Background:

Menstrual cycle characteristics—including age at menarche and cycle length— have been associated with ovarian cancer risk in White women. However, the associations between menstrual cycle characteristics and ovarian cancer risk among Black women have been sparsely studied.

Methods:

Using the Ovarian Cancer in Women of African Ancestry (OCWAA) Consortium that includes 1,024 Black and 2,910 White women diagnosed with epithelial ovarian cancer (EOC) and 2,325 Black and 7,549 White matched controls, we investigated associations between menstrual cycle characteristics (age at menarche, age at menstrual regularity, cycle length, and ever missing three periods) and EOC risk by race and menopausal status. Multivariable logistic regression was used to calculate odds ratios (OR) and 95% confidence intervals (CI).

Results:

Black women were more likely to be <11 years at menarche than White women (controls: 9.9% vs. 6.0%). Compared with ≥15 years at menarche, <11 years was associated with increased EOC risk for White (OR = 1.25; 95% CI, 0.99–1.57) but not Black women (OR = 1.10; 95% CI, 0.80–1.55). Among White women only, the association was greater for premenopausal (OR = 2.20; 95% CI, 1.31–3.68) than postmenopausal women (OR = 1.06; 95% CI, 0.82–1.38). Irregular cycle length was inversely associated with risk for White (OR = 0.78; 95% CI, 0.62–0.99) but not Black women (OR = 1.06; 95% CI, 0.68–1.66).

Conclusions:

Earlier age at menarche and cycle irregularity are associated with increased EOC risk for White but not Black women.

Impact:

Associations between menstrual cycle characteristics and EOC risk were not uniform by race.

Ovarian cancer incidence is about 30% higher among White than Black women (1). Reasons for this racial disparity are not fully understood—in part due to lack of sufficient sample sizes to examine differences by race—but are likely to be multifactorial and interrelated (2). Although incidence is lower, ovarian cancer survival is poorer among Black women, even among women diagnosed with localized disease (3). Therefore, identifying factors contributing to race differences is critical to reduce disparities and improve ovarian cancer outcomes.

Factors that decrease the lifetime number of ovulatory cycles, including pregnancy, breastfeeding, and oral contraceptive use, have been established as risk-reducing for ovarian cancer (4–7). Ovulation disrupts the ovarian epithelium and promotes rapid cell division and proliferation, which increases the potential for malignant transformation (8). Earlier age at menarche, which could result in more years of ovulation, has been posited as an additional reproductive-related ovarian cancer risk factor and has been associated with an increased risk of other hormonally driven cancers, including breast and endometrial cancers (9–11). However, epidemiologic evidence for an association between age at menarche and ovarian cancer risk has been inconsistent and race differences in the association have not been adequately examined (11–15). Other menstrual cycle characteristics, including cycle irregularity, cycle length, and missing periods may also contribute to ovarian cancer risk. A large pooled analysis of 14 case–control studies found irregular menstrual cycles and long menstrual cycles were each associated with a decreased risk of ovarian cancer (16), but other smaller studies have found null associations or associations only for specific ovarian cancer histotypes (17, 18). Cycle irregularity and missing periods may be caused by a variety of underlying factors, including endocrine disorders or uterine fibroids, and could result in fewer ovulatory cycles. One notable limitation of these previous studies is that they were all conducted in entirely or majority White populations. Although there are racial differences in the distributions of menstrual cycle characteristics (19), for example menarche has been shown to be consistently earlier in Black girls than White girls (20, 21), it is not known whether there are also racial differences in the associations between menstrual cycle characteristics and ovarian cancer risk. Understanding whether associations between menstrual cycle characteristics and ovarian cancer differ by race may help inform ovarian cancer etiology and explain racial disparities.

Most epidemiologic studies of ovarian cancer lack sufficient sample sizes of Black women to investigate risk associations separately by race. Using data from the Ovarian Cancer in Women of African Ancestry (OCWAA) consortium, we investigated the associations between four menstrual cycle characteristics—age at menarche, age at menstrual regularity, cycle length, and ever missing three periods—and risk of ovarian cancer, separately for Black and White women. The OCWAA consortium consists of harmonized data from seven well-established studies (22–28), and includes the largest number of Black women diagnosed with ovarian cancer in an epidemiologic study to date (29).

Study population

The OCWAA consortium includes data from more than 4,000 women diagnosed with epithelial ovarian cancer (EOC), and race-, age-, and site-matched controls compiled from four case–control studies and three case–control studies that were nested within large, prospective cohorts. The OCWAA consortium has been previously described in detail (29). Briefly, data from patient questionnaires, medical records, and cancer registry records were harmonized for participants from the seven individual studies. The four case–control studies include: the African American Cancer Epidemiology Study (AACES; ref. 22), the Cook County Case–Control Study (CCCCS; ref. 23), the Los Angeles County Ovarian Cancer Study (LACOCS; ref. 24), and the North Carolina Ovarian Cancer Study (NCOCS; ref. 25). The nested case–control studies were within the Black Women's Health Study (BWHS) (26), the Multiethnic Cohort Study (MEC; ref. 27), and the Women's Health Initiative (WHI; ref. 28). Each study obtained informed consent from its participants. For the three cohort studies, consent was determined as follows. For MEC, receipt of a completed, mailed baseline questionnaire was considered implicit consent to participate. For BWHS, receipt of a completed baseline questionnaire was considered implicit consent to participate. Finally for WHI, participants provided written consent. The individual studies and the OCWAA Consortium were approved by the relevant Institutional Review Boards.

The analytic dataset included participants with data on any of the four menstrual cycle characteristics of interest (i.e., age at menarche, age at menstrual regularity, cycle length, or ever missed three consecutive periods; Supplementary Table S1). Participants missing information on all four characteristics were excluded. Approximately 84% of participants in the OCWAA consortium had information on at least one of the four menstrual cycle characteristics and were therefore included in the current analyses.

Exposure and covariate assessment

Participant race in all OCWAA studies was determined by self-report. Both Hispanic and non-Hispanic ethnicities were included, however only 2.0% of White participants and 0.5% of Black participants were of Hispanic ethnicity.

Other demographic, clinical, and medical history data were obtained by in-person or telephone interviews or mailed questionnaires. Self-reported age at menarche was collected in all seven studies. Age at menstrual regularity was collected in all studies except BWHS as either the number of months after first menstrual period before regularity (AACES, NCOCS, LACOCS) or the age at which periods became regular (MEC, CCCCS, WHI). Information on the average length of the menstrual cycle and whether subjects ever missed three consecutive periods was available in three studies only (AACES, NCOCS, and LACOCS; Supplementary Table S1). For participants who reported months until menstrual regularity, age at menstrual regularity was calculated by adding the number of months to the self-reported age at menarche because number of months until regularity was not collected by all studies. Participants who reported never reaching menstrual regularity were excluded from age at regularity analyses. Both age variables were analyzed as four-level categorical variables (<11, 11–12, 13–14, ≥15 years).

Additional patient characteristics included age at diagnosis for cases or at interview for controls, educational attainment (high school or less, some college, college graduate, or graduate/professional school), marital status (single, married, separated, or widowed), parity (0, 1–3, or >3 pregnancies), duration of oral contraceptive use (never, 1–5, or >5 years), body mass index (BMI, <25, 25–<30, 30–<35, or ≥35 kg/m2), smoking status (never, former, or current), history of tubal ligation (yes or no), first degree family history of breast or ovarian cancer (yes or no), menopausal status at diagnosis (premenopausal or postmenopausal), and postmenopausal hormone use (yes or no).

Outcome assessment

Eligible cases were diagnosed with EOC, the most common type of ovarian cancer, accounting for more than 90% of all ovarian cancer cases (30). The four OCWAA case–control studies identified cases through population-based cancer registries and the case–control studies nested within cohort studies identified cases through self-report or linkage to statewide cancer registries. Each study obtained pathology data to confirm EOC diagnosis. EOC histotype was classified into seven mutually exclusive subtypes using both morphology and grade information as previously described (29, 31). Specifically, cases were classified as high-grade serous, low-grade serous, endometrioid, clear cell, mucinous, carcinosarcoma, or other histotype. Cases with serous histology were classified as low-grade serous if tumor grade was 1 and as high-grade serous if tumor grade was 2 or higher. Endometrioid tumors with grades 3 or 4 (N = 141 White cases, 33 Black cases) were recategorized as high-grade serous due to their biological similarity to high-grade serous tumors and the challenges with distinguishing the two histotypes (32, 33). A sensitivity analysis, excluding high-grade endometrioid tumors from the high-grade serous category was performed.

Statistical analysis

Descriptive statistics were used to summarize subject characteristics by case/control status and by race as frequency and percent for categorical variables or mean and SD for continuous variables. Multivariable logistic regression was used to calculate adjusted odds ratios (aOR) and 95% confidence intervals (CI) for the associations between menstrual cycle characteristics and ovarian cancer risk. Data from the seven studies was pooled and heterogeneity by study site was quantified by calculating Cochran Q P value. To control for study site heterogeneity, a random effect for study site was assessed in each model. If all variance components were nonzero, then the random effect for study site was included in the model. Otherwise, the model reduced to fixed effects only. Multivariable models were adjusted for age at diagnosis (continuous) and potential confounders, identified from previous literature and graphical representation of relationships between variables (i.e., directed acyclic graphs). Because menstrual cycle characteristics (especially age at menarche) occur prior to other reproductive-related ovarian cancer risk factors, multivariable models were only adjusted for height, weight (young adult), and education (as a proxy measure for socioeconomic status). All analyses were stratified by race (Black or White). Models examining the associations for age at menarche and age at menstrual regularity were also stratified by menopausal status. Ptrend was calculated by considering age at menarche or age at regularity as a continuous variable (34). Statistical heterogeneity by race and menopausal status was assessed by joint Wald chi-square tests of the interaction terms.

Subgroup analyses

EOC is a heterogeneous disease that can be further subdivided into histotypes that have distinct gene expression patterns, molecular characteristics, and clinical features (31, 35). Therefore, we assessed race-specific associations for age at menarche and age at menstrual regularity, separately for high-grade serous tumors, the most common histotype (31). The other histotypes (low-grade serous, endometrioid, clear cell, mucinous, carcinosarcoma, and other) were grouped together because modest sample sizes precluded meaningful separate analyses. For the histotype analysis only, polytomous logistic regression was used to calculate aORs and 95% CIs. The outcomes in these models were high-grade serous or other histotypes and the reference group was all eligible controls.

Genetic disposition to being taller has been associated with increased risk of ovarian cancer (36) and age at menarche has been associated with height (37, 38). Therefore, to determine whether the association between age at menarche or age at menstrual regularity and EOC risk is modified by adult height, we performed a subgroup analysis stratified by height. Cases and controls were classified as being in the upper quartile for height versus the lower three quartiles, based on heights from controls with nonmissing data for age at menarche or age at menstrual regularity.

We also assessed whether associations between age at menarche or age at menstrual regularity and EOC risk differed by obesity at diagnosis/interview (as determined by BMI ≥30 kg/m2) because overweight/obesity has been associated with anovulation and may impact menstrual cycle characteristics. Because of small numbers, 13–14 years and ≥15 years were combined into one age category for associations among premenopausal women.

Finally, we repeated the analysis examining race-specific associations between age at menarche and EOC risk excluding AACES and NCOCS because these two studies have individually reported results for age at menarche among Black women (13, 14), and together they account for 66% of the Black cases included in the OCWAA consortium. The data underlying this article will be shared on reasonable request to the OCWAA Consortium.

A total of 3,934 cases with EOC and 9,874 matched controls were included in the analysis, with Black women accounting for 24% (1,024 cases, 2,325 controls) of the sample population. Demographic and clinical characteristics by race and case/control status are shown in Table 1. Black cases and controls were younger, had higher BMI at diagnosis/interview, lower educational attainment, and were more likely to be divorced, separated or never married than White cases and controls. Among both Black and White women separately, cases were more likely than controls to be nulliparous (among Black women: 18.3% vs. 15.7% and among White women: 23.7% vs. 18.4%) and to have a family history of breast or ovarian cancer (among Black women: 27.6% vs. 15.6% and among White women: 20.9% vs. 16.9%).

Table 1.

Demographic and clinical characteristics of the OCWAA consortium study population by race and case/control status.

