Abstract
Objective
To investigate whether obesity and hormone therapy (HT) are associated with ovarian cancer risk among women in the California Teachers Study cohort.
Methods
Of 56,091 women age ≥45 years, 277 developed epithelial ovarian cancer between 1995 and 2007. Multivariate Cox regression was performed.
Results
Among women who never used HT, greater adult weight gain, waist circumference and waist-to-height ratio, but not adult BMI, increased risk of ovarian cancer. Compared to women who never used HT and had a stable adult weight, risk of ovarian cancer was increased in women who gained ≥40 lb (relative risk (RR) 1.8, 95% confidence interval (CI): 1.0–3.0) or used HT for >5 years (RR 2.3 95% CI: 1.3–4.1). Having both exposures (RR 1.9, 95% CI: 0.99–3.5), however, did not increase risk more than having either alone. Results were similar for waist circumference and weight-to-height ratio; however, differences across HT groups were not statistically significant.
Conclusions
This study suggests that abdominal adiposity and weight gain, but not overall obesity, increase ovarian cancer risk and that there may be a threshold level beyond which additional hormones, whether exogenous or endogenous, do not result in additional elevation in risk. However, large pooled analyses are needed to confirm these findings.
Keywords: Ovarian cancer, Obesity, Abdominal adiposity, Hormone therapy
Introduction
The impact of obesity on the risk of ovarian cancer has not been established. Individual studies, pooled analyses and meta-analyses of ovarian cancer risk and obesity have yielded mixed results [1–4]. Conversely, long-term hormone therapy (HT) use has been positively associated with ovarian cancer risk [5–9]. The joint relationship between these factors remains unclear.
Obesity causes physiological changes that lead to perturbation of steroid hormone levels. When menstrual cycles cease, adipose tissue becomes a primary source of estrogens through the aromatization of adrenal androgens [10]. Greater overall and abdominal adiposity after menopause are independently associated with lower levels of sex hormone–binding globulin (SHBG) [11, 12], which resultsin higher levels of non-protein-bound, biologically available estrogen [12]. Hormone therapy (HT) also plays a role in the risk of hormone-related cancers by increasing circulating hormone levels [13].
Few studies have evaluated whether the effect of obesity on the risk of ovarian cancer varies by HT use, or whether these risk associations differ by various measures of overall and abdominal adiposity. If there is an interaction between HT use and obesity on ovarian cancer risk, the discrepant results between studies may be related to differences in the proportion and duration of HT use in the various populations studied. Therefore, we investigated the effects of obesity and HT use on the risk of epithelial ovarian cancer among women age ≥ 45 years in the large, prospective California Teachers Study (CTS) cohort.
Materials and methods
Study population
The CTS cohort was established in 1995–1996 and includes 133,479 active and retired female teachers and administrators recruited through the California State Teachers Retirement System [14]. Participants completed a baseline questionnaire that collected information on family history of cancer and other conditions, menstrual and reproductive history, a brief health history that included self-reported weight and height, living environment, diet, alcohol and tobacco use. Two years later, a follow-up questionnaire collected self-reported measurements of waist and hip circumference; 99,529 participants (75%) completed the follow-up questionnaire.
The analytic subcohort for the present analysis was created after excluding, sequentially, women who at baseline did not live in California (n = 8,867); consented to participate only in breast cancer research (n = 18); did not provide adequate information on personal history of cancer (n = 662); had a history of ovarian cancer (identified by self-report or by linkage with the California Cancer Registry [CCR]) (n = 641); reported having had a bilateral oophorectomy (n = 14,422); were not between the ages of 45 and 84 years (n = 33,696 younger than age 45 years and n = 1,874 older than 84 years); had missing or unreliable data on height, weight or body mass index (BMI) (n = 3,490); had unknown HT use or duration (n = 6,201); reported using progestin-alone HT exclusively (n = 850: excluded due to an insufficient number of cases (n = 4) for stratified analysis); or had missing data for one or more of the confounding factors included in the analysis (n = 6,667). We restricted the analysis to women age ≥45 years so that all women were likely to be in or have passed through the peri- or postmenopausal period; thus, all women who were included could potentially have used HT. Of the 56,091 women eligible for analysis, 277 were diagnosed with incident invasive epithelial ovarian cancer after joining the cohort and on or before December 31, 2007. For waist and hip circumference analyses, we further excluded, sequentially, women who did not complete the follow-up questionnaire (n = 12,886); were diagnosed with ovarian cancer between baseline and completing the follow-up questionnaire (n = 37); moved out of California before completing the follow-up questionnaire (n = 409); or had missing or unreliable waist or hip circumference data (n = 4,600). Among the remaining 38,159 women, 166 were diagnosed with incident invasive epithelial ovarian cancer after completing the follow-up questionnaire and on or before December 31, 2007.
The CTS study was approved by the Institutional Review Boards of the Cancer Prevention Institute of Cal-ifornia, the California Health and Human Services Agency, the University of California, Irvine, the University of Southern California, and the City of Hope.
Follow-up
The CTS cohort is followed annually for cancer diagnoses, death, and changes of address. Annual linkage between the CCR and the cohort membership is used to identify incident cancer cases. The CCR is a population-based cancer registry that is anchored in state legislation that mandates reporting and is estimated to be over 99% complete [15]. The high standards maintained by the CCR ensure that follow-up for cancer outcomes are virtually complete as long as the cohort members reside in California. Linkage between the CTS cohort and the CCR database is based on full name, date of birth, address and Social Security number, and includes manual review of possible matches.
