HDL and Reverse Cholesterol Transport
Bibliographic record
Abstract
Cardiovascular disease, with atherosclerosis as the major underlying factor, remains the leading cause of death worldwide. It is well established that cholesterol ester-enriched foam cells are the hallmark of atherosclerotic plaques. Multiple lines of evidence support that enhancing foam cell cholesterol efflux by HDL (high-density lipoprotein) particles, the first step of reverse cholesterol transport (RCT), is a promising antiatherogenic strategy. Yet, excitement towards the therapeutic potential of manipulating RCT for the treatment of cardiovascular disease has faded because of the lack of the association between cardiovascular disease risk and what was typically measured in intervention trials, namely HDL cholesterol, which has an inconsistent relationship to HDL function and RCT. In this review, we will summarize some of the potential reasons for this inconsistency, update the mechanisms of RCT, and highlight conditions in which impaired HDL function or RCT contributes to vascular disease. On balance, the evidence still argues for further research to better understand how HDL functionality contributes to RCT to develop prevention and treatment strategies to reduce the risk of cardiovascular disease.
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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".