Lifetime ovulatory years and risk of epithelial ovarian cancer: a multinational pooled analysis

Abstract

Background

The role of ovulation in epithelial ovarian cancer (EOC) is supported by the consistent protective effects of parity and oral contraceptive use. Whether these factors protect through anovulation alone remains unclear. We explored the association between lifetime ovulatory years (LOY) and EOC.

Methods

LOY was calculated using 12 algorithms. Odds ratios (ORs) and 95% confidence intervals (CIs) estimated the association between LOY or LOY components and EOC among 26 204 control participants and 21 267 case patients from 25 studies. To assess whether LOY components act through ovulation suppression alone, we compared beta coefficients obtained from regression models with expected estimates assuming 1 year of ovulation suppression has the same effect regardless of source.

Results

LOY was associated with increased EOC risk (OR per year increase = 1.014, 95% CI = 1.009 to 1.020 to OR per year increase = 1.044, 95% CI = 1.041 to 1.048). Individual LOY components, except age at menarche, also associated with EOC. The estimated model coefficient for oral contraceptive use and pregnancies were 4.45 times and 12- to 15-fold greater than expected, respectively. LOY was associated with high-grade serous, low-grade serous, endometrioid, and clear cell histotypes (ORs per year increase = 1.054, 1.040, 1.065, and 1.098, respectively) but not mucinous tumors. Estimated coefficients of LOY components were close to expected estimates for high-grade serous but larger than expected for low-grade serous, endometrioid, and clear cell histotypes.

Conclusions

LOY is positively associated with nonmucinous EOC. Differences between estimated and expected model coefficients for LOY components suggest factors beyond ovulation underlie the associations between LOY components and EOC in general and for non-HGSOC.

Epithelial ovarian cancer (EOC) is the most lethal gynecologic malignancy. The consistent protective effects of oral contraceptives (OC) (1-3), bearing children (3,4), and breastfeeding (5), which all suppress ovulation, suggest that ovulation may play a key role in disease origin (6). In support of this hypothesis, lifetime ovulatory years (LOY) have been associated with increased EOC risk (2,7-14). However, differences in how studies define LOY and categorize exposure make it challenging to quantify the LOY-EOC relationship (15). Moreover, it remains unclear whether the mechanism whereby LOY components exert their impacts is through ovulation suppression alone or other means (7).

Although EOC is considered a set of diseases defined by histologic subtypes (histotypes), the relationship between LOY and EOC histotypes remains understudied. Although LOY might be associated with specific EOC subtypes (2,10-14), no individual study has had a large enough sample size to undertake a detailed histotype-specific analysis to evaluate the actual vs expected effects of individual LOY components to assess whether the mechanism of action of these components is solely by ovulation suppression.

To investigate the effects of LOY and its components on EOC, we pooled data from 25 case-control studies from the Ovarian Cancer Association Consortium (OCAC). Our goals were to 1) quantify the LOY-EOC association overall and for individual histotypes, 2) assess the impact of LOY definition on the LOY-EOC relationship, and 3) determine whether the relationship between LOY components and EOC is beyond ovulation suppression.

Methods

Study population

This study included 25 case-control studies (see Table 1) (16-42) from OCAC (43). Participants provided informed consent for original studies, whose protocols were approved by their respective institutional review boards.

Table 1.

Characteristics of the 25 case-control studies from the Ovarian Cancer Association Consortium, conducted in Asia, Australia, Europe, and North America from 1989 to present and included in the lifetime ovulatory years (LOY) analyses

