Obesity and risk of ovarian cancer subtypes: evidence from the Ovarian Cancer Association Consortium

in Endocrine-Related Cancer

Whilst previous studies have reported that higher BMI increases a woman's risk of developing ovarian cancer, associations for the different histological subtypes have not been well defined. As the prevalence of obesity has increased dramatically, and classification of ovarian histology has improved in the last decade, we sought to examine the association in a pooled analysis of recent studies participating in the Ovarian Cancer Association Consortium. We evaluated the association between BMI (recent, maximum and in young adulthood) and ovarian cancer risk using original data from 15 case–control studies (13 548 cases and 17 913 controls). We combined study-specific adjusted odds ratios (ORs) using a random-effects model. We further examined the associations by histological subtype, menopausal status and post-menopausal hormone use. High BMI (all time-points) was associated with increased risk. This was most pronounced for borderline serous (recent BMI: pooled OR=1.24 per 5 kg/m2; 95% CI 1.18–1.30), invasive endometrioid (1.17; 1.11–1.23) and invasive mucinous (1.19; 1.06–1.32) tumours. There was no association with serous invasive cancer overall (0.98; 0.94–1.02), but increased risks for low-grade serous invasive tumours (1.13, 1.03–1.25) and in pre-menopausal women (1.11; 1.04–1.18). Among post-menopausal women, the associations did not differ between hormone replacement therapy users and non-users. Whilst obesity appears to increase risk of the less common histological subtypes of ovarian cancer, it does not increase risk of high-grade invasive serous cancers, and reducing BMI is therefore unlikely to prevent the majority of ovarian cancer deaths. Other modifiable factors must be identified to control this disease.

Abstract

Whilst previous studies have reported that higher BMI increases a woman's risk of developing ovarian cancer, associations for the different histological subtypes have not been well defined. As the prevalence of obesity has increased dramatically, and classification of ovarian histology has improved in the last decade, we sought to examine the association in a pooled analysis of recent studies participating in the Ovarian Cancer Association Consortium. We evaluated the association between BMI (recent, maximum and in young adulthood) and ovarian cancer risk using original data from 15 case–control studies (13 548 cases and 17 913 controls). We combined study-specific adjusted odds ratios (ORs) using a random-effects model. We further examined the associations by histological subtype, menopausal status and post-menopausal hormone use. High BMI (all time-points) was associated with increased risk. This was most pronounced for borderline serous (recent BMI: pooled OR=1.24 per 5 kg/m2; 95% CI 1.18–1.30), invasive endometrioid (1.17; 1.11–1.23) and invasive mucinous (1.19; 1.06–1.32) tumours. There was no association with serous invasive cancer overall (0.98; 0.94–1.02), but increased risks for low-grade serous invasive tumours (1.13, 1.03–1.25) and in pre-menopausal women (1.11; 1.04–1.18). Among post-menopausal women, the associations did not differ between hormone replacement therapy users and non-users. Whilst obesity appears to increase risk of the less common histological subtypes of ovarian cancer, it does not increase risk of high-grade invasive serous cancers, and reducing BMI is therefore unlikely to prevent the majority of ovarian cancer deaths. Other modifiable factors must be identified to control this disease.

Introduction

It is widely accepted that being overweight or obese increases a woman's risk of developing endometrial and post-menopausal breast cancer (Calle & Kaaks 2004). The association with ovarian cancer is less clear, largely because individual studies have had insufficient power to reliably detect moderate effects or to consider the different histological subtypes of ovarian cancer. In 2008, a pooled analysis of cohort studies concluded that BMI was associated with ovarian cancer in pre-menopausal women only, however this analysis only included 2000 cases and thus also had limited power to evaluate the different histological subtypes separately (Schouten et al. 2008). A recent pooled analysis conducted to overcome these limitations concluded that among women who have not used hormone replacement therapy (HRT), the risk of ovarian cancer increases by 10% for every 5 kg/m2 increase in BMI (Collaborative Group on Epidemiological Studies of Ovarian Cancer 2012). This association did not vary significantly for the different histological subtypes of ovarian cancer, with the exception of borderline serous cancers where the excess relative risk (RR) was substantially greater than for the other tumour types. There was no increase in risk with increasing BMI among women who had used HRT.

However, the mean year of diagnosis of the cases in the studies included in the previous report was 1992 (Collaborative Group on Epidemiological Studies of Ovarian Cancer 2012) and over the last few decades, most countries have seen dramatic increases in the prevalence of overweight and obesity (Finucane et al. 2011). Classification of the different histological subtypes of ovarian cancer has also improved in recent years (Gilks & Prat 2009) and it is possible that misclassification in earlier studies might have masked differences between the histological subtypes. In particular, it is now recognised that low- and high-grade invasive serous cancers are distinct entities and that many cancers previously described as high-grade endometrioid tumours should really be classified as high-grade serous cancers (Gilks & Prat 2009). We therefore sought to confirm the results of the previous analysis in a second, independent pooled analysis using data from more recent studies that met the inclusion criteria for the Ovarian Cancer Association Consortium (OCAC) collaboration (Ramus et al. 2008). We examined the associations by histological subtype and tumour grade and by menopausal status and HRT use because, if the effects of obesity on ovarian cancer risk are mediated through oestrogenic pathways, then any association between BMI and risk may be more evident among women who have not used exogenous oestrogens. We also evaluated the relation between body size at different ages and ovarian cancer risk.

