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. 2015 Jul 7;113(5):817–826. doi: 10.1038/bjc.2015.245

Obesity and survival among women with ovarian cancer: results from the Ovarian Cancer Association Consortium

C M Nagle 1,*, S C Dixon 1,2, A Jensen 3, S K Kjaer 3,4, F Modugno 5,6,7, A deFazio 8,9, S Fereday 10, J Hung 8,9, S E Johnatty 11; Australian Ovarian Cancer Study Group1,10, P A Fasching 12,13, M W Beckmann 13, D Lambrechts 14,15, I Vergote 16, E Van Nieuwenhuysen 16, S Lambrechts 16, H A Risch 17, M A Rossing 18, J A Doherty 19, K G Wicklund 18, J Chang-Claude 20, M T Goodman 21, R B Ness 22, K Moysich 23, F Heitz 24,25, A du Bois 24,25, P Harter 24,25, I Schwaab 26, K Matsuo 27, S Hosono 28, E L Goode 29, R A Vierkant 29, M C Larson 29, B L Fridley 30, C Høgdall 4, J M Schildkraut 31, R P Weber 31, D W Cramer 32, K L Terry 32, E V Bandera 33, L Paddock 34, L Rodriguez-Rodriguez 33, N Wentzensen 35, H P Yang 35, L A Brinton 35, J Lissowska 36, E Høgdall 3,37, L Lundvall 4, A Whittemore 38, V McGuire 38, W Sieh 38, J Rothstein 38, R Sutphen 39, H Anton-Culver 40, A Ziogas 40, C L Pearce 41, A H Wu 41, P M Webb 1,2, for the Ovarian Cancer Association Consortium
PMCID: PMC4559823  PMID: 26151456

Abstract

Background:

Observational studies have reported a modest association between obesity and risk of ovarian cancer; however, whether it is also associated with survival and whether this association varies for the different histologic subtypes are not clear. We undertook an international collaborative analysis to assess the association between body mass index (BMI), assessed shortly before diagnosis, progression-free survival (PFS), ovarian cancer-specific survival and overall survival (OS) among women with invasive ovarian cancer.

Methods:

We used original data from 21 studies, which included 12 390 women with ovarian carcinoma. We combined study-specific adjusted hazard ratios (HRs) using random-effects models to estimate pooled HRs (pHR). We further explored associations by histologic subtype.

Results:

Overall, 6715 (54%) deaths occurred during follow-up. A significant OS disadvantage was observed for women who were obese (BMI: 30–34.9, pHR: 1.10 (95% confidence intervals (CIs): 0.99–1.23); BMI: ⩾35, pHR: 1.12 (95% CI: 1.01–1.25)). Results were similar for PFS and ovarian cancer-specific survival. In analyses stratified by histologic subtype, associations were strongest for women with low-grade serous (pHR: 1.12 per 5 kg m−2) and endometrioid subtypes (pHR: 1.08 per 5 kg m−2), and more modest for the high-grade serous (pHR: 1.04 per 5 kg m−2) subtype, but only the association with high-grade serous cancers was significant.

Conclusions:

Higher BMI is associated with adverse survival among the majority of women with ovarian cancer.

Keywords: ovarian cancer, obesity, overall survival, progression-free survival, ovarian cancer-specific survival

Ovarian cancer survival is poor, with only ∼30–50% of women alive 5 years after diagnosis and over 140 000 deaths globally per year (Jemal et al, 2011). The key prognostic factors – age, stage and grade of tumour – are not modifiable at diagnosis. Understanding how potentially modifiable factors such as obesity influence survival following a diagnosis of ovarian cancer could potentially be harnessed as a means of reducing a woman's risk of cancer progression or recurrence.

Women who are overweight or obese have increased risks of developing many types of cancer, including ovarian, when compared with women of normal weight (Reeves et al, 2007). Two large pooled analyses recently confirmed that this increased risk for ovarian cancer is modest (odds ratios ∼10% increase in risk per 5 kg m−2 increase in body mass index (BMI) (Collaborative Group on Epidemiological Studies of Ovarian Cancer, 2012; Olsen et al, 2013) and may be restricted to non-high-grade serous subtypes (Olsen et al, 2013). Evidence that obesity is a poor prognostic factor for several malignancies including the breast, prostate and colon is increasing (Calle et al, 2003; Parekh et al, 2012) and several lines of evidence suggest that obesity may also be associated with poor survival among women with ovarian cancer (Ptak et al, 2013; Diaz et al, 2013; Makowski et al, 2014). A recent meta-analysis of 14 studies concluded that women with ovarian cancer, who were obese, had 17% worse survival compared with those of normal weight (Protani et al, 2012). However, the studies in this meta-analysis varied greatly in the timing of obesity measurement: from usual adult weight to weight at the time of diagnosis, or at the commencement of chemotherapy. Most of the studies included had a relatively small sample size (median=301) and, as a consequence, variation by histologic subtype could not be investigated. Furthermore, few studies had examined progression-free survival (PFS) or ovarian cancer-specific survival.