Black (N = 3,349)White (N = 10,459)
CasesControlsCasesControls
(N = 1,024)(N = 2,325)(N = 2,910)(N = 7,549)
Mean (SD)Mean (SD)Mean (SD)Mean (SD)
Overall 
 Age at diagnosis/interview (years) 58 (11.4) 58 (13.2) 62 (11.9) 65 (12.6) 
 BMI at diagnosis/interview (kg/m232 (8.0) 30 (7.3) 26 (6.0) 26 (5.8) 
 BMI at age 18 (kg/m222 (4.6) 22 (4.4) 21 (3.2) 21 (3.1) 
 Height at diagnosis/interview (m) 1.64 (0.1) 1.64 (0.1) 1.63 (0.1) 1.63 (0.1) 
Premenopausal (N = 2,678) 
 Age at diagnosis/interview (years) 45 (7.0) 43 (7.7) 45 (6.6) 44 (7.2) 
 BMI at age 18 (kg/m224 (5.5) 23 (5.3) 22 (4.1) 21 (3.6) 
 Height at diagnosis/interview (m) 1.65 (0.1) 1.65 (0.1) 1.64 (0.1) 1.65 (0.1) 
Postmenopausal (N = 11,130) 
 Age at diagnosis/interview (years) 63 (8.7) 64 (9.7) 66 (9.1) 69 (9.0) 
 BMI at diagnosis/interview (kg/m222 (4.2) 21 (3.9) 21 (2.9) 21 (3.0) 
 Height at diagnosis/interview (m) 1.64 (0.1) 1.64 (0.1) 1.63 (0.1) 1.63 (0.1) 
Black (N = 3,349)White (N = 10,459)
CasesControlsCasesControls
(N = 1,024)(N = 2,325)(N = 2,910)(N = 7,549)
Mean (SD)Mean (SD)Mean (SD)Mean (SD)
Overall 
 Age at diagnosis/interview (years) 58 (11.4) 58 (13.2) 62 (11.9) 65 (12.6) 
 BMI at diagnosis/interview (kg/m232 (8.0) 30 (7.3) 26 (6.0) 26 (5.8) 
 BMI at age 18 (kg/m222 (4.6) 22 (4.4) 21 (3.2) 21 (3.1) 
 Height at diagnosis/interview (m) 1.64 (0.1) 1.64 (0.1) 1.63 (0.1) 1.63 (0.1) 
Premenopausal (N = 2,678) 
 Age at diagnosis/interview (years) 45 (7.0) 43 (7.7) 45 (6.6) 44 (7.2) 
 BMI at age 18 (kg/m224 (5.5) 23 (5.3) 22 (4.1) 21 (3.6) 
 Height at diagnosis/interview (m) 1.65 (0.1) 1.65 (0.1) 1.64 (0.1) 1.65 (0.1) 
Postmenopausal (N = 11,130) 
 Age at diagnosis/interview (years) 63 (8.7) 64 (9.7) 66 (9.1) 69 (9.0) 
 BMI at diagnosis/interview (kg/m222 (4.2) 21 (3.9) 21 (2.9) 21 (3.0) 
 Height at diagnosis/interview (m) 1.64 (0.1) 1.64 (0.1) 1.63 (0.1) 1.63 (0.1) 
N (%)N (%)N (%)N (%)
Demographic characteristics 
Study site 
 AACES 558 (54.5) 746 (32.1) — — 
 BWHS 90 (8.8) 586 (25.2) — — 
 CCCCS 43 (4.2) 79 (3.4) 232 (8.0) 413 (5.5) 
 LACOCS 127 (12.4) 145 (6.2) 1,176 (40.4) 1,805 (23.9) 
 MEC 80 (7.8) 453 (19.5) 141 (4.9) 829 (11.0) 
 NCOCS 108 (10.6) 181 (7.8) 791 (27.2) 847 (11.2) 
 WHI 18 (1.8) 135 (5.8) 570 (19.6) 3,655 (48.4) 
Education     
 High school, GED or less 415 (40.5) 769 (33.1) 623 (21.4) 1,454 (19.3) 
 Some college 265 (25.9) 700 (30.1) 797 (27.4) 2,168 (28.7) 
 College graduate 201 (19.6) 454 (19.5) 688 (23.6) 1,472 (19.5) 
 Graduate or professional school 143 (14.0) 402 (17.3) 802 (27.6) 2,455 (32.5) 
Marital status 
 Single 164 (17.3) 383 (17.3) 166 (6.8) 363 (5.2) 
 Married or living with partner 360 (37.9) 917 (41.3) 1,551 (63.3) 4,348 (62.8) 
 Divorced or separated 249 (26.2) 569 (25.6) 338 (13.8) 941 (13.6) 
 Widowed 178 (18.7) 350 (15.8) 394 (16.1) 1,269 (18.3) 
 Unknown 73 (—) 106 (—) 431 (—) 628 (—) 
Clinical characteristicsa 
BMI 
 Normal weight (<25 kg/m2182 (17.8) 524 (22.6) 1,515 (52.4) 3,737 (49.7) 
 Overweight (25−<30 kg/m2298 (29.2) 749 (32.3) 766 (26.5) 2,267 (30.2) 
 Obese (30−<35 kg/m2269 (26.3) 538 (23.2) 383 (13.2) 946 (12.6) 
 Very obese (35+ kg/m2273 (26.7) 506 (21.8) 228 (7.9) 570 (7.6) 
 Unknown 2 (–) 8 (—) 18 (—) 29 (—) 
Heightb 
 Upper quartile (≥1.68 m) 408 (39.8) 811 (34.9) 881 (30.3) 2,088 (27.7) 
 Below upper quartile (<1.68 m) 616 (60.2) 1,514 (65.1) 2,029 (69.7) 5,461 (72.3) 
Smoking status 
 Never 544 (53.2) 1,219 (52.7) 1,455 (50.3) 3,771 (50.3) 
 Former 343 (33.5) 673 (29.1) 1,151 (39.8) 3,034 (40.5) 
 Current 136 (13.3) 422 (18.2) 289 (10.0) 692 (9.2) 
 Unknown 1 (—) 11 (—) 15 (—) 52 (—) 
Menopausal status     
 Premenopausal 262 (25.6) 680 (29.3) 566 (19.5) 1,170 (15.5) 
 Postmenopausal 762 (74.4) 1,645 (70.8) 2,344 (80.6) 6,379 (84.5) 
Medical history 
Family history of breast or ovarian cancer 
 Yes 262 (27.6) 347 (15.6) 593 (20.9) 1215 (16.9) 
 No 688 (72.4) 1,872 (84.4) 2,239 (79.1) 5,996 (83.2) 
 Unknown 74 (—) 106 (—) 78 (—) 338 (—) 
Parityc 
 0 pregnancies 187 (18.3) 363 (15.7) 690 (23.7) 1,382 (18.4) 
 1−3 pregnancies 401 (39.2) 1,034 (44.6) 1,259 (43.3) 3,056 (40.7) 
 3+ pregnancies 435 (42.5) 920 (39.7) 958 (33.0) 3,075 (40.9) 
 Unknown 1 (—) 8 (—) 3 (—) 36 (—) 
Oral contraceptive use 
 Never 385 (38.2) 876 (38.2) 1,352 (47.0) 3,793 (50.5) 
 <5 years 367 (36.4) 744 (32.4) 905 (31.5) 1,834 (24.4) 
 5+ years 257 (25.5) 675 (29.4) 619 (21.5) 1,884 (25.1) 
 Unknown 15 (—) 30 (—) 34 (—) 38 (—) 
Tubal ligation 
 Yes 307 (30.3) 715 (31.6) 432 (14.9) 1,336 (17.7) 
 No 707 (69.7) 1,549 (68.4) 2,476 (85.1) 6,202 (82.3) 
 Unknown 10 (—) 61 (—) 2 (—) 11 (—) 
Postmenopausal hormone 
 Yes 234 (23.0) 587 (25.6) 1,474 (50.8) 4,045 (53.7) 
 No 783 (77.0) 1,710 (74.4) 1,429 (49.2) 3,484 (46.3) 
 Unknown 7 (—) 28 (—) 7 (—) 20 (—) 
N (%)N (%)N (%)N (%)
Demographic characteristics 
Study site 
 AACES 558 (54.5) 746 (32.1) — — 
 BWHS 90 (8.8) 586 (25.2) — — 
 CCCCS 43 (4.2) 79 (3.4) 232 (8.0) 413 (5.5) 
 LACOCS 127 (12.4) 145 (6.2) 1,176 (40.4) 1,805 (23.9) 
 MEC 80 (7.8) 453 (19.5) 141 (4.9) 829 (11.0) 
 NCOCS 108 (10.6) 181 (7.8) 791 (27.2) 847 (11.2) 
 WHI 18 (1.8) 135 (5.8) 570 (19.6) 3,655 (48.4) 
Education     
 High school, GED or less 415 (40.5) 769 (33.1) 623 (21.4) 1,454 (19.3) 
 Some college 265 (25.9) 700 (30.1) 797 (27.4) 2,168 (28.7) 
 College graduate 201 (19.6) 454 (19.5) 688 (23.6) 1,472 (19.5) 
 Graduate or professional school 143 (14.0) 402 (17.3) 802 (27.6) 2,455 (32.5) 
Marital status 
 Single 164 (17.3) 383 (17.3) 166 (6.8) 363 (5.2) 
 Married or living with partner 360 (37.9) 917 (41.3) 1,551 (63.3) 4,348 (62.8) 
 Divorced or separated 249 (26.2) 569 (25.6) 338 (13.8) 941 (13.6) 
 Widowed 178 (18.7) 350 (15.8) 394 (16.1) 1,269 (18.3) 
 Unknown 73 (—) 106 (—) 431 (—) 628 (—) 
Clinical characteristicsa 
BMI 
 Normal weight (<25 kg/m2182 (17.8) 524 (22.6) 1,515 (52.4) 3,737 (49.7) 
 Overweight (25−<30 kg/m2298 (29.2) 749 (32.3) 766 (26.5) 2,267 (30.2) 
 Obese (30−<35 kg/m2269 (26.3) 538 (23.2) 383 (13.2) 946 (12.6) 
 Very obese (35+ kg/m2273 (26.7) 506 (21.8) 228 (7.9) 570 (7.6) 
 Unknown 2 (–) 8 (—) 18 (—) 29 (—) 
Heightb 
 Upper quartile (≥1.68 m) 408 (39.8) 811 (34.9) 881 (30.3) 2,088 (27.7) 
 Below upper quartile (<1.68 m) 616 (60.2) 1,514 (65.1) 2,029 (69.7) 5,461 (72.3) 
Smoking status 
 Never 544 (53.2) 1,219 (52.7) 1,455 (50.3) 3,771 (50.3) 
 Former 343 (33.5) 673 (29.1) 1,151 (39.8) 3,034 (40.5) 
 Current 136 (13.3) 422 (18.2) 289 (10.0) 692 (9.2) 
 Unknown 1 (—) 11 (—) 15 (—) 52 (—) 
Menopausal status     
 Premenopausal 262 (25.6) 680 (29.3) 566 (19.5) 1,170 (15.5) 
 Postmenopausal 762 (74.4) 1,645 (70.8) 2,344 (80.6) 6,379 (84.5) 
Medical history 
Family history of breast or ovarian cancer 
 Yes 262 (27.6) 347 (15.6) 593 (20.9) 1215 (16.9) 
 No 688 (72.4) 1,872 (84.4) 2,239 (79.1) 5,996 (83.2) 
 Unknown 74 (—) 106 (—) 78 (—) 338 (—) 
Parityc 
 0 pregnancies 187 (18.3) 363 (15.7) 690 (23.7) 1,382 (18.4) 
 1−3 pregnancies 401 (39.2) 1,034 (44.6) 1,259 (43.3) 3,056 (40.7) 
 3+ pregnancies 435 (42.5) 920 (39.7) 958 (33.0) 3,075 (40.9) 
 Unknown 1 (—) 8 (—) 3 (—) 36 (—) 
Oral contraceptive use 
 Never 385 (38.2) 876 (38.2) 1,352 (47.0) 3,793 (50.5) 
 <5 years 367 (36.4) 744 (32.4) 905 (31.5) 1,834 (24.4) 
 5+ years 257 (25.5) 675 (29.4) 619 (21.5) 1,884 (25.1) 
 Unknown 15 (—) 30 (—) 34 (—) 38 (—) 
Tubal ligation 
 Yes 307 (30.3) 715 (31.6) 432 (14.9) 1,336 (17.7) 
 No 707 (69.7) 1,549 (68.4) 2,476 (85.1) 6,202 (82.3) 
 Unknown 10 (—) 61 (—) 2 (—) 11 (—) 
Postmenopausal hormone 
 Yes 234 (23.0) 587 (25.6) 1,474 (50.8) 4,045 (53.7) 
 No 783 (77.0) 1,710 (74.4) 1,429 (49.2) 3,484 (46.3) 
 Unknown 7 (—) 28 (—) 7 (—) 20 (—) 

aAssessed at diagnosis (cases) or at time of interview (controls).

bQuartiles for height were determined based on heights from controls with non-missing data for age at menarche or age at regularity.

cDefined as delivery at 24 weeks gestation or later.

