Genetics of high-density lipoproteins
Bibliographic record
Abstract
PURPOSE OF REVIEW: High-density lipoproteins have multi-factorial anti-atherosclerosis properties: they have potent anti-oxidant effects and prevent the oxidation of low-density lipoproteins; they have anti-inflammatory effects; they modulate vascular endothelial cell function and transport cholesterol back to the liver for excretion into the bile - a process called reverse cholesterol transport. The present review focuses on genetic aspects of high-density lipoprotein metabolism, with genomic approaches used to identify genes that regulate high-density lipoproteins in humans. RECENT FINDINGS: Disorders of the many genes that code for proteins, including transporters, enzymes, receptors, transfer proteins and lipases, involved in high-density lipoprotein metabolism have been identified in humans as causing extremes of high-density lipoprotein cholesterol, and provide potential novel therapeutic avenues. These, however, explain fewer than 5% of the causes of low high-density lipoprotein cholesterol in the general population. SUMMARY: Genome-wide linkage studies of large cohorts, with discrete as well as quantitative trait loci analyses, followed by association studies have enabled the identification of large chromosomal regions that may harbor genes that modulate high-density lipoprotein cholesterol levels in humans. Using mouse genetics, the results of the HapMap project and novel genetic approaches will allow the discovery of novel genes in high-density lipoprotein metabolism.
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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 | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".