Costimulation Blockade Disrupts CD4+ T Cell Memory Pathways and Uncouples Their Link to Decline in β-Cell Function in Type 1 Diabetes
Bibliographic record
Abstract
Abstract We previously reported that costimulation blockade by abatacept limits the decline of β-cell function and the frequency of circulating CD4+ central memory T cells (TCM) (CD45RO+CD62L+) in new-onset type 1 diabetes. In human subjects receiving placebo, we found a significant association between an increase in CD4+ TCM cells and the decline of β-cell function. To extend and refine these findings, we examined changes in human CD4+ and CD8+ naive and memory T cell subsets at greater resolution using polychromatic flow and mass cytometry. In the placebo group, we successfully reproduced the original finding of a significant association between TCM and β-cell function and extended this to other T cell subsets. Furthermore, we show that abatacept treatment significantly alters the frequencies of a majority of CD4+ conventional and regulatory T cell subsets; in general, Ag-naive subsets increase and Ag-experienced subsets decrease, whereas CD8+ T cell subsets are relatively resistant to drug effects, indicating a lesser reliance on CD28-mediated costimulation. Importantly, abatacept uncouples the relationship between changes in T cell subsets and β-cell function that is a component of the natural history of the disease. Although these data suggest immunological markers for predicting change in β-cell function in type 1 diabetes, the finding that abatacept blunts this relationship renders the biomarkers nonpredictive for this type of therapy. In sum, our findings point to a novel mechanism of action for this successful immunotherapy that may guide other disease-modifying approaches for type 1 diabetes.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 |
| 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 | 0.001 |
| 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".