The effects of inositol supplementation on lipid profiles among patients with metabolic diseases: a systematic review and meta-analysis of randomized controlled trials
Bibliographic record
Abstract
BACKGROUND: Several studies have evaluated the effect of inositol supplementation on lipid profiles among population with metabolic diseases; however, the findings are controversial. This review of randomized controlled trials (RCTs) was performed to summarize the evidence of the effects of inositol supplementation on lipid profiles among population with metabolic diseases. METHODS: statistics were used. Data were pooled by using the random-effect model and standardized mean difference (SMD) was considered as summary of the effect size. RESULTS: Overall, 14 RCTs were included into meta-analysis. Pooled results showed that inositol supplementation among patients with metabolic diseases significantly decreased triglycerides (SMD - 1.24; 95% CI, - 1.84, - 0.64; P < 0.001), total- (SMD - 1.09; 95% CI, - 1.83, - 0.55; P < 0.001), and LDL-cholesterol levels (SMD - 1.31; 95% CI, - 2.23, - 0.39; P = 0.005). There was no effect of inositol supplementation on HDL-cholesterol levels (SMD 0.20; 95% CI, - 0.27, 0.67; P = 0.40). CONCLUSIONS: Inositol supplementation may result in reduction in triglycerides, total- and LDL-cholesterol levels, but did not affect HDL-cholesterol levels among patients with metabolic diseases. Additional prospective studies regarding the effect of inositol supplementation on lipid profiles in patients with metabolic diseases are necessary.
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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 | 0.012 | 0.030 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.019 | 0.030 |
| Bibliometrics | 0.006 | 0.007 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 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".