Abstract
Background
The incidence of differentiated thyroid cancer (DTC) is higher in women than in men but whether sex steroid hormones contribute to this difference remains unclear. Studies of reproductive and hormonal factors and thyroid cancer risk have provided inconsistent results.
Methods
Original data from 1 252 907 women in 16 cohorts in North America, Europe, Australia and Asia were combined to evaluate associations of DTC risk with reproductive and hormonal factors. Multivariable-adjusted Cox proportional hazard models were used to estimate hazard ratios (HRs) and 95% CIs.
Results
During follow-up, 2142 women were diagnosed with DTC. Factors associated with higher risk of DTC included younger age at menarche (<10 vs 10–11 years; HR, 1.28; 95% CI, 1.00–1.64), younger (<40; HR, 1.31; 95% CI, 1.05–1.62) and older (≥55; HR, 1.33; 95% CI, 1.05–1.68) ages at menopause (vs 40–44 years), ever use of menopausal hormone therapy (HR, 1.16; 95% CI, 1.02–1.33) and previous hysterectomy (HR, 1.25; 95% CI, 1.13–1.39) or bilateral oophorectomy (HR, 1.14; 95% CI, 1.00–1.29). Factors associated with lower risk included longer-term use (≥5 vs <5 years) of oral contraceptives (HR, 0.86; 95% CI, 0.76–0.96) among those who ever used oral contraception and baseline post-menopausal status (HR, 0.82; 95% CI, 0.70–0.96). No associations were observed for parity, duration of menopausal hormone therapy use or lifetime number of reproductive years or ovulatory cycles.
Conclusions
Our study provides some evidence linking reproductive and hormonal factors with risk of DTC. Results should be interpreted cautiously considering the modest strength of the associations and potential for exposure misclassification and detection bias. Prospective studies of pre-diagnostic circulating sex steroid hormone measurements and DTC risk may provide additional insight.
Keywords: Thyroid cancer, parity, menarche, menopause, oral contraceptives, hysterectomy, hormone replacement therapy, oophorectomy
Key Messages.
Results suggest an association between reproductive and hormonal factors and risk for differentiated thyroid cancer (DTC) in females. Higher risks were observed for younger age at menarche, younger and older ages at menopause, ever use of oral contraceptives and menopausal hormone therapy, and previous hysterectomy or bilateral oophorectomy. Lower risks were observed for longer-term use (≥5 vs <5 years) of oral contraceptives and baseline post-menopausal status.
Observed heterogeneity across cohorts for associations with hysterectomy and oophorectomy may reflect variability in the timing of these events in relation to other reproductive and hormonal exposures, as well as with thyroid cancer diagnosis.
Large prospective studies with more detailed exposure information, information on other factors (e.g. breastfeeding), pre-diagnostic measures of sex steroid hormone concentrations and mode of DTC diagnosis, are needed.
Introduction
Thyroid cancer is the fifth most-commonly diagnosed cancer among women worldwide and the third most-commonly diagnosed cancer among reproductive age women.1 Thyroid cancer disproportionately affects women and has an age-adjusted global incidence rate that is 3-fold higher than that of men.1,2 Higher rates are most pronounced during women’s reproductive years and are not explained by known or suspected thyroid cancer risk factors, including ionizing radiation or obesity.1,3 Higher exposure to endogenous sex steroid hormones over the life course has been thought to at least partly contribute to the female predominance of this disease, with oestrogen playing a role in thyroid cancer growth and development.4 Thyroid tumours commonly express oestrogen receptors and experimental studies have demonstrated a growth-promoting effect of oestrogen on benign and malignant thyroid cells.5
Reproductive and hormonal factors, such as parity and age at menarche and at menopause, and use of exogenous hormones, are considered proxies for lifetime exposure to sex steroid hormones, particularly oestrogens and progestogens, and have been associated with risk of other female-predominant or female-specific cancers.6–8,9,10 Early thyroid cancer case–control studies suggested that some of these factors, including later menarche, menopause, age at first birth, current use of oral contraceptives (OC) and use of fertility treatments, may be associated with slightly higher risk of thyroid cancer.10,11 However, findings from more recent case–control and prospective studies have been inconsistent.9,12–14
We conducted the first pooled analysis of prospective studies on reproductive and hormonal factors and risk of differentiated thyroid cancer (DTC, accounting for ∼95% of all thyroid cancers) as the first primary cancer15 by combining individual-level data across 16 prospective cohorts in North America, Europe, Australia and Asia. We hypothesized that female reproductive characteristics representing higher circulating concentrations of sex steroid hormones, namely oestrogen, would be associated with an increased risk of DTC.
Methods
Study population
Cohorts participating in the National Cancer Institute’s (NCI) Cohort Consortium were eligible for inclusion if the baseline year occurred on or after 1970 and the study ascertained data on parity, age at menarche, menopausal status, OC use, menopausal hormone therapy (MHT) use, previous hysterectomy and/or previous oophorectomy. Data on the female participants from 16 prospective cohort studies were available: National Institutes of Health American Association of Retired Persons Diet and Health (AARP); Breast Cancer Detection Demonstration Project (BCDDP); Cancer Prevention Study II (CPSII); California Teachers Study (CTS); European Prospective Investigation into Cancer and Nutrition (EPIC); Iowa Women’s Health Study (IWHS); Melbourne Collaborative Cohort Study (MCCS); New York University Women’s Health Study (NYUWHS); Prostate, Lung, Colorectal, and Ovarian Cancer Screening Trial (PLCO); Sister Study (SISTER); Swedish Mammography Cohort (SMC); Shanghai Women’s Health Study (SWHS); US Radiological Technologists Study (USRT); VITamins And Lifestyle Study (VITAL); Women’s Health Study (WHS); and the Swedish Women’s Lifestyle and Health Study (WLH). Design and recruitment details for each study have been previously published.16–34
From 1 321 371 eligible female study participants with accrued follow-up time, individuals were excluded if baseline age or age at end of follow-up were missing (N = 2214) or if diagnosed with any cancer other than non-melanoma skin cancer before completing the baseline questionnaire (N = 66 250). The final analysis contained a pooled cohort of 1 252 907 women, including 2142 incident cases of DTC diagnosed during follow-up of the individual cohorts.
Case ascertainment
The follow-up date began when participants completed the baseline questionnaire and continued until occurrence of any first primary cancer other than non-melanoma skin cancer, loss to follow-up, death or the cohort-specific administrative end date. Information on cancer incidence was obtained via linkage to local, state or national cancer registries (AARP, CTS, IWHS, MCCS, SMC, VITAL and WLH), medical record confirmation of self-report (PLCO, SISTER, USRT and WHS) or a combination approach (BCDDP, CPSII, EPIC, NYUWHS and SWHS). Cases were those with a reported diagnosis of first primary DTC [International Classification of Disease for Oncology, 3rd Edition (ICD-O-3), topography code C73] with histologic types defined using ICD-O-3 morphology codes (papillary: 8050, 8260, 8340–8344, 8350, 8450–8460 and follicular: 8290, 8330–8335).35 Population characteristics and case counts by study are available in Table 1.
Table 1.
