Treg gene signatures predict and measure type 1 diabetes trajectory
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
BACKGROUND: Multiple therapeutic strategies to restore immune regulation and slow type 1 diabetes (T1D) progression are in development and testing. A major challenge has been defining biomarkers to prospectively identify subjects likely to benefit from immunotherapy and/or measure intervention effects. We previously found that, compared with healthy controls, Tregs from children with new-onset T1D have an altered Treg gene signature (TGS), suggesting that this could be an immunoregulatory biomarker. METHODS: nanoString was used to assess the TGS in sorted Tregs (CD4+CD25hiCD127lo) or peripheral blood mononuclear cells (PBMCs) from individuals with T1D or type 2 diabetes, healthy controls, or T1D recipients of immunotherapy. Biomarker discovery pipelines were developed and applied to various sample group comparisons. RESULTS: Compared with controls, the TGS in isolated Tregs or PBMCs was altered in adult new-onset and cross-sectional T1D cohorts, with sensitivity or specificity of biomarkers increased by including T1D-associated SNPs in algorithms. The TGS was distinct in T1D versus type 2 diabetes, indicating disease-specific alterations. TGS measurement at the time of T1D onset revealed an algorithm that accurately predicted future rapid versus slow C-peptide decline, as determined by longitudinal analysis of placebo arms of START and T1DAL trials. The same algorithm stratified participants in a phase I/II clinical trial of ustekinumab (αIL-12/23p40) for future rapid versus slow C-peptide decline. CONCLUSION: These data suggest that biomarkers based on measuring TGSs could be a new approach to stratify patients and monitor autoimmune activity in T1D. FUNDING: JDRF (1-PNF-2015-113-Q-R, 2-PAR-2015-123-Q-R, 3-SRA-2016-209-Q-R, 3-PDF-2014-217-A-N), the JDRF Canadian Clinical Trials Network, the National Institute of Allergy and Infectious Diseases of the National Institutes of Health (UM1AI109565 and FY15ITN168), and BCCHRI.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it