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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: [email protected]
1
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
2
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.
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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
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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
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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.
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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).
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Results
We identified twenty six prospective studies (22 publications, 23 risk estimates) (721;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 casecontrol 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;3234;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%
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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).
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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
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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;2126;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
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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).
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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
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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
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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 selfreported 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 (>1600018000) 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
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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
16
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.
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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 doseresponse 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
24