Study	Region	Study name	Study period	Case type	Method of data collection	Age, mean (SD), y	Control participants, No. (%)	Case patients, No. (%)
AUS (16)	Australia	Australian Ovarian Cancer Study/Australian Cancer Study	2002-2006	Population-based	Self-completed questionnaire	56.88 (12.28)	1506 (43.2)	1984 (56.8)
BAV (17)	Germany	Bavarian Ovarian Cancer Cases and Controls	2002-2006	Hospital or Clinic-based	Interview	57.31 (13.77)	629 (47.9)	684 (52.1)
CON (18)	USA	Connecticut Ovarian Cancer Study	1998-2003	Population-based	Interview	55.27 (11.04)	551 (52.6)	497 (47.4)
DOV (19)	USA	Diseases of the Ovary and their Evaluation	2002-2009	Population-based	Interview	55.78 (9.26)	1849 (54.2)	1562 (45.8)
GER (20)	Germany	German Ovarian Cancer Study	1993-1996	Population-based	Self-completed questionnaire	55.07 (12.24)	533 (67.4)	258 (32.6)
HAW (21)	USA	Hawaii Ovarian Cancer Case-Control Study	1993-2008	Population-based	Interview	54.98 (14.28)	1103 (55.2)	895 (44.8)
HOP (22)	USA	Hormones and Ovarian cancer PrEdiction	2003-2009	Population-based	Interview	58.66 (12.52)	1802 (68.3)	836 (31.7)
JPN (23)	Japan	Hospital-based Research Program at Aichi Cancer Center	2001-2005	Hospital or Clinic-based	Interview	52.36 (11.17)	233 (60.5)	152 (39.5)
MAY (24)	USA	Mayo Clinic Ovarian Cancer Case-Control Study	1999-2018	Hospital or Clinic-based	Interview	60.51 (13.58)	2299 (55.5)	1846 (44.5)
MCC (25)a	Australia	Melbourne Collaborative Cohort Study	1990-2008	Defined cohort	Self-completed questionnaire	64.07 (9.62)	471 (73.1)	173 (26.9)
NCO (26)	USA	North Carolina Ovarian Cancer Study	1999-2008	Population-based	Interview	55.28 (11.53)	1085 (47.6)	1195 (42.4)
NEC (27)	USA	New England Case Control Study	1992-2003	Population-based	Interview	53.54 (12.35)	2100 (50.0)	2075 (49.7)
NJO (28)	USA	New Jersey Ovarian Cancer Study	2002-2008	Population-based	Interview	61.48 (11.60)	458 (65.9)	237 (34.1)
NTH (29,30)	Netherlands	Nijmegen Ovarian Cancer Study	1997-2008	Population-based	Self-completed questionnaire	55.90 (10.79)	600 (69.4)	265 (30.6)
OVA	Canada	Ovarian Cancer in Alberta and British Columbia	2002-2012	Population-based	Self-completed questionnaire 2002-2004; interview 2004-2012	56.81 (10.62)	2698 (62.2)	1637 (37.8)
POL (31)	Poland	Polish Ovarian Cancer Case Control Study	2000-2003	Population-based	Interview	55.70 (10.62)	1128 (79.3)	294 (20.7)
SON (32)	Canada	Southern Ontario Ovarian Cancer Study	1989-1993	Population-based	Interview	56.86 (11.97)	564 (55.6)	450 (44.4)
STA (33)	USA	Family Registry for Ovarian Cancer AND Genetic Epidemiology of Ovarian Cancer	1997-2001	Population-based	Interview	47.77 (10.07)	567 (46.0)	665 (54.0)
SWH (34)	China	Shanghai Women’s Health Study	1996-present	Defined cohort	Interview	53.36 (9.70)	986 (86.6)	152 (13.4)
TBO (35)	USA	Tampa Bay Ovarian Cancer Study	2000-present	Population-based	Interview	60.53 (10.85)	205 (41.8)	285 (58.2)
TOR (36)	Canada	Familial Ovarian Tumour Study (FOTS) AND Health Watch (HW)	1995-1999 and 2000-2003	Population-based	Interview	56.62 (12.77)	322 (21.6)	1167 (78.4)
UCI (37)	USA	University California Irvine Ovarian Study	1993-2005	Population-based	Interview	54.29 (13.17)	614 (49.1)	636 (50.9)
UKO (38)	UK	United Kingdom Ovarian cancer Population Study	2006-2010	Hospital or Clinic-based	Interview	63.06 (8.93)	1182 (58.5)	839 (41.5)
USC (39-41)	USA	Los Angeles County Case-Control Studies of Ovarian Cancer	1992-2009	Population-based	Interview	55.07 (12.41)	2595 (52.2)	2380 (47.8)
VTL (42)a	USA	VITamins And Lifestyle Cohort Study	2000-2010	Defined cohort	Self-completed questionnaire	68.19 (7.62)	124 (54.6)	103 (45.4)
Total						56.55 (12.20)	26204 (55.2)	21267 (44.8)

^aEmployed a nested-case control study design within a cohort study.

Study variables and LOY calculation

OCAC’s harmonized core data provided LOY component variables: age at last menstrual period (LMP) before diagnosis (case participants) or interview (control participants); age at menarche; number of pregnancies; number of full-term births; and total durations of pregnancy, breastfeeding, and OC use.

LOY was calculated with 12 algorithms (Supplementary Table 1, available online) (8) using the formula: where “menstrual span” was calculated from age at LMP minus age at menarche. The algorithms were divided into 4 classes based on how “years of anovulation” was defined (see Figure 1).

Figure 1.

Flowchart for algorithms to calculate lifetime ovulatory years. OC = oral contraceptive.

$$\mathrm{LOY}=\mathrm{menstrual}\mathrm{ }\mathrm{span}\mathrm{ }–\mathrm{ }\mathrm{years}\mathrm{ }\mathrm{of}\mathrm{ }\mathrm{anovulation},$$

Seven studies recorded age at LMP (cases = 6881 [32.4% of total]; controls = 8316 [31.7% of total]). For the remaining studies, we imputed age at LMP (see Figure 2) (44) and assessed the imputation algorithm by comparing actual vs imputed age at LMP for the 7 sites (Supplementary Table 2, available online). Sites with 50% or more missing values in any LOY component except age at LMP were excluded from algorithms using those components (Supplementary Table 3, available online) (45,46).

Figure 2.

Flowchart for imputation of age at last menstrual period (LMP). HRT = hormone replacement therapy.

Variables considered a priori as potential confounders included age at diagnosis (cases) or interview (controls), self-reported race (Asian, Black, Other, Unknown, White, where Other was defined by each participating study as not Asian, Black, or White), education, body mass index (BMI) 1 year to 5 years prior, family history of ovarian or breast cancer in a first-degree relative, smoking status, history of endometriosis, and tubal ligation.

Statistical analyses

Assessment of study heterogeneity

We used random effects meta-analysis to assess interstudy LOY-EOC heterogeneity. Because we observed no substantive heterogeneity (Supplementary Figure 1, available online), we used the pooled data set adjusted for study site for all analyses.

Correlations between LOY values among algorithms and between LOY and LOY components

We used Pearson correlation to assess pairwise correlations of LOY calculated among algorithms limiting analyses to observations with complete data for each algorithm in the pairwise comparison. Pearson correlation was also used to assess the correlations of individual components with LOY calculated by each algorithm.

Estimation of LOY-EOC association

Multivariable logistic regression was used to estimate odds ratios (ORs) with 95% confidence intervals (CIs) for the association between LOY and EOC overall and by histotype. Models were adjusted for study site, age at diagnosis or interview, race, education, BMI, smoking status, and family history. Inclusion of tubal ligation and endometriosis in models did not alter any findings; thus, tubal ligation and endometriosis were omitted from final models. Because OCAC only recorded total months of breastfeeding across all live births and not months per breastfeeding episode, to account for return of ovulation once food is introduced typically at 6 months, we performed sensitivity analyses replacing breastfeeding duration with either 1) number of live births times the average duration of breastfeeding per live birth if the average duration was less than 6 months or 2) number of live births times 6 months if the average duration was 6 months or greater. Similar sensitivity analyses were performed for algorithms containing a term for breastfeeding duration (algorithms I-L). Sensitivity analyses were performed with multiple imputation by chained equation to assess the effect of missing values on LOY-EOC associations (47) including the same covariates as main models. Nested imputations were done for number of pregnancies, number of full-term births, duration of breastfeeding, and duration of OC use using the binary variables of ever pregnant, ever breastfed, and OC use, respectively. Imputations were done 5 times with auxiliary variables defined as Pearson correlation larger than 0.4 (48). Sensitivity analyses also examined limiting models to population-based studies and using only observations with complete data for all variables.

