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      Mineral Nutrition and the Risk of Chronic Diseases: A Mendelian Randomization Study

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      , * ,
      Nutrients
      MDPI
      calcium, magnesium, iron, copper, zinc, Mendelian randomization, chronic diseases

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          Abstract

          We applied Mendelian randomization analyses to investigate the potential causality between blood minerals (calcium, magnesium, iron, copper, and zinc) and osteoporosis (OP), gout, rheumatoid arthritis (RA), type 2 diabetes (T2D), Alzheimer’s disease (AD), bipolar disorder (BD), schizophrenia, Parkinson’s disease and major depressive disorder. Single nucleotide polymorphisms (SNPs) that are independent ( r 2 < 0.01) and are strongly related to minerals ( p < 5 × 10 −8) are selected as instrumental variables. Each standard deviation increase in magnesium (0.16 mmol/L) is associated with an 8.94-fold increase in the risk of RA ( p = 0.044) and an 8.78-fold increase in BD ( p = 0.040) but a 0.10 g/cm 2 increase in bone density related to OP ( p = 0.014). Each per-unit increase in copper is associated with a 0.87-fold increase in the risk of AD ( p = 0.050) and BD ( p = 0.010). In addition, there is suggestive evidence that calcium is positively correlated (OR = 1.36, p = 0.030) and iron is negatively correlated with T2D risk (OR = 0.89, p = 0.010); both magnesium (OR = 0.26, p = 0.013) and iron (OR = 0.71, p = 0.047) are negatively correlated with gout risk. In the sensitivity analysis, causal estimation is not affected by pleiotropy. This study supports the long-standing hypothesis that magnesium supplementation can increase RA and BD risks and decrease OP risk and that copper intake can reduce AD and BD risks. This study will be helpful to address some controversial debates on the relationships between minerals and chronic diseases.

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          Using published data in Mendelian randomization: a blueprint for efficient identification of causal risk factors

          Finding individual-level data for adequately-powered Mendelian randomization analyses may be problematic. As publicly-available summarized data on genetic associations with disease outcomes from large consortia are becoming more abundant, use of published data is an attractive analysis strategy for obtaining precise estimates of the causal effects of risk factors on outcomes. We detail the necessary steps for conducting Mendelian randomization investigations using published data, and present novel statistical methods for combining data on the associations of multiple (correlated or uncorrelated) genetic variants with the risk factor and outcome into a single causal effect estimate. A two-sample analysis strategy may be employed, in which evidence on the gene-risk factor and gene-outcome associations are taken from different data sources. These approaches allow the efficient identification of risk factors that are suitable targets for clinical intervention from published data, although the ability to assess the assumptions necessary for causal inference is diminished. Methods and guidance are illustrated using the example of the causal effect of serum calcium levels on fasting glucose concentrations. The estimated causal effect of a 1 standard deviation (0.13 mmol/L) increase in calcium levels on fasting glucose (mM) using a single lead variant from the CASR gene region is 0.044 (95 % credible interval −0.002, 0.100). In contrast, using our method to account for the correlation between variants, the corresponding estimate using 17 genetic variants is 0.022 (95 % credible interval 0.009, 0.035), a more clearly positive causal effect. Electronic supplementary material The online version of this article (doi:10.1007/s10654-015-0011-z) contains supplementary material, which is available to authorized users.
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            Recent Developments in Mendelian Randomization Studies

            Purpose of Review Mendelian randomization (MR) is a strategy for evaluating causality in observational epidemiological studies. MR exploits the fact that genotypes are not generally susceptible to reverse causation and confounding, due to their fixed nature and Mendel’s First and Second Laws of Inheritance. MR has the potential to provide information on causality in many situations where randomized controlled trials are not possible, but the results of MR studies must be interpreted carefully to avoid drawing erroneous conclusions. Recent Findings In this review, we outline the principles behind MR, as well as assumptions and limitations of the method. Extensions to the basic approach are discussed, including two-sample MR, bidirectional MR, two-step MR, multivariable MR, and factorial MR. We also consider some new applications and recent developments in the methodology, including its ability to inform drug development, automation of the method using tools such as MR-Base, and phenome-wide and hypothesis-free MR. Summary In conjunction with the growing availability of large-scale genomic databases, higher level of automation and increased robustness of the methods, MR promises to be a valuable strategy to examine causality in complex biological/omics networks, inform drug development and prioritize intervention targets for disease prevention in the future.
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              Mendelian randomization: prospects, potentials, and limitations.

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                Author and article information

                Journal
                Nutrients
                Nutrients
                nutrients
                Nutrients
                MDPI
                2072-6643
                12 February 2019
                February 2019
                : 11
                : 2
                : 378
                Affiliations
                Hubei Key Laboratory of Agricultural Bioinformatics, College of Informatics, Huazhong Agricultural University, Wuhan 4300700, China; chengww@ 123456webmail.hzau.edu.cn (W.-W.C.); zhy630@ 123456mail.hzau.edu.cn (H.-Y.Z.)
                Author notes
                [* ]Correspondence: stony@ 123456mail.hzau.edu.cn ; Tel.: +86-27-8728-5085
                Author information
                https://orcid.org/0000-0002-8078-4401
                Article
                nutrients-11-00378
                10.3390/nu11020378
                6412267
                30759836
                13c7cb23-ad25-4e2a-81ff-67724d1ab6cd
                © 2019 by the authors.

                Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license ( http://creativecommons.org/licenses/by/4.0/).

                History
                : 07 January 2019
                : 08 February 2019
                Categories
                Article

                Nutrition & Dietetics
                calcium,magnesium,iron,copper,zinc,mendelian randomization,chronic diseases
                Nutrition & Dietetics
                calcium, magnesium, iron, copper, zinc, mendelian randomization, chronic diseases

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