Whole grain consumption and the risk of colorectal cancer:

Anthropometric factors and ovarian cancer risk: A systematic review and nonlinear dose-response meta-analysis of prospective studies

Dagfinn Aune1,2, Deborah A. Navarro Rosenblatt1, Doris Sau Man Chan1, Leila Abar1, Snieguole Vingeliene1, Ana Rita Vieira1, Darren C. Greenwood3, Teresa Norat1.

Affiliations

1 Department of Epidemiology and Biostatistics, School of Public Health Imperial College London, London, United Kingdom

2 Department of Public Health, Faculty of Medicine, Norwegian University of Science and Technology, Trondheim, Norway

2 Biostatistics Unit, Centre for Epidemiology and Biostatistics, University of Leeds, Leeds, United Kingdom

Correspondence to: Mr. Dagfinn Aune, Department of Epidemiology and Biostatistics,

School of Public Health, Imperial College London, St. Mary's Campus, Norfolk Place, Paddington, London W2 1PG, UK.

Telephone: +44 (0) 20 7594 8478

E-mail: d.aune@imperial.ac.uk

Novelty and impact: Evidence for an association between obesity and ovarian cancer is inconclusive. In this meta-analysis the authors found evidence of a nonlinear positive association between body mass index and weight and ovarian cancer risk, with a significant association from a body mass index of 28 and above. In addition, greater height was associated with increased risk. For the first time there is strong evidence from cohort studies that greater body fatness increases ovarian cancer risk.

Key words: Body mass index, weight, height, ovarian cancer, meta-analysis.

Word count (introduction through conclusion): 3468

Number of figures: 4

Number of tables: 1

Number of supplementary tables: 5

Number of references: 64

Abstract

In the World Cancer Research Fund/American Institute for Cancer Research report from 2007 the evidence relating body fatness to ovarian cancer risk was considered inconclusive, while the evidence supported a probably causal relationship between adult attained height and increased risk. Several additional cohort studies have since been published, and therefore we conducted an updated meta-analysis of the evidence as part of the Continuous Update Project. We searched PubMed and several other databases up to December 2013. Summary relative risks (RRs) were calculated using a random effects model. The summary relative risk for a 5 unit increment in BMI was 1.07 (95% CI: 1.02-1.11, I2=53%, n=26 studies). There was evidence of a nonlinear association, pnonlinearity<0.0001, with risk increasing significantly from BMI~28 and above. The summary RR per 5 unit increase in BMI in early adulthood was 1.12 (95% CI: 1.05-1.20, I2=0%, pheterogeneity=0.54, n=6), per 5 kg increase in body weight was 1.03 (95% CI: 1.02-1.05, I2=0%, n=4) and per 10 cm increase in waist circumference was 1.06 (95% CI: 1.00-1.12, I2=0%, n=6). No association was found for weight gain, hip circumference or waist-to-hip ratio. The summary RR per 10 cm increase in height was 1.16 (95% CI: 1.11-1.21, I2=32%, n=16). In conclusion, greater body fatness as measured by body mass index and weight are positively associated risk of ovarian cancer, and in addition, greater height is associated with increased risk. Further studies are needed to clarify whether abdominal fatness and weight gain is associated with risk.

Introduction

Ovarian cancer is the 7th most common cancer in women and accounts for 3.6% of all new cancer cases in women (1). Ovarian cancer is often diagnosed at advanced stages because there are often few symptoms at the early stages, thus patients have a poor survival, with 5 year survival rates of 45% (2). Globally there is a wide variation in ovarian cancer rates and they are high in high-income countries and are increasing in countries undergoing economic development. In Japan there was a 4-fold increase in mortality rates between 1950 and 1997 (3). Currently there are no established methods of screening for early detection, thus, primary prevention by altering modifiable risk factors may be a promising method to reduce the ovarian cancer burden.

In the World Cancer Research Fund/American Institute for Cancer Research report from 2007 “Food, Nutrition, Physical Activity and the Prevention of Cancer: A Global Perspective” it was stated that the evidence relating body fatness to ovarian cancer risk was inconclusive, while the evidence that greater height increases ovarian cancer risk was considered probable (4). A pooled analysis of cohort studies suggested a positive association between BMI and ovarian cancer risk in premenopausal women, but not in postmenopausal women (5), while a meta-analysis of 13 cohort studies did not find a statistically significant association (6). Since the WCRF/AICR 2007 report, seventeen additional cohort studies (15 publications) with 3556 cases among >2.3 million participants have been published on BMI (7-21) and twelve additional cohort studies with 8529 cases among >2.5 million participants have been published on height (11;14;16;19;21-26) in relation to ovarian cancer risk. Therefore we conducted an updated systematic review and dose-response meta-analysis of anthropometric factors and ovarian cancer risk. We particularly wanted to clarify the strength and shape of the dose-response relationship between the anthropometric measures and ovarian cancer risk, potential modification by menopausal status, and to investigate potential heterogeneity by subgroup and meta-regression analyses.

