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In Diabetes ; h5-index 94.0

This study aims to model genetic, immunologic, metabolomic and proteomic biomarkers for development of islet autoimmunity (IA) and progression to type 1 diabetes in a prospective high-risk cohort. We studied 67 children: 42 who developed IA (20/42 progressed to diabetes) and 25 controls matched for sex and age. Biomarkers were assessed at four time points: earliest available sample, just prior to IA, just after IA, and just prior to diabetes onset. Predictors of IA and progression to diabetes were identified across disparate sources using an integrative machine learning algorithm and optimization-based feature selection. Our integrative approach was predictive of IA (AUC 0.91) and progression to diabetes (AUC 0.92) based on standard cross-validation (CV). Amongst the strongest predictors of IA were change in serum ascorbate, 3-methyl-oxobutyrate, and the PTPN22 (rs2476601) polymorphism. Serum glucose, ADP fibrinogen, and mannose were amongst the strongest predictors of progression to diabetes. This proof-of-principle analysis is the first study to integrate large, diverse biomarker data sets into a limited number of features, highlighting differences in pathways leading to IA from those predicting progression to diabetes. Integrated models, if validated in independent populations, could provide novel clues concerning the pathways leading to IA and type 1 diabetes.

Frohnert Brigitte I, Webb-Robertson Bobbie-Jo, Bramer Lisa M, Reehl Sara M, Waugh Kathy, Steck Andrea K, Norris Jill M, Rewers Marian

2019-Nov-18