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 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 | 0.015 | 0.063 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".