Epidemiology

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

Corresponding Author

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

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

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, Tel.: +44-20-7594-8478, E-mail: d.aune@imperial.ac.uk Search for more papers by this author

Deborah A. Navarro Rosenblatt,

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

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Doris Sau Man Chan,

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

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Leila Abar,

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

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Snieguole Vingeliene,

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

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Ana Rita Vieira,

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

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Darren C. Greenwood,

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

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Teresa Norat,

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

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Dagfinn Aune,

Corresponding Author

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

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

Deborah A. Navarro Rosenblatt,

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

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Doris Sau Man Chan,

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

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Leila Abar,

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

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Snieguole Vingeliene,

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

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Ana Rita Vieira,

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

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Darren C. Greenwood,

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

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Teresa Norat,

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

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First published: 24 September 2014

https://doi.org/10.1002/ijc.29207

Citations: 52

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 20th of August 2014. Summary relative risks (RRs) were calculated using a random effects model. The summary relative risk for a 5-U increment in BMI was 1.07 (95% CI: 1.03–1.11, I² = 54%, n = 28 studies). There was evidence of a nonlinear association, p_nonlinearity < 0.0001, with risk increasing significantly from BMI∼28 and above. The summary RR per 5 U increase in BMI in early adulthood was 1.12 (95% CI: 1.05–1.20, I² = 0%, p_{heterogeneity}= 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.

Abstract

What's new?

While past investigations of relationships between obesity and ovarian cancer yielded inconclusive results, recent cohort studies have offered new insight, warranting reassessment of potential associations. In this meta-analysis, the authors found evidence of a nonlinear positive association between ovarian cancer risk and body mass index (BMI) and weight, with a significant association for a BMI of 28 or higher. Risk also rose significantly with increasing height. Thus, strong evidence that greater body fatness increases ovarian cancer risk has amassed from cohort studies, which has important public health implications, particularly in light of rising obesity rates.

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 fourfold 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, 19 additional cohort studies (17 publications) with 7,420 cases among >5.1 million participants have been published on BMI7-23 and twelve additional cohort studies with 8,529 cases among >2.5 million participants have been published on height11, 14, 16, 19, 21, 24-28 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 20th of August 2014. The search terms used are provided in the Supporting Information Appendix. A prespecified protocol was followed for the review (http://www.dietandcancerreport.org/cup/current_progress/ovarian_cancer.php) and we followed standard criteria for meta-analyses of observational studies.29 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, weight, weight gain, waist circumference, waist-to-hip ratio, hip circumference, or height and risk of ovarian cancer 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, at least three categories of the anthropometric variable and a quantitative measure of the exposure and the total number of cases and person-years had to be available in the publication. We identified 42 potentially relevant full-text publications.7-28, 30-49 We excluded two studies which did not provide any risk estimates,42, 49 one study of relative weight as the exposure,41 two studies of obesity diagnosis,43, 45 and five duplicate publications.39, 44, 46-48 Two publications33, 37 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. Two studies17, 23 were excluded from the analyses of waist circumference and waist-to-hip ratio as they reported results in less than three categories.

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 August 2014 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 U increment in BMI, 5 kg increment in weight and weight gain, 10 cm increment in waist circumference, hip circumference, and height and for a 0.1 U increment in waist-to-hip ratio.50 The average of the natural logarithm of the RRs was estimated and the RR from each study was weighted using random effects weighting.50 A two-tailed p < 0.05 was considered statistically significant.

We used the method described by Greenland and Longnecker51 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.52 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.53 A potential nonlinear dose–response relationship between anthropometric measures and ovarian cancer was examined by using fractional polynomial models.54 We used a likelihood ratio test to assess the difference between the nonlinear and linear models to test for nonlinearity.54

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 I^2.55 Small study effects, such as publication bias, were assessed by inspecting the funnel plots for asymmetry and with Egger's test56 and Begg's test,57 with the results considered to indicate small study effects when p < 0.10. Study quality was assessed using the Newcastle–Ottawa scale.58 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).

