Alessandra Breschi, Yuliang Wang, Sarah Short, Wilman Luk, David Erani, Pouya Kheradpour, Peter Cimermancic, Gary J Tong, Jean Philippe Martin, Manway Liu, Lulu Cao, Daniel Liu, Ranee Chatterjee, Lydia Coulter Kwee, Thomas M Snyder, Andrew Han, Katherine Drake, Charles C Kim
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Abstract
Background: Understanding diabetes at the molecular level can help refine diagnostic approaches and personalized treatment efforts.
Methods: We generated proteomic data from plasma collected from participants enrolled in the longitudinal observational cohort study Project Baseline Health Study (PBHS) (evaluated cohort, n = 738, 27.9% of the total PBHS cohort), and integrated those data with information from their medical history and laboratory tests to determine diabetes status. We then identified biomarker proteins associated with diabetes status.
Results: Here we identify 87 differentially expressed proteins in people with diabetes compared to those without diabetes, 71 of which show higher expression. This proteomic profile, integrated with clinical data into a logistic regression model, can discriminate diabetes status with over 85% balanced accuracy.
Conclusions: Our approach indicates that proteomic data can enhance diabetes phenotyping, showing potential for marker-based stratification of diabetes diagnosis. These results suggest that a holistic molecular-clinical approach to diagnosis might help personalize treatments or interventions for people with diabetes.
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