Association of Glycemic Variability in Type 1 Diabetes With Progression of Microvascular Outcomes in the Diabetes Control and Complications Trial
Bibliographic record
Abstract
OBJECTIVE The Diabetes Control and Complications Trial (DCCT) demonstrated the beneficial effects of intensive versus conventional therapy on the development and progression of microvascular complications of type 1 diabetes. These beneficial effects were almost completely explained by the difference between groups in the levels of HbA1c, which in turn were associated with the risk of these complications. We assessed the association of glucose variability within and between quarterly 7-point glucose profiles with the development and progression of retinopathy, nephropathy, and cardiovascular autonomic neuropathy during the DCCT. RESEARCH DESIGN AND METHODS Measures of variability included the within-day and updated mean (over time) of the SD, mean amplitude of glycemic excursions (MAGE), and M-value, and the longitudinal within-day, between-day, and total variances. Imputation methods filled in the 16.3% of expected glucose values that were missing. RESULTS Cox proportional hazards models assessed the association of each measure of glycemic variation, as a time-dependent covariate, with the risk of retinopathy and nephropathy, and a longitudinal logistic regression model did likewise for cardiovascular autonomic neuropathy. Adjusted for mean blood glucose, no measure of within-day variability was associated with any outcome. Only the longitudinal mean M-value (over time) was significantly associated with microalbuminuria when adjusted for the longitudinal mean blood glucose and corrected for multiple tests using the Holm procedure. CONCLUSIONS Overall, within-day glycemic variability, as determined from quarterly glucose profiles, does not play an apparent role in the development of microvascular complications beyond the influence of the mean glucose.
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| 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".