Baseline characteristics of participants in the pooled analysis, by cohort
| Cohort | Cohort acronym | Geographic location | Participants | Cases | Calendar period of follow-up (range) | Years of follow-up, mean (SD) | Baseline age, mean (SD) | Parity (live births), mean (range) | Menarche age, mean (SD) | Ever oral contraceptive use | Ever menopausal hormone therapy use | Hysterectomy | Oophorectomy |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| NIH-AARP Diet & Health Study | AARP | USA | 202 531 | 474 | 1995–2011 | 13.4 (4.2) | 61.9 (5.4) | 2.5 (0, 11) | 12.0 (1.7) | 39.2% | 52.8% | 40.7% | 28.2% |
| Breast Cancer Detection Demonstration Project | BCDDP | USA | 42 182 | 33 | 1987–1997 | 8.2 (1.7) | 62.0 (8.2) | 2.5 (0, 15) | 12.8 (1.5) | 30.6% | 66.1% | 43.3% | 33.7% |
| Cancer Prevention Study II | CPSII | USA | 82 604 | 125 | 1992–2009 | 12.8 (3.5) | 62.1 (6.6) | 2.9 (0, 9) | 12.7 (1.5) | 38.3% | 56.4% | 35.1% | 24.9% |
| California Teachers Study | CTS | USA | 105 667 | 265 | 1995–2010 | 12.8 (4.1) | 52.3 (13.9) | 1.7 (0, 13) | 12.5 (1.5) | 66.2% | NA | 20.7% | 14.7% |
| European Prospective Investigation into Cancer and Nutrition | EPIC | Europe (multiple sites) | 345 158 | 474 | 1991–2010 | 11.0 (2.7) | 50.9 (9.9) | 1.9 (0, 17) | 13.1 (1.5) | 56.9% | 24.2% | 10.2% | 6.1% |
| Iowa Women’s Health Study | IWHS | USA | 37 982 | 64 | 1986–2005 | 16.4 (5.5) | 62.2 (4.2) | 3.1 (0, 14) | 12.9 (1.5) | 18.9% | 38.3% | 32.4% | 25.7% |
| Melbourne Collaborative Cohort Study | MCCS | Australia | 22 354 | 30 | 1990–2009 | 15.2 (3.8) | 54.8 (8.6) | 2.4 (0, 16) | 13.1 (1.6) | 58.7% | 24.7% | 20.5% | 8.8% |
| New York University Women’s Health Study | NYU WHS | USA | 13 358 | 46 | 1985–2007 | 18.7 (5.2) | 50.6 (8.7) | 1.5 (0, 12) | 12.6 (1.5) | 35.1% | NA | 13.7% | 12.4% |
| Prostate, Lung, Colorectal, and Ovarian Cancer Screening Study | PLCO | USA | 70 976 | 89 | 1993–2006 | 8.5 (2.6) | 63.0 (5.4) | 3.1 (0, 22) | 13.2 (1.6) | 54.2% | 67.2% | 35.4% | 19.7% |
| Sister Study | SISTER | USA | 47 805 | 118 | 2003–2012 | 8.3 (2.3) | 55.4 (8.9) | 2.0 (0, 12) | 12.6 (1.5) | 84.2% | 44.4% | 30.1% | 23.4% |
| Swedish Mammography Cohort | SMC | Sweden | 37 169 | 14 | 1998–2008 | 9.7 (2.2) | 62.4 (9.3) | 2.2 (0, 13) | 13.2 (1.3) | 56.7% | 45.2% | 1.5% | 9.4% |
| Shanghai Women’s Health Study | SWHS | China | 74 933 | 143 | 1996–2009 | 10.8 (1.9) | 52.6 (9.1) | 1.8 (0, 10) | 14.9 (1.7) | 20.4% | NA | 5.4% | 4.2% |
| United States Radiological Technologists Study | USRT | USA | 50 510 | 93 | 1994–2006 | 9.0 (1.5) | 47.4 (8.4) | 1.9 (0, 8) | 12.5 (1.4) | 75.0% | 30.3% | 22.9% | 13.3% |
| VITamins and Lifestyle Study | VITAL | USA | 33 730 | 74 | 2000–2009 | 7.4 (2.0) | 61.0 (7.4) | 2.3 (0, 5) | 12.4 (1.9) | 71.6% | 59.0% | 36.4% | 21.4% |
| Women’s Health Study | WHS | USA | 39 779 | 96 | 1993–2010 | 14.7 (3.9) | 54.7 (7.0) | 2.5 (0, 6) | 12.4 (1.5) | 69.3% | 49.9% | 15.8% | 20.1% |
| Swedish Women’s Lifestyle and Health Study | WLH | Sweden | 46 169 | 4 | 1991–2006 | 14.8 (1.8) | 40.2 (5.8) | 1.9 (0, 4) | 13.0 (1.4) | 82.9% | 26.5% | 10.9% | 5.7% |
AARP, American Association of Retired Persons; NIH, National Institutes of Health.
Exposure assessment and data standardization
Cohort participants completed self-administered questionnaires at baseline that elicited information on general demographics (age, sex, race/ethnicity, education, marital status), lifestyle factors (cigarette smoking, alcohol intake, physical activity) and personal medical history. Reproductive and hormonal factor data were self-reported for all cohorts. Information on parity, menarche, oral contraceptive use, hysterectomy, oophorectomy and menopause was provided by all 16 cohorts, largely as these were considered potential covariates in an earlier pooled analysis.34 For WHS, it was not possible to distinguish between participants who did not have a prior hysterectomy and those with unknown information; thus, WHS was dropped from models for hysterectomy. MHT use was provided by 13 studies (AARP, BCDDP, CPSII, EPIC, IWHS, MCCS, PLCO, SISTER, SMC, USRT, VITAL, WHS and WLH). Because the level of detail on exposure variables and covariates differed across the cohorts, we created harmonized variables for the aggregated cohort using standardized definitions and categories. More information on exposure assessment and data standardization is available in Table 1.
The number of reproductive years was estimated by subtracting age at menarche from age at menopause, restricting to post-menopausal women.36 Because some questionnaires elicited age at menarche and age at menopause categorically, we used 1 minus the high end of the range for the lowest categories (e.g. 9 for ages <10 years), the midpoint for the middle categories (e.g. 10.5 for ages 10–11 years) and the bottom end of the range for the highest categories (e.g. 16 for ages ≥16 years) to calculate the number of reproductive years. Total number of lifetime ovulatory cycles was estimated by subtracting duration of OC use and 36 weeks for each live birth from the number of reproductive years, and assuming that these cycles averaged 28.1 days.37,38 For these calculations, numerical values were reassigned for each category of OC use using the same method as for categorical designations of age at menarche and age at menopause. Negative values of reproductive years or ovulatory cycles were reassigned as 0 (N = 301). The total numbers of reproductive years and lifetime ovulatory cycles were divided into quartiles based on the distribution among women without DTC to create categories of reproductive years (0 to <31), (31 to <36), (36 to <39) and (≥39), and categories of ovulatory cycles (0 to <320), (320 to <402), (402 to <467) and (≥467).
Statistical analysis
Cox proportional-hazards models with attained age as the time metric and stratified by cohort were used to calculate hazard ratios (HRs) and 95% CIs for the association between hormonal and reproductive factors and DTC. We also separately evaluated associations by DTC subtype (e.g. papillary and follicular carcinoma). Models were adjusted for known and potential confounding factors including self-reported race (White, Black, Asian/Pacific Islander, other, missing), education (less than high school, high-school graduate, some college, college graduate, missing), baseline body mass index (BMI) (<18.5, 18.5–24.9, 25.0–29.9, ≥30.0, missing), smoking status (never, former, current, missing) and alcohol intake (0, <10 g/day, ≥10 g/day, missing). Missingness was handled through inclusion of a missing-indicator variable in the models.
