• Record: found
  • Abstract: found
  • Article: found
Is Open Access

Obesity and Risk of Colorectal Cancer: A Systematic Review of Prospective Studies

Read this article at

      There is no author summary for this article yet. Authors can add summaries to their articles on ScienceOpen to make them more accessible to a non-specialist audience.



      Mounting evidence indicates that obesity may be associated with the risk of colorectal cancer (CRC). To conduct a systematic review of prospective studies assessing the association of obesity with the risk of CRC using meta-analysis.

      Methodology/Principal Findings

      Relevant studies were identified by a search of MEDLINE and EMBASE databases before January 2012, with no restrictions. We also reviewed reference lists from retrieved articles. We included prospective studies that reported relative risk (RR) estimates with 95% confidence intervals (CIs) for the association between general obesity [measured using body mass index (BMI)] or central obesity [measured using waist circumference (WC)] and the risk of colorectal, colon, or rectal cancer. Approximately 9, 000, 000 participants from several countries were included in this analysis. 41 studies on general obesity and 13 studies on central obesity were included in the meta-analysis. The pooled RRs of CRC for the obese vs. normal category of BMI were 1.334 (95% CI, 1.253–1.420), and the highest vs. lowest category of WC were 1.455 (95% CI, 1.327–1.596). There was heterogeneity among studies of BMI (P<0.001) but not among studies of WC (P = 0.323).


      Both of general and central obesity were positively associated with the risk of CRC in this meta-analysis.

      Related collections

      Most cited references 66

      • Record: found
      • Abstract: found
      • Article: not found

      Quantifying heterogeneity in a meta-analysis.

      The extent of heterogeneity in a meta-analysis partly determines the difficulty in drawing overall conclusions. This extent may be measured by estimating a between-study variance, but interpretation is then specific to a particular treatment effect metric. A test for the existence of heterogeneity exists, but depends on the number of studies in the meta-analysis. We develop measures of the impact of heterogeneity on a meta-analysis, from mathematical criteria, that are independent of the number of studies and the treatment effect metric. We derive and propose three suitable statistics: H is the square root of the chi2 heterogeneity statistic divided by its degrees of freedom; R is the ratio of the standard error of the underlying mean from a random effects meta-analysis to the standard error of a fixed effect meta-analytic estimate, and I2 is a transformation of (H) that describes the proportion of total variation in study estimates that is due to heterogeneity. We discuss interpretation, interval estimates and other properties of these measures and examine them in five example data sets showing different amounts of heterogeneity. We conclude that H and I2, which can usually be calculated for published meta-analyses, are particularly useful summaries of the impact of heterogeneity. One or both should be presented in published meta-analyses in preference to the test for heterogeneity. Copyright 2002 John Wiley & Sons, Ltd.
        • Record: found
        • Abstract: found
        • Article: not found

        Bias in meta-analysis detected by a simple, graphical test.

        Funnel plots (plots of effect estimates against sample size) may be useful to detect bias in meta-analyses that were later contradicted by large trials. We examined whether a simple test of asymmetry of funnel plots predicts discordance of results when meta-analyses are compared to large trials, and we assessed the prevalence of bias in published meta-analyses. Medline search to identify pairs consisting of a meta-analysis and a single large trial (concordance of results was assumed if effects were in the same direction and the meta-analytic estimate was within 30% of the trial); analysis of funnel plots from 37 meta-analyses identified from a hand search of four leading general medicine journals 1993-6 and 38 meta-analyses from the second 1996 issue of the Cochrane Database of Systematic Reviews. Degree of funnel plot asymmetry as measured by the intercept from regression of standard normal deviates against precision. In the eight pairs of meta-analysis and large trial that were identified (five from cardiovascular medicine, one from diabetic medicine, one from geriatric medicine, one from perinatal medicine) there were four concordant and four discordant pairs. In all cases discordance was due to meta-analyses showing larger effects. Funnel plot asymmetry was present in three out of four discordant pairs but in none of concordant pairs. In 14 (38%) journal meta-analyses and 5 (13%) Cochrane reviews, funnel plot asymmetry indicated that there was bias. A simple analysis of funnel plots provides a useful test for the likely presence of bias in meta-analyses, but as the capacity to detect bias will be limited when meta-analyses are based on a limited number of small trials the results from such analyses should be treated with considerable caution.
          • Record: found
          • Abstract: found
          • Article: not found