Results

We identified twenty eight prospective studies (24 publications, 25 risk estimates)7-23, 30, 32, 34-36, 38, 40 that were included in the analyses of BMI and ovarian cancer risk (Supporting Information Table 1). One publication reported a combined result for three nested case-control studies34 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, 24, 35, 36, 38 four studies were included in the analysis of weight and ovarian cancer,14, 21, 35, 40 six cohort studies14, 17, 21, 33, 35, 37 were included in the analysis of weight gain and ovarian cancer risk. Six cohort studies (five publications)14-16, 21, 31 were included in the analysis of waist circumference, and five cohort studies (four publications)14, 15, 21, 38 were included in the analysis of waist-to-hip ratio, and four cohort studies (three publications)14, 15, 21 were included in the analysis of hip circumference and ovarian cancer risk. Sixteen prospective studies (15 publications, 16 risk estimates)11, 14, 16, 19, 21, 24-28, 32, 35, 36, 38, 40 were included in the analysis of height and ovarian cancer (Supporting Information Table 2). The characteristics of the included studies are provided in Supporting Information Tables 1 and 2. Most of the studies were from Europe and the US and used self-reported weight and height (Supporting Information Tables 1 and 2).

BMI

Twenty eight prospective studies (24 publications, 25 risk estimates)7-19, 22, 23, 30, 32, 34-36, 38-40 were included in the overall dose-response analysis of BMI and ovarian cancer risk and included a total of 19,825 cases among 6,681,795 participants. Ten studies were from the US, eleven were from Europe, six were from Asia, and one was from Australia (Supporting Information Table 1). The summary RR for a 5-U increment in BMI was 1.07 (95% CI: 1.03–1.11), with moderate heterogeneity, I² = 54%, p_{heterogeneity} = 0.001 (Fig. 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%, p_{heterogeneity} = 0.14. There was no evidence of small study effects with Egger's test, p = 0.19, or with Begg's test, p = 0.83 and when visually inspected the funnel plot showed no sign of asymmetry (Supporting Information Fig. 1). There was evidence of a nonlinear association between BMI and ovarian cancer risk, p_nonlinearity < 0.0001 (Fig. 1b, Supporting Information Table 3), with risk increasing significantly from a BMI of 27.6 and more steeply at higher BMI levels.

**Figure 1**
Open in figure viewer PowerPoint

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

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, 24, 35, 36, 38 Four studies were from the US and two were from Europe (Supporting Information Table 1). The summary RR per 5 U increase in BMI in early adulthood was 1.12 (1.05–1.20, I² = 0%, p_{heterogeneity} = 0.54) (Fig. 1c). There was evidence of a nonlinear association between BMI and ovarian cancer risk, p_nonlinearity < 0.0001 (Fig. 1d, Supporting Information Table 3), with risk increasing significantly from BMI∼30 and more steeply at higher BMI levels.

Weight

Four cohort studies14, 21, 35, 40 were included in the analysis of weight and included 1,149 cases among 344,718 participants. Two studies were from Europe, one was from the US and one study was from Asia (Supporting Information Table 1). The summary RR per 5 kg increase in weight was 1.03 (95% CI: 1.02–1.05, I² = 0%, p_{heterogeneity} = 0.46) (Fig. 2a). There was no evidence of a nonlinear association between weight and ovarian cancer risk, p_nonlinearity = 0.73 (Fig. 2b, Supporting Information Table 3).

Weight gain

Six cohort studies14, 17, 21, 33, 35, 37 were included in the analysis of weight gain and ovarian cancer risk and included 1338 cases among 313,066 participants. Three studies were from the Europe, two was from the US and one was from Asia (Supporting Information Table 1). The summary RR per 5 kg of weight gained between early adulthood (age 18–25 years) and baseline of the studies was 1.02 (95% CI: 0.96–1.09, I² = 67%, p_{heterogeneity} = 0.01) (Fig. 2c). Although there was indication of nonlinearity in the analysis, p_nonlinearity = 0.02, there was no evidence of an association across the range of weight gain observed and ovarian cancer risk (Fig. 2d, Supporting Information Table 3).

Waist circumference

Six cohort studies (five publications)14-16, 21, 31 were included in the analysis of waist circumference and ovarian cancer risk and included 1,360 cases among 436,579 participants. Three studies were from Europe, two were from the US and one was from Asia (Supporting Information 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 (Fig. 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, p_nonlinearity = 0.69 (Fig. 3b, Supporting Information Table 4).