Using data from 21 case–control studies from the Ovarian Cancer Association Consortium (OCAC), we sought to evaluate the association between BMI and survival (PFS, ovarian cancer-specific and overall survival (OS)), overall and by histologic subtype, in over 12 000 women with invasive epithelial ovarian cancer.

Materials and Methods

The OCAC consortium was founded in 2005 to combine data from individual case–control studies, to provide increased accuracy of estimates of genetic associations with ovarian cancer (Ramus et al, 2008). Twenty-one studies summarized in Table 1 provided BMI data and clinical follow-up information, allowing calculation of 5-year survival estimates for invasive ovarian, fallopian tube or peritoneal cancer cases (full study name and acronym are listed in Supplementary Table 1). All of the studies were approved by their institutional review board and all study participants provided informed consent.

Table 1. Characteristics of 21 OCAC studies included in analysis.

Site Country Source of cases Diagnosis years Age range Number of cases Reference period for BMI measurement (before diagnosis) Median (range) BMI (kg m−2) Median (range) follow-up time among living (years) Number (%) dead 5-Year survival (%)
AUS (Merritt et al, 2008) Australia Population 2002–2006 20–80 1404 1 Year 26.1 (46.7) 7.3 (8.0) 875 (62.3) 48.5
BAV (Song et al, 2009) Germany Hospital/Clinic 2002–2006 22–84 431 5 Years 25.9 (34.2) 5.6 (25.1) 236 (54.8) 47.4
BEL (Song et al, 2009) Belgium Hospital/Clinic 2007–2012 18–85 477 1 Year 24.7 (34.5) 3.5 (28.8) 133 (27.9) 70.0
CON (Risch et al, 2006) USA Population 1998–2003 36–81 388 5 Years 24.6 (43.6) 8.3 (10.1) 224 (57.7) 57.6
DOV (Rossing et al, 2007; Bodelon et al, 2012) USA Population 2002–2005 35–74 1146 5 Years 25.1 (44.8) 4.4 (8.8) 486 (42.4) 55.0
GER (Royar et al, 2001) Germany Population 1993–1996 21–75 240 At diagnosis 24.4 (40.6) 14.5 (3.9) 167 (69.6) 47.1
HAW (Goodman et al, 2008; Lurie et al, 2008) USA Population 1993–2008 24–85 429 5 Years 25.1 (36.9) 7.3 (16.5) 217 (50.6) 62.2
HOP (Lo-Ciganic et al, 2012) USA Population 2003–2009 25–85 652 1 Year 27.4 (51.1) 5.1 (9.1) 335 (51.4) 51.3
HSK (du Bois et al, 2003; Harter et al, 2011) Germany Hospital/clinic 2000–2007 29–80 111 At diagnosis 24.2 (21.3) 5.0 (10.7) 65 (58.6) 48.2
JPN (Hamajima et al, 2001) Japan Hospital/clinic 2001–2005 23–75 65 1 Year 22.4 (12.5) 3.6 (9.2) 29 (44.6) 44.1
MAC+MAY (Kelemen et al, 2008; Goode et al, 2010, 2011) USA Hospital/clinic 1999–2012 21–85 944 1 Year 26.6 (41.3) 3.3 (22.0) 503 (53.3) 44.0
MAL (Glud et al, 2004; Soegaard et al, 2007) Denmark Population 1994–1999 32–80 573 5 Years 23.6 (42.2) 13.8 (4.5) 438 (76.4) 43.5
NCO (Schildkraut et al, 2008, 2010) USA Population 1999–2008 22–74 916 1 Year 26.6 (47.4) 7.2 (8.6) 551 (60.2) 50.2
NEC (Terry et al, 2005; Merritt et al, 2013) USA Population 1992–2003 21–77 847 1 Year 24.6 (50.3) 13.3 (10.6) 490 (57.9) 58.7
NJO (Bandera et al, 2011; Chandran et al, 2011; Gifkins et al, 2012) USA Population 2002–2008 25–81 240 1 Year 25.8 (51.4) 6.2 (7.4) 113 (47.1) 61.0
POL (Garcia-Closas et al, 2007) Poland Population 2000–2003 24–74 268 5 Years 23.9 (21.9) 5.3 (7.1) 142 (53.0) 49.0
PVD (Hogdall et al, 2010; Risum et al, 2011) Denmark Hospital/clinic 2004–2012 30–84 191 At diagnosis 24.2 (32.1) 4.8 (4.9) 102 (53.4) 46.6
STA (McGuire et al, 2004) USA Population 1997–2001 21–64 499 At diagnosis 24.4 (44.7) 11.0 (13.3) 284 (56.9) 54.6
TBO (Pal et al, 2005, 2007; Lacour et al, 2011) USA Population 2000–2012 26–85 245 At diagnosis 24.8 (32.9) 5.9 (7.9) 131 (53.5) 49.9
UCI (Ziogas et al, 2000) USA Population 1993–2005 21–84 394 1 Year 24.1 (41.2) 8.7 (17.6) 179 (45.4) 73.5
USC (Pike et al, 2004; Wu et al, 2009) USA Population 1992–2009 20–84 1930 1 Year 24.6 (42.4) 8.0 (17.7) 1015 (52.6) 57.4
TOTAL       18–85 12 390   25.1 (54.6) 6.9 (28.8) 6715 (54.2) 53.6
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Analysis variables