Material and methods

Search strategy

Relevant studies of anthropometric measures and ovarian cancer risk were identified by searching several databases up to the end of December 2005, including Pubmed, Embase, CAB Abstracts, ISI Web of Science, BIOSIS, LILACS, Cochrane library, CINAHL, AMED, National Research Register, and In Process Medline initially. However, because all the relevant studies were identified by the PubMed search, a change to the protocol was made and in the updated searches only Pubmed was searched from 1st January 2006 to 31st of December 2013. The search terms used are provided in the Supplementary Appendix. A prespecified protocol was followed for the review (http://www.dietandcancerreport.org/cup/current_progress/ovarian_cancer.php) and we used standard criteria for meta-analyses of observational studies (27). In addition, we also searched the reference lists of all the studies that were included in the analysis and the reference lists of a published meta-analysis (6).

Study selection

Published prospective cohort studies, case-cohort studies, or nested case-control studies of the association between BMI, waist circumference, or waist-to-hip ratio and ovarian cancer risk incidence or mortality were included. Grey literature and unpublished studies were not included. Relative risk estimates and 95% confidence intervals had to be available in the publication and for the dose-response analysis, a quantitative measure of the exposure and the total number of cases and person-years had to be available in the publication. We identified 39 potentially relevant full-text publications (7-26;28-46). We excluded one study which did not provide any risk estimates (40), one study of relative weight as the exposure (39), two studies of obesity diagnosis (41;43), and five duplicate publications (37;42;44-46). Two publications (31;35) were partly overlapping with subsequent publications from the studies (11;15), but were used in the analysis of weight gain because the most recent publications did not report on weight gain.

Data extraction

The following information was extracted from each study: The first author’s last name, publication year, country where the study was conducted, the study name, follow-up period, sample size, age, number of cases, assessment method of anthropometric factors (measured vs. self-reported), RRs and 95% CIs, and variables adjusted for in the analysis. Several reviewers at the Istituto Nazionale Tumori, Milan conducted the search and data extraction of articles published up to December 2005, during the systematic literature review for the WCRF/AICR report (4). The search and data extraction from January 2006 and up to December 2013 was conducted by one author (DANR) and was checked for accuracy by one author (DA).

Statistical analysis

We used a random effects model to calculate summary RRs and 95% CIs for a 5 unit increment in BMI, 5 kg increment in weight and weight gain, 10 cm increment in waist circumference and height and for a 0.1 unit increment in waist-to-hip ratio (47). The average of the natural logarithm of the RRs was estimated and the RR from each study was weighted by the inverse of its variance. A two-tailed p<0.05 was considered statistically significant.

We used the method described by Greenland and Longnecker (48) to calculate linear slopes and 95% CIs from the natural logs of the RRs and CIs across categories of anthropometric measures. The method requires that the distribution of cases and person-years or non-cases and the RRs with the variance estimates for at least three quantitative exposure categories are known. We estimated the distribution of cases or person-years in studies that did not report these, but reported the total number of cases and person-years using a previously described method (49). The mean anthropometric measure for each category was assigned to the corresponding relative risk for each study and for studies that reported these measures by ranges we estimated the mean in each category using the method described by Chene and Thompson (50). A potential nonlinear dose-response relationship between anthropometric measures and ovarian cancer was examined by using fractional polynomial models (51). We used a likelihood ratio test to assess the difference between the nonlinear and linear models to test for nonlinearity (51).

Subgroup and meta-regression analyses were conducted to investigate potential sources of heterogeneity and heterogeneity between studies was quantitatively assessed by the Q test and I2 (52). Small study effects, such as publication bias, were assessed by inspecting the funnel plots for asymmetry and with Egger’s test (53) and Begg’s test (54), with the results considered to indicate small study effects when p<0.10. Study quality was assessed using the Newcastle-Ottawa scale (55). Sensitivity analyses excluding one study at a time were conducted to clarify whether the results were robust or sensitive to the influence of single studies. The statistical analyses were conducted using Stata version 12.0 software (StataCorp, College Station, TX, USA).

Results

We identified twenty six prospective studies (22 publications, 23 risk estimates) (7-21;28;30;32-34;36;38) that were included in the analyses of BMI and ovarian cancer risk (Supplementary Table 1). One publication reported a combined result for three nested case-control studies (32) and another publication reported results from two cohort studies (15). Six cohort studies (five publications) were included in the analysis of BMI in early adulthood (age 18-29 years) and risk of ovarian cancer (13;22;33;34;36), four studies were included in the analysis of weight and ovarian cancer (14;21;33;38), six cohort studies (five publications) (14-16;21;29) were included in the analysis of waist circumference, and five cohort studies (four publications) (14;15;21;36) were included in the analysis of waist-to-hip ratio and ovarian cancer. Sixteen prospective studies (15 publications, 16 risk estimates) (11;14;16;19;21-26;30;33;34;36;38) were included in the analysis of height and ovarian cancer (Supplementary Table 2). The characteristics of the included studies are provided in Supplementary Table 1 and Supplementary Table 2. Most of the studies were from Europe and the US and used self-reported weight and height (Supplementary Table 1 and Supplementary Table 2).