Waist-to-hip ratio

Five cohort studies (four publications)14, 15, 21, 38 were included in the analysis of waist-to-hip ratio and ovarian cancer risk and included 1,329 cases among 417,558 participants. Three studies were from the US, one was from Europe and one was from Asia (Supporting Information Table 1). The summary RR for a 0.1 U increment in waist-to-hip ratio was 1.00 (95% CI: 0.93–1.07) with no significant heterogeneity I² = 0%, p = 0.47 (Fig. 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, p_nonlinearity = 0.99 (Fig. 3d, Supporting Information 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 1,106 cases among 386,177 participants. Two studies were from the US, one from Europe and one from Asia (Supporting Information Table 1). The summary RR was 1.04 (95% CI: 0.82–1.33, I² = 72%, p = 0.01) (Fig. 4a). Although the test for nonlinearity was significant, p_nonlinearity = 0.003, there was no significant association between hip circumference and ovarian cancer within the range reported by the studies (Fig. 4b, Supporting Information Table 4).

Height

Sixteen cohort studies (15 publications, 16 risk estimates)11, 14, 16, 19, 21, 24-28, 32, 35, 36, 38, 40 were included in the analysis of height and ovarian cancer and included 18,531 cases among 3,937,281 participants. Seven studies were from the US, six were from Europe, two were from Asia and one from Australia (Supporting Information 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) (Fig. 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 (Supporting Information Fig. 2). There was no evidence of a nonlinear association, p_nonlinearity = 0.11 (Fig. 4d, Supporting Information 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).

Table 1. Subgroup analyses of BMI and ovarian cancer

		BMI, per 5 kg m⁻²					Height, per 10 cm
		n	RR (95% CI)	I² (%)	P_ha^a p for heterogeneity within each subgroup.	P_hb^b p for heterogeneity between subgroups with meta-regression analysis.	n	RR (95% CI)	I² (%)	P_ha^a p for heterogeneity within each subgroup.	P_hb^b p for heterogeneity between subgroups with meta-regression analysis.
All studies		25	1.07 (1.03-1.11)	54.2	0.001		16	1.16 (1.11-1.20)	25.3	0.17
Duration of follow-up
<10 yrs follow-up		8	1.09 (1.06-1.12)	0	0.91	0.24	4	1.22 (1.09-1.38)	54.4	0.09	0.39
≥10 yrs follow-up		17	1.06 (1.00-1.12)	59.6	0.001		12	1.14 (1.10-1.19)	10.5	0.34
Assessment of weight/height
Measured		10	1.09 (1.1-1.17)	74.4	<0.0001	0.47	8	1.14 (1.08-1.21)	20.3	0.27	0.60
Self-reported		14	1.07 (1.04-1.11)	0	0.83		8	1.18 (1.10-1.26)	35.3	0.15
Measured and self-reported		1	0.68 (0.49-0.95)				0
Geographic location
Europe		8	1.06 (1.00-1.12)	73.1	<0.0001	0.88	5	1.17 (1.11-1.23)	35.4	0.19	0.98
America		9	1.07 (1.03-1.11)	0	0.68		7	1.13 (1.06-1.19)	18.9	0.29
Europe and America		1	0.68 (0.49-0.95)				0
Australia		1	1.22 (1.00-1.48)				1	1.13 (0.82-1.55)
Asia		6	1.26 (1.09-1.46)	0	0.45		3	1.34 (1.08-1.68)	22.0	0.28
Number of cases
Cases <100		6	1.06 (0.88-1.29)	0	0.49	0.25	1	1.03 (0.68-1.56)			0.76
Cases 100<200		7	1.14 (1.00-1.29)	55.3	0.04		4	1.20 (1.09-1.33)	0	0.52
Cases ≥200		12	1.05 (1.01-1.10)	62.7	0.002		11	1.15 (1.10-1.21)	40.0	0.08
Menopausal status
Premenopausal		8	1.13 (1.02-1.26)	72.6	0.001	0.65/0.25³	1	1.28 (0.94-1.72)			0.34/ 0.40³
Postmenopausal		13	1.05 (1.01-1.09)	42.5	0.05		5	1.10 (1.04-1.17)	0	0.42
Pre- and postmenopausal		11	1.08 (0.98-1.20)	43.7	0.06		11	1.18 (1.12-1.24)	32.8	0.14
Study quality
0–3		0				0.83	0				0.12
4–6		6	1.07 (1.02-1.12)	0	0.72		1	1.35 (1.13-1.60)
7–9		19	1.07 (1.02-1.12)	62.9	<0.0001		15	1.16 (1.11-1.20)	16.9	0.27
Adjustment for confounders
Age at menarche	Yes	8	1.10 (1.05-1.14)	0	0.54	0.28	7	1.15 (1.09-1.22)	17.4	0.30	0.90
	No	17	1.06 (1.00-1.11)	60.8	0.001		9	1.17 (1.10-1.24)	37.6	0.12
Parity	Yes	14	1.08 (1.04-1.12)	20.7	0.23	0.57	13	1.15 (1.10-1.20)	17.6	0.27	0.79
	No	11	1.07 (0.99-1.15)	66.9	0.001		3	1.28 (1.04-1.58)	63.7	0.06
Menopausal status	Yes	8	1.07 (1.00-1.14)	41.4	0.10	0.96	6	1.13 (1.04-1.23)	41.3	0.13	0.40
	No	17	1.07 (1.02-1.13)	57.9	0.001		10	1.17 (1.12-1.21)	15.1	0.30
Hormone replacement therapy	Yes	11	1.09 (1.05-1.13)	0	0.78	0.23	10	1.13 (1.06-1.20)	22.9	0.23	0.44
	No	14	1.09 (1.05-1.13)	0	0.78		6	1.18 (1.12-1.24)	31.3	0.20
Oral contraceptive use	Yes	10	1.08 (1.02-1.13)	39.0	0.10	0.75	10	1.16 (1.09-1.24)	39.2	0.10	0.97
	No	15	1.07 (1.01-1.13)	58.9	0.002		6	1.15 (1.11-1.19)	4.3	0.39
Tubal ligation	Yes	3	1.04 (0.95-1.13)	19.6	0.29	0.59	2	1.26 (1.02-1.57)	58.0	0.12	0.46
	No	22	1.08 (1.03-1.12)	57.7	<0.0001		14	1.15 (1.11-1.20)	22.6	0.21
Alcohol	Yes	7	1.08 (1.04-1.11)	0	0.71	0.92	4	1.18 (1.07-1.31)	40.9	0.17	0.42
	No	18	1.07 (1.02-1.13)	62.3	<0.0001		12	1.15 (1.10-1.20)	24.4	0.20
Smoking	Yes	13	1.09 (1.04-1.13)	17.4	0.27	0.38	9	1.17 (1.11-1.24)	15.9	0.30	0.36
	No	12	1.06 (1.00-1.13)	61.6	0.003		7	1.15 (1.08-1.22)	38.5	0.14
Height	Yes	6	1.05 (0.97-1.12)	69.9	0.005	0.26
	No	19	1.08 (1.04-1.13)	27.3	0.13
BMI	Yes						7	1.16 (1.10-1.21)	38.8	0.13	0.95
	No						9	1.16 (1.08-1.26)	21.6	0.25
Physical activity	Yes	9	1.09 (1.05-1.12)	0	0.63	0.36	6	1.19 (1.09-1.30)	46.3	0.10	0.63
	No	16	1.06 (1.00-1.12)	63.6	<0.0001		10	1.14 (1.10-1.19)	13.1	0.32
Breastfeeding	Yes	2	1.16 (0.98-1.38)	0	0.33	0.44	3	1.25 (0.98-1.61)	42.4	0.18	0.35
	No	23	1.07 (1.02-1.11)	56.0	0.001		13	1.15 (1.11-1.19)	20.9	0.23

