The effect of childhood cow's milk intake and HLA-DR genotype on risk of islet autoimmunity and type 1 diabetes: The Diabetes Autoimmunity Study in the Young
Bibliographic record
Abstract
BACKGROUND: Cow's milk intake has been inconsistently associated with islet autoimmunity (IA) and type 1 diabetes (T1D) development. Genetic and environmental factors may modify the effect of cow's milk on IA and T1D risk. METHODS: The Diabetes Autoimmunity Study in the Young (DAISY) follows children at increased T1D risk of IA (presence of autoantibodies to insulin, GAD65, or IA-2 twice in succession) and T1D development. We examined 1835 DAISY children with data on cow's milk intake: 143 developed IA, 40 subsequently developed T1D. Cow's milk protein and lactose intake were calculated from prospectively collected parent- and self-reported food frequency questionnaires (FFQ). High risk HLA-DR genotype: HLA-DR3/4,DQB1*0302; low/moderate risk: all other genotypes. We examined interactions between cow's milk intake, age at cow's milk introduction, and HLA-DR genotype in IA and T1D development. Interaction models contained the base terms (e.g., cow's milk protein and HLA-DR genotype) and an interaction term (e.g., cow's milk protein*HLA-DR genotype). RESULTS: In survival models adjusted for total calories, FFQ type, T1D family history, and ethnicity, greater cow's milk protein intake was associated with increased IA risk in children with low/moderate risk HLA-DR genotypes [hazard ratio (HR): 1.41, 95% confidence interval (CI): 1.08-1.84], but not in children with high risk HLA-DR genotypes. Cow's milk protein intake was associated with progression to T1D (HR: 1.59, CI: 1.13-2.25) in children with IA. CONCLUSIONS: Greater cow's milk intake may increase risk of IA and progression to T1D. Early in the T1D disease process, cow's milk intake may be more influential in children with low/moderate genetic T1D risk.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 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".