Lipolytically Modified Triglyceride-Enriched HDLs Are Rapidly Cleared From the Circulation
Bibliographic record
Abstract
The precise biochemical mechanisms underlying the reduction of HDL levels in hypertriglyceridemic states are currently not known. In humans, we showed that triglyceride (TG) enrichment of HDL, as occurs in hypertriglyceridemic states, enhances the clearance of HDL-associated apolipoprotein A-I (apoA-I) from the circulation. In the New Zealand White rabbit (an animal model naturally deficient in hepatic lipase [HL]), however, TG enrichment of HDL is not sufficient to alter the clearance of either the protein or lipid moieties of HDL. In the present study, therefore, we determined in the New Zealand White rabbit the combined effects of ex vivo TG enrichment and lipolytic transformation of HDL by HL on the subsequent metabolic clearance of HDL apoA-I. Results of the in vivo kinetic studies (n=18 animals) showed that apoA-I associated with TG-enriched rabbit HDL modified ex vivo by catalytically active HL was cleared 22% more rapidly versus TG-enriched HDL incubated with heat-inactivated HL, and 26% more rapidly than fasting (TG-poor) HDL incubated with active HL (P<0.05 for both). Furthermore, a strong correlation was observed between the HDL TG content and apoA-I fractional catabolic rate (0.59, P<0.05) in the combined active HL groups. These data establish that TG enrichment of HDL with subsequent lipolysis by HL enhances HDL apoA-I clearance, but neither TG enrichment of HDL without HL lipolysis nor HL lipolysis in the absence of previous TG enrichment of HDL is sufficient to enhance HDL clearance. These data further support the important interaction between HDL TG enrichment and HL action in the pathogenesis of HDL lowering in hypertriglyceridemic states.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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".