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Abstract
Adults with type 2 diabetes mellitus (T2DM) are at increased risk for stroke, myocardial infarction, and cardiovascular death, yet individual risk is heterogeneous and incompletely captured by clinical models. In the Exenatide Study of Cardiovascular Event Lowering (EXSCEL), adults with T2DM were randomized to a GLP-1 RA (exenatide) or placebo and followed longitudinally for major adverse cardiovascular events (MACE). High-throughoput discovery proteomics was done in plasma collected at baseline and 12-months. Proteins associated with time-to-MACE were identified using multivariable regression and incorporated into supervised machine learning models. A multi-protein score was developed and externally validated in two independent population-based and trial cohorts, Cardiovascular Health Study and the Prospective Multicentre Imaging Study for Evaluation of Chest Pain (PROMISE). The proteomic score showed incremental improvement in cardiovascular risk discrimination beyond clinical factors alone, and several proteins were consistently prioritized across modeling approaches. The protein score and a top-ranked protein, tetranectin, were modified by GLP-1 RA treatment, and a decrease in the protein score was associated with improved outcomes, supporting modifiability of MACE risk. External validation confirmed generalizability across cohorts with and without diabetes. Together, these findings demonstrate that plasma proteomic signatures can enhance cardiovascular risk stratification and identify treatment-responsive biomarkers in T2DM, supporting their potential role in precision prevention strategies.
Authors
Kristin M. Corey, Maggie Nguyen, Michael Y. Mi, Megan E. Ramaker, Ilya Zhbannikov, Harald Sourij, G. Michael Felker, Naveed Sattar, Jennifer B. Green, Pamela S. Douglas, Robert E. Gerszten, Robert J. Mentz, Adrian F. Hernandez, Rury R. Holman, Bruce M. Psaty, James S. Floyd, Svati H. Shah
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