Distributions of menstrual cycle characteristics and associations with EOC are shown in Table 2. Compared with White women, Black women were more likely to be <11 years (cases: 9.9% vs. 6.8%; controls: 9.9% vs. 6.0%) or ≥15 years at menarche (cases: 12.2% vs. 10.0%; controls: 12.3% vs. 10.3%). Overall, compared with oldest age at menarche (≥15 years), youngest age at menarche (<11 years) was associated with increased risk of EOC for White (aOR = 1.25; 95% CI, 0.99–1.57) but not Black women (aOR = 1.10; 95% CI, 0.80–1.55; Pheterogeneity = 0.58; Table 2). Among Black women, there was no trend in the association between age at menarche and ovarian cancer (Ptrend = 0.56) but among White women, EOC risk decreased with increasing age at menarche (11–12 years: aOR = 1.13; 95% CI, 0.96–1.32; 13–14 years: aOR = 1.10; 95% CI, 0.94–1.29; Ptrend = 0.07). The risk pattern was observed among premenopausal White women (Ptrend = 0.002) but not postmenopausal women (Ptrend = 0.76 and Pheterogeneity = 0.04) and not among Black women, regardless of menopausal status (Pheterogeneity = 0.85).

Table 2.

Associations between menstrual cycle characteristics and EOC among women in the OCWAA consortium by race and menopausal status.

BlackWhite
CasesControlsCasesControls
N (%)N (%)aOR (95% CI)P value for Q statisticN (%)N (%)aOR (95% CI)P value for Q statisticRace Phet
Age at Menarche 
Overall 
 <11 years 101 (9.9) 231 (9.9) 1.10 (0.80–1.55) 0.34 198 (6.8) 450 (6.0) 1.25 (0.99–1.57) 0.34 0.58 
 11–12 years 416 (40.7) 978 (42.1) 1.17 (0.91–1.51) 0.16 1,206 (41.5) 3,077 (40.8) 1.13 (0.96–1.32) 0.09  
 13–14 years 380 (37.2) 830 (35.7) 1.19 (0.92–1.53) 0.18 1,213 (41.7) 3,241 (43.0) 1.10 (0.94–1.29) 0.15  
 15+ years 125 (12.2) 286 (12.3) Reference  292 (10.0) 776 (10.3) Reference   
Ptrend   0.56    0.07   
 Menopause Phet   0.85    0.04   
Premenopausal 
 <11 years 33 (12.6) 77 (11.3) 1.18 (0.63–2.18) 0.28 51 (9.0) 69 (5.9) 2.20 (1.31–3.68) 0.24 0.39 
 11–12 years 119 (45.4) 314 (46.2) 1.13 (0.69–1.84) 0.64 255 (45.1) 479 (40.9) 1.58 (1.07–2.33) 0.08  
 13–14 years 78 (29.8) 212 (31.2) 1.11 (0.66–1.87) 0.53 219 (38.7) 495 (42.3) 1.37 (0.93–2.03) 0.17  
 15+ years 32 (12.2) 77 (11.3) Reference  41 (7.2) 127 (10.9) Reference   
Ptrend   0.63    0.002   
Postmenopausal 
 <11 years 68 (9.0) 154 (9.4) 1.09 (0.73–1.62) 0.95 147 (6.3) 381 (6.0) 1.06 (0.82–1.38) 0.61 0.66 
 11–12 years 297 (39.1) 664 (40.4) 1.22 (0.91–1.65) 0.09 951 (40.6) 2,598 (40.8) 1.04 (0.87–1.24) 0.18  
 13–14 years 302 (39.7) 618 (37.6) 1.24 (0.92–1.68) 0.57 994 (42.4) 2,746 (43.1) 1.05 (0.88–1.25) 0.27  
 15+ years 93 (12.2) 209 (12.7) Reference  251 (10.7) 649 (10.2) Reference   
Ptrend   0.66    0.76   
Age at regularity 
Overall (N = 12,073) 
 <11 years 72 (8.3) 147 (9.0) 0.96 (0.67–1.39) 0.3 165 (6.0) 292 (4.3) 1.34 (1.06–1.70) 0.29 0.19 
 11–12 years 336 (38.8) 599 (36.5) 1.23 (0.95–1.59) 0.13 1,045 (38.1) 2,275 (33.3) 1.23 (1.07–1.42) 0.02  
 13–14 years 331 (38.2) 596 (36.3) 1.26 (0.97–1.63) 0.09 1,139 (41.6) 2,910 (42.6) 1.19 (1.04–1.37) 0.15  
 15+ years 127 (14.7) 299 (18.2) Reference  392 (14.3) 1,348 (19.8) Reference   
Ptrend   0.84    0.004   
 Menopause Phet   0.51    0.09   
Premenopausal (N = 2,327) 
 <11 years 22 (10.1) 49 (11.7) 0.94 (0.47–1.85) 0.23 45 (8.2) 61 (5.3) 2.17 (1.32–3.57) 0.17 0.04 
 11–12 years 92 (42.4) 186 (44.4) 1.12 (0.67–1.87) 0.49 236 (43.0) 424 (37.1) 1.61 (1.15–2.26) 0.03  
 13–14 years 71 (32.7) 113 (27.0) 1.52 (0.89–2.61) 0.38 207 (37.7) 474 (41.5) 1.32 (0.94–1.86) 0.43  
 15+ years 32 (14.8) 71 (17.0) Reference  61 (11.1) 183 (16.0) Reference   
Ptrend   0.55    <0.001   
Postmenopausal (N = 9,746) 
 <11 years 50 (7.7) 98 (8.0) 1.00 (0.65–1.55) 0.89 120 (5.5) 231 (4.1) 1.16 (0.88–1.52) 0.47 0.58 
 11–12 years 244 (37.6) 413 (33.8) 1.33 (0.98–1.80) 0.05 809 (36.9) 1,851 (32.6) 1.16 (0.99–1.36) 0.18  
 13–14 years 260 (40.1) 483 (39.5) 1.25 (0.93–1.68) 0.21 932 (42.5) 2,436 (42.9) 1.18 (1.01–1.37) 0.35  
 15+ years 95 (14.6) 228 (18.7) Reference  331 (15.1) 1,165 (20.5) Reference   
Ptrend   0.45    0.18   
Cycle length 
Overall (N = 5,048) 
 ≤ 25 days 53 (7.3) 75 (7.6) 0.99 (0.68–1.43) 0.85 57 (4.1) 88 (4.6) 0.90 (0.63–1.27) <0.01 0.66 
 26–30 days 623 (85.8) 836 (84.6) Reference  1,156 (82.2) 1,468 (76.1) Reference   
 31+ days 12 (1.7) 25 (2.5) 0.60 (0.30–1.22) 0.35 68 (4.8) 146 (7.6) 0.65 (0.48–0.88) 0.84  
 Irregular 38 (5.2) 52 (5.3) 1.06 (0.68–1.66) 0.45 125 (8.9) 226 (11.7) 0.78 (0.62–0.99) 0.07  
Ever missed three consecutive periodsa 
Overall (N = 5,270) 
 No 616 (88.6) 868 (86.9) Reference  1,327 (87.0) 1,730 (84.4) Reference  0.71 
 Yes 79 (11.4) 131 (13.1) 0.92 (0.68–1.25) 0.72 198 (13.0) 321 (15.7) 0.80 (0.21–3.04) 0.14  
BlackWhite
CasesControlsCasesControls
N (%)N (%)aOR (95% CI)P value for Q statisticN (%)N (%)aOR (95% CI)P value for Q statisticRace Phet
Age at Menarche 
Overall 
 <11 years 101 (9.9) 231 (9.9) 1.10 (0.80–1.55) 0.34 198 (6.8) 450 (6.0) 1.25 (0.99–1.57) 0.34 0.58 
 11–12 years 416 (40.7) 978 (42.1) 1.17 (0.91–1.51) 0.16 1,206 (41.5) 3,077 (40.8) 1.13 (0.96–1.32) 0.09  
 13–14 years 380 (37.2) 830 (35.7) 1.19 (0.92–1.53) 0.18 1,213 (41.7) 3,241 (43.0) 1.10 (0.94–1.29) 0.15  
 15+ years 125 (12.2) 286 (12.3) Reference  292 (10.0) 776 (10.3) Reference   
Ptrend   0.56    0.07   
 Menopause Phet   0.85    0.04   
Premenopausal 
 <11 years 33 (12.6) 77 (11.3) 1.18 (0.63–2.18) 0.28 51 (9.0) 69 (5.9) 2.20 (1.31–3.68) 0.24 0.39 
 11–12 years 119 (45.4) 314 (46.2) 1.13 (0.69–1.84) 0.64 255 (45.1) 479 (40.9) 1.58 (1.07–2.33) 0.08  
 13–14 years 78 (29.8) 212 (31.2) 1.11 (0.66–1.87) 0.53 219 (38.7) 495 (42.3) 1.37 (0.93–2.03) 0.17  
 15+ years 32 (12.2) 77 (11.3) Reference  41 (7.2) 127 (10.9) Reference   
Ptrend   0.63    0.002   
Postmenopausal 
 <11 years 68 (9.0) 154 (9.4) 1.09 (0.73–1.62) 0.95 147 (6.3) 381 (6.0) 1.06 (0.82–1.38) 0.61 0.66 
 11–12 years 297 (39.1) 664 (40.4) 1.22 (0.91–1.65) 0.09 951 (40.6) 2,598 (40.8) 1.04 (0.87–1.24) 0.18  
 13–14 years 302 (39.7) 618 (37.6) 1.24 (0.92–1.68) 0.57 994 (42.4) 2,746 (43.1) 1.05 (0.88–1.25) 0.27  
 15+ years 93 (12.2) 209 (12.7) Reference  251 (10.7) 649 (10.2) Reference   
Ptrend   0.66    0.76   
Age at regularity 
Overall (N = 12,073) 
 <11 years 72 (8.3) 147 (9.0) 0.96 (0.67–1.39) 0.3 165 (6.0) 292 (4.3) 1.34 (1.06–1.70) 0.29 0.19 
 11–12 years 336 (38.8) 599 (36.5) 1.23 (0.95–1.59) 0.13 1,045 (38.1) 2,275 (33.3) 1.23 (1.07–1.42) 0.02  
 13–14 years 331 (38.2) 596 (36.3) 1.26 (0.97–1.63) 0.09 1,139 (41.6) 2,910 (42.6) 1.19 (1.04–1.37) 0.15  
 15+ years 127 (14.7) 299 (18.2) Reference  392 (14.3) 1,348 (19.8) Reference   
Ptrend   0.84    0.004   
 Menopause Phet   0.51    0.09   
Premenopausal (N = 2,327) 
 <11 years 22 (10.1) 49 (11.7) 0.94 (0.47–1.85) 0.23 45 (8.2) 61 (5.3) 2.17 (1.32–3.57) 0.17 0.04 
 11–12 years 92 (42.4) 186 (44.4) 1.12 (0.67–1.87) 0.49 236 (43.0) 424 (37.1) 1.61 (1.15–2.26) 0.03  
 13–14 years 71 (32.7) 113 (27.0) 1.52 (0.89–2.61) 0.38 207 (37.7) 474 (41.5) 1.32 (0.94–1.86) 0.43  
 15+ years 32 (14.8) 71 (17.0) Reference  61 (11.1) 183 (16.0) Reference   
Ptrend   0.55    <0.001   
Postmenopausal (N = 9,746) 
 <11 years 50 (7.7) 98 (8.0) 1.00 (0.65–1.55) 0.89 120 (5.5) 231 (4.1) 1.16 (0.88–1.52) 0.47 0.58 
 11–12 years 244 (37.6) 413 (33.8) 1.33 (0.98–1.80) 0.05 809 (36.9) 1,851 (32.6) 1.16 (0.99–1.36) 0.18  
 13–14 years 260 (40.1) 483 (39.5) 1.25 (0.93–1.68) 0.21 932 (42.5) 2,436 (42.9) 1.18 (1.01–1.37) 0.35  
 15+ years 95 (14.6) 228 (18.7) Reference  331 (15.1) 1,165 (20.5) Reference   
Ptrend   0.45    0.18   
Cycle length 
Overall (N = 5,048) 
 ≤ 25 days 53 (7.3) 75 (7.6) 0.99 (0.68–1.43) 0.85 57 (4.1) 88 (4.6) 0.90 (0.63–1.27) <0.01 0.66 
 26–30 days 623 (85.8) 836 (84.6) Reference  1,156 (82.2) 1,468 (76.1) Reference   
 31+ days 12 (1.7) 25 (2.5) 0.60 (0.30–1.22) 0.35 68 (4.8) 146 (7.6) 0.65 (0.48–0.88) 0.84  
 Irregular 38 (5.2) 52 (5.3) 1.06 (0.68–1.66) 0.45 125 (8.9) 226 (11.7) 0.78 (0.62–0.99) 0.07  
Ever missed three consecutive periodsa 
Overall (N = 5,270) 
 No 616 (88.6) 868 (86.9) Reference  1,327 (87.0) 1,730 (84.4) Reference  0.71 
 Yes 79 (11.4) 131 (13.1) 0.92 (0.68–1.25) 0.72 198 (13.0) 321 (15.7) 0.80 (0.21–3.04) 0.14  

Note: All models adjusted for site, age, education, young adult weight, and height.

aRandom effect for study site included in overall model and model within White women.

