Efficacy and Safety of Alogliptin in Elderly Patients With Type 2 Diabetes Mellitus
Bibliographic record
Abstract
BACKGROUND: In Japan, with increasing age of the population, diabetic patients often become in need of hemodialysis due to diabetic nephropathy, and thus there is a demand for development of diabetic treatments that take into account renal effects in the elderly. No previous studies of alogliptin had focused on Japanese elderly subjects; we therefore assessed the effects of alogliptin in elderly individuals using available data. METHODS: Laboratory data were compiled for 1 year at intervals of 3 months following the start of alogliptin treatment. The subjects were divided into three groups by age: < 65 years (n = 110), 65 - 74 years (n = 87), and ≥ 75 years (n = 93). Laboratory values in comparison with baseline were compared within groups at various time points, and changes from baseline were compared among the different groups. RESULTS: Hemoglobin A1c (HbA1c) levels decreased significantly from baseline values in all groups at and after month 3: the change at month 12 was -0.74±1.45% for the age group < 65, -0.47±1.02% for the age group 65 - 74, and -0.42±1.11% for the age group ≥ 75. The 12-month change in estimated glomerular filtration rate (eGFR) was -6.5 ± 12.0 for the age group < 65, -2.0 ± 8.4 for the age group 65 - 74, and -1.5 ± 10.0 for the age group ≥ 75; the reduction in the age group < 65 was significant, whereas the reduction in the age groups ≥ 65 was not. CONCLUSIONS: Alogliptin significantly lowers HbA1c levels in the elderly and can be used without posing any safety issues, including renal effects, thus contributing to safe blood glucose control in clinical practice.
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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 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".