          Meta-analysis of observational studies in epidemiology: a proposal for reporting. Meta-analysis Of Observational Studies in Epidemiology (MOOSE) group.

           T Sipe,  D Rennie,  D Stroup (2000)
          Because of the pressure for timely, informed decisions in public health and clinical practice and the explosion of information in the scientific literature, research results must be synthesized. Meta-analyses are increasingly used to address this problem, and they often evaluate observational studies. A workshop was held in Atlanta, Ga, in April 1997, to examine the reporting of meta-analyses of observational studies and to make recommendations to aid authors, reviewers, editors, and readers. Twenty-seven participants were selected by a steering committee, based on expertise in clinical practice, trials, statistics, epidemiology, social sciences, and biomedical editing. Deliberations of the workshop were open to other interested scientists. Funding for this activity was provided by the Centers for Disease Control and Prevention. We conducted a systematic review of the published literature on the conduct and reporting of meta-analyses in observational studies using MEDLINE, Educational Research Information Center (ERIC), PsycLIT, and the Current Index to Statistics. We also examined reference lists of the 32 studies retrieved and contacted experts in the field. Participants were assigned to small-group discussions on the subjects of bias, searching and abstracting, heterogeneity, study categorization, and statistical methods. From the material presented at the workshop, the authors developed a checklist summarizing recommendations for reporting meta-analyses of observational studies. The checklist and supporting evidence were circulated to all conference attendees and additional experts. All suggestions for revisions were addressed. The proposed checklist contains specifications for reporting of meta-analyses of observational studies in epidemiology, including background, search strategy, methods, results, discussion, and conclusion. Use of the checklist should improve the usefulness of meta-analyses for authors, reviewers, editors, readers, and decision makers. An evaluation plan is suggested and research areas are explored.

            Author and article information

            [1 ]Department of Surgery, Shanghai Tenth People's Hospital, Affiliated to Tongji University, Shanghai, People’s Republic of China
            [2 ]Department of Surgery, The Sixth People’s Hospital Affiliated to Shanghai Jiao Tong University, Shanghai, People’s Republic of China
            The University of Texas M. D. Anderson Cancer Center, United States of America
            Author notes

            Competing Interests: The authors have declared that no competing interests exist.

            Conceived and designed the experiments: YLM YZY HLQ. Performed the experiments: YZY FW. Analyzed the data: YZY PZ CZS YZ. Contributed reagents/materials/analysis tools: YZY PZ CZS YZ. Wrote the paper: YZY HLQ YLM.

            Role: Editor
            PLoS One
            PLoS ONE
            PLoS ONE
            Public Library of Science (San Francisco, USA )
            17 January 2013
            : 8
            : 1
            23349764 3547959 PONE-D-12-25799 10.1371/journal.pone.0053916

            This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.

            Pages: 1
            This work was financially sponsored by the Shanghai Rising-Star Program (No. 11QA1404800), the Grants from the National Natural Science Foundation of China (No. 81001069), and Shanghai Science and Technology Development Fund (No. 12140902300 and No. 12410707400). The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.
            Research Article
            Clinical Research Design
            Systematic Reviews
            Gastroenterology and Hepatology
            Gastrointestinal Cancers
            Cancer Risk Factors
            Lifestyle Causes of Cancer
            Cancers and Neoplasms
            Gastrointestinal Tumors
            Rectal Cancer
            Cancer Prevention



            Comment on this article