From HDL-cholesterol to HDL-function: cholesterol efflux capacity determinants
Bibliographic record
Abstract
PURPOSE OF REVIEW: The validity of HDL-cholesterol (HDL-C) elevation as a therapeutic target has been questioned, in comparison to enhancing HDL functionality. Cholesterol efflux capacity (CEC) is an in-vitro assay that measures the ability of an individual's HDL to promote cholesterol efflux from cholesterol donor cells such as macrophages. CEC of HDL is a predictor of cardiovascular risk independent of HDL-C levels. However, molecular determinants of CEC and the effects of diseases and therapeutic interventions on CEC have not been completely defined. RECENT FINDINGS: We review here recent findings on elevated HDL-C and disease risk, as well as determinants of CEC, from genetics and proteomics to pathophysiology and therapeutic interventions that contribute to our understanding of CEC as a biomarker of HDL functionality. SUMMARY: Elevated HDL-C levels are not always protective against cardiovascular disease and mortality. CEC is a heritable trait, and genetic polymorphisms in genes involved in HDL and triglycerides metabolism are associated with CEC. Multiple HDL proteins correlate positively with CEC levels and inversely with noncalcified plaque burden. Differences in CEC assays that make comparisons between studies difficult are also emphasized. CEC should be measured in clinical trials of lipid-modifying and anti-inflammatory therapies to determine whether increases are cardioprotective.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 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 | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 0.002 |
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".