The Effects of Selenium Supplementation on Glucose Metabolism and Lipid Profiles Among Patients with Metabolic Diseases: A Systematic Review and Meta-Analysis of Randomized Controlled Trials
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Bibliographic record
Abstract
This systematic review and meta-analysis of randomized controlled trials (RCTs) was conducted to summarize the effect of selenium administration on glucose metabolism and lipid profiles among patients with diseases related to metabolic syndrome (MetS). We searched the following databases up to May 2017: MEDLINE, EMBASE, Web of Science, and Cochrane Central Register of Controlled Trials. The relevant data were extracted and assessed for quality of the studies according to the Cochrane risk of bias tool. Data were pooled using the inverse variance method and expressed as standardized mean difference (MDs) with 95% confidence intervals (95% CI). Five studies were included in the meta-analyses. The results showed that selenium supplementation significantly reduced insulin levels (SMD -0.42; 95% CI, -0.83 to -0.01) and increased quantitative insulin sensitivity check index (QUICKI) (SMD 0.83; 95% CI, 0.58 to 1.09). Selenium supplementation had no beneficial effects on other glucose homeostasis parameters, such as fasting plasma glucose (FPG) (SMD -0.29; 95% CI, -0.73 to 0.15), homeostasis model assessment of insulin resistance (HOMA-IR) (SMD -0.80; 95% CI, -1.58 to -0.03), and lipid profiles, such as triglycerides (SMD -0.42; 95% CI, -0.83 to -0.01), VLDL- (SMD -0.42; 95% CI, -0.83 to -0.01), total- (SMD -0.42; 95% CI, -0.83 to -0.01), LDL- (SMD 0.02; 95% CI, -0.20 to 0.24), and HDL-cholesterol (SMD 0.16; 95% CI, -0.06 to -0.38). Overall, this meta-analysis showed that selenium administration may lead to an improvement in insulin and QUICKI, but did not affect FPG, HOMA-IR, and lipid profiles.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.037 | 0.039 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.058 | 0.005 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it