California and national mortality files are used to ascertain date and cause of death. Changes of address are obtained through annual mailings, responses from participants, and record linkages with multiple sources, including the US Postal Service National Change of Address database.
Follow-up time was calculated as the number of days between the date the baseline questionnaire was completed and the earliest occurrence of a first diagnosis of ovarian cancer (International Classification of Diseases for Oncology-3 (ICD-O-3) site code C56.9), death, a permanent (over 4 months long) move out of California or December 31, 2007. For waist circumference analyses, follow-up time was calculated as the number of days between the date the follow-up questionnaire was completed and the earliest outcome/censoring event as defined previously. For analysis, cases were restricted to invasive epithelial ovarian cancers (ICD-O-3 histology codes 8010, 8012, 8020, 8041, 8050, 8070, 8071, 8120, 8130, 8140, 8255, 8260, 8310, 8323, 8380, 8382, 8441, 8450, 8460, 8461, 8470, 8480, 8481, 8490, 8560, 9014) [16]; 23 women with other or unknown histologic types of ovarian cancer and 26 women with borderline ovarian cancer were censored at the time of their diagnoses. Analyses were limited to invasive epithelial ovarian cancer since over 90% of the invasive cases were this type.
Body size assessment
Height (in feet and inches) and weight (in pounds) were self-reported for age 18 and at the time of completing the baseline questionnaire. Body mass index (BMI) for both time periods was calculated as weight (converted to kilograms) divided by height (converted to meters and squared) and is a measure of overall adiposity that is independent of height (Pearson correlation coefficient of –0.04 in our study population). Extreme BMI values, defined as BMI at baseline <16 kg/m2 or BMI at age 18 < 15 or ≥ 54.9 kg/m2, were considered unreliable and these women were excluded from analysis. Weight change since age 18 was calculated as the absolute gain or loss of weight (in pounds) between age 18 and baseline.
In conjunction with the follow-up questionnaire, participants were provided with a standard heavy-weight flexible paper tape measure and written illustrated instructions on how to measure their waist and hip circumferences (in inches). Waist was measured as the circumference one inch above the navel. Hips were measured at the largest circumference between waist and thighs. Women were instructed to measure and record each circumference twice. For each circumference (waist or hips), if the two repeated measurements were within 3 inches of each other, the average was used in the analysis. If the two repeated measurements were over 3 inches apart or if either measurement was less than 20 inches, the data were considered unreliable and the woman was excluded from the analysis. In addition, if the final waist and hip circumference values were extreme in relation to each other [i.e., waist circumference less than 26.5 inches (10th percentile) and hip circumference greater than 45 inches (90th percentile) or waist circumference greater than 39.5 inches (90th percentile) and hip circumference less than 35.5 inches (10th percentile)], the data were considered unreliable and the woman was excluded from the analysis. Waist-to-hip ratio (WHR) was calculated as waist circumference divided by hip circumference, and waist-to-height ratio (WHtR) was calculated as waist circumference divided by height (in inches). WHR is a measure of body fat distribution (abdominal vs. hip) but reflects both fat and muscle, whereas WHtR is considered a measure of visceral fat that is independent of height/body stature (Pearson correlation coefficient of -0.14 in our study population) [17]; these two ratios, along with waist circumference, were considered as measures of abdominal adiposity.
Data analysis
Relative risks (RR; hazard rate ratios) and 95% confidence intervals (CI) were estimated using multivariate Cox proportional hazards regression models with age (in days) as the time-scale. Models were stratified by age (in years) at baseline and adjusted for race/ethnicity (non-Hispanic white, non-white including Hispanic), oral contraceptive (OC) use and duration (never used, <5 years, ≥5 years), number of full-term pregnancies (0, 1, ≥2), wine consumption in the year preceding baseline (grams per day; continuous), strenuous and moderate physical activity in the 3 years before baseline (hours/week; both strenuous and moderate activities <0.5, intermediate, either ≥3.01), smoking history at baseline (never, former, current), history of tubal ligation (no, yes) and height at baseline (inches; continuous). These covariates were included based on their independent association with risk in our cohort and prior knowledge of ovarian cancer risk factors [18, 19]. We additionally investigated HT type, total daily caloric intake, history of simple hysterectomy and family history of ovarian cancer as potential confounding variables, but they were not independently associated with risk in multivariate models and were therefore not included in the final models reported here. When included as a potential confounder, weight at age 18 was included as a continuous variable.
Women were classified by their history of HT use at baseline as follows: never used HT, total HT duration ≤5 years or total HT duration >5 years. We classified by short- and long-term HT as risk of ovarian cancer is positively associated with long-term HT, both in the literature [7–9] and in our data. The sample size was too small to further classify by HT type.
We tested the assumption of proportional hazards for each adjustment variable and main effect using a likelihood ratio test of interaction with the time-scale (continuous) based on cross-product terms. Adjustment variables were coded as defined previously, and main effects were categorized as presented in the tables. There were no violations of the proportional hazards assumption.
Likelihood ratio tests for interaction across HT use and duration (never used HT, total HT duration ≤5 years, total HT duration >5 years) were computed based on cross-product terms with body size measures categorized as presented in the tables.
Because early symptoms of ovarian cancer can include abdominal fullness or bloating, difficulty eating, anorexia or weight loss [20–23], we conducted a secondary analysis in which we excluded the first 3 years (and additionally the first 5 years) of follow-up to remove the possibility that body size measures were affected by early symptoms. For these analyses, follow-up began at 3 years and 1 day (or 5 years and 1 day) after the baseline questionnaire was completed (n = 202 cases and n = 169 cases, respectively) or the follow-up questionnaire was completed (for waist circumference analyses; n = 122 cases and n = 90 cases, respectively).
