Zinc Transporter-8 Autoantibodies Improve Prediction of Type 1 Diabetes in Relatives Positive for the Standard Biochemical Autoantibodies
Bibliographic record
Abstract
OBJECTIVE: We assessed diabetes risk associated with zinc transporter-8 antibodies (ZnT8A), islet cell antibodies (ICA), and HLA type and age in relatives of people with type 1 diabetes with the standard biochemical autoantibodies (BAA) to insulin (IAA), GAD65 (GAD65A), and/or insulinoma-associated protein 2 antigen (IA-2A). RESEARCH DESIGN AND METHODS: For this analysis, 2,256 relatives positive for at least one BAA, of whom 142 developed diabetes, were tested for ZnT8A, ICA, and HLA genotype followed by biannual oral glucose tolerance tests. ZnT8A were also tested in 911 randomly chosen antibody-negative relatives. RESULTS: ZnT8A were associated with the other BAA (548 of 2,256 [24.3%] BAA(+) vs. 8 of 911 [0.8%] BAA(-), P < 0.001) and BAA number (177 of 1,683 [10.5%] single-, 221 of 384 [57.6%] double-, and 150 of 189 [79.4%] triple-BAA positivity, P < 0.001). The 4-year diabetes risk was higher in single BAA(+) relatives with ZnT8A than ZnT8A(-) relatives (31 vs. 7%, P < 0.001). In multivariable analysis, age ≤ 20 years (hazard ratio 2.13, P = 0.03), IA-2A (2.15, P = 0.005), IAA (1.73, P = 0.01), ICA (2.37, P = 0.002), and ZnT8A (1.87, P = 0.03) independently predicted diabetes, whereas HLA type (high and moderate vs. low risk) and GAD65A did not (P = 0.81 and 0.86, respectively). CONCLUSIONS: In relatives with one standard BAA, ZnT8A identified a subset at higher diabetes risk. ZnT8A predicted diabetes independently of ICA, the standard BAA, age, and HLA type. ZnT8A should be included in type 1 diabetes prediction and prevention studies.
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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".