Comparisons of Metabolic Measures to Predict T1D vs Detect a Preventive Treatment Effect in High-Risk Individuals
Year: 2024
Type: article
Abstract: CONTEXT: Metabolic measures are frequently used to predict type 1 diabetes (T1D) and to understand effects of disease-modifying therapies. OBJECTIVE: Compare metabolic endpoints for their ability to detect preventive treatment effects and predict T1D. METHODS: 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, a multicenter clinical trial investigating 14-day intravenous teplizumab infusion and (2) predicting T1D in the TrialNet Pathway to Prevention natural history study. For each metabolic measure, t-Values from t tests for detecting a treatment effect were compared with chi-square values from proportional hazards regression for predicting T1D. 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. CONCLUSION: 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.
Institutions: Indiana University Indianapolis, Indiana University School of Medicine, Indiana University – Purdue University Indianapolis, University of South Florida, University of Florida Health, University of Florida, University of Minnesota, Yale University, Baylor College of Medicine, Texas Children's Hospital, University of Miami
Language: English
Subfield: Genetics
Domain: Life Sciences
Open Access status: hybrid
Estimated APC paid: $3,938
Funders: Doris Duke Charitable Foundation, John Templeton Foundation, National Institutes of Health, National Institute of Allergy and Infectious Diseases, National Institute of Diabetes and Digestive and Kidney Diseases, National Institute of Child Health and Human Development, National Center for Advancing Translational Sciences, Eunice Kennedy Shriver National Institute of Child Health and Human Development
Awards: G1044130170, G1058663374, G1293678405, G1335891746, G1567340913, G1636177350, G1667355478, G1778119441, G180872007, G1874622916, G1979237160, Type 1 Diabetes-A Proposal for Prevention & Intervention, CLINICAL AFFILIATE OF THE TYPE 1 DIABETES TRIALNET, G2003004475, G2078056140, G215324815, Chicagoland Diabetes TrialNet Clinical Center, G2325591987, G2564331524, G2579296307, G266093542, G312575218, G3345811275, G3631080139, Type 1 Diabetes Genetic Risk Score in TrialNet, G3726356400, G3933238600, G3998258134, G4055536045, G4326787791, G4404410777, G447712914, G4552347576, G457535738, G4914271708, G491993124, G4960854521, Type 1 Diabetes TrialNet: Toronto Clinical Center, G5271169946, G5473625662, G5514976981, G5564421729, G5964144280, G6107671091, G6173851391, G66187487, G6699656948, G6862536402, G695377212, G701880105, G70843923, G7319114294, G7500853074, G7617495748, G8114795067, COVID-19 Fund to Retain Clinical Scientists, G881227773, G8857125704, G9000050447, G9000053785, G9000070396, G9000074482, G9000098994, G9000103942, G9000115250, G9000137445, G9000153846, G9000159741, G9000180806, G9000182208, G9000183277, G9000207758, G9000254921, G9000308555, G9000319557, G9000351500, G9000351679, Yale Clinical and Translational Science Award
OpenAlex ID: W4391211773