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      Serum magnesium and the risk of prediabetes: a population-based cohort study

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          Abstract

          Aims/hypothesis

          Previous studies have found an association between serum magnesium and incident diabetes; however, this association may be due to reverse causation, whereby diabetes may induce urinary magnesium loss. In contrast, in prediabetes (defined as impaired fasting glucose), serum glucose levels are below the threshold for urinary magnesium wasting and, hence, unlikely to influence serum magnesium levels. Thus, to study the directionality of the association between serum magnesium levels and diabetes, we investigated its association with prediabetes. We also investigated whether magnesium-regulating genes influence diabetes risk through serum magnesium levels. Additionally, we quantified the effect of insulin resistance in the association between serum magnesium levels and diabetes risk.

          Methods

          Within the population-based Rotterdam Study, we used Cox models, adjusted for age, sex, lifestyle factors, comorbidities, kidney function, serum levels of electrolytes and diuretic use, to study the association between serum magnesium and prediabetes/diabetes. In addition, we performed two mediation analyses: (1) to study if common genetic variation in eight magnesium-regulating genes influence diabetes risk through serum magnesium levels; and (2) to quantify the proportion of the effect of serum magnesium levels on diabetes that is mediated through insulin resistance (quantified by HOMA-IR).

          Results

          A total of 8555 participants (mean age, 64.7 years; median follow-up, 5.7 years) with normal glucose levels (mean ± SD: 5.46 ± 0.58 mmol/l) at baseline were included. A 0.1 mmol/l decrease in serum magnesium level was associated with an increase in diabetes risk (HR 1.18 [95% CI 1.04, 1.33]), confirming findings from previous studies. Of interest, a similar association was found between serum magnesium levels and prediabetes risk (HR 1.12 [95% CI 1.01, 1.25]). Genetic variation in CLDN19, CNNM2, FXYD2, SLC41A2, and TRPM6 significantly influenced diabetes risk ( p < 0.05), and for CNNM2, FXYD2, SLC41A2 and TRPM6 this risk was completely mediated by serum magnesium levels. We found that 29.1% of the effect of serum magnesium levels on diabetes was mediated through insulin resistance, whereas for prediabetes 13.4% was mediated through insulin resistance.

          Conclusions/interpretation

          Low serum magnesium levels are associated with an increased risk of prediabetes and this increased risk is similar to that of diabetes. Furthermore, common variants in magnesium-regulating genes modify diabetes risk through serum magnesium levels. Both findings support a potential causal role of magnesium in the development of diabetes, where the hypothesised pathway is partly mediated through insulin resistance.

          Electronic supplementary material

          The online version of this article (doi:10.1007/s00125-017-4224-4) contains peer-reviewed but unedited supplementary material, which is available to authorised users.

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          Most cited references26

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          Genotype imputation.

          Genotype imputation is now an essential tool in the analysis of genome-wide association scans. This technique allows geneticists to accurately evaluate the evidence for association at genetic markers that are not directly genotyped. Genotype imputation is particularly useful for combining results across studies that rely on different genotyping platforms but also increases the power of individual scans. Here, we review the history and theoretical underpinnings of the technique. To illustrate performance of the approach, we summarize results from several gene mapping studies. Finally, we preview the role of genotype imputation in an era when whole genome resequencing is becoming increasingly common.
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            Graphical methods for assessing violations of the proportional hazards assumption in Cox regression.

            K. Hess (1995)
            A major assumption of the Cox proportional hazards model is that the effect of a given covariate does not change over time. If this assumption is violated, the simple Cox model is invalid, and more sophisticated analyses are required. This paper describes eight graphical methods for detecting violations of the proportional hazards assumption and demonstrates each on three published datasets with a single binary covariate. I discuss the relative merits of these methods. Smoothed plots of the scaled Schoenfeld residuals are recommended for assessing PH violations because they provide precise usable information about the time dependence of the covariate effects.
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              Lifetime risk of developing impaired glucose metabolism and eventual progression from prediabetes to type 2 diabetes: a prospective cohort study.

              Data are scarce for the lifetime risk of developing impaired glucose metabolism, including prediabetes, as are data for the risk of eventual progression from prediabetes to diabetes and for initiation of insulin treatment in previously untreated patients with diabetes. We aimed to calculate the lifetime risk of the full range of glucose impairments, from normoglycaemia to prediabetes, type 2 diabetes, and eventual insulin use.
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                Author and article information

                Contributors
                b.stricker@erasmusmc.nl
                Journal
                Diabetologia
                Diabetologia
                Diabetologia
                Springer Berlin Heidelberg (Berlin/Heidelberg )
                0012-186X
                1432-0428
                21 February 2017
                21 February 2017
                2017
                : 60
                : 5
                : 843-853
                Affiliations
                [1 ]GRID grid.5645.2, ISNI 000000040459992X, Department of Epidemiology, , Erasmus University Medical Center Rotterdam, ; P.O. Box 2040, 3000 CA Rotterdam, the Netherlands
                [2 ]GRID grid.5645.2, ISNI 000000040459992X, Department of Internal Medicine, , Erasmus University Medical Center Rotterdam, ; Rotterdam, the Netherlands
                [3 ]Inspectorate for Health Care, Utrecht, the Netherlands
                [4 ]GRID grid.10417.33, ISNI 0000 0004 0444 9382, Department of Physiology, Radboud Institute for Molecular Life Sciences, , Radboud University Medical Center, ; Nijmegen, the Netherlands
                [5 ]GRID grid.4991.5, ISNI 0000 0004 1936 8948, Department of Physiology, Anatomy and Genetics, , University of Oxford, ; Oxford, UK
                [6 ]GRID grid.38142.3c, ISNI 000000041936754X, Department of Epidemiology, , Harvard T.H. Chan School of Public Health, ; Boston, MA USA
                Article
                4224
                10.1007/s00125-017-4224-4
                6518103
                28224192
                7bc0878c-c124-47ba-877e-be9bcc13968b
                © The Author(s) 2017

                Open Access This article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made.

                History
                : 8 December 2016
                : 27 January 2017
                Funding
                Funded by: Netherlands Genomics Initiative
                Funded by: FundRef http://dx.doi.org/10.13039/501100000780, European Commission;
                Funded by: FundRef http://dx.doi.org/10.13039/501100002999, Ministerie van Volksgezondheid, Welzijn en Sport;
                Funded by: FundRef http://dx.doi.org/10.13039/501100001828, Erasmus Universiteit Rotterdam;
                Funded by: FundRef http://dx.doi.org/10.13039/501100003245, Ministerie van Onderwijs, Cultuur en Wetenschap;
                Funded by: FundRef http://dx.doi.org/10.13039/501100003061, Erasmus Medisch Centrum;
                Funded by: FundRef http://dx.doi.org/10.13039/501100001826, ZonMw;
                Funded by: Research Institute for Diseases in the Elderly
                Funded by: FundRef http://dx.doi.org/10.13039/501100003246, Nederlandse Organisatie voor Wetenschappelijk Onderzoek;
                Categories
                Article
                Custom metadata
                © Springer-Verlag Berlin Heidelberg 2017

                Endocrinology & Diabetes
                diabetes,epidemiology,insulin resistance,magnesium,magnesium regulating genes,mediation,population-based cohort,prediabetes,single nucleotide polymorphism

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