Between-study heterogeneity was assessed using a random-effects meta-analysis to calculate the I2 index,39 with 0% indicating no heterogeneity and an I2 of >50% representing substantial heterogeneity.40P-values for trend were obtained from the Wald test for categorical variables modelled continuously.
We conducted sensitivity analyses to identify whether certain reproductive factors were independently associated with thyroid cancer risk. One such analysis used a model mutually adjusted for oophorectomy and hysterectomy. Another model, restricted to post-menopausal women at baseline, mutually adjusted for oophorectomy, hysterectomy and ever use of MHT. All analyses were performed using SAS v9.4 (SAS Cary Institute Inc., Cary, NC).
Results
Over a mean 12.8 years of follow-up, 2142 first primary DTCs were identified among 1 252 907 individuals. The mean age at baseline was 54.8 years (range 40.2–63.0) and 86.6% of DTCs were diagnosed at age ≥50 years. Cohort characteristics are described in Table 1. The three largest studies were AARP, EPIC and CTS, with 474, 474 and 265 DTC cases, respectively. Together, AARP, EPIC and CTS accounted for 52% of participants and 57% of DTC cases.
Table 2 describes a univariate comparison of the number (%) of DTC cases and non-cases, as well as the total number of participants (%), according to baseline hormonal and reproductive factors. No major differences were observed by DTC case status apart from a slightly higher proportion of DTC cases than non-cases reporting a hysterectomy (28.6% vs 22.9%), ever use of MHT (58.4% vs 53.4%) and >5 years’ duration of use of MHT (51.4% vs 47.5%).
Table 2.
Number (%) of cases with differentiated thyroid cancer and total number (%) of participants according to hormonal and reproductive factors
| Factor | Cases | Non-cases | Total |
|---|---|---|---|
| Age at menarche (years) | |||
| <10 | 78 (3.6%) | 28 568 (2.3%) | 28 646 (2.3%) |
| 10–11 | 482 (22.5%) | 248 509 (19.9%) | 248 991 (19.9%) |
| 12 to <14 | 1018 (47.6%) | 593 384 (47.4%) | 594 402 (47.4%) |
| 14 to <16 | 412 (19.2%) | 284 832 (22.8%) | 285 244 (22.8%) |
| ≥16 | 128 (6.0%) | 73 558 (5.9%) | 73 686 (5.9%) |
| Unknown | 24 (1.1%) | 21 914 (1.8%) | 21 938 (1.8%) |
| Parity | |||
| 0 | 293 (13.7%) | 177 022 (14.2%) | 177 315 (14.2%) |
| 1 | 339 (15.8%) | 185 316 (14.8%) | 185 655 (14.8%) |
| 2 | 719 (33.6%) | 397 673 (31.8%) | 398 392 (31.8%) |
| 3–4 | 598 (27.9%) | 369 103 (29.5%) | 369 701 (29.5%) |
| ≥5 | 138 (6.4%) | 85 674 (6.8%) | 85 812 (6.8%) |
| Unknown | 55 (2.6%) | 35 977 (2.9%) | 36 032 (2.9%) |
| Oral contraceptive use | |||
| Never | 927 (43.3%) | 573 054 (45.8%) | 573 981 (45.8%) |
| Ever | 1177 (54.9%) | 656 912 (52.5%) | 658 089 (52.5%) |
| Unknown | 38 (1.8%) | 20 799 (1.7%) | 20 837 (1.7%) |
| Duration of oral contraceptive use (years) | |||
| <5 | 594 (50.5%) | 321 575 (49.0%) | 322 169 (49.0%) |
| ≥5 | 528 (44.9%) | 303 808 (46.2%) | 304 336 (46.2%) |
| Unknown | 55 (4.7%) | 31 529 (4.8%) | 31 584 (4.8%) |
| Hysterectomy | |||
| No | 1400 (68.4%) | 875 355 (72.3%) | 876 755 (72.3%) |
| Yes | 586 (28.6%) | 277 897 (22.9%) | 278 483 (23.0%) |
| Unknown | 60 (2.9%) | 57 830 (4.8%) | 57 890 (4.8%) |
| Oophorectomy | |||
| No | 1630 (76.1%) | 951 748 (76.1%) | 953 378 (76.1%) |
| Unilateral | 102 (4.8%) | 55 789 (4.5%) | 55 891 (4.5%) |
| Bilateral | 288 (13.4%) | 138 658 (11.1%) | 138 946 (11.1%) |
| Yes, number of ovaries removed unknown | 9 (0.4%) | 3266 (0.3%) | 3275 (0.3%) |
| Unknown | 113 (5.3%) | 101 304 (8.1%) | 101 417 (8.1%) |
| Menopausal status | |||
| Pre-menopausal | 589 (28.7%) | 292 150 (25.8%) | 292 739 (25.8%) |
| Post-menopausal | 1276 (62.3%) | 751 650 (66.3%) | 752 926 (66.3%) |
| Unknown | 184 (9.0%) | 89 913 (7.9%) | 90 097 (7.9%) |
| Age at menopausea (years) | |||
| <40 | 178 (13.0%) | 84 211 (10.3%) | 84 389 (10.3%) |
| 40–44 | 151 (11.1%) | 97 437 (11.9%) | 97 588 (11.9%) |
| 45 to <50 | 329 (24.1%) | 211 265 (25.7%) | 211 594 (25.7%) |
| 50 to <55 | 458 (33.6%) | 288 490 (35.1%) | 288 948 (35.1%) |
| ≥55 | 137 (10.0%) | 74 500 (9.1%) | 74 637 (9.1%) |
| Unknown | 111 (8.1%) | 65 275 (7.9%) | 65 386 (7.9%) |
| Menopausal hormone therapy usea | |||
| Never | 485 (40.6%) | 325 609 (44.7%) | 326 094 (44.7%) |
| Ever | 698 (58.4%) | 388 937 (53.4%) | 389 635 (53.4%) |
| Unknown | 13 (1.1%) | 13 859 (1.9%) | 13 872 (1.9%) |
| Duration of menopausal hormone therapy usea (years) | |||
| ≤5 | 275 (46.4%) | 153 318 (46.9%) | 153 593 (46.9%) |
| >5 | 305 (51.4%) | 155 357 (47.5%) | 155 662 (47.5%) |
| Unknown | 13 (2.2%) | 18 384 (5.6%) | 18 397 (5.6%) |
| Reproductive yearsa | |||
| Quartile 1 (0 to <31 years) | 340 (27.3%) | 186 826 (24.9%) | 187 166 (24.9%) |
| Quartile 2 (31 to <36 years) | 225 (18%) | 153 327 (20.4%) | 153 552 (20.4%) |
| Quartile 3 (36 to <39 years) | 235 (18.8%) | 156 426 (20.9%) | 156 661 (20.9%) |
| Quartile 4 (≥39 years) | 447 (35.8%) | 253 332 (33.8%) | 253 779 (33.8%) |
| Ovulatory cyclesa | |||
| Quartile 1 (0 to <320) | 251 (26.3%) | 133 228 (24.9%) | 133 479 (24.9%) |
| Quartile 2 (320 to <402) | 212 (22.2%) | 131 338 (24.5%) | 131 550 (24.5%) |
| Quartile 3 (402 to <467) | 257 (26.9%) | 134 841 (25.2%) | 135 098 (25.2%) |
| Quartile 4 (≥467) | 235 (24.6%) | 136 285 (25.4%) | 136 520 (25.4%) |
Restricted to post-menopausal women.
