Effect of sitagliptin therapy on triglyceride‐rich lipoprotein kinetics in patients with type 2 diabetes
Bibliographic record
Abstract
AIM: To investigate the effects of sitagliptin therapy on the kinetics of triglyceride-rich lipoprotein (TRL) apolipoprotein (apo)B-48, VLDL apoB-100, apoE and apoC-III in patients with type 2 diabetes. METHODS: Twenty-two subjects with type 2 diabetes were recruited in this double-blind crossover study, during which the subjects received sitagliptin (100 mg/day) or placebo for a 6-week period each. At the end of each phase of treatment, the in vivo kinetics of the different apolipoproteins were assessed using a primed-constant infusion of l-[5,5,5-D3]leucine for 12 h, with the participants in a constantly fed state. RESULTS: Sitagliptin therapy significantly reduced fasting plasma triglyceride (-15.4%, p = 0.03), apoB-48 (-16.3%, p = 0.03) and free fatty acid concentrations (-9.5%, p = 0.04), as well as plasma HbA1c (placebo: 7.0% ± 0.8 vs. sitagliptin: 6.6% ± 0.7, p < 0.0001) and plasma glucose levels (-13.5%, p = 0.001), without any significant effect on insulin levels. Kinetic results showed that treatment with sitagliptin significantly reduced the pool size of TRL apoB-48 by -20.8% (p = 0.03), paralleled by a reduction in the production rate of these particles (-16.0%, p = 0.03). The VLDL apoB-100 pool size was also significantly decreased by sitagliptin therapy (-9.3%, p = 0.03), mainly because of a reduction in the hepatic secretion of these lipoproteins, although this difference did not reach statistical significance (-9.2%, p = 0.06). CONCLUSIONS: Treatment with sitagliptin for 6 weeks reduced triglyceride-rich apoB-containing lipoprotein levels by reducing the synthesis of these particles.
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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 | 0.001 | 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".