Increasing the Accuracy of Oral Glucose Tolerance Testing and Extending Its Application to Individuals With Normal Glucose Tolerance for the Prediction of Type 1 Diabetes
Bibliographic record
Abstract
OBJECTIVE: We assessed the extent to which both standard and alternative indexes from 2-h oral glucose tolerance testing predict type 1 diabetes and whether oral glucose tolerance tests (OGTTs) predict type 1 diabetes in individuals with normal glucose tolerance. RESEARCH DESIGN AND METHODS: The prediction of type 1 diabetes from baseline OGTTs was studied in 704 Diabetes Prevention Trial-Type 1 participants (islet-cell autoantibody [ICA]-positive relatives of type 1 diabetic patients). The maximum follow-up was 7.4 years. Analyses utilized receiver-operator curves (ROCs), proportional hazards models, and survival curves. RESULTS: ROC areas under the curve (ROCAUCs) for both the AUC glucose (0.73 +/- 0.02) and an OGTT prediction index (0.78 +/- 0.02) were higher (P < 0.001) than those for the fasting (0.53 +/- 0.02) and 2-h glucose (0.66 +/- 0.02). ROCAUCs for the 60- and 90-min glucose (0.71 +/- 0.02 and 0.72 +/- 0.02, respectively) were also higher (P < 0.01) than those for the fasting and 2-h glucose. Among individuals with normal glucose tolerance, OGTTs were highly predictive, with 4th versus 1st quartile hazard ratios for the 2-h glucose, AUC glucose, and OGTT prediction index ranging from 3.77 to 5.30 (P < 0.001 for all). CONCLUSIONS: Certain alternative OGTT indexes appear to better predict type 1 diabetes than standard OGTT indexes in ICA-positive relatives of type 1 diabetic patients. Moreover, even among those with normal glucose tolerance, OGTTs are strongly predictive. This suggests that subtle metabolic abnormalities are present several years before the diagnosis of type 1 diabetes.
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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 | 0.000 | 0.000 |
| 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 | 0.000 | 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".