Materials and methods

OCAC was founded in 2005 to foster collaborative efforts in discovering and validating associations between genetic polymorphisms and ovarian cancer risk. A detailed description has been provided elsewhere (Ramus et al. 2008) but, briefly, studies were eligible for inclusion if they included at least 200 cases of ovarian cancer and 200 controls, with controls from broadly the same population as cases, and provided DNA for genetic analyses. Table 1 summarizes the characteristics of the 15 case–control studies (14 population-based and one clinic-based) that provided data for these analyses (Ziogas et al. 2000, Royar et al. 2001, Glud et al. 2004, Pike et al. 2004, Hoyo et al. 2005, Terry et al. 2005, Risch et al. 2006, Garcia-Closas et al. 2007, Rossing et al. 2007, Kelemen et al. 2008, Lurie et al. 2008, Merritt et al. 2008, Moorman et al. 2008, Wu et al. 2009, Balogun et al. 2011, Bandera et al. 2011, Ness et al. 2011). Race/ethnicity was categorised as non-Hispanic White (88%), Hispanic White (3%), Black (4%), Asian (3%) or other (2%). All studies had ethics approval, and all study participants provided informed consent.

Table 1

Characteristics of the 15 studies included in the pooled analyses of BMI and ovarian cancer

Number of:HistologyBMI measurement
StudyDiagnosis (years)Age (range)Geographic locationCasesaControlsaControl sources (response rate)Invasive cases (%)Borderline cases (%)Control sources (response rate)RecentEarly adulthoodMaximum
Clinic-based
 Mayo Clinic Ovarian Cancer Case Control Study (MAY)2000–200820–91Upper Midwest, USA715945Mayo Clinic (84%)Ser 405 (57%)Ser 59 (8%)Women seeking general examinations (65%)Y
Muc 19 (3%)Muc 27 (4%)
End 100 (14%)Other 14 (2%)
CC 44 (6%)
Other 47 (7%)
Population-based
 Australian Ovarian Cancer Study and Australian Cancer Study (Ovarian Cancer; AUS)2002–200618–80Australia15791485Cancer registries, treatment centres (84%)Ser 756 (48%)Ser 150 (9%)Electoral roll (47%)YYY
Muc 49 (3%)Muc 169 (11%)
End 150 (9%)Other 15 (1%)
CC 98 (6%)
Other 192 (12%)
 Connecticut Ovary Study (CON)1998–200334–81Connecticut, USA483551Cancer registries, pathology departments (69%)Ser 221 (46%)Ser 69 (14%)Random digit dialling (61%)YY
Muc 19 (4%)Muc 36 (7%)
End 74 (15%)Other 4 (1%)
CC 35 (7%)
Other 25 (5%)
 Diseases of the Ovary and their Evaluation Study (DOV)2002–200535–74Washington, USA15691848Cancer Surveillance System, SEER (77%)Ser 672 (43%)Ser 234 (15%)Random digit dialling (69%)YYY
Muc 33 (2%)Muc 156 (10%)
End 187 (12%)Other 24 (2%)
CC 87 (6%)
Other 176 (11%)
 German Ovarian Cancer Study (GER)1993–199821–75Germany254519 Hospital admissions (58%)Ser 106 (42%)Ser 15 (6%)Population registries (51%)YY
Muc 26 (10%)Muc 9 (4%)
End 26 (10%)Other 3 (1%)
CC 6 (2%)
Other 63 (25%)
 Hawaii Ovarian Cancer Study (HAW)1993–200818–93Hawaii, USA8831089Cancer registry (78%)Ser 312 (35%)Ser 88 (10%)Department of Health Annual Survey (80%)YY
Muc 70 (8%)Muc 87 (10%)
End 116 (13%)Other 7 (1%)
CC 81 (9%)
Other 122 (14%)
 Hormones and Ovarian Cancer Prediction (HOP)2003–200925–80New York, Ohio and Pennsylvania, USA7711803Cancer registries, pathology databases, physician offices (69%)Ser 364 (47%)Ser 58 (8%)Random digit dialling (81%)YYY
Muc 36 (5%)Muc 29 (4%)
End 97 (13%)Other 10 (1%)
CC 52 (7%)
Other 125 (16%)
 The Danish Malignant Ovarian Tumour Study (MAL)1994–199935–79Denmark7441552Danish Cancer Registry, 16 gynaecologic departments 79%)Ser 337 (45%)Ser 103 (14%)Danish Central Population Register (67%)YY
Muc 50 (7%)Muc 87 (12%)
End 75 (10%)Other 10 (1%)
CC 43 (6%)
Other 39 (5%)
 North Carolina Ovarian Cancer Study (NCO)1999–200720–75North Carolina, USA10871083North Carolina Central Cancer Registry (70%)Ser 470 (43%)Ser 155 (14%)Random digit dialling (63%)YYY
Muc 43 (4%)Muc 64 (6%)
End 138 (13%)Other 5 (0%)
CC 88 (8%)
Other 122 (11%)
 New England-based Case–Control Study of Ovarian Cancer (NEC)1992–200818–78New England, USA19602097Hospital tumour boards, State cancer registries (72%)Ser 819 (43%)Ser 242 (13%)Random digit dialling and townbook selection (69%)YY
Muc 89 (5%)Muc 145 (8%)
End 296 (16%)Other 35 (2%)
CC 192 (10%)
Other 70 (4%)
 New Jersey Ovarian Cancer Study (NJO)2004–200823–96New Jersey, USA224448NJ State Cancer Registry (47%)Ser 129 (58%)Random digit dialling, Medicare and Medicaid lists, area sampling (40%)YYY
Muc 11 (5%)
End 31 (14%)
CC 30 (13%)
Other 23 (10%)
 Polish Ovarian Cancer Study (POL)2001–200324–74Poland2831071Hospitals in Warsaw and Lodz (71%)Ser 116 (41%)Ser 17 (6%)Electoral roll (67%)YY
Muc 19 (7%)Muc 3 (1%)
End 39 (14%)Other 1 (0%)
CC 10 (4%)
Other 78 (28%)
 UC Irvine Ovarian Cancer Study (UCI)1994–200418–86Orange and San Diego counties, USA588565Orange County Cancer Surveillance Program, Tumour Registry (70%)Ser 211 (36%)Ser 122 (21%)Random digit dialling (80%)YY
Muc 28 (5%)Muc 74 (13%)
End 72 (12%)Other 1 (0%)
CC 37 (6%)
Other 43 (7%)
 UK Ovarian Cancer Population Study (UKO)2006–201050–76UK6871026Gynaecologic oncology NHS centres (86%)Ser 348 (51%)Post-menopausal women participating in UKCTOPCSb (97%)Y
Muc 69 (10%)
End 106 (15%)
CC 65 (9%)
Other 99 (14%)
 Los Angeles County Case–Control Studies of Ovarian Cancer (USC)1993–200919–86Los Angeles, USA17211831Cancer Surveillance Program of Los Angeles (73%)Ser 826 (48%) Muc 112 (7%) End 183 (11%) CC 87 (5%) Other 110 (6%)Ser 240 (14%) Muc 158 (9%) Other 5 (0%)Neighbourhood controls (73%)YY

