The NIH has awarded two new research supplements to an existing $4M award to investigate the potential role of IgE antibodies to alpha-gal in driving vascular inflammation in cardiovascular disease (CVD). The innovative new studies will address the impact of IgE to alpha-gal and CVD on Alzheimer’s disease and will develop precision medicine approaches using artificial intelligence and machine learning tools. Currently, heart disease and Alzheimer’s disease are the leading causes of death in the U.S. The first award to Loren D. Erickson, Ph.D. of the Carter Immunology Center and Department of Microbiology, Immunology and Cancer Biology and member of the iPRIME initiative, is part of a 5-year multi-PI NIH sponsored grant with Coleen A. McNamara, MD of the Carter Immunology Center and Department of Medicine, Cardiovascular Division, and Co-PI of the iPRIME initiative to study the effects of IgE to alpha-gal on atherosclerotic plaque development and immune mechanisms mediating IgE sensitization to alpha-gal linked to atherosclerotic cardiovascular disease in mice and humans. This new funding will support collaborative studies with John R. Lukens, Ph.D. of the Carter Immunology Center, the Center for Brain Immunology and Glia, and the Department of Neuroscience to test the function of IgE antibodies to alpha-gal on Alzheimer’s disease-related brain inflammation and cognitive impairment during chronic exposure to dietary alpha-gal. These studies leverage expertise in the fields of atherosclerosis, allergy, and Alzheimer’s disease to test for the first time whether IgE-mediated vascular inflammation may be a mechanism by which coronary artery disease and Alzheimer’s disease are causally linked. Such information could serve to identify those with coronary artery disease at risk for Alzheimer’s disease and identify potential mechanisms that could lead to biomarker discovery and approaches to reduce this risk. For the second award, Drs. Erickson and McNamara will work with Stefan Bekiranov, Ph.D. of the Carter Immunology Center, Department of Biochemistry and Molecular Genetics, and co-PI of the iPRIME initiative to adapt an explainable single-cell machine learning framework for CVD patient single-cell mass cytometry data. The study will enable a powerful AI framework to be developed that predicts patients’ risk of CVD while identifying the cells and their states driving the predictions of patient risk. This framework could accelerate drug discovery and enable AI-driven precision medicine from a routine blood draw for the treatment of CVD. The data used for the study will be made AI-ready and shared with the biomedical community for other research teams to further advance precision medicine.