Adaptive metabolic and inflammatory responses identified using accelerated aging metrics are linked to adverse outcomes in severe SARS-CoV-2 infection

• Published in: The journals of gerontology. Series A, Biological sciences and medical sciences
• Main Authors: Márquez-Salinas, Alejandro, Fermín-Martínez, Carlos A, Antonio-Villa, Neftalí Eduardo, Vargas-Vázquez, Arsenio, C Guerra, Enrique, Campos-Muñoz, Alejandro, Zavala-Romero, Lilian, Mehta, Roopa, Bahena-López, Jessica Paola, Ortiz-Brizuela, Edgar, González-Lara, María Fernanda, Roman-Montes, Carla M, Martinez-Guerra, Bernardo A, Ponce de Leon, Alfredo, Sifuentes-Osornio, José, Gutiérrez-Robledo, Luis Miguel, Aguilar-Salinas, Carlos A, Bello-Chavolla, Omar Yaxmehen
• Format: Journal Article
• Language: English
• Published: United States 01.08.2021
• Subjects: COVID-19; PhenoAge; SARS-CoV2; Inflammaging; Biological Aging
• ISSN: 1758-535X
• DOI: 10.1093/gerona/glab078

Chronological age (CA) is a predictor of adverse COVID-19 outcomes; however, CA alone does not capture individual responses to SARS-CoV-2 infection. Here, we evaluated the influence of aging metrics PhenoAge and PhenoAgeAccel to predict adverse COVID-19 outcomes. Furthermore, we sought to model adaptive metabolic and inflammatory responses to severe SARS-CoV-2 infection using individual PhenoAge components. In this retrospective cohort study, we assessed cases admitted to a COVID-19 reference center in Mexico City. PhenoAge and PhenoAgeAccel were estimated using laboratory values at admission. Cox proportional hazards models were fitted to estimate risk for COVID-19 lethality and adverse outcomes (ICU admission, intubation, or death). To explore reproducible patterns which model adaptive responses to SARS-CoV-2 infection, we used k-means clustering using PhenoAge components. We included 1068 subjects of whom 222 presented critical illness and 218 died. PhenoAge was a better predictor of adverse outcomes and lethality compared to CA and SpO2 and its predictive capacity was sustained for all age groups. Patients with responses associated to PhenoAgeAccel>0 had higher risk of death and critical illness compared to those with lower values (log-rank p<0.001). Using unsupervised clustering we identified four adaptive responses to SARS-CoV-2 infection: 1) Inflammaging associated with CA, 2) metabolic dysfunction associated with cardio-metabolic comorbidities, 3) unfavorable hematological response, and 4) response associated with favorable outcomes. Adaptive responses related to accelerated aging metrics are linked to adverse COVID-19 outcomes and have unique and distinguishable features. PhenoAge is a better predictor of adverse outcomes compared to CA.