Since particular subtypes of epithelial ovarian cancer may be more related to weight gain or loss [24], where feasible, we examined the association of BMI with risk of subtypes of epithelial ovarian cancer (n = 136 serous, n = 16 mucinous, n = 34 endometrioid, n = 20 clear cell and n = 37 adenocarcinoma NOS cases); these analyses, however, were not stratified by HT use.
Results
The median age at baseline was 55 years (interquartile range (IQR) 49–64 years). The median follow-up was 12.1 years for analyses using baseline measures and 10.1 years for the waist circumference analyses. Compared to women of normal weight, women who were obese at baseline were more likely to have had lower levels of physical activity in the 3 years before baseline, been overweight at age 18, gained 40 or more pounds since age 18 and had a waist circumference of 35 or more inches (Table 1).
Table 1.
Selected baseline characteristics of women age ≥45 years in the California Teachers Study (CTS) cohort by body mass index (BMI)
| Characteristic | BMI (Body mass index)
|
||||||||
|---|---|---|---|---|---|---|---|---|---|
| <25
|
25–29
|
≥30
|
|||||||
| Median (IQR) | n | (%) | Median (IQR) | n | (%) | Median (IQR) | n | (%) | |
| BMI (kg/m2) | 32,941 | 58.7 | 14,980 | 26.7 | 8,170 | 14.6 | |||
| Age (years) | 54 (49–64) | 56 (50–66) | 55 (49–64) | ||||||
| Wine g/d | 3.3 (0–3.3) | 3.3 (0–3.3) | 3.3 (0–3.3) | ||||||
| Height (inches) | 65 (63–66) | 65 (63–66) | 64 (63–66) | ||||||
| Race | |||||||||
| White | 29,790 | 90.4 | 13,354 | 89.2 | 7,136 | 87.3 | |||
| Non-white | 3,151 | 9.6 | 1,626 | 10.9 | 1,034 | 12.7 | |||
| Strenuous and moderate physical activity (hours/week) in the 3 years before baseline | |||||||||
| Both < 0.5 h/week | 7,305 | 22.2 | 4,320 | 28.8 | 3,444 | 42.2 | |||
| Intermediate | 14,820 | 45.0 | 6,879 | 45.9 | 3,471 | 42.5 | |||
| Either ≥3.01 h/week | 10,816 | 32.8 | 3,781 | 25.2 | 1,255 | 15.4 | |||
| Total number of full-term pregnancies | |||||||||
| None | 7,140 | 21.7 | 2,945 | 19.7 | 1,917 | 23.5 | |||
| 1 | 4,945 | 15.0 | 2,015 | 13.5 | 1,146 | 14.0 | |||
| ≥2 | 20,856 | 63.3 | 10,020 | 66.9 | 5,107 | 62.5 | |||
| Oral contraceptive duration (years) | |||||||||
| Never used | 11,031 | 33.5 | 5,918 | 39.5 | 3,119 | 38.2 | |||
| <5 | 9,899 | 30.1 | 4,179 | 27.9 | 2,504 | 30.7 | |||
| ≥5 | 12,011 | 36.5 | 4,883 | 32.6 | 2,547 | 31.2 | |||
| Smoking history | |||||||||
| Never | 20,048 | 60.9 | 9,065 | 60.5 | 5,040 | 61.7 | |||
| Former | 10,911 | 33.1 | 5,036 | 33.6 | 2,752 | 33.7 | |||
| Current | 1,982 | 6.0 | 879 | 5.9 | 378 | 4.6 | |||
| History of tubal ligation | |||||||||
| No | 27,684 | 84.0 | 12,524 | 83.6 | 6,822 | 83.5 | |||
| Yes | 5,257 | 16.0 | 2,456 | 16.4 | 1,348 | 16.5 | |||
| Hormone therapy use (years) | |||||||||
| Never used | 15,651 | 47.5 | 6,937 | 46.3 | 4,366 | 53.4 | |||
| ≤5 | 9,164 | 27.8 | 4,235 | 28.3 | 2,256 | 27.6 | |||
| >5 | 8,126 | 24.7 | 3,808 | 25.4 | 1,548 | 19.0 | |||
| BMI at age 18 (kg/m2) | |||||||||
| <25 | 29,918 | 92.2 | 13,668 | 92.5 | 6,278 | 77.7 | |||
| ≥25 | 2,536 | 7.8 | 1,104 | 7.5 | 1,804 | 22.3 | |||
| Missing or unreliable | 487 | 208 | 88 | ||||||
| Weight change (pounds) since age 18 | |||||||||
| Loss, >10 lb | 4,823 | 14.8 | 355 | 2.4 | 55 | 0.7 | |||
| Stable, ±10 lb | 13,566 | 41.6 | 864 | 5.8 | 146 | 1.8 | |||
| Gain, 11–39 lb | 13,705 | 42.0 | 8,128 | 54.8 | 739 | 9.1 | |||
| ≥40 lb | 511 | 1.6 | 5,493 | 37.0 | 7,174 | 88.4 | |||
| Missing or unreliable | 336 | 140 | 56 | ||||||
| Waist (inches)a | |||||||||
| <35 | 21,087 | 88.7 | 4,509 | 46.1 | 409 | 8.9 | |||
| ≥35 | 2,687 | 11.3 | 5,272 | 53.9 | 4,195 | 91.1 | |||
| Waist-to-height ratioa | |||||||||
| <0.55 | 21,834 | 91.8 | 5,445 | 55.7 | 527 | 11.5 | |||
| ≥0.55 | 1,940 | 8.2 | 4,336 | 44.3 | 4,077 | 88.6 | |||
| Waist-to-hip ratioa | |||||||||
| <0.80 | 13,900 | 58.5 | 3,086 | 31.6 | 783 | 17.0 | |||
| ≥0.80 | 9,874 | 41.5 | 6,695 | 68.5 | 3,821 | 83.0 | |||
IQR interquartile range, BMI body mass index, E estrogen, P progestin
Available only among participants who completed the 1997 follow-up questionnaire