Table 3 shows multivariable-adjusted HRs for reproductive and hormonal factors and DTC risk. Younger age at menarche (<10 vs 10–11 years) was associated with higher DTC risk (HR, 1.28; 95% CI, 1.00–1.64), as was baseline history of hysterectomy (HR, 1.25; 95% CI, 1.13–1.39) and bilateral oophorectomy (HR, 1.14; 95% CI, 1.00–1.29). Unilateral oophorectomy was not associated with DTC risk (HR, 1.06, 95% CI, 0.87–1.30). Among ever OC users, greater duration of use (≥5 vs <5 years) was associated with lower DTC risk (HR, 0.86; 95% CI, 0.76–0.96). Baseline post-menopausal status also was associated with lower DTC risk (HR, 0.82; 95% CI, 0.70–0.96). Younger (<40 vs 40–44 years; HR, 1.31; 95% CI, 1.05–1.62) and older (≥55 vs 40–44 years; HR, 1.33; 95% CI, 1.05–1.68) ages at menopause were associated with higher DTC risk. Among women who were post-menopausal at baseline, ever use of MHT was associated with higher DTC risk (HR, 1.16; 95% CI, 1.02–1.33). The other reproductive factors examined were not associated with DTC risk, including parity and, among post-menopausal women at baseline, duration of MHT use and estimated number of reproductive years or ovulatory cycles.
Table 3.
Hazard ratios and 95% confidence intervals for differentiated thyroid cancer according to hormonal and reproductive factors
| Factor | Cases | Person-years | HR a (95% CI) |
|---|---|---|---|
| Age at menarche (years) | |||
| <10 | 78 | 327 175 | 1.28 (1.00–1.64) |
| 10–11 | 482 | 2 976 607 | 1 (reference) |
| 12 to <14 | 1018 | 6 916 907 | 0.99 (0.89–1.11) |
| 14 to <16 | 412 | 3 244 178 | 0.92 (0.80–1.05) |
| ≥16 | 128 | 816 317 | 1.09 (0.88–1.35) |
| Unknown | 24 | 260 635 | 0.72 (0.48–1.09) |
| P-trend = 0.06 | |||
| Continuous (per year) | 0.98 (0.96–1.01) | ||
| Parity | |||
| 0 | 293 | 2 073 725 | 0.89 (0.75–1.05) |
| 1 | 339 | 2 103 623 | 1 (reference) |
| 2 | 719 | 4 590 952 | 1.01 (0.88–1.17) |
| 3–4 | 598 | 4 327 991 | 0.90 (0.77–1.04) |
| ≥5 | 138 | 1 020 111 | 0.88 (0.71–1.08) |
| Unknown | 55 | 425 418 | 0.85 (0.63–1.14) |
| P-trend = 0.14 | |||
| Continuous (per live birth)b | 0.99 (0.96–1.02) | ||
| Oral contraceptive use | |||
| Never | 927 | 6 689 707 | 1 (reference) |
| Ever | 1177 | 7 584 294 | 1.09 (0.99–1.20) |
| Unknown | 38 | 267 818 | 0.97 (0.69–1.37) |
| Duration of oral contraceptive use (years) | |||
| <5 | 594 | 3 787 714 | 1 (reference) |
| ≥5 | 528 | 3 445 763 | 0.86 (0.76–0.96) |
| Unknown | 55 | 350 818 | 1.24 (0.92–1.66) |
| Hysterectomy | |||
| No | 1400 | 10 013 122 | 1 (reference) |
| Yes | 586 | 3 218 411 | 1.25 (1.13–1.39) |
| Unknown | 60 | 724 542 | 0.66 (0.51–0.87) |
| Oophorectomy | |||
| No | 1630 | 11 018 045 | 1 (reference) |
| Unilateral | 102 | 650 402 | 1.06 (0.87–1.30) |
| Bilateral | 288 | 1 632 944 | 1.14 (1.00–1.29) |
| Yes, number of ovaries removed unknown | 9 | 35 911 | 1.42 (0.72–2.78) |
| Unknown | 113 | 1 204 518 | 0.71 (0.58–0.88) |
| Menopausal status | |||
| Pre-menopausal | 589 | 3 422 628 | 1 (reference) |
| Post-menopausal | 1276 | 8 807 350 | 0.82 (0.70–0.96) |
| Unknown | 184 | 1 054 167 | 1.04 (0.86–1.26) |
| Age at menopausec (years) | |||
| <40 | 178 | 999 112 | 1.31 (1.05–1.62) |
| 40–44 | 151 | 1 138 789 | 1 (reference) |
| 45 to <50 | 329 | 2 437 133 | 1.06 (0.87–1.28) |
| 50 to <55 | 458 | 3 318 156 | 1.10 (0.91–1.33) |
| ≥55 | 137 | 803 705 | 1.33 (1.05–1.68) |
| Unknown | 111 | 702 735 | 1.35 (1.04, 1.76) |
| P-trend = 0.01 | |||
| Continuous (per year)c | 1.00 (0.99–1.01) | ||
| Menopausal hormone therapy usec | |||
| Never | 485 | 3 780 032 | 1 (reference) |
| Ever | 698 | 4 356 044 | 1.16 (1.02–1.33) |
| Unknown | 13 | 159 025 | 0.65 (0.37–1.17) |
| Duration of menopausal hormone therapy usec (years) | |||
| ≤5 | 275 | 1 664 767 | 1 (reference) |
| >5 | 305 | 1 710 651 | 0.98 (0.81–1.18) |
| Unknown | 13 | 177 700 | 0.76 (0.43–1.37) |
| Reproductive yearsc | |||
| Quartile 1 (0 to <31 years) | 340 | 2 190 834 | 1 (reference) |
| Quartile 2 (31 to <36 years) | 225 | 1 749 059 | 0.88 (0.74–1.05) |
| Quartile 3 (36 to <39 years) | 235 | 1 796 601 | 0.92 (0.78–1.09) |
| Quartile 4 (≥39 years) | 447 | 2 892 888 | 0.99 (0.85–1.14) |
| P-trend = 0.42 | |||
| Continuous (per year) | 1.00 (0.99–1.01) | ||
| Ovulatory cyclesc | |||
| Quartile 1 (0 to <320) | 251 | 1 553 939 | 1 (reference) |
| Quartile 2 (320 to <402) | 212 | 1 551 509 | 0.86 (0.71–1.03) |
| Quartile 3 (402 to <467) | 257 | 1 601 262 | 1.01 (0.85–1.21) |
| Quartile 4 (≥467) | 235 | 1 607 859 | 0.88 (0.73–1.06) |
| P-trend = 0.17 | |||
| Continuous (per cycle) | 1.00 (1.00–1.00) |
HR, hazard ratio.
P-trend calculated using the Wald test for the exposure term modelled continuously using ordinal groups.
Adjusted for attained age (used as time metric), race, education, body mass index, alcohol intake and smoking.
Restricted to women with at least one live birth.
Restricted to post-menopausal women.
Heterogeneity across studies was observed for associations of hysterectomy (I2= 27.4%, P = 0.007) and bilateral oophorectomy (I2= 43.0%, P = 0.04) with DTC risk (Supplementary Figures, available as Supplementary data at IJE online). The positive associations for hysterectomy and DTC risk were observed in most of the larger studies, accounting for the overall positive pooled estimate. Associations for bilateral oophorectomy appeared less consistent across studies.
