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Adaptive Metabolic and Inflammatory Responses Identified Using Accelerated Aging Metrics Are Linked to Adverse Outcomes in Severe SARS-CoV-2 Infection.
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.
  • Márquez-Salinas A; Research Division, Instituto Nacional de Geriatría, Mexico City, Mexico.
  • Fermín-Martínez CA; MD/PhD (PECEM), Faculty of Medicine, Universidad Nacional Autónoma de México, Mexico City, Mexico.
  • Antonio-Villa NE; MD/PhD (PECEM), Faculty of Medicine, Universidad Nacional Autónoma de México, Mexico City, Mexico.
  • Vargas-Vázquez A; Unidad de Investigación de Enfermedades Metabólicas, Instituto Nacional de Ciencias Médicas y Nutrición Salvador Zubirán, Mexico City, Mexico.
  • Guerra EC; MD/PhD (PECEM), Faculty of Medicine, Universidad Nacional Autónoma de México, Mexico City, Mexico.
  • Campos-Muñoz A; Unidad de Investigación de Enfermedades Metabólicas, Instituto Nacional de Ciencias Médicas y Nutrición Salvador Zubirán, Mexico City, Mexico.
  • Zavala-Romero L; MD/PhD (PECEM), Faculty of Medicine, Universidad Nacional Autónoma de México, Mexico City, Mexico.
  • Mehta R; Unidad de Investigación de Enfermedades Metabólicas, Instituto Nacional de Ciencias Médicas y Nutrición Salvador Zubirán, Mexico City, Mexico.
  • Bahena-López JP; Research Division, Instituto Nacional de Geriatría, Mexico City, Mexico.
  • Ortiz-Brizuela E; MD/PhD (PECEM), Faculty of Medicine, Universidad Nacional Autónoma de México, Mexico City, Mexico.
  • González-Lara MF; Unidad de Investigación de Enfermedades Metabólicas, Instituto Nacional de Ciencias Médicas y Nutrición Salvador Zubirán, Mexico City, Mexico.
  • Roman-Montes CM; AFINES, Faculty of Medicine, Universidad Nacional Autónoma de México, Mexico City, Mexico.
  • Martinez-Guerra BA; Unidad de Investigación de Enfermedades Metabólicas, Instituto Nacional de Ciencias Médicas y Nutrición Salvador Zubirán, Mexico City, Mexico.
  • Ponce de Leon A; MD/PhD (PECEM), Faculty of Medicine, Universidad Nacional Autónoma de México, Mexico City, Mexico.
  • Sifuentes-Osornio J; Infectology Department, Instituto Nacional de Ciencias Médicas y Nutrición Salvador Zubirán, Mexico City, Mexico.
  • Gutiérrez-Robledo LM; Direction of Medicine, Instituto Nacional de Ciencias Médicas y Nutrición Salvador Zubirán, Mexico City, Mexico.
  • Aguilar-Salinas CA; Direction of Medicine, Instituto Nacional de Ciencias Médicas y Nutrición Salvador Zubirán, Mexico City, Mexico.
  • Bello-Chavolla OY; Direction of Medicine, Instituto Nacional de Ciencias Médicas y Nutrición Salvador Zubirán, Mexico City, Mexico.
J Gerontol A Biol Sci Med Sci ; 76(8): e117-e126, 2021 07 13.
Article in English | MEDLINE | ID: covidwho-1132490
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.
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Full text: Available Collection: International databases Database: MEDLINE Main subject: Aging / Models, Statistical / COVID-19 / Inflammation / Metabolism Type of study: Cohort study / Experimental Studies / Observational study / Prognostic study / Randomized controlled trials Limits: Female / Humans / Male / Middle aged Country/Region as subject: Mexico Language: English Journal: J Gerontol A Biol Sci Med Sci Journal subject: Geriatrics Year: 2021 Document Type: Article Affiliation country: Gerona

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Full text: Available Collection: International databases Database: MEDLINE Main subject: Aging / Models, Statistical / COVID-19 / Inflammation / Metabolism Type of study: Cohort study / Experimental Studies / Observational study / Prognostic study / Randomized controlled trials Limits: Female / Humans / Male / Middle aged Country/Region as subject: Mexico Language: English Journal: J Gerontol A Biol Sci Med Sci Journal subject: Geriatrics Year: 2021 Document Type: Article Affiliation country: Gerona