To assess the relationship between LOY and EOC histotypes, we present results using algorithm K because this algorithm most closely reflects lifetime ovulatory years accounting for OC use, pregnancy type, and breastfeeding.

Prior studies suggest that the relationship between LOY and EOC may not be linear (49); thus, we constructed models using LOY and log(LOY). Because log(LOY) did not improve model fit when included with LOY and models with LOY alone provided a better fit than those with log(LOY) alone, we report only analyses using LOY.

Estimation of EOC risk related to LOY components: observed vs expected estimates

The association of each LOY component and EOC risk overall and separately for each histotype was estimated using multivariable logistic regression adjusted for study site, age at diagnosis (case patients) or interview (control participants), race, education, BMI 1 to 5 years prior to diagnosis (case patients) or interview (control participants), smoking status, family history, and other LOY components.

To assess whether each component acts through ovulation suppression alone, we compared expected beta coefficient with actual estimates obtained from regression models (7). Based on the incessant ovulation hypothesis, 1 year of ovulation suppression should have the same effect on the log odds of EOC regardless of origin. Thus, if we assign one as the expected beta coefficient for age at LMP per year (indicating that a 1-year increase in LMP, which would increase LOY by 1, would increase the log odds by 1), then the expected beta coefficient for age at menarche per year would be −1 because each additional year increase would decrease LOY by 1 year and hence decrease the log odds by 1. Similarly, the expected beta coefficients for OC use per year, number of incomplete pregnancies (assumed to be 3 months or 0.25 years), number of full-term births (assumed to be 9 months or 0.75 years), and breastfeeding per year would be −1, −0.25, −0.75, and −1, respectively.

We then computed the relative coefficients, defined as the actual coefficients from regression models divided by the actual coefficient of age at LMP. This set the relative coefficient for age at LMP to 1, just as in the expected model. This enabled us to compare the relative coefficient estimates with their expected counterparts. To assess the statistical significance of individual components, χ² statistics and P values were obtained from the likelihood-ratio test for the removal of each component from the full model. Sensitivity analyses examined limiting models to population-based studies and using only observations with complete data for all variables.

All statistical tests were 2-sided and performed in Stata/SE version 16.1 (StataCorp, College Station, TX, USA).

Results

Study population

Among the 25 studies, there were 26 204 control participants and 21 267 case patients (Table 2). Compared with controls, cases were more likely to have a family history of breast or ovarian cancer and a history of endometriosis, be hysterectomized, and be obese or overweight. Controls were more likely to have never smoked, be premenopausal, and have had a tubal ligation. Cases reported a shorter total duration of OC use and breastfeeding and fewer total pregnancies.

Table 2.

Characteristics of ovarian cancer cases and controls included in the lifetime ovulatory years (LOY) analyses

Variables	Control participants,	Case patients,
	(n = 26 204)	(n = 21 267)
Age, mean (SD), y	56.51 (12.06)	56.59 (12.36)
Race, No. (%)a
Asian	2019 (7.7)	1227 (5.8)
Black	566 (2.2)	460 (2.2)
Other	775 (3.0)	692 (3.3)
Unknown	258 (1.0)	203 (1.0)
White	22 586 (86.2)	18 685 (87.9)
Education, No. (%)
Less than high school	2857 (10.9)	2512 (11.8)
Completed high school	6508 (24.8)	5309 (25.0)
Completed some college	5573 (21.3)	4849 (22.8)
Completed college or university bachelor degree	4727 (18.0)	3344 (15.7)
Completed graduate or professorial degree	3139 (12.0)	2271 (10.7)
Unknown	3400 (13.0)	2982 (14.0)
BMI at age 18 y, No. (%)
<18.5 kg/m2	2637 (10.1)	2008 (9.4)
18.5-24.9 kg/m2	10 697 (40.8)	8809 (41.4)
25-29.9 kg/m2	992 (3.8)	1002 (4.7)
≥30 kg/m2	310 (1.2)	353 (1.7)
Unknown	11 568 (44.2)	9095 (42.8)
BMI 1 or 5 years prior, No. (%)
<18.5 kg/m2	286 (1.1)	274 (1.3)
18.5-24.9 kg/m2	7472 (28.5)	5672 (26.7)
25-29.9 kg/m2	4541 (17.3)	3570 (16.8)
≥30 kg/m2	3074 (11.7)	3021 (14.2)
Unknown	10 831 (41.3)	8730 (41.1)
Smoking status, No. (%)
Never smoker	13 311 (50.8)	10 106 (47.5)
Former smoker	2900 (11.1)	2682 (12.6)
Current smoker	7449 (28.4)	5930 (27.9)
Unknown	2544 (9.7)	2549 (12.0)
Family history of breast or ovarian cancer in first-degree relative, No. (%)
No	16 038 (61.2)	11 574 (54.4)
Yes	1569 (6.0)	1808 (8.5)
Unknown	8597 (32.8)	7885 (37.1)
Tubal ligation, No. (%)
No	16 351 (62.4)	15 035 (70.7)
Yes	5138 (19.6)	3345 (15.7)
Unknown	4715 (18.0)	2887 (13.6)
Menopausal status, No. (%)
Pre/perimenopausal	8206 (31.3)	5775 (27.2)
Postmenopausal	16 749 (63.9)	14 422 (67.8)
Unknown	1249 (4.8)	1070 (5.0)
Endometriosis, No. (%)
No	18 294 (69.8)	15 128 (71.1)
Yes	1291 (4.9)	1615 (7.6)
Unknown	6619 (25.3)	4524 (21.3)
Hysterectomy prediagnosis (cases) or interview (controls), No. (%)
No	20 969 (80.0)	14 562 (68.5)
Yes	4004 (15.3)	5008 (23.6)
Unknown	1231 (4.7)	1697 (8.0)
Hormone replacement therapy, No. (%)
No	15 547 (59.3)	13 097 (61.6)
Yes	7472 (28.5)	5921 (27.8)
Unknown	3185 (12.2)	2249 (10.6)
Components of lifetime ovulatory years
Age at last menstrual period before diagnosis or interview, No. (%)	26 204 (100.0)	21 267 (100.0)
Mean (SD), y	48.77 (6.03)	48.84 (6.4)
Age at menarche, No. (%)	25 255 (96.4)	20 101 (94.5)
Mean (SD), y	12.91 (1.7)	12.79 (1.6)
Duration of oral contraceptive use, No. (%)	24 948 (95.2)	19 762 (92.9)
Mean (SD), mo	52.12 (71.3)	37.42 (59.3)
No. of pregnancies, regardless of outcome, No. (%)	25 429 (97.0)	20 429 (96.1)
Mean (SD)	2.75 (1.8)	2.40 (1.9)
Total number of months of being pregnant, regardless of outcome(s), No. (%)	14 438 (55.1)	12 195 (57.3)
Mean No. (SD)	21.42 (22.3)	16.39 (17.6)
Total number of full-term births, No. (%)	22 835 (87.1)	18 304 (86.1)
Mean No. (SD)	2.13 (1.5)	1.85 (1.6)
Total months of breastfeeding, No. (%)	18 578 (70.1)	13 619 (64.0)
Mean (SD), mo	9.52 (14.4)	6.86 (13.1)
Behavior and histotypes, No. (%)
Invasive	—	17 465 (82.1)
High-grade serous	—	7492 (71.8)
Low-grade serous	—	513 (4.9)
Serous (unknown grade)	—	2418 (23.2)
Endometrioid	—	2536 (14.5)
Mucinous	—	1134 (6.5)
Clear cell	—	1310 (7.5)
Mixed	—	566 (3.2)
Others	—	1496 (8.6)
Low malignant potential (borderline tumors)	—	3602 (16.9)
Unknown behavior	—	200 (0.9)