Tables 4 provides the HRs and 95% CIs by histologic type of DTC. Results were largely consistent for papillary and follicular thyroid cancer, with some exceptions. Namely, OC use, hysterectomy and bilateral oophorectomy were positively associated with risk of papillary, but not follicular, thyroid cancer.
Table 4.
Hazard ratios and 95% confidence intervals for papillary and follicular thyroid cancer according to hormonal and reproductive factors
| Papillary thyroid cancer |
Follicular thyroid cancer |
|||||
|---|---|---|---|---|---|---|
| Factor | Cases | Person-years | HR a (95% CI) | Cases | Person-years | HR a (95% CI) |
| Age at menarche, years | ||||||
| <10 | 66 | 327 175 | 1.23 (0.94–1.60) | 12 | 327 175 | 1.68 (0.89–3.16) |
| 10–11 | 424 | 2 976 607 | 1 (reference) | 58 | 2 976 607 | 1 (reference) |
| 12 to <14 | 900 | 6 916 907 | 0.99 (0.88–1.11) | 118 | 6 916 907 | 1.00 (0.73–1.39) |
| 14 to <16 | 352 | 3 244 178 | 0.88 (0.76–1.03) | 60 | 3 244 178 | 1.15 (0.79–1.70) |
| ≥16 | 114 | 816 317 | 1.06 (0.85–1.34) | 14 | 816 317 | 1.24 (0.66–2.31) |
| Unknown | 24 | 260 635 | 0.83 (0.55–1.26) | 0 | 260 635 | – |
| P-trend = 0.15 | P-trend = 0.60 | |||||
| Continuous (per year) | 0.98 (0.95–1.01) | 1.02 (0.95–1.11) | ||||
| Parity | ||||||
| 0 | 259 | 2 073 725 | 0.86 (0.72–1.02) | 34 | 2 073 725 | 1.19 (0.71–1.99) |
| 1 | 309 | 2 103 623 | 1 (reference) | 30 | 2 103 623 | 1 (reference) |
| 2 | 628 | 4 590 952 | 0.98 (0.85–1.14) | 91 | 4 590 952 | 1.30 (0.84–2.00) |
| 3–4 | 518 | 4 327 991 | 0.88 (0.75–1.03) | 80 | 4 327 991 | 1.07 (0.68–1.67) |
| ≥5 | 118 | 1 020 111 | 0.87 (0.69–1.09) | 20 | 1 020 111 | 0.97 (0.53–1.77) |
| Unknown | 48 | 425 418 | 0.83 (0.61–1.14) | 7 | 425 418 | 0.95 (0.40–2.25) |
| P-trend = 0.19 | P-trend = 0.70 | |||||
| Continuous (per live birth)b | 1.00 (0.97–1.03) | 0.96 (0.89–1.04) | ||||
| Oral contraceptive use | ||||||
| Never | 794 | 6 689 707 | 1 (reference) | 133 | 6 689 707 | 0.97 (0.74–1.28) |
| Ever | 1052 | 7 584 294 | 1.11 (1.00–1.24) | 125 | 7 584 294 | 0.60 (0.20–1.75) |
| Unknown | 34 | 267 818 | 1.03 (0.72–1.48) | 4 | 267 818 | 1.11 (0.54–2.28) |
| Duration of oral contraceptive use (years) | ||||||
| <5 | 531 | 3 787 714 | 1 (reference) | 63 | 3 787 714 | 1 (reference) |
| ≥5 | 471 | 3 445 763 | 0.85 (0.75–0.97) | 57 | 3 445 763 | 0.87 (0.60–1.25) |
| Unknown | 50 | 350 818 | 1.33 (0.98–1.81) | 5 | 350 818 | 0.67 (0.25–1.82) |
| Hysterectomy | ||||||
| No | 1229 | 10 013 122 | 1 (reference) | 171 | 10 013 122 | 1 (reference) |
| Yes | 515 | 3 218 411 | 1.28 (1.14–1.43) | 71 | 3 218 411 | 1.07 (0.80–1.44) |
| Unknown | 50 | 724 542 | 0.67 (0.50–0.90) | 10 | 724 542 | 0.63 (0.32–1.24) |
| Oophorectomy | ||||||
| No | 1432 | 11 018 045 | 1 (reference) | 198 | 11 018 045 | 1 (reference) |
| Unilateral | 92 | 650 402 | 1.11 (0.90–1.38) | 10 | 650 402 | 0.75 (0.40–1.42) |
| Bilateral | 253 | 1 632 944 | 1.16 (1.01–1.33) | 35 | 1 632 944 | 0.98 (0.68–1.43) |
| Yes, number of ovaries removed unknown | 8 | 35 911 | 1.35 (0.66–2.76) | 1 | 35 911 | 2.23 (0.29–17.32) |
| Unknown | 95 | 1 204 518 | 0.74 (0.59–0.93) | 18 | 1 204 518 | 0.57 (0.32–1.02) |
| Menopausal status | ||||||
| Pre-menopausal | 537 | 3 422 628 | 1 (reference) | 52 | 3 422 628 | 1 (reference) |
| Post-menopausal | 1106 | 8 807 350 | 0.82 (0.70–0.97) | 170 | 8 807 350 | 0.81 (0.50–1.30) |
| Unknown | 159 | 1 054 167 | 1.04 (0.85–1.27) | 25 | 1 054 167 | 1.07 (0.63–1.82) |
| Age at menopausec (years) | ||||||
| <40 | 158 | 999 112 | 1.29 (1.02–1.62) | 20 | 999 112 | 1.47 (0.75–2.87) |
| 40–44 | 136 | 1 138 789 | 1 (reference) | 15 | 1 138 789 | 1 (reference) |
| 45 to <50 | 284 | 2 437 133 | 1.00 (0.82–1.23) | 45 | 2 437 133 | 1.54 (0.85–2.77) |
| 50 to <55 | 395 | 3 318 156 | 1.04 (0.86–1.27) | 63 | 3 318 156 | 1.62 (0.91–2.86) |
| ≥55 | 113 | 803 705 | 1.19 (0.92–1.54) | 24 | 803 705 | 2.63 (1.37–5.07) |
| Unknown | 97 | 702 735 | 1.28 (0.97–1.69) | 14 | 702 735 | 2.09 (0.97–4.52) |
| P-trend = 0.05 | P-trend = 0.08 | |||||
| Continuous (per year)c | 1.00 (0.99–1.01) | 1.02 (1.00–1.05) | ||||
| Menopausal hormone therapy usec | ||||||
| Never | 406 | 3 780 032 | 1 (reference) | 79 | 3 780 032 | 1 (reference) |
| Ever | 610 | 4 356 044 | 1.17 (1.03–1.34) | 88 | 4 356 044 | 1.07 (0.78–1.47) |
| Unknown | 13 | 159 025 | 0.81 (0.46–1.41) | 0 | 159 025 | 0.00 (0.00–0.00) |
| Duration of menopausal hormone therapy usec (years) | ||||||
| ≤5 | 246 | 1 664 767 | 1 (reference) | 29 | 1 664 767 | 1 (reference) |
| >5 | 264 | 1 710 651 | 0.93 (0.76–1.15) | 41 | 1 710 651 | 1.33 (0.77–2.30) |
| Unknown | 12 | 177 700 | 0.77 (0.42–1.42) | 1 | 177 700 | 0.67 (0.09–5.10) |
| Reproductive yearsc | ||||||
| Quartile 1 (0 to <31 years) | 302 | 2 190 834 | 1 (reference) | 38 | 2 190 834 | 1 (reference) |
| Quartile 2 (31 to <36 years) | 194 | 1 749 059 | 0.85 (0.71–1.02) | 31 | 1 749 059 | 1.15 (0.71–1.87) |
| Quartile 3 (36 to <39 years) | 198 | 1 796 601 | 0.87 (0.72–1.04) | 37 | 1 796 601 | 1.35 (0.85–2.15) |
| Quartile 4 (≥39 years) | 386 | 2 892 888 | 0.95 (0.81–1.11) | 61 | 2 892 888 | 1.30 (0.86–1.96) |
| P-trend = 0.25 | P-trend = 0.22 | |||||
| Continuous (per year) | 1.00 (0.99–1.01) | 1.02 (0.99–1.04) | ||||
| Ovulatory cyclesc | ||||||
| Quartile 1 (0 to <320) | 223 | 1 553 939 | 1 (reference) | 28 | 1 553 939 | 1 (reference) |
| Quartile 2 (320 to <402) | 187 | 1 551 509 | 0.85 (0.70–1.03) | 25 | 1 551 509 | 0.92 (0.53–1.59) |
| Quartile 3 (402 to <467) | 217 | 1 601 262 | 0.96 (0.80–1.16) | 40 | 1 601 262 | 1.42 (0.87–2.33) |
| Quartile 4 (≥467) | 195 | 1 607 859 | 0.82 (0.67–1.01) | 40 | 1 607 859 | 1.37 (0.83–2.27) |
| P-trend = 0.15 | P-trend = 0.23 | |||||
| Continuous (per cycle) | 1.00 (1.00–1.00) | 1.00 (1.00–1.00) | ||||
HR, hazard ratio.