In all but two studies (HSK and PVD), both height and weight were self-reported. For 10 studies, women recalled their weight ∼1 year before diagnosis (AUS (Merritt et al, 2008), BEL (Song et al, 2009), HOP (Lo-Ciganic et al, 2012), JPN (Hamajima et al, 2001), MAC (Goode et al, 2011) and MAY (Kelemen et al, 2008; Goode et al, 2010), NCO (Schildkraut et al, 2008, 2010), NEC (Terry et al, 2005; Merritt et al, 2013), NJO (Bandera et al, 2011; Chandran et al, 2011; Gifkins et al, 2012), UCI (Ziogas et al, 2000) and USC (Pike et al, 2004; Wu et al, 2009)); for six studies, they were asked to recall their weight ∼5 years before diagnosis (BAV (Song et al, 2009), CON (Risch et al, 2006), DOV (Rossing et al, 2007; Bodelon et al, 2012), HAW (Goodman et al, 2008; Lurie et al, 2008), MAL (Glud et al, 2004; Soegaard et al, 2007) and POL (Garcia-Closas et al, 2007)); for three studies, it was recalled weight around the time of diagnosis (GER (Royar et al, 2001), STA (McGuire et al, 2004) and TBO (Lacour et al, 2011; Pal et al, 2005, 2007)); and for two studies, it was reported in the medical records around the time of diagnosis (PVD (Hogdall et al, 2010; Risum et al, 2011) and HSK (du Bois et al, 2003; Harter et al, 2011). This information was used to calculate BMI as weight in kilograms divided by the square of height in metres (kg m−2), which was classified using the World Health Organization (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') (World Health Organisation, 1995). Among the 21 OCAC studies, 756 (5.3%) women were with missing BMI information. Of the 12 390 women included in the analyses, BMI ranged from 13.7 to 68.3 kg m−2. Three hundred and seven women were underweight (BMI <18.5) and 71 (0.6%) had BMI values >50 kg m−2.

Covariate information

Each OCAC study submitted their data to Duke University using a standard format. Here the variables were reviewed, discrepancies were checked with individual studies and data merged where necessary. The data included information about variables potentially associated with BMI and/or survival: age (at diagnosis), tumour stage summarized from International Federation of Gynecology and Obstetrics or the Surveillance, Epidemiology, and End Results (SEER) stage (localized, regional, distant and unknown) and tumour grade (well, moderately, poorly undifferentiated and unknown). Residual disease remaining after primary cytoreductive surgery (no macroscopic disease, macroscopic disease ⩽2 cm, macroscopic disease, macroscopic disease, size unknown and tumour not resected) was reported in 9 of the studies and cigarette smoking status (never, current and former smoker) was reported in 17 studies.

