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
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.017 | 0.038 |
| Meta-epidemiology (narrow) | 0.004 | 0.002 |
| Meta-epidemiology (broad) | 0.025 | 0.038 |
| Bibliometrics | 0.009 | 0.008 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".