Ser, serous; Muc, mucinous; End, endometrioid; CC, clear cell; other includes both ‘other’ histologies and subjects with unknown histology.

Numbers of participants with body-size information.

United Kingdom Collaborative Trial of Ovarian Cancer Screening.

Analysis variables

There was some variation in the way weight information was collected by the individual studies (Supplementary Table 1, see section on supplementary data given at the end of this article). Weight in early adulthood was reported by 14 studies (all except MAY); this was reported as weight at age 18 for nine studies and at age 20 for two studies (AUS and GER), while three studies reported weight ‘in your 20s’ (CON, MAL and USC). Recent weight was reported by 11 studies (AUS, CON, DOV, HOP, MAL, MAY, NCO, NJO, NEC, UCI and USC); for most studies this was reported as weight 1 year prior to diagnosis/reference date, but 5 years prior to diagnosis/reference date was used for four studies (CON, DOV, MAL and USC). To minimise overlap between our analyses of recent weight and the previous pooled analysis (The Collaborative Group on Epidemiological Studies of Ovarian Cancer 2012), we excluded two studies (GER and HAW) that were included in the previous analysis, but included two studies (NEC and USC) that had contributed only part of their data to the previous analysis (total overlap ∼1200 cases). Maximum weight was reported by eight studies (AUS, DOV, GER, HAW, HOP, NCO, NJO and POL). BMI, calculated as weight in kilograms divided by the square of height in metres (kg/m2), was classified using the WHO definitions of obesity (<18.5 ‘underweight’; 18.5–24.9 ‘normal weight’; 25–29.9 ‘overweight’; 30–34.9 ‘class I obesity’; 35–39.9 ‘class II obesity’ and ≥40 ‘class III obesity’; WHO 1995). For subgroup analyses there were small numbers in the upper classes of obesity for BMI in early adulthood, so these groups were combined.

Covariate information

Each case–control study provided information on potential confounding variables including age, cancer grade, race/ethnicity, parity, breastfeeding, oral contraceptive (OC) and HRT use, family history of breast or ovarian cancer in a first-degree relative, menopausal status and history of hysterectomy or tubal ligation. All data were cleaned and checked for internal consistency and clarification was provided by the original investigators when needed.

Statistical analysis

We used Stukel's two-stage method of analysis to obtain study-specific odds ratios (ORs) and pooled ORs (pORs) and 95% CIs (Stukel et al. 2001). In the first stage, each study was analysed separately, controlling for study-specific confounders. The pooled exposure effect was estimated in a second stage using a meta-analytic approach. A weighted average of the log RR was estimated, taking into account the random effects using the method of DerSimonian & Laird (1986). Statistical heterogeneity among studies was evaluated using Cochran's Q-test and I2 statistics (Higgins & Thompson 2002). All models were stratified by age in 5-year groups and adjusted for parity (0, 1, 2, 3, 4+ full-term births), OC use (0, ≤60 and >60 months) and family history of breast or ovarian cancer in a first-degree relative. We also adjusted study-specific results for race/ethnicity (non-Hispanic White, Hispanic White, Black, Asian and other) where more than 10% of the study population was not classified as non-Hispanic White and inclusion of a term for race/ethnicity altered the OR by 10% or more. Other potential confounders considered but not included in final models since they did not make any material change to the BMI associations were breastfeeding, history of hysterectomy, tubal ligation, menopausal status and HRT. Adjusting for history of endometriosis made no material change to the pooled estimates for the endometrioid or clear cell subtypes and thus it was not included in final models. Data on smoking status were not available for all studies, however including smoking status in models where it was available did not result in significant changes to the pooled estimates and thus it was not included in final models. Covariate data were mostly complete and uniformly coded for all studies with a few exceptions. The parity variable included all full-term births (live and still births) for all studies except MAY which recorded only live births. Secondly, tubal ligation and breastfeeding data were unavailable for the MAY study. These missing covariates were therefore not included in the first-stage models for this study.

We initially computed ORs for each of the primary exposure variables for invasive and borderline cancers separately and then further classified tumours by their histological subtype (serous, mucinous, endometrioid and clear cell). In the subtype-specific models, adjacent levels of confounders were collapsed where necessary to avoid zero cells in the two-stage models. Where heterogeneity was evident, we examined the data for potential sources of this heterogeneity including type of control group (population vs hospital-based) and style of questionnaire (self-completed vs in-person interview). The RR of ovarian cancer per 5 kg/m2 increase in BMI was estimated by fitting a log-linear trend across categories of BMI (18.5–<20, 20−, 22.5−, 25−, 27.5−, 30−, 32.5−, 35−, 37.5−, 40+ kg/m2) using the overall median value within each category, except for the top category where we used the site-specific median as this varied between sites. Since we were interested in the effects of being overweight and speculated that the relation between BMI and cancer risk might not be linear at very low BMI levels, these analyses excluded women in the ‘underweight’ range (BMI <18.5 kg/m2).