Clinical data, definitions and analysis

Vital status and survival time were determined by each study and OS time was calculated from date of diagnosis to date of death, or last follow-up. Cause of death information was available for 1511 of the 6715 women who had died (23%) and, of these, most (94%) had died from ovarian cancer; thus, all-cause mortality was used as the primary outcome. Where time from diagnosis to study recruitment was provided (19 of the 21 studies, not available for HSK and PVD), the left truncation was used to account for time elapsed between date of diagnosis and date of study recruitment, in order to reduce potential survivorship biases arising from the exclusion of eligible women who had died before recruitment. Additional analyses were conducted using death from ovarian cancer as the end point and PFS (defined by each study as time from date of diagnosis to the first confirmed sign of disease progression (clinical, biochemical (i.e., CA125) or radiological progression), death or last follow-up date) where such data were available.

Statistical analysis

We used a two-stage method of analysis. In the first stage, each study was analysed separately. For each study, we used Cox proportional hazards regression models to compute adjusted hazard ratios (HRs) and 95% confidence intervals (CIs) for the association between BMI (standard WHO categories and per 5 kg m−2 increase) and survival. The HR for ovarian cancer per 5 kg m−2 increase in BMI was estimated by fitting a log linear trend across intervals of BMI (2.5 units) using the overall median value for each interval, except for the top interval where we used site-specific median values rounded to the nearest integer, to account for the greater variability of BMI in the upper category across study sites. We repeated this analysis using the continuous value of BMI for each woman. We expected that the relationship between BMI and survival might not be linear at very low BMI levels; hence, we excluded women in the ‘underweight' range (BMI <18.5 kg m−2) from these analyses. All study-specific models were adjusted for age (continuous), tumour stage (classified as localized, regional and distant) and tumour grade (well differentiated, moderately differentiated and poorly/undifferentiated). We also adjusted study-specific models for race/ethnicity (non-Hispanic White, Hispanic White, Black, Asian and others) where more than 5% of the study population was not classified as the predominant race. Data on residual disease and cigarette smoking status were not available for all studies; however, we did include these variables in models restricted to studies where they were available. For each study-specific model, we examined possible violation of the proportional hazards assumption by evaluating whether covariate associations with BMI varied over time and allowing them to vary if needed. In the second stage, the pooled BMI effect (pooled HR (pHR)) was calculated using random-effects meta-analysis according to the method of DerSimonian and Laird (1986).

We computed pHRs for BMI for all invasive ovarian cases. Where between-study heterogeneity was evident, we examined the data for potential sources of this heterogeneity considering sample size, study design, study site/region, diagnosis years and median follow-up times (calculated using the reverse Kaplan–Meier method (Clark et al, 2003; Schemper and Smith, 1996)), median BMI, 5-year survival per cent, race/ethnicity and timing of BMI measurement. We also conducted subgroup analyses to examine whether associations between BMI and survival were modified by histologic subtype (low- (well differentiated) and high-grade tumours (moderate/poorly/undifferentiated), serous, mucinous, endometrioid and clear cell cancers), where these data were available. The statistical significance of any observed stratum differences was assessed by including a cross-product term in survival models. All P-values are two-sided. Analyses were performed using SAS 9.2 (SAS Institute, Cary, NC, USA) and Stata 11 (College Station, TX, USA).

Results

Table 1 shows the characteristics of the 21 studies that contributed data from a total of 12 390 women with invasive epithelial ovarian cancer to this analysis. Twelve studies were conducted in the United States, seven in Europe and one each in Australia and Japan. The number of women with invasive epithelial ovarian cancer participating in the studies ranged from 65 (JAP) to 1930 (USC). Women were 18–85 years of age at diagnosis between 1992 and 2012 (most of the OCAC studies capped recruitment at 80 or 85 years and also excluded women younger than 18 years; thus, our cases are slightly younger than those reported in the SEER programme); 15 studies were population based and 6 were hospital/clinic based (two studies from the same population, MAC and MAY, were combined). During the follow-up period, half (54%) of the women had died. The median follow-up time among living women was 6.9 years (maximum 14.5 years). The median BMI was 25.1 kg m−2 (interquartile range: 22.3–29.3 kg m−2).

The clinical characteristics of all the women included in this analysis are shown in Table 2. Women had a mean age of 58 years at diagnosis and the majority had serous (62%), poorly differentiated (56%) and/or distant stage (64%) cancers. Among those with residual disease data available (n=3856), 47% had no macroscopic disease after cytoreductive surgery.

Table 2. Clinical characteristics of 12 390 participants included in analysis.

