Altered immune response and inflammation characterize patients with aortic valve sclerosis in acute myocardial infarction

Background: Aortic valve sclerosis (AVSc) presents similar pathogenetic mechanisms to coronary artery disease (CAD) and is associated with both short- and long-term mortality. In the context of acute myocardial infarction (AMI), evidence of specific systemic mechanisms characterizing AVSc are currently lacking. Objective: To evaluate the systemic pathophysiological landscape that might differentiate AVSc from non-AVSc subjects in AMI. Methods: Whole-blood transcriptome of AVSc (n=44) and no-AVSc (n=66) patients with AMI was assessed by RNA-sequencing. A bioinformatic workflow, including feature selection, differential expression and enrichment analysis was performed to identify gene expression signatures discriminating AVSc from no-AVSc and infer functional association. Multivariable Cox regression analysis was used to estimate hazard ratio of AVSc versus no-AVSc patients. Results: A signature of 100 informative genes allowed classifying AVSc from no-AVSc with 94% accuracy. Significant differences in 143 genes were also identified, 30 of which withstood association independently of age and previous AMI, or cardiac interventions. Functional inference unveiled significant association of AVSc with acute inflammatory and type I interferons (IFNs) response, platelet activation and hemostasis. AMI patients with AVSc showed a significantly higher incidence of adverse cardiovascular events within 10 years of follow-up with a full adjusted hazard ratio of 2.4 (95% CI 1.3–4.5). Conclusions: During AMI, AVSc patients showed increased type I IFNs response and associated adverse cardiovascular outcomes at follow-up. Novel pharmacological therapies aiming to inhibit response to type I IFNs during or immediately after AMI might improve poor cardiovascular outcomes of AMI patients with AVSc. Overall design: A total of 110 whole-blood sample of AMI patients with AVSc or without AVSC have been used for RNA-sequencing. The population included 44 AMI with AVSc and 66 AMI without AVSc. A feature selection approach was used to identify a small set of informative genes to discriminate AVSc from no-AVSc patients. Differential expression and enrichment analysis were used to infer functional associations with the two sub-groups of patients. Raw data for GSM6745956-GSM6745979 can be downloaded from PRJNA400456.