We also conducted subgroup analyses to assess the interaction between recent BMI, menopausal status and use of any HRT (pre-/peri-menopausal, post-menopausal and never used HRT, post-menopausal and had used HRT). There was some heterogeneity in how menopausal status was defined across studies, so we also conducted analyses stratified by age at diagnosis (<50 and ≥50 years). To avoid problems with zero cells in some studies in these and other subgroup analyses, we pooled all data and computed ORs using logistic regression stratified by study site and age in 5-year groups in order to maximise the statistical power. The statistical significance of any observed stratum-specific differences was then assessed by including a cross-product term (using the continuous BMI variables defined above) in regression models.

Analyses were conducted using SAS (SAS Institute, Cary, NC, USA) and Stata 10 (College Station, TX, USA).

Results

Eleven studies contributed to analyses of recent BMI, eight studies for maximum BMI and 14 studies for BMI in early adulthood (Table 1). Using the two-stage method of analysis, we observed significantly increased risks of both invasive and borderline ovarian cancers associated with higher BMI at all three time-points. The association was modest for invasive tumours with an increase in risk of 4% per 5 kg/m2 for recent BMI and 8% for BMI in early adulthood, but was stronger for borderline tumours with increases of 15–18% per 5 kg/m2 for the different time-points (Table 2).

Table 2

Adjusteda pooled ORs (95% CIs) for ovarian cancer in relation to BMI, by tumour behaviourb

InvasiveBorderline
BMI (kg/m2)StudiesI2 (%)CasesControlspOR (95%CI)Studies I2 (%)CasesControlspOR (95%CI)
Recent BMI
 <18.51126.61832821.08 (0.84–1.39)100.0572811.13 (0.82–1.55)
 18.5–24.9 (ref)11402067961.010108065991.0
 25–29.91131.2250040771.00 (0.92–1.09)1013.866239301.23 (1.09–1.39)
 30–34.9110.0116618081.06 (0.97–1.16)101.137917411.61 (1.40–1.85)
 35–39.9111.75116921.21 (1.07–1.38)100.01506721.68 (1.37–2.06)
 ≥40110.03835031.22 (1.05–1.41)90.01374861.96 (1.57–2.46)
 Per 5 kg/m2c1147.71.04 (1.00–1.08)*90.01.18 (1.14–1.23)
Maximum BMI
 <18.560.024331.22 (0.69–2.14)30.05191.00 (0.33–3.03)
 18.5–24.9 (ref)8139326831.0 729625481.0
 25–29.986.2142725661.02 (0.92–1.13)719.727524091.13 (0.91–1.41)
 30–34.980.082313351.17 (1.04–1.31)717.019912361.58 (1.24–2.03)
 35–39.9860.23885921.29 (0.99–1.68)*60.01055441.70 (1.30–2.22)
 ≥40815.93104901.16 (0.96–1.41)530.51084551.90 (1.35–2.68)
 Per 5 kg/m2c845.31.06 (1.01–1.11)735.81.17 (1.08–1.26)
BMI early adulthood
 <18.5140.0164629310.94 (0.88–1.01)1213.041627180.97 (0.85–1.11)
 18.5–24.9 (ref)14727812 3641.0 12181911 2451.0
 25–29.9140.078811511.12 (1.01–1.24)110.02489831.27 (1.09–1.49)
 30–34.9140.01762331.21 (0.98–1.49)100.0662101.32 (0.98–1.78)
 ≥35120.0761081.08 (0.78–1.49)100.0431001.86 (1.25–2.78)
 Per 5 kg/m2c141.08 (1.03–1.14)121.15 (1.08–1.24)

*Significant heterogeneity noted (P value for heterogeneity <0.05).

Stratified by age in 5-year groups and adjusted for parity (0, 1, 2, 3, 4+ full-term births), hormonal contraceptive use (0, ≤60 and >60 months), family history of breast or ovarian cancer in a first-degree relative and, where appropriate, race/ethnicity.

Numbers may not sum to total because of missing data.

Excludes women in the underweight range (BMI <18.5 kg/m2).

Results of the pooled analyses stratified by histological subtype are presented in Tables 3 and 4 for invasive and borderline tumours respectively. Overall, risk of invasive serous cancer was not associated with any measure of BMI (Table 3). However, stratification by tumour grade (data available for 91% of cases) revealed positive associations between all measures of BMI and risk of low-grade (G1) invasive serous tumours (OR=1.13, 1.18 and 1.24 per 5 kg/m2 for recent, maximum and young adult BMI respectively, all P<0.01) but not high-grade (G2–G4) tumours (OR=0.96, 0.96 and 0.98 respectively). Higher BMI (all BMI variables) was significantly associated with an increased risk of invasive endometrioid ovarian cancer. This association was restricted to low- and intermediate-grade (G1 and G2) tumours (OR per 5 kg/m2 1.25, 1.22 and 1.20 for recent, maximum and young adulthood BMI respectively, all P≤0.001) and was not seen for high-grade (G3 and G4) endometrioid cancers (OR=0.97, 1.02 and 0.90 respectively) (data on grade available for 93% of cases). The associations between BMI and invasive mucinous and clear cell cancers were less clear, with increased risks of both tumour types associated with high recent BMI and, for mucinous cancers, BMI in young adulthood, but not maximum BMI. The results for recent BMI were essentially unaltered when we restricted the analysis to include only studies that assessed weight around 5 years prior to diagnosis to reduce potential bias due to recent weight loss in cases. Considering all non-serous invasive cancers together, the association with recent BMI remained significant after adjusting for maximum BMI or BMI in young adulthood, however after adjusting for recent BMI there was no association with either maximum BMI (OR=1.02, 95% CI 0.95–1.11 per 5 kg/m2) or BMI in young adulthood (OR=0.96, 95% CI 0.86–1.08 per 5 kg/m2).

