Glycemic Variability in Patients With Early Type 2 Diabetes: The Impact of Improvement in β-Cell Function
Bibliographic record
Abstract
OBJECTIVE Increased glycemic variability has been reported to be associated with the risk of hypoglycemia and possibly diabetes complications and is believed to be due to β-cell dysfunction. However, it is not known whether improvement in β-cell function can reduce glycemic variability. Because short-term intensive insulin therapy (IIT) can improve β-cell function in early type 2 diabetes (T2DM), our objective was to determine whether the β-cell functional recovery induced by this therapy is associated with decreased glycemic variability. RESEARCH DESIGN AND METHODS Sixty-one patients with T2DM of 3.0 years mean duration underwent 4 weeks of IIT, which consisted of basal insulin detemir and premeal insulin aspart. Glucose variability was assessed in both the first and the last week by the coefficient of variation of capillary glucose on daily 6-point self-monitoring profiles. β-Cell function before and after IIT was assessed with the Insulin Secretion-Sensitivity Index-2 (ISSI-2). RESULTS Between the first and the last week on IIT, 55.7% of patients had a reduction in glucose variability. Change in glucose variability was negatively correlated with the change in β-cell function (ISSI-2) (r = -0.34, P = 0.008). On multiple linear regression analyses, percentage change in ISSI-2 emerged as the only factor independently associated with the change in glucose variability (standardized β = -0.42, P = 0.03). Moreover, patients with an increase in ISSI-2 ≥25% experienced a reduction in glucose variability compared with their peers who had almost no change (-0.041 ± 0.06 vs. -0.0002 ± 0.04, respectively; P = 0.006). CONCLUSIONS In early T2DM, glycemic variability is a modifiable parameter that can be reduced by improving β-cell function with short-term IIT.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".