New Insights Into the Regulation of HDL Metabolism and Reverse Cholesterol Transport
Bibliographic record
Abstract
The metabolism of high-density lipoproteins (HDL), which are inversely related to risk of atherosclerotic cardiovascular disease, involves a complex interplay of factors regulating HDL synthesis, intravascular remodeling, and catabolism. The individual lipid and apolipoprotein components of HDL are mostly assembled after secretion, are frequently exchanged with or transferred to other lipoproteins, are actively remodeled within the plasma compartment, and are often cleared separately from one another. HDL is believed to play a key role in the process of reverse cholesterol transport (RCT), in which it promotes the efflux of excess cholesterol from peripheral tissues and returns it to the liver for biliary excretion. This review will emphasize 3 major evolving themes regarding HDL metabolism and RCT. The first theme is that HDL is a universal plasma acceptor lipoprotein for cholesterol efflux from not only peripheral tissues but also hepatocytes, which are a major source of cholesterol efflux to HDL. Furthermore, although efflux of cholesterol from macrophages represents only a tiny fraction of overall cellular cholesterol efflux, it is the most important with regard to atherosclerosis, suggesting that it be specifically termed macrophage RCT. The second theme is the critical role that intravascular remodeling of HDL by lipid transfer factors, lipases, cell surface receptors, and non-HDL lipoproteins play in determining the ultimate metabolic fate of HDL and plasma HDL-c concentrations. The third theme is the growing appreciation that insulin resistance underlies the majority of cases of low HDL-c in humans and the mechanisms by which insulin resistance influences HDL metabolism. Progress in our understanding of HDL metabolism and macrophage reverse cholesterol transport will increase the likelihood of developing novel therapies to raise plasma HDL concentrations and promote macrophage RCT and in proving that these new therapeutic interventions prevent or cause regression of atherosclerosis in humans.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 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 | 0.003 |
| 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".