Efficacy and Safety of Adding Sitagliptin in Type 2 Diabetes Patients on Insulin: Age-Stratified Comparison at One Year in the ASSIST-K Study
Bibliographic record
Abstract
BACKGROUND: Sitagliptin, the first dipeptidyl peptidase-4 inhibitor, has demonstrated efficacy and safety as monotherapy and as add-on therapy to oral antidiabetic agents or insulin. However, there have been few reports about sitagliptin in elderly patients. The ASSIST-K observational study was performed in patients with type 2 diabetes mellitus (T2DM) receiving sitagliptin as add-on therapy to insulin. Changes of hemoglobin A1c (HbA1c), body weight, and the estimated glomerular filtration rate (eGFR), as well as adverse events, were investigated over 12 months in age-stratified groups. METHODS: Among outpatients with T2DM treated at member institutions of Kanagawa Physicians Association, those starting sitagliptin as add-on therapy to insulin were followed for 12 months. HbA1c (National Glycohemoglobin Standardization Program), body weight, and eGFR were the efficacy endpoints, while adverse events were investigated to assess safety. Patients were stratified into three age groups (≤ 64 years, 65 - 74 years, and ≥ 75 years) for comparison of the endpoints. RESULTS: Among 937 patients on insulin before starting sitagliptin, 821 patients were analyzed after excluding those without HbA1c data at baseline and 12 months. The two groups of elderly patients (65 - 74 years and ≥75 years) had more complications and their HbA1c was lower at initiation of sitagliptin therapy. The dose of sitagliptin, daily number of insulin injections, and number of concomitant oral antidiabetic agents were all lower in the elderly patients. HbA1c showed a significant decrease after initiation of sitagliptin in all age groups, and there were no significant intergroup differences in the change of HbA1c at 12 months. Body weight did not change significantly in any group. eGFR decreased significantly in all groups, with no significant intergroup differences at 12 months. Regarding adverse events, there were no significant intergroup differences in the incidence of severe hypoglycemia, gastrointestinal symptoms, or constipation. CONCLUSIONS: Despite baseline differences in demographic factors and medications, sitagliptin showed good efficacy and safety in all age groups of patients receiving it as add-on therapy to insulin during routine management of T2DM. Adding sitagliptin to insulin achieves similar efficacy and safety outcomes at 12 months in both elderly and non-elderly T2DM 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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 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".