^aRace was self-reported by participants and provided to the OCAC Core. The category “other” refers to lack of self-identification as Asian, Black, or White. BMI = body mass index; OCAC = Ovarian Cancer Association Consortium.

LOY estimations and correlations

Among the 12 algorithms, median LOY ranged from 31.67 (interquartile range [IQR] = 25.50-35.20) to 35.75 (IQR = 32.50-37.50) years (Figure 3; Supplementary Table 4, available online). Pairwise LOY correlations ranged from 0.75 between the algorithms in the first class (inclusive of pregnancies only) and the third class (inclusive of pregnancies, OC use, and breastfeeding) to at least 0.99 for correlations within the same class (Supplementary Table 5, available online). Correlations between individual components and LOY are presented in Supplementary Table 6 (available online). As algorithm complexity increased, correlations between age at LMP and LOY decreased. OC duration was moderately negatively correlated with LOY (rho range: −0.68 to −0.69); correlations between the other components and LOY were low.

Figure 3.

Distribution of lifetime ovulatory years calculated from 12 different algorithms.

Estimation of LOY-EOC association

Odds ratios for LOY per year increase across the 12 algorithms ranged from 1.014 (95% CI = 1.009 to 1.020) to 1.044 (95% CI = 1.041 to 1.048) (Table 3). Associations with LOY calculated from the third class of algorithms (inclusive of pregnancies, OC use, and breastfeeding) were not changed when months of breastfeeding were truncated at 6 for participants reporting more than 6 months per birth (data not shown). LOY associations remain unchanged when adjusting models in the first class of algorithms (which included only pregnancies) for OC and breastfeeding duration, as well as when adjusting the second class of algorithms (which included pregnancies and OC duration) for breastfeeding duration (data not shown). Sensitivity analyses with multiple imputations of missing values did not alter LOY-EOC associations (Table 3). Sensitivity analyses limited to population-based studies and those limited to observations with complete data also did not alter the LOY-EOC association (data not shown).

Table 3.

Odds ratio for ovarian cancer per lifetime ovulatory year using complete data and full data with imputation

Lifetime ovulatory years algorithm	Main analysesa (complete data only)			Sensitivity analysesa (includes imputed data)
	Control participants	Case patients	Odds ratiob (95% CI)	Odds ratiob (95% CI)
First class of algorithms—anovulation due to pregnancy
Algorithm A	25 081	20 046	1.018 (1.013 to 1.022)	1.015 (1.011 to 1.020)
Algorithm B	22 519	18 013	1.014 (1.009 to 1.020)	1.012 (1.007 to 1.017)
Algorithm C	22 509	18 003	1.016 (1.011 to 1.021)	1.014 (1.009 to 1.019)
Algorithm Dc	13 617	10 689	1.016 (1.010 to 1.023)	1.009 (1.003 to 1.016)
Second class of algorithms—anovulation due to pregnancy and OC use
Algorithm E	24 480	19 323	1.044 (1.041 to 1.048)	1.043 (1.039 to 1.046)
Algorithm Fd	22 316	17 772	1.043 (1.039 to 1.046)	1.042 (1.039 to 1.046)
Algorithm Gd	22 306	17 762	1.043 (1.040 to 1.047)	1.043 (1.039 to 1.047)
Algorithm Hc,d	13 515	10 576	1.043 (1.039 to 1.048)	1.041 (1.036 to 1.045)
Third class of algorithms—anovulation due to pregnancy, OC use, and breastfeeding
Algorithm Ie	14 900	11 829	1.041 (1.036 to 1.045)	1.047 (1.043 to 1.051)
Algorithm Jf	14 902	11 339	1.041 (1.036 to 1.045)	1.046 (1.042 to 1.050)
Algorithm Kf	14 900	11 329	1.041 (1.036 to 1.046)	1.046 (1.042 to 1.050)
Algorithm L	8473	6498	1.040 (1.034 to 1.046)	1.047 (1.042 to 1.052)

^aMain analyses included participants without missing values in any component for LOY calculation; sensitivity analyses included all participants with imputation. CI = confidence interval; MCC = Melbourne Collaborative Cohort Study; NTC = Nijmegen Ovarian Cancer Study; OC = oral contraceptive; TBO = Tampa Bay Ovarian Cancer Study.

