Treatment options for low high-density lipoproteins
Bibliographic record
Abstract
PURPOSE OF REVIEW: To examine the current and future therapeutic option of HDL-based therapies. RECENT FINDINGS: The inverse association between plasma level of high-density lipoprotein cholesterol (HDL-C) is strong and coherent across the population studied. In-vitro and in-vivo studies show the strong biological plausibility for HDL as a therapeutic target. Mendelian randomization does not support HDL-C as a causal (protective) cardiovascular risk factor, and clinical data does not support the concept that raising HDL-cholesterol mass alters the outcomes. Better biomarkers of HDL function are being examined in the clinical trials. These include cellular cholesterol efflux, antioxidant and anti-inflammatory effects, effects on vascular endothelial cells (inflammation and nitric oxide release) and endothelial progenitor cells. Novel therapeutic agents that alter HDL function are in advanced phase 3 trials and in early preclinical trials. These include inhibitors of cholesteryl ester transfer protein, reconstituted proteoliposomes, apolipoprotein A-I and HDL mimetic peptides and small molecules that increase apo A-I production rate. SUMMARY: Targeting HDL-C has, to date, not led to changes in the cardiovascular outcomes. Novel therapeutic advances target the HDL function. In keeping with the recent 2013 American College of Cardiology/American Heart Association Guideline on the treatment of blood cholesterol to reduce atherosclerotic cardiovascular risk in adults, the major focus of prevention lies with LDL-cholesterol reduction.
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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.008 | 0.003 |
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".