Lipid effects of glucagon-like peptide 1 receptor analogs
Bibliographic record
Abstract
PURPOSE OF REVIEW: Glucagon-like peptide-1 receptor agonists (GLP-1RAs) are becoming more prominent as a therapeutic choice in diabetes management and their use is being expanded to other indications, such as obesity. Dyslipidemia and cardiovascular disease are common co-morbidities in these populations and understanding the impact of this class of medications on the lipid profile may be an important consideration. RECENT FINDINGS: Several GLP-1RAs trials demonstrate them to be safe and potentially beneficial for cardiovascular outcomes; improvements in surrogate markers of atherosclerosis have also been observed. Lipid data collected as secondary outcomes from large clinical trials as well as some smaller dedicated trials show that GLP-1RAs can modestly lower low-density lipoprotein (LDL) and total cholesterol (C), and most show modest fasting triglyceride (TG) lowering. Effects on high-density lipoprotein-C have been less consistent. Some have also demonstrated substantial blunting of the postprandial rise in serum TGs. Favorable effects on lipoprotein metabolism, with reduced levels of small dense LDL particles and decreased atherogenic potential of oxidized LDL, have also been seen. Mechanisms underlying these observations have been investigated. SUMMARY: This review summarizes the data available on the lipid effects of GLP-1RAs, and explores the current understanding of the mechanisms underlying these observed effects.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.002 |
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".