Ricerc@Sapienza

Ischemic Heart disease (IHD) is not anymore synonymous with obstructive (Obs) flow-limiting coronary artery disease (CAD), especially in women that are more commonly affected by non-obstructive (non-Obs) disease (i.e., In preliminary studies we developed an interpretable machine learning-based model that discriminates non-Obs and Obs CAD through features that capture i) the reduction of physiological functional reserve of individuals (i.e. frailty); ii) the inflammatory burden; and iii) the complexity that gender encompasses (identity, role, relations and institutionalized gender). We found that while Obs CAD associates with increased frailty index, older age and a cytokine signature characterized by IL-1ß, IL-12p70 and IL-33, non-Obs CAD is more likely to associate with a feminine gender score and with a cytokine signature characterized by IL-18, IL-8, IL-23. Thus, we hypothesize that different thrombo-inflammatory mechanisms underlie different types of CAD.
To test this hypothesis, we here propose 3 tasks to be pursued by a coordinated effort of 3 research units. The Unit lead by L. Stefanini will compare platelet reactivity in Obs and non-Obs CAD patients (task 1). The Unit lead by S. Piconese will characterize by multi-color flow cytometry the immunological profile of these patients and the crosstalk between platelets and immune cells in collaboration with Unit Stefanini (task 2). The Unit lead by V. De Cicco will use data obtained in tasks 1-2 to compute a simplified model of platelet activation to predict how inflammatory cytokines affect platelet function (task 3). In summary, the proposed project will employ state-of-the-art flow cytometry and mathematical techniques to dissect the mechanisms underlying non-Obs and Obs CAD. We anticipate that these studies will facilitate the tailoring of safer patient-specific therapies.