH. Cancer incidence data from Louisiana were collected by the Louisiana Tumor Registry, Louisiana State University Health Sciences Center School of Public Health, New Orleans, LA. Cancer incidence data from New Jersey were collected by the New Jersey State Cancer Registry, The Rutgers Cancer Institute of New Jersey, New Brunswick, NJ. Cancer incidence data from North Carolina were collected by the North Carolina Central Cancer Registry, Raleigh, NC. Cancer incidence data from Pennsylvania were supplied by the Division of Health Statistics and Research, Pennsylvania Department of Health, Harrisburg, PA. The Pennsylvania Department of Health specifically disclaims responsibility for any analyses, interpretations or conclusions. Cancer incidence data from Arizona were collected by the Arizona Cancer Registry, Division of Public Health Services, Arizona Department of Health Services, Phoenix, AZ. Cancer incidence data from Texas were collected by the Texas Cancer Registry, Cancer Epidemiology and Surveillance Branch, Texas Department of State Health Services, Austin, TX. Cancer incidence data from Nevada were collected by the Nevada Central Cancer Registry, Division of Public and Behavioral Health, State of Nevada Department of Health and Human Services, Carson City, NV.

NYUWHS: The NYUWHS is supported by grants UM1 CA182934 and P30 CA16087 from the NCI and by grant P30 ES000260 from the National Institute of Environmental Health Sciences.

PHS: PHS is supported by grants from the NCI (CA-34933, CA-40360, and CA-097193) and from the National Heart, Lung, and Blood Institute (HL-26490 and HL-34595), NIH, Bethesda, MD.

PLCO: The PLCO Cancer Screening Trial is supported by contracts from the NCI.

RERF: The Radiation Effects Research Foundation (RERF), Hiroshima and Nagasaki, Japan is a public interest foundation funded by the Japanese Ministry of Health, Labour and Welfare (MHLW) and the US Department of Energy (DOE). The research was also funded in part through DOE award DE-HS0000031 to the National Academy of Sciences. This publication was supported by RERF Research Protocol A2-13. The views of the authors do not necessarily reflect those of the two governments.

SCHS: This study is supported by the NCI (R01CA080205, R01CA144034, UM1CA182876).

SCS: The Shanghai Cohort Study is supported by the NCI (R01CA043092, R01CA144034, UM1CA182876).

SISTER: The Sister Study is supported by the Intramural Research Program of the NIH, National Institute of Environmental Health Sciences (ZO1-ES-044005). Support for data collection and study and data management are provided by Social & Scientific Systems, Inc., and Westat, Inc., Durham, NC.

SMC: This cohort is supported by the Swedish Research Council (Research Infrastructure SIMPLER), the Swedish Cancer Foundation, and by Strategic Funds from Karolinska Institutet, Stockholm, Sweden.

VITAL: The VITAL study was supported by the NIH grant K05-CA154337 (NCI and Office of Dietary Supplements).

WHI: The WHI program is funded by the National Heart, Lung, and Blood Institute, NIH, U.S. Department of Health and Human Services through contracts, HHSN268201600018C, HHSN268201600001C, HHSN268201600002C, HHSN268201600003C, and HHSN268201600004C. WHI would like to additionally acknowledge the following short list of WHI investigators: Program Office: Jacques Rossouw, Shari Ludlam, Joan McGowan, Leslie Ford, and Nancy Geller (National Heart, Lung, and Blood Institute, Bethesda, Maryland); Clinical Coordinating Center: Garnet Anderson, Ross Prentice, Andrea LaCroix, and Charles Kooperberg (Fred Hutchinson Cancer Research Center, Seattle, WA); Investigators and Academic Centers: JoAnn E. Manson (Brigham and Women's Hospital, Harvard Medical School, Boston, MA); Barbara V. Howard (MedStar Health Research Institute/Howard University, Washington, DC); Marcia L. Stefanick (Stanford Prevention Research Center, Stanford, CA); Rebecca Jackson (The Ohio State University, Columbus, OH); Cynthia A. Thomson (University of Arizona, Tucson/Phoenix, AZ); Jean Wactawski-Wende (University at Buffalo, Buffalo, NY); Marian Limacher (University of Florida, Gainesville/Jacksonville, FL); Jennifer Robinson (University of Iowa, Iowa City/Davenport, IA); Lewis Kuller (University of Pittsburgh, Pittsburgh, PA); Sally Shumaker (Wake Forest University School of Medicine, Winston-Salem, NC); Robert Brunner (University of Nevada, Reno, NV); and Women's Health Initiative Memory Study: Mark Espeland (Wake Forest University School of Medicine, Winston-Salem, NC). For a list of all the investigators who have contributed to WHI Science, please visit: https://www.whi.org/researchers/Documents%20%20Write%20a%20Paper/WHI%20Investigator%20Long%20List.pdf.

WHS: WHS was supported by grants CA047988, HL043851, HL080467, and HL099355.

WLHS: The WLHS project was supported by the Swedish Research Council (Grant No. 521-2011-295) and a Distinguished Professor Award at Karolinska Institutet to Hans-Olov Adami (Grant No. 2368/10-221).

The costs of publication of this article were defrayed in part by the payment of page charges. This article must therefore be hereby marked advertisement in accordance with 18 U.S.C. Section 1734 solely to indicate this fact.

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