BMI

Twenty six prospective studies (22 publications, 23 risk estimates) (7-19;28;30;32-34;36-38) were included in the overall dose-response analysis of BMI and ovarian cancer risk and included a total of 16111 cases among 3818137 participants. Ten studies were from the US, ten were from Europe, five were from Asia, and one was from Australia (Supplementary Table 1). The summary RR for a 5 unit increment in BMI was 1.07 (95% CI: 1.02-1.11), with moderate heterogeneity, I2=53%, pheterogeneity=0.001 (Figure 1a). In sensitivity analyses excluding one study at a time, the summary RR in the overall analysis ranged from 1.06 (95% CI: 1.02-1.10) when the Shanghai Women’s Health Study was excluded to 1.08 (95% CI: 1.04-1.12) when the Norwegian Tuberculosis Screening Study was excluded. The heterogeneity was explained by the latter study and when excluded I2=25%, pheterogeneity=0.14. There was no evidence of small study effects with Egger’s test, p=0.16, or with Begg’s test, p=0.79 and when visually inspected the funnel plot showed no sign of asymmetry (Supplementary Figure 1). There was evidence of a nonlinear association between BMI and ovarian cancer risk, pnonlinearity<0.0001 (Figure 1b, Supplementary Table 3), with risk increasing significantly from BMI~28 and more steeply at higher BMI levels.

BMI in early adulthood (age 18-29 years)

Six cohort studies (five publications) were included in the analysis of BMI in early adulthood (age 18-29 years) and risk of ovarian cancer (13;22;33;34;36). Four studies were from the US and two were from Europe (Supplementary Table 1). The summary RR per 5 units increase in BMI in early adulthood was 1.12 (1.05-1.20, I2=0%, pheterogeneity=0.54) (Figure 1c). There was evidence of a nonlinear association between BMI and ovarian cancer risk, pnonlinearity<0.0001 (Figure 1d, Supplementary Table 3), with risk increasing significantly from BMI~30 and more steeply at higher BMI levels.

Weight

Four cohort studies (14;21;33;38) were included in the analysis of weight and included 1149 cases among 344718 participants. Two studies were from Europe, one was from the US and one study was from Asia (Supplementary Table 1). The summary RR per 5 kg increase in weight was 1.03 (95% CI: 1.02-1.05, I2=0%, pheterogeneity=0.46) (Figure 2a). There was no evidence of a nonlinear association between weight and ovarian cancer risk, pnonlinearity=0.73 (Figure 2b, Supplementary Table 3).

Weight gain

Five cohort studies (14;21;31;33;35) were included in the analysis of weight gain and ovarian cancer risk and included 1166 cases among 311430 participants. Three studies were from the Europe, one was from the US and one was from Asia (Supplementary Table 1). The summary RR per 5 kg of weight gained between early adulthood (age 18-25 years) and baseline of the studies was 1.03 (95% CI: 0.95-1.12, I2=74%, pheterogeneity=0.004) (Figure 2c). Although there was indication of nonlinearity in the analysis, pnonlinearity=0.046, there was no evidence of an association across the range of weight gain observed and ovarian cancer risk (Figure 2d, Supplementary Table 3).

Waist circumference

Six cohort studies (five publications) (14-16;21;29) were included in the analysis of waist circumference and ovarian cancer risk and included 1360 cases among 436579 participants. Three studies were from Europe, two were from the US and one was from Asia (Supplementary Table 1). The summary RR for a 10 cm increase in waist circumference was 1.06 (95% CI: 1.00-1.12) with no evidence of heterogeneity, I2=0%, p=0.49 (Figure 3a). The summary RR ranged from 1.04 (95% CI: 0.98-1.11) when the Iowa Women’s Health Study was excluded to 1.07 (95% CI: 0.99-1.16) when the EPIC Study was excluded. There was no evidence of a nonlinear association between waist circumference and ovarian cancer risk, pnonlinearity=0.69 (Figure 3b, Supplementary Table 4).

Waist-to-hip ratio

Five cohort studies (four publications) (14;15;21;36) were included in the analysis of waist-to-hip ratio and ovarian cancer risk and included 1329 cases among 417558 participants. Three studies were from the US, one was from Europe and one was from Asia (Supplementary Table 1). The summary RR for a 0.1 unit increment in waist-to-hip ratio was 1.00 (95% CI: 0.93-1.07) with no significant heterogeneity I2=0%, p=0.47 (Figure 3c). The summary RR ranged from 0.97 (95% CI: 0.90-1.05) when the Iowa Women’s Health Study was excluded to 1.07 (95% CI: 0.95-1.21) when the EPIC Study was excluded. There was no evidence of a nonlinear association between waist-to-hip ratio and ovarian cancer risk, pnonlinearity=0.99 (Figure 3d, Supplementary Table 4).

Hip circumference

Four cohort studies (three publications) (14;15;21) were included in the analysis of hip circumference and ovarian cancer risk and included 1106 cases among 386177 participants. Two studies were from the US, one from Europe and one from Asia (Supplementary Table 1). The summary RR was 1.04 (95% CI: 0.82-1.33, I2=72%, p=0.01) (Figure 4a). Although the test for nonlinearity was significant, pnonlinearity=0.003, there was no significant association between hip circumference and ovarian cancer within the range reported by the studies (Figure 4b, Supplementary Table 4).

