Determinants of reversibility of β-cell dysfunction in response to short-term intensive insulin therapy in patients with early type 2 diabetes
Bibliographic record
Abstract
Short-term intensive insulin therapy (IIT) can improve pancreatic β-cell function when administered early in the course of type 2 diabetes mellitus (T2DM). However, the degree of improvement in response to this therapy varies between patients. Thus, we sought to characterize the determinants of improvement in β-cell function in response to short-term IIT in early T2DM. Sixty-three patients with mean 3.0 ± 2.1 yr duration of T2DM and Hb A1c of 6.8 ± 0.8% underwent 4 wk of IIT consisting of basal insulin detemir and premeal insulin aspart, with oral glucose tolerance test administered at baseline and 1 day post-IIT. β-Cell function before and after IIT was assessed by Insulin Secretion Sensitivity Index-2 (ISSI-2). Reversibility of β-cell dysfunction was defined as percentage change in ISSI-2 of ≥25%. Overall, the study population experienced an increase in ISSI-2 from baseline to post-IIT (P = 0.01), with one-third of participants achieving ≥25% improvement in ISSI-2. Compared with their peers, those with increases in ISSI-2 of ≥25% had greater decrements in fasting glucose (P < 0.0001), Hb A1c (P = 0.001), ALT (P = 0.04), AST (P = 0.02), and HOMA-IR (P < 0.0001). On logistical regression analysis, baseline Hb A1c (OR = 2.83, 95% CI 1.16-6.88, P = 0.02) and change in HOMA-IR (OR = 0.008, 95%CI 0.0004-0.16, P = 0.001) emerged as independent predictors of reversibility of β-cell dysfunction. Indeed, reversibility of β-cell dysfunction was achieved in only those participants in whom IIT yielded an improvement in HOMA-IR. In conclusion, decline in HOMA-IR may be a key determinant of improvement of β-cell function in response to short-term IIT, suggesting a fundamental contribution of insulin resistance to the reversible component of β-cell dysfunction in early T2DM.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".