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      Magnesium Intake and Risk of Type 2 Diabetes : Meta-analysis of prospective cohort studies

      , BSC 1 , , MD, PHD 2 , 3 , , MD, SCD 2 , 3 , , MD, PHD 1

      Diabetes Care

      American Diabetes Association

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          Abstract

          OBJECTIVE

          Emerging epidemiological evidence suggests that higher magnesium intake may reduce diabetes incidence. We aimed to examine the association between magnesium intake and risk of type 2 diabetes by conducting a meta-analysis of prospective cohort studies.

          RESEARCH DESIGN AND METHODS

          We conducted a PubMed database search through January 2011 to identify prospective cohort studies of magnesium intake and risk of type 2 diabetes. Reference lists of retrieved articles were also reviewed. A random-effects model was used to compute the summary risk estimates.

          RESULTS

          Meta-analysis of 13 prospective cohort studies involving 536,318 participants and 24,516 cases detected a significant inverse association between magnesium intake and risk of type 2 diabetes (relative risk [RR] 0.78 [95% CI 0.73–0.84]). This association was not substantially modified by geographic region, follow-up length, sex, or family history of type 2 diabetes. A significant inverse association was observed in overweight (BMI ≥25 kg/m 2) but not in normal-weight individuals (BMI <25 kg/m 2), although test for interaction was not statistically significant ( P interaction = 0.13). In the dose-response analysis, the summary RR of type 2 diabetes for every 100 mg/day increment in magnesium intake was 0.86 (95% CI 0.82–0.89). Sensitivity analyses restricted to studies with adjustment for cereal fiber intake yielded similar results. Little evidence of publication bias was observed.

          CONCLUSIONS

          This meta-analysis provides further evidence supporting that magnesium intake is significantly inversely associated with risk of type 2 diabetes in a dose-response manner.

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          Most cited references 40

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          Measuring inconsistency in meta-analyses.

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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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              Reduction in the incidence of type 2 diabetes with lifestyle intervention or metformin.

              Type 2 diabetes affects approximately 8 percent of adults in the United States. Some risk factors--elevated plasma glucose concentrations in the fasting state and after an oral glucose load, overweight, and a sedentary lifestyle--are potentially reversible. We hypothesized that modifying these factors with a lifestyle-intervention program or the administration of metformin would prevent or delay the development of diabetes. We randomly assigned 3234 nondiabetic persons with elevated fasting and post-load plasma glucose concentrations to placebo, metformin (850 mg twice daily), or a lifestyle-modification program with the goals of at least a 7 percent weight loss and at least 150 minutes of physical activity per week. The mean age of the participants was 51 years, and the mean body-mass index (the weight in kilograms divided by the square of the height in meters) was 34.0; 68 percent were women, and 45 percent were members of minority groups. The average follow-up was 2.8 years. The incidence of diabetes was 11.0, 7.8, and 4.8 cases per 100 person-years in the placebo, metformin, and lifestyle groups, respectively. The lifestyle intervention reduced the incidence by 58 percent (95 percent confidence interval, 48 to 66 percent) and metformin by 31 percent (95 percent confidence interval, 17 to 43 percent), as compared with placebo; the lifestyle intervention was significantly more effective than metformin. To prevent one case of diabetes during a period of three years, 6.9 persons would have to participate in the lifestyle-intervention program, and 13.9 would have to receive metformin. Lifestyle changes and treatment with metformin both reduced the incidence of diabetes in persons at high risk. The lifestyle intervention was more effective than metformin.
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                Author and article information

                Journal
                Diabetes Care
                diacare
                dcare
                Diabetes Care
                Diabetes Care
                American Diabetes Association
                0149-5992
                1935-5548
                September 2011
                19 August 2011
                : 34
                : 9
                : 2116-2122
                Affiliations
                1Department of Nutrition and Food Hygiene, School of Radiation Medicine and Public Health, Soochow University, Suzhou, China
                2Department of Nutrition, Gillings School of Global Public Health and School of Medicine, University of North Carolina at Chapel Hill, Chapel Hill, North Carolina
                3Department of Epidemiology, Gillings School of Global Public Health and School of Medicine, University of North Carolina at Chapel Hill, Chapel Hill, North Carolina
                Author notes
                Corresponding author: Li-Qiang Qin, dongjy@ 123456mail3.sysu.edu.cn .
                Article
                0518
                10.2337/dc11-0518
                3161260
                21868780
                © 2011 by the American Diabetes Association.

                Readers may use this article as long as the work is properly cited, the use is educational and not for profit, and the work is not altered. See http://creativecommons.org/licenses/by-nc-nd/3.0/ for details.

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                Categories
                Reviews/Commentaries/ADA Statements
                Meta-Analysis

                Endocrinology & Diabetes

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