Magnesium and Metabolic Syndrome: A Dose-response Meta-analysis

Overview

Magnesium is an essential mineral found in many foods; rich sources include whole grains, green leafy vegetables, coffee, and legumes. Magnesium is a critical cofactor in \>300 enzymatic reactions, including those related to energy metabolism. Reduced magnesium intake and serum concentrations have been detected, both cross-sectionally and prospectively,in type 2 diabetes and insulin resistance, hypertension, dyslipidemia, and cardiovascular diseases. Different studies have reported inadequate magnesium intake and low serum magnesium concentrations may correlated also with metabolic syndrome, defined as a cluster of metabolic disorders including obesity, hypertension, dyslipidemia and impaired glucose tolerance or diabetes mellitus. Previous studies on this subject, however, reported contradicting results. Some investigations reported inadequate magnesium intake and low serum magnesium concentrations while others did not. To our knowledge, the epidemiological evidence on the relation between dietary magnesium intake and risk of metabolic syndrome has not yet been summarized.Therefore, the investigators will perform a systematic review and dose-response meta-analysis to assess the association between dietary and circulating magnesium level and risk of metabolic syndrome.

Background: Increasing evidence has suggested inadequate magnesium intake and low serum magnesium concentrations may correlated with metabolic syndrome. However, whether or not dietary or circulating magnesium at usual intakes or concentrations influences risk of metabolic syndrome is inconsistent Objective:To our knowledge, the epidemiological evidence on the relation between dietary magnesium intake and risk of metabolic syndrome has not yet been summarized.Therefore, to improve evidence-based guidance for dietary guidelines, the investigators will perform a systematic review and dose-response meta-analysis to assess the association between dietary and circulating magnesium level and risk of metabolic syndrome. Design: The planning, conduct and reporting of the proposed meta-analyses will follow Preferred Reporting Items for Systematic reviews and Meta-Analyses (PRISMA) guidelines. Data search and screening: The PubMed, Cochrane CENTRAL, and EMBASE databases via Elsevier will be searched using appropriate search terms. Study selection: Published articles will be included: Randomized, controlled studies, case-control studies, cross-sectional studies and cohort studies. All studies enrolled adults (age 18 years) and reported metabolic syndrome as the outcome of interest and magnesium intake or serum magnesium concentration as a risk factor or intervention. They reported adjusted risk ratios, including odds ratios, relative risks, hazard ratios with 95% confidence intervals (CIs), or they reported sufficient data to calculate these values for 3 or more quantitative categories of dietary magnesium intake levels. They reported risk ratios with metabolic syndrome criteria according to the National Cholesterol Education Panel (NCEP), modified NCEP, American Heart Association/National Heart Lung and Blood Institute , and harmonized definition . The investigators selected articles written in English that were published in their entirety. Data extraction: Titles and/or abstracts of studies retrieved using the search strategy will be screened independently by two review investigators to identify studies that potentially meet the inclusion criteria outlined above. The full text of these potentially eligible studies will be retrieved and independently assessed for eligibility by two investigators with disagreements being resolved by consensus. Data will be extracted from multivariate-adjusted models. Extracted information will included: first author's surname, publication year, name of the cohort study, study location (nation), years of follow-up (in case of cohort study design), sex, age, sample size (prevalence of metabolic syndrome and total number of participants), dietary magnesium intake(mg/day), serum magnesium concentration, covariates adjusted for in the multivariable analysis, and risk ratios with their 95% confidence intervals for each category of dietary magnesium intake and serum magnesium concentrations. The investigators will extract risk ratios that reflect the greatest degree of adjustment for potential confounders. The study investigator will assess the risk of bias in included studies using a tool for assessing the risk of bias for nonrandomized studies (RoBANs) which contain the following eight domains: comparability of groups, selection of participants, confounding variables, measurement of exposure, blinding of outcome assessment, adequacy of outcome assessment, incomplete outcome data and selective outcome reporting. Domains of RoBANs have the following characteristics: Comparability of groups: Selection bias caused by the inadequate selection of comparable groups. Selection of participants: Selection bias caused by the inadequate selection of participants. Measurement of exposure: Performance bias caused by the inadequate confirmation and consideration of confounding variable. Blinding of outcome assessments: Detection bias caused by the inadequate blinding of outcome assessments. Incomplete outcome data: Attrition bias caused by the inadequate handling of incomplete outcome data. Selective outcome reporting: Reporting bias caused by the selective reporting of outcome. Adequacy of outcome assessments: Performance bias caused by the inadequate confirmation of outcomes. RoBANS was developed to be used for the assessment of all study designs except for randomized controlled studies. RoBANS includes criteria for judging the risk of bias for each domain. Any discrepancies will be checked with the study investigator. A final copy of the form from each trial will be checked with the appropriate trial investigator for verification. Outcomes: The investigators will perform a two stage random-effects dose-risk meta-analysis to examine a linear dose-response relationship between dose of magnesium intake and metabolic syndrome using a generalized least-squares method taking into account random effects. This method constructs a covariance estimate for dose-specific log relative risks (RRs) within each study and then estimates the dose-response relation, accounting for between- and within-study variation. Reported hazard ratios and odds ratios were assumed to approximate RRs. For the dose-response meta-analysis, The investigators will generalized least-squares regression (GLST), which take into account the correlation between estimates for different expose level, to compute study-specific slopes. This method requires that the number of cases and controls (or person-years) and the RR with its variance estimate for at least three quantitative exposure categories were known. For studies that dose not provide this information, the investigators will estimate the dose-response slopes using variance-weighted least-squares regression (VWLS). Both the methods (GLST and VWLS) required median or mean intake for each category of intake level. For studies that report the concentrations as ranges of magnesium intake only, the investigators will estimate the midpoint in each category by calculating the average of the lower and upper bound. If the upper boundary for the highest category is not provided, the investigators will assume that the boundary has the same amplitude as the adjacent category. When the lowest category is open-ended, the investigators will set the lower boundary to the same amplitude as the adjacent category. In the case of different categorizations of magnesium intake and serum concentrations across studies, the investigators will choose the lowest level as the reference category. Dose responses of dietary magnesium intake will be standardized based on unweighted median differences between the highest and lowest quartile category medians across all studies. For the meta-analysis, the statistical heterogeneity between the studies will be assessed using the Q and I-squared statistics. For the Q statistic, heterogeneity was considered present if p \< 0.1. The investigators will define low, moderate and high heterogeneity as I-squared values of 25%, 50% and 75%, respectively. Publication bias will be evaluated using Begg's test. In the presence of publication bias, the P values for Begg's test are less than 0.05. The investigators will conduct sensitivity analyses to evaluate potential sources of heterogeneity in the analyses. The investigators will conduct subgroup meta-analyses according to the study design (cross-sectional study, cohort cohort or nested case-control study), study quality, definition of metabolic syndrome, gender, age, and geographic area of the study