Expression of Human Hepatic Lipase in the Rabbit Model Preferentially Enhances the Clearance of Triglyceride-Enriched Versus Native High-Density Lipoprotein Apolipoprotein A-I
Bibliographic record
Abstract
BACKGROUND: We have shown previously that triglyceride (TG) enrichment of HDL, as occurs in hypertriglyceridemic states, contributes to HDL lowering in humans by enhancing the clearance of HDL apolipoprotein (apo) A-I from the circulation. In the New Zealand White rabbit, an animal naturally deficient in hepatic lipase (HL), we demonstrated that TG enrichment of HDL per se is not sufficient to enhance HDL clearance in the absence of ex vivo lipolysis by HL. Here, we examined in the rabbit the interaction between in vivo HL lipolytic action and HDL TG enrichment on the subsequent metabolic clearance of HDL apoA-I. METHODS AND RESULTS: The clearance of HDL, TG-enriched with human VLDL (12% mass TG), was compared with a simultaneously injected native rabbit HDL tracer (8% TG) 5 to 7 days after injection of recombinant (r) adenovirus expressing either the human HL or lacZ transgene (n=6 animals each). In rHL-Adv rabbits, HL activity levels were 2- to 7-fold higher (versus rlacZ-Adv controls; P<0.01), and there were significant (P<0.05) reductions in HDL TG (-18%), cholesterol (-21%), cholesteryl ester (-24%), and phospholipid (-14%). Moreover, the clearance of TG-enriched versus native HDL was significantly greater (by 50%; 0.122+/-0.022 versus 0.081+/-0.015 pools/h; P<0.01) in rHL-Adv rabbits but not in controls. CONCLUSIONS: These studies have shown that TG enrichment of HDL in the presence but not in the absence of in vivo expression of moderate levels of lipolytically active HL results in enhanced HDL clearance, demonstrating the important interaction between 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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".