n denotes the number of risk estimates
^a p for heterogeneity within each subgroup.
^b p for heterogeneity between subgroups with meta-regression analysis.
^c P for heterogeneity between premenopausal and postmenopausal women (excluding studies with mixed menopausal status).

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 studies4-6, 59 (Supporting Information 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 20 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 19,825 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 from a BMI of 27.6. 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 women5 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 analyses5, 59, 60 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 estrogens61 and reduced levels of sex hormone-binding globulin,62 however, epidemiological data are limited and inconsistent.61, 63 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,64 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.36 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 or by inspection of the funnel plots.

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.65 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 (>18,000–19,000) 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 27.6 kg m⁻² and above. However, this finding should not be interpreted as having a BMI below 27.6 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, 66, 67 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

The authors thank the systematic literature review team at the Istituto Nazionale Tumori, Milan for their contributions to the ovarian cancer database. 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.

Supporting Information

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Citing Literature

Volume136, Issue8

15 April 2015

Pages 1888-1898

Filename	Description
ijc29207-sup-0001-suppinfo01.ppt1 MB	Supporting Information
ijc29207-sup-0002-suppinfo01.doc159.5 KB	Supporting Information
ijc29207-sup-0003-suppinfo02.doc46 KB	Supporting Information
ijc29207-sup-0004-suppinfo01.docx86.1 KB	Supporting Information

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

Abstract

Abstract