Compared to women who never used HT, the risk of epithelial ovarian cancer was significantly increased among women who used HT for >5 years (RR 1.6, 95% CI: 1.2–2.2), but not among short-term HT users (RR = 1.0, 95% CI: 0.75–1.4). Risk did not differ by HT type. Among women who never used HT (n = 26,954), the median age at baseline was 50 years (IQR 47–61 years). Among women who used HT for ≤5 years (n = 15,655), the median age at baseline was 54 years (IQR 51–61). Thirty percent of these women used estrogen-alone therapy (ET) exclusively, 69% used combined estrogen-plus-progester-one therapy (EPT) exclusively and 1% used both types. At baseline, 14% were currently using ET, 57% were currently using EPT, and 30% were past users. Among women who used HT for >5 years (n = 13,482), the median age at baseline was 62 years (IQR 57–69). Thirty-three percent of these women used ET exclusively, 45% used EPT exclusively, and 23% used both types. At baseline, 34% were currently using ET, 54% were currently using EPT, 12% were past users, and <1% had unknown current HT use.
In this subcohort of women age ≥45 years, obesity was not associated with risk of epithelial ovarian cancer overall (RR 0.85, 95% CI: 0.58–1.2 for BMI ≥ 30 vs.<25 kg/m2) or among subgroups defined by HT use (Table 2). Among women who never used HT, greater weight gain since age 18 (RR 1.8, 95% CI: 1.0–3.0 for ≥40 lb vs stable weight), a larger waist circumference (RR 1.8, 95% CI: 1.1–3.0 for ≥35 vs. <35 inches), and a larger waist-to-height ratio (RR 1.8, 95% CI: 1.1–3.1 for ≥0.55 vs. <0.55) were signifi-cantly associated with higher risk of ovarian cancer.
Table 2.
Body size and hormone therapy use and risk of epithelial ovarian cancer in women age ≥ 45 years in the California Teachers Study cohort
| Never used HT
|
HT ≤ 5 years
|
HT > 5 years
|
||||||||||
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| Cases | Person-years | RRa | 95% CI | Cases | Person-years | RRa | 95% CI | Cases | Person-years | RRa | 95% CI | |
| BMI (kg/m2) | ||||||||||||
| <25 | 57 | 175,216 | 1.0 | Reference | 41 | 101,462 | 1.2 | 0.76, 1.8 | 64 | 88,130 | 1.8 | 1.2, 2.6 |
| 25–29 | 29 | 77,031 | 1.1 | 0.71, 1.8 | 21 | 46,700 | 1.3 | 0.74, 2.1 | 30 | 41,282 | 1.7 | 1.1, 2.8 |
| ≥30 | 21 | 48,237 | 1.2 | 0.72, 2.0 | 6 | 24,856 | 0.65 | 0.28, 1.5 | 8 | 16,507 | 1.1 | 0.53, 2.5 |
| BMI at age 18 (kg/m2) | ||||||||||||
| <25 | 90 | 263,607 | 1.0 | Reference | 57 | 154,751 | 1.0 | 0.71, 1.4 | 91 | 132,900 | 1.6 | 1.1, 2.2 |
| ≥25 | 16 | 32,733 | 1.4 | 0.82, 2.4 | 7 | 16,074 | 1.1 | 0.52, 2.5 | 9 | 10,986 | 1.9 | 0.94, 3.9 |
| Weight change (since age 18)b | ||||||||||||
| Loss, >10 lb | 15 | 29,294 | 1.8 | 0.94, 3.5 | 9 | 15,187 | 2.0 | 0.90, 4.4 | 12 | 12,446 | 2.9 | 1.4, 6.1 |
| Stable, ±10 lb | 22 | 78,662 | 1.0 | Reference | 28 | 43,887 | 2.2 | 1.2, 3.9 | 31 | 38,541 | 2.3 | 1.3, 4.1 |
| Gain, 11–39 lb | 31 | 117,098 | 0.96 | 0.55, 1.7 | 17 | 71,428 | 0.81 | 0.42, 1.5 | 37 | 62,356 | 1.7 | 0.98, 3.0 |
| ≥40 lb | 38 | 72,570 | 1.8 | 1.0, 3.0 | 13 | 41,293 | 1.0 | 0.51, 2.1 | 20 | 31,092 | 1.9 | 0.99, 3.5 |
| Waist circumference (inches) | ||||||||||||
| <35 | 32 | 113,564 | 1.0 | Reference | 29 | 69,763 | 1.4 | 0.83, 2.4 | 41 | 60,898 | 2.2 | 1.3, 3.6 |
| ≥35 | 29 | 52,592 | 1.8 | 1.1, 3.0 | 13 | 31,086 | 1.4 | 0.69, 2.6 | 22 | 28,210 | 2.4 | 1.3, 4.3 |
| Waist-to-height ratio | ||||||||||||
| <0.55 | 35 | 120,779 | 1.0 | Reference | 33 | 74,915 | 1.5 | 0.88, 2.4 | 42 | 65,405 | 2.0 | 1.2, 3.3 |
| ≥0.5 5 | 26 | 45,378 | 1.8 | 1.1, 3.1 | 9 | 25,935 | 1.1 | 0.52, 2.4 | 21 | 23,703 | 2.7 | 1.5, 4.9 |
| Waist-to-hip ratio | ||||||||||||
| <0.80 | 29 | 80,796 | 1.0 | Reference | 23 | 48,774 | 1.2 | 0.69, 2.2 | 25 | 38,331 | 1.6 | 0.88, 2.9 |
| ≥0.80 | 32 | 85,360 | 0.95 | 0.56, 1.6 | 19 | 52,076 | 0.90 | 0.49, 1.7 | 38 | 50,777 | 1.7 | 0.96, 2.9 |
HT hormone therapy, BMI body mass index, RR relative risk, CI confidence interval
Adjusted for race, oral contraceptive use and duration, number of full-term pregnancies, wine consumption, physical activity, smoking history, history of tubal ligation and height; age was the time-scale, and analyses were stratified by age at baseline
Additionally adjusted for weight at 18
Long-term HT use increased epithelial ovarian cancer risk among non-obese women (RR 1.8, 95% CI: 1.2–2.6 for BMI < 25 kg/m2 and HT use >5 years and RR 1.7, 95% CI: 1.1–2.8 for BMI 25–29 and HT use >5 years, both compared to BMI < 25 and never used HT) (Table 2). No increase in risk associated with HT use was observed among those who were obese (RR 1.1, 95% CI: 0.53–2.5 for BMI > 30 and HT use >5 years); although, the latter estimate was based on small numbers.
