Serum fatty acids and risk of developing islet autoimmunity: A nested <scp>case–control</scp> study within the <scp>TRIGR</scp> birth cohort
Bibliographic record
Abstract
BACKGROUND: Circulating fatty acids have been linked to development of type 1 diabetes. OBJECTIVES: To study the prospective associations of serum fatty acids with the risk of islet autoimmunity in high-risk children. METHODS: A nested case-control selection was carried out within the TRIGR cohort, which included infants with HLA (DQB1 or DQA1)-conferred disease susceptibility and a first-degree relative with type 1 diabetes, born between 2002 and 2007 in 15 countries and followed-up until 2017. The present study included 244 case children positive for at least two islet autoantibodies (ICA, IAA, GADA, and IA-2A) and two control children were matched for country and age. Proportions of 26 serum fatty acids at cord blood and at 6, 12, and 18 months of age were assessed using gas-chromatography. RESULTS: The average proportions of the following fatty acids were associated with an increased risk of islet autoimmunity, adjusted for sex, HLA risk, and maternal type 1 diabetes: pentadecanoic acid (15:0) (OR 3.41: 95% CI 1.70, 6.85), heptadecanoic acid (iso 17:0) (2.64: 1.62, 4.28) and (anteiso 17:0) (2.27: 1.39, 3.70), stearic acid (18:0) (23.8: 2.32, 244.6), and conjugated linoleic acid (18:2n-7) (2.60: 1.47, 4.59). Breastfeeding and not having maternal type 1 diabetes were positively associated with levels of the above-mentioned fatty acids. N-3 fatty acids were not consistently associated with islet autoimmunity. CONCLUSIONS: We found direct associations of pentadecanoic acid, heptadecanoic acid, stearic acid, and conjugated linoleic acid with the risk of islet autoimmunity. Further studies are needed to understand the complex role of fatty acids in the development of type 1 diabetes.
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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 | 0.002 |
| 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".