Table 3

Adjusteda pooled ORs (95% CIs) for invasive ovarian cancer in relation to BMI, by histological subtypeb

SerousMucinousEndometrioidClear cell
Studies (n)Controls (n)Cases (n)pOR (95% CI)Cases (n)pOR (95% CI)Cases (n)pOR (95% CI)Cases (n)pOR (95% CI)
Recent BMI11
 <18.5 282910.93 (0.72–1.20)192.48 (1.03–4.51)331.47 (0.98–2.21)182.69 (1.34–5.41)
 18.5–24.9 (ref)679624751.02071.05921.03531.0
 25–29.9407714770.93 (0.86–1.02)1341.19 (0.95–1.50)3801.12 (0.96–1.30)2271.05 (0.79–1.40)*
 30–34.918086650.94 (0.84–1.05)681.48 (0.92–2.37)*2051.37 (1.14–1.64)981.14 (0.79–1.63)*
 35–39.96922751.06 (0.90–1.23)292.03 (1.10–3.77)971.74 (1.36–2.23)481.59 (1.14–2.24)
 ≥405031700.89 (0.74–1.08)292.70 (1.76–4.16)821.86 (1.42–2.24)371.58 (1.04–2.40)
 Per 5 kg/m2c0.98 (0.94–1.02)1.19 (1.06–1.32)1.17 (1.11–1.23)1.06 (0.96–1.17)*
Maximum BMI8
 18.5–24.9 (ref)26837931.0861.01771.01201.0
 25–29.925667870.93 (0.73–1.17)*841.22 (0.88–1.69)1941.20 (0.96–1.45)1120.95 (0.72–1.26)
 30–34.913354451.03 (0.89–1.18)341.08 (0.70–1.67)1291.63 (1.26–2.10)691.22 (0.88–1.70)
 35–39.95921991.18 (0.81–1.72)*201.30 (0.74–2.27)671.78 (1.29–2.46)331.30 (0.84–2.00)
 ≥404901470.98 (0.68–1.41)*171.37 (0.76–2.46)601.82 (1.29–2.56)271.12 (0.70–1.82)
 Per 5 kg/m2c1.00 (0.93–1.07)1.05 (0.94–1.17)1.18 (1.09–1.28)1.04 (0.95–1.13)
Early adult14
 <18.529319180.94 (0.86–1.03)1020.94 (0.74–1.19)2430.93 (0.80–1.09)1641.08 (0.83–1.39)
 18.5–24.9 (ref)12 36441611.04651.011211.06481.0
 25–29.911514011.04 (0.92–1.18)541.20 (0.88–1.64)1501.33 (1.10–1.62)641.05 (0.75–1.45)
 30–34.9231731.03 (0.78–1.37)191.90 (1.12–3.21)391.51 (1.03–2.21)141.10 (0.61–1.99)
 ≥35110361.15 (0.75–1.76)72.18 (0.96–4.95)181.85 (1.05–3.24)62.73 (1.08–6.88)
 Per 5 kg/m2c1.02 (0.95–1.10)1.22 (1.07–1.40)1.14 (1.04–1.25)1.02 (0.89–1.16)

*Significant heterogeneity noted (P value for heterogeneity <0.05).

Stratified by age in 5-year groups and adjusted for parity (0, 1, 2, 3, 4+ full-term births), hormonal contraceptive use (0, ≤60 and >60 months), family history of breast or ovarian cancer in a first-degree relative and, where appropriate, race/ethnicity; pooled across study sites using random effects models.

Numbers may not sum to total because of missing data.

Excludes women in the underweight range (BMI <18.5 kg/m2).

Table 4

Adjusteda pooled ORs (95% CIs) for borderline ovarian cancer in relation to BMI, by histological subtype

SerousbMucinousb
Studies (n)Controls (n)Cases (n)pOR (95% CI)Cases (n)pOR (95% CI)
Recent BMI10
 <18.5281231.12 (0.70–1.79)331.61 (1.08–2.39)
 18.5–24.9 (ref)65995681.04541.0
 25–29.939304031.40 (1.22–1.62)2341.08 (0.91–1.28)
 30–34.917412361.86 (1.55–2.24)1221.32 (1.05–1.67)
 35–39.96721012.11 (1.66–2.70)411.29 (0.91–1.84)
 ≥40486852.23 (1.69–2.94)411.68 (1.16–2.43)
 Per 5 kg/m2c1.24 (1.18–1.30)1.09 (1.02–1.16)
Maximum BMI7
 18.5–24.9 (ref)25481351.01381.0
 25–29.924091531.39 (1.00–1.93)1130.99 (0.75–1.30)
 30–34.912361152.00 (1.51–2.65)781.39 (0.99–1.96)
 35–39.9544662.40 (1.71–3.38)351.26 (0.68–2.32)
 ≥40455712.73 (1.92–3.88)301.29 (0.79–2.11)
 Per 5 kg/m2c1.25 (1.17–1.34)1.09 (0.98–1.21)
BMI early adulthood12
 <18.527182220.90 (0.77–1.06)1711.07 (0.88–1.31)
 18.5–24.9 (ref)11 24510341.06991.0
 25–29.99831521.40 (1.12–1.74)861.22 (0.95–1.55)
 30–34.9210401.48 (1.03–2.14)261.57 (1.00–2.47)
 ≥35100292.34 (1.47–3.74)122.00 (1.00–4.01)
 Per 5 kg/m2c1.22 (1.12–1.33)1.11 (0.99–1.24)

Stratified by age in 5-year groups and adjusted for parity (0, 1, 2, 3, 4+ full-term births), hormonal contraceptive use (0, ≤60 and >60 months), family history of breast or ovarian cancer in a first-degree relative and, where appropriate, race/ethnicity; pooled across study sites using random effects models.

