Insulin Resistance and Progression to Type 1 Diabetes in the European Nicotinamide Diabetes Intervention Trial (ENDIT)
Bibliographic record
Abstract
OBJECTIVE: Insulin resistance can modulate progression to type 1 diabetes in individuals with ongoing islet autoimmunity. We wanted to see whether measures of insulin resistance improved risk assessment in islet cell antibody (ICA)-positive relatives when added to other immune and metabolic markers. RESEARCH DESIGN AND METHODS: The retrospective cohort analysis included 213 family members participating in the European Nicotinamide Diabetes Intervention Trial. All were aged <25 years, with at least one islet antibody in addition to ICA >or=20 Juvenile Diabetes Foundation units. Median length of follow-up was 4.21 years, and 105 individuals developed diabetes. Oral and intravenous glucose tolerance tests were performed at baseline; antibodies to GAD, IA-2, and insulin were determined by radioimmunoassay; and insulin resistance was estimated by homeostasis model assessment. Risk was assessed by Cox regression analysis RESULTS: The overall cumulative risk of diabetes within 5 years was 54.1% (95% CI 46.0-62.3). Multivariate analysis confirmed that baseline first-phase insulin response (FPIR) quartile (P < 0.0001), number of additional antibody markers (P < 0.0001), and 120-min glucose in the oral glucose tolerance test (P < 0.0001) were independent determinants of risk of progression, whereas addition of homeostasis model assessment of insulin resistance (HOMA2-IR) achieved only borderline significance (P = 0.06). HOMA2-IR was an independent determinant in participants with loss of FPIR (P = 0.025) but not in those with preserved FPIR (P = 0.3). CONCLUSIONS: These data suggest that insulin resistance accelerates progression to type 1 diabetes in antibody-positive relatives in whom insulin secretion is markedly reduced but does not affect progression when insulin secretion is relatively well preserved.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".