Abstract
Background
Adult obesity is a strong risk factor for endometrial cancer (EC); however, associations of early life obesity with EC are inconclusive. We evaluated associations of young adulthood (18–21 years) and adulthood (at enrolment) body mass index (BMI) and weight change with EC risk in the Epidemiology of Endometrial Cancer Consortium (E2C2).
Methods
We pooled data from nine case-control and 11 cohort studies in E2C2. We performed multivariable logistic regression analyses to estimate odds ratios (OR) and 95% confidence intervals (95% CI) for BMI (kg/m2) in young adulthood and adulthood, with adjustment for BMI in adulthood and young adulthood, respectively. We evaluated categorical changes in weight (5-kg increments) and BMI from young adulthood to adulthood, and stratified analyses by histology, menopausal status, race and ethnicity, hormone replacement therapy (HRT) use and diabetes.
Results
We included 14 859 cases and 40 859 controls. Obesity in adulthood (OR = 2.85, 95% CI = 2.47–3.29) and young adulthood (OR = 1.26, 95% CI = 1.06–1.50) were positively associated with EC risk. Weight gain and BMI gain were positively associated with EC; weight loss was inversely associated with EC. Young adulthood obesity was more strongly associated with EC among cases diagnosed with endometrioid histology, those who were pre/perimenopausal, non-Hispanic White and non-Hispanic Black, among never HRT users and non-diabetics.
Conclusions
Young adulthood obesity is associated with EC risk, even after accounting for BMI in adulthood. Weight gain is also associated with EC risk, whereas weight loss is inversely associated. Achieving and maintaining a healthy weight over the life course is important for EC prevention efforts.
Keywords: Endometrial cancer, gynaecology, epidemiology, uterine cancer, obesity, life course exposures, BMI, weight change, weight loss, BMI change
Key Messages.
Obesity in young adulthood is positively associated with risk of endometrial cancer, even after accounting for body mass index (BMI) in adulthood.
Young adulthood obesity is most strongly associated with risk of endometrial cancer among endometrioid subtypes, those who were pre/perimenopausal, non-Hispanic White and non-Hispanic Black, among never hormone replacement therapy (HRT) users and non-diabetics.
Weight gain over the life course is also positively associated with risk of endometrial cancer, whereas weight loss is inversely associated.
Introduction
Endometrial cancer (EC) is the most commonly diagnosed gynaecological cancer in the USA1 and the second most commonly diagnosed worldwide.2 EC is primarily diagnosed in postmenopausal women, and EC incidence has increased over the past four decades3–6 particularly among younger ages.7 These trends are thought to be partially driven by increasing rates of obesity, a well-established risk factor for EC, particularly for the more common and less aggressive endometrioid subtypes.8,9 Obesity at younger ages and/or weight change over the life course may influence EC risk due to cumulative effects of long-term obesity exposure and/or by influencing reproductive processes associated with EC risk.10,11 However, research regarding the relationship between EC and early life obesity is inconclusive. Some previous studies suggest that elevated body mass index (BMI)12–19 and/or weight gain12,15,18–24 earlier in life may increase EC risk; others show no or reduced risk, highlighting the need for a large pooled analysis.20,24–27 Here we evaluated associations of obesity in young adulthood and adulthood and weight change with EC risk, using data from 20 studies in the Epidemiology of Endometrial Cancer Consortium (E2C2).
Methods
Participating studies
The E2C2 core database includes data from 32 studies across 11 countries. Eligible studies contributed data for BMI between ages 18–21 years (i.e. ‘young adulthood’), BMI at enrolment (i.e. ‘adulthood’), age and for covariates including race and/or ethnicity, education, smoking status, age at menarche, parity, oral contraceptive use and hormone replacement therapy (HRT) use. Informed consent was obtained from all study participants at the time of enrolment, as required by each study’s institutional review board.
Data collection
De-identified data were harmonized across all studies by the Memorial Sloan Kettering Cancer Center E2C2 coordinating centre. Cohort studies were included in the database as nested case-control studies, with cases matched to controls in a 1 to 4 ratio using birth year, time of cohort entry and other criteria as determined by individual studies.28 Risk factor data were obtained from baseline questionnaires and follow-up cycles for most cohort studies and were based on a specific reference date before diagnosis (cases) or enrolment (controls) for all case-control studies. Age at adulthood is defined as age at diagnosis (cases), or age at interview or age in the year that their matched case was diagnosed with EC (controls) for all case-control and cohort studies. Adulthood height (metres) and weight (kilograms) were self-reported or measured directly. Young adulthood BMI was based on adulthood height and self-reported weight at age 18–21 (except the Italian Multicentre Study, which defined young adulthood at age 30). Menopausal status was recorded at baseline. All other covariates were self-reported. All studies reported tumour histology using ICD-O-3 codes, except the Polish Case Control Study, the Shanghai Endometrial Cancer Study and the Nurses’ Health Study, which provided summary histological type.
Statistical methods
Descriptive statistics were used to summarize demographic, socioeconomic, behavioural and reproductive variables by case status. We performed pooled multiple logistic regression analyses to estimate odds ratios (OR) and 95% confidence intervals (95% CI) for associations of EC with young adulthood BMI [underweight (<18.5 kg/m2), normal (18.5 to <25 kg/m2; reference), overweight (25 to <30 kg/m2), obese (30+ kg/m2)] and adulthood BMI, using the same cut points as above with underweight and normal BMI grouped (hereafter listed as ‘normal’) because of low numbers (939 underweight, 1.7%).29 BMI values for Asian participants were classified according to the World Health Organization’s recommendations as underweight (<18.5 kg/m2), normal (18.5 to <23 kg/m2), overweight (23 to <25 kg/m2), and obese (25+ kg/m2).30,31 We evaluated measures of BMI and weight change from young adulthood to adulthood, defined categorically as: maintain/gain to a normal BMI, lose to a normal BMI, gain to an overweight BMI, lose to an overweight BMI, and maintain/gain to an obese BMI. Weight change was categorically defined: <-10 kg, -10 to <-5 kg, -5 to <-2 kg, -2 to 2 kg (stable reference group), +2 to <5 kg, +5 to <10 kg, +10 to <15 kg, +15 to <20 kg, +20 to <25 kg, 25+ kg. In multivariable models, we included age (continuous), race and ethnicity (non-Hispanic White, non-Hispanic Black, Hispanic, non-Hispanic Asian, mixed/other/Hawaiian or Pacific Islander), education (high school or less, some college/associates degree or technical school, college or above), smoking status (never, former, current), age at menarche (continuous), parity (continuous), HRT use (ever, never) and oral contraceptive use (ever, never). We report multivariable-adjusted associations of BMI in young adulthood and adulthood separately, as well as with mutual adjustment for adulthood BMI and young adulthood BMI, respectively. Analyses of weight change were adjusted for all covariates and young adulthood BMI. We performed stratified analyses to evaluate whether the associations of weight change with EC were modified by young adulthood BMI (combining underweight with normal and overweight with obesity due to low cell counts). As sensitivity analyses, we performed a stratified analysis by study design (case-control and cohort), as well as a random-effects meta-analysis of study-specific odds ratios and estimated heterogeneity across studies.
