Predictive utility of remnant cholesterol in atherosclerotic cardiovascular disease
Bibliographic record
Abstract
PURPOSE OF REVIEW: Remnant cholesterol (RC) is the cholesterol carried in lipoproteins derived from the catabolism of chylomicrons and very low-density lipoproteins. Evidence supporting the causal relationship of RC with atherosclerotic cardiovascular disease (ASVD) is accumulating rapidly. The number of impactful contributions to this field are increasing and provide a pathophysiological insight into the current residual cardiovascular risk beyond low-density cholesterol (LDL)-cholesterol (LDL-C). They also raise the question of whether RC should be used in prediction models and become the target of new therapeutic interventions. The intent of this review is to highlight the recent advances on the role of RC in atherogenesis and the validation of RC as a predictor of ASVD. RECENT FINDINGS: Numerous prospective and retrospective cohorts helped validate a significant causal relationship of RC with various forms of ASVD, independent of LDL-C. A recent large Mendelian randomization study reinforced the existence of this relationship and showed that the risk of atherosclerotic events was driven nearly entirely by a direct effect of RC. SUMMARY: Both available and accumulating evidence suggest that a lifelong reduction in RC could translate into a substantial reduction in ASVD risk. The data support a revision of current guidelines to incorporate RC as an independent risk factor for ASVD. We propose that early screening of RC should be implemented and that RC lowering should become the target of future drug developments.
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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 | 0.002 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.003 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".