Numbers may not sum to total because of missing data.

Excludes women in the underweight range (BMI <18.5 kg/m2).

Increasing BMI (all BMI variables) was associated with increased risks of both borderline serous and mucinous ovarian cancers, with significant trends with increasing BMI that were stronger for borderline serous cancers (20–25% increase per 5 kg/m2) than borderline mucinous cancers (9–11% per 5 kg/m2; Table 4).

Although there was some heterogeneity among studies for some of the pooled estimates, heterogeneity for the estimates per 5 kg/m2 only reached statistical significance for recent BMI and risk of clear cell tumours and the combined group of all invasive tumours; sensitivity analyses by study design features suggested that no single factor could explain this observed heterogeneity.

When we combined all tumour types and stratified by ever use of HRT, we observed a significant association between BMI and cancer risk among women who had not used HRT (OR per 5 kg/m2=1.10; 95% CI 1.07–1.14) but no association among women who had used HRT (1.02; 0.97–1.07). However, we saw markedly different patterns of association when we considered pre- and post-menopausal women and the different histological subtypes of cancer separately (Table 5). When we stratified by menopausal status and use of HRT, we saw significant interaction for recent BMI and risk of invasive serous cancers (P≤0.001). A significant trend of increasing risk with increasing BMI was observed in pre-menopausal women, with no association among post-menopausal women who had never used HRT, and a significant inverse association among those who had used HRT. Further stratification of the pre-menopausal group suggested that the positive association was stronger for G1 (OR=1.34, 95% CI 1.14–1.59) but still statistically significant for G2–G4 tumours (OR=1.07, 95% CI 1.00–1.15; P<0.05). A similar pattern was seen in analyses of maximum BMI and BMI in young adulthood (data not shown), suggesting that the lack of a positive association among post-menopausal women was not simply an artefact due to recent weight loss among women with serous cancer. For all other invasive subtypes combined, the association was somewhat stronger among pre-menopausal women than post-menopausal women but did not differ by HRT use among post-menopausal women. The association with borderline tumours did not vary by menopausal status or HRT use. When we stratified by age at diagnosis (<50 and ≥50 years) instead of menopausal status the results did not differ materially (data not shown).

Table 5

Adjusteda ORs (95% CIs) for ovarian cancer in relation to recent BMI, by menopausal status and use of HRT

Invasive serousbAll other invasive cancersbAll borderlineb
Controls (n)Cases (n)OR (95% CI)Cases (n)OR (95% CI)Cases (n)OR (95% CI)
Pre-menopausal women
 18.5–24.9 (ref)20494841.05141.05291.0
 25–29.99192721.23 (1.03–1.47)2751.26 (1.06–1.51)2541.22 (1.02–1.46)
 30–34.94171211.21 (0.96–1.54)1391.40 (1.11–1.76)1471.63 (1.30–2.05)
 35–39.9152551.50 (1.07–2.10)761.78 (1.30–2.45)652.00 (1.44–2.78)
 ≥40136471.43 (0.99–2.06)721.81 (1.30–2.52)521.76 (1.22–2.53)
 Per 5 kg/m2c1.11 (1.04–1.18)1.17 (1.11–1.24)1.19 (1.12–1.27)
Post-menopausal women, no HRT
 18.5–24.9 (ref)13436521.03471.01571.0
 25–29.910544250.87 (0.74–1.01)3121.20 (1.00–1.43)1241.17 (0.90–1.51)
 30–34.95222160.93 (0.77–1.12)1531.24 (0.99–1.55)821.60 (1.19–2.16)
 35–39.9226870.89 (0.67–1.16)671.24 (0.91–1.69)301.36 (0.88–2.09)
 ≥40157610.87 (0.63–1.21)651.64 (1.18–2.29)332.12 (1.37–3.29)
 Per 5 kg/m2c0.97 (0.92–1.03)1.10 (1.03–1.17)1.17 (1.08–1.27)
Post-menopausal women who used HRT
 18.5–24.9 (ref)16507781.03131.01381.0
 25–29.911234400.86 (0.75–1.00)2211.08 (0.89–1.31)1121.35 (1.03–1.76)
 30–34.94801830.86 (0.71–1.05)1011.19 (0.92–1.54)601.64 (1.18–2.28)
 35–39.9167751.08 (0.78–1.45)311.15 (0.76–1.74)201.67 (1.00–2.78)
 ≥40111230.49 (0.30–0.77)311.64 (1.06–2.52)131.48 (0.80–2.76)
 Per 5 kg/m2c0.92 (0.87–0.98)1.09 (1.01–1.18)1.16 (1.05–1.28)

Stratified by study site (AUS, DOV, HOP, MAY, NEC, NJO, UCI and USC) and age in 5-year groups, and adjusted for parity (0, 1, 2, 3, 4+ full-term births), hormonal contraceptive use (0, ≤60 and >60 months), family history of breast or ovarian cancer in a first-degree relative.

Numbers may not sum to total because of missing data.

Excludes women in the underweight range (BMI <18.5 kg/m2).

