Effect of Fenofibrate on Plasma Lipoprotein Composition and Kinetics in Patients With Complete Hepatic Lipase Deficiency
Bibliographic record
Abstract
OBJECTIVE: The goal of this study was to characterize the effect of microcoated fenofibrate (160 mg/day for 6 months) on plasma lipoprotein composition and kinetics in 2 patients with complete hepatic lipase (HL) deficiency. METHODS AND RESULTS: Fenofibrate treatment normalized the plasma lipoprotein profile of patients with complete HL deficiency, as evidenced by significant reductions in the plasma concentration of cholesterol (-49%) and triglycerides (-82%) and a significant increase in low-density lipoprotein (LDL) size (251.5+/-1.8 versus 263.5+/-0.7 A). The in vivo kinetics of very low-density lipoprotein (VLDL), intermediate-density lipoprotein (IDL), and LDL apolipoprotein (apo)B-100 and plasma apoA-I and apoA-II were studied using a primed-constant infusion of L-[5,5,5-D3]-leucine for 12 hours in the fasted state. Fenofibrate treatment in complete HL-deficient patients substantially decreased plasma concentrations of VLDL, IDL, and LDL apoB-100 attributable to important increases in VLDL (+325%), IDL (+129%), and LDL (+218%) apoB-100 fractional catabolic rates (FCR). IDL apoB-100 FCR nevertheless remained 60% lower after treatment compared with values obtained in controls (n=5). The kinetics of plasma apoA-I and apoA-II as well as the capacity of total plasma and of high-density lipoprotein particles to efflux cellular cholesterol from normal human skin fibroblasts was not altered by fenofibrate. CONCLUSIONS: Fenofibrate therapy exerts a pronounced antiatherogenic effect on triglyceride-rich lipoproteins even in the complete absence of HL.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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 | 0.000 |
| 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".