Two‐year trial of intermittent insulin therapy vs metformin for the preservation of β‐cell function after initial short‐term intensive insulin induction in early type 2 diabetes
Bibliographic record
Abstract
AIMS: To test the hypothesis that "induction" intensive insulin therapy (IIT) needs to be followed by "maintenance therapy" to preserve β-cell function, and to evaluate the impact on β-cell function over 2 years of two approaches to maintenance therapy: intermittent short-term IIT every 3 months vs daily metformin. MATERIALS AND METHODS: In this trial, 24 adults with a mean type 2 diabetes mellitus (T2DM) duration of 2.0 ± 1.7 years and glycated haemoglobin (HbA1c) levels 6.4 ± 0.1% (46 ± 1.1mmol/mol) were randomized to 3 weeks of induction IIT (glargine, lispro) followed by either repeat IIT for up to 2 weeks every 3 months or daily metformin. Participants underwent serial assessment of β-cell function using the Insulin Secretion-Sensitivity Index-2 (ISSI-2) on an oral glucose tolerance test every 3 months. RESULTS: The primary outcome of baseline-adjusted ISSI-2 at 2 years was higher in the metformin arm compared with intermittent IIT (245.0 ± 31.7 vs 142.2 ± 18.4; P = .008). Baseline-adjusted HbA1c at 2 years (secondary outcome) was lower in the metformin arm (6.0 ± 0.2% vs 7.3 ± 0.2%; P = .0006) (42 ± 2.2 vs 56 ± 2.2mmol/mol). At study completion, 66.7% of participants randomized to metformin had an HbA1c concentration ≤ 6.0% (≤42mmol/mol), compared with 8.3% of those on intermittent IIT (P = .009). There were no differences in insulin sensitivity. CONCLUSION: After induction IIT, metformin was superior to intermittent IIT for maintaining β-cell function and glycaemic control over 2 years. The strategy of induction and maintenance therapy to preserve β-cell function warrants exploration 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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".