182-OR: C-Peptide and Metabolic Outcomes in Immunotherapy Studies of New-Onset Type 1 Diabetes
Bibliographic record
Abstract
Background: Despite advances in insulin therapy, metabolic outcomes in T1D remain suboptimal. Immunotherapy to preserve beta cell function has enormous potential but the metabolic benefits and the size of trial needed to demonstrate them remain unclear, hindering drug development. Methods: Our dataset comprised 1315 adults and 1396 children enrolled in 20 immunotherapy intervention trials within 100 days of diagnosis. End points assessed were AUC c-peptide, HbA1c, IDAAC, Beta-2 Score and hypoglycemia. Differences in outcomes between active and control arms in positive and negative studies were assessed using the Wilcoxon rank test. Results: C-peptide preservation in positive studies resulted in greater improvements in HbA1c within 3 months of beginning therapy. Beyond the initial 3 month “honeymoon” period, 20% greater preservation of C-peptide in active versus placebo subjects was associated with a 0.5% lower HbA1c. Higher initial C-peptide levels and greater C-peptide preservation were associated with better glycemic outcomes. Sample size for HbA1c, IDAAC, and Beta-2 Score required 2-3 times as many subjects per armto demonstrate a difference at 6 months as compared to C-peptide. Smaller samples sizes were required in children. Longer studies are required to demonstrate durability but not efficacy. Hypoglycaemia rates required substantially larger sample sizes and more than 1 year follow-up. Conclusion: Immunotherapy to preserve beta cell function is effective at improving metabolic outcomes in new-onset T1D. Beyond the initial 3 month period, improvements in HbA1c are proportional to C-peptide preservation. Early intervention and sustained high C-peptide levels are required for ongoing benefits. Disclosure P.Taylor: None. M.Rigby: None. P.Gottlieb: Advisory Panel; ViaCyte, Inc., Board Member; ImmunoMolecular Therapeutics, Research Support; Imcyse, Hemsley Charitable Trust, Novartis, National Institute of Diabetes and Digestive and Kidney Diseases, Precigen, Inc., Dompé, Nova Pharmaceuticals, Provention Bio, Inc. C.Dayan: Advisory Panel; AstraZeneca, Consultant; Provention Bio, Sanofi, Avotres Inc., Other Relationship; Dompé, Merck & Co., Inc. K.S.Collins: None. S.Karpen: None. E.Atabakhsh: None. S.Ahmed: None. M.Marinac: None. E.Latres: None. A.Lam: None. P.A.Senior: Advisory Panel; Novo Nordisk Canada Inc., Consultant; Novo Nordisk Canada Inc., Bayer Inc., Viatris Inc., Vertex Pharmaceuticals Incorporated, ViaCyte, Inc., Insulet Corporation. Funding JDRF; Diabetes UK
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 | 0.008 | 0.015 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.004 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.016 | 0.001 |
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".