The associations between age at menstrual regularity and EOC followed a similar pattern as the associations between age at menarche and EOC. Overall, younger ages at menstrual regularity were associated with increased risk of EOC among White (Ptrend = 0.004) but not Black (Ptrend = 0.84) women. The trend among White women was driven by associations among premenopausal (<11 years aOR = 2.17; 95% CI, 1.32, 3.57; 11–12 years: aOR = 1.61; 95% CI, 1.15–2.26; 13–14 years aOR = 1.32; 95% CI, 0.94–1.86; Ptrend < 0.001) but not postmenopausal women (<11 years aOR = 1.16; 95% CI, 0.88–1.52; 11–12 years aOR = 1.16; 95% CI, 0.99–1.36; 13–14 years aOR = 1.18; 95% CI, 1.01–1.37; Ptrend = 0.18). No trend was observed among either premenopausal (Ptrend = 0.55) or postmenopausal (Ptrend = 0.45) Black women.

Black control women were more likely to report shorter (<25 days) menstrual cycles (7.6% vs. 4.6%) but less likely to report longer cycles (31+ days: 2.5% vs. 7.6%) than White control women (Table 2). However, associations between cycle length and EOC were similar by race (Pheterogeneity = 0.66). Compared with cycle length 26 to 30 days, longer cycle length was associated with reduced EOC risk for both Black (aOR = 0.60; 95% CI, 0.30–1.22) and White (aOR = 0.65; 95% CI, 0.48–0.88) women, whereas shorter cycle length was not associated with risk for either Black (aOR = 0.99; 95% CI, 0.68–1.43) or White (aOR = 0.90; 95% CI, 0.63–1.27) women. Irregular menstrual cycle length was less common among Black than White control women (e.g., 5.3% vs. 11.7%) and inversely associated with risk for White (aOR = 0.78; 95% CI, 0.62–0.99) but not Black women (OR = 1.06; 95% CI, 0.68–1.66). Ever missing three consecutive periods was not associated with EOC for both Black and White women (Pheterogeneity = 0.71).

Subgroup analyses

Analyses for age at menarche and age at regularity were repeated for subgroups of women to identify other potential differences in the associations. Risk patterns by EOC histotype are presented in Table 3. Associations between age at menarche and EOC did not differ consistently between high-grade serous and other histotypes for either Black (Pheterogeneity = 0.98) or White (Pheterogeneity = 0.24) women. Associations between age at menstrual regularity and EOC were also similar by histotype for both Black and White women. Findings by histotype were largely similar in a sensitivity analysis excluding high-grade endometrioid tumors from the high-grade serous category (Supplementary Table S2).

Table 3.

Associations between menstrual cycle characteristics and epithelial ovarian cancer among women in the OCWAA consortium by race, menopausal status and histotype.

BlackWhite
ControlsHGS casesOther histotype casesHGSOther histotypeControlsHGS casesOther histotype casesHGSOther histotype
N (%)N (%)N (%)aOR (95% CI)aOR (95% CI)hist. PhetN (%)N (%)N (%)aOR (95% CI)aOR (95% CI)hist. Phet
Age at Menarche 
Overall 
 <11 years 231 (9.9) 62 (9.6) 39 (10.5) 1.09 (0.74–1.62) 1.15 (071–1.84) 0.98 450 (6.0) 113 (6.4) 85 (7.5) 1.29 (0.98–1.71) 1.18 (0.86–1.62) 0.24 
 11–12 years 978 (42.1) 248 (38.2) 168 (45.0) 1.12 (0.83–1.51) 1.26 (0.88–1.82)  3,077 (40.8) 730 (41.1) 476 (42.1) 1.20 (0.99–1.46) 1.03 (0.82–1.28)  
 13–14 years 830 (35.7) 259 (39.9) 121 (32.4) 1.26 (0.94–1.70) 1.05 (0.72–1.53)  3,241 (43.0) 764 (43.0) 449 (39.7) 1.20 (0.99–1.45) 0.96 (0.77–1.20)  
 15+ years 286 (12.3) 80 (12.3) 45 (12.1) Reference Reference  776 (10.3) 170 (9.6) 122 (10.8) Reference Reference  
Ptrend    0.92 0.21     0.11 0.23  
 Menopause Phet    0.87 0.90     0.05 0.09  
Premenopausal 
 <11 years 77 (11.3) 18 (12.6) 15 (12.6) 1.16 (0.54–2.50) 1.18 (0.52–2.70) 0.99 69 (5.9) 22 (8.6) 29 (9.3) 2.58 (1.26–5.30) 1.97 (1.06–3.66) 0.38 
 11–12 years 314 (46.2) 62 (43.4) 57 (47.9) 1.09 (0.59–2.01) 1.19 (0.61–2.29)  479 (40.9) 116 (45.5) 139 (44.7) 1.89 (1.07–3.32) 1.39 (0.86–2.23)  
 13–14 years 212 (31.2) 45 (31.5) 33 (27.7) 1.18 (0.62–2.24) 1.05 (0.52–2.12)  495 (42.3) 101 (39.6) 118 (37.9) 1.66 (0.94–2.93) 1.19 (0.74–1.93)  
 15+ years 77 (11.3) 18 (12.6) 14 (11.8) Reference Reference  127 (10.9) 16 (6.3) 25 (8.0) Reference Reference  
Ptrend    0.87 0.56     0.01 0.02  
Postmenopausal 
 <11 years 154 (9.4) 44 (8.7) 24 (9.5) 1.06 (0.67–1.68) 1.15 (0.64–2.07) 0.97 381 (6.0) 91 (6.0) 56 (6.8) 1.11 (0.82–1.51) 0.99 (0.68–1.43) 0.23 
 11–12 years 664 (40.4) 186 (36.8) 111 (43.7) 1.16 (0.82–1.64) 1.33 (0.86–2.07)  2,598 (40.8) 614 (40.3) 337 (41.1) 1.11 (0.90–1.37) 0.93 (0.73–1.20)  
 13–14 years 618 (37.6) 214 (42.3) 88 (34.7) 1.32 (0.94–1.86) 1.09 (0.69–1.71)  2746 (43.1) 663 (43.6) 331 (40.3) 1.15 (0.93–1.41) 0.90 (0.70–1.16)  
 15+ years 209 (12.7) 62 (12.3) 31 (12.2) Reference Reference  649 (10.2) 154 (10.1) 97 (11.8) Reference Reference  
Ptrend    0.87 0.25     0.68 0.99  
Age at Regularity 
Overall 
 <11 years 147 (9.0) 44 (8.0) 28 (8.9) 0.92 (0.60–1.42) 1.04 (0.61–1.75) 0.77 292 (4.3) 92 (5.5) 73 (6.8) 1.31 (0.98–1.74) 1.39 (1.01–1.92) 0.83 
 11–12 years 599 (36.5) 196 (35.5) 140 (44.6) 1.12 (0.82–1.52) 1.42 (0.98–2.06)  2,275 (33.3) 630 (37.7) 415 (38.9) 1.25 (1.06–1.49) 1.20 (0.98–1.48)  
 13–14 years 596 (36.3) 230 (41.7) 101 (32.2) 1.33 (0.98–1.80) 1.12 (0.76–1.64)  2,910 (42.6) 713 (42.6) 426 (39.9) 1.24 (1.05–1.47) 1.12 (0.92–1.38)  
 15+ years 299 (18.2) 82 (14.9) 45 (14.3) Reference Reference  1,348 (19.8) 238 (14.2) 154 (14.4) Reference Reference  
Ptrend    0.53 0.21     0.03 0.02  
 Menopause Phet    0.38 0.61     0.15 0.26  
Premenopausal 
 <11 years 49 (11.7) 11 (9.5) 11 (10.9) 0.74 (0.31–1.75) 1.24 (0.49–3.11) 0.26 61 (5.3) 18 (7.3) 27 (8.9) 2.20 (1.10–4.37) 2.17 (1.20–3.91) 0.62 
 11–12 years 186 (44.4) 45 (38.8) 47 (46.5) 0.86 (0.46–1.61) 1.55 (0.76–3.17)  424 (37.1) 107 (43.3) 129 (42.7) 1.79 (1.11–2.88) 1.50 (0.99–2.27)  
 13–14 years 113 (27.0) 40 (34.5) 31 (30.7) 1.33 (0.70–2.56) 1.82 (0.86–3.88)  474 (41.5) 97 (39.3) 110 (36.4) 1.52 (0.94–2.45) 1.20 (0.79–1.82)  
 15+ years 71 (17.0) 20 (17.2) 12 (11.9) Reference Reference  183 (16.0) 25 (10.1) 36 (11.9) Reference Reference  
Ptrend    0.21 0.74     0.01 0.004  
Postmenopausal 
 <11 years 98 (8.0) 33 (7.6) 17 (8.0) 1.00 (0.60–1.66) 1.02 (0.53–1.95) 0.82 231 (4.1) 74 (5.2) 46 (6.0) 1.15 (0.83–1.57) 1.17 (0.79–1.72) 0.78 
 11–12 years 413 (33.8) 151 (34.6) 93 (43.7) 1.26 (0.88–1.79) 1.47 (0.94–2.28)  1,851 (32.6) 523 (36.7) 286 (37.3) 1.18 (0.98–1.42) 1.12 (0.88–1.42)  
 13–14 years 483 (39.5) 190 (43.6) 70 (32.9) 1.39 (0.99–1.96) 0.99 (0.63–1.55)  2,436 (42.9) 616 (43.2) 316 (41.3) 1.21 (1.01–1.45) 1.11 (0.88–1.40)  
 15+ years 228 (18.7) 62 (14.2) 33 (15.5) Reference Reference  1,165 (20.5) 213 (14.9) 118 (15.4) Reference Reference  
Ptrend    0.93 0.15     0.26 0.35  
BlackWhite
ControlsHGS casesOther histotype casesHGSOther histotypeControlsHGS casesOther histotype casesHGSOther histotype
N (%)N (%)N (%)aOR (95% CI)aOR (95% CI)hist. PhetN (%)N (%)N (%)aOR (95% CI)aOR (95% CI)hist. Phet
Age at Menarche 
Overall 
 <11 years 231 (9.9) 62 (9.6) 39 (10.5) 1.09 (0.74–1.62) 1.15 (071–1.84) 0.98 450 (6.0) 113 (6.4) 85 (7.5) 1.29 (0.98–1.71) 1.18 (0.86–1.62) 0.24 
 11–12 years 978 (42.1) 248 (38.2) 168 (45.0) 1.12 (0.83–1.51) 1.26 (0.88–1.82)  3,077 (40.8) 730 (41.1) 476 (42.1) 1.20 (0.99–1.46) 1.03 (0.82–1.28)  
 13–14 years 830 (35.7) 259 (39.9) 121 (32.4) 1.26 (0.94–1.70) 1.05 (0.72–1.53)  3,241 (43.0) 764 (43.0) 449 (39.7) 1.20 (0.99–1.45) 0.96 (0.77–1.20)  
 15+ years 286 (12.3) 80 (12.3) 45 (12.1) Reference Reference  776 (10.3) 170 (9.6) 122 (10.8) Reference Reference  
Ptrend    0.92 0.21     0.11 0.23  
 Menopause Phet    0.87 0.90     0.05 0.09  
Premenopausal 
 <11 years 77 (11.3) 18 (12.6) 15 (12.6) 1.16 (0.54–2.50) 1.18 (0.52–2.70) 0.99 69 (5.9) 22 (8.6) 29 (9.3) 2.58 (1.26–5.30) 1.97 (1.06–3.66) 0.38 
 11–12 years 314 (46.2) 62 (43.4) 57 (47.9) 1.09 (0.59–2.01) 1.19 (0.61–2.29)  479 (40.9) 116 (45.5) 139 (44.7) 1.89 (1.07–3.32) 1.39 (0.86–2.23)  
 13–14 years 212 (31.2) 45 (31.5) 33 (27.7) 1.18 (0.62–2.24) 1.05 (0.52–2.12)  495 (42.3) 101 (39.6) 118 (37.9) 1.66 (0.94–2.93) 1.19 (0.74–1.93)  
 15+ years 77 (11.3) 18 (12.6) 14 (11.8) Reference Reference  127 (10.9) 16 (6.3) 25 (8.0) Reference Reference  
Ptrend    0.87 0.56     0.01 0.02  
Postmenopausal 
 <11 years 154 (9.4) 44 (8.7) 24 (9.5) 1.06 (0.67–1.68) 1.15 (0.64–2.07) 0.97 381 (6.0) 91 (6.0) 56 (6.8) 1.11 (0.82–1.51) 0.99 (0.68–1.43) 0.23 
 11–12 years 664 (40.4) 186 (36.8) 111 (43.7) 1.16 (0.82–1.64) 1.33 (0.86–2.07)  2,598 (40.8) 614 (40.3) 337 (41.1) 1.11 (0.90–1.37) 0.93 (0.73–1.20)  
 13–14 years 618 (37.6) 214 (42.3) 88 (34.7) 1.32 (0.94–1.86) 1.09 (0.69–1.71)  2746 (43.1) 663 (43.6) 331 (40.3) 1.15 (0.93–1.41) 0.90 (0.70–1.16)  
 15+ years 209 (12.7) 62 (12.3) 31 (12.2) Reference Reference  649 (10.2) 154 (10.1) 97 (11.8) Reference Reference  
Ptrend    0.87 0.25     0.68 0.99  
Age at Regularity 
Overall 
 <11 years 147 (9.0) 44 (8.0) 28 (8.9) 0.92 (0.60–1.42) 1.04 (0.61–1.75) 0.77 292 (4.3) 92 (5.5) 73 (6.8) 1.31 (0.98–1.74) 1.39 (1.01–1.92) 0.83 
 11–12 years 599 (36.5) 196 (35.5) 140 (44.6) 1.12 (0.82–1.52) 1.42 (0.98–2.06)  2,275 (33.3) 630 (37.7) 415 (38.9) 1.25 (1.06–1.49) 1.20 (0.98–1.48)  
 13–14 years 596 (36.3) 230 (41.7) 101 (32.2) 1.33 (0.98–1.80) 1.12 (0.76–1.64)  2,910 (42.6) 713 (42.6) 426 (39.9) 1.24 (1.05–1.47) 1.12 (0.92–1.38)  
 15+ years 299 (18.2) 82 (14.9) 45 (14.3) Reference Reference  1,348 (19.8) 238 (14.2) 154 (14.4) Reference Reference  
Ptrend    0.53 0.21     0.03 0.02  
 Menopause Phet    0.38 0.61     0.15 0.26  
Premenopausal 
 <11 years 49 (11.7) 11 (9.5) 11 (10.9) 0.74 (0.31–1.75) 1.24 (0.49–3.11) 0.26 61 (5.3) 18 (7.3) 27 (8.9) 2.20 (1.10–4.37) 2.17 (1.20–3.91) 0.62 
 11–12 years 186 (44.4) 45 (38.8) 47 (46.5) 0.86 (0.46–1.61) 1.55 (0.76–3.17)  424 (37.1) 107 (43.3) 129 (42.7) 1.79 (1.11–2.88) 1.50 (0.99–2.27)  
 13–14 years 113 (27.0) 40 (34.5) 31 (30.7) 1.33 (0.70–2.56) 1.82 (0.86–3.88)  474 (41.5) 97 (39.3) 110 (36.4) 1.52 (0.94–2.45) 1.20 (0.79–1.82)  
 15+ years 71 (17.0) 20 (17.2) 12 (11.9) Reference Reference  183 (16.0) 25 (10.1) 36 (11.9) Reference Reference  
Ptrend    0.21 0.74     0.01 0.004  
Postmenopausal 
 <11 years 98 (8.0) 33 (7.6) 17 (8.0) 1.00 (0.60–1.66) 1.02 (0.53–1.95) 0.82 231 (4.1) 74 (5.2) 46 (6.0) 1.15 (0.83–1.57) 1.17 (0.79–1.72) 0.78 
 11–12 years 413 (33.8) 151 (34.6) 93 (43.7) 1.26 (0.88–1.79) 1.47 (0.94–2.28)  1,851 (32.6) 523 (36.7) 286 (37.3) 1.18 (0.98–1.42) 1.12 (0.88–1.42)  
 13–14 years 483 (39.5) 190 (43.6) 70 (32.9) 1.39 (0.99–1.96) 0.99 (0.63–1.55)  2,436 (42.9) 616 (43.2) 316 (41.3) 1.21 (1.01–1.45) 1.11 (0.88–1.40)  
 15+ years 228 (18.7) 62 (14.2) 33 (15.5) Reference Reference  1,165 (20.5) 213 (14.9) 118 (15.4) Reference Reference  
Ptrend    0.93 0.15     0.26 0.35  