Compared to women who never used HT and had a stable adult weight, risk of epithelial ovarian cancer was increased in women who gained ≥40 lb (RR 1.8, 95% CI: 1.0–3.0) or used HT for >5 years (RR 2.3 95% CI: 1.3–4.1) (Table 2). Having both exposures (RR 1.9, 95% CI: 0.99–3.5) did not increase risk more than having either alone (P-interaction = 0.05). Similar patterns were observed for waist and WHtR. Differences across HT groups, however, were not statistically significant for these other body measures (P-interaction > 0.05). In addition, compared to women who never used HT and had a stable adult weight, there was a suggestion of an increased risk of ovarian cancer among women who lost >10 lb, regardless of HT use (RR1.8, 95% CI: 0.94–3.5 for never HT, RR 2.0, 95% CI: 0.90–4.4 for HT ≤ 5 years and RR 2.9, 95% CI: 1.4–6.1 for HT > 5 years), although the number of cases was small.
Excluding the first 3 years of follow-up, results for Table 2 were similar. For example, compared to women who never used HT and had a stable adult weight, women who gained ≥40 lb or used HT for >5 years or both, had an increased risk of epithelial ovarian cancer (RR 1.9, 95% CI: 1.0–3.6; RR 2.3, 95% CI: 1.1–4.5; and RR 1.7, 95% CI: 0.76–3.6, respectively). With the same comparison group, for women who lost >10 lb since age 18, there was a statistically significant increased risk of ovarian cancer among those who had never used HT (RR 2.2, 95% CI: 1.0–4.5; n = 13 cases) and those with long-term HT use (RR 3.3, 95% CI: 1.4–7.6; n = 9 cases), but not among those who had used HT for ≤5 years (RR 1.2, 95% CI: 0.40–3.7; n = 4 cases). Excluding the first 5 years of follow-up, results were attenuated for every measure given the small number of cases in each subgroup.
Overall obesity decreased risk of serous tumors (RR 0.53, 95% CI: 0.28–0.99 for BMI ≥ 30 vs. <30 kg/m2), but increased risk of clear cell tumors (RR 2.7, 95% CI: 0.99–7.3). No association with risk was observed for the other histologic subtypes, although statistical power was low due to the small number of cases.
Discussion
Results from our prospective cohort suggest that abdominal adiposity and weight gain, but not overall obesity, increased epithelial ovarian cancer risk in HT non-users. In addition, long-term HT use increased risk; however, risk was not further increased among women with both exposures. Interestingly, adult weight loss was associated with increased ovarian cancer risk, and this finding persisted when the first 3 or 5 years of follow-up were excluded. The association between overall obesity and ovarian cancer risk may vary by histologic subtypes; however, larger studies or pooled analyses are needed to adequately address this issue.
The association between ovarian cancer and obesity in postmenopausal women in the recent literature is mixed. Consistent with our study, most studies, including a pooled analysis of 12 cohort studies, have found no association in postmenopausal women [2, 4, 25–31], although a few earlier studies did find a positive association for one or more of the obesity measures assessed [3, 25, 31].
Three recent cohort studies investigated the relationship between obesity and risk of epithelial ovarian cancer by HT use, two in postmenopausal women and one in women age 50–71. The European Prospective Investigation into Cancer and Nutrition (EPIC) found that higher BMI and larger hip circumference increased risk of epithelial ovarian cancer in postmenopausal women, but waist circumference, WHR and adult weight change were not associated with risk [32]. The Nurses’ Health Study found evidence for the decreased risk of epithelial ovarian cancer with larger hip circumference in postmenopausal women, but no association with risk for BMI, waist circumference or WHR [33]. In both studies, the results for BMI did not differ by HT use. In contrast, the National Institutes of Health AARP Diet and Health Study found that BMI was significantly associated with increased risk in women who never used HT (RR 1.8, 95% CI: 1.2–2.8 ≥ 30 vs. <25 kg/m2), but not among those who ever used HT (RR 0.96, 95% CI: 0.65–1.4) (P-interaction = 0.02) [34]. Findings were similar for BMI at age 18 (P-interaction = 0.02), but there was no association with adult weight gain in either group.
