Predictive Modeling of Type 1 Diabetes Stages Using Disparate Data Sources

• Published in: Diabetes (New York, N.Y.) Vol. 69; no. 2; pp. 238 - 248
• Main Authors: Frohnert, Brigitte I., Webb-Robertson, Bobbie-Jo, Bramer, Lisa M., Reehl, Sara M., Waugh, Kathy, Steck, Andrea K., Norris, Jill M., Rewers, Marian
• Format: Journal Article
• Language: English
• Published: United States American Diabetes Association 01.02.2020
• Subjects: Adenosine Diphosphate - metabolism; Adolescent; Ascorbic acid; Ascorbic Acid - blood; Autoimmunity; Biomarkers; Biomarkers - blood; Butyrates - blood; Case-Control Studies; Child; Child, Preschool; Diabetes; Diabetes mellitus (insulin dependent); Diabetes Mellitus, Type 1 - blood; Diabetes Mellitus, Type 1 - immunology; Female; Fibrinogen; Fibrinogen - metabolism; Genetics/Genomes/Proteomics/Metabolomics; Humans; Infant; Learning algorithms; Machine learning; Male; Mannose; Mannose - blood; Metabolomics; Models, Biological; Polymorphism, Genetic; Protein Tyrosine Phosphatase, Non-Receptor Type 22 - genetics; Protein-tyrosine-phosphatase; Young Adult
• ISSN: 0012-1797; 1939-327X; 1939-327X
• DOI: 10.2337/db18-1263

This study aims to model genetic, immunologic, metabolomics, 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 of 42 progressed to diabetes) and 25 control subjects...