Note: All models are adjusted for site, age, education, young adult weight, and height.

Abbreviation: HGS, high-grade serous.

Associations between age at menarche and EOC were not different by height overall for Black (Pheterogeneity = 0.43) or White (Pheterogeneity = 0.99) women. However, differences by height were observed among premenopausal Black women (Pheterogeneity = 0.02, Table 4). Among premenopausal Black women in the upper quartile for height, compared with ≥15 years at menarche, ages <11, 11–12, and 13–14 years were associated with increased risk of EOC with aORs of 2.94 (95% CI, 1.04–8.37), 1.60 (95% CI, 0.70–3.64), and 2.53 (95% CI, 1.09–5.87), respectively (Ptrend = 0.40; Table 4). In contrast, among premenopausal Black women in the lower three quartiles for height, earlier ages at menarche were not associated with EOC risk (<11 years aOR = 0.66; 95% CI, 0.30–1.45; 11–12 years aOR = 0.89; 95% CI, 0.47–2.12; 13–14 years aOR = 0.59; 95% CI, 0.30–1.18). Associations between age at menstrual regularity and EOC also differed by height for premenopausal Black women (Pheterogeneity = 0.03). Associations were more pronounced among women in the upper quartile for height (<11 years aOR = 1.62; 95% CI, 0.48–5.52; 11–12 years aOR = 1.21; 95% CI, 0.52–2.81; 13–14 years aOR = 3.31; 95% CI, 1.39–7.92) than among women in the lower three quartiles for height (<11 years aOR = 0.73; 95% CI, 0.31–1.68; 11–12 years aOR = 1.08; 95% CI, 0.56–2.07; 13–14 years aOR = 0.85; 95% CI, 0.41–1.74); however, associations did not follow a monotonic trend for either group. EOC risk patterns among White women did not differ by height (Supplementary Table S3).

Table 4.

Association between menstrual cycle and EOC among Black women in the OCWAA consortium by menopausal status and height.

Upper quartile of heightaLower three quartiles of heighta
Black casesBlack controlsBlack casesBlack controls
N (%)N (%)aOR (95% CI)N (%)N (%)aOR (95% CI)Phet for height
Overall 
 <11 years 35 (8.6) 61 (7.5) 1.37 (0.77–2.42) 66 (10.7) 170 (11.2) 1.05 (0.69–1.59) 0.43 
 11–12 years 154 (37.8) 337 (41.6) 1.08 (0.72–1.62) 262 (42.6) 641 (42.3) 1.24 (0.89–1.71)  
 13–14 years 166 (40.8) 300 (37.0) 1.27 (0.84–1.90) 214 (34.8) 530 (35.0) 1.14 (0.82–1.60)  
 15+ years 52 (12.8) 113 (13.9) Reference 73 (11.9) 173 (11.4) Reference  
Ptrend   0.55   0.73  
 Menopause Phet   0.19   0.13  
Premenopausal 
 <11 years 14 (11.9) 25 (8.9) 2.94 (1.04–8.37) 19 (13.2) 52 (13.1) 0.66 (0.30–1.45) 0.02 
 11–12 years 49 (41.5) 135 (47.9) 1.60 (0.70–3.64) 70 (48.6) 179 (45.0) 0.89 (0.47–1.69)  
 13–14 years 45 (38.1) 83 (29.4) 2.53 (1.09–5.87) 33 (22.9) 129 (32.4) 0.59 (0.30–1.18)  
 15+ years 10 (8.5) 39 (13.8) Reference 22 (15.3) 38 (9.6) Reference  
Ptrend   0.40   0.89  
Postmenopausal 
 <11 years 21 (7.3) 36 (6.8) 1.00 (0.49–2.05) 47 (10.0) 118 (10.6) 1.22 (0.74–1.99) 0.53 
 11–12 years 105 (36.3) 202 (38.2) 0.95 (0.59–1.55) 192 (40.8) 462 (41.4) 1.44 (0.98–2.12)  
 13–14 years 121 (41.9) 217 (41.0) 1.02 (0.63–1.65) 181 (38.4) 401 (35.9) 1.45 (0.99–2.14)  
 15+ years 42 (14.5) 74 (14.0) Reference 51 (10.8) 135 (12.1) Reference  
Ptrend   0.83   0.45  
Age at regularity 
Overall 
 <11 years 22 (6.4) 36 (6.8) 1.08 (0.56–2.07) 50 (9.6) 111 (10.0) 0.94 (0.60–1.47) 0.41 
 11–12 years 123 (35.8) 191 (36.0) 1.18 (0.77–1.80) 213 (40.8) 408 (36.8) 1.26 (0.91–1.75)  
 13–14 years 146 (42.4) 194 (36.5) 1.49 (0.99–2.26) 185 (35.4) 402 (36.2) 1.14 (0.82–1.59)  
 15+ years 53 (15.4) 110 (20.7) Reference 74 (14.2) 189 (17.0) Reference  
Ptrend   0.99   0.67  
 Menopause Phet   0.06   0.67  
Premenopausal 
 <11 years 7 (7.4) 13 (7.9) 1.62 (0.48–5.52) 15 (12.3) 36 (14.1) 0.73 (0.31–1.68) 0.03 
 11–12 years 34 (35.8) 76 (46.3) 1.21 (0.52–2.81) 58 (47.5) 110 (43.1) 1.08 (0.56–2.07)  
 13–14 years 42 (44.2) 42 (25.6) 3.31 (1.39–7.92) 29 (23.8) 71 (27.8) 0.85 (0.41–1.74)  
 15+ years 12 (12.6) 33 (20.1) Reference 20 (16.4) 38 (14.9) Reference  
Ptrend   0.55   0.82  
Postmenopausal 
 <11 years 15 (6.0) 23 (6.3) 1.03 (0.47–2.29) 35 (8.8) 75 (8.8) 1.04 (0.61–1.77) 0.99 
 11–12 years 89 (35.7) 115 (31.3) 1.29 (0.78–2.14) 155 (38.8) 298 (34.9) 1.38 (0.94–2.03)  
 13–14 years 104 (41.8) 152 (41.4) 1.22 (0.75–1.99) 156 (39.0) 331 (38.7) 1.30 (0.88–1.90)  
 15+ years 41 (16.5) 77 (21.0) Reference 54 (13.5) 151 (17.7) Reference  
Ptrend   0.57   0.51  
Upper quartile of heightaLower three quartiles of heighta
Black casesBlack controlsBlack casesBlack controls
N (%)N (%)aOR (95% CI)N (%)N (%)aOR (95% CI)Phet for height
Overall 
 <11 years 35 (8.6) 61 (7.5) 1.37 (0.77–2.42) 66 (10.7) 170 (11.2) 1.05 (0.69–1.59) 0.43 
 11–12 years 154 (37.8) 337 (41.6) 1.08 (0.72–1.62) 262 (42.6) 641 (42.3) 1.24 (0.89–1.71)  
 13–14 years 166 (40.8) 300 (37.0) 1.27 (0.84–1.90) 214 (34.8) 530 (35.0) 1.14 (0.82–1.60)  
 15+ years 52 (12.8) 113 (13.9) Reference 73 (11.9) 173 (11.4) Reference  
Ptrend   0.55   0.73  
 Menopause Phet   0.19   0.13  
Premenopausal 
 <11 years 14 (11.9) 25 (8.9) 2.94 (1.04–8.37) 19 (13.2) 52 (13.1) 0.66 (0.30–1.45) 0.02 
 11–12 years 49 (41.5) 135 (47.9) 1.60 (0.70–3.64) 70 (48.6) 179 (45.0) 0.89 (0.47–1.69)  
 13–14 years 45 (38.1) 83 (29.4) 2.53 (1.09–5.87) 33 (22.9) 129 (32.4) 0.59 (0.30–1.18)  
 15+ years 10 (8.5) 39 (13.8) Reference 22 (15.3) 38 (9.6) Reference  
Ptrend   0.40   0.89  
Postmenopausal 
 <11 years 21 (7.3) 36 (6.8) 1.00 (0.49–2.05) 47 (10.0) 118 (10.6) 1.22 (0.74–1.99) 0.53 
 11–12 years 105 (36.3) 202 (38.2) 0.95 (0.59–1.55) 192 (40.8) 462 (41.4) 1.44 (0.98–2.12)  
 13–14 years 121 (41.9) 217 (41.0) 1.02 (0.63–1.65) 181 (38.4) 401 (35.9) 1.45 (0.99–2.14)  
 15+ years 42 (14.5) 74 (14.0) Reference 51 (10.8) 135 (12.1) Reference  
Ptrend   0.83   0.45  
Age at regularity 
Overall 
 <11 years 22 (6.4) 36 (6.8) 1.08 (0.56–2.07) 50 (9.6) 111 (10.0) 0.94 (0.60–1.47) 0.41 
 11–12 years 123 (35.8) 191 (36.0) 1.18 (0.77–1.80) 213 (40.8) 408 (36.8) 1.26 (0.91–1.75)  
 13–14 years 146 (42.4) 194 (36.5) 1.49 (0.99–2.26) 185 (35.4) 402 (36.2) 1.14 (0.82–1.59)  
 15+ years 53 (15.4) 110 (20.7) Reference 74 (14.2) 189 (17.0) Reference  
Ptrend   0.99   0.67  
 Menopause Phet   0.06   0.67  
Premenopausal 
 <11 years 7 (7.4) 13 (7.9) 1.62 (0.48–5.52) 15 (12.3) 36 (14.1) 0.73 (0.31–1.68) 0.03 
 11–12 years 34 (35.8) 76 (46.3) 1.21 (0.52–2.81) 58 (47.5) 110 (43.1) 1.08 (0.56–2.07)  
 13–14 years 42 (44.2) 42 (25.6) 3.31 (1.39–7.92) 29 (23.8) 71 (27.8) 0.85 (0.41–1.74)  
 15+ years 12 (12.6) 33 (20.1) Reference 20 (16.4) 38 (14.9) Reference  
Ptrend   0.55   0.82  
Postmenopausal 
 <11 years 15 (6.0) 23 (6.3) 1.03 (0.47–2.29) 35 (8.8) 75 (8.8) 1.04 (0.61–1.77) 0.99 
 11–12 years 89 (35.7) 115 (31.3) 1.29 (0.78–2.14) 155 (38.8) 298 (34.9) 1.38 (0.94–2.03)  
 13–14 years 104 (41.8) 152 (41.4) 1.22 (0.75–1.99) 156 (39.0) 331 (38.7) 1.30 (0.88–1.90)  
 15+ years 41 (16.5) 77 (21.0) Reference 54 (13.5) 151 (17.7) Reference  
Ptrend   0.57   0.51  