^bAdjusted for study site, age, self-reported race (Asian, Black, Other [as defined by participants as not being Asian, Black, or White], Unknown, White), education (less than high school, completed high school, completed some college, completed college or university bachelor degree, completed graduate or professorial degree, unknown), body mass index 1 or 5 years prior (underweight, normal, overweight, obese, unknown), smoking status (never, former, current, unknown), and family history (yes, no, unknown).

^cTBO was excluded from the sensitivity analyses because of limited numbers within site to impute missing values.

^dMCC was excluded from the sensitivity analyses because of limited numbers within site to impute missing values.

^eNTH was excluded from the sensitivity analyses because of failure to converge on observed data.

^fNTH was excluded from the sensitivity analyses because of limited numbers within site to impute missing values.

Estimation of EOC risk related to LOY components: observed vs expected estimates

Individual components in LOY, except for age at menarche, were associated with EOC (Table 4). There were substantial deviations between relative estimated coefficients and expected estimates for each component. The estimated coefficient of OC use per year was 4.45 times larger than expected, and estimates for pregnancies were 11- to 15-fold greater than expected regardless of pregnancy type. Estimated coefficient of breastfeeding per year was −13.45, instead of the expected −1. Results were similar when truncating breastfeeding at 6 months per full-term birth, when limiting analyses to population-based studies and when limiting analyses to observations with complete data (data not shown).

Table 4.

Odds ratios, expected beta coefficients, and normalized beta coefficients for ovarian cancer by individual components of lifetime ovulatory years

Lifetime ovulatory years component	OR (95% CI)a	Expected estimate of coefficient	Normalized coefficientb	P for removal of component from model
Age at last menstrual period before diagnosis or interview
Per year	1.011 (1.004 to 1.019)	1 (defined)	1	.004
Age at menarche
Per year	1.002 (0.985 to 1.018)	−1	0.13	.86
Duration of oral contraceptive use, y
Per year	0.950 (0.945 to 0.956)	−1	−4.45	<.001
No. of incomplete pregnancies
Per pregnancy	0.968 (0.944 to 0.992)	−0.25	−2.89	.009
Number of full-term births
Per pregnancy	0.877 (0.857 to 0.897)	−0.75	−11.53	<.001
Total years of breastfeeding
Per year	0.858 (0.816 to 0.901)	−1.0	−13.45	<.001

^aAdjusted for study site, age, self-reported race (Asian, Black, Other [as defined by participants as not being Asian, Black or White], Unknown, White), education (less than high school, completed high school, completed some college, completed college or university bachelor degree, completed graduate or professorial degree, unknown), body mass index 1 or 5 years prior (underweight, normal, overweight, obese, unknown), smoking status (never, former, current, unknown), family history (yes, no, unknown), and other components of lifetime ovulatory cycles in the model. CI = confidence interval; OR = odds ratio.

^bNormalized to the beta coefficient of age at last menstrual period.

Histotype-specific estimation for LOY and individual components: observed vs expected estimates

LOY was associated with invasive high-grade serous (HGSOC; OR per year = 1.054, 95% CI = 1.048 to 1.061), low-grade serous (LGSOC; OR = 1.040, 95% CI = 1.019 to 1.061), endometrioid (OR = 1.065, 95% CI = 1.053 to 1.076), and clear cell (OR = 1.098, 95% CI = 1.079 to 1.117) but not mucinous EOC (OR = 1.006, 95% CI = 0.992 to 1.019) (Table 5). Except for breastfeeding, estimated coefficients of LOY components were close to expected for HGSOC. In contrast, estimated coefficients of individual components, except for age at menarche, were larger than the expected for LGSOC, endometrioid, and clear cell cancers.

Table 5.

Odds ratios, expected beta coefficients, and normalized beta coefficients for ovarian cancer histotypes by individual components of lifetime ovulatory years

