Long-Term Effect of Alogliptin on Glycemic Control in Japanese Patients With Type 2 Diabetes: A 3.5-Year Observational Study
Bibliographic record
Abstract
BACKGROUND: The goal of the current study was to investigate the long-term effects (after 3 years or more) of alogliptin on glycemic control in Japanese patients with type 2 diabetes. METHODS: We retrospectively studied the effect of alogliptin on glycemic control in the patients with type 2 diabetes who had participated in our previous 3-month study and who continued to take alogliptin for at least 36 months. RESULTS: The mean duration of alogliptin treatment was 42.8 ± 2.2 months. In all 39 patients, a significant reduction in hemoglobin A1c (HbA1c) levels was noted between the baseline and final visit: 7.8±0.6% to 7.2±1.0% (P = 0.0001). A significant reduction in HbA1c levels was found in a subgroup of patients who did not change their anti-diabetic drugs or did decrease the dose of their sulfonylureas (SUs) or did change to a lower strength repaglinide (n = 32): 7.7±0.6% to 7.2±1.0% (P = 0.0005). A significant decrease in low-density lipoprotein cholesterol (LDL-C) levels was observed in all of the patients that had LDL-C levels determined (P = 0.0406) (n = 37), and in a subgroup of patients who had not taken either statins, fibrates, or pioglitazone, or who had taken one or more of these drugs but the doses were not changed during the observation period (P = 0.0250) (n = 27). CONCLUSION: The current study found that alogliptin performed well for glycemic control when evaluated by HbA1c levels in a long-term observation period exceeding 3 years in Japanese patients with type 2 diabetes. Alogliptin may also decrease circulating LDL-C levels with long-term use.
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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 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| 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".