Height

Sixteen cohort studies (15 publications, 16 risk estimates) (11;14;16;19;21-26;30;33;34;36;38) were included in the analysis of height and ovarian cancer and included 18531 cases among 3937281 participants. Seven studies were from the US, six were from Europe, two were from Asia and one from Australia (Supplementary Table 1). The summary RR per 10 cm increase in height was 1.16 (95% CI: 1.11-1.20, I2=25%, p=0.17) (Figure 4c). The summary RR ranged from 1.15 (95% CI: 1.11-1.18) when the Korea National Health Insurance Corporation Study was excluded to 1.17 (95% CI: 1.12-1.22) when the Cancer Prevention Study 2 was excluded. There was no evidence of publication bias with Egger’s test, p=0.34 or Begg’s test, p=0.18 or by inspection of the funnel plot (Supplementary Figure 2). There was little evidence of a nonlinear association, pnonlinearity=0.11 (Figure 4d, Supplementary Table 4).

Subgroup and sensitivity analyses

In subgroup analyses the results for BMI and ovarian cancer were in the direction of increased risk in most subgroups, although not always statistically significant (Table 1). There was no evidence of heterogeneity between subgroups when stratified by duration of follow-up, assessment of weight and height, geographic location, number of cases, menopausal status, study quality, and adjustment for confounding factors (Table 1). In subgroup analyses of height and ovarian cancer, the significant positive association persisted across most subgroups and there was no evidence of between subgroup heterogeneity in any of the subgroup analyses (Table 1).

Discussion

We found an increased risk of ovarian cancer with greater BMI, BMI in young adulthood and weight, and a marginally significant positive association with waist circumference, but there was no association for weight gain, hip circumference or waist-to-hip ratio. Greater height was associated with an increased risk of ovarian cancer as well. To our knowledge this is the first meta-analysis to show a significant association between greater BMI and ovarian cancer in prospective studies. Previous pooled analyses and meta-analyses showed non-significant and slightly weaker associations when restricted to prospective studies (4-6;56) (Supplementary Table 5), while in this analysis we had a larger number of cases and studies and thus more statistical power to detect an association. The present meta-analysis includes 8 of the 12 studies included in the pooled analysis of prospective studies (5), but in addition 15 other studies that were not included in the pooled analysis. Although it is possible lack of inclusion of the 4 studies that had not published data separately and therefore were not included in the current meta-analysis (but were included in the pooled analysis) might have led to a slightly exaggerated association, these studies 4 studies included only a total of 666 cases compared to 16111 cases in the present meta-analysis, thus we consider it less likely that the present results would have been substantially altered if these additional studies would have been included. To our knowledge this is the first meta-analysis to report a potential nonlinear association between BMI and ovarian cancer, and there appeared to be a suggestive threshold around a BMI of 28. In addition, BMI in early adulthood (age 18-29 years) appeared to be somewhat more strongly associated with increased risk than BMI in middle age. This is consistent with a pooled analysis of 12 cohort studies which found a positive association among premenopausal women, but not among postmenopausal women (5) and our subgroup analyses which showed a somewhat higher risk estimate in premenopausal compared to postmenopausal women (although there was no between subgroup heterogeneity by menopausal status). This might also partly explain the lack of association between weight gained between early adulthood and middle age and risk of ovarian cancer. There was probably too few studies included to be able to show an association with abdominal adiposity, so further studies are needed to clarify whether or not abdominal fatness is associated with ovarian cancer risk. Consistent with previous pooled analyses (5;56;57) we found a significant increase in the risk of ovarian cancer with greater height.

Mechanisms

Greater body fatness has been proposed to influence ovarian cancer risk through several mechanisms, including increased levels of endogenous estrogens (58) and reduced levels of sex hormone-binding globulin (59), however, epidemiological data are limited and inconsistent (58;60). The mechanisms underlying the association between greater height and increased ovarian cancer risk are also not well understood. Differences in height is determined by genetic and environmental influences, including childhood and adolescent nutrition and infections (61), and an increased cancer risk with greater height may reflect variations in circulating levels of insulin-like growth factors or a greater number of cells, with subsequent greater chance of DNA mutations. However, further studies are needed to clarify the mechanisms by which greater body fatness and height increase ovarian cancer risk.

Limitations of the study

The main limitation of this meta-analysis is the low number of cohort studies available reporting on abdominal and hip adiposity which limited our statistical power and the possibility to conduct subgroup and sensitivity analyses for these anthropometric measures, and to test for publication bias. There was moderate heterogeneity in the analysis of BMI, but this appeared to be driven by a large Norwegian study, which had no information on hormonal, reproductive, and other risk factors (34). In addition, although there was no heterogeneity between subgroups, there was in general lower heterogeneity in subgroups with adjustment for confounding factors. There was low heterogeneity in the analysis of height. Although it is possible that the positive association between BMI and weight and ovarian cancer risk could be partly due to unmeasured or residual confounding by other lifestyle factors, such as lower physical activity or dietary factors, few dietary risk factors have been identified for ovarian cancer and the data regarding physical activity and ovarian cancer are inconsistent and mostly null (4). The association between height and ovarian cancer risk persisted in several subgroup analyses. As a meta-analysis of published literature it is possible the small study effects may have affected the results, but we did not find evidence of small study effects with either Egger’s test or Begg’s test.