Characteristica Nb %
Age
Age, years (median) 58.0  
 <40 741 6.0
 40– <50 2286 18.5
 50– <60 3847 31.1
 60– <70 3515 28.4
 70– <85 2001 16.2
Histology
Serous 7530 62.4
 Serous low-grade 500 4.1
 Serous high-grade 6443 53.4
Mucinous 733 6.1
Endometrioid 1683 14.0
Clear cell 896 7.4
Other histology 1222 10.1
Stage
Localized 2066 17.0
Regional 2285 18.8
Distant 7809 64.2
Grade
Well-differentiated 1336 12.1
Moderately differentiated 2597 23.5
Poorly differentiated 6158 55.7
Undifferentiated 975 8.8
Residual disease after surgery
No macroscopic disease 1804 46.8
Macroscopic disease ⩽2 cm 974 25.3
Macroscopic disease >2 cm 250 6.5
Macroscopic disease, size unknown 713 18.5
Tumour not resected 115 3.0
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a

Participants with unknown histology (n=326), unknown serous high- or low-grade status (n=587), stage (n=230), grade (n=1324) and residual disease (n=8534) were not included in percentages.

b

Numbers may not add to 12 390 due to missing data.

In multivariate analyses (all histologies combined), we found that women who were overweight (BMI: 25–29.9), obese (BMI: 30–34.9) and morbidly obese (BMI: ⩾35) experienced worse survival compared with women within the normal weight range (pHRs: 1.05 (95% CIs: 0.96–1.15), 1.10 (95% CIs: 0.99–1.22) and 1.15 (95% CIs: 0.98–1.37), respectively). However, in the overweight and morbidly obese groups there was significant heterogeneity between the studies (all P<0.05). Using BMI as a continuous variable, risk of death increased by 3% for each 5-unit increase in BMI over 18.5 kg m−2 (HR: 1.03, (95% CIs: 1.00–1.07)); however, again significant heterogeneity was present between the studies (I2 40%, P=0.03) (Figure 1). Exploration of the heterogeneity showed that the largest difference in pHR was seen for study size with no apparent heterogeneity among the 18 studies, with ⩾200 participants but significant heterogeneity among those with fewer women (Figure 2). The pHR per 5-unit increase in BMI kg m−2 was 1.03 (95% CIs: 1.01–1.06) for studies with ⩾200 women vs 1.21 for studies with <200 women (95% CIs: 0.75–1.96). We also saw significant heterogeneity in other strata, in all except one instance (diagnosis years); the group with significant heterogeneity included at least two, if not all three of the small studies. When we repeated these analyses excluding the three small studies (HSK, JPN and PVD), we found that women who were obese still experienced worse survival (pHR: 1.10 (95% CIs: 0.99–1.23)) than women within the normal weight range (Table 3). This association was similar for those who were morbidly obese (pHR 1.12 (95% CIs: 1.01–1.25)) and the pHR per 5-unit increase in BMI kg m−2 was 1.03 (95% CIs: 1.00–1.06); I2 9%, P=0.35) (Table 3). The evidence of heterogeneity disappeared in the obese and morbidly obese groups; however, there remained significant heterogeneity between the studies in the overweight group (I2 46%, P=0.02). When we further excluded the study site where the confidence interval did not include the pooled estimate (MAL), there was no remaining heterogeneity in the overweight group (I2: 10.4%, P=0.3).

Figure 1.

Figure 1

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The association between BMI (per 5 kg m−2) and OS following a diagnosis of invasive ovarian cancer, all subtypes, overall and by study site. Estimates are adjusted for age at diagnosis (continuous), stage (local/regional/distant/unknown), grade (well-/moderately-/poorly plus undifferentiated/unknown) and ethnicity (if <95% of participants at a site shared a common ethnicity) estimates are further adjusted for the interaction of age, stage, grade and/or race with time as appropriate at each site.

Figure 2.

Figure 2

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The association between BMI (per 5 kg m−2) and OS following a diagnosis of invasive ovarian cancer, all subtypes in 21 studies, stratified by study characteristics. Estimates are adjusted for age at diagnosis (continuous), stage (local/regional/distant/unknown), grade (well-/moderately-/poorly plus undifferentiated/unknown) and ethnicity (if <95% of participants at a site shared a common ethnicity) estimates are further adjusted for the interaction of age, stage, grade and/or race with time as appropriate at each site. Study site region ‘Other'=AUS and JPN.

Table 3. The association between BMI and OS following a diagnosis of invasive ovarian cancer, all subtypes, two-stage pooled analysis, studies where N⩾200 (18 studies).