P-trend calculated using the Wald test for the exposure term modelled continuously using ordinal groups.
Adjusted for attained age (used as time metric), race, education, body mass index, alcohol intake and smoking.
Restricted to women with at least one live birth.
Restricted to post-menopausal women.
Mutual adjustment of baseline oophorectomy and hysterectomy attenuated the point estimate for bilateral oophorectomy (HR, 0.97; 95% CI, 0.83–1.14) but did not change that for hysterectomy (HR, 1.25; 95% CI, 1.10–1.42). In a model restricted to post-menopausal women at baseline, mutual adjustment of oophorectomy, hysterectomy and ever use of MHT attenuated the point estimate for bilateral oophorectomy (HR, 0.99; 95% CI, 0.83–1.17) and MHT (HR, 1.08; 95% CI, 0.95–1.23) but not hysterectomy (HR, 1.29; 95% CI, 1.11–1.49).
Discussion
This is the first pooled analysis of prospective studies and one of the largest studies to date investigating DTC risk in relation to hormonal and reproductive factors. We found that DTC risk was associated with several reproductive and hormonal factors, which are thought to be reflective of exposure to oestrogens and progestogens over the life course, including younger age at menarche, both younger and older ages at menopause, hysterectomy, bilateral oophorectomy, and OC and MHT use, providing some evidence that lifetime exposure to sex steroid hormones is associated with risk of first primary DTC. However, in mutually adjusted models, the risk of DTC associated with hysterectomy remained elevated whereas that of bilateral oophorectomy and MHT were attenuated, which may indicate a role of factors other than sex steroid hormones (e.g. detection bias). Moreover, DTC risk declined, rather than increased, with greater duration of use of OCs. Other proxy measures of higher or prolonged sex steroid levels were not associated with DTC risk, including parity, duration of MHT use and estimated number of reproductive years and ovulatory cycles.
Because pregnancy is a time of increased exposure to oestrogens and progestogens, we hypothesized that a greater number of live births would be associated with higher DTC risk. We observed no association between parity and DTC risk overall, and the results were heterogeneous across studies. Although parity has been linked to other female or female-predominant cancers,41 the underlying biological mechanisms are known or suspected to differ across these outcomes and may not be completely relevant for DTC. The inability of our study to detect an association between parity and DTC could be due a lack of information on pregnancy loss (miscarriages, abortions or stillbirths) and other relevant exposures, such as breastfeeding. The elevated risk of thyroid cancer observed in some studies immediately after pregnancy may reflect hormone-driven progression of pre-existing cancer or it may be due to greater medical surveillance during pregnancy, which increases the likelihood of thyroid cancer detection and diagnosis.42–44 Studies evaluating these associations in more detail would be informative.
Results from this pooled analysis indicated that onset of menarche prior to age 10 years was associated with higher DTC risk. Early menarche is an indicator for greater lifetime number of ovulatory cycles and an elevated exposure to female sex steroid hormones at younger ages.45 However, we found no association for estimated number of ovulatory cycles or reproductive years with DTC risk. The lower risk of DTC in the post-menopausal period is in agreement with previous studies,42,46,47 although some studies found no association.44,48–51 The lower DTC risk observed after menopause is consistent with the substantial reduction in circulating sex steroid hormones, although it could also reflect more frequent opportunities for incidental thyroid cancer detection during the reproductive years and perimenopausal period.
Determinants of age at menopause are important to consider as younger and older ages at menopause were both associated with higher risk of DTC. Consistently with previous studies,50,52 we observed a higher DTC risk in women who had previously undergone a hysterectomy or bilateral oophorectomy. Most bilateral oophorectomies occur concurrently with hysterectomy and more than half of all hysterectomies are performed between the ages of 35 and 45 years.53,54 Prior to 2002, >90% of women used MHT after bilateral oophorectomies likely, in part, because of data that demonstrated MHT use shortly after the time of surgery was effective in controlling symptoms, as well as reducing morbidity and mortality.55,56 In this study, ever use of MHT, but not duration of use, was associated with higher DTC risk. A more direct influence of MHT use on DTC risk could explain the discrepant findings for post-menopausal status, age at menopause and surgical procedures known to induce menopause. However, only hysterectomy remained associated with DTC risk in models mutually adjusted for hysterectomy, oophorectomy and MHT use. To some extent, the higher DTC risk associated with hysterectomy may reflect more frequent opportunities for incidental detection of thyroid cancer.42 In a recent case–control study in Australia, 20% of the observed association between hysterectomy and thyroid cancer was mediated by frequency of healthcare utilization.57
Ever use of OCs was not associated with DTC in our study, consistently with several previous cohort studies on this topic.42,44,46,48,49 Although we did not observe an association between DTC risk and ever use of OCs, our study indicated that longer duration of OC use was associated with lower first primary DTC risk among women who did use OCs. Past studies have reported both inverse associations42,47 and no association44,48–51 with duration of OC use. One possible explanation for these inconsistencies is that the formulations for OCs are variable and have changed over time. Similarly, lack of information on the formulation of MHT could account for some of the inconsistent findings in our study and previous studies. Most of the cohorts did not collect information on OC and MHT formulations, however.34
Women are more likely to develop thyroid disorders than men, which may be due in part to the effects of oestrogen on the hypothalamic–pituitary–thyroid (HPT) axis.58 The resulting increase in thyroid stimulating hormone (TSH) related to an active HPT axis has long been hypothesized to increase thyroid cancer risk; however, recent prospective studies have demonstrated inverse, rather than positive, associations of TSH with thyroid cancer risk.57,58 Oestrogen affects the thyroid directly by stimulating thyroid growth and secretion of thyroglobulin, which is a thyroid hormone precursor, and indirectly by increasing pituitary secretion of TSH.57,58 Initial TSH increases among euthyroid women quickly return to normal whereas longer-term increases occur among women with hypothyroidism.58 However, in this study, we were unable to assess differences in associations for women with, vs without, thyroid dysfunction.
