HDL and cardiovascular risk: is cholesterol in particle subclasses relevant?
Bibliographic record
Abstract
This editorial refers to ‘HDL cholesterol subclasses, myocardial infarction, and mortality in secondary prevention: the Lipoprotein Investigators Collaborative’†, by S.S. Martin et al. on page 22. Dyslipidaemia is one of the major risk factors for the development of cardiovascular disease, which remains the leading cause of death in the world. Despite major advances in the treatment of dyslipidaemia, residual cardiovascular risk remains high in a significant number of patients, a fact that has prompted intensive investigation of medications raising HDL-cholesterol (HDL-C). Indeed, robust epidemiological evidence had shown that low HDL-C was inversely related to coronary outcomes in the pre-statin era, and even in patients treated aggressively with statins in the Treating-to-New-Targets (TNT) trial. However, the failure of several HDL- C-raising drugs including torcetrapib and niacin in statin-treated patients, combined with genetic studies, has recently challenged the relevance of the cholesterol content of HDL (i.e. HDL-C) (reviewed in Tujeda and Rader1). As a consequence, the focus on HDL-C has now started to shift away from a cholesterol-centric view towards alternative indexes of HDL such as particle size, subclass distribution, and measures of HDL functionality. However, this approach remains vague, and further progress is hampered by the facts that HDLs are highly heterogeneous particles and various HDL subfractionation methods exist, which also differ in their nomenclature, making comparisons across these methods challenging. This complexity has been compounded over the last 10 years by inconsistencies in the literature on HDL-C subclasses and function.
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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 | 0.004 | 0.025 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.010 | 0.010 |
| Insufficient payload (model declined to judge) | 0.003 | 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".