Dysglycemia and Index60 as Prediagnostic End Points for Type 1 Diabetes Prevention Trials
Bibliographic record
Abstract
OBJECTIVE We assessed dysglycemia and a T1D Diagnostic Index60 (Index60) ≥1.00 (on the basis of fasting C-peptide, 60-min glucose, and 60-min C-peptide levels) as prediagnostic end points for type 1 diabetes among Type 1 Diabetes TrialNet Pathway to Prevention Study participants. RESEARCH DESIGN AND METHODS Two cohorts were analyzed: 1) baseline normoglycemic oral glucose tolerance tests (OGTTs) with an incident dysglycemic OGTT and 2) baseline Index60 <1.00 OGTTs with an incident Index60 ≥1.00 OGTT. Incident dysglycemic OGTTs were divided into those with (DYS/IND+) and without (DYS/IND−) concomitant Index60 ≥1.00. Incident Index60 ≥1.00 OGTTs were divided into those with (IND/DYS+) and without (IND/DYS−) concomitant dysglycemia. RESULTS The cumulative incidence for type 1 diabetes was greater after IND/DYS− than after DYS/IND− (P < 0.01). Within the normoglycemic cohort, the cumulative incidence of type 1 diabetes was higher after DYS/IND+ than after DYS/IND− (P < 0.001), whereas within the Index60 <1.00 cohort, the cumulative incidence after IND/DYS+ and after IND/DYS− did not differ significantly. Among nonprogressors, type 1 diabetes risk at the last OGTT was greater for IND/DYS− than for DYS/IND− (P < 0.001). Hazard ratios (HRs) of DYS/IND− with age and 30- to 0-min C-peptide were positive (P < 0.001 for both), whereas HRs of type 1 diabetes with these variables were inverse (P < 0.001 for both). In contrast, HRs of IND/DYS− and type 1 diabetes with age and 30- to 0-min C-peptide were consistent (all inverse [P < 0.01 for all]). CONCLUSIONS The findings suggest that incident dysglycemia without Index60 ≥1.00 is a suboptimal prediagnostic end point for type 1 diabetes. Measures that include both glucose and C-peptide levels, such as Index60 ≥1.00, appear better suited as prediagnostic end points.
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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 | 0.012 | 0.017 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".