Integration of the circulating miRNome and clinical information to predict 90-day mortality in elderly COVID-19 subphenotypes

Elderly patients with COVID-19 exhibit significant elevated mortality. Our research seeks to assess whether microRNA (miRNA) profiling provides prognostic information in this population. Multicenter study including hospitalized COVID-19 patients aged ≥65 years (n=763). Subphenotypes were identified using sociodemographic and clinical data via elbow criterion-based clustering. Plasma miRNome was profiled using qPCR. VSURF was applied to develop clinical and miRNA-based models for 90-day mortality prediction. Functional analyses were performed. Mean age was 79 years; 44.0% were women. The 90-day mortality rate was 26.7%. Thirteen candidate miRNAs were identified in the screening phase (n=39), but none were associated with mortality in the overall cohort (n=340). Three subphenotypes (eCOVID-1, -2 and -3) with distinct clinical features and mortality risks were identified. Differential miRNA expression patterns and mechanisms were found across subphenotypes. In eCOVID-2, miR-106b-3p was the strongest predictor. Metabolic and immune processes were linked to the fatal outcome in eCOVID-2. Plasma miRNAs provide complementary prognostic information when combined with clinical variables in models for elderly COVID-19 subphenotypes.