Effect of Short-term Intensive Insulin Therapy on Post-challenge Hyperglucagonemia in Early Type 2 Diabetes
Bibliographic record
Abstract
CONTEXT: Hyperglucagonemia is a characteristic feature of type 2 diabetes (T2DM) that has been postulated to be due to β-cell dysfunction and the resultant loss of insulin-mediated α-cell suppression. When administered in early T2DM, short-term intensive insulin therapy (IIT) can improve β-cell function, resulting in reduced glycemic variability. OBJECTIVE: To evaluate the impact of IIT on hyperglucagonemia and its associations with β-cell function and glycemic variability. Design/Setting/Participants/Intervention: Sixty-two patients with T2DM of mean 3.0 ± 2.1 years duration and glycated hemoglobin of 6.8 ± 0.7% underwent 4 weeks of IIT, consisting of basal detemir and premeal insulin aspart. MAIN OUTCOME MEASURES: Glucagon response was measured by area under the glucagon curve (AUCglucagon) on oral glucose tolerance test at baseline and 1 day post-IIT. β-Cell function before and after IIT was assessed by Insulin Secretion-Sensitivity Index-2 and ΔISR0-120/Δglucose0-120*Matsuda index (where ISR is the prehepatic insulin secretion rate determined by C-peptide deconvolution). Glucose variability was assessed in both the first and last weeks by the coefficient of variation of capillary glucose on daily six-point self-monitoring profiles. RESULTS: Both Insulin Secretion-Sensitivity Index-2 and ΔISR0-120/Δglucose0-120*Matsuda index demonstrated improvement in β-cell function after IIT (both P ≤ .02), accompanied by reduced glycemic variability (P = .05). There was a marked reduction in AUCglucagon after IIT, as compared to baseline (P < .001). However, the decrease in AUCglucagon was not associated with the change in either β-cell measure (both P ≥ .34) or glucose variability (P = .37). CONCLUSIONS: Short-term IIT can reduce post-challenge hyperglucagonemia in early T2DM, but this effect does not appear to be due to improved β-cell function.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.004 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.002 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".