Effect of Rimonabant on the High-Triglyceride/ Low–HDL-Cholesterol Dyslipidemia, Intraabdominal Adiposity, and Liver Fat
Bibliographic record
Abstract
BACKGROUND: Rimonabant, the first selective cannabinoid type 1 (CB1) receptor antagonist, improves cardiometabolic risk factors in overweight/obese patients. ADAGIO-Lipids assessed the effect of rimonabant on cardiometabolic risk factors and intraabdominal and liver fat. METHODS AND RESULTS: 803 abdominally obese patients with atherogenic dyslipidemia (increased triglycerides [TG] or reduced high-density lipoprotein-cholesterol [HDL-C]) were randomized to placebo or rimonabant 20 mg/d for 1 year. HDL-C and TG were coprimary end points. Intraabdominal (visceral) and liver fat were measured by computed tomography in a subgroup of 231 patients. In total, 73% of rimonabant- and 70% of placebo-treated patients completed the study treatment. Rimonabant 20 mg produced significantly greater changes from baseline versus placebo in HDL-C (+7.4%) and TG levels (-18%; P<0.0001), as well as low-density lipoprotein (LDL) and HDL particle sizes, apolipoprotein A1 and B, HDL2, HDL3, C-reactive protein, and adiponectin levels (all P<0.05). Rimonabant decreased abdominal subcutaneous adipose tissue (AT) cross-sectional area by 5.1% compared to placebo (P<0.005), with a greater reduction in visceral AT (-10.1% compared to placebo; P<0.0005), thereby reducing the ratio of visceral/subcutaneous AT (P<0.05). Rimonabant significantly reduced liver fat content (liver/spleen attenuation ratio; P<0.005). Systolic (-3.3 mm Hg) and diastolic (-2.4 mm Hg) blood pressure were significantly reduced with rimonabant versus placebo (P<0.0001). The safety profile of rimonabant was consistent with previous studies; gastrointestinal, nervous system, psychiatric, and general adverse events were more common with rimonabant 20 mg. CONCLUSIONS: In abdominally obese patients with atherogenic dyslipidemia, rimonabant 20 mg significantly improved multiple cardiometabolic risk markers and induced significant reductions in both intraabdominal and liver fat.
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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) | 0.002 | 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".