Identification of autoimmune type 1 diabetes and multiple organ‐specific autoantibodies in adult‐onset non‐insulin‐requiring diabetes in China: A population‐based multicentre nationwide survey
Bibliographic record
Abstract
AIMS: To investigate the prevalence of adult-onset autoimmune diabetes (ADM) and predisposition to autoimmune diseases by quantifying serum organ-specific autoantibodies in people with phenotype of type 2 diabetes (T2D). MATERIALS AND METHODS: We included a nationally representative sample of 46 239 adults aged ≥20 years from 14 provinces, of whom 4671 had diabetes, plus 1000 control subjects with normal glucose tolerance (NGT). Participants were screened centrally for autoantibodies to glutamic acid decarboxylase (GAD), islet antigen 2 (IA2) and zinc transporter isoform-8 (Znt8) and were defined as having ADM where positive for these antibodies. We then assayed thyroid peroxidase (TPO), tissue transglutaminase (tTG) and 21-hydroxylase (21-OH) autoantibodies in randomly selected participants with ADM and in age-matched, sex-matched and non-ADM controls with T2D plus controls with NGT. RESULTS: Post-normalization, the standardized prevalence rate of ADM was 6.0% (95% confidence interval [CI] 5.3-6.8) in initially non-insulin-requiring participants with ADM, corresponding to six million adults in China, in whom adjusted antibody positivity was: TPO autoantibodies 16.3% (95% CI 10.8-21.8), tTG autoantibodies 2.1% (95% CI 0.0-4.2), and 21-OH autoantibodies 1.8% (95% CI -0.2 to 3.8). Those participants with ADM who were GAD autoantibody-positive had high risk of TPO autoantibody positivity (odds ratio [OR] 2.39, P = 0.0031) and tTG autoantibody positivity (OR 6.98, P = 0.027), while those positive for IA2 autoantibodies had a high risk of tTG autoantibody positivity (OR 19.05, P = 0.001). CONCLUSIONS: A proportion of people with phenotype of T2D in China have ADM, with diabetes-associated autoantibodies, and may be at risk of developing other organ-specific autoimmune diseases; therefore, it may be clinically relevant to consider screening such Chinese populations.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| 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) | 0.001 | 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".