Thyroid, Celiac, and Other Autoimmune Diseases and the Risk of Incident Type 1 Diabetes in Young Adulthood
Bibliographic record
Abstract
OBJECTIVE: The association between autoimmune diseases and type 1 diabetes (T1D) is mostly based on studies among people with T1D at baseline. We assessed the risk of incident T1D among adolescents with other autoimmune diseases. RESEARCH DESIGN AND METHODS: Included were all Israeli adolescents without a history of dysglycemia, aged 16-19 years, undergoing medical evaluation before mandatory military service between January 1996 and December 2016. Data were linked with information on adult-onset T1D from the Israeli National Diabetes Registry. The cohort was dichotomized by the presence of any autoimmune disease. Cox proportional hazards modeling was applied. RESULTS: A total of 1,426,362 people were included, of whom 38,766 (2.7%) had a history of autoimmunity at study entry (10,333 with autoimmune thyroid disease [AITD] and 9,603 with celiac disease). Over 15,810,751 person-years of follow-up, there were 37 and 740 incident cases of T1D among people with and without autoimmunity, respectively, and a crude incident rate of 9.6 and 4.8 cases per 105 person-years, respectively. In a multivariable model adjusted for sex, birth year, and sociodemographic variables, the hazard ratio (HR) for incident T1D among people with autoimmunity was 2.19 (95% CI 1.57-3.04) versus those without. Results persisted when islet autoantibody data were used as mandatory criteria for T1D case definition (HR 2.22, 95% CI 1.13-4.35). The HRs among people with AITD and celiac disease were 3.99 (2.5-6.4) and 2.82 (1.46-5.45), respectively. CONCLUSIONS: Autoimmune diseases in late adolescence were associated with an increased risk of T1D in adulthood in both sexes, especially among those with AITD and celiac disease.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".