The Association between IgG4 Antibodies to Dietary Factors, Islet Autoimmunity and Type 1 Diabetes: The Diabetes Autoimmunity Study in the Young
Bibliographic record
Abstract
BACKGROUND: Infant dietary exposures have been linked to type 1 diabetes (T1D) development. IgG4 antibody responses to food antigens are associated with food intolerances but have not been explored prospectively in the period preceding T1D. METHODS: Using a case-cohort design, IgG4 antibodies to ß-lactoglobulin, gluten, and ovalbumin were measured in plasma collected annually from 260 DAISY participants. Of those, 77 developed islet autoimmunity (IA), defined as positive for either insulin, GAD65 or IA-2 autoantibodies on two consecutive visits, and 22 developed T1D. RESULTS: In mixed model analysis adjusting for HLA-DR status, T1D family history, age and ethnicity, higher ß-lactoglobulin IgG4 concentrations were associated with shorter breastfeeding duration (beta = -0.03, 95% Confidence Interval: -0.05, -0.006) and earlier first cow's milk exposure (beta = -0.04, 95% Confidence Interval: -0.08, 0.00). Higher gluten IgG4 was associated with older age at gluten introduction (beta = 0.06, 95% Confidence Interval: 0.00, 0.13). In proportional hazards analysis adjusting for HLA-DR status, T1D family history and ethnicity, IgG4 against individual or multiple dietary antigens throughout childhood were not associated with IA. In addition, mean antigen-specific IgG4 concentrations in infancy (age <2 years) were not associated with risk of IA nor progression to T1D. Higher ovalbumin IgG4 at first IA positive visit was marginally associated with progression to T1D (Hazard Ratio: 1.39, 95% Confidence Interval: 1.00, 1.92). CONCLUSION: We found no association between the IgG4 response to β-lactoglobulin, gluten, and the development of either IA or T1D. The association between higher ovalbumin and progression to T1D in children with IA should be explored in other populations.
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 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".