Measurement errors in the assessment of height and weight may have influenced the findings, however, studies have found a high correlation between self-reported and measured height and weight (62). In addition, there was no heterogeneity between subgroups when studies were stratified by whether weight and height was measured or self-reported and the results persisted in studies that measured weight and height, lending further credibility to self-reported anthropometric measures.

Strengths of the study

Strengths of this meta-analysis include the prospective design of the studies, which avoids recall bias and reduces the possibility of selection bias, the large number of cases (>16000-18000) and participants in the BMI and height analyses which provided statistical power to detect moderate associations, the detailed subgroup and sensitivity analyses which allowed for investigation of sources of heterogeneity, and the nonlinear analyses which allowed us to examine the shape of the dose-response relationships. All the studies included in the analyses had a medium or high study quality, and there was no evidence of a difference in the results when stratified by study quality scores.

Conclusions, clinical and public health implications, and recommendations for future studies

For the first time there is a considerable amount of evidence from cohort studies showing that greater BMI significantly increases ovarian cancer risk and this adds to a growing body of evidence linking body fatness to several different cancers (4). Because of the growing obesity epidemic worldwide these findings have important public health implications with additional ovarian cancer cases likely to be caused by obesity if the trend continues. There was evidence of a nonlinear association between BMI and ovarian cancer, with risk increasing more strongly in the overweight and obese range of BMI than in the normal range and becoming statistically significant from about 28 kg/m2 and above. However, this finding should not be interpreted as having a BMI below 28 is safe, since the optimal BMI for preventing other cancers and chronic diseases is likely to be lower and there is evidence of increased risk even within the high normal range for some cancers and other chronic diseases (4;63;64). In summary, we found that greater BMI and weight are associated with increased ovarian cancer risk, and in addition, greater height is associated with increased risk. Further studies are needed to clarify whether abdominal or hip adiposity is associated with risk and potential modification by menopausal status and hormone therapy use.

Contributors

The systematic literature review team at the Istituto Nazionale Tumori, Milan conducted the search, data selection and data extraction up to December 2005. DANR did the updated literature search. DA and DANR did the updated data extraction, and DA did the study selection, statistical analyses and wrote the first draft of the original manuscript. DCG was expert statistical advisor and contributed towards the statistical analyses. All authors contributed to the interpretation of the data and the revision of the manuscript. TN wrote the protocol of the study and is the PI of the Continuous Update Project at Imperial College.

Acknowledgement: We thank the systematic literature review team at the Istituto Nazionale Tumori, Milan for their contributions to the ovarian cancer database. This work was funded by the World Cancer Research Fund (grant number 2007/SP01) as part of the Continuous Update Project. The views expressed in this review are the opinions of the authors. They may not represent the views of WCRF International/AICR and may differ from those in future updates of the evidence related to food, nutrition, physical activity and cancer risk. All authors had full access to all of the data in the study. D. Aune takes responsibility for the integrity of the data and the accuracy of the data analysis.

Conflict of interest: The authors declare that there are no conflicts of interest.

Reference List

(1) Ferlay J, Soerjomataram I, Ervik M, Dikshit R, Eser S, Mathers C, Rebelp M, Parkin DM, Forman D, Bray F. GLOBOCAN 2012: Estimated Cancer Incidence, Mortality and Prevalence Worldwide in 2012. Lyon, France: International Agency for Research on Cancer 2013. http://globocan.iarc.fr, accessed 08.04.2014.

(2) Aletti GD, Gallenberg MM, Cliby WA, Jatoi A, Hartmann LC. Current management strategies for ovarian cancer. Mayo Clin Proc 2007 Jun;82(6):751-70.

(3) Li XM, Ganmaa D, Sato A. The experience of Japan as a clue to the etiology of breast and ovarian cancers: relationship between death from both malignancies and dietary practices. Med Hypotheses 2003 Feb;60(2):268-75.

(4) World Cancer Research Fund/American Insitute for Cancer Research. Food, Nutrition, Physical Activity and the Prevention of Cancer: a Global Perspective. p. Washington DC: AICR, 2007.

(5) Schouten LJ, Rivera C, Hunter DJ, Spiegelman D, Adami HO, Arslan A, Beeson WL, van den Brandt PA, Buring JE, Folsom AR, Fraser GE, Freudenheim JL, et al. Height, body mass index, and ovarian cancer: a pooled analysis of 12 cohort studies. Cancer Epidemiol Biomarkers Prev 2008 Apr;17(4):902-12.

(6) Renehan AG, Tyson M, Egger M, Heller RF, Zwahlen M. Body-mass index and incidence of cancer: a systematic review and meta-analysis of prospective observational studies. Lancet 2008 Feb 16;371(9612):569-78.

(7) Rapp K, Schroeder J, Klenk J, Stoehr S, Ulmer H, Concin H, Diem G, Oberaigner W, Weiland SK. Obesity and incidence of cancer: a large cohort study of over 145,000 adults in Austria. Br J Cancer 2005 Oct 31;93(9):1062-7.

