Low-glycaemic index diets in the management of blood lipids: a systematic review and meta-analysis
Bibliographic record
Abstract
BACKGROUND: Dietary glycaemic index (GI) is a measure of the postprandial glycaemic response to carbohydrates. Observational studies have found increased triglycerides and decreased high-density lipoprotein levels in patients consuming higher GI foods. OBJECTIVE: Our aim was to review and synthesize the evidence on the effect of low-glucose index diets on serum lipid levels. METHODS: We conducted a systematic review and meta-analysis of randomized controlled trials on the effect of low-GI diets on serum lipid levels. We searched PubMed, Embase and Cochrane Library for published, English-language, randomized controlled trials comparing low-GI and high-GI diets for the management of blood lipids in the general population with at least 4 weeks of follow-up. We conducted a meta-analysis assuming a random effects model. RESULTS: Four studies met the criteria for inclusion in the systematic review and meta-analysis. The individual studies did not always show a significant effect of a low-GI diet on serum lipids; however, when combined in a meta-analysis, low-GI diets were shown to have a significant effect on decreasing total cholesterol and low-density lipoprotein (LDL) cholesterol over a short time span (5-12 weeks). There was no significant effect on high-density lipoprotein or triglyceride levels. The forest plots for total cholesterol and LDL cholesterol did not show significant statistical heterogeneity (I (2) = 0%). CONCLUSION: This meta-analysis suggests that a low-GI diet may help lower total and LDL cholesterol. The generalizability of these findings is likely limited by heterogeneity in individual study definitions of low- or high-GI diets.
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 | 0.012 | 0.028 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.020 | 0.034 |
| Bibliometrics | 0.007 | 0.007 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".