BMI (kg m−2) Study sites (n)a Cases (n) I2 (%) pHRb 95% CI
<18.5 18 284 29.7 1.18 0.94–1.48
18.5–24.9 (Ref) 18 5385 – REF  
25–29.9 18 3374 46.0 1.03c 0.95–1.13
30–34.9 18 1547 34.9 1.10 0.99–1.23
⩾35 17 1097 9.5 1.12 1.01–1.25
Per 5 kg m−2 d 18 11 403 9.1 1.03 1.00–1.06
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Abbreviations: BMI=body mass index; CI=confidence interval; HR=hazard ratio; pHR=pooled HR; OS=overall survival.

a

Excludes study sites HSK, JPN, PVD.

b

Pooled HR combined study site-specific estimates adjusting for age at diagnosis (continuous), stage (local/regional/distant/unknown), grade (well-/moderately-/poorly plus undifferentiated/unknown) and ethnicity (if <95% of participants at a site shared a common ethnicity) estimates are further adjusted for the interaction of age, stage, grade and/or race with time as appropriate at each site.

c

Significant heterogeneity noted (P-value for heterogeneity 0.017).

d

Excludes participants with BMI <18.5 kg m−2.

Data on residual disease were not available for all studies; however, including this variable in models restricted to studies where the data were available did not result in appreciable changes to the pooled estimates (Supplementary Table 2). Similarly, data on cigarette smoking status were not available for all studies and including this variable in models did not result in appreciable changes (Supplementary Table 3).

The results stratified by histologic subtype are shown in Figure 3. This analysis included only the 12 studies with adequate numbers of cases and events to generate estimates for each histologic subtype. The strongest associations were seen for the low-grade serous and endometrioid subtypes, but neither result was significant (pHR: 1.12 (95% CIs: 0.96–1.31) and 1.08 (95% CIs: 0.95–1.23), respectively, per 5-unit increase in BMI). A more modest but significant association was observed for the high-grade serous subtype (pHR per 5-unit increase in BMI: 1.04 (95% CIs: 1.00–1.09)). No association was noted between BMI and survival among women with mucinous or clear cell tumours. Tests for heterogeneity (between the four main subtypes and for low- vs high-grade serous subtypes) did not reach statistical significance (both P=1.0).

Figure 3.

Figure 3

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The association between BMI (per 5 kg m−2) and OS following a diagnosis of invasive ovarian cancer, by histologic subtype, two-stage pooled analysis. Pooled HR combined study site-specific estimates adjusting for age at diagnosis (continuous), stage (local/regional/distant/unknown), grade (well-/moderately-/poorly plus undifferentiated/unknown) (except for low- and high-grade serous estimates) and ethnicity (if <95% of participants at a site shared a common ethnicity) estimates are further adjusted for the interaction of age, stage, grade and/or race with time as appropriate at each site. Excludes participants with BMI <18.5 kg m−2. Includes study sites with adequate numbers of cases and events to generate an estimate for each histologic group. Pooled HR for serous, mucinous, endometrioid and clear-cell includes study sites: AUS, BAV, CON, DOV, HAW, HOP, MAL, NCO, NEC, STA, TBO and USC. Pooled HR for serous low-grade and serous high-grade includes study sites: AUS, BAV, BEL, DOV, HOP, MAL, NCO, NEC, NJO, STA, UCI and USC.

We also assessed PFS (in 11 studies, n=4133 cases) and ovarian cancer-specific survival (in 9 studies, n=3091) and similar results were noted to those for OS. For the 11 studies where we had both PFS and OS data, the pHR for obese women (BMI: ⩾30) compared with women within the normal weight range was 1.10 (95% CIs: 0.99–1.23) for PFS and 1.12 (955 CIs: 1.01–1.26) for OS. In the nine studies with the cause of death data, the pHR for obese women (BMI: ⩾30) compared with women within the normal weight range was 1.17 (95% CIs: 1.00–1.37) for ovarian-cancer specific survival and 1.16 (95% CIs: 1.03–1.31) for OS.

Discussion

This study is, to our knowledge, the largest evaluation to date, of BMI and survival following a diagnosis of ovarian cancer. We found that obesity was associated with a 10–12% OS disadvantage among women with ovarian cancer and results were similar for PFS and ovarian cancer-specific mortality. In subtype analyses, associations were strongest for women with low-grade serous and endometrioid cancers, and more modest for high-grade serous cancers (12%, 8% and 4% increases in mortality per 5-unit increase in BMI, respectively), but only the association with high-grade serous cancers, by far the largest subgroup, reached statistical significance. No increase in risk was noted for the less common clear-cell or mucinous subtypes, which are estimated to account for ∼8% of all epithelial ovarian cancers.