(8) Kuriyama S, Tsubono Y, Hozawa A, Shimazu T, Suzuki Y, Koizumi Y, Suzuki Y, Ohmori K, Nishino Y, Tsuji I. Obesity and risk of cancer in Japan. Int J Cancer 2005 Jan 1;113(1):148-57.

(9) Kiani F, Knutsen S, Singh P, Ursin G, Fraser G. Dietary risk factors for ovarian cancer: the Adventist Health Study (United States). Cancer Causes Control 2006 Mar;17(2):137-46.

(10) Reeves GK, Pirie K, Beral V, Green J, Spencer E, Bull D. Cancer incidence and mortality in relation to body mass index in the Million Women Study: cohort study. BMJ 2007 Dec 1;335(7630):1134.

(11) Lundqvist E, Kaprio J, Verkasalo PK, Pukkala E, Koskenvuo M, Soderberg KC, Feychting M. Co-twin control and cohort analyses of body mass index and height in relation to breast, prostate, ovarian, corpus uteri, colon and rectal cancer among Swedish and Finnish twins. Int J Cancer 2007 Aug 15;121(4):810-8.

(12) Song YM, Sung J, Ha M. Obesity and risk of cancer in postmenopausal Korean women. J Clin Oncol 2008 Jul 10;26(20):3395-402.

(13) Leitzmann MF, Koebnick C, Danforth KN, Brinton LA, Moore SC, Hollenbeck AR, Schatzkin A, Lacey JV, Jr. Body mass index and risk of ovarian cancer. Cancer 2009 Feb 15;115(4):812-22.

(14) Lahmann PH, Cust AE, Friedenreich CM, Schulz M, Lukanova A, Kaaks R, Lundin E, Tjonneland A, Halkjaer J, Severinsen MT, Overvad K, Fournier A, et al. Anthropometric measures and epithelial ovarian cancer risk in the European Prospective Investigation into Cancer and Nutrition. Int J Cancer 2010 May 15;126(10):2404-15.

(15) Kotsopoulos J, Baer HJ, Tworoger SS. Anthropometric measures and risk of epithelial ovarian cancer: results from the nurses' health study. Obesity (Silver Spring) 2010 Aug;18(8):1625-31.

(16) Chionh F, Baglietto L, Krishnan K, English DR, MacInnis RJ, Gertig DM, Hopper JL, Giles GG. Physical activity, body size and composition, and risk of ovarian cancer. Cancer Causes Control 2010 Dec;21(12):2183-94.

(17) Canchola AJ, Chang ET, Bernstein L, Largent JA, Reynolds P, Deapen D, Henderson KD, Ursin G, Horn-Ross PL. Body size and the risk of ovarian cancer by hormone therapy use in the California Teachers Study cohort. Cancer Causes Control 2010 Dec;21(12):2241-8.

(18) Andreotti G, Hou L, Beane Freeman LE, Mahajan R, Koutros S, Coble J, Lubin J, Blair A, Hoppin JA, Alavanja M. Body mass index, agricultural pesticide use, and cancer incidence in the Agricultural Health Study cohort. Cancer Causes Control 2010 Nov;21(11):1759-75.

(19) Weiderpass E, Sandin S, Inoue M, Shimazu T, Iwasaki M, Sasazuki S, Sawada N, Yamaji T, Tsugane S. Risk factors for epithelial ovarian cancer in Japan - results from the Japan Public Health Center-based Prospective Study cohort. Int J Oncol 2012 Jan;40(1):21-30.

(20) Khan MM, Khan A, Nojima M, Suzuki S, Fujino Y, Tokudome S, Tamakoshi K, Mori M, Tamakoshi A. Ovarian cancer mortality among women aged 40-79 years in relation to reproductive factors and body mass index: latest evidence from the Japan Collaborative Cohort study. J Gynecol Oncol 2013 Jul;24(3):249-57.

(21) Ma X, Beeghly-Fadiel A, Shu XO, Li H, Yang G, Gao YT, Zheng W. Anthropometric measures and epithelial ovarian cancer risk among Chinese women: results from the Shanghai Women's Health Study. Br J Cancer 2013 Aug 6;109(3):751-5.

(22) 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 Dec 2;99(11):1916-22.

(23) Sung J, Song YM, Lawlor DA, Smith GD, Ebrahim S. Height and site-specific cancer risk: A cohort study of a korean adult population. Am J Epidemiol 2009 Jul 1;170(1):53-64.

(24) Green J, Cairns BJ, Casabonne D, Wright FL, Reeves G, Beral V. Height and cancer incidence in the Million Women Study: prospective cohort, and meta-analysis of prospective studies of height and total cancer risk. Lancet Oncol 2011 Aug;12(8):785-94.

(25) Kabat GC, Heo M, Kamensky V, Miller AB, Rohan TE. Adult height in relation to risk of cancer in a cohort of Canadian women. Int J Cancer 2013 Mar 1;132(5):1125-32.