This study has several strengths, including the prospective study design that limited the potential for differential recall and selection biases,10,11 the large number of included studies from across the world, the ability to assess differences by histologic type and the ability to control for potential confounding factors, including socio-economic indicators, BMI, smoking and alcohol intake. Pooling of the data resulted in a relatively large number of cases, allowing more precise estimation of these associations than could have been achieved from any of these studies individually. Utilizing a standardized format for each exposure lowered the potential for bias due to methodological heterogeneity across studies.
Our study had other limitations not already mentioned above. Most cohort members were peri- or post-menopausal at study entry and were diagnosed with thyroid cancer at age ≥50 years. This precluded us from investigating risk factors for pre-menopausal thyroid cancer. Some participants, especially those enrolled at older ages, may have had difficulty recalling age at menarche and age at starting and stopping OC use. In addition to recall errors, which are likely to be non-differential in these studies and expected to bias associations towards the null, the factors examined in our study may be poor proxies of cumulative sex steroid hormone exposure or exposures occurring during potentially etiologically relevant time windows (e.g. puberty or pregnancy). We lacked detailed information on the timing of some of these exposures in relation to one another and to subsequent thyroid cancer diagnosis. Additionally, residual confounding for socio-economic factors or healthy-user bias is also possible, especially in countries without universal healthcare, such as the USA. Confounding by iodine status may be possible as dietary iodine is often inadequate in pregnancy and residing in regions characterized by mild-to-moderate iodine deficiency can further increase the risk of developing severe iodine deficiency in pregnancy. Finally, we cannot rule out detection bias as a potential explanation for some of our findings, as some reproductive and hormonal factors may be associated with greater likelihood of incidental detection of thyroid cancer and we did not have information on the mode of thyroid cancer detection.
In conclusion, our study provides some evidence linking reproductive and hormonal factors with risk of DTC. Results should be interpreted cautiously considering the modest strength of the associations and potential for exposure misclassification and detection bias. Prospective studies of pre-diagnostic circulating sex steroid hormone measurements and DTC risk would overcome many of these limitations and may provide additional insight about a potential role of sex steroid hormones, including oestrogen, in the aetiology of DTC.
Disclaimer
The opinions, findings and conclusions expressed herein are those of the author(s) and do not necessarily reflect the official views of the State of California, Department of Public Health, the National Cancer Institute, the National Institutes of Health, the Centers for Disease Control and Prevention or their Contractors and Subcontractors, or the Regents of the University of California, or any of its programmes. 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.
Ethics approval
All studies received ethics and data-sharing approval from their local Institutional Review Board. Study participants provided informed consent, wherever applicable, and participated under ethically approved protocols. Data sets shared with the NCI were de-identified.
Supplementary Material
Acknowledgements
We are grateful to Eric Boyd at Information Management Services, Inc. for biomedical computing support and to the late Dr Leslie Bernstein for feedback on the initial proposal and early drafts of this manuscript.
AARP: This research was supported [in part] by the Intramural Research Program of the National Institutes of Health (NIH), National Cancer Institute. 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, Georgia. Cancer incidence data from California were collected by the California Cancer Registry, California Department of Public Health’s Cancer Surveillance and Research Branch, Sacramento, California. Cancer incidence data from the Detroit metropolitan area were collected by the Michigan Cancer Surveillance Program, Community Health Administration, Lansing, Michigan. The Florida cancer incidence data used in this report were collected by the Florida Cancer Data System (FCDC) (Miami, Florida) under contract with the Florida Department of Health (FDOH), Tallahassee, Florida. 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, Louisiana. Cancer incidence data from New Jersey were collected by the New Jersey State Cancer Registry, The Rutgers Cancer Institute of New Jersey, New Brunswick, New Jersey. Cancer incidence data from North Carolina were collected by the North Carolina Central Cancer Registry, Raleigh, North Carolina. Cancer incidence data from Pennsylvania were supplied by the Division of Health Statistics and Research, Pennsylvania Department of Health, Harrisburg, Pennsylvania. 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, Arizona. Cancer incidence data from Texas were collected by the Texas Cancer Registry, Cancer Epidemiology and Surveillance Branch, Texas Department of State Health Services, Austin, Texas. 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, Nevada. We are indebted to the participants in the NIH-AARP Diet and Health Study for their outstanding co-operation. We also thank Sigurd Hermansen and Kerry Grace Morrissey from Westat for study outcomes ascertainment and management, and Leslie Carroll at Information Management Services for data support and analysis
BCDDP: This study is supported by the Intramural Research Program of the Division of Cancer Epidemiology and Genetics of the NCI at the NIH.
CPSII: The American Cancer Society funds the creation, maintenance and updating of the CPSII cohort. The authors would like to acknowledge the contribution to this study from central cancer registries supported through the Centers for Disease Control and Prevention's National Program of Cancer Registries and cancer registries supported by the National Cancer Institute's Surveillance Epidemiology and End Results Program.
CTS: The CTS and the research reported in this publication were supported by the National Cancer Institute of the National Institutes of Health under award numbers U01-CA199277, P30-CA033572, P30-CA023100, UM1- CA164917 and R01-CA077398. The content is solely the responsibility of the authors and does not necessarily represent the official views of the National Cancer Institute or the National Institutes of Health. The collection of cancer incidence data used in the CTS 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, under cooperative agreement 5NU58DP006344; the National Cancer Institute’s Surveillance, Epidemiology and End Results Program under contract HHSN261201800032I awarded to the University of California, San Francisco, contract HHSN261201800015I awarded to the University of Southern California and contract HHSN261201800009I awarded to the Public Health Institute. The opinions, findings and conclusions expressed herein are those of the author(s) and do not necessarily reflect the official views of the State of California, Department of Public Health, the National Cancer Institute, the National Institutes of Health, the Centers for Disease Control and Prevention or their Contractors and Subcontractors, or the Regents of the University of California or any of its programmes. The authors would like to thank the California Teachers Study Steering Committee that is responsible for the formation and maintenance of the study within which this research was conducted. A full list of CTS team members is available at https://www.calteachersstudy.org/team.
EPIC: The co-ordination of EPIC-Europe is financially supported by the International Agency for Research on Cancer (IARC) and also by the Department of Epidemiology and Biostatistics, School of Public Health, Imperial College London, which has additional infrastructure support provided by the National Institute for Health and Care Research (NIHR) Imperial Biomedical Research Centre.
The national cohorts are supported by: Danish Cancer Society (Denmark); 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 Institute of Human Nutrition Potsdam-Rehbruecke (DIfE), Federal Ministry of Education and Research (BMBF) (Germany); Associazione Italiana per la Ricerca sul Cancro-AIRC-Italy, Compagnia di SanPaolo and National Research Council (Italy); Dutch Ministry of Public Health, Welfare and Sports (VWS), Netherlands Cancer Registry (NKR), LK Research Funds, Dutch Prevention Funds, Dutch ZON (Zorg Onderzoek Nederland), World Cancer Research Fund (WCRF), Statistics Netherlands (Netherlands); Health Research Fund (FIS)—Instituto de Salud Carlos III (ISCIII), Regional Governments of Andalucía, Asturias, Basque Country, Murcia and Navarra, and the Catalan Institute of Oncology—ICO (Spain); Swedish Cancer Society, Swedish Research Council and County Councils of Skåne and Västerbotten (Sweden); Cancer Research UK (14136 to EPIC-Norfolk; C8221/A29017 to EPIC-Oxford), Medical Research Council (1000143 to EPIC-Norfolk; MR/M012190/1 to EPIC-Oxford) (UK).