Component of lifetime ovulatory years	Expected estimate of coefficient	Low malignant potential		Invasive high-grade serous		Invasive low-grade serous		Invasive endometrioid		Invasive mucinous		Invasive clear cell
		(n = 2014)		(n = 4139)		(n = 282)		(n = 1322)		(n = 602)		(n = 547)
		ORa (95% CI)	β̂b	ORa (95% CI)	β̂b	ORa (95% CI)	β̂b	ORa (95% CI)	β̂b	ORa (95% CI)	β̂c	ORa (95% CI)	β̂b
Lifetime ovulatory yearsc,d
Per year		1.015 (1.007 to 1.024)	—	1.054 (1.048 to 1.061)	—	1.040 (1.019 to 1.061)	—	1.065 (1.053 to 1.076)	—	1.006 (0.992 to 1.019)	—	1.098 (1.079 to 1.117)	—
Age at last menstrual period before diagnosis or interview
Per year	1	0.981 (0.967 to 0.995)	1	1.056 (1.044 to 1.069)	1	1.010 (0.976 to 1.044)	1	1.031 (1.013 to 1.049)	1	0.977 (0.955 to 1.000)	1	1.086 (1.057 to 1.117)	1
Age at menarche
Per year	−1	1.027 (0.995 to 1.059)	−1.360	1.000 (0.977 to 1.023)	−0.008	0.961 (0.891 to 1.037)	−4.157	1.003 (0.967 to 1.041)	0.093	1.076 (1.023 to 1.132)	−3.208	0.948 (0.867 to 1.002)	−0.643
Duration of oral contraceptive use, y
Per year	−1	0.973 (0.962 to 0.983)	1.441	0.948 (0.940 to 0.956)	−0.975	0.953 (0.929 to 0.978)	−5.042	0.928 (0.914 to 0.942)	−2.472	0.973 (0.955 to 0.991)	1.186	0.925 (0.904 to 0.947)	−0.942
No. of incomplete pregnancies
Per pregnancy	−0.25	1.00 (0.96 to 1.04)	0.082	0.99 (0.95 to 1.02)	−0.245	0.89 (0.78 to 1.01)	−12.484	0.93 (0.87 to 0.98)	−2.533	0.94 (0.87 to 1.03)	2.516	0.85 (0.77 to 0.94)	−1.924
Total number of full-term births
Per pregnancy	−0.75	0.82 (0.79 to 0.86)	10.085	0.94 (0.91 to 0.97)	−1.170	0.92 (0.82 to 1.02)	−9.356	0.74 (0.70 to 0.78)	−9.884	0.88 (0.82 to 0.95)	5.417	0.63 (0.57 to 0.69)	−5.583
Total years of breastfeeding
Per year	−1	0.963 (0.867 to 1.069)	1.970	0.827 (0.771 to 0.886)	−3.467	0.808 (0.627 to 1.041)	−22.533	0.830 (0.727 to 0.948)	−6.120	1.010 (0.858 to 1.190)	−0.455	0.893 (0.723 to 1.103)	−1.370

^aAdjusted for study site, age, self-reported race (Asian, Black, Other [as defined by participants as not being Asian, Black or White], Unknown, White), education (less than high school, completed high school, completed some college, completed college or university bachelor degree, completed graduate or professorial degree, unknown), body mass index 1 or 5 years prior (underweight, normal, overweight, obese, unknown), smoking status (never, former, current, unknown), family history (yes, no, unknown), and other components of lifetime ovulatory cycles in the model. β = estimated coefficient; CI = confidence interval; OC = oral contraceptive; OR = odds ratio.

^bNormalized to the beta coefficient of age at last menstrual period.

^cAdjusted for study site, age, self-reported race (Asian, Black, Other [as defined by participants as not being Asian, Black, or White], Unknown, White), education (less than high school, completed high school, completed some college, completed college or university bachelor degree, completed graduate or professorial degree, unknown), body mass index 1 or 5 years prior (underweight, normal, overweight, obese, unknown), smoking status (never, former, current, unknown), and family history (yes, no, unknown).

^dUsing algorithm K with complete data: (age at last menstrual period—age at menarche) – years of OC use—(0.25*number of incomplete pregnancies)—(0.75*number of full-term births) – years of breastfeeding. This algorithm was chosen because it most closely accounts for expected ovulation suppression due to pregnancies, OC use, and breastfeeding.

Discussion

Pooling data from 25 case-control studies, we show a positive association between LOY and EOC, with each year of ovulation associated with a 4% increase in risk. We also found a positive association between LOY and HGSOC, LGSOC, endometrioid, and clear cell EOC but not with mucinous tumors. These LOY-EOC associations were not altered when using different algorithms to compute LOY or when imputing missing data. We further found that LOY components, except age at menarche, were associated with EOC, with the magnitude of these associations varying substantially from expectation if their mechanism of action was solely ovulation suppression. There was also notable heterogeneity in these component-specific findings among EOC histotypes. Together, these data suggest that reproductive factors comprising LOY exert their effects through means beyond ovulation suppression, and those relationships vary by EOC subtype.

Most prior studies report a positive relationship between LOY and EOC (2,7-14,50-63). Differences in LOY definitions among studies make it challenging to compare specific findings across studies. In the present study, we defined LOY from available harmonized data using 12 algorithms. Like the Polish Cancer study (8) (1 of the 25 studies in this analysis), we found a high correlation for LOY among algorithms, although point estimates varied depending on the algorithm. When assessing overall EOC per 1-year increase in LOY, estimates ranged from 1.01 to 1.04, which is similar to estimates reported by the US Nurses’ Health Study (1976-2006) and Nurses’ Health Study II (1989-2005) (OR = 1.07, 95% CI = 1.05 to 1.08) (10). Although it is reassuring that our results are similar to previous work, because each study used different LOY algorithms and units of presentation (eg, quartiles, ovulatory cycles) (15), a direct comparison of estimated magnitudes is not possible. A standardized definition of LOY would facilitate cross-study comparisons and allow for more robust interstudy analyses. Our findings confirm that among algorithms that account for menstrual span, number of pregnancies, total duration of OC use, and total duration of breastfeeding, point estimates for the LOY-EOC relationship are similar. Defining LOY using these factors would facilitate interstudy analyses.

We report differences in the association of LOY with EOC subtypes. We report a positive association between LOY and HGSOC and LGSOC. Whereas previous studies have reported a positive association between LOY and risk of serous tumors (2,10-15), only the Ovarian Cancer Cohort Consortium (OC3) (14) reported results separately for HGSOC, also finding a positive association. Separating serous EOC analyses is important because HGSOC and LGSOC are distinct diseases (64,65). Also consistent with most (10-14) but not all previous studies (2,15), we found positive associations between LOY and clear cell and endometrioid but not mucinous tumors. These results are consistent with epidemiologic evidence that suggests a different risk-factor profile for mucinous EOC (3,66).

