HDL and LDL cholesterol significantly influence β-cell function in type 2 diabetes mellitus
Bibliographic record
Abstract
PURPOSE OF REVIEW: Patients with type 2 diabetes mellitus (T2DM) display significant abnormalities in both LDL and HDL particles. Recent data suggest that these changes in lipoprotein particles could contribute to the pathogenesis of T2DM. In this review, we focus on these abnormalities and discuss their possible impact on beta-cell function and beta-cell mass. RECENT FINDINGS: Infusion of reconstituted HDL in T2DM patients improves beta-cell function, whereas carriers of loss-of-function mutations in the cholesterol transporter ABCA1, who have decreased HDL levels, have impaired beta-cell function. In addition, recent studies show that HDL protects against stress-induced beta-cell apoptosis in vitro. Finally, increasing evidence points to a role for islet inflammation in the pathogenesis of T2DM. ABCA1 and ABCG1 may also modulate these inflammatory responses, suggesting an additional pathway by which HDL may impact T2DM. SUMMARY: Recent findings indicate that HDL protects beta-cells from cholesterol-induced beta-cell dysfunction, stress-induced apoptosis and islet inflammation. As the protective properties of HDL are compromised in patients with metabolic syndrome and T2DM, dysfunctional HDL metabolism could contribute to the pathogenesis of T2DM. Therapeutic normalization of both the quantity and quality of HDL particles may be a novel approach to prevent or treat T2DM.
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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 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| 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".