Growth and development of islet autoimmunity and type 1 diabetes in children genetically at risk
Bibliographic record
Abstract
Aims/hypothesis: We aimed to evaluate the relationship between childhood growth measures and risk of developing islet autoimmunity (IA) and type 1 diabetes in children with an affected first-degree relative and increased HLA-conferred risk. We hypothesised that being overweight or obese during childhood is associated with a greater risk of IA and type 1 diabetes. Methods: Participants in a randomised infant feeding trial (N = 2149) were measured at 12 month intervals for weight and length/height and followed for IA (at least one positive out of insulin autoantibodies, islet antigen-2 autoantibody, GAD autoantibody and zinc transporter 8 autoantibody) and development of type 1 diabetes from birth to 10–14 years. In this secondary analysis, Cox proportional hazard regression models were adjusted for birthweight and length z score, sex, HLA risk, maternal type 1 diabetes, mode of delivery and breastfeeding duration, and stratified by residence region (Australia, Canada, Northern Europe, Southern Europe, Central Europe and the USA). Longitudinal exposures were studied both by time-varying Cox proportional hazard regression and by joint modelling. Multiple testing was considered using family-wise error rate at 0.05. Results: In the Trial to Reduce IDDM in the Genetically at Risk (TRIGR) population, 305 (14.2%) developed IA and 172 (8%) developed type 1 diabetes. The proportions of children overweight (including obese) and obese only were 28% and 9% at 10 years, respectively. Annual growth measures were not associated with IA, but being overweight at 2–10 years of life was associated with a twofold increase in the development of type 1 diabetes (HR 2.39; 95% CI 1.46, 3.92; p < 0.001 in time-varying Cox regression), and similarly with joint modelling. Conclusions/interpretation: In children at genetic risk of type 1 diabetes, being overweight at 2–10 years of age is associated with increased risk of progression from multiple IA to type 1 diabetes and with development of type 1 diabetes, but not with development of IA. Future studies should assess the impact of weight management strategies on these outcomes. Trial registration: ClinicalTrials.gov NCT00179777 Graphical abstract: [Figure not available: see fulltext.]
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 | 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 | 0.001 |
| 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".