Longitudinal Assessment of Pancreas Volume by MRI Predicts Progression to Stage 3 Type 1 Diabetes
Year: 2023
Type: article
Study design: Observational Study
Abstract: OBJECTIVE: This multicenter prospective cohort study compared pancreas volume as assessed by MRI, metabolic scores derived from oral glucose tolerance testing (OGTT), and a combination of pancreas volume and metabolic scores for predicting progression to stage 3 type 1 diabetes (T1D) in individuals with multiple diabetes-related autoantibodies. RESEARCH DESIGN AND METHODS: Pancreas MRI was performed in 65 multiple autoantibody-positive participants enrolled in the Type 1 Diabetes TrialNet Pathway to Prevention study. Prediction of progression to stage 3 T1D was assessed using pancreas volume index (PVI), OGTT-derived Index60 score and Diabetes Prevention Trial-Type 1 Risk Score (DPTRS), and a combination of PVI and DPTRS. RESULTS: PVI, Index60, and DPTRS were all significantly different at study entry in 11 individuals who subsequently experienced progression to stage 3 T1D compared with 54 participants who did not experience progression (P < 0.005). PVI did not correlate with metabolic testing across individual study participants. PVI declined longitudinally in the 11 individuals diagnosed with stage 3 T1D, whereas Index60 and DPTRS increased. The area under the receiver operating characteristic curve for predicting progression to stage 3 from measurements at study entry was 0.76 for PVI, 0.79 for Index60, 0.79 for DPTRS, and 0.91 for PVI plus DPTRS. CONCLUSIONS: These findings suggest that measures of pancreas volume and metabolism reflect distinct components of risk for developing stage 3 type 1 diabetes and that a combination of these measures may provide superior prediction than either alone.
Source: Diabetes Care
DOI: 10.2337/dc23-1681
Authors: John Virostko, Jordan J. Wright, Jonathan M. Williams, Melissa A. Hilmes, Taylor M. Triolo, Hali Broncucia, Liping Du, Hakmook Kang, Shreya Nallaparaju, Leah Galvez Valencia, Demetra Reyes, Brenna Hammel, William E. Russell, Louis H. Philipson, Michaela Waibel, Thomas W.H. Kay, Helen E. Thomas, Siri Atma W. Greeley, Andrea K. Steck, Alvin C. Powers, Daniel J. Moore
Institutions: The University of Texas at Austin, Vanderbilt University, VA Tennessee Valley Healthcare System, Vanderbilt University Medical Center, University of Colorado Anschutz, University of Chicago, The University of Melbourne, St Vincent's Hospital Melbourne, Monroe Carell Jr. Children's Hospital
Language: English
Subfield: Genetics
Domain: Life Sciences
Open Access status: bronze
Funders: U.S. Department of Veterans Affairs, Juvenile Diabetes Research Foundation International, Vanderbilt University, Leona M. and Harry B. Helmsley Charitable Trust, Juvenile Diabetes Research Foundation United States of America, National Institutes of Health, Vanderbilt Diabetes Research and Training Center, Vanderbilt University Medical Center, 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, Division of Diabetes, Endocrinology, and Metabolic Diseases
Awards: G117257630, G1182793083, G1506509853, G18062229, G2019238319, G2451518850, G2920425998, Deep Learning of Pancreas MRI to Predict Progression of T1D., G3362710062, Replacement and Upgrade of a 3T MR Scanner for Research, G4044942779, G4335585434, G4425699080, G4904569727, G5212145964, G5223711344, G5679716143, G6312945067, G6662478338, G6676018288, The Vanderbilt Institute for Clinical and Translational Research (VICTR), G862854160, G8838464003, G9000033427, G9000275154, Vanderbilt Diabetes Research Center, dkNET Coordinating Unit: Harnessing the Power of AI and Data Science forCollaborative Discovery and Sharing in the DK Community, G9000990861
OpenAlex ID: W4390298201