We performed stratified analyses with tests for interaction by menopausal status (pre- and perimenopausal and postmenopausal), race and ethnicity, HRT use and diabetes, in addition to analyses stratified by histological subtype [grouped as endometroid or type I, including adenocarcinoma not otherwise specified (NOS), and non-endometrioid or type II]. All analyses were performed in Stata/SE 16.0.
Results
Study and population characteristics
A total of 14 859 cases and 40 859 controls (N = 55 718) from 20 studies were included in this analysis (Table 1). Participant characteristics are shown in Table 2. The mean age was 64.4 (SD = 9.7) years, with cases being slightly younger (mean = 63.3, SD = 9.8) than controls (mean = 64.9, SD = 9.7). Most participants were non-Hispanic White, but a larger proportion of controls (81.2%) identified as non-Hispanic White than did cases (76.5%). Cases were more likely than controls to have a high school education or less (43.8% and 40.0%, respectively), and to be never smokers (61.8% and 54.9%, respectively). HRT (61.2% vs 58.4%) and oral contraceptive use (58.1% vs 54.2%) were more common among cases than controls. Mean age at menarche was the same in both cases and controls (mean = 12.7, SD = 1.6). Most participants reported having ≥1 live birth, but nulliparity was more common among cases (17.9%) than controls (13.4%). Most participants were postmenopausal and non-diabetic. Among EC cases, endometrioid was the most common subtype (51.5%), followed by adenocarcinoma NOS (26.3%) and non-endometrioid (10.6%).
Table 1.
Characteristics of 20 studies in the Epidemiology of Endometrial Cancer Consortium (E2C2)
| Study namea | Total | Cases | Controls | Locationb | Recruitment period | Age at ‘young adulthood’ |
|---|---|---|---|---|---|---|
| Case-control | ||||||
| Australian National Endometrial Cancer Study | 2175 | 1434 | 741 | Australia | 2005–07 | 20 |
| Estrogen, Diet, Genetics, and Endometrial Cancer | 936 | 469 | 467 | NJ, USA | 2001–05 | 18 |
| FHCC Endometrial Cancer Case-Control Studies | 1746 | 881 | 865 | WA, USA | 1994–2005 | 18 |
| Hawaii Endometrial Cancer Case-Control Study | 843 | 332 | 511 | HI, USA | 1988–93 | 21 |
| Italian Mulitcentre Study | 1362 | 454 | 908 | 3 Italian sites | 1992–2006 | 30c |
| Polish Case Control Study (NCI) | 2476 | 551 | 1925 | Warsaw and Lodz, Poland | 2001–03 | 20 |
| Screenwide | 371 | 180 | 191 | 3 Spanish sites | 2017–21 | 20 |
| Shanghai Endometrial Cancer Study | 2411 | 1199 | 1212 | Shanghai, China | 1997–2004 | 20 |
| US Endometrial Case-Control Study | 753 | 433 | 320 | 5 USA sites | 1987–90 | 20 |
| Cohort | ||||||
| AARP-NIH Diet and Health Study Cohort | 9250 | 1850 | 7400 | 6 USA states | 1995–96 | 18 |
| Black Women's Health Study | 1375 | 275 | 1100 | USA | 1995 | 18 |
| California Teachers Study | 6465 | 1293 | 5172 | CA, USA | 1995–96 | 18 |
| Canadian National Breast Screening Study | 3840 | 768 | 3072 | 15 Canadian sites | 1980–85 | 20 |
| Cancer Prevention Study II | 6685 | 1337 | 5348 | 21 USA states | 1992–93 | 18 |
| Iowa Women's Health Study Cohort | 2765 | 553 | 2212 | IA, USA | 1986 | 18 |
| Multiethnic Cohort Study | 4955 | 996 | 3959 | HI/CA, USA | 1993–96 | 21 |
| Nurses' Health Study | 2291 | 650 | 1641 | 11 USA states | 1976 | 18 |
| Prostate Lung Colorectal Ovarian NCI Study | 3389 | 673 | 2716 | 10 USA sites | 1993–2001 | 20 |
| Southern Community Cohort Study | 280 | 56 | 224 | 12 USA states | 2002–09 | 21 |
| Women's Lifestyle and Health Study | 1350 | 475 | 875 | Sweden | 1991 | 18 |
| Total | 55718 | 14 859 | 40 859 |
FHCC, Fred Hutchinson Cancer Center; NCI, United States National Cancer Institute; AARP, formerly known as the American Association of Retired Persons; NIH, United States National Institutes of Health.
NJ, New Jersey; USA, United States of America; WA, Washington; CA, California; IA, Iowa; HI, Hawaii.
Young adulthood in the Italian Multicentre Study was defined at age 30. A sensitivity analysis was performed, and effect estimates were not affected by the inclusion of this study in our models.
Table 2.
Population characteristics in 20 studies in the Epidemiology of Endometrial Cancer Consortium (E2C2)a,b
| Cases (n = 14 859) | Controls (n = 40 859) | |
|---|---|---|
| Adulthood age (years) | ||
| Mean (SDc) | 63.3 (9.8) | 64.9 (9.7) |
| Race/ethnicity | ||
| Non-Hispanic White | 11360 (76.5) | 33177 (81.2) |
| Non-Hispanic Black | 720 (4.8) | 2690 (6.6) |
| Hispanic | 484 (3.3) | 1234 (3.0) |
| Asian | 1802 (12.1) | 2858 (7.0) |
| Mixed/Other/Hawaiian + Pacific Islander | 296 (2.0) | 733 (1.8) |
| Missing | 197 (1.3) | 167 (0.4) |
| Education | ||
| High school or less | 6504 (43.8) | 16359 (40.0) |
| Some college/Associates or Tech | 3764 (25.3) | 10306 (25.2) |
| College or above | 4394 (29.6) | 13729 (33.6) |
| Missing | 197 (1.3) | 465 (1.1) |
| Smoking status | ||
| Never | 9184 (61.8) | 22413 (54.9) |
| Former | 4212 (28.3) | 12578 (30.8) |
| Current | 1288 (8.7) | 5384 (13.2) |
| Missing | 175 (1.2) | 484 (1.2) |
| Age at menarche | ||
| Mean (SDc) | 12.7 (1.6) | 12.7 (1.6) |
| Missing; n (%) | 167 (1.1) | 399 (1.0) |
| Parity | ||
| 0 | 2664 (17.9) | 5486 (13.4) |
| 1 to 2 | 6415 (43.2) | 16807 (41.1) |
| 3+ | 5639 (38.0) | 18218 (44.6) |
| Missing | 141 (0.9) | 348 (0.9) |
| Hormone replacement therapy (HRT) use | ||
| Ever | 9097 (61.2) | 23857 (58.4) |
| Never | 5303 (35.7) | 15955 (39.0) |
| Missing | 459 (3.1) | 1047 (2.6) |
| Oral contraceptive (OC) use | ||
| Ever | 8631 (58.1) | 22146 (54.2) |
| Never | 5888 (39.6) | 18030 (44.1) |
| Missing | 340 (2.3) | 683 (1.7) |
| Menopausal status at baseline | ||
| Pre/Peri | 4448 (29.9) | 14210 (34.8) |
| Post | 8893 (59.9) | 24627 (60.3) |
| Missing | 1518 (10.2) | 2022 (4.95) |
| Diabetes status at baseline | ||
| Yes | 1810 (12.2) | 2458 (6.0) |
| No | 12117 (81.6) | 35066 (85.8) |
| Missing | 932 (6.3) | 3335 (8.2) |
| Cancer subtyped | ||
| Endometrioid | 7646 (51.5) | – |
| Non-endometrioid | 1573 (10.6) | – |
| Adenocarcinoma NOSe | 3913 (26.3) | – |
| Other | 502 (3.4) | – |
| Missing | 1225 (8.2) | – |
Counts and percentages shown as n (%) are reported for categorical variables.
