Key factors of the deranged antiviral response in elderly patients with COVID-19: a machine-learning analysis

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.

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Journal
GeroScience
Published
2026-04-22
DOI
https://doi.org/10.1007/s11357-026-02212-z
Primary Topic
COVID-19 Clinical Research Studies
Type
article
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article

Key factors of the deranged antiviral response in elderly patients with COVID-19: a machine-learning analysis

Víctor Sagredo Meneses, Alejandro Álvaro‐Meca, Sonia Campo, Tamara Postigo-Casado et al.
GeroScience
COVID-19 Clinical Research Studies
article

Key factors of the deranged antiviral response in elderly patients with COVID-19: a machine-learning analysis

Víctor Sagredo Meneses, Alejandro Álvaro‐Meca, Sonia Campo, Tamara Postigo-Casado, Félix del Campo, Maria José Villegas, Tomás Ruíz Albi, M.C. Martín Delgado, Caridad Martín López, Raúl López‐Izquierdo, Wysali Trapiello, Ainhoa Arroyo Domingo, Ana P. Tedim, Jesús F. Bermejo-Martín, Adrián Ceccato, Ruth Noemí Jorge García, Marta Molinero, Emilio Maseda, Amanda de la Fuente, Amalia Martínez de la Gándara, Raquel Almansa, Milagros González Rivera, Manel Perez‐Pons, Eva Ayllon, Pablo Vidal-Cortes, Isidoro Martínez, Elena Bustamante‐Munguira, Luis Inglada, Luis Jorge Valdivia, Tania Luis-García, Julio de Frutos Arribas, Fátima Rodríguez‐Jara, Nieves Carbonell, Marta Domínguez‐Gil, L.J. García Frade, Oliver Norberto Gutiérrez-Pérez, Ana Motos, Julia Gómez Barquero, Jessica González, Sandra Campos‐Fernández, Jesús Caballero, Lorenzo Socías, Elena Gallego Curto, Luis Tamayo, Ignacio Martínez Varela, Juan López Messa, Ana Loza Vázquez, María del Carmen de la Torre, Noelia Jorge, José María Eiros, Ángel Estella, Laura González-González, Alejandro Úbeda Iglesias, Antoni Torres, Felipe Pérez-García, Jessica Abadia-Otero, Ferrán Barbé, Nadia García-Mateo, Anna Moncusí-Moix, Alicia Ortega, David de Gonzalo-Calvo, Daniel Vélez-Serrano, Salvador Resino, Gerard Torres, Clara Gort-Paniello, CIBERES-U. C. I.-COVID group, Laia Fernández-Barat
article en

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.

GeroScience
Universidad de Valladolid (ES), Institució Catalana de Recerca i Estudis Avançats (ES), Universidad Complutense de Madrid (ES), Universidad Rey Juan Carlos (ES), Universidad de Salamanca (ES), Instituto de Salud Carlos III (ES), Hospital Universitari Arnau de Vilanova (ES), Centro de Investigación Biomédica en Red de Enfermedades Respiratorias (ES), Biomedical Research Networking Center in Bioengineering, Biomaterials and Nanomedicine (ES), Centro de Investigación Biomédica en Red (ES), Junta de Castilla y León (ES), Instituto de Investigación Biomédica de Salamanca (ES), Hospital Clínico Universitario de Valladolid (ES), Hospital Universitario Río Hortega (ES), Consorci Institut D'Investigacions Biomediques August Pi I Sunyer (ES)
Fundació la Marató de TV3, Universidad de Salamanca, Instituto de Salud Carlos III
Good health and well-being
Openalex Percentile: Top 13%
COVID-19 Clinical Research Studies
DOIOpen Access PDFOpenAlex
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