IWHS: This study is funded by a grant from the National Cancer Institute (R01 CA39742).
MCCS: This study was made possible by the contribution of many people, including the original investigators and the diligent team who recruited the participants and who continue working on follow-up. This work was supported by infrastructure from Cancer Council Victoria and grants from the National Health and Medical Research Council of Australia 209057 and 251533.
NYUWHS: This study is supported by funding from the NCI: U01CA182934 (mPI: Zeleniuch-Jacquotte and Chen).
PLCO: The PLCO Cancer Screening Trial is funded by the NCI. This research also was supported by contracts from the Division of Cancer Prevention of the NCI and by the Intramural Research Program of the Division of Cancer Epidemiology and Genetics of the NCI at the NIH.
SISTER: This 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. Aimee D’Aloisio, Sandra Halverson, Dan Scharf and David Shore helped to prepare the data for this analysis.
SWHS: This is supported in part by research grants from the National Cancer Institute (R37 CA070867, UM1 CA182910, R01 CA082729 and UM1 CA173640). The authors thank research staff members for their contribution.
USRT: The authors thank Jerry Reid at the American Registry of Radiologic Technologists for continued support of this study; Allison Iwan and Diane Kampa at the University of Minnesota for study management and data collection; and Jeremy Miller at Information Management Services, Inc. for biomedical computing support. This study is supported by the Intramural Research Program of the Division of Cancer Epidemiology and Genetics of the NCI at the NIH.
VITAL: Emily White was supported by the grant K05-CA154337 (National Cancer Institute and Office of Dietary Supplements).
WHS: Funding for WHS is supported by funding from the following grants: CA047988, CA182913, HL043851, HL080467 and HL099355.
Contributor Information
Thomas J O’Grady, Division of Cancer Epidemiology and Genetics, National Cancer Institute, Rockville, MD, USA; Department of Epidemiology and Biostatistics, University at Albany School of Public Health, Albany, NY, USA.
Sabina Rinaldi, International Agency for Research on Cancer (IARC/WHO), Lyon, France.
Kara A Michels, Division of Cancer Epidemiology and Genetics, National Cancer Institute, Rockville, MD, USA; Department of Epidemiology and Population Health, Albert Einstein College of Medicine, Bronx, NY, USA.
Hans-Olov Adami, Clinical Effectiveness Group, Institute of Health and Society, University of Oslo, Oslo, Norway; Department of Medical Epidemiology and Biostatistics, Karolinska Institutet, Stockholm, Sweden.
Julie E Buring, Division of Preventive Medicine, Department of Medicine, Brigham and Women’s Hospital and Harvard Medical School, Boston, MA, USA; Department of Epidemiology, Harvard TH Chan School of Public Health, Boston, MA, USA.
Yu Chen, Division of Epidemiology, Department of Population Health and NYU Cancer Institute, NYU School of Medicine, New York, NY, USA.
Tess V Clendenen, Division of Epidemiology, Department of Population Health and NYU Cancer Institute, NYU School of Medicine, New York, NY, USA.
Aimee D’Aloisio, Social & Scientific Systems, DLH Holdings Corporation, Durham, NC, USA.
Jessica Clague DeHart, School of Community and Global Health, Claremont Graduate University, Claremont, CA, USA.
Silvia Franceschi, Aviano Cancer Institute, IRCCS, Aviano, Italy.
Neal D Freedman, Division of Cancer Epidemiology and Genetics, National Cancer Institute, Rockville, MD, USA.
Gretchen L Gierach, Division of Cancer Epidemiology and Genetics, National Cancer Institute, Rockville, MD, USA.
Graham G Giles, Cancer Epidemiology Division, Cancer Council Victoria, Melbourne, Australia; Centre for Epidemiology and Biostatistics, Melbourne School of Population and Global Health, The University of Melbourne, Carlton, Australia; Precision Medicine, School of Clinical Sciences at Monash Health, Monash University, Clayton, VIC, Australia.
James V Lacey, Division of Health Analytics Department of Computational and Quantitative Medicine, Beckman Research Institute, City of Hope, Atlanta, GA, USA.
I-Min Lee, Division of Preventive Medicine, Department of Medicine, Brigham and Women’s Hospital and Harvard Medical School, Boston, MA, USA; Department of Epidemiology, Harvard TH Chan School of Public Health, Boston, MA, USA.
Linda M Liao, Division of Cancer Epidemiology and Genetics, National Cancer Institute, Rockville, MD, USA.
Martha S Linet, Division of Cancer Epidemiology and Genetics, National Cancer Institute, Rockville, MD, USA.
Marjorie L McCullough, Department of Population Science, American Cancer Society, Atlanta, GA, USA.
Alpa V Patel, Department of Population Science, American Cancer Society, Atlanta, GA, USA.
Anna Prizment, Division of Hematology, Oncology and Transplantation, Department of Medicine, University of Minnesota, Minneapolis, MN, USA.
Kim Robien, Exercise and Nutrition Sciences, Milken Institute School of Public Health, George Washington University, Washington, DC, USA.
Dale P Sandler, Epidemiology Branch, National Institute of Environmental Health Sciences, Research Triangle Park, NC, USA.
Rachael Stolzenberg-Solomon, Division of Cancer Epidemiology and Genetics, National Cancer Institute, Rockville, MD, USA.
Elisabete Weiderpass, International Agency for Research on Cancer (IARC/WHO), Lyon, France.
Emily White, Public Health Sciences Division, Fred Hutchinson Cancer Research Center, Seattle, WA, USA; Department of Epidemiology, University of Washington, Seattle, Washington, USA.
Alicja Wolk, Division of Nutritional Epidemiology, Institute of Environmental Medicine, Karolinska Institutet, Stockholm, Sweden.
Wei Zheng, Division of Epidemiology, Department of Medicine, Vanderbilt Epidemiology Center, Vanderbilt-Ingram Cancer Center, Vanderbilt University School of Medicine, Nashville, TN, USA.
Amy Berrington de Gonzalez, Division of Cancer Epidemiology and Genetics, National Cancer Institute, Rockville, MD, USA; The Institute of Cancer Research, London, UK.
Cari M Kitahara, Division of Cancer Epidemiology and Genetics, National Cancer Institute, Rockville, MD, USA.
Data availability
The data underlying this pooled analysis were provided by cohorts participating in the NCI Cohort Consortium under a data-use agreement. Researchers interested in the individual-level data may submit an inquiry to the corresponding author and the principal investigators of the individual cohorts.
Supplementary data
Supplementary data are available at IJE online.
Author contributions
T.J.O. and C.M.K. conceived of and designed the study. All authors (except T.J.O.) acquired the data. C.M.K. and T.J.O. performed the data analysis, created tables and compiled the results. T.O.G. produced the initial draft. All authors read and critically reviewed the manuscript for intellectual content. T.J.O. and C.M.K. revised the manuscript. C.M.K. supervised the work. All authors reviewed the manuscript and approved the final version.
Funding
This work was supported by the Intramural Research Program of the US National Cancer Institute, National Institutes of Health. Cohort-specific funding is described in the Acknowledgements.
Conflict of interest
None declared.
References
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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 pooled analysis were provided by cohorts participating in the NCI Cohort Consortium under a data-use agreement. Researchers interested in the individual-level data may submit an inquiry to the corresponding author and the principal investigators of the individual cohorts.