Means and standard deviations shown as µ (SD) are reported for continuous variables.
SD, standard deviation.
All studies reported tumour histology using ICD-O-3 codes, except the Polish Case Control Study, the Shanghai Endometrial Cancer Study, and the Nurses’ Health Study, which provided summary histological type.
NOS: Not otherwise specified.
Associations of BMI with EC risk
BMI exposure distributions in the analysis population and associations of young adulthood and adulthood BMI with EC are shown in Table 3. Both overweight and obesity in young adulthood were more prevalent among cases (9.0% and 3.7%, respectively) than controls (5.7% and 1.7%, respectively). Adulthood obesity was more prevalent among cases (39.0%) than controls (19.3%). Between time points, more controls than cases lost weight to a normal BMI (31.5% vs 21.2%, respectively), whereas more cases than controls maintained or gained weight to an obese BMI (34.2% vs 16.6%, respectively).
Table 3.
Associations of life course body mass index with endometrial cancer (EC) in 20 studies in the Epidemiology of Endometrial Cancer Consortium (E2C2), with odds ratios (OR) and 95% confidence intervals (95% CI)
| Cases | Controls | Model 1b | Model 2c | |
|---|---|---|---|---|
| Young adulthood BMIa | P-trend <0.0001 | P-trend = 0.03 | ||
|
| ||||
| Underweight | 1853 (12.5) | 5617 (13.7) | 0.88 (0.82–0.94) | 1.01 (0.93–1.09) |
| Normal | 9139 (61.5) | 25923 (63.4) | Ref | Ref |
| Overweight | 1341 (9.0) | 2328 (5.7) | 1.51 (1.37–1.67) | 1.13 (1.01–1.25) |
| Obese | 555 (3.7) | 680 (1.7) | 1.90 (1.60–2.26) | 1.26 (1.06–1.50) |
|
| ||||
| Adulthood BMIa | P–trend <0.0001 | P–trend <0.0001 | ||
|
| ||||
| Underweight/normal | 4812 (32.4) | 19860 (48.6) | Ref | Ref |
| Overweight | 3945 (26.5) | 12292 (30.1) | 1.35 (1.27–1.45) | 1.38 (1.27–1.49) |
| Obese | 5794 (39.0) | 7868 (19.3) | 2.99 (2.62–3.40) | 2.85 (2.47–3.29) |
|
| ||||
| BMI changea,d | P–trend <0.0001 | |||
|
| ||||
| Maintain/gain to normal | 1071 (7.2) | 4229 (10.4) | Ref | – |
| Lose to normal | 3155 (21.2) | 12867 (31.5) | 1.02 (0.94–1.11) | – |
| Maintain/gain to overweight | 3465 (23.3) | 10344 (25.3) | 1.40 (1.27–1.55) | – |
| Lose to overweight | 40 (0.3) | 112 (0.3) | 1.26 (0.75–2.09) | – |
| Maintain/gain to obese | 5075 (34.2) | 6793 (16.6) | 2.98 (2.57–3.45) | – |
BMI, body mass index; HRT, hormone replacement therapy.
Model 1: adjusted for age at adulthood, race/ethnicity, education, smoking status, age at menarche, parity (continuous), oral contraceptive use and HRT use (hereafter listed as ‘covariates’) data.
Model 2: mutually adjusted for young adulthood, adulthood BMI and covariates.
Maintain/gain from normal to normal BMI (reference); lose from overweight to normal BMI; maintain/gain from ≤overweight to overweight BMI; lose from obese to overweight BMI; maintain/gain from ≤obese to obese BMI.
In multivariable models without adjustment for baseline BMI, being underweight in young adulthood decreased odds of EC compared with those with a normal BMI (OR = 0.88, 95% CI = 0.82–0.94). Conversely, young adulthood overweight and obesity were positively associated with EC (OR = 1.51, 95% CI = 1.37–1.67; OR = 1.90, 95% CI = 1.60–2.26; respectively). These associations remained, although attenuated, after additional adjustment for adulthood BMI (overweight: OR = 1.13, 95% CI = 1.01–1.25; obese: OR = 1.26, 95% CI = 1.06–1.50). Being underweight in young adulthood was not associated with EC (OR = 1.01, 95% CI = 0.93–1.09) after accounting for adulthood BMI. We observed strong positive associations of adulthood overweight and obesity with EC (overweight: OR = 1.35, 95% CI = 1.27–1.45; obese: OR = 2.99, 95% CI = 2.62–3.40), even after additional adjustment for young adulthood BMI (overweight: OR = 1.38, 95% CI = 1.27–1.49; obese: OR = 2.85, 95% CI = 2.47–3.29).
With respect to change in BMI category over time, we observed a positive association of maintaining/gaining to overweight (OR = 1.40, 95% CI = 1.27–1.55) and with maintaining/gaining to obesity (OR = 2.98, 95% CI = 2.57–3.45) with EC, when compared with maintaining/gaining to a normal BMI. Categories defined by a reduction in BMI between the two time periods (i.e. lose to normal and lose to overweight), though directionally positive, were not associated with EC.
In sensitivity analyses, results stratified by study design were similar between cohort and case-control studies (data not shown). Results from meta-analyses evaluating the summary-level effect estimates and heterogeneity across studies for associations of young adulthood and adulthood BMI and BMI change were similar to those from the pooled multivariable logistic regression analyses. Heterogeneity across studies was not observed for most variables (Supplementary Table S1, Supplementary Figures S1–S3, available as Supplementary data at IJE online).
