Increased T Cell Proliferative Responses to Islet Antigens Identify Clinical Responders to Anti-CD20 Monoclonal Antibody (Rituximab) Therapy in Type 1 Diabetes
Bibliographic record
Abstract
Type 1 diabetes mellitus is believed to be due to the autoimmune destruction of β-cells by T lymphocytes, but a single course of rituximab, a monoclonal anti-CD20 B lymphocyte Ab, can attenuate C-peptide loss over the first year of disease. The effects of B cell depletion on disease-associated T cell responses have not been studied. We compare changes in lymphocyte subsets, T cell proliferative responses to disease-associated target Ags, and C-peptide levels of participants who did (responders) or did not (nonresponders) show signs of β-cell preservation 1 y after rituximab therapy in a placebo-controlled TrialNet trial. Rituximab decreased B lymphocyte levels after four weekly doses of mAb. T cell proliferative responses to diabetes-associated Ags were present at baseline in 75% of anti-CD20- and 82% of placebo-treated subjects and were not different over time. However, in rituximab-treated subjects with significant C-peptide preservation at 6 mo (58%), the proliferative responses to diabetes-associated total (p = 0.032), islet-specific (p = 0.048), and neuronal autoantigens (p = 0.005) increased over the 12-mo observation period. This relationship was not seen in placebo-treated patients. We conclude that in patients with type 1 diabetes mellitus, anti-B cell mAb causes increased proliferative responses to diabetes Ags and attenuated β-cell loss. The way in which these responses affect the disease course remains unknown.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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 | 0.001 | 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".