Dissociation between the Insulin-Sensitizing Effect of Rosiglitazone and Its Effect on Hepatic and Intestinal Lipoprotein Production
Bibliographic record
Abstract
CONTEXT: Despite its potent, well-documented insulin-sensitizing effects, rosiglitazone (RSG) does not effectively ameliorate the hypertriglyceridemia of insulin-resistant or diabetic individuals and has even been shown to slightly but significantly increase triglyceride-rich lipoproteins (TRL) in some studies. The mechanism of this effect is currently not known. OBJECTIVE: We investigated the effect of RSG treatment on TRL metabolism. DESIGN: This was a 12-wk, single-sequence, cross-over study of rosiglitazone vs. placebo for 6 wk. PARTICIPANTS: Participants included 17 nondiabetic men with a broad range of insulin sensitivity. INTERVENTION: INTERVENTION included rosiglitazone 8 mg/d vs. placebo for 6 wk. MAIN OUTCOME MEASURE: TRL metabolism (concentration, production and catabolic rates) was assessed in a constant fed state with a 12-h primed constant infusion of [D3]l-leucine and multicompartmental modeling. RESULTS: RSG treatment resulted in significant insulin sensitization with no change in body weight. Fasting plasma triglyceride (TG) concentration, however, was higher with RSG vs. placebo (P = 0.0006), as were fasting and fed TRL-TG, TRL-apoB-48, and TRL-apoB-100 (fed TRL-apoB-48: 0.93 +/- 0.08 vs. 0.76 +/- 0.07 mg/dl, P =0.017, and fed TRL-apoB-100: 15.57 +/- 0.90 vs. 13.71 +/- 1.27 mg/dl, P = 0.029). This small but significant increase in plasma TRL concentration was explained by a tendency for RSG to increase TRL production and reduce particle clearance, as indicated by the significantly increased production to clearance ratios for both apoB-48-containing (0.43 +/- 0.03 vs. 0.34 +/- 0.03, P = 0.048) and apoB-100-containing (7.0 +/- 0.4 vs. 6.2 +/- 0.6, P = 0.029) TRL. CONCLUSION: These data indicate dissociation between the insulin-sensitizing effects of RSG and absence of anticipated reductions in production rates of apoB-100- and apoB-48-containing-TRL particles, which may explain the absence of TG lowering seen in humans treated with this agent.
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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 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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".