Interleukin‐1 antagonism moderates the inflammatory state associated with Type 1 diabetes during clinical trials conducted at disease onset
Bibliographic record
Abstract
It was hypothesized that IL-1 antagonism would preserve β-cell function in new onset Type 1 diabetes (T1D). However, the Anti-Interleukin-1 in Diabetes Action (AIDA) and TrialNet Canakinumab (TN-14) trials failed to show efficacy of IL-1 receptor antagonist (IL-1Ra) or canakinumab, as measured by stimulated C-peptide response. Additional measures are needed to define immune state changes associated with therapeutic responses. Here, we studied these trial participants with plasma-induced transcriptional analysis. In blinded analyses, 70.2% of AIDA and 68.9% of TN-14 participants were correctly called to their treatment arm. While the transcriptional signatures from the two trials were distinct, both therapies achieved varying immunomodulation consistent with IL-1 inhibition. On average, IL-1 antagonism resulted in modest normalization relative to healthy controls. At endpoint, signatures were quantified using a gene ontology-based inflammatory index, and an inverse relationship was observed between measured inflammation and stimulated C-peptide response in IL-1Ra- and canakinumab-treated patients. Cytokine neutralization studies showed that IL-1α and IL-1β additively contribute to the T1D inflammatory state. Finally, analyses of baseline signatures were indicative of later therapeutic response. Despite the absence of clinical efficacy by IL-1 antagonist therapy, transcriptional analysis detected immunomodulation and may yield new insight when applied to other clinical trials.
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 | 0.004 | 0.003 |
| 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 | 0.001 | 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".