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Published on: October 26, 2017
Integration of the circulating miRNome and clinical information to predict 90-day mortality in elderly COVID-19
Manel Perez-Pons1,2, Iván D Benítez2,3,4, Marta Molinero1,2
1Clinical and Molecular Phenotyping, Biomedical Research Institute of Lleida - Dr. Pifarré Foundation, IRBLleida, Lleida, Spain.
Insights
MicroRNA profiling offers prognostic insights for elderly COVID-19 patients. Specific microRNAs, like miR-106b-3p in one subphenotype, improve mortality prediction beyond clinical factors.
Area of Science:
- Biomedical research
- Genomics
- Geriatric medicine
Background:
- Elderly individuals with COVID-19 face high mortality risks.
- Prognostic markers are crucial for managing severe cases in this demographic.
Purpose of the Study:
- To investigate the prognostic value of microRNA (miRNA) profiling in elderly COVID-19 patients.
- To develop predictive models for 90-day mortality using clinical and miRNA data.
Main Methods:
- A multicenter study involving 763 hospitalized COVID-19 patients aged ≥65 years.
- Clinical subphenotyping using the k-prototypes algorithm.
- Plasma miRNome profiling via qPCR and predictive model development using VSURF.
Main Results:
- Three distinct clinical subphenotypes (eCOVID-1, -2, -3) with varying mortality risks were identified.
- No single miRNA predicted mortality in the entire cohort, necessitating subphenotype analysis.
- In the eCOVID-2 subphenotype, miR-106b-3p significantly improved 90-day mortality prediction (AUC 0.75 vs. 0.66).
Conclusions:
- Plasma miRNAs provide prognostic information complementary to clinical variables in elderly COVID-19 subphenotypes.
- Subphenotype-specific miRNA analysis is essential for accurate mortality risk assessment in this population.
Abstract:
Elderly patients with coronavirus disease 2019 (COVID-19) exhibit high mortality rates. We assessed whether microRNA (miRNA) profiling provides prognostic information in this population. This multicenter study included hospitalized COVID-19 patients aged ≥65 years (n = 763). Clinical subphenotypes were identified using clinical data through k-prototypes algorithm. Plasma miRNome was profiled using qPCR. Clinical and miRNA-based models predicting 90-day mortality were developed using Variable Selection Using Random Forests (VSURF). Median age was 79 years, 44.0% were female, and 90-day mortality was 26.7%. Although 13 candidate miRNAs were identified during screening (n = 39), none were associated with mortality in the full derivation cohort (n = 340), leading to a subphenotype-stratified analysis. Three subphenotypes (elderly COVID-1 [eCOVID-1], -2, and -3) with distinct clinical features and mortality risks were identified. In eCOVID-2, miR-106b-3p was the strongest predictor of 90-day mortality and improved model discrimination beyond clinical variables alone area under the curve (AUC: 0.75 vs. 0.66). Plasma miRNAs provide complementary prognostic information when integrated with clinical variables in elderly COVID-19 subphenotypes.