Note: All models adjusted for site, age, education, and young adult weight.

aUpper quartile cutpoint is 1.68 m.

Results stratified by BMI are shown in Supplementary Table S4. Among both White and Black women, overall trends for age at menarche were similar by BMI category, however the association for age at menarche <11 years, compared with ≥13 years, were of greater magnitude among premenopausal White women with BMI ≥30 kg/m2 (aOR = 2.43; 95% CI, 1.10–5.36) than among premenopausal White women with BMI <30 kg/m2 (aOR = 1.59; 95% CI, 0.98–2.57). Overall trends for age at regularity were also similar by BMI category for both White and Black women but the association for <11 years among premenopausal White women was of greater magnitude for women with BMI ≥30 kg/m2 (aOR = 3.07; 95% CI, 1.29–7.38) than among women with BMI <30 kg/m2 (aOR = 1.68; 95% CI, 1.02–2.76).

Finally, we repeated the main analyses for age at menarche excluding AACES and NCOCS from the pooled dataset (Supplementary Table S5). Results among White women were similar to the primary analyses but results among Black women differed. With 35% of the Black cases and 60% of the Black controls remaining after exclusion, compared with ≥15 years, younger ages at menarche were associated with an increased risk of EOC among Black women overall (Ptrend = 0.06); the trend was driven by associations among premenopausal women (Ptrend = 0.04) but not postmenopausal women (Ptrend = 0.27). For example, ages at menarche of <11, 11–12, and 13–14 years, compared with ≥15 years, were associated with increased risk of EOC among premenopausal Black women with aORs of 2.94 (95% CI, 0.88–9.76), 2.24 (95% CI, 0.80–6.33), and 1.68 (95% CI, 0.56–4.98), respectively.

In this large study, associations between menstrual cycle characteristics and risk of EOC were not uniform among Black and White women. Near null associations between age at menarche and EOC were observed among Black women regardless of menopausal status. In contrast, younger ages at menarche were associated with an increased risk of EOC among premenopausal White women; this trend was not observed among postmenopausal White women. Associations between earlier age at menstrual regularity and risk of EOC were similarly observed among premenopausal White but not Black women. In addition, irregular cycle length was associated with reduced risk for White but not Black women. In contrast, associations between longer menstrual cycle length (≥31 days) and ever missing three consecutive periods were similar for Black and White women, although estimates for these two factors were less precise because the sample of Black women was only about one-third that of White women.

Prior investigations of the association between age at menarche and ovarian cancer risk have mostly found older ages at menarche to be associated with reduced ovarian cancer risk (11, 40), which is supported by our findings among White women. These previous reports rarely examined associations in non-White populations or by menopausal status. A large 2013 meta-analysis consisting of 27 observational studies (22 case–control, 5 cohort studies) found a 15% decrease in ovarian cancer risk comparing the oldest with the youngest age at menarche category, in both case–control (RR = 0.84; 95% CI, 0.75–0.97) and cohort (RR = 0.89; 95% CI, 0.76–1.03) studies (15). A more recent pooled analysis of eight cohort studies (none of which were included in the 2013 meta-analysis; ref. 15) consisting mostly of US and European White women found a small decrease in ovarian cancer risk (RR = 0.98; 95% CI, 0.95–1.01) with each additional year delay in the age at menarche (11). Race was not considered in the pooled analysis of cohort studies (11) and the meta-analysis only examined associations separately for Asians, Americans, and Europeans (15). Two studies (AACES and NCOCS), included in the OCWAA consortium, have provided information on age at menarche and risk of ovarian cancer in Black women (13, 14). In the AACES study, menarche at ages 12 to 13 years (compared with younger than 12 years) was associated with increased EOC risk (OR = 1.3; 95% CI, 1.0–1.8) among postmenopausal Black women, but this association was not found in premenopausal women (13). After excluding AACES and NCOCS, which accounted for two-thirds of the Black cases in OCWAA, we observed an increase in EOC risk for youngest ages at menarche among premenopausal Black women, which is in contrast to the results observed among all women in OCWAA.

Literature on associations between other menstrual cycle characteristics and EOC is more limited. A pooled analysis of more than 13,000 ovarian cancer cases from 14 case–control studies (including NCOCS and LACOCS), as part of the Ovarian Cancer Association Consortium (OCAC), found a decrease in EOC risk associated with both irregular and long (>35 days) menstrual cycles (16). The analyses were adjusted for race, but race-specific associations were not estimated. Smaller case–control studies conducted in majority White populations have also found inverse associations for longer or irregular menstrual cycles (17, 18). In contrast, a prospective analysis using the Child Health and Development Studies (CHDS) cohort established during 1959–1966 and followed through 2011 via linkage to the California Cancer Registry, found elevated ovarian cancer risk with irregular menstrual cycles, after adjustment for race and ethnicity, oral contraceptive use, parity, and other factors (41). In addition, older age at menarche was associated with increased risk of ovarian cancer in the CHDS cohort, also inconsistent with our results and results reported in the meta-analysis (15) and pooled cohort analysis (11).

Age at menarche is influenced by several factors, including body weight and height, which likely reflect the contribution of a myriad additional and complex factors such as overall nutrition and health status (38, 42–45). Some studies have shown that over the last several decades, age at menarche has decreased, whereas adult height has increased (46, 47). Taller height has been associated with earlier menarche in multiethnic populations (38). However, in a large study of mainly European whites, women with earlier menarche ultimately reached a shorter adult height (37). Our subgroup analyses did not show significant differences by height among White women, however, among premenopausal Black women there was a nonlinear increase in EOC for earlier ages at menarche among women in the highest quartile of height. Differences by BMI were not significant for either race group, although associations for the youngest age at menarche were of greater magnitude among premenopausal White women with BMI ≥30 kg/m2 than among premenopausal White women with BMI <30 kg/m2.

Histotype differences in the associations between menstrual cycle characteristics and risk of ovarian cancer have been explored in a few previous studies. In the 2013 meta-analysis, age at menarche was not associated with risk of invasive serous ovarian cancer but this was based on only two studies (15). In an OCAC pooled analysis, both irregular cycles and long cycles (>35 days) were associated with decreased risk of high grade serous EOC (16). In a pooled analysis that included 1.3 million women in the Ovarian Cancer Cohort Consortium (OC3), differences in associations between reproductive characteristics—including age at menarche—and risk of ovarian cancer were observed. Oldest age at menarche (≥15 years), compared with youngest age (≤11 years), was associated with reduced risk of clear cell (RR = 0.55; 95% CI, 0.34–0.90) but not endometrioid (RR = 0.98; 95% CI, 0.73–1.31) or mucinous (RR = 1.13; 95% CI, 0.76–1.66) tumors (40). Our subgroup analysis did not find meaningful differences in the race-specific associations by histotype, however, data were sparse and we were unable to examine histotypes other than high grade serous. Investigating histotype differences in race-specific associations is an important area of future inquiry.

The association between earlier age at menarche and increased ovarian cancer risk may be partly explained by the incessant ovulation hypothesis, which suggests there is a positive association between frequency of ovulation and ovarian cancer risk, because earlier menarcheal age results in an increase in the lifetime number of ovulations (48). In addition, early menarche is associated with a more rapid onset of ovulatory cycles and a tendency to sustain higher levels of circulating estrogens (49). Although a role of sex steroid hormones in the etiology of ovarian cancer is biologically compelling, the mechanism for ovarian carcinogenesis has not been well characterized. Exogenous hormones, administered as menopausal hormone therapy, have been associated with an increased risk of ovarian cancer (50), whereas those administered earlier in life, as oral contraceptives, have been associated with a reduced risk of ovarian cancer (4). Evidence for the role of endogenous hormones is conflicting and limited by small sample sizes or nonrepresentative patient populations (e.g., pregnant women; refs. 51–54).

