Association between family history, early growth and the risk of beta cell autoimmunity in children at risk for type 1 diabetes
Bibliographic record
Abstract
AIMS/HYPOTHESIS: The aim of this work was to examine the relationship between family history of type 1 diabetes, birthweight, growth during the first 2 years and development of multiple beta cell autoantibodies in children with a first-degree relative with type 1 diabetes and HLA-conferred disease susceptibility. METHODS: In a secondary analysis of the Trial to Reduce IDDM in the Genetically at Risk (TRIGR), clinical characteristics and development of beta cell autoantibodies were compared in relation to family history of type 1 diabetes (mother vs father vs sibling) in 2074 children from families with a single affected family member. RESULTS: Multiple autoantibodies (≥2 of 5 measured) developed in 277 (13%) children: 107 (10%), 114 (16%) and 56 (18%) born with a mother, father or sibling with type 1 diabetes, respectively (p < 0.001). The HR for time to multiple autoimmunity was 0.54 (95% CI 0.39, 0.75) in offspring of affected mothers (n = 107/1046, p < 0.001) and 0.81 (95% CI 0.59, 1.11) (n = 114/722, p = 0.19) in offspring of affected fathers, compared with participants with a sibling with type 1 diabetes (comparator group n = 56/306). The time to the first autoantibody present (to insulin, GAD, tyrosine phosphatase-related insulinoma-associated 2 molecules, islet cell or zinc transporter 8) was similar in the three groups. Height velocity (z score/year) in the first 24 months was independently associated with developing multiple antibodies in the total cohort (HR 1.31 [95% CI 1.01, 1.70], p = 0.04). A higher birthweight in children born to an affected mother vs affected father or an affected sibling was not related to the risk of multiple autoimmunity. CONCLUSIONS/INTERPRETATION: The risk of developing multiple autoantibodies was lower in children with maternal type 1 diabetes. For the whole group, this risk of developing multiple autoantibodies was independent of birthweight but was greater in those with increased height velocity during the first 2 years of life. However, the risk associated with paternal type 1 diabetes was not linked to differences in birthweight or early growth. TRIAL REGISTRATION: ClinicalTrials.gov NCT00179777 Graphical abstract.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| 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 | 0.001 | 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".