Discussion

The results of our pooled analysis confirm that being overweight or obese is associated with an overall increased risk of both invasive and borderline ovarian cancers, however for invasive cancers this association appears to be restricted to the non-serous and low-grade serous subtypes. Furthermore, most of our risk estimates were very consistent with those from a previous pooled analysis (Collaborative Group on Epidemiological Studies of Ovarian Cancer 2012) with a strong increase in risk of borderline serous cancer (pOR/RR=1.24 per 5 kg/m2 in our analysis vs 1.29 in the previous report) and intermediate risks for clear cell (1.06 vs 1.05) and invasive (1.19 vs 1.15) and borderline (1.09 vs 1.06) mucinous cancers. Like the previous report, we saw no increase in risk of invasive serous cancer overall (0.98 vs 1.00), however we did see an increased risk of low-grade invasive serous cancers (OR=1.13) which are now thought to arise via a different aetiological pathway from their high-grade counterparts. The only subtype for which our results differed appreciably was invasive endometrioid cancers where we saw a 17% increase in risk per 5 kg/m2 overall, and a 25% increase after excluding high-grade endometrioid cancers which are likely to be misclassified as serous tumours (Gilks & Prat 2009), compared with only an 8% increase in the previous study (Collaborative Group on Epidemiological Studies of Ovarian Cancer 2012).

Since endometrioid ovarian tumours are histologically similar to endometrial cancer (Russell 1994), which is strongly associated with obesity (Crosbie et al. 2010), it seems plausible that obesity might also be a relatively strong risk factor for this subtype of ovarian cancer. The roughly 70–80% risk increases we observed even among the groups of women with highest BMI were, however, considerably lower than the ninefold risk previously reported for endometrial cancer (Crosbie et al. 2010). Historically, the histopathological classification of ovarian cancer cell types has only been modestly reproducible (Hernandez et al. 1984, Cramer et al. 1987, Sakamoto et al. 1994), and particularly problematic was the specific diagnosis of serous vs endometrioid carcinomas (Stalsberg et al. 1988). A recent development is the recognition that many carcinomas formally considered high-grade endometrioid are better classified as high-grade serous (Gilks & Prat 2009, Kobel et al. 2010, Madore et al. 2010). When we excluded high-grade endometrioid tumours from our analysis the associations with BMI were considerably strengthened while, as for invasive serous cancers, we saw no association with high-grade endometrioid tumours. It is thus possible that misclassification of serous and endometrioid tumours may explain, in part, why a significant association between obesity and endometrioid ovarian cancers has not previously been consistently reported and why it was not observed in the previous large pooled analysis which included mostly older studies and did not consider tumour grade (Collaborative Group on Epidemiological Studies of Ovarian Cancer 2012). Time trends in the use of various regimens of HRT, as well as the increasing prevalence of obesity over calendar time, may also play a role.

As in the previous pooled analysis, we observed an association between increasing BMI and risk of borderline ovarian tumours, with the strength of the association somewhat stronger for serous than mucinous tumours. High BMI has been associated with benign ovarian tumours (Jordan et al. 2007), and there is evidence from epidemiological, histopathological and molecular studies that these borderline tumours may develop from benign tumours in a neoplastic progression (Jordan et al. 2006). Our finding that low-grade but not high-grade invasive serous tumours were also associated with BMI supports this theory of progression for low-grade serous cancers.

We can only speculate as to why we observed heterogeneity in the association between BMI and risk of invasive serous tumours between pre- and post-menopausal women, however this could not be explained by a higher proportion of G1 tumours in the pre-menopausal group. The endocrine consequences of obesity may have differential effects on the pathogenesis of serous ovarian cancer in pre- and post-menopausal women. Whilst post-menopausal obesity is associated with higher levels of endogenous oestrogen due to the synthesis of oestrogen in body fat (Key et al. 2001), in pre-menopausal women, obesity lowers sex hormone-binding globulin (Key et al. 2001, Tworoger et al. 2006) but does not significantly influence the levels of oestrogens and androgens as the ovaries produce more steroids than the peripheral fat tissue. Other hormonal factors that may mediate the relationship between obesity and risk of ovarian cancer include progesterone (Risch 1998) and insulin (Calle & Kaaks 2004). Compared with women of ‘normal’ weight, pre-menopausal obese women have reduced serum progesterone levels due to an increase in anovulatory cycles (Key et al. 2001), and there is a significant body of evidence suggesting that progesterone plays a protective role in ovarian carcinogenesis (Risch 1998). Obesity is associated with increased insulin levels, which lead to increases in the insulin-like growth factor 1 (IGF1; Calle & Kaaks 2004). There is no clear relation between adiposity and IGF1, however high levels of IGF1 have been associated with ovarian cancer in women younger than 55 years of age (Lukanova et al. 2002).

Our observation that the positive association with BMI was stronger among pre-menopausal women is consistent with the earlier analysis of cohort studies (Schouten et al. 2008). However, in contrast to the recent pooled analysis (Collaborative Group on Epidemiological Studies of Ovarian Cancer 2012), we found no suggestion of effect modification by use of HRT in post-menopausal women. Although the overall association did appear to be restricted to women who had never used HRT, this was driven by the stronger associations seen among pre-menopausal women who rarely use HRT. Similarly, the apparent lack of association among HRT users was driven by the strong inverse association with invasive serous cancers, the most common histological subtype, in this group. For the cancers that showed an overall association with BMI, non-serous invasive and borderline cancers, the risk estimates among post-menopausal women did not differ by use of HRT. Whilst data on recent or current use of menopausal hormonal therapy were not available for the current analyses, the possibility that recent use may modify the relationship between BMI and ovarian cancer risk deserves further exploration.

