Article ; Online: Comparisons of Metabolic Measures to Predict T1D vs. Detect a Preventive Treatment Effect in High-Risk Individuals.
The Journal of clinical endocrinology and metabolism
2024
Abstract: Context: Metabolic measures are frequently used to predict T1D and to understand effects of disease-modifying therapies.: Objective: Compare metabolic endpoints for their ability to detect preventive treatment effects and predict T1D.: Design: Six- ...
Abstract | Context: Metabolic measures are frequently used to predict T1D and to understand effects of disease-modifying therapies. Objective: Compare metabolic endpoints for their ability to detect preventive treatment effects and predict T1D. Design: Six-month changes in metabolic endpoints were assessed for: 1) detecting treatment effects by comparing placebo and treatment arms from the randomized controlled teplizumab prevention trial and 2) predicting T1D in the TrialNet Pathway to Prevention natural history study. Setting: Multicenter clinical trial network. Intervention: 14-day intravenous teplizumab infusion. Main outcome measures: T-values from t tests for detecting a treatment effect were compared to Chi-square values from proportional hazards regression for predicting T1D for each metabolic measure. Patients or other participants: Participants in the teplizumab prevention trial and participants in the Pathway to Prevention study selected with the same inclusion criteria used for the teplizumab trial were studied. Results: Six-month changes in glucose-based endpoints predicted diabetes better than C-peptide-based endpoints, yet the latter were better at detecting a teplizumab effect. Combined measures of glucose and C-peptide were more balanced than measures of glucose alone or C-peptide alone for predicting diabetes and detecting a teplizumab effect. Conclusions: The capacity of a metabolic endpoint to detect a treatment effect does not necessarily correspond to its accuracy for predicting T1D. However, combined glucose and C-peptide endpoints appear to be effective for both predicting diabetes and detecting a response to immunotherapy. These findings suggest that combined glucose and C-peptide endpoints should be incorporated into the design of future T1D prevention trials. |
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Language | English |
Publishing date | 2024-01-24 |
Publishing country | United States |
Document type | Journal Article |
ZDB-ID | 3029-6 |
ISSN | 1945-7197 ; 0021-972X |
ISSN (online) | 1945-7197 |
ISSN | 0021-972X |
DOI | 10.1210/clinem/dgae048 |
Database | MEDical Literature Analysis and Retrieval System OnLINE |
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