Results regarding the associations between LOY components and EOC appeared consistent with previous studies (7,8,10,12,58,62). Beyond considering statistical significance, our study also compared the magnitudes of each component’s effect on EOC risk and found the actual magnitudes varied substantially from expectation (7). Based on the incessant ovulation hypothesis (6), women with the same LOY should have the same estimated risk if ovulation is the only etiologic mechanism underlying the relationship between the components of LOY and EOC. However, consistent with 2 case-control studies (7,62), we show that pregnancy, OC use, and breastfeeding are associated with stronger protective effects than would be expected based on ovulation suppression alone. Moreover, the protection from 1 year of pregnancy, whether complete or incomplete, was substantially greater than that of 1 year of OC use (7). Together, these data imply that mechanisms beyond ovulation suppression, such as hormonal alterations (67,68) or inflammation (69), contribute to the LOY-EOC association. They further imply differences in the mechanisms whereby individual LOY components impact EOC risk, especially for non-HGSOC subtypes, suggesting that a model of EOC risk incorporating just LOY and not its component parts would be insufficient in fully capturing the effects of exposure to LOY components.

Our results indicate heterogeneity in the associations between LOY components and histotype-specific risk. Notably, except for breastfeeding, the estimated coefficients for HGSOC were close to expected if only ovulation suppression underlies the component-HGSOC relationship. This suggests that ovulation may be the primary etiologic mechanism for HGSOC; however, because HGSOC is believed to arise in the fimbriated end of the fallopian tube and not the ovary (70-72), ovulation effects must extend beyond ovarian surface epithelium trauma, as originally proposed by Fathalla (6). Notably, during ovulation, fallopian tube fimbria come in close proximity to the site of ovulation, directly exposing the fimbria to ovarian follicular fluid. In vitro studies show that normal fallopian tube epithelia exposed to follicular fluid aspirates develop TP53 mutations, a hallmark of HGSOC (73). Moreover, follicular fluid has both mutagenic and tumorigenic effects facilitating the full transformation process for developing HGSOC from the fallopian tube (74-77). Thus, follicular fluid may be the link between greater number of ovulations and HGSOC.

In contrast to HGSOC, factors beyond ovulation suppression underlie the link between LOY and other histotypes. For LGSOC, endometrioid and clear cell histotypes, we found that actual coefficient estimates were substantially larger than expected for OC use, pregnancies, and breastfeeding. This suggests that other mechanisms, such as increased progestin exposure (78), may play a role in the protective effects of these factors.

Although we did not find any association between LOY and mucinous EOC, we report associations for several LOY components. Thus, factors other than ovulation may be driving mucinous carcinogenesis. Moreover, the relationship between LOY components and mucinous disease varied from that of other histotypes. Together, these observations suggest that factors underlying the relationship between exposures and EOC vary based on histotype and confirm the unique origin of mucinous cancers (79,80).

The major strength of our work was pooling 25 case-control studies, allowing us to estimate more precisely the LOY-EOC association overall and by histotype. The large data set also enabled comparison of different LOY definitions and their impact on the LOY-EOC relationship. For LOY components, the sample size enabled us to separate the effects of ovulation suppression from other potential etiologic mechanisms. The range of studies from 4 continents and 9 countries supports the generalizability of our findings.

Despite these strengths, there are several limitations. Because all but 2 studies (25,42) employed a retrospective case-control design, recall and selection bias are always a concern. Regardless of study design limitations, our estimates were consistent with previous prospective studies, including the US Nurses’ Health Study and US Nurses’ Health Study II studies (10) and the OC3 pooled analysis of prospective studies (14). We made some assumptions about LOY components that may impact results. If age at LMP was unknown, we imputed it using an algorithm based on average age at menopause by country, age at first hormone replacement therapy use, or age at hysterectomy. We compared the observed and imputed age at LMP from 7 sites, conducted sensitivity analyses using LOY calculated from the imputed value for those sites, and noted no differences in observed associations. To prevent overestimating the duration of anovulation from breastfeeding, we repeated analyses capping women at 6 months of breastfeeding per live birth. Results were unchanged.

In conclusion, increasing LOY is associated with increased EOC risk, as well as the risk of HGSOC, LGSOC, endometrioid, and clear cell histotypes. Although point estimates varied slightly, the association between LOY and EOC was not altered when LOY was calculated in different ways using core components. Our study also indicated heterogeneity in the expected estimated coefficients of each LOY component on histotype-specific EOC. Together, our findings suggest that ovulation suppression is not the sole mechanism whereby reproductive factors affect EOC overall and for non-HGSOC histotypes. Identifying these mechanisms and understanding their individual and joint roles can provide deeper insight into disease etiology and potential risk-reducing approaches.

Funding

The Ovarian Cancer Association Consortium is funded by generous contributions from its research investigators and through anonymous donations. The Ovarian Cancer Association Consortium was funded by a grant from the Ovarian Cancer Research Fund.

Funding for individual studies:

