Revising the high-density lipoprotein targeting strategies – Insights from human and preclinical studies
Bibliographic record
Abstract
In recent years, the high-density lipoprotein (HDL) hypothesis has been challenged. Several completed randomized clinical trials continue to fall short in demonstrating HDL, or at least HDL-cholesterol (HDL-C) levels, as being a consistent target in the prevention of cardiovascular diseases. However, population studies and findings in lipid modifying trials continue to strongly support HDL-C as a superb risk predictor. It is increasingly evident that the complexity of HDL metabolism confounds the use of HDL-C concentration as a unified target. However, important insights continue to emerge from the post hoc analyses of recently completed (i) fibrate-based FIELD and ACCORD trials, including the unexpected beneficial effect of fibrates in microvascular diseases, (ii) the niacin-based AIM-HIGH and HPS2-THRIVE studies, (iii) recombinant HDL-based as well as (iv) the completed CETP inhibitor-based trials. These together with on-going mechanistic studies on novel pathways, which include the unique roles of microRNAs, post-translational remodeling of HDL and novel pathways related to HDL modulators will provide valuable insights to guide how best to refocus and redesign the conceptual framework for selecting HDL-based targets.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.016 | 0.065 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.007 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.006 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".