Effect of Anagliptin on Glycemic and Lipid Profile in Patients With Type 2 Diabetes Mellitus
Bibliographic record
Abstract
BACKGROUND: Anagliptin is a dipeptidyl peptidase-4 (DPP-4) inhibitor expected to improve the lipid profile as well as glycemic control. However, findings from large-scale prospective trials have not been obtained. METHODS: We performed a multicenter prospective trial in patients with type 2 diabetes receiving anagliptin to evaluate its effect on glycemic control and the lipid profile. A total of 95 patients received anagliptin at 200 mg twice daily. Markers of glucose and lipid metabolism were measured at baseline and after 12 and 24 weeks of administration, and the absolute changes and percent changes were determined. RESULTS: Both HbA1c and plasma glucose were significantly decreased by anagliptin therapy. Regarding the lipid profile, total cholesterol (TC) showed a significant decrease at 12 weeks, while TC, low-density lipoprotein cholesterol (LDL-C) and high-density lipoprotein cholesterol (HDL-C) were significantly decreased at 24 weeks. Multivariate analysis revealed that female sex was an independent predictor of greater reduction of TC, LDL-C, and HDL-C, while a baseline TC level ≥ 200 mg/dL predicted greater reduction of TC and a baseline HDL-C level ≥ 40 mg/dL predicted greater reduction of LDL-C and HDL-C. CONCLUSIONS: This study suggested that anagliptin significantly reduced TC, LDL-C, and HDL-C levels, as well as improving glycemic control, particularly in female patients.
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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) | 0.000 | 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) | 0.001 | 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".