Initial short‐term intensive insulin therapy as a strategy for evaluating the preservation of beta‐cell function with oral antidiabetic medications: a pilot study with sitagliptin
Bibliographic record
Abstract
AIM: Studies evaluating the effects of oral antidiabetic drugs (OADs) on beta-cell function in type 2 diabetes mellitus (T2DM) are confounded by an inability to establish the actual baseline degree of beta-cell dysfunction, independent of the deleterious effects of hyperglycaemia (glucotoxicity). Because intensive insulin therapy (IIT) can induce normoglycaemia, we reasoned that short-term IIT could enable evaluation of the beta-cell protective capacity of OADs, free from confounding hyperglycaemia. We applied this strategy to assess the effect of sitagliptin on beta-cell function. METHODS: In this pilot study, 37 patients with T2DM of 6.0 + 6.4 years duration and A1c 7.0 + 0.8% on 0-2 OADs were switched to 4-8 weeks of IIT consisting of basal detemir and premeal insulin aspart. Subjects achieving fasting glucose <7.0 mmol/l 1 day after completing IIT (n = 21) were then randomized to metformin with either sitagliptin (n = 10) or placebo (n = 11). Subjects were followed for 48 weeks, with serial assessment of beta-cell function [ratio of AUC(Cpep) to AUC(gluc) over Homeostasis Model Assessment of Insulin Resistance (HOMA-IR) (AUC(Cpep/gluc) /HOMA-IR)] on 4-h meal tests. RESULTS: During the study, fasting glucagon-like-peptide-1 was higher (p = 0.003) and A1c lower in the sitagliptin arm (p = 0.016). Nevertheless, although beta-cell function improved during the IIT phase, it declined similarly in both arms over time (p = 0.61). By study end, AUC(Cpep/gluc) /HOMA-IR was not significantly different between the placebo and sitagliptin arms (median 71.2 vs 80.4; p = 0.36). CONCLUSIONS: Pretreatment IIT can provide a useful strategy for evaluating the beta-cell protective capacity of diabetes interventions. In this pilot study, improved A1c with sitagliptin could not be attributed to a significant effect on preservation of beta-cell function.
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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 | 0.003 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 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".