METHODS: We systematically searched PubMed/MEDLINE, ISI/WOS, and Scopus (from their commencements up to Jan 2016) for relevant studies examining the association between intake of selenium and glycemic indices. The data were extracted from relevant qualified studies and estimated using the random-effect or pooled model and standardized mean difference (SMD) with 95% confidence interval (CI).
RESULTS: Twelve articles published between 2004 and 2016 were included. In all the studies, the participants were randomly assigned to an intervention group (n = 757) or a control group(n = 684). All the studies were double blind, placebo controlled trials. Selenium supplementation resulted in a significant decrease in homeostasis model of assessment-estimated β-cell function (HOMA-B) (SMD: -0.63; 95%CI: -0.89 to -0.38) and a significant increase in quantitative insulin sensitivity check index (QUICKI) (SMD: by 0.74; 95%CI: 0.49 to 0.1) as compared with the controls. There were no statistically significant improvements in glycemic indices, such as fasting plasma glucose (FPG), insulin, homeostasis model of assessment-estimated insulin resistance (HOMA-IR), Hemoglobin A1c (HbA1c) and adiponectin.
CONCLUSION: This meta-analysis indicated that selenium supplementation significantly decreased HOMA-B and increased QUICKI score. There was no statistically significant improvement in FPG, insulin, HOMA-IR, HbA1c and adiponectin indices following selenium supplementation.
Methods: A systematic search was conducted through PubMed/Medline, Institute of Scientific Information, and Scopus, until 2017 based on the search terms of metabolic syndrome (MetS) and cardio metabolic risk factors. Random-effect model was used to perform a meta-analysis and estimate the pooled SE, SP and correlation coefficient (CC).
Results: A total of 41 full texts were selected for systematic review. The pooled SE of greater NC to predict MetS was 65% (95% CI 58, 72) and 77% (95% CI 55, 99) in adult and children, respectively. Additionally, the pooled SP was 66% (95% CI 60, 72) and 66% (95% CI 48, 84) in adult and children, respectively. According to the results of meta-analysis in adults, NC had a positive and significant correlation with fasting blood sugar (FBS) (CC: 0.16, 95% CI 0.13, 0.20), HOMA-IR (0.38, 95% CI 0.25, 0.50), total cholesterol (TC) (0.07 95% CI 0.02, 0.12), triglyceride (TG) concentrations (0.23, 95% CI 0.19, 0.28) and low density lipoprotein cholesterol (LDL-C) (0.14, 95% CI 0.07, 0.22). Among children, NC was positively associated with FBS (CC: 0.12, 95% CI 0.07, 0.16), TG (CC: 0.21, 95% CI 0.17, 0.25), and TC concentrations (CC: 0.07, 95% CI 0.02, 0.12). However, it was not significant for LDL-C.
Conclusion: NC has a good predictive value to identify some cardiometabolic risk factors. There was a positive association between high NC and most cardiometabolic risk factors. However due to high heterogeneity, findings should be declared with caution.