Associations of weight change with EC risk
Most study participants gained weight between young adulthood and adulthood, with about half gaining at least 10 kg (49.3%) and 16.0% gained >25 kg (Table 4). In the overall model accounting for young adulthood BMI, losing weight was inversely associated with odds of EC when compared with those who maintained a stable weight (ORs ranging from 0.67 for a <10-kg loss to 0.84 for a 2–5-kg loss). A dose-response relationship was observed for categories of weight gain with increased odds of EC (ORs ranging from 1.19 to 2.78; P-trend <0.0001). Patterns were generally similar in analyses stratified by young adulthood BMI, though the inverse associations of weight loss were attenuated among the overweight/obese stratum, likely due to small numbers. Positive associations of weight gain with EC were stronger in all strata among those with overweight or obesity in young adulthood.
Table 4.
Associations of weight change (kg) with endometrial cancer in 20 studies in the Epidemiology of Endometrial Cancer Consortium (E2C2) with odds ratios (OR) and 95% confidence intervals (95% CI), stratified by young adulthood body mass index (BMI)
| Total | Model 1a | Model 2b | Underweight/normala,c (n = 32 532) |
Overweight/obesea,c (n = 4904) |
|||
|---|---|---|---|---|---|---|---|
| Weight change | N (%) | P-trend <0.0001 | P-trend <0.0001 | n (%) | P-trend <0.0001 | n (%) | P-trend <0.0001 |
| Loss (<-10) | 809 (1.5) | 1.00 (0.70–1.43) | 0.67 (0.46–0.96) | 201 (0.5) | 0.69 (0.41–1.17) | 600 (12.2) | 0.87 (0.57–1.32) |
| Loss (-10 to <-5) | 1061 (1.9) | 0.79 (0.69–0.91) | 0.68 (0.58–0.78) | 686 (1.6) | 0.71 (0.61–0.82) | 371 (7.6) | 0.79 (0.58–1.08) |
| Loss (-5 to <-2) | 1678 (3.0) | 0.88 (0.76–1.02) | 0.84 (0.73–0.97) | 1380 (3.2) | 0.81 (0.69–0.96) | 292 (6.0) | 1.08 (0.71–1.64) |
| Stable (-2 to 2) | 3535 (6.3) | 0.92 (0.82–1.04) | Ref | 3167 (7.5) | Ref | 362 (7.4) | Ref |
| Gain (+2 to <5) | 4411 (7.9) | 0.996 (0.87–1.14) | 0.95 (0.84–1.07) | 4120 (9.7) | 0.92 (0.82–1.04) | 278 (5.7) | 1.11 (0.82–1.52) |
| Gain (+5 to <10) | 8335 (15.0) | 1.16 (1.02–1.32) | 1.02 (0.89–1.17) | 7795 (18.3) | 0.99 (0.86–1.15) | 521 (10.6) | 1.25 (0.87–1.79) |
| Gain (+10 to <15) | 7730 (13.9) | 1.37 (1.20–1.56) | 1.19 (1.05–1.36) | 7248 (17.0) | 1.15 (1.00–1.33) | 469 (9.6) | 1.44 (1.00–2.08) |
| Gain (+15 to <20) | 6061 (10.9) | 1.61 (1.36–1.90) | 1.4 (1.23–1.60) | 5636 (13.3) | 1.33 (1.16–1.52) | 412 (8.4) | 2.14 (1.59–2.87) |
| Gain (+20 to <25) | 4723 (8.5) | 2.79 (2.28–3.41) | 1.64 (1.39–1.94) | 4338 (10.2) | 1.58 (1.33–1.87) | 375 (7.7) | 2.15 (1.53–3.02) |
| Gain (≥ +25) | 8930 (16.0) | 1.00 (0.70–1.43) | 2.78 (2.29–3.39) | 7708 (18.1) | 2.66 (2.17–3.25) | 1195 (24.4) | 3.75 (2.65–5.31) |
Model adjusted for covariates.
Model adjusted for covariates and young adulthood BMI.
Underweight/normal and overweight/obese BMI categories were combined due to low cell counts. Effect estimates were not affected when underweight individuals were removed in a sensitivity analysis.
Associations of BMI with EC risk by histology, menopausal status, race and ethnicity, HRT use and diabetes
In analyses stratified by histological subtype (Table 5), obesity in young adulthood was associated with endometrioid/adenocarcinoma NOS EC (OR = 1.28, 95% CI = 1.05–1.57), whereas this association was attenuated for cases with non-endometrioid EC. Both overweight and obesity in adulthood were positively associated with both subtypes of EC; however, obesity was more strongly associated with endometrioid/adenocarcinoma NOS EC (OR = 3.12, 95% CI = 2.63–3.71) than with non-endometrioid EC (OR = 2.49, 95% CI = 2.18–2.85). Likewise, associations of BMI change were similar between strata, with stronger associations of maintaining/gaining to obesity with endometrioid/adenocarcinoma EC (OR 3.23, 95% CI = 2.77–3.78) than with non-endometrioid EC (OR 2.45, 95% CI = 1.90–3.17).
Table 5.
Associations of life course obesity with endometrial cancer in 18 studiesa,b in the Epidemiology of Endometrial Cancer Consortium (E2C2) with odds ratios (OR) and 95% confidence intervals (95% CI), stratified by EC subtype
| Endometrioid or Adenocarcinoma NOSd (n = 11 559)c | Non-Endometrioid (n = 1573) | |
|---|---|---|
| Young adulthood BMId,e | P-trend = 0.12 | P-trend = 0.35 |
|
| ||
| Underweight | 1.02 (0.93–1.12) | 1.05 (0.84–1.30) |
| Normal | Ref | Ref |
| Overweight | 1.09 (0.94–1.27) | 1.08 (0.93–1.25) |
| Obese | 1.28 (1.05–1.57) | 1.14 (0.92–1.42) |
|
| ||
| Adulthood BMId,e | P–trend < 0.0001 | P–trend < 0.0001 |
|
| ||
| Underweight/normal | Ref | Ref |
| Overweight | 1.40 (1.28–1.53) | 1.42 (1.22–1.65) |
| Obese | 3.12 (2.63–3.71) | 2.49 (2.18–2.85) |
|
| ||
| BMI changed,f | P–trend < 0.0001 | P–trend < 0.0001 |
|
| ||
| Maintain/gain to normal | Ref | Ref |
| Lose to normal | 1.02 (0.94–1.10) | 0.98 (0.72–1.32) |
| Maintain/gain to overweight | 1.41 (1.28–1.56) | 1.37 (1.05–1.80) |
| Lose to overweight | 1.53 (0.93–2.52) | 0.66 (0.27–1.65) |
| Maintain/gain to obese | 3.23 (2.77–3.78) | 2.45 (1.90–3.17) |
The Italian Multicentre Study and the Women’s Lifestyle and Health Study were excluded from this analysis due to failure to report histology. A sensitivity analysis was performed, and the removal of the studies did not affect estimates in the overall models.