(26) Kabat GC, Anderson ML, Heo M, Hosgood HD, III, Kamensky V, Bea JW, Hou L, Lane DS, Wactawski-Wende J, Manson JE, Rohan TE. Adult stature and risk of cancer at different anatomic sites in a cohort of postmenopausal women. Cancer Epidemiol Biomarkers Prev 2013 Aug;22(8):1353-63.

(27) Stroup DF, Berlin JA, Morton SC, Olkin I, Williamson GD, Rennie D, Moher D, Becker BJ, Sipe TA, Thacker SB. Meta-analysis of observational studies in epidemiology: a proposal for reporting. Meta-analysis Of Observational Studies in Epidemiology (MOOSE) group. JAMA 2000 Apr 19;283(15):2008-12.

(28) Tornberg SA, Carstensen JM. Relationship between Quetelet's index and cancer of breast and female genital tract in 47,000 women followed for 25 years. Br J Cancer 1994 Feb;69(2):358-61.

(29) Folsom AR, Kushi LH, Anderson KE, Mink PJ, Olson JE, Hong CP, Sellers TA, Lazovich D, Prineas RJ. Associations of general and abdominal obesity with multiple health outcomes in older women: the Iowa Women's Health Study. Arch Intern Med 2000 Jul 24;160(14):2117-28.

(30) Rodriguez C, Calle EE, Fakhrabadi-Shokoohi D, Jacobs EJ, Thun MJ. Body mass index, height, and the risk of ovarian cancer mortality in a prospective cohort of postmenopausal women. Cancer Epidemiol Biomarkers Prev 2002 Sep;11(9):822-8.

(31) Fairfield KM, Willett WC, Rosner BA, Manson JE, Speizer FE, Hankinson SE. Obesity, weight gain, and ovarian cancer. Obstet Gynecol 2002 Aug;100(2):288-96.

(32) Lukanova A, Toniolo P, Lundin E, Micheli A, Akhmedkhanov A, Muti P, Zeleniuch-Jacquotte A, Biessy C, Lenner P, Krogh V, Berrino F, Hallmans G, et al. Body mass index in relation to ovarian cancer: a multi-centre nested case-control study. Int J Cancer 2002 Jun 1;99(4):603-8.

(33) 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 Mar 1;157(5):424-33.

(34) Engeland A, Tretli S, Bjorge T. Height, body mass index, and ovarian cancer: a follow-up of 1.1 million Norwegian women. J Natl Cancer Inst 2003 Aug 20;95(16):1244-8.

(35) Jonsson F, Wolk A, Pedersen NL, Lichtenstein P, Terry P, Ahlbom A, Feychting M. Obesity and hormone-dependent tumors: cohort and co-twin control studies based on the Swedish Twin Registry. Int J Cancer 2003 Sep 10;106(4):594-9.

(36) Anderson JP, Ross JA, Folsom AR. Anthropometric variables, physical activity, and incidence of ovarian cancer: The Iowa Women's Health Study. Cancer 2004 Apr 1;100(7):1515-21.

(37) Niwa Y, Yatsuya H, Tamakoshi K, Nishio K, Kondo T, Lin Y, Suzuki S, Wakai K, Tokudome S, Yamamoto A, Hamajima N, Toyoshima H, et al. Relationship between body mass index and the risk of ovarian cancer in the Japanese population: findings from the Japanese Collaborate Cohort (JACC) study. J Obstet Gynaecol Res 2005 Oct;31(5):452-8.

(38) Lacey JV, Jr., Leitzmann M, Brinton LA, Lubin JH, Sherman ME, Schatzkin A, Schairer C. Weight, height, and body mass index and risk for ovarian cancer in a cohort study. Ann Epidemiol 2006 Dec;16(12):869-76.

(39) Lew EA, Garfinkel L. Variations in mortality by weight among 750,000 men and women. J Chronic Dis 1979;32(8):563-76.

(40) Lapidus L, Helgesson O, Merck C, Bjorntorp P. Adipose tissue distribution and female carcinomas. A 12-year follow-up of participants in the population study of women in Gothenburg, Sweden. Int J Obes 1988;12(4):361-8.

(41) Moller H, Mellemgaard A, Lindvig K, Olsen JH. Obesity and cancer risk: a Danish record-linkage study. Eur J Cancer 1994;30A(3):344-50.

(42) Mink PJ, Folsom AR, Sellers TA, Kushi LH. Physical activity, waist-to-hip ratio, and other risk factors for ovarian cancer: a follow-up study of older women. Epidemiology 1996 Jan;7(1):38-45.

(43) Wolk A, Gridley G, Svensson M, Nyren O, McLaughlin JK, Fraumeni JF, Adam HO. A prospective study of obesity and cancer risk (Sweden). Cancer Causes Control 2001 Jan;12(1):13-21.

(44) Calle EE, Rodriguez C, Walker-Thurmond K, Thun MJ. Overweight, obesity, and mortality from cancer in a prospectively studied cohort of U.S. adults. N Engl J Med 2003 Apr 24;348(17):1625-38.

(45) Brandstedt J, Nodin B, Manjer J, Jirstrom K. Anthropometric factors and ovarian cancer risk in the Malmo Diet and Cancer Study. Cancer Epidemiol 2011 Oct;35(5):432-7.

