INTEGRATED CLINICAL-MOLECULAR APPROACH WITH PLASMA MIRNOME PROFILING PREDICTS SUBPHENOTYPE-SPECIFIC MORTALITY IN ELDERLY COVID-19

Background

Elderly patients with viral respiratory infections, who exhibit considerable clinical heterogeneity, present a high mortality risk. Circulating microRNAs (miRNAs) are promising biomarkers for predicting outcomes, but their prognostic value in this complex population remains unclear. The main objective of this study is to assess whether miRNA profiling provides prognostic information in elderly COVID-19 patients.

Methods

Multicenter study including hospitalized COVID-19 patients aged ≥ 65 years [n=763, COVID-Ponent (NCT04824677), participation of several CIBERES groups]. Subphenotypes were identified using sociodemographic, baseline, infection-timing and admission-registered clinical data via elbow criteria-based cluster analysis. Plasma miRNome was profiled using qPCR. VSURF was applied to develop clinical-molecular models for 90-day mortality. Functional analyses based on an external RNA-seq dataset were performed using GO, Reactome, and KEGG databases.

Results

Results: The mean age was 79 years, with 44.0% women. The 90-day mortality rate was 26.7%. Thirteen candidate miRNAs were identified during the screening phase (n=39), but no association with 90-day mortality was found in the derivation cohort (n=340). Three subphenotypes (eCOVID-1, -2 and -3) with distinct clinical features and mortality risks (22.0%-34.2%) were identified. Differential miRNA expression patterns and molecular mechanisms were found across subphenotypes. In eCOVID-2 (eldest and most comorbid subphenotype) miR-106b-3p was the strongest predictor, selected in a VSURF model with PaO₂/FiO₂, age, and CRP. Metabolic and immune processes were linked to the fatal outcome in eCOVID-2.

Conclusions

Plasma miRNAs lack biomarker value in the overall population but provide complementary prognostic information when combined with clinical variables in specific elderly COVID-19 subphenotypes. These results offer a novel framework for stratifying elderly patients in the context of viral respiratory infections.

Funding

La Fundació La Marató de TV3 (202108-30/-31). Institut Català de la Salut and Gestió de Serveis Sanitaris. Instituto de Salud Carlos III (ISCIII) (CIBERESUCICOVID, COV20/00110), co-funded by the European Union and Centro de Investigación Biomedica En Red – Enfermedades Respiratorias (CIBERES). CIBERES (CB07/06/2008). UNESPA (Donation Program “estar preparados”). SEPAR (1225/2022). ISCIII co-funded by the European Union (PFIS: FI23/00022, for MPP; FI21/00187 MM; and Miguel Servet 2020: CP20/00041, for DdGC). MCGH: IRBLleida/Diputación de Lleida. CR: PERIS (SLT028/23/000191) and AGAUR-FI ajuts (2024 FI-1 00863). FB: ICREA Academia program.