Effect of Fish Oil on Insulin Sensitivity in Children: A Systematic Review and Meta-Analysis of Randomized, Controlled Trials
Bibliographic record
Abstract
OBJECTIVES: Fish oil has been shown to reduce the risk of metabolic disorders. However, the effects of fish oil intervention on glucose metabolism and insulin sensitivity are still controversial, especially in children. The current meta-analysis aimed to evaluate the effects of fish oil intervention on insulin sensitivity in children. METHODS: The Cochrane Library, PubMed, Embase, Web of Science, ClinicalTrials.gov and China National Knowledge Infrastructure databases were searched up to August 2020 for relevant studies evaluating fish oil intake compared with placebo on insulin sensitivity indications (Homeostatic Model for Insulin Resistance). A fixed-effects model was used to calculate the pooled effect. RESULTS: A total of 13 studies with 1,132 participants (567 in placebo group and 565 in fish oil group) were included in the meta-analysis. Compared with the placebo group, fish oil intervention had beneficial effects on insulin sensitivity in the pooled analysis (weighted mean difference, -0.219; 95% confidence interval, -0.392 to -0.046; p=0.013). In subgroup analyses, when the fish oil intervention period was short-term (≤6 months), low dose (eicosapentaenoic acid + docosahexaenoic acid dose <1.5 g/day) and high ratio (eicosapentaenoic acid to docosahexaenoic acid ≥1), it could improve insulin sensitivity. No heterogeneity was found for the pooled and subgroup analyses. CONCLUSION: Fish oil intervention has a beneficial effect on insulin sensitivity in children.
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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 | 0.008 | 0.022 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.019 | 0.029 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.003 | 0.003 |
| 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".