Strengths of our study include the large number of cases and controls made possible by pooling data from 15 individual case–control studies. Individual-level data were combined into a single dataset following a rigorous data cleaning and harmonisation protocol, giving enhanced ability to control for confounding in individual studies (Stukel et al. 2001). Pooling these data increased our statistical power to examine BMI in relation to the different histological subtypes of ovarian cancer, and allowed subgroup analyses to examine the effects by tumour grade, age, menopausal status, and for post-menopausal women, by use of HRT. Additionally, all studies contributing to the pooled analyses were conducted in the past two decades and, aside from early cases from the NEC and USC studies, a total of ∼1200 cases (10%), there was no overlap with the previous pooled analysis (Collaborative Group on Epidemiological Studies of Ovarian Cancer 2012). Histological misclassification is likely to be considerably less of a concern for these recent studies than in studies conducted in the more distant past, although some degree of misclassification remains likely.

However, as with any pooled analysis, some limitations must be acknowledged. First the majority of the studies included in the pooled analyses relied upon retrospective self-reports of weight and height. Research has shown that women with higher BMI are more likely to underestimate weight, whereas underweight women are more likely to overestimate body weight (Kuskowska-Wolk et al. 1989, Troy et al. 1995, Lawlor et al. 2002, Taylor et al. 2006); this may have attenuated the true associations. We cannot exclude the possibility of selection bias due to self-selection of more health conscious women, who are less likely to be overweight or obese, into control groups; this would have lead to overstated risk estimates. Such misclassification, however, is likely to be non-differential with respect to the different histological subtypes. Finally, weight loss several years before the time of cancer diagnosis would, if present, bias risk estimates towards the null although the similar patterns of risk seen for all three measures of BMI, and for analyses of recent BMI restricted to studies that asked women to report their usual weight ∼5 years prior to diagnosis, suggest that this has not occurred to any great extent.

In summary, obesity appears to moderately increase the risk of developing the less common histological subtypes of ovarian cancer, particularly borderline and low-grade invasive serous cancers and endometrioid cancers. With the possible exception of pre-menopausal women, it does not, however, appear to increase risk of the more common high-grade invasive serous cancers that account for the majority of ovarian cancer deaths.

Supplementary data

This is linked to the online version of the paper at http://dx.doi.org/10.1530/ERC-12-0395.

Declaration of interest

The authors declare that there is no conflict of interest that could be perceived as prejudicing the impartiality of the research reported.

Funding

This work was supported by donations by the family and friends of Kathryn Sladek Smith to the Ovarian Cancer Research Fund. The studies that contributed to this analysis were funded by the National Institutes of Health (grant numbers R01 CA13689101A1, CA14089, CA17054, CA61132, CA63464, N01 PC67010 and R03 CA113148 (for USC), R01 CA112523 and R01 CA87538 (for DOV), R01 CA58598, N01 CN67001 and N01 PC35137 (for HAW), 5R01 CA074850 and 5R01 CA080742 (for CON), R01 CA76016 (for NCO), R01-CA-122443 (for MAY), CA054419-10, P40 CA1005009 (for NEC), K07 CA095666, R01 CA83918 and K22 CA138563 CA58860 (for NJO), CA58860, CA92044 (for UCI) and RO1 CA61107 (for MAL); the California Cancer Research Program (grant numbers 0001389V20170 and 2110200 (for USC)); the California Department of Health Services (subcontract 050E8709 (for USC)); the UCIBCS component of this research (UCI) was supported by the National Institutes of Health (grant numbers CA58860, CA92044) and the Lon V Smith Foundation (grant number LVS39420); the German Federal Ministry of Education and Research of Germany, Programme of Clinical Biomedical Research (grant number 01GB9401), German Cancer Research Center and University of Ulm (for GER); the Eve Appeal, Oak Foundation, the University College Hospital, National Institute for Health Research Biomedical Research Centre and the Royal Marsden Hospital, Biomedical Research Centre (for UKO); the National Health and Medical Research Council of Australia (grant number 199600), US Army Medical Research and Materiel Command (grant numbers DAMD 170110729 and W81XWH0610220), Cancer Council Tasmania and Cancer Foundation of Western Australia (for AUS); the US Department of Defence (grant number W81XWH-10-1-0280 (for NEC)); the Cancer Institute of New Jersey (for NJO); Mermaid 1 and the Danish Cancer Society (for MAL); Intramural Research Funds of the National Cancer Institute, Department of Health and Human Services, USA (for POL).

Acknowledgements

We are grateful to the family and friends of Kathryn Sladek Smith for their generous support of the International OCAC through their donations to the Ovarian Cancer Research Fund. We thank all the individuals who took part in these studies and the project staff of all the participating studies. We thank Ursula Eilber and Tanja Koehler for their technical assistance for the German Ovarian Cancer study (GER). The Australian group gratefully acknowledges the contribution of all the clinical and scientific collaborators (see http://www.aocstudy.org)). The Connecticut Ovary Study group gratefully acknowledges the cooperation of 30 Connecticut hospitals, including Stamford Hospital, in allowing patient access. The NJ Ovarian Cancer Study group gratefully acknowledges the contribution of collaborators and staff at the New Jersey State Cancer Registry, the Cancer Institute of New Jersey and Memorial Sloan-Kettering Cancer Center. The POCS thanks Drs Mark Sherman and Nicolas Wentzensen from the National Cancer Institute, USA, Drs Neonila Szeszenia-Dabrowska and Beata Peplonska of the Nofer Institute of Occupational Medicine (Lodz, Poland), Witold Zatonski of the Department of Cancer Epidemiology and Prevention, The M Sklodowska-Curie Cancer Center and Institute of Oncology (Warsaw, Poland), and Pei Chao and Michael Stagner from Information Management Services (Sliver Spring, MD, USA), for their valuable contributions to the study. Some of the data used in the CON Study were obtained from the Connecticut Tumor Registry, Connecticut Department of Public Health.

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