By comparison, our study found a positive association between risk of epithelial ovarian cancer and adult weight gain, waist circumference and WHtR in women who never used HT. However, among HT users, substantial weight gain had no impact on risk. Thus, while substantial abdominal weight gain and long-term HT use each increased the risk of epithelial ovarian cancer, having both exposures did not increase risk more than either alone. This pattern implies a threshold (rather than a multiplicative) effect beyond which additional exogenous and endogenous hormones did not continue to increase risk.
A pooled analysis of case–control studies reported differences by histologic subtypes of epithelial ovarian cancer [24]. Higher BMI was associated with a non-significant increased risk for endometrioid and mucinous tumors and decreased risk for serous tumors. However, the EPIC study found no association between BMI and subtypes of epithelial ovarian cancer [32]. Consistent with the pooled analysis, we found that higher BMI decreased the risk for serous tumors, while conversely, increased the risk for clear cell tumors, but was not associated with other histo-logic subtypes, including endometrioid and mucinous.
A potential limitation of our study is the possibility of error in self-reported anthropometric measurements. Such error could be the result of lack of knowledge, the desire to report a socially more normative value or measurement error (for waist and hip circumferences). However, to improve the accuracy of measured waist and hip circumferences, participants were provided with specific written and pictorial instructions and a standard tape measure and were asked to take and record their measurements twice. In addition, the prospective study design eliminated recall bias and, thus, if measurement error occurred in the self-reports, it was unlikely to differ systematically between cases and non-cases. However, socially desirable responses would have been likely to attenuate elevated risk estimates. Finally, in an ancillary validation study conducted within the cohort, comparison of the self-reported measurements to measurements taken by trained interviewers suggested excellent validity, with Pearson correlation coefficients of 0.87, 0.93, 0.85, and 0.87 for weight, height, waist circumference, and hip circumference, respectively. Another limitation of the study was the small number of cases, particularly when stratified by duration of HT use; the small sample size resulted in limited power to detect differences within and across these strata. However, since HT use and duration may have been relevant to disentangling the mixed results observed to date, we presented the data accordingly. Nevertheless, combining different HT types together within strata of HT duration may have obscured effects, if different preparations have differential impact on the association between obesity and risk. However, HT type was not related to ovarian cancer risk in our subcohort (data not shown). An additional limitation was the relatively large number of women excluded due to missing data. However, in a comparison of the 56,091 women included in the present analysis with the 17,208 women excluded from analyses for missing data, none of the body size measures differed (data not shown); thus, there was no evidence of inclusion bias. Finally, we were unable to update cohort members’ anthropometric and HT data during the decade-long follow-up period included in this analysis.
Strengths of our study include the prospective design minimizing recall bias, detailed exposure information, and virtually complete case ascertainment minimizing selection bias due to loss to follow-up.
In conclusion, our findings show that either substantial abdominal weight gain or long-term HT use increased the risk of epithelial ovarian cancer, emphasizing the importance of weight control and HT cessation in ovarian cancer prevention. Having both exposures, however, did not increase risk more than either alone. This pattern suggests a threshold effect of hormonal exposures, such that after a certain level is achieved, regardless of its source, no additional risk is incurred.
Acknowledgments
This research was supported by grant R01 CA77398 from the National Cancer Institute and contract 97–10500 from the California Breast Cancer Research Fund. The funding sources did not contribute to the design or conduct of the study, nor to the writing or submission of this manuscript. The collection of cancer incidence data used in this study was supported by the California Department of Public Health as part of the statewide cancer reporting program mandated by California Health and Safety Code Sect. 103885; the National Cancer Institute’s Surveillance, Epidemiology and End Results Program under contract N01-PC-35136 awarded to the Cancer Prevention Institute of California (formerly the Northern California Cancer Center), contract N01-PC-35139 awarded to the University of Southern California, and contract N02-PC-15105 awarded to the Public Health Institute; and the Centers for Disease Control and Prevention’s National Program of Cancer Registries, under agreement #U55/CCR921930-02 awarded to the Public Health Institute. The ideas and opinions expressed herein are those of the authors and endorsement by the State of California, Department of Public Health, the National Cancer Institute, and the Centers for Disease Control and Prevention or their contractors and subcontractors is not intended nor should be inferred. The authors would like to thank the CTS Steering Committee who are responsible for the formation and maintenance of the cohort within which this study was conducted but who did not directly contribute to the current paper: Hoda Anton-Culver, Christina A. Clarke, James V. Lacey Jr., Huiyan Ma, David O. Nelson, Susan L. Neuhausen, Rich Pinder, Frederick Schumacher, Daniel O. Stram, Sophia S. Wang, Dee W. West, and Argyrios Ziogas.
Contributor Information
Alison J. Canchola, Email: Alison.Canchola@CPIC.org, Cancer Prevention Institute of California, 2201 Walnut Avenue, Suite 300, Fremont, CA, 94538, USA
Ellen T. Chang, Cancer Prevention Institute of California, 2201 Walnut Avenue, Suite 300, Fremont, CA, 94538, USA. Division of Epidemiology, Department of Health Research and Policy, Stanford University School of Medicine, Stanford, CA, USA
Leslie Bernstein, Division of Cancer Etiology, Department of Population Sciences, City of Hope National Medical Center, Duarte, CA, USA.
