Abstract
Rationale: Pathogenesis of human coronary atherosclerosis is tightly associated with the imbalance of inflammation and resolution in the local immune microenvironment of AS plaques. However, how the peripheral immune system dynamically changes along with disease progression in humans remains unclear. As a result, the minimally-invasive clinical biomarkers that can sensitively distinguish different stages of human coronary atherosclerosis are still lacking. Methods: We performed single-cell Cytometry by Time-Of-Flight (CyTOF) analyses to comprehensively profile the compositions and phenotypes of CD45+ cells derived from 83 human peripheral blood samples with two independent antibody-staining panels (T cell panel and myeloid cell panel). Clinical associations between the frequencies of peripheral immune cell subsets with AS plaque burdens of coronary arteries (Gensini score) and serum lipids were also examined. By integrating immune and clinical features, we established novel CVD risk prediction models to stratify patients in different disease stages. Results: We revealed the disease stage-associated peripheral immune features for patients with coronary atherosclerosis (CAS) and atherosclerotic cardiovascular disease (ASCVD), and also identified the specific peripheral immune cell subsets that were tightly associated with the disease severity of coronary arteries (Gensini score). By integrating these peripheral immune signatures with clinical features, we have established a disease progression prediction (DPP) model that could precisely discriminate CAS patients from ASCVD patients with high prediction accuracy (ROC-AUC = 0.88). Conclusion: The progression of coronary atherosclerosis is accompanied by significant alterations of the peripheral immune system, including the changes in the distributions as well as phenotypic functions of specific immune cell subsets. The indicated stage-specific peripheral immune signatures thus become promising minimally-invasive liquid biomarkers that could help to potentially diagnose and monitor the CVD progression in humans.