Journal Article Ahead of print
PubMed: 42020919 DOI: 10.1007/s11357-026-02212-z
Key factors of the deranged antiviral response in elderly patients with COVID-19: a machine-learning analysis.
Geroscience
Authors: Tamara Postigo-Casado, Alicia Ortega, Alejandro Álvaro-Meca, Daniel Vélez-Serrano, Nadia García-Mateo, Ana P Tedim, Raquel Almansa, Jose María Eiros, David de Gonzalo-Calvo, Anna Moncusí-Moix, Clara Gort-Paniello, Manel Perez-Pons, Marta Molinero, Marta Dominguez-Gil, Amanda de la Fuente, Laura González-González, Tania Luis-García, Fátima Rodríguez-Jara, Noelia Jorge, Jessica González, Gerard Torres, Oliver Norberto Gutiérrez-Pérez, María José Villegas, Sonia Campo, Eva Ayllón, Tomás Ruiz Albi, Julio de Frutos Arribas, Ainhoa Arroyo Domingo, Jessica Abadia-Otero, Julia Gómez Barquero, Wysali Trapiello, Luis Javier García Frade, Luis Inglada, Félix Del Campo, Ferrán Barbé, Antoni Torres, Raúl López-Izquierdo, Jesús F Bermejo-Martin
Affiliations
- Group for Biomedical Research in Sepsis (BioSepsis). Instituto de Investigación Biomédica de Salamanca, (IBSAL), Gerencia Regional de Salud de Castilla y León, Paseo de San Vicente, 58-182, 37007, Salamanca, Spain.
- Centro de Investigación Biomédica en Red en Enfermedades Respiratorias (CIBERES), Instituto de Salud Carlos III, Avenida de Monforte de Lemos, 3-5, 28029, Madrid, Spain.
- Department of Preventive Medicine and Public Health, Faculty of Health Science, Universidad Rey Juan Carlos, Madrid, Spain.
- Centro de Investigación Biomédica en Red en Enfermedades Infecciosas (CIBERINFEC), Instituto de Salud Carlos III, Avenida de Monforte de Lemos, 3-5, 28029, Madrid, Spain.
- Department of Statistics and Operations Research, Universidad Complutense de Madrid, Madrid, Spain.
- Department of Cellular Biology, Hystology and Pharmacology, University of Valladolid, Av. Ramón y Cajal, 7, 47005, Valladolid, Spain.
- Microbiology Service, Hospital Universitario Rio Hortega, Gerencia Regional de Salud de Castilla y León, Calle Dulzaina, 2, 47012, Valladolid, Spain.
- Translational Research in Respiratory Medicine, University Hospital Arnau de Vilanova and Santa Maria, IRB Lleida, Av. Alcalde Rovira Roure, 80, 25198, Lleida, Spain.
- Hematology Service. Hospital Universitario Río Hortega, Gerencia Regional de Salud de Castilla y León, Calle Dulzaina, 2, 47012, Valladolid, Spain.
- Pneumology Service, Hospital Universitario Río Hortega, Gerencia Regional de Salud de Castilla y León, Calle Dulzaina, 2, 47012, Valladolid, Spain.
- Deparment of Medicine, Dermatology and Toxicology, School of Medicine, University of Valladolid, Av. Ramón y Cajal, 7, 47005, Valladolid, Spain.
- Internal Medicine Service, Hospital Universitario Río Hortega, Gerencia Regional de Salud de Castilla y León, Calle Dulzaina, 2, 47012, Valladolid, Spain.
- Clinical Analysis Service. Hospital, Clínico Universitario de Valladolid, Gerencia Regional de Salud de Castilla y León, Av. Ramón y Cajal, 3, 47003, Valladolid, Spain.
- Centro de Investigación Biomédica en Red en Bioingeniería, Biomateriales y Nanomedicina (CIBER-BBN), Instituto de Salud Carlos III, Av. de Monforte de Lemos, 3-5, 28029, Madrid, Spain.
- Catalan Institution for Research and Advanced Studies, Passeig de Lluís Companys, 23, 08010, Barcelona, Spain.
- Department of Pulmonology, Institut D'Investigacions August Pi I Sunyer (IDIBAPS), Hospital Clinic de Barcelona, Universidad de Barcelona, Carrer del Rosselló, 149, 08036, Barcelona, Spain.
- Emergency Department, Hospital Universitario Rio Hortega, Gerencia Regional de Salud de Castilla y León, Calle Dulzaina, 2, 47012, Valladolid, Spain.
- Group for Biomedical Research in Sepsis (BioSepsis). Instituto de Investigación Biomédica de Salamanca, (IBSAL), Gerencia Regional de Salud de Castilla y León, Paseo de San Vicente, 58-182, 37007, Salamanca, Spain.
- Centro de Investigación Biomédica en Red en Enfermedades Respiratorias (CIBERES), Instituto de Salud Carlos III, Avenida de Monforte de Lemos, 3-5, 28029, Madrid, Spain.
- Department of Medicine, Faculty of Medicine, University of Salamanca, Salamanca, Spain.
Abstract
Age is a well-known risk factor to develop severe viral respiratory infections, including severe COVID-19. This study aimed to identify the biological alterations linked to severe disease in elderly patients with COVID-19. For this purpose, we employed a derivation cohort with 450 SARS-CoV-2 infected and unvaccinated patients admitted to hospital wards and a validation cohort with 244 SARS-CoV-2 infected and unvaccinated patients admitted to hospital Intensive Care Unit (ICU). Twenty-one biomarkers were measured in plasma samples from patients upon admission, including SARS-CoV-2 RNA, IgG antibodies, and protein biomarkers. Patient cohorts were divided into two groups based on age: adult (≤ 70 years old) and elderly (> 70 years old) patients. In the derivation cohort, 90-day mortality rate observed in the adult group was 6.0% whereas in the elderly group it rises to 31.6%, same trend was noticed regarding the validation cohort, with 11.2% versus 40.3% 90-day mortality rates for adult and elderly groups, respectively. The machine-learning framework XGBoost-SHAP, fed with the plasma biomarkers information, was used to profile an age-related host response to SARS-CoV-2 infection. Based on SHAP plot, elderly patients had a strong thrombo-inflammatory response profile (significantly elevated plasma levels of: lipocalin-2, endothelin-1, D-dimer) combined with deficient adaptive and cytotoxic antiviral responses. Model performance evaluated with the validation cohort confirmed the robustness and generalizability of the model developed (AUC = 0.710). In conclusion, the machine learning approach we built allowed us to identify the presence of a deranged host response in elderly patients with COVID-19 linked to poor viral control and increased mortality.
MeSH
Mortality, Aging, Biomarkers, Machine Learning, Aged, Covid-19, Intensive Care Units, Learning, Antiviral Agents, Infections, Plasma