Associations Between Serum Fatty Acids and Immunological Markers in Children Developing Islet Autoimmunity—The TRIGR Nested Case–Control Study
Bibliographic record
Abstract
Aims Altered immune functions as well as fatty acid intake and status have been associated with the development of type 1 diabetes. We aimed to study the relationship between fatty acids and immunological markers in young children with increased genetic risk for type 1 diabetes in order to define putative mechanisms related to development of islet autoimmunity. Methods Serum samples for fatty acid and immunological marker measurements were obtained in the Trial to Reduce IDDM in the Genetically at Risk (TRIGR) ancillary study (Divia) from children born between 2002 and 2007 in 15 countries. Case children (n = 95) were defined as having repeated positivity for at least two out of four diabetes-associated autoantibodies. For each case child, control children were selected matched for country and date of birth (n = 173). Serum fatty acids and immunological markers were measured from cord serum and at the age of 6 and 12 months. Spearman correlation coefficients were calculated between fatty acids and immunological markers. Results Correlations between circulating fatty acids and immunological markers were different in case children who developed islet autoimmunity than in control children already at birth continuing across the first year of life. In case children, saturated fatty acids (SFAs) showed stronger correlations with immunological markers, while in controls, polyunsaturated fatty acids (PUFAs) showed stronger correlations. Conclusions In cases, SFAs were associated with several immunological markers (CXCL10, IL-6, IL-9, IL-17, and CM-CSF) previously linked to the type 1 diabetes disease process. Findings indicate that fatty acids could have immunomodulatory potential in the early phase of the disease development, although causality between fatty acids and the immunological pathways remains to be explored. Trial registry number NCT00179777
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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 | 0.002 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 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".