Adaptive Metabolic and Inflammatory Responses Identified Using Accelerated Aging Metrics Are Linked to Adverse Outcomes in Severe SARS-CoV-2 Infection. (16th March 2021)

• Record Type: Journal Article
• Authors: Márquez-Salinas, Alejandro; Fermín-Martínez, Carlos A; Antonio-Villa, Neftalí Eduardo; Vargas-Vázquez, Arsenio; Guerra, Enrique C; 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
• Editors: Newman, Anne B
• Is Part Of: Journals of gerontology. Volume 76:Number 8(2021)
• Page Start: e117
• Page End: e126
• Publication Date: 2021-03-16
• Subjects: Biological aging -- COVID-19 -- Inflammaging -- PhenoAge -- SARS-CoV2; Geriatrics -- Periodicals; Gerontology -- Periodicals; 618.97
• DOI: 10.1093/gerona/glab078
• Languages: English
• ISSNs: 1079-5006

Abstract

Abstract: Background: Chronological age (CA) is a predictor of adverse coronavirus disease 2019 (COVID-19) outcomes; however, CA alone does not capture individual responses to severe acute respiratory syndrome coronavirus 2 (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. Method: 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 (intensive care unit 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. Results: 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 < .001). Using unsupervised clustering, we identified 4 adaptive responses to SARS-CoV-2 infection: (i) inflammaging associated with CA, (ii) metabolic dysfunction associated with cardiometabolic comorbidities, (iii) unfavorable hematological response, and (iv) response associated with favorable outcomes. Conclusions: 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.