Effect of weight loss, independent of change in diet composition, on apolipoprotein AI kinetic in men with metabolic syndrome
Bibliographic record
Abstract
We investigated the effect of weight loss, independent of change in diet composition, on HDL and apoAI metabolism in men with metabolic syndrome (MetS). Subjects (19 men with MetS [NCEP-ATPIII]) were fed an isoenergetic Mediterranean-style diet for 5 weeks (all foods provided). Participants then underwent a 20-week free-living period during which they were counseled to restrict energy intake, after which they were again fed an isoenergetic Mediterranean-style diet for 5 weeks. At the end of the two controlled diets, participants received a single bolus of [5,5,5-(2)H(3)] (L)-leucine, and fasting blood samples were collected over a 96 h period. ApoAI kinetic was assessed using multicompartmental modeling of the tracer enrichment data. Participants achieved a 9.1 ± 2.8% reduction in body weight (P < 0.001). Weight loss resulted in an increase in plasma HDL-cholesterol (HDL-C) concentrations of 6.0% (P = 0.059) and HDL(3)-C of 7.9% (P = 0.045), attributable to a reduction in apoAI fractional catabolic rate (-7.8%; P = 0.046) with no change in apoAI production rate (2.2%; P = 0.58). These data indicate that weight loss, independent of variation in diet composition, increases plasma HDL primarily by delaying the catabolism of apoAI.
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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 | 0.000 | 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".