Joan A. Largent, Department of Epidemiology, College of Health Sciences, University of California Irvine, Irvine, CA, USA
Peggy Reynolds, Cancer Prevention Institute of California, 2201 Walnut Avenue, Suite 300, Fremont, CA, 94538, USA. Division of Epidemiology, Department of Health Research and Policy, Stanford University School of Medicine, Stanford, CA, USA.
Dennis Deapen, Department of Preventive Medicine, University of Southern California Keck School of Medicine, Los Angeles, CA, USA.
Katherine D. Henderson, Division of Cancer Etiology, Department of Population Sciences, City of Hope National Medical Center, Duarte, CA, USA
Giske Ursin, Department of Nutrition, University of Oslo, Oslo, Norway Department of Preventive Medicine, University of Southern California Keck School of Medicine, Los Angeles, CA, USA.
Pamela L. Horn-Ross, Cancer Prevention Institute of California, 2201 Walnut Avenue, Suite 300, Fremont, CA, 94538, USA Division of Epidemiology, Department of Health Research and Policy, Stanford University School of Medicine, Stanford, CA, USA
References
- 1.World Cancer Research Fund and the American Institute for Cancer Research. Food, Nutrition and the Prevention of Cancer: A Global Perspective. 2007. [DOI] [PubMed] [Google Scholar]
- 2.Olsen CM, Green AC, Whiteman DC, Sadeghi S, Kolahdooz F, Webb PM. Obesity and the risk of epithelial ovarian cancer: a systematic review and meta-analysis. Eur J Cancer. 2007;43:690–709. doi: 10.1016/j.ejca.2006.11.010. [DOI] [PubMed] [Google Scholar]
- 3.Purdie DM, Bain CJ, Webb PM, Whiteman DC, Pirozzo S, Green AC. Body size and ovarian cancer: case-control study and systematic review (Australia) Cancer Causes Control. 2001;12:855–863. doi: 10.1023/a:1012267619561. [DOI] [PubMed] [Google Scholar]
- 4.Schouten LJ, Rivera C, Hunter DJ, et al. Height, body mass index, and ovarian cancer: a pooled analysis of 12 cohort studies. Cancer Epidemiol Biomarkers Prev. 2008;17:902–912. doi: 10.1158/1055-9965.EPI-07-2524. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 5.Folsom AR, Anderson JP, Ross JA. Estrogen replacement therapy and ovarian cancer. Epidemiology. 2004;15:100–104. doi: 10.1097/01.ede.0000091606.31903.8e. [DOI] [PubMed] [Google Scholar]
- 6.Lacey JV, Jr, Mink PJ, Lubin JH, et al. Menopausal hormone replacement therapy and risk of ovarian cancer. JAMA. 2002;288:334–341. doi: 10.1001/jama.288.3.334. [DOI] [PubMed] [Google Scholar]
- 7.Lacey JV, Jr, Brinton LA, Leitzmann MF, et al. Menopausal hormone therapy and ovarian cancer risk in the National Institutes of Health-AARP Diet and Health Study Cohort. J Natl Cancer Inst. 2006;98:1397–1405. doi: 10.1093/jnci/djj375. [DOI] [PubMed] [Google Scholar]
- 8.Riman T, Dickman PW, Nilsson S, et al. Hormone replacement therapy and the risk of invasive epithelial ovarian cancer in Swedish women. J Natl Cancer Inst. 2002;94:497–504. doi: 10.1093/jnci/94.7.497. [DOI] [PubMed] [Google Scholar]
- 9.Vo C, Carney ME. Ovarian cancer hormonal and environmental risk effect. Obstet Gynecol Clin North Am. 2007;34:687–700. viii. doi: 10.1016/j.ogc.2007.09.008. [DOI] [PubMed] [Google Scholar]
- 10.Key TJ, Allen NE, Verkasalo PK, Banks E. Energy balance and cancer: the role of sex hormones. Proc Nutr Soc. 2001;60:81–89. doi: 10.1079/pns200068. [DOI] [PubMed] [Google Scholar]
- 11.Kaye SA, Folsom AR, Soler JT, Prineas RJ, Potter JD. Associations of body mass and fat distribution with sex hormone concentrations in postmenopausal women. Int J Epidemiol. 1991;20:151–156. doi: 10.1093/ije/20.1.151. [DOI] [PubMed] [Google Scholar]
- 12.Potischman N, Swanson CA, Siiteri P, Hoover RN. Reversal of relation between body mass and endogenous estrogen concentrations with menopausal status. J Natl Cancer Inst. 1996;88:756–758. doi: 10.1093/jnci/88.11.756. [DOI] [PubMed] [Google Scholar]
- 13.Bernstein L. The risk of breast, endometrial and ovarian cancer in users of hormonal preparations. Basic Clin Pharmacol Toxicol. 2006;98:288–296. doi: 10.1111/j.1742-7843.2006.pto_277.x. [DOI] [PubMed] [Google Scholar]
- 14.Bernstein L, Allen M, Anton-Culver H, et al. High breast cancer incidence rates among California teachers: results from the California Teachers Study (United States) Cancer Causes Control. 2002;13:625–635. doi: 10.1023/a:1019552126105. [DOI] [PubMed] [Google Scholar]
