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Publication Information
Affiliation
Department of Population Health and Immunity, Walter and Eliza Hall Institute, Parkville, VIC, Australia.; Department of Medical Biology, University of Melbourne, Parkville, VIC, Australia.; University of Florida Diabetes Institute, Gainesville, FL, USA.; Department of Pediatrics and Diabetes Center, University of California at San Francisco, San Francisco, CA, USA.; Center for Diabetes and Metabolic Diseases, Indiana University School of Medicine, Indianapolis, IN, USA.; Barbara Davis Center, University of Colorado School of Medicine, Aurora, CO, USA.; Helmholtz Zentrum München, Institute of Diabetes Research, German Research Center for Environmental Health, Munich-Neuherberg, Germany.; Helmholtz Zentrum München, Institute of Diabetes Research, German Research Center for Environmental Health, Munich-Neuherberg, Germany.; Department of Clinical Sciences, Lund University/CRC, Skåne University Hospital, Malmö, Sweden. ake.lernmark@med.lu.se.; Center for Diabetes and Metabolic Diseases, Indiana University School of Medicine, Indianapolis, IN, USA.; Division of Endocrinology, Department of Pediatrics, Hospital for Sick Children, University of Toronto, Toronto, ON, Canada.; Department of Diabetes and Endocrinology, Royal Melbourne Hospital, Parkville, VIC, Australia.; Department of Population Health and Immunity, Walter and Eliza Hall Institute, Parkville, VIC, Australia.; Department of Population Health and Immunity, Walter and Eliza Hall Institute, Parkville, VIC, Australia. wentworth@wehi.edu.au.
Authors
Bediaga Naiara G, Li-Wai-Suen Connie S N, Haller Michael J, Gitelman Stephen E, Evans-Molina Carmella, Gottlieb Peter A, Hippich Markus, Ziegler Anette-Gabriele, Lernmark Ake, DiMeglio Linda A, Wherrett Diane K, Colman Peter G, Harrison Leonard C, Wentworth John M
Studies
Abstract
Accurate prediction of disease progression in individuals with pre-symptomatic type 1 diabetes has potential to prevent ketoacidosis and accelerate development of disease-modifying therapies. Current tools for predicting risk require multiple blood samples taken during an OGTT. Our aim was to develop and validate a simpler tool based on a single blood draw.