PR001050 (Project)

Description:Biomarkers are of paramount importance for early disease detection and are particularly valuable in type 1 diabetes (T1D) to prevent significant ß cell loss before the onset of clinical symptoms. Thus far, single-omics studies have failed to identify such T1D biomarkers. Here, we present proof-of-concept studies to demonstrate the potential for identifying integrated biomarker signature(s) of T1D using parallel multi-omics. Blood from human subjects at high risk for T1D (and healthy controls; n=4 each) were subjected to parallel unlabeled proteomics, metabolomics, lipidomics, and transcriptomics. The integrated dataset was analyzed using Ingenuity Pathway Analysis (IPA) software for disturbances in the at-risk subjects compared to the controls.