Lipid effects of sodium-glucose cotransporter 2 inhibitors
Bibliographic record
Abstract
PURPOSE OF REVIEW: Sodium-glucose cotransporter 2 (SGLT2) inhibitors are widely used antihyperglycemic drugs that show remarkable cardiorenal protective effects in patients with or without type 2 diabetes. Furthermore, they are effective among patients across a wide range of baseline renal and cardiac function. Numerous mechanisms have been evaluated to understand these remarkable clinical benefits. From an early stage, these agents were noted to affect the plasma lipid profile. Here we review lipid profile alterations attributable to SGLT2 inhibitors and also some mechanisms explored in model systems and human studies. RECENT FINDINGS: SGLT2 inhibitors given to patients with diabetes as monotherapy shift substrate utilization from carbohydrates to lipids, and have mild effects on the lipid profile. Increased LDL cholesterol appears to be associated with increased hepatic production and decreased catabolism. Increased HDL cholesterol and decreased triglycerides appear to be associated with improved insulin sensitivity and increased lipolysis. Lipid effects of SGLT2 inhibitors are further modulated by background therapy with other diabetes medications and statins. SUMMARY: The minor lipid profile alterations observed in patients treated with SGLT2 inhibitors are offset by the staggering range of beneficial pleiotropic mechanisms that likely explain the marked cardiorenal benefits of these agents.
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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) | 0.003 | 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".