High-density lipoproteins and cardiovascular disease: 2010 update
Bibliographic record
Abstract
High-density lipoprotein-cholesterol (HDL-C) is a continuous inverse cardiovascular risk factor. The mechanisms by which HDLs protect against atherosclerosis are multiple. The major effect is thought to be reverse cholesterol transport, the mechanism by which excess cellular cholesterol is returned to the liver for excretion in the bile. HDLs also have pleiotropic roles: they decrease inflammation, prevent low-density lipoprotein oxidation, vascular endothelial cell apoptosis and thrombosis, and improve vascular endothelial function. Recent studies suggest that nascent HDL particles are metabolized rapidly and that their components (Apo AI, cholesterol and phospholipids) are rapidly exchanged within lipoprotein classes. There are many causes of HDL-C deficiency. Using Mendelian randomization, several groups have concluded that many genetic forms of HDL deficiency do not increase cardiovascular risk. This raises the controversial issue of the causality of low HDL-C as a cardiovascular risk factor, rather than a marker of cardiovascular health. This is reflected in the importance of lifestyle in determining HDL-C levels. The treatment of low HDL-C remains controversial, in part because the only currently available effective medication, niacin, is relatively poorly tolerated and outcomes studies on cardiovascular disease prevention are still pending.
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) | 0.002 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.008 | 0.005 |
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".