Several mechanisms have been proposed to underlie the effects of obesity on ovarian cancer outcomes. Makowski et al (2014) recently showed that the obese state promotes tumour progression in animal models of serous ovarian cancer and concluded that metabolic consequences of obesity may be involved in the pathogenesis of ovarian cancer. Aberrant adipokine production, specifically upregulation of leptin and downregulation of adiponectin in the obese state, may explain an association between obesity and ovarian cancer outcomes. Leptin has both mitogenic and anti-apoptotic properties in cancer cell lines and is involved in promoting angiogenesis (Khandekar et al, 2011; Chen et al, 2013; Ptak et al, 2013). Conversely, adiponectin has anti-proliferative effects through the induction of apoptosis (Kelesidis et al, 2006; Barb et al, 2007). In a recent cohort study of 161 women with advanced-stage ovarian cancer, Diaz et al (2013) found that women with increased leptin to adiponectin (L:A) ratios experienced significantly shorter disease-free survival time than those with low L:A ratio. Obesity may also affect ovarian cancer survival through its effect on inflammatory cytokines, markers of insulin resistance and obesity-related hormones such as oestrogen, through the conversion of androgens to oestrogen in adipose tissue. In-vitro studies have shown that oestrogens have pro-proliferative actions on ovarian cancer cells (Galtier-Dereure et al, 1992; Langdon et al, 1994; Karlan et al, 1995). The oestrogen receptor is expressed in up to 80% of epithelial ovarian cancers with the highest expression in serous and endometrioid tumours (Modugno et al, 2012; Sieh et al, 2013), the two subtypes in this study with the strongest associations. Finally, oestrogen may also have a role in the motility and invasion of ovarian cancer cells (Hua et al, 2008; Zhu et al, 2012).

From a treatment perspective, obese women may have worse survival because of the practice of dose capping when prescribing chemotherapy (Modesitt and van Nagell, 2005; Pavelka et al, 2006; Poniewierski et al, 2008; Au-Yeung et al, 2014). Dose capping involves the use of ideal rather than the actual body weight when calculating the dose to be given or dose capping at a body surface area of 2.0 m2 (equivalent to a BMI of ∼30 kg m−2) and occurs largely, it is thought, due to concerns regarding the potential for chemotherapy-related toxicities if the full dose is given (Field et al, 2008). Evidence from an Australian study of 330 women with late-stage ovarian cancer has shown that underdosing of carboplatin was common among the obese women (Au-Yeung et al, 2014). They also reported that reduced dose intensity of carboplatin was associated with worse PFS. Recently, published guidelines from the American Society of Clinical Oncology recommend that full weight-based doses of chemotherapy be used to treat obese patients with cancer, in particular where the goal of treatment is cure (Griggs et al, 2012).

The strengths of our study include the large sample size, which allowed us to examine associations both overall and separately for the different histologic subtypes of ovarian cancer. We included age, ethnicity, clinical factors and study site in our models, and sensitivity analysis suggested that any residual confounding by cigarette smoking would have been minimal. We also assessed PFS and ovarian cancer-specific survival, where such data were available, and the results were essentially unchanged, suggesting that obesity is not just increasing non-ovarian cancer deaths, but progression and cause-specific survival. However, most studies relied on retrospective self-reports of weight and height; hence, there is some potential for recall error; however, it is unlikely to have differed by outcome and thus our results may underestimate the true magnitude of the association. It is possible that our measure of usual weight (before diagnosis) may be influenced by weight loss due to cachexia or weight gain due to the presence of ascites, both of which may be presenting symptoms for ovarian cancer, in particular in women with advanced disease. However, the adverse association between obesity and ovarian cancer survival appeared consistent regardless of when BMI was measured, suggesting this is not a major problem. One potential limitation of our analysis is that the data were not originally collected to look at survival and, as a result, clinical data were not always available and/or complete. However, the major advantage of using the data in this way is cost effectiveness; the time, effort and cost associated with collecting similar data from an equal number of women with ovarian cancer for a new study specifically looking at survival would be prohibitive.

In conclusion, this analysis of data from OCAC has shown that obesity before or at ovarian cancer diagnosis is associated with worse survival, when compared with women within the normal-weight range. As ovarian cancer remains a highly fatal disease and the prevalence of obesity continues to increase, studies focusing on causal mechanisms involved in adverse survival are needed.

