High-Dose Resveratrol Treatment for 2 Weeks Inhibits Intestinal and Hepatic Lipoprotein Production in Overweight/Obese Men
Bibliographic record
Abstract
OBJECTIVE: Overproduction of hepatic apolipoprotein B (apoB)-100 containing very low-density lipoprotein particles and intestinal apoB-48 containing chylomicrons contributes to hypertriglyceridemia seen in conditions such as obesity and insulin resistance. Some, but not all, preclinical and clinical studies have demonstrated that the polyphenol resveratrol ameliorates insulin resistance and hypertriglyceridemia. Here, we assessed intestinal and hepatic lipoprotein turnover, in humans, after 2 weeks of treatment with resveratrol (1000 mg daily for week 1 followed by 2000 mg daily for week 2) or placebo. APPROACH AND RESULTS: Eight overweight or obese individuals with mild hypertriglyceridemia were studied on 2 occasions, 4 to 6 weeks apart, after treatment with resveratrol or placebo in a randomized, double-blinded, crossover study. Steady-state lipoprotein kinetics was assessed in a constant fed state with a primed, constant infusion of deuterated leucine. Resveratrol treatment did not significantly affect insulin sensitivity (homeostasis model of assessment of insulin resistance), fasting or fed plasma triglyceride concentration. Resveratrol reduced apoB-48 production rate by 22% (P=0.007) with no significant effect on fractional catabolic rate. Resveratrol reduced apoB-100 production rate by 27% (P=0.02) and fractional catabolic rate by 26% (P=0.04). CONCLUSIONS: These results indicate that 2 weeks of high-dose resveratrol reduces intestinal and hepatic lipoprotein particle production. Long-term studies are needed to evaluate the potential clinical benefits of resveratrol in patients with hypertriglyceridemia, who have increased concentrations of triglyceride-rich lipoprotein apoB-100 and apoB-48. CLINICAL TRIAL REGISTRATION URL: www.clinicaltrials.gov. Unique identifier: NCT01451918.
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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) | 0.001 | 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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".