- 15.Kwong SL, Perkins CI, Morris CR, et al. Cancer Surveillance Section. California Department of Health Services; Sacramento, CA: 2001. Dec, Cancer in Cali-fornia: 1988–1999. [Google Scholar]
- 16.Chen VW, Ruiz B, Killeen JL, Cote TR, Wu XC, Correa CN. Pathology and classification of ovarian tumors. Cancer. 2003;97:2631–2642. doi: 10.1002/cncr.11345. [DOI] [PubMed] [Google Scholar]
- 17.Molarius A, Seidell JC. Selection of anthropometric indicators for classification of abdominal fatness–a critical review. Int J Obes Relat Metab Disord. 1998;22:719–727. doi: 10.1038/sj.ijo.0800660. [DOI] [PubMed] [Google Scholar]
- 18.Chang ET, Canchola AJ, Lee VS, et al. Wine and other alcohol consumption and risk of ovarian cancer in the California Teachers Study cohort. Cancer Causes Control. 2007;18:91–103. doi: 10.1007/s10552-006-0083-x. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 19.Chang ET, Lee VS, Canchola AJ, et al. Diet and risk of ovarian cancer in the California Teachers Study cohort. Am J Epidemiol. 2007;165:802–813. doi: 10.1093/aje/kwk065. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 20.Hamilton W, Menon W. Easily missed? Ovarian cancer. Brit Med J. 2010;340:96–97. [Google Scholar]
- 21.Lataifeh I, Marsden DE, Robertson G, Gebski V, Hacker NF. Presenting symptoms of epithelial ovarian cancer. Aust N Z J Obstet Gynaecol. 2005;45:211–214. doi: 10.1111/j.1479-828X.2005.00391.x. [DOI] [PubMed] [Google Scholar]
- 22.Roett MA, Evans P. Ovarian cancer: an overview. Am Fam Physician. 2009;80:609–616. [PubMed] [Google Scholar]
- 23.Webb PM, Purdie DM, Grover S, Jordan S, Dick ML, Green AC. Symptoms and diagnosis of borderline, early and advanced epithelial ovarian cancer. Gynecol Oncol. 2004;92:232–239. doi: 10.1016/j.ygyno.2003.09.005. [DOI] [PubMed] [Google Scholar]
- 24.Kurian AW, Balise RR, McGuire V, Whittemore AS. Histologic types of epithelial ovarian cancer: have they different risk factors? Gynecol Oncol. 2005;96:520–530. doi: 10.1016/j.ygyno.2004.10.037. [DOI] [PubMed] [Google Scholar]
- 25.Anderson JP, Ross JA, Folsom AR. Anthropometric variables, physical activity, and incidence of ovarian cancer: the Iowa Women’s Health Study. Cancer. 2004;100:1515–1521. doi: 10.1002/cncr.20146. [DOI] [PubMed] [Google Scholar]
- 26.Baer HJ, Hankinson SE, Tworoger SS. Body size in early life and risk of epithelial ovarian cancer: results from the Nurses’ Health Studies. Br J Cancer. 2008;99:1916–1922. doi: 10.1038/sj.bjc.6604742. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 27.Beehler GP, Sekhon M, Baker JA, et al. Risk of ovarian cancer associated with BMI varies by menopausal status. J Nutr. 2006;136:2881–2886. doi: 10.1093/jn/136.11.2881. [DOI] [PubMed] [Google Scholar]
- 28.Fairfield KM, Willett WC, Rosner BA, Manson JE, Speizer FE, Hankinson SE. Obesity, weight gain, and ovarian cancer. Obstet Gynecol. 2002;100:288–296. doi: 10.1016/s0029-7844(02)02053-7. [DOI] [PubMed] [Google Scholar]
- 29.Kuper H, Cramer DW, Titus-Ernstoff L. Risk of ovarian cancer in the United States in relation to anthropometric measures: does the association depend on menopausal status? Cancer Causes Control. 2002;13:455–463. doi: 10.1023/a:1015751105039. [DOI] [PubMed] [Google Scholar]
- 30.Peterson NB, Trentham-Dietz A, Newcomb PA, et al. Relation of anthropometric measurements to ovarian cancer risk in a population-based case-control study (United States) Cancer Causes Control. 2006;17:459–467. doi: 10.1007/s10552-005-0416-1. [DOI] [PubMed] [Google Scholar]
- 31.Schouten LJ, Goldbohm RA, van den Brandt PA. Height, weight, weight change, and ovarian cancer risk in the Netherlands cohort study on diet and cancer. Am J Epidemiol. 2003;157:424–433. doi: 10.1093/aje/kwf224. [DOI] [PubMed] [Google Scholar]
- 32.Lahmann PH, Cust AE, Friedenreich CM, et al. Anthropometric measures and epithelial ovarian cancer risk in the European Prospective Investigation into Cancer and Nutrition. Int J Cancer. 2009;126:2404–2415. doi: 10.1002/ijc.24952. [DOI] [PubMed] [Google Scholar]
- 33.Kotsopoulos J, Baer HJ, Tworoger SS. Anthropometric Measures and Risk of Epithelial Ovarian Cancer: results from the nurses’ health study. Obesity. 2009;18:1625–1631. doi: 10.1038/oby.2009.461. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 34.Leitzmann MF, Koebnick C, Danforth KN, et al. Body mass index and risk of ovarian cancer. Cancer. 2009;115:812–822. doi: 10.1002/cncr.24086. [DOI] [PMC free article] [PubMed] [Google Scholar]