All studies reported tumour histology using ICD-O-3 codes, except the Polish Case Control Study, the Shanghai Endometrial Cancer Study, and the Nurses’ Health Study, which provided summary histological type.
Endometrioid and adenocarcinoma NOS histology were combined to avoid misclassification of adenocarcinoma NOS cases. A sensitivity analysis was performed and endometrioid estimates were not affected when adenocarcinoma NOS cases were separated into a separate stratum.
BMI, body mass index; NOS, not otherwise sepcified.
Model is mutually adjusted for young adulthood, adulthood BMI and covariates.
BMI change is modelled with adjustment for covariates and without adjustment for young adulthood BMI.
Results stratified by menopausal status were directionally similar to the overall models (Table 6); however, obesity in young adulthood was more associated with EC among pre/perimenopausal participants (OR = 1.52, 95% CI = 1.09–2.13). Associations of overweight and obesity in adulthood with EC were similar by menopausal status. Additionally, losing to an overweight BMI was associated with EC among pre/perimenopausal participants (OR = 1.89, 95% CI = 1.26–2.85). Interactions between BMI and menopausal status were not observed.
Table 6.
Associations of life course obesity with EC in 18 studiesa in the Epidemiology of Endometrial Cancer Consortium (E2C2) with odds ratios (OR) and 95% confidence intervals (95% CI), stratified by menopausal status
| Pre/peri-menopausal (n = 18 659) | Postmenopausal (n = 33 520) | |
|---|---|---|
| Young adulthood BMIb,c,d | P-trend = 0.09 | P-trend = 0.03 |
|
| ||
| Underweight | 1.03 (0.93–1.14) | 0.99 (0.90–1.09) |
| Normal | Ref | Ref |
| Overweight | 1.16 (0.92–1.46) | 1.13 (1.03–1.24) |
| Obese | 1.52 (1.09–2.13) | 1.18 (0.99–1.40) |
|
| ||
| Adulthood BMIb,c,d | P–trend <0.0001 | P–trend <0.0001 |
|
| ||
| Underweight/normal | Ref | Ref |
| Overweight | 1.41 (1.24–1.61) | 1.34 (1.24–1.46) |
| Obese | 2.90 (2.41–3.48) | 2.78 (2.35–3.28) |
|
| ||
| BMI changeb,e | P–trend <0.0001 | P–trend <0.0001 |
|
| ||
| Maintain/gain to normal | Ref | Ref |
| Lose to normal | 1.07 (0.94–1.23) | 1.03 (0.92–1.15) |
| Maintain/gain to overweight | 1.47 (1.23–1.77) | 1.38 (1.21–1.56) |
| Lose to overweight | 1.89 (1.26–2.85) | 1.13 (0.59–2.18) |
| Maintain/gain to obese | 3.21 (2.67–3.86) | 2.91 (2.43–3.47) |
The Fred Hutchinson Cancer Center (FHCC) Endometrial Cancer Case-Control Studies and the Women’s Lifestyle and Health Study were excluded from this analysis due to failure to report menopausal status. A sensitivity analysis was performed, and the removal of the studies did not affect estimates in the overall models.
BMI, body mass index.
Model is mutually adjusted for young adulthood BMI, adulthood BMI and covariates.
No interactions between menopausal status and BMI were observed; young adulthood BMI chi square = 4.08 (P = 0.26); adulthood BMI chi square = 1.00 (P = 0.61).
BMI change is modelled with adjustment for covariates and without adjustment for young adulthood BMI.
In analyses stratified by race and ethnicity (Table 7), young adulthood obesity was associated with EC among non-Hispanic White (OR = 1.32, 95% CI = 1.07–1.63) and non-Hispanic Black (OR = 1.57, 95% CI = 1.30–1.91) participants. Among Hispanic participants, underweight in young adulthood was positively associated with EC (OR = 1.39, 95% CI = 1.15–1.67). Associations of adulthood obesity and EC were observed among all race/ethnicity strata and were strongest among Hispanic (OR = 3.53, 95% CI = 2.93–4.25) and non-Hispanic White (OR = 3.07, 95% CI = 2.59–3.64) participants. Maintaining/gaining to obesity was most strongly associated with EC among Hispanic participants (OR = 3.53, 95% CI = 2.44–5.10). Interactions between race and ethnicity and BMI were observed at both time points. Anthropometric measures by race and ethnicity are shown in Supplementary Table S2 (available as Supplementary data at IJE online).
Table 7.
Associations of life course obesity with endometrial cancer in 20 studies in the Epidemiology of Endometrial Cancer Consortium (E2C2) with odds ratios (OR) and 95% confidence intervals (95% CI), stratified by race and ethnicitya
| Non-Hispanic White (n = 44 537) | Non-Hispanic Black (n = 3410) | Hispanic (n = 1718) | Non-Hispanic Asian (n = 4660) | |
|---|---|---|---|---|
| Young adulthood BMIb,c,d | P-trend = 0.07 | P-trend <0.0001 | P-trend = 0.01 | P-trend = 0.04 |
|
| ||||
| Underweight | 0.98 (0.89-1.09) | 0.90 (0.73-1.11) | 1.39 (1.15-1.67) | 1.04 (0.91-1.20) |
| Normal | Ref | Ref | Ref | Ref |
| Overweight | 1.10 (0.99-1.22) | 1.44 (1.20-1.74) | 1.40 (1.09-1.80) | 0.96 (0.74-1.25) |
| Obese | 1.32 (1.07-1.63) | 1.57 (1.30-1.91) | 1.04 (0.62-1.75) | 0.97 (0.72-1.31) |
|
| ||||
| Adulthood BMIb,c,d | P-trend <0.0001 | P-trend <0.0001 | P-trend <0.0001 | P-trend <0.0001 |
|
| ||||
| Underweight/normal | Ref | Ref | Ref | Ref |
| Overweight | 1.41 (1.30–1.53) | 1.07 (0.86–1.35) | 1.86 (1.52–2.27) | 1.59 (1.41–1.79) |
| Obese | 3.07 (2.59–3.64) | 2.10 (1.82–2.42) | 3.53 (2.93–4.25) | 2.76 (2.34–3.25) |
|
| ||||
| BMI changed,e | P-trend <0.0001 | P-trend <0.0001 | – | P-trend <0.0001 |
|
| ||||
| Maintain/gain to normal | Ref | Ref | Ref | Ref |
| Lose to normal | 1.02 (0.92–1.13) | 1.12 (0.73–1.72) | 1.12 (0.79–1.58) | 1.13 (0.98–1.30) |
| Maintain/gain to overweight | 1.43 (1.28–1.61) | 1.18 (0.72–1.93) | 1.77 (1.25–2.51) | 1.69 (1.49–1.90) |
| Lose to overweight | 1.51 (0.89–2.57) | 1.54 (0.18–13.04) | – | 0.54 (0.19–1.50) |
| Maintain/gain to obese | 3.22 (2.69–3.85) | 2.50 (1.65–3.78) | 3.53 (2.44–5.10) | 2.88 (2.38–3.49) |
Participants identified as Mixed, Other, and Hawaiian/Pacific Islander were excluded due to low numbers.