AUS: The Australian Ovarian Cancer Study was supported by the U.S. Army Medical Research and Materiel Command (DAMD17-01-1-0729), National Health & Medical Research Council of Australia (199600, 400413 and 400281), Cancer Councils of New South Wales, Victoria, Queensland, South Australia and Tasmania and Cancer Foundation of Western Australia (Multi-State Applications 191, 211 and 182). The Australian Ovarian Cancer Study gratefully acknowledges additional support from Ovarian Cancer Australia and the Peter MacCallum Foundation; BAV: Funds of the University of Erlangen-Nuremberg; CON: United States National Institutes of Health (R01-CA063678, R01-CA074850; R01-CA080742); DOV: United States National Institutes of Health R01-CA112523 and R01-CA87538; GER: German Federal Ministry of Education and Research, Programme of Clinical Biomedical Research (01 GB 9401) and the German Cancer Research Center (DKFZ); HAW: United States National Institutes of Health (R01-CA58598, N01-CN-55424 and N01-PC-67001); HOP: University of Pittsburgh School of Medicine Dean’s Faculty Advancement Award (F. Modugno), Department of Defense (DAMD17-02-1-0669, OC20085) and United States National Cancer Institute (R21-CA267050, K07-CA080668, R01-CA95023, MO1-RR000056). Funding was also provided by the United States National Science Foundation (DGE-2217399-Modugno). The views expressed are those of the authors and do not necessarily reflect the views of the National Science Foundation; JPN: Grant-in-Aid for the Third Term Comprehensive 10-Year Strategy for Cancer Control from the Ministry of Health, Labour and Welfare; MAY: United States National Institutes of Health (R01-CA122443, P30-CA15083, P50-CA136393); Mayo Foundation; Minnesota Ovarian Cancer Alliance; Fred C. and Katherine B. Andersen Foundation; MCC: Cohort recruitment was funded by VicHealth and Cancer Council Victoria. Additional support from the Australian National Health and Medical Research Council grants 209057, 396414, and 1074383 and infrastructure support provided by Cancer Council Victoria; NCO: United States National Institutes of Health (R01-CA76016) and the Department of Defense (DAMD17-02-1-0666); NEC: United States National Institutes of Health R01-CA54419 and P50-CA105009 and Department of Defense W81XWH-10-1-02802; NJO: United States National Cancer Institute (NIH-K07 CA095666, R01-CA83918, NIH-K22-CA138563, and P30-CA072720) and the Cancer Institute of New Jersey; NTH: Radboud University Medical Centre; OVA: This work was supported by Canadian Institutes of Health Research grant (MOP-86727) and by United States National Institute of Health R01CA160669-01A1; POL: Intramural Research Program of the National Cancer Institute; SON: National Health Research and Development Program, Health Canada, grant 6613-1415-53; STA: United States National Institutes of Health grants U01 CA71966 and U01 CA69417; SWH: United States National Institutes of Health grant R37-CA070867 and UM1-CA182910; TBO: United States National Institutes of Health (R01-CA106414-A2), American Cancer Society (CRTG-00-196-01-CCE), Department of Defense (DAMD17-98-1-8659), Celma Mastry Ovarian Cancer Foundation; TOR: United States National Institutes of Health grants R01 CA063678 and R01 CA063682; UCI: United States National Institutes of Health R01-CA058860 and the Lon V Smith Foundation grant LVS-39420; UKO: The UKOPS study was funded by The Eve Appeal (The Oak Foundation) with contribution to author’s salary through Medical Research Council core funding MC_UU_00004/01 and the National Institute for Health Research University College London Hospitals Biomedical Research Centre. UM and AGM are supported by salary contributions through Medical Research Council core funding (MC_UU_00004/01); USC: United States National Institutes of Health P01CA17054, P30CA14089, R01CA61132, N01PC67010, R03CA113148, R03CA115195, N01CN025403, and California Cancer Research Program (00-01389 V-20170, 2II0200); VTL: United States National Institutes of Health K05-CA154337.

Notes

Role of the funder: Provision of funding only. Funders had no role in the execution of this study.

Disclosures: Elisa Bandera is currently a member of an Advisory Board for the Pfizer Clinical Trial Diversity Initiative. Usha Menon held personal shares in Abcodia between April 1, 2011, and October 30, 2021. Penelope Webb received grant funding for an unrelated study of ovarian cancer and a speaker’s honorarium from AstraZeneca. No other authors reported any disclosures. Harvey A. Risch, a JNCI Associate Editor and co-author on this article, was not involved in the editorial review or decision to publish the manuscript.

Author contributions: ZF: Conceptualization, Methodology, Investigation, Writing—Original Draft, Writing—Review and Editing. MMB: Conceptualization, Methodology, Investigation, Writing—Review and Editing. SI, SJ, NW, PMW: Writing—Original Draft, Writing—Review and Editing, Data Curation. HAR: Conceptualization, Methodology, Investigation, Writing—Review and Editing, Data Curation. FM: Conceptualization, Methodology, Investigation, Supervision, Writing—Original Draft, Writing—Review and Editing, Funding Acquisition, Resources, Data Curation. KKHA, HAC, EVB, MWB, AB, ABW, JCC, LSC, DWC, KLCH, JAD, ABE, PAF, RTF, SAG, AGM, GGG, ELG, MTG, AOCSG, HRH, AH, RK, LAK, MK, JK, NDL, AWL, KM, VM, JRM, UM, RLM, KBM, CLP, MCP, BOQ, SJR, MJR, JHR, JMS, WS, RS, KLT, PJT, LT, AMVA, EW, ASW, AHW, WZ, AZ, SET, LT, TS: Writing—Review and Editing, Data Curation.

Acknowledgements: We thank all the study participants who contributed to this study and all the researchers, clinicians, technical and administrative staff who have made this work possible.

Acknowledgements for individual studies:

AUS: We acknowledge the cooperation of the participating institutions in Australia, and the contribution of the study nurses, research assistants and all clinical and scientific collaborators. The complete Study Group can be found at www.aocstudy.org. CON: The cooperation of the 32 Connecticut hospitals, including Stamford Hospital, in allowing patient access, is gratefully acknowledged. This study was approved by the State of Connecticut Department of Public Health Human Investigation Committee. Certain data used in this study were obtained from the Connecticut Tumor Registry in the Connecticut Department of Public Health. The authors assume full responsibility for analyses and interpretation of these data; MCC: Cases and their vital status were ascertained through the Victorian Cancer Registry and the Australian Institute of Health and Welfare, including the National Death Index and the Australian Cancer Database; NJO: We thank all participants and research staff at the Rutgers Cancer Institute of New Jersey, the New Jersey State Cancer Registry, and Memorial Sloan-Kettering Cancer Center; SWH: We thank the participants and the research staff of the Shanghai Women’s Health Study for making this study possible.

Data Availability

The data generated in this study are not publicly available due to restrictions of some included studies’ informed consent. The corresponding author will facilitate access to data through existing data request processes for OCAC.