Neuronal T-Cell Autoreactivity Is Amplified in Overweight Children With New-Onset Insulin-Requiring Diabetes
Bibliographic record
Abstract
OBJECTIVE: Disease-associated T-cell autoreactivities are seen in most type 1 diabetic patients and are thought to emerge before islet autoantibodies, but host factors that impact autoimmune elements remain uncertain. We assessed if adiposity and measures of insulin sensitivity impact T- and B-cell autoimmunity in children with insulin-requiring diabetes. RESEARCH DESIGN AND METHODS: Insulin-requiring children and adolescents diagnosed between January 2004 and June 2008 were studied (n = 261): age 9.7 ± 4 years, 92% white, and 60% male. T-cell responses to 10 diabetes-associated antigens, β-cell autoantibodies (GADA, IA-2A, IAA, and ICA), BMI z score (BMIz), and waist percentile were measured at onset and 3 months later. RESULTS: All but one subject had either T- or B-cell autoimmunity. Diabetes-associated T-cell autoreactivities were found in 92% of subjects. Higher amplitude T-cell autoreactivities to neuronal diabetes-associated autoantigens were seen in those with the highest BMIz quintile, BMI ≥85th percentile (P < 0.05), and waist circumference ≥85th percentile (P < 0.05). There were no relationships between the number of T-cell reactivities or T-cell diversity with adiposity measures or autoantibody number or type. Patients with positive T-cell reactivities but without autoantibodies had the highest BMIz (P = 0.006). CONCLUSIONS: Our observations link obesity and diabetes-related autoimmunity, suggesting an amplification of neuronal T-cell autoimmunity associated with adiposity and/or insulin resistance, with obesity-related inflammation possibly enhancing islet autoimmunity.
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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".