BMI, body mass index.
Model is mutually adjusted for young adulthood BMI, adulthood BMI and covariates without race/ethnicity.
Interactions between race/ethnicity and BMI were observed; young adulthood BMI chi square = 101.16 (P <0.0001); adulthood BMI chi square = 67.33 (P <0.0001).
BMI change is modelled with adjustment for covariates without race/ethnicity and without adjustment for young adulthood BMI.
Results stratified by HRT use (Table 8) were generally similar to the overall model, but associations of adulthood BMI and BMI change were weaker among those with a history of HRT use and stronger among never users. Associations of young adulthood overweight and obesity were attenuated to null among ever users. Interestingly, underweight BMI in young adulthood among ever users was inversely associated with EC. Interactions between HRT use and BMI were observed at both time points.
Table 8.
Associations of life course obesity with endometrial cancer in 20 studies in the Epidemiology of Endometrial Cancer Consortium (E2C2) with odds ratios (OR) and 95% confidence intervals (95% CI), stratified by hormone replacement therapy (HRT) use
| Never (n = 32 954) | Ever (n = 21 258) | |
|---|---|---|
| Young adulthood BMIa,b,c | P-trend = 0.01 | P-trend = 0.09 |
|
| ||
| Underweight | 1.09 (0.97–1.23) | 0.91 (0.83–0.99) |
| Normal | Ref | Ref |
| Overweight | 1.18 (1.05–1.32) | 0.97 (0.83–1.14) |
| Obese | 1.28 (1.03–1.60) | 1.08 (0.84–1.39) |
|
| ||
| Adulthood BMIa,b,c | P–trend < 0.0001 | P–trend < 0.0001 |
|
| ||
| Underweight/normal | Ref | Ref |
| Overweight | 1.61 (1.44–1.81) | 1.18 (1.10–1.26) |
| Obese | 3.82 (3.13–4.66) | 1.73 (1.56–1.91) |
|
| ||
| BMI changea,c | P–trend < 0.0001 | P–trend < 0.0001 |
|
| ||
| Maintain/gain to normal | Ref | Ref |
| Lose to normal | 0.92 (0.82–1.04) | 1.11 (0.98–1.25) |
| Maintain/gain to overweight | 1.52 (1.35–1.71) | 1.27 (1.12–1.43) |
| Lose to overweight | 1.61 (0.89–2.93) | 0.82 (0.47–1.44) |
| Maintain/gain to obese | 3.71 (3.13–4.40) | 1.88 (1.61–2.20) |
BMI, body mass index.
Model is mutually adjusted for young adulthood BMI, adulthood BMI and covariates without race/ethnicity.
Interactions between HRT use and BMI were observed; young adulthood BMI chi square = 9.07 (P = 0.03); adulthood BMI chi square = 113.71 (P <0.0001).
BMI change is modelled with adjustment for covariates without race/ethnicity and without adjustment for young adulthood BMI.
When stratified by diabetes status (Supplementary Table S3, available as Supplementary data at IJE online), results were generally similar to the overall model. No substantial differences in associations were observed between strata, though the association of young adulthood obesity was attenuated to null among those with diabetes. The interaction between diabetes and BMI was only observed in young adulthood.
Discussion
Our analyses revealed that overweight and obesity in young adulthood were associated with increased EC risk, even after accounting for BMI in adulthood. We demonstrated that increasing weight loss was inversely associated and increasing weight gain was positively associated with EC risk, in dose-response relationships. Further, we confirmed the strong positive associations of overweight and obesity in adulthood with EC risk.12,13,15,16,18–21,24,26–28,32–35
Our findings clarify the existing inconsistent literature on young adulthood BMI and EC, with some studies showing positive associations12–19 and others reporting no association.20,23–27 Discrepancies may be due to differences in BMI cut points 12,13,15,16,26,27,32 or alternative measures used to assess body mass.12,16,17,23,25 Differences may also be attributed to sample size, analytical approaches and lack of adjustment for important confounders.12,14–18,24–27,32,36 Some studies did not adjust for adulthood BMI,13–15,19,25,26,32 which may have resulted in stronger associations of young adulthood BMI with EC.
Like other studies,12,15,18–24 our findings suggest that weight gain from young adulthood to adulthood is associated with an increased EC risk, particularly among those who gain ≥25 kg. These associations were generally similar irrespective of young adulthood BMI. In contrast to the increased EC risk observed for weight gain, we found that weight loss between young adulthood and adulthood was inversely associated with EC risk, particularly among individuals with an underweight/normal BMI in young adulthood.
Participants who developed or maintained overweight or obesity in adulthood from young adulthood to adulthood had an increased EC risk. Contrastingly, individuals with overweight or obesity in young adulthood but a normal adulthood BMI were not at increased EC risk, similar to the inverse associations we observed for weight loss.
Proposed biological mechanisms linking adult overweight and obesity with EC risk include alterations in insulin signalling, sex hormone pathways and inflammatory pathways. Obesity is hypothesized to influence EC risk via increased levels of unopposed estrogen exposure through changes in ovulation (among premenopausal women), androgen aromatization (among postmenopausal women) and decreased production of sex hormone-binding globulin (SHBG).9,37,38 Obesity-induced chronic inflammation may also contribute to endometrial carcinogenesis by increasing the expression of inflammatory cytokines, stimulating angiogenesis and cell proliferation.11,39,40 Observed associations for young adulthood obesity and weight gain may reflect an increased risk from cumulative life course exposure to these hormonal, metabolic and immune disturbances.9,11 Further research is needed to better understand how weight change over time interacts with critical periods of hormonally related processes such as menarche, pregnancy and menopause.
Similar to previous research evaluating adulthood BMI,28,41 young adulthood obesity was more so associated with endometrioid/adenocarcinoma NOS EC than with non-endometrioid subtypes. Stratified by menopausal status, the associations of obesity in young adulthood with EC were stronger among pre/perimenopausal participants than among postmenopausal participants, in line with other studies.12 Understanding the relationships between EC risk and obesity at different hormonal stages is increasingly important, as EC incidence among people ≤50 years old has been increasing annually by an average of 1.6% for the past two decades.3
With respect to differences by race and ethnicity, young adulthood overweight and obesity were associated with EC risk among non-Hispanic White and Black participants and were strongest among non-Hispanic Black participants. Interestingly, being underweight in young adulthood was positively associated with EC among Hispanic participants; more research is needed to better understand this observation. Associations of adulthood obesity with EC were strongest among non-Hispanic White and Hispanic participants and lowest among non-Hispanic Black participants. This contrasts with previous research that observed stronger associations of obesity with EC among non-Hispanic Black participants in comparison with other racial and ethnic groups.13 Due to low numbers in our stratified analyses, we were unable to investigate the relationship between cancer subtype, race/ethnicity and BMI.