Acknowledgments

We are grateful to the family and friends of Kathryn Sladek Smith for their generous support of Ovarian Cancer Association Consortium through their donations to the Ovarian Cancer Research Fund. The Australian Ovarian Cancer Study Management Group (D Bowtell, G Chenevix-Trench, A deFazio, D Gertig, A Green and PM Webb) and ACS Investigators (A Green, P Parsons, N Hayward, PM Webb and D Whiteman) thank all the clinical and scientific collaborators (see http://www.aocstudy.org/) and the women who participated in these studies for their contribution. We thank Gilian Peuteman, Thomas Van Brussel and Dominiek Smeets for technical assistance (for BEL). 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. We assume full responsibility for analyses and interpretation of these data (for CON). The German Ovarian Cancer Study (GER) thank Ursula Eilber for competent technical assistance (for GER). Funding: The Ovarian Cancer Association Consortium is supported by a grant from the Ovarian Cancer Research Fund. This work was supported by the following: National Institutes of Health (R01-CA61107 (for MAL); R01-CA95023 and R01-CA126841 (for HOP); P01-CA17054 (for USC); NIH-K07 CA095666, R01-CA83918, NIH-K22-CA138563 and P30-CA072720 (for NJO); (R01-CA074850 and R01-CA080742 (for CON); R01-CA112523 and R01-CA087538 (for DOV); R01-CA058598, N01-CN-55424 and N01-PC-67001 (for HAW); R01-CA122443, P50-CA136393 and P30-CA15083 (for MAC and MAY); R01-CA076016 (for NCO); R01-CA054419 and P50-CA105009 (for NEC); U01-CA71966, R01-CA016056 and K07-CA143047 (for STA); R01-CA106414 (for TBO); R01CA058860, R01CA092044 and PSA042205 (for UCI); Department of Defense (DAMD17-02-1-0666 (for NCO); W81XWH-10-1-02802 (for NEC); DAMD17-02-1-0669 (for HOP); DAMD17-01-1-0729 (for AUS); DAMD17-98-1-8659 (for TBO); National Cancer Institute K07-CA80668 (for HOP); NIH/National Center for Research Resources/General Clinical Research Center grant MO1-RR000056 and P50-CA159981 (for HOP); National Health and Medical Research Council of Australia, Cancer Councils of New South Wales, Victoria, Queensland, South Australia and Tasmania, Cancer Foundation of Western Australia; National Health and Medical Research Council of Australia (199600 and 400281) (for AUS); Danish Cancer Society, Copenhagen, Denmark (94 222 52) and the Mermaid I project (for MAL); FM supported by funding from K07-CA080668 (for HOP); California Cancer Research Program (00-01389V-20170, R03-CA113148, R03-CA115195, N01-CN25403 and 2II0200) (for USC); ELAN Funds of the University of Erlangen-Nuremberg (for BAV); Nationaal Kankerplan (for BEL); German Federal Ministry of Education and Research, Programme of Clinical Biomedical Research (01 GB 9401) and data management by the German Cancer Research Center (for GER); Grant-in-Aid for Scientific Research on Priority Areas from the Ministry of Education, Science, Sports, Culture and Technology of Japan, by a Grant-in-Aid for the Third Term Comprehensive 10-Year Strategy for Cancer Control from Ministry Health, Labour and Welfare of Japan, and by a grant from Takeda Science Foundation (for JPN); Mayo Foundation, Minnesota Ovarian Cancer Alliance, Fred C. and Katherine B. Andersen Foundation (for MAC and MAY); Cancer Institute of New Jersey (for NJO); Intramural Research Program of the National Cancer Institute (for POL); Cancer Prevention Institute of California (U01-CA69417) (for STA); American Cancer Society (CRTG-00-196-01-CCE), Celma Mastry Ovarian Cancer Foundation (for TBO); and Lon V Smith Foundation grant LVS-39420 (for UCI). PW is supported by a Fellowship from NHMRC and CN is supported by NHMRC Program grant 552429 (for AUS). AdF is supported by the University of Sydney Cancer Research Fund and the Cancer Institute NSW through the Sydney-West Translational Cancer Research Centre (for AUS).

The authors declare no conflict of interest.

Footnotes

Supplementary Information accompanies this paper on British Journal of Cancer website (http://www.nature.com/bjc)

This work is published under the standard license to publish agreement. After 12 months the work will become freely available and the license terms will switch to a Creative Commons Attribution-NonCommercial-Share Alike 4.0 Unported License

Supplementary Material

Supplementary Tables
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