Journal article
Simplifying prediction of disease progression in pre-symptomatic type 1 diabetes using a single blood sample
Naiara G Bediaga, Connie SN Li-Wai-Suen, Michael J Haller, Stephen E Gitelman, Carmella Evans-Molina, Peter A Gottlieb, Markus Hippich, Anette-Gabriele Ziegler, Ake Lernmark, Linda A DiMeglio, Diane K Wherrett, Peter G Colman, Leonard C Harrison, John M Wentworth
DIABETOLOGIA | SPRINGER | Published : 2021
Abstract
AIMS/HYPOTHESIS: 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. METHODS: Models to predict disease progression using a single OGTT time point (0, 30, 60, 90 or 120 min) were developed using TrialNet data collected from relatives with type 1 diabetes and validated in independent populations at high genetic risk of type 1 diabetes (TrialNet, Diabetes Prevention Trial-Type 1, The Environ..
View full abstractGrants
Awarded by JDRF
Awarded by Australian National Health and Medical Research Council (NHMRC)
Awarded by Eunice Kennedy Shriver National Institute of Child Health and Human Development
Awarded by 'National Institute of Allergy and Infectious Diseases and The Eunice Kennedy Shriver National Institute of Child Health and Human Development'
Awarded by LifeScience-Stiftung
Awarded by Bavarian State Ministry of Health and Care
Awarded by The LeonaM. and Harry B. Helmsley Charitable Trust
Awarded by Deutsche Diabetes-Stiftung
Funding Acknowledgements
Open access funding provided by Lund University. This work was supported by JDRF (1-SRA-2020-900 to JMW and PGC) and the Australian National Health and Medical Research Council (NHMRC) (Program Grant APP 1150425 and Leadership Investigator Grant APP 1173945 to LCH). This work was made possible through Victorian State Government Operational Infrastructure Support and the Australian NHMRC Research Institute Infrastructure Support Scheme. We acknowledge the support of the Type 1 Diabetes TrialNet Study Group, which identified study participants and provided samples and follow-up data for this study. The Type 1 Diabetes TrialNet Study Group is a clinical trials network funded by the National Institutes of Health (NIH) through the National Institute of Diabetes and Digestive and Kidney Diseases, the National Institute of Allergy and Infectious Diseases and The Eunice Kennedy Shriver National Institute of Child Health and Human Development, through the cooperative agreements U01 DK061010, U01 DK061034, U01 DK061042, U01 DK061058, U01 DK085453, U01 DK085461, U01 DK085465, U01 DK085466, U01 DK085476, U01 DK085499, U01 DK085504, U01 DK085509, U01 DK103180, U01 DK103153, U01 DK103266, U01 DK103282, U01 DK106984, U01 DK106994, U01 DK107013, U01 DK107014, UC4 DK106993, UC4 DK11700901 and U01 DK106693-02, and JDRF. The contents of this article are solely the responsibility of the authors and do not necessarily represent the official views of the NIH or JDRF. The Fr1da study was supported by grants from LifeScience-Stiftung (HMGU 2014.01 and HMGU 2016.01), JDRF International (1-SRA-2014-310-M-R and 3-SRA-2015-72-M-R), the Bavarian State Ministry of Health and Care (Gesund.Leben.Bayern, LP00228), The LeonaM. and Harry B. Helmsley Charitable Trust (G-1911-03274), Deutsche Diabetes-Stiftung (364/11/14) and the German Federal Ministry of Education and Research to the German Center for Diabetes Research (DZD e.V.).