Thematic review series: Patient-Oriented Research. What have we learned about HDL metabolism from kinetics studies in humans?
Bibliographic record
Abstract
Plasma measurements of lipids, lipoproteins, and apolipoproteins provide information on the static levels of these fractions without providing key information on the dynamic fluxes of lipoproteins in the circulation. Kinetics studies, in contrast, provide additional information on the production and clearance rates of lipoproteins and the flow of lipids and apolipoproteins through lipoprotein fractions. This information is crucial in accurately delineating the metabolism of HDL in plasma, because plasma concentrations of HDL are the net result of the de novo production and catabolism of HDL as well as the recycling of HDL particles and the contribution to HDL from components of other lipoproteins. Studies aimed at measuring the metabolism of HDL particles have shown that HDL metabolism in vivo is complex and consists of multiple components. Kinetics studies provide a window into the metabolism of HDL, allowing us to better understand the mechanisms of HDL decrease in human conditions and the functionality of HDL particles. Here, we review the progress in our understanding of HDL metabolism derived from in vivo kinetics studies, focusing primarily on studies in humans but also reviewing key studies in animal models.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.003 | 0.001 |
| Bibliometrics | 0.003 | 0.004 |
| 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) | 0.015 | 0.010 |
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".