High-Density Lipoprotein Metabolism and Function in Cardiovascular Diseases: What about Aging and Diet Effects?
Bibliographic record
Abstract
Cardiovascular diseases (CVDs) have become the leading global cause of mortality, prompting a heightened focus on identifying precise indicators for their assessment and treatment. In this perspective, the plasma levels of HDL have emerged as a pivotal focus, given the demonstrable correlation between plasma levels and cardiovascular events, rendering them a noteworthy biomarker. However, it is crucial to acknowledge that HDLs, while intricate, are not presently a direct therapeutic target, necessitating a more nuanced understanding of their dynamic remodeling throughout their life cycle. HDLs exhibit several anti-atherosclerotic properties that define their functionality. This functionality of HDLs, which is independent of their concentration, may be impaired in certain risk factors for CVD. Moreover, because HDLs are dynamic parameters, in which HDL particles present different atheroprotective properties, it remains difficult to interpret the association between HDL level and CVD risk. Besides the antioxidant and anti-inflammatory activities of HDLs, their capacity to mediate cholesterol efflux, a key metric of HDL functionality, represents the main anti-atherosclerotic property of HDL. In this review, we will discuss the HDL components and HDL structure that may affect their functionality and we will review the mechanism by which HDL mediates cholesterol efflux. We will give a brief examination of the effects of aging and diet on HDL structure 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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 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".