In some previous studies of ovarian cancer, reproductive factors, including parity and oral contraceptive use and duration, were more strongly associated with premenopausal than postmenopausal ovarian cancer risk (13, 55). These and other reproductive-related exposures, including age at menarche, have occurred in the more distant past for postmenopausal than premenopausal women. Difficulty with recall, resulting in differential misclassification by age, may have contributed to our weaker findings in postmenopausal women. However, differences in associations with ovarian cancer risk by menopausal status may also represent specific periods of susceptibility. The lifetime number of ovulatory cycles is influenced by many factors, including age at menopause. Therefore, associations among premenopausal women may reflect a greater lifetime number of ovulatory cycles in women who are still ovulating. A better understanding of the underlying reasons contributing to effect modification by menopausal status is needed.

Biological reasons for the largely null association between age at menarche and risk of EOC among Black women, regardless of menopausal status, are unclear. One potential mechanism is obesity-related anovulation, which may reduce ovarian cancer risk by decreasing the lifetime number of ovulations. Among premenopausal women with EOC in our study, only 32% of White but 78% of Black cases were overweight or obese. A lower proportion of overweight/obesity observed among Black cases (59%) after excluding AACES and NCOCS could partly explain the inverse association among premenopausal Black women after the restriction, similar to the association observed among premenopausal White women. Although obesity has been associated with an increased risk of ovarian cancer in previous studies, the association may differ by menopausal status, age at menarche, race, and histotype (56, 57). Subgroup analyses in a 2012 meta-analysis that included 47 epidemiologic studies found a small increased risk of ovarian cancer with higher BMI for all women [relative risk (RR) per 5 kg/m2 increase in BMI = 1.05; SE = 0.011]; risk was slightly higher for premenopausal women (RR = 1.12; SE = 0.024), weaker for women with younger (<13 years) ages at menarche (RR = 1.05; SE = 0.015) and largely null for non-White women (RR = 0.98; SE = 0.059; ref. 56). Unpublished OCWAA consortium results showed BMI ≥30 kg/m2 was associated with an overall increased risk of EOC for Black but not White women and no association for high-grade serous EOC for either race group (58). The Black cases in our analysis were more likely to be premenopausal at diagnosis, but also more likely to have younger ages at menarche. Stratifying by BMI in our analysis did not explain null results among Black women. An additional consideration may be race differences in age at menopause, which combined with age at menarche, parity, oral contraceptive use, and breastfeeding duration, determine the lifetime number of ovulatory cycles. These data are not yet harmonized for OCWAA, and previous reports of race differences in timing of menopause have been mixed (59–62). Disentangling the complex interactions between these factors is beyond the scope of this study but is an important area of future research.

There are some limitations to our current study, most notable is the potential for misclassification of the exposure. Menstrual cycle characteristics were ascertained by patient self-report, which is subject to misclassification and potentially differential by case/control status or age. Exposure information for the four case–control studies was collected several decades after menarche for most women, and around the time of cancer diagnosis for cases. Recall may also be differential with respect to age and menopausal status because the length of time since onset of menarche and other menstrual cycle characteristics is longer for postmenopausal women. Because of small sample sizes, we were unable to examine associations separately for histotypes other than high-grade serous, potentially masking heterogeneity in associations by tumor histotype. Despite these limitations, OCWAA is a rich resource with sufficient sample size to examine ovarian cancer risk factors by race. This study is the largest to investigate race differences in the association between menstrual cycle characteristics and ovarian cancer risk but the sample size of Black cases and controls was still only about one-third the sample size of White cases and controls.

In conclusion, we observed differences in the associations between certain menstrual cycle characteristics (i.e., age at menarche, age at menstrual regularity, and irregular menstrual cycles) and risk of EOC by race. Earlier ages at menarche and menstrual regularity were associated with an increased risk of EOC for premenopausal White women but not Black women. As age at menarche among girls decreases, this warrants further attention. Recent estimates from the National Health Statistics Report showed 14% of Black girls but only 9% of White girls had reached menarche by 10 years of age (63). We also found irregular cycles reduced risk for White but not Black women, whereas long menstrual cycles appeared to reduce risk for both race groups. Future studies should examine EOC risk factors by race and investigate reasons for these disparities.

L.C. Peres reports grants from NCI outside the submitted work. T.N. Bethea reports grants from NCI during the conduct of the study. E.V. Bandera reports grants from NCI during the conduct of the study. E.R. Myers reports grants from NCI during the conduct of the study; personal fees from Moderna, Inc., Merck, Inc., Hologic, Inc, and AbbVie, Inc. outside the submitted work. A. Beeghly-Fadiel reports grants from NIH NCI U54 CA163072 MVTCP Pilot Study during the conduct of the study; grants from NIH NCI U54 MD010722 Precision Medicine Pilot Study outside the submitted work. P.G. Moorman reports grants from NCI during the conduct of the study. No disclosures were reported by the other authors.

R. Nash: Writing–original draft, writing–review and editing. C.E. Johnson: Formal analysis, writing–review and editing. H.R. Harris: Writing–review and editing. L.C. Peres: Writing–review and editing. C.E. Joslin: Writing–review and editing. T.N. Bethea: Writing–review and editing. E.V. Bandera: Writing–review and editing. H.M. Ochs-Balcom: Writing–review and editing. E.R. Myers: Writing–review and editing. K.A. Guertin: Writing–review and editing. F. Camacho: Writing–review and editing. A. Beeghly-Fadiel: Writing–review and editing. P.G. Moorman: Writing–review and editing. V.W. Setiawan: Writing–review and editing. L. Rosenberg: Funding acquisition, writing–review and editing. J.M. Schildkraut: Conceptualization, resources, funding acquisition, project administration, writing–review and editing. A.H. Wu: Conceptualization, supervision, methodology, writing–original draft, writing–review and editing.

The OCWAA Consortium was supported by the US NCI (R01-CA207260, to J.M. Schildkraut and L. Rosenberg and K01-CA212056, to T.N. Bethea). AACES was funded by NCI (R01-CA142081, to J.M. Schildkraut); BWHS is funded by NIH (R01-CA058420, UM1-CA164974, and U01-CA164974, to L. Rosenberg); CCCS was funded by NIH/NCI (R01-CA61093, to K. Rosenblatt); LACOCS was funded by NCI (R01-CA17054, to M. Pike, R01-CA58598, to M. Goodman and A.H. Wu, and Cancer Center Core grant P30-CA014089, to B. Henderson and A.H. Wu) and by the California Cancer Research Program (2II0200, to A.H. Wu); and NCOCS was funded by NCI (R01-CA076016, to J.M. Schildkraut). The WHI program is funded by the National Heart, Lung, and Blood Institute through contracts HHSN268201600018C, HHSN268201600001C, HHSN268201600002C, HHSN268201600003C, and HHSN268201600004C. Additional grants to support WHI inclusion in OCWAA include UM1-CA173642-05 (to G.L. Anderson), NHLBI-CSB-WH-2016-01-CM, and NHLBI-75N92021D00002. R. Nash was additionally supported by an award from the US NCI (F31CA268737). Pathology data were obtained from the following state cancer registries (AZ, CA, CO, CT, DE, DC, FL, GA, IL, IN, KY, LA, MD, MA, MI, NJ, NY, NC, OK, PA, SC, TN, TX, and VA), and results reported do not necessarily represent their views. The IRBs of participating institutions and cancer registries have approved these studies, as required.