The associations observed in analyses stratified by HRT use were largely similar to previous findings, for both young adulthood BMI42 and adulthood BMI,18,42 with associations being stronger among never HRT users.18,20,42 Along with these previous studies, our findings suggest that HRT modifies associations of obesity with EC risk.
This analysis has several strengths: notably, a large sample size with detailed covariate information. The large number of cases and detailed histology allowed us to investigate associations of young adulthood anthropometric measures with EC subtype. Further, using WHO-recommended BMI cut points for Asian populations allows for more accurate estimation of EC risk for Asian and Asian American participants.30,31 However, despite being the largest analysis to date, data were still limited among some racial and ethnic groups; further research is needed to evaluate associations of BMI throughout the life course with EC risk in diverse populations. Further, we were unable to assess EC risk among Asian ethnic subgroups, and more research is necessary to address potential ethnic differences in EC risk. Additionally, young adulthood BMI was based on self-reported measures which may introduce additional biases and measurement errors. However, several studies have evaluated the validity of self-reported weight compared with measured weight in young adulthood and have shown good agreement between both measures.43–45 An important limitation is that only two time points were included in this analysis; thus, we were unable to assess the effect of weight cycling or accurately estimate the cumulative effects of obesity over time. We also lacked information on potential confounders including breastfeeding, tamoxifen use, type and duration of HRT and/or oral contraceptive use, and conditions such as polycystic ovarian syndrome.
Conclusions
In the largest analysis conducted to date, we demonstrate that young adulthood obesity is associated with increased EC risk, even after accounting for adulthood BMI. Further, we show that gaining weight between young adulthood and adulthood is associated with increased EC risk, whereas weight loss is inversely associated with the development of EC. Associations of young adulthood obesity with EC were strongest among endometrioid/adenocarcinoma NOS cases, pre/perimenopausal and non-Hispanic Black participants, never HRT users and non-diabetics. Our findings emphasize the importance of maintaining a healthy weight for EC prevention efforts.
Ethics approval
All studies were approved by the relevant institutional review boards and participants provided informed consent.
Supplementary Material
Acknowledgements
The authors would like to thank the participants and staff of each study for their valuable contributions, and 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 and WY.
Contributor Information
Summer V Harvey, Division of Cancer Epidemiology and Genetics, National Cancer Institute, Rockville, MD, USA.
Nicolas Wentzensen, Division of Cancer Epidemiology and Genetics, National Cancer Institute, Rockville, MD, USA.
Kimberly Bertrand, Slone Epidemiology Center, at Boston University, Boston, MA, USA.
Amanda Black, Division of Cancer Epidemiology and Genetics, National Cancer Institute, Rockville, MD, USA.
Louise A Brinton, Division of Cancer Epidemiology and Genetics, National Cancer Institute, Rockville, MD, USA.
Chu Chen, Division of Public Health Sciences, Fred Hutchinson Cancer Center, Seattle, WA, USA.
Laura Costas, Cancer Epidemiology Research Programme IDIBELL, Catalan Institute of Oncology, Hospitalet de Llobregat, Barcelona, Spain; Consortium for Biomedical Research in Epidemiology and Public Health (CIBERESP), Madrid, Spain.
Luigino Dal Maso, Cancer Epidemiology Unit, Centro di Riferimento Oncologico, Aviano, Italy.
Immaculata De Vivo, Channing Division of Network Medicine, Department of Medicine, Brigham and Women's Hospital, and Harvard Medical School, Boston, MA, USA.
Mengmeng Du, Department of Epidemiology and Biostatistics, Memorial Sloan Kettering Cancer Center, New York, NY, USA.
Montserrat Garcia-Closas, Division of Cancer Epidemiology and Genetics, National Cancer Institute, Rockville, MD, USA.
Marc T Goodman, Cedars-Sinai Cancer and Department of Medicine, Cedars-Sinai Medical Center, Los Angeles, CA, USA.
Jessica Gorzelitz, Division of Cancer Epidemiology and Genetics, Metabolic Epidemiology Branch, National Cancer Institute, Bethesda, MD, USA.
Lisa Johnson, Division of Public Health Sciences, Fred Hutchinson Cancer Center, Seattle, WA, USA.
James V Lacey, Division of Health Analytics, Department of Computational and Quantitative Medicine, Beckman Research Institute, City of Hope National Medical Center, Duarte, CA, USA.
Linda Liao, Division of Cancer Epidemiology and Genetics, Metabolic Epidemiology Branch, National Cancer Institute, Bethesda, MD, USA.
Loren Lipworth, Division of Epidemiology, Department of Medicine, Vanderbilt Epidemiology Center, Vanderbilt-Ingram Cancer Center, Vanderbilt University Medical Center, Nashville, TN, USA.
Jolanta Lissowska, Department of Cancer Epidemiology and Prevention, M. Sklodowska-Curie National Research Institute of Oncology, Warsaw, Poland.
Anthony B Miller, Dalla Lana School of Public Health, University of Toronto, Toronto, ON, Canada.
Kelli O'Connell, Department of Epidemiology and Biostatistics, Memorial Sloan Kettering Cancer Center, New York, NY, USA.
Tracy A O’Mara, Cancer Research Program, QIMR Berghofer Medical Research Institute, Brisbane, QLD, Australia.
Xiao Ou, Division of Epidemiology, Department of Medicine, Vanderbilt Epidemiology Center, Vanderbilt-Ingram Cancer Center, Vanderbilt University Medical Center, Nashville, TN, USA.
Julie R Palmer, Slone Epidemiology Center, at Boston University, Boston, MA, USA.
Alpa V Patel, Department of Population Science, American Cancer Society, Atlanta, GA, USA.
Sonia Paytubi, Cancer Epidemiology Research Programme IDIBELL, Catalan Institute of Oncology, Hospitalet de Llobregat, Barcelona, Spain.
Beatriz Pelegrina, Cancer Epidemiology Research Programme IDIBELL, Catalan Institute of Oncology, Hospitalet de Llobregat, Barcelona, Spain; Consortium for Biomedical Research in Epidemiology and Public Health (CIBERESP), Madrid, Spain.
Stacey Petruzella, Department of Epidemiology and Biostatistics, Memorial Sloan Kettering Cancer Center, New York, NY, USA.
Anna Prizment, Division of Hematology, Oncology and Transplantation, University of Minnesota, Minneapolis, MN, USA.
Thomas Rohan, Department of Epidemiology and Population Health, Albert Einstein College of Medicine, Bronx, NY, USA.
