C-peptide and metabolic outcomes in trials of disease modifying therapy in new-onset type 1 diabetes: an individual participant meta-analysis
Bibliographic record
Abstract
Background Metabolic outcomes in type 1 diabetes remain suboptimal. Disease modifying therapy to prevent β-cell loss presents an alternative treatment framework but the effect on metabolic outcomes is unclear. We, therefore, aimed to define the relationship between insulin C-peptide as a marker of β-cell function and metabolic outcomes in new-onset type 1 diabetes. Methods 21 trials of disease-modifying interventions within 100 days of type 1 diabetes diagnosis comprising 1315 adults (ie, those 18 years and older) and 1396 children (ie, those younger than 18 years) were combined. Endpoints assessed were stimulated area under the curve C-peptide, HbA 1c , insulin use, hypoglycaemic events, and composite scores (such as insulin dose adjusted A 1c , total daily insulin, U/kg per day, and BETA-2 score). Positive studies were defined as those meeting their primary endpoint. Differences in outcomes between active and control groups were assessed using the Wilcoxon rank test. Findings 6 months after treatment, a 24·8% greater C-peptide preservation in positive studies was associated with a 0·55% lower HbA 1c (p<0·0001), with differences being detectable as early as 3 months. Cross-sectional analysis, combining positive and negative studies, was consistent with this proportionality: a 55% improvement in C-peptide preservation was associated with 0·64% lower HbA 1c (p<0·0001). Higher initial C-peptide levels and greater preservation were associated with greater improvement in HbA 1c . For HbA 1c , IDAAC, and BETA-2 score, sample size predictions indicated that 2–3 times as many participants per group would be required to show a difference at 6 months as compared with C-peptide. Detecting a reduction in hypoglycaemia was affected by reporting methods. Interpretation Interventions that preserve β-cell function are effective at improving metabolic outcomes in new-onset type 1 diabetes, confirming their potential as adjuncts to insulin. We have shown that improvements in HbA 1c are directly proportional to the degree of C-peptide preservation, quantifying this relationship, and supporting the use of C-peptides as a surrogate endpoint in clinical trials. Funding JDRF and Diabetes UK.
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How this classification was reachedexpand
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 | 0.003 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.008 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".