Factors Predicting Therapeutic Efficacy of Combination Treatment With Sitagliptin and Insulin in Type 2 Diabetic Patients: The ASSIST-K Study
Bibliographic record
Abstract
BACKGROUND: It is unclear whether dipeptidyl peptidase-4 inhibitors decrease hemoglobin A1c (HbA1c) in a glucose-dependent manner in patients on insulin therapy who have impaired insulin secretion. This study investigated factors influencing the efficacy of sitagliptin when used concomitantly with insulin to treat type 2 diabetes mellitus (T2DM) in the real-world setting. METHODS: A retrospective study was conducted of 1,004 T2DM patients at 36 Japanese clinics associated with the Diabetes Task Force of the Kanagawa Physicians Association. Eligible patients had been on insulin for at least 6 months, with a baseline HbA1c of 7.0% (53 mmol/mol) or higher. Baseline characteristics and laboratory data from 495 patients were subjected to multiple regression analysis to identify factors influencing the change of HbA1c. RESULTS: Most patients (n = 809) received sitagliptin at a dose of 50 mg. In the 1,004 patients, HbA1c decreased by 0.74% (6 mmol/mol) and body weight increased by 0.1 kg after 6 months of combination therapy. Multiple regression analysis showed that a higher baseline HbA1c, older age, and lower body mass index influenced the change of HbA1c after 6 months. Hypoglycemic symptoms occurred in 7.4%, but none were severe. CONCLUSIONS: These results emphasize the importance of a higher HbA1c at the commencement of sitagliptin therapy in patients on insulin. Glucose-dependent suppression of glucagon secretion by sitagliptin may be useful in patients with impaired insulin secretion. Sitagliptin can be used concomitantly with insulin irrespective of the insulin regimen, duration of insulin treatment, and concomitant medications.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".