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      Coffee Consumption and Risk of Breast Cancer: An Up-To-Date Meta-Analysis

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          Abstract

          Objectives

          This updated meta-analysis was conducted to assess the association between coffee consumption and breast cancer risk.

          Methods

          We conducted a systematic search updated July 2012 to identify observational studies providing quantitative estimates for breast cancer risk in relation to coffee consumption. Pooled relative risks (RRs) with 95% confidence intervals (CIs) were calculated using a random-effects model, and generalized least square trend estimation was used to assess dose–response relationships.

          Results

          A total of 26 studies (16 cohort and 10 case–control studies) on coffee intake with 49497 breast cancer cases were included in the meta-analysis. The pooled RR showed a borderline significant influence of highest coffee consumption (RR = 0.96; 95% CI 0.93–1.00), low-to moderate coffee consumption (RR = 0.99; 95% CI 0.95–1.04), or an increment of 2 cups/day of coffee consumption (RR = 0.98; 95% CI 0.97–1.00) on the risk of breast cancer. In stratified analysis, a significant inverse association was observed in ER-negative subgroup. However, no significant association was noted in the others.

          Conclusions

          Our findings suggest that increased coffee intake is not associated with a significantly reduced risk of breast cancer, but we observe an inverse association in ER-negative subgroup analysis. More large studies are needed to determine subgroups to obtain more valuable data on coffee drinking and breast cancer risk.

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          Most cited references 48

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          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.
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            Global cancer statistics, 2002.

             D Parkin,  F Bray,  J Ferlay (2005)
            Estimates of the worldwide incidence, mortality and prevalence of 26 cancers in the year 2002 are now available in the GLOBOCAN series of the International Agency for Research on Cancer. The results are presented here in summary form, including the geographic variation between 20 large "areas" of the world. Overall, there were 10.9 million new cases, 6.7 million deaths, and 24.6 million persons alive with cancer (within three years of diagnosis). The most commonly diagnosed cancers are lung (1.35 million), breast (1.15 million), and colorectal (1 million); the most common causes of cancer death are lung cancer (1.18 million deaths), stomach cancer (700,000 deaths), and liver cancer (598,000 deaths). The most prevalent cancer in the world is breast cancer (4.4 million survivors up to 5 years following diagnosis). There are striking variations in the risk of different cancers by geographic area. Most of the international variation is due to exposure to known or suspected risk factors related to lifestyle or environment, and provides a clear challenge to prevention.
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              Methods for trend estimation from summarized dose-response data, with applications to meta-analysis.

              Meta-analysis often requires pooling of correlated estimates to compute regression slopes (trends) across different exposure or treatment levels. The authors propose two methods that account for the correlations but require only the summary estimates and marginal data from the studies. These methods provide more efficient estimates of regression slope, more accurate variance estimates, and more valid heterogeneity tests than those previously available. One method also allows estimation of nonlinear trend components, such as quadratic effects. The authors illustrate these methods in a meta-analysis of alcohol use and breast cancer.
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                Author and article information

                Contributors
                Role: Editor
                Journal
                PLoS One
                PLoS ONE
                plos
                plosone
                PLoS ONE
                Public Library of Science (San Francisco, USA )
                1932-6203
                2013
                4 January 2013
                : 8
                : 1
                Affiliations
                [1 ]Department of Oncology, Xuzhou Medical College, Xuzhou, People's Republic of China
                [2 ]Department of Pathology, Xuzhou Medical College, Xuzhou, People's Republic of China
                [3 ]Department of General Surgery, Jiangsu Cancer Hospital, Nanjing, People's Republic of China
                [4 ]Clinical Laboratory Center, Jiangsu Cancer Hospital, Nanjing, People's Republic of China
                [5 ]Department of Radiation Oncology, Jiangsu Cancer Hospital, Nanjing, People's Republic of China
                [6 ]Oncology of Central Laboratory, Jiangsu Cancer Hospital, Nanjing, People's Republic of China
                King Faisal Specialist Hospital & Research Center, Saudi Arabia
                Author notes

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

                Conceived and designed the experiments: JHT XJL. Performed the experiments: XJL ZJR. Analyzed the data: JHZ JHT XJL. Contributed reagents/materials/analysis tools: JWQ JZW MHJ. Wrote the paper: XJL.

                Article
                PONE-D-12-22560
                10.1371/journal.pone.0052681
                3537715
                23308117

                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.

                Page count
                Pages: 7
                Funding
                This work was supported by grants from the National Natural Science Foundation of China (81272470). The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.
                Categories
                Research Article
                Medicine
                Clinical Research Design
                Case-Control Studies
                Cohort Studies
                Meta-Analyses
                Survey Research
                Nutrition
                Oncology
                Cancer Risk Factors
                Public Health
                Behavioral and Social Aspects of Health
                Preventive Medicine

                Uncategorized

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