A Regulatory T-Cell Gene Signature Is a Specific and Sensitive Biomarker to Identify Children With New-Onset Type 1 Diabetes
Bibliographic record
Abstract
Type 1 diabetes (T1D) is caused by immune-mediated destruction of insulin-producing β-cells. Insufficient control of autoreactive T cells by regulatory T cells (Tregs) is believed to contribute to disease pathogenesis, but changes in Treg function are difficult to quantify because of the lack of Treg-exclusive markers in humans and the complexity of functional experiments. We established a new way to track Tregs by using a gene signature that discriminates between Tregs and conventional T cells regardless of their activation states. The resulting 31-gene panel was validated with the NanoString nCounter platform and then measured in sorted CD4(+)CD25(hi)CD127(lo) Tregs from children with T1D and age-matched control subjects. By using biomarker discovery analysis, we found that expression of a combination of six genes, including TNFRSF1B (CD120b) and FOXP3, was significantly different between Tregs from subjects with new-onset T1D and control subjects, resulting in a sensitive (mean ± SD 0.86 ± 0.14) and specific (0.78 ± 0.18) biomarker algorithm. Thus, although the proportion of Tregs in peripheral blood is similar between children with T1D and control subjects, significant changes in gene expression can be detected early in disease process. These findings provide new insight into the mechanisms underlying the failure to control autoimmunity in T1D and might lead to a biomarker test to monitor Tregs throughout disease progression.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".