Islet Autoantibody Seroconversion in the DPT-1 Study
Bibliographic record
Abstract
OBJECTIVE: Although type 1 diabetes autoimmunity frequently begins in childhood, little is known about the relationship between age and autoimmunity development. Our aim was to determine the timing of seroconversion to diabetes-associated autoantibody (DAA) positivity and risk in first- and second-degree relatives of patients with type 1 diabetes. RESEARCH DESIGN AND METHODS: Study subjects were identified through the Diabetes Prevention Trial-Type 1 (DPT-1). Children 3-18 years of age (n = 42,447) were screened for DAAs; 1,454 were ICA positive (≥ 10 JDF units), 1,758 were GAD65 positive, and 899 were ICA512 positive at the time of initial screening. Subjects who were initially antibody negative (n = 39,212) were recalled for rescreening, and 11,813 returned for rescreening. RESULTS: DAA seroconversion occurred in 469 (4%) children; 258 seroconverted to ICA, 234 to GAD65, and 99 to ICA512. The median time to seroconversion was 2 years. The 2-year risk for DAAs was highest in early childhood. For each 1-year increase in age in this cohort, the risk of any autoantibody seroconversion (HR 0.95, 95% CI 0.92-0.97) decreased by 5%, and for any two autoantibodies risk decreased by 13% (0.87, 0.82-0.93). CONCLUSIONS: Risk of autoantibody seroconversion among children followed in DPT-1 is age dependent. Younger children have the highest risk for DAAs, with the majority of children seroconverting by 13 years of age (75%). This suggests that annual screenings should be started in early childhood and continued through early adolescence to identify the majority of subjects at risk for type 1 diabetes and eligible for prevention trials.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| 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".