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

1.
Peres
LC
,
Schildkraut
JM
.
Racial/ethnic disparities in ovarian cancer research
.
Adv Cancer Res
2020
;
146
:
1
21
.
2.
Peres
LC
,
Bethea
TN
,
Camacho
TF
,
Bandera
EV
,
Beeghly-Fadiel
A
,
Chyn
DL
, et al
.
Racial differences in population attributable risk for epithelial ovarian cancer in the OCWAA consortium
.
J Natl Cancer Inst
2021
;
113
:
710
8
.
3.
Siegel
RL
,
Miller
KD
,
Jemal
A
.
Cancer statistics, 2020
.
CA Cancer J Clin
2020
;
70
:
7
30
.
4.
Collaborative Group on Epidemiological Studies of Ovarian C
,
Beral
V
,
Doll
R
,
Hermon
C
,
Peto
R
,
Reeves
G
.
Ovarian cancer and oral contraceptives: collaborative reanalysis of data from 45 epidemiological studies including 23,257 women with ovarian cancer and 87,303 controls
.
Lancet
2008
;
371
:
303
14
.
5.
Whittemore
AS
,
Harris
R
,
Itnyre
J
.
Characteristics relating to ovarian cancer risk: collaborative analysis of 12 US case-control studies. II. Invasive epithelial ovarian cancers in white women. collaborative ovarian cancer group
.
Am J Epidemiol
1992
;
136
:
1184
203
.
6.
Gaitskell
K
,
Green
J
,
Pirie
K
,
Barnes
I
,
Hermon
C
,
Reeves
GK
, et al
.
Histological subtypes of ovarian cancer associated with parity and breastfeeding in the prospective million women study
.
Int J Cancer
2018
;
142
:
281
9
.
7.
Li
DP
,
Du
C
,
Zhang
ZM
,
Li
GX
,
Yu
ZF
,
Wang
X
, et al
.
Breastfeeding and ovarian cancer risk: a systematic review and meta-analysis of 40 epidemiological studies
.
Asian Pac J Cancer Prev
2014
;
15
:
4829
37
.
8.
Risch
HA
.
Hormonal etiology of epithelial ovarian cancer, with a hypothesis concerning the role of androgens and progesterone
.
J Natl Cancer Inst
1998
;
90
:
1774
86
.
9.
Collaborative Group on Hormonal Factors in Breast Cancer. Menarche, menopause, and breast cancer risk: individual participant meta-analysis, including 118 964 women with breast cancer from 117 epidemiological studies
.
Lancet Oncol
2012
;
13
:
1141
51
.
10.
Gong
TT
,
Wang
YL
,
Ma
XX
.
Age at menarche and endometrial cancer risk: a dose-response meta-analysis of prospective studies
.
Sci Rep
2015
;
5
:
14051
.
11.
Fuhrman
BJ
,
Moore
SC
,
Byrne
C
,
Makhoul
I
,
Kitahara
CM
,
Berrington de Gonzalez
A
, et al
.
Association of the age at menarche with site-specific cancer risks in pooled data from nine cohorts
.
Cancer Res
2021
;
81
:
2246
55
.
12.
Braem
MG
,
Onland-Moret
NC
,
van den Brandt
PA
,
Goldbohm
RA
,
Peeters
PH
,
Kruitwagen
RF
, et al
.
Reproductive and hormonal factors in association with ovarian cancer in the Netherlands cohort study
.
Am J Epidemiol
2010
;
172
:
1181
9
.
13.
Moorman
PG
,
Alberg
AJ
,
Bandera
EV
,
Barnholtz-Sloan
J
,
Bondy
M
,
Cote
ML
, et al
.
Reproductive factors and ovarian cancer risk in African-American women
.
Ann Epidemiol
2016
;
26
:
654
62
.
14.
Moorman
PG
,
Palmieri
RT
,
Akushevich
L
,
Berchuck
A
,
Schildkraut
JM
.
Ovarian cancer risk factors in African-American and white women
.
Am J Epidemiol
2009
;
170
:
598
606
.
15.
Gong
TT
,
Wu
QJ
,
Vogtmann
E
,
Lin
B
,
Wang
YL
.
Age at menarche and risk of ovarian cancer: a meta-analysis of epidemiological studies
.
Int J Cancer
2013
;
132
:
2894
900
.
16.
Harris
HR
,
Babic
A
,
Webb
PM
,
Nagle
CM
,
Jordan
SJ
,
Risch
HA
, et al
.
Polycystic ovary syndrome, oligomenorrhea, and risk of ovarian cancer histotypes: evidence from the ovarian cancer association consortium
.
Cancer Epidemiol Biomarkers Prev
2018
;
27
:
174
82
.
17.
Harris
HR
,
Titus
LJ
,
Cramer
DW
,
Terry
KL
.
Long and irregular menstrual cycles, polycystic ovary syndrome, and ovarian cancer risk in a population-based case-control study
.
Int J Cancer
2017
;
140
:
285
91
.
18.
Titus-Ernstoff
L
,
Perez
K
,
Cramer
DW
,
Harlow
BL
,
Baron
JA
,
Greenberg
ER
.
Menstrual and reproductive factors in relation to ovarian cancer risk
.
Br J Cancer
2001
;
84
:
714
21
.
19.
John
EM
,
Whittemore
AS
,
Harris
R
,
Itnyre
J
.
Characteristics relating to ovarian cancer risk: collaborative analysis of seven U.S. case-control studies. Epithelial ovarian cancer in black women. Collaborative Ovarian Cancer Group
.
J Natl Cancer Inst
1993
;
85
:
142
7
.
20.
Chumlea
WC
,
Schubert
CM
,
Roche
AF
,
Kulin
HE
,
Lee
PA
,
Himes
JH
, et al
.
Age at menarche and racial comparisons in US girls
.
Pediatrics
2003
;
111
:
110
3
.
21.
Freedman
DS
,
Khan
LK
,
Serdula
MK
,
Dietz
WH
,
Srinivasan
SR
,
Berenson
GS
.
Relation of age at menarche to race, time period, and anthropometric dimensions: the Bogalusa Heart Study
.
Pediatrics
2002
;
110
:
e43
.
22.
Schildkraut
JM
,
Alberg
AJ
,
Bandera
EV
,
Barnholtz-Sloan
J
,
Bondy
M
,
Cote
ML
, et al
.
A multi-center population-based case-control study of ovarian cancer in African-American women: the African American Cancer Epidemiology Study (AACES)
.
BMC Cancer
2014
;
14
:
688
.
23.
Peterson
CE
,
Rauscher
GH
,
Johnson
TP
,
Kirschner
CV
,
Freels
S
,
Barrett
RE
, et al
.
The effect of neighborhood disadvantage on the racial disparity in ovarian cancer-specific survival in a large hospital-based study in cook county, Illinois
.
Front Public Health
2015
;
3
:
8
.
24.
Wu
AH
,
Pearce
CL
,
Tseng
CC
,
Templeman
C
,
Pike
MC
.
Markers of inflammation and risk of ovarian cancer in Los Angeles county
.
Int J Cancer
2009
;
124
:
1409
15
.
25.
Schildkraut
JM
,
Moorman
PG
,
Halabi
S
,
Calingaert
B
,
Marks
JR
,
Berchuck
A
.
Analgesic drug use and risk of ovarian cancer
.
Epidemiology
2006
;
17
:
104
7
.
26.
Rosenberg
L
,
Adams-Campbell
L
,
Palmer
JR
.
The black women's health study: a follow-up study for causes and preventions of illness
.
J Am Med Womens Assoc (1972)
1995
;
50
:
56
8
.
27.
Kolonel
LN
,
Henderson
BE
,
Hankin
JH
,
Nomura
AM
,
Wilkens
LR
,
Pike
MC
, et al
.
A multiethnic cohort in Hawaii and Los Angeles: baseline characteristics
.
Am J Epidemiol
2000
;
151
:
346
57
.
28.
Hays
J
,
Hunt
JR
,
Hubbell
FA
,
Anderson
GL
,
Limacher
M
,
Allen
C
, et al
.
The women's health initiative recruitment methods and results
.
Ann Epidemiol
2003
;
13
(
9 Suppl
):
S18
77
.
29.
Schildkraut
JM
,
Peres
LC
,
Bethea
TN
,
Camacho
F
,
Chyn
D
,
Cloyd
EK
, et al
.
Ovarian Cancer in Women of African Ancestry (OCWAA) consortium: a resource of harmonized data from eight epidemiologic studies of African American and white women
.
Cancer Causes Control
2019
;
30
:
967
78
.
30.
American Cancer Society
.
Cancer facts & figures 2018 special section: ovarian cancer
.
Atlanta: American Cancer Society;
2018
.
31.
Peres
LC
,
Cushing-Haugen
KL
,
Kobel
M
,
Harris
HR
,
Berchuck
A
,
Rossing
MA
, et al
.
Invasive epithelial ovarian cancer survival by histotype and disease stage
.
J Natl Cancer Inst
2019
;
111
:
60
8
.
32.
Gilks
CB
,
Prat
J
.
Ovarian carcinoma pathology and genetics: recent advances
.
Hum Pathol
2009
;
40
:
1213
23
.
33.
Kurman
RJ
,
Carcangiu
ML
,
Herrington
CS
, et al
.
WHO classification of tumours of female reproductive organs
.
Lyon, France: International Agency for Research on Cancer;
2014
.
34.
Liu
H
,
Merck Research Labs. Cochran-Armitage trend test using SAS
.
PharmaSUG Conference
;
2007
.
35.
Marquez
RT
,
Baggerly
KA
,
Patterson
AP
,
Liu
J
,
Broaddus
R
,
Frumovitz
M
, et al
.
Patterns of gene expression in different histotypes of epithelial ovarian cancer correlate with those in normal fallopian tube, endometrium, and colon
.
Clin Cancer Res
2005
;
11
:
6116
26
.
36.
Dixon-Suen
SC
,
Nagle
CM
,
Thrift
AP
,
Pharoah
PDP
,
Ewing
A
,
Pearce
CL
, et al
.
Adult height is associated with increased risk of ovarian cancer: a Mendelian randomisation study
.
Br J Cancer
2018
;
118
:
1123
9
.
37.
Onland-Moret
NC
,
Peeters
PH
,
van Gils
CH
,
Clavel-Chapelon
F
,
Key
T
,
Tjonneland
A
, et al
.
Age at menarche in relation to adult height: the EPIC study
.
Am J Epidemiol
2005
;
162
:
623
32
.
38.
Koprowski
C
,
Ross
RK
,
Mack
WJ
,
Henderson
BE
,
Bernstein
L
.
Diet, body size and menarche in a multiethnic cohort
.
Br J Cancer
1999
;
79
:
1907
11
.
39.
Moorman
PG
,
Calingaert
B
,
Palmieri
RT
,
Iversen
ES
,
Bentley
RC
,
Halabi
S
, et al
.
Hormonal risk factors for ovarian cancer in premenopausal and postmenopausal women
.
Am J Epidemiol
2008
;
167
:
1059
69
.
40.
Wentzensen
N
,
Poole
EM
,
Trabert
B
,
White
E
,
Arslan
AA
,
Patel
AV
, et al
.
Ovarian cancer risk factors by histologic subtype: an analysis from the ovarian cancer cohort consortium
.
J Clin Oncol
2016
;
34
:
2888
98
.
41.
Cirillo
PM
,
Wang
ET
,
Cedars
MI
,
Chen
LM
,
Cohn
BA
.
Irregular menses predicts ovarian cancer: prospective evidence from the child health and development studies
.
Int J Cancer
2016
;
139
:
1009
17
.
42.
Bratke
H
,
Bruserud
IS
,
Brannsether
B
,
Assmus
J
,
Bjerknes
R
,
Roelants
M
, et al
.
Timing of menarche in Norwegian girls: associations with body mass index, waist circumference and skinfold thickness
.
BMC Pediatr
2017
;
17
:
138
.
43.
Meyer
F
,
Moisan
J
,
Marcoux
D
,
Bouchard
C
.
Dietary and physical determinants of menarche
.
Epidemiology
1990
;
1
:
377
81
.
44.
Maclure
M
,
Travis
LB
,
Willett
W
,
MacMahon
B
.
A prospective cohort study of nutrient intake and age at menarche
.
Am J Clin Nutr
1991
;
54
:
649
56
.
45.
Merzenich
H
,
Boeing
H
,
Wahrendorf
J
.
Dietary fat and sports activity as determinants for age at menarche
.
Am J Epidemiol
1993
;
138
:
217
24
.
46.
Anderson
SE
,
Dallal
GE
,
Must
A
.
Relative weight and race influence average age at menarche: results from two nationally representative surveys of US girls studied 25 years apart
.
Pediatrics
2003
;
111
(
4 Pt 1
):
844
50
.
47.
Nichols
HB
,
Trentham-Dietz
A
,
Hampton
JM
,
Titus-Ernstoff
L
,
Egan
KM
,
Willett
WC
, et al
.
From menarche to menopause: trends among US Women born from 1912 to 1969
.
Am J Epidemiol
2006
;
164
:
1003
11
.
48.
Fathalla
MF
.
Incessant ovulation—a factor in ovarian neoplasia?
Lancet
1971
;
2
:
163
.
49.
Vihko
R
,
Apter
D
.
Endocrine characteristics of adolescent menstrual cycles: impact of early menarche
.
J Steroid Biochem
1984
;
20
:
231
6
.
50.
Collaborative Group on Epidemiological Studies of Ovarian Cancer
,
Beral
V
,
Gaitskell
K
,
Hermon
C
,
Moser
K
,
Reeves
G
, et al
.
Menopausal hormone use and ovarian cancer risk: individual participant meta-analysis of 52 epidemiological studies
.
Lancet
2015
;
385
:
1835
42
.
51.
Gharwan
H
,
Bunch
KP
,
Annunziata
CM
.
The role of reproductive hormones in epithelial ovarian carcinogenesis
.
Endocr Relat Cancer
2015
;
22
:
R339
63
.
52.
Helzlsouer
KJ
,
Alberg
AJ
,
Gordon
GB
,
Longcope
C
,
Bush
TL
,
Hoffman
SC
, et al
.
Serum gonadotropins and steroid hormones and the development of ovarian cancer
.
JAMA
1995
;
274
:
1926
30
.
53.
Lukanova
A
,
Lundin
E
,
Akhmedkhanov
A
,
Micheli
A
,
Rinaldi
S
,
Zeleniuch-Jacquotte
A
, et al
.
Circulating levels of sex steroid hormones and risk of ovarian cancer
.
Int J Cancer
2003
;
104
:
636
42
.
54.
Schock
H
,
Surcel
HM
,
Zeleniuch-Jacquotte
A
,
Grankvist
K
,
Lakso
HA
,
Fortner
RT
, et al
.
Early pregnancy sex steroids and maternal risk of epithelial ovarian cancer
.
Endocr Relat Cancer
2014
;
21
:
831
44
.
55.
Tung
KH
,
Wilkens
LR
,
Wu
AH
,
McDuffie
K
,
Nomura
AM
,
Kolonel
LN
, et al
.
Effect of anovulation factors on pre- and postmenopausal ovarian cancer risk: revisiting the incessant ovulation hypothesis
.
Am J Epidemiol
2005
;
161
:
321
9
.
56.
Collaborative Group on Epidemiological Studies of Ovarian Cancer
.
Ovarian cancer and body size: individual participant meta-analysis including 25,157 women with ovarian cancer from 47 epidemiological studies
.
PLoS Med
2012
;
9
:
e1001200
.
57.
Olsen
CM
,
Nagle
CM
,
Whiteman
DC
,
Ness
R
,
Pearce
CL
,
Pike
MC
, et al
.
Obesity and risk of ovarian cancer subtypes: evidence from the Ovarian Cancer Association Consortium
.
Endocr Relat Cancer
2013
;
20
:
251
62
.
58.
Bandera
EV
,
Camacho
F
,
Chyn
D
,
Cloyd
EK
,
Bethea
TN
,
Beeghly-Fadiel
A
., et al
.
Racial disparities in body mass index and ovarian cancer risk in the OCWAA Consortium
[abstract]. In:
Proceedings of the Twelfth AACR Conference on the Science of Cancer Health Disparities in Racial/Ethnic Minorities and the Medically Underserved
;
2019 Sep 20–23
;
San Francisco, CA. Philadelphia (PA)
:
AACR
;
Cancer Epidemiol Biomarkers Prev 2020;29(6 Suppl_2):Abstract nr C070
.
59.
McKnight
KK
,
Wellons
MF
,
Sites
CK
,
Roth
DL
,
Szychowski
JM
,
Halanych
JH
, et al
.
Racial and regional differences in age at menopause in the United States: findings from the REasons for geographic and racial differences in stroke (REGARDS) study
.
Am J Obstet Gynecol
2011
;
205
:
353
.
60.
Bromberger
JT
,
Matthews
KA
,
Kuller
LH
,
Wing
RR
,
Meilahn
EN
,
Plantinga
P
.
Prospective study of the determinants of age at menopause
.
Am J Epidemiol
1997
;
145
:
124
33
.
61.
Gold
EB
.
The timing of the age at which natural menopause occurs
.
Obstet Gynecol Clin North Am
2011
;
38
:
425
40
.
62.
Gold
EB
,
Bromberger
J
,
Crawford
S
,
Samuels
S
,
Greendale
GA
,
Harlow
SD
, et al
.
Factors associated with age at natural menopause in a multiethnic sample of midlife women
.
Am J Epidemiol
2001
;
153
:
865
74
.
63.
Martinez
GM
.
Trends and patterns in menarche in the United States: 1995 through 2013–2017
.
Natl Health Stat Report
2020
:
1
12
.

Supplementary data