Sven Sandin, Department of Medical Epidemiology and Biostatistics, Karolinska Institutet, Stockholm, Sweden.
Veronica Wendy Setiawan, Department of Population and Public Health Sciences, Keck School of Medicine, University of Southern California, Los Angeles, CA, USA.
Rashmi Sinha, Division of Cancer Epidemiology and Genetics, Metabolic Epidemiology Branch, National Cancer Institute, Bethesda, MD, USA.
Britton Trabert, Division of Cancer Epidemiology and Genetics, National Cancer Institute, Rockville, MD, USA.
Penelope M Webb, Population Health Department, QIMR Berghofer Medical Research Institute, Brisbane, QLD, Australia.
Lynne R Wilkens, Epidemiology Program, University of Hawaii Cancer Center, Honolulu, HI, USA.
Wanghong Xu, Department of Epidemiology, Fudan University School of Public Health, Shanghai, China.
Hannah P Yang, Division of Cancer Epidemiology and Genetics, National Cancer Institute, Rockville, MD, USA.
Wei Zheng, Division of Epidemiology, Department of Medicine, Vanderbilt Epidemiology Center, Vanderbilt-Ingram Cancer Center, Vanderbilt University Medical Center, Nashville, TN, USA.
Megan A Clarke, Division of Cancer Epidemiology and Genetics, National Cancer Institute, Rockville, MD, USA.
Data availability
The data underlying this analysis were provided by the Epidemiology of Endometrial Cancer Consortium (E2C2) under a data use agreement. Researchers interested in the E2C2 data may submit an inquiry online [https://epi.grants.cancer.gov/eecc/membership.html].
Supplementary data
Supplementary data are available at IJE online.
Author contributions
S.V.H., N.W. and M.A.C. designed the analysis, directed its implementation and prepared the manuscript. All other listed authors are representatives from individual studies within E2C2 and assisted the project by providing data and suggesting revisions during the manuscript editing process. All authors of this paper have read and approved the final version submitted.
Funding
This work was supported by the Intramural Research Programs of the NCI, NIH, Department of Health and Human Services, United States. The E2C2 Data Coordinating Center at Memorial Sloan Kettering Cancer Center and multiple authors are supported by the National Cancer Institute Grant U01 CA250476. The Data Coordinating Center is additionally supported by NCI P30 CA008748. The content is solely the responsibility of the authors and does not necessarily represent the official views of the funders. The funders had no role in study design, data collection, analysis, decision to publish or manuscript preparation.
AARP: Intramural Research Programs of the National Cancer Institute, National Institutes of Health, Department of Health and Human Services, United States [https://dietandhealth.cancer.gov/acknowledgement.html].
ANECS: National Health and Medical Research Council of Australia (Grants No. APP339435, APP1073898, APP1061341 and APP1061779; Grant No. APP1173346 to P.M.W.); Cancer Council Tasmania (Grants No. 403031 and 457636).
BWHS: National Institutes of Health (Grants No. R01CA058420, U01CA164974 and R03CA169888).
CTS: National Cancer Institute of the National Institutes of Health (Grants No. U01-CA199277, P30-CA033572, P30-CA023100, UM1-CA164917 and R01-CA077398); the collection of cancer incidence data was supported by the California Department of Public Health pursuant to California Health and Safety Code Section 103885; Centers for Disease Control and Prevention’s National Program of Cancer Registries (cooperative agreement 5NU58DP006344); National Cancer Institute’s Surveillance, Epidemiology and End Results Program (Contracts No. HHSN261201800032I, HHSN261201800015I, and HHSN261201800009I).
CNBSS: Canadian Breast Cancer Research Alliance, the Canadian Breast Cancer Research Initiative, the Canadian Cancer Society, Health and Welfare Canada, the National Cancer Institute of Canada, the Alberta Heritage Fund for Cancer Research, Manitoba Health Services Commission, Medical Research Council of Canada, le Ministère de la Santé et des Services Soçiaux du Québec, the Nova Scotia Department of Health and the Ontario Ministry of Health.
CPS II: Centers for Disease Control and Prevention's National Program of Cancer Registries; cancer registries supported by the National Cancer Institute's Surveillance Epidemiology and End Results Program.
EDGE: National Cancer Institute of the National Institutes of Health (Grants No. U01 CA250476 and P30 CA008748; Grant No. R01CA83918 to K.O., M.D. and S.P.).
FHCC: National Cancer Institute of the National Institutes of Health (Grants No. R35 CA39779, R01 CA47749, R01 CA75977, N01 HD 2 3166, K05 CA92002, R01 CA105212, and R01 CA87538); additional funding from the Fred Hutchison Cancer Center.
HAWAII: National Cancer Institute of the National Institutes of Health (Grants No. R35 CA39779, R01 CA47749, R01 CA75977, N01 HD 2 3166, K05 CA92002, R01 CA105212, R01 CA87538; Contracts No. N01-CN-05223 and N01-CN-55424) and the United States Public Health Service (Grants No. P01-CA-33619, R01-CA-58598, R01-CA-55700 and P20-CA-57113).
IWHS: National Institutes of Health (Grant No. R01 CA39742).
IMS: Italian Association for Cancer Research (Grant No. 1468).
MECS: National Institutes of Health (Grants No. U01CA164973 and RO3CA135632).
NHS: National Institutes of Health (Grants No. 2R01 CA082838 and P01 CA87969).
PLCO: Intramural Research Programs of the National Cancer Institute, National Institutes of Health, Department of Health and Human Services, United States.
POL: Intramural Research Programs of the National Cancer Institute, National Institutes of Health, Department of Health and Human Services, United States (Grant No. ZIA CP010126).
Screenwide: Carlos III Health Institute (Grants No. PIE16/00049 and PI19/01835); CIBERESP (Grant No. CB06/02/0073); CIBERONC (Grants No. CB16/12/00401, CM19/00216, FI20/00031, MV21/00061 and MV20/00029), co-financed by the European Regional Development Fund ERDF, a way to build Europe; Generalitat de Catalunya (Research Groups 2017SGR01085, 2017SGR01718 and 2017SGR00735); institutional support from CERCA Programme/Generalitat de Catalunya.
SHANGHAI: National Institutes of Health (Grant No. R01 CA092585).
SCCS: National Institutes of Health (Grant No. U01CA202979).
USEC: Intramural Research Programs of the National Cancer Institute, National Institutes of Health, Department of Health and Human Services, United States.
WLHS: Swedish Research Council (Grant No. 521–2011-2955).
Conflict of interest
None declared.
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Associated Data
This section collects any data citations, data availability statements, or supplementary materials included in this article.
Supplementary Materials
Data Availability Statement
The data underlying this analysis were provided by the Epidemiology of Endometrial Cancer Consortium (E2C2) under a data use agreement. Researchers interested in the E2C2 data may submit an inquiry online [https://epi.grants.cancer.gov/eecc/membership.html].