(46) Yang HP, Trabert B, Murphy MA, Sherman ME, Sampson JN, Brinton LA, Hartge P, Hollenbeck A, Park Y, Wentzensen N. Ovarian cancer risk factors by histologic subtypes in the NIH-AARP Diet and Health Study. Int J Cancer 2012 Aug 15;131(4):938-48.

(47) DerSimonian R, Laird N. Meta-analysis in clinical trials. Control Clin Trials 1986 Sep;7(3):177-88.

(48) Greenland S, Longnecker MP. Methods for trend estimation from summarized dose-response data, with applications to meta-analysis. Am J Epidemiol 1992 Jun 1;135(11):1301-9.

(49) Aune D, Greenwood DC, Chan DS, Vieira R, Vieira AR, Navarro Rosenblatt DA, Cade JE, Burley VJ, Norat T. Body mass index, abdominal fatness and pancreatic cancer risk: a systematic review and non-linear dose-response meta-analysis of prospective studies. Ann Oncol 2012 Apr;23(4):843-52.

(50) Chene G, Thompson SG. Methods for summarizing the risk associations of quantitative variables in epidemiologic studies in a consistent form. Am J Epidemiol 1996 Sep 15;144(6):610-21.

(51) Royston P. A strategy for modelling the effect of a continuous covariate in medicine and epidemiology. Stat Med 2000 Jul 30;19(14):1831-47.

(52) Higgins JP, Thompson SG. Quantifying heterogeneity in a meta-analysis. Stat Med 2002 Jun 15;21(11):1539-58.

(53) Egger M, Davey SG, Schneider M, Minder C. Bias in meta-analysis detected by a simple, graphical test. BMJ 1997 Sep 13;315(7109):629-34.

(54) Begg CB, Mazumdar M. Operating characteristics of a rank correlation test for publication bias. Biometrics 1994 Dec;50(4):1088-101.

(55) Wells G, Shea B, O'Connell D., Peterson J, Welch V, Losos M, Tugwell P. The Newcastle-Ottawa Scale (NOS) for assessing the quality of nonrandomised studies in meta-analyses. http://www.ohri.ca/programs/clinical_epidemiology/oxford.asp, Accessed 13.08.2014.

(56) Collaborative Group on Epidemiological Studies of Ovarian Cancer. Ovarian cancer and body size: individual participant meta-analysis including 25,157 women with ovarian cancer from 47 epidemiological studies. PLoS Med 2012;9(4):e1001200.

(57) Emerging Risk Factors Collaboration. Adult height and the risk of cause-specific death and vascular morbidity in 1 million people: individual participant meta-analysis. Int J Epidemiol 2012 Oct;41(5):1419-33.

(58) Lukanova A, Kaaks R. Endogenous hormones and ovarian cancer: epidemiology and current hypotheses. Cancer Epidemiol Biomarkers Prev 2005 Jan;14(1):98-107.

(59) Tworoger SS, Eliassen AH, Missmer SA, Baer H, Rich-Edwards J, Michels KB, Barbieri RL, Dowsett M, Hankinson SE. Birthweight and body size throughout life in relation to sex hormones and prolactin concentrations in premenopausal women. Cancer Epidemiol Biomarkers Prev 2006 Dec;15(12):2494-501.

(60) Rinaldi S, Dossus L, Lukanova A, Peeters PH, Allen NE, Key T, Bingham S, Khaw KT, Trichopoulos D, Trichopoulou A, Oikonomou E, Pera G, et al. Endogenous androgens and risk of epithelial ovarian cancer: results from the European Prospective Investigation into Cancer and Nutrition (EPIC). Cancer Epidemiol Biomarkers Prev 2007 Jan;16(1):23-9.

(61) Gunnell D, Okasha M, Smith GD, Oliver SE, Sandhu J, Holly JM. Height, leg length, and cancer risk: a systematic review. Epidemiol Rev 2001;23(2):313-42.

(62) Rimm EB, Stampfer MJ, Colditz GA, Chute CG, Litin LB, Willett WC. Validity of self-reported waist and hip circumferences in men and women. Epidemiology 1990 Nov;1(6):466-73.

(63) Rana JS, Li TY, Manson JE, Hu FB. Adiposity compared with physical inactivity and risk of type 2 diabetes in women. Diabetes Care 2007 Jan;30(1):53-8.

(64) Field AE, Coakley EH, Must A, Spadano JL, Laird N, Dietz WH, Rimm E, Colditz GA. Impact of overweight on the risk of developing common chronic diseases during a 10-year period. Arch Intern Med 2001 Jul 9;161(13):1581-6.

Figure legends

Figure 1. BMI and BMI in early adulthood ovarian cancer risk, linear (per 5 BMI units) and nonlinear dose-response analyses

Figure 2: Weight and weight change and ovarian cancer, linear (per 5 kg) and nonlinear dose-response analysis

Figure 3. Waist circumference and waist-to-hip ratio and ovarian cancer, linear (per 10 cm and 0.1 unit, respectively) and nonlinear dose-response analyses

Figure 4: Hips circumference and height and ovarian cancer, linear (per 10 cm) and nonlinear dose-response

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