Comprehensive Efficacy of the Dipeptidyl Peptidase 4 Inhibitor Alogliptin in Practical Clinical Settings: A Prospective Multi-Center Interventional Observational Study
Bibliographic record
Abstract
BACKGROUND: This study aimed to verify the safety and efficacy, including glycemic control, of the selective dipeptidyl peptidase 4 inhibitor alogliptin in patients with type 2 diabetes. METHODS: This study used a multi-center, open-label, prospective observational design. Type 2 diabetes patients who were undergoing dietary therapy and/or exercise therapy alone without sufficient glycemic control (hemoglobin A1c (HbA1c) ≥ 6.5% and < 10%) were administered alogliptin (25 mg/day). The long-term effects (6 and 12 months) on blood glucose, blood pressure, heart rate, body weight and lipids were assessed. RESULTS: A final 50 patients were included with a high prevalence of hypertension (77%) and dyslipidemia (72%), and a mean duration of diabetes of 4.5 years. Pre-treatment HbA1c was 7.5% and was significantly decreased at 6 and 12 months (6M: 6.4%, 12M: 6.2%; P < 0.02 vs. 0M, respectively). Body weight, blood pressure and low-density lipoprotein cholesterol were significantly decreased by 6 months and maintained at 12 months. Triglycerides showed a significant decrease at 12 months. No significant differences were observed in HbA1c decrease for different grade of age, duration of diabetes, body mass index and renal function. The degree of decrease in HbA1c was most strongly correlated with pre-treatment HbA1c. Adverse events were noted in three patients, with no serious outcomes. CONCLUSION: The blood glucose-lowering effect and safety of alogliptin were demonstrated regardless of baseline HbA1c, although its effect appeared stronger with higher pre-treatment HbA1c values. Additionally, alogliptin appears useful for managing atherosclerotic risk factors such as body weight and blood pressure.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.003 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| 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".