Adaptive metabolic and inflammatory responses identified using accelerated aging metrics are linked to adverse outcomes in severe SARS-CoV-2 infection
ABSTRACT INTRODUCTION 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.METHODS 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/PhenoAccelAge components.RESULTS We included 1068 subjects of whom 401 presented critical illness and 204 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.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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Preprint| Erscheinungsjahr: |
2020 |
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| Erschienen: |
2020 |
| Enthalten in: |
bioRxiv.org - (2020) vom: 27. Nov. Zur Gesamtaufnahme - year:2020 |
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| Sprache: |
Englisch |
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| Beteiligte Personen: |
Márquez-Salinas, Alejandro [VerfasserIn] |
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| Links: |
Volltext [kostenfrei] |
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| doi: |
10.1101/2020.11.03.20225375 |
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| PPN (Katalog-ID): |
XBI019262426 |
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| 520 | |a ABSTRACT INTRODUCTION 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.METHODS 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/PhenoAccelAge components.RESULTS We included 1068 subjects of whom 401 presented critical illness and 204 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.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. | ||
| 700 | 1 | |a Fermín-Martínez, Carlos A. |e verfasserin |4 aut | |
| 700 | 1 | |a Antonio-Villa, Neftalí Eduardo |e verfasserin |4 aut | |
| 700 | 1 | |a Vargas-Vázquez, Arsenio |e verfasserin |4 aut | |
| 700 | 1 | |a Guerra, Enrique C. |e verfasserin |4 aut | |
| 700 | 1 | |a Campos-Muñoz, Alejandro |e verfasserin |4 aut | |
| 700 | 1 | |a Zavala-Romero, Lilian |e verfasserin |4 aut | |
| 700 | 1 | |a Mehta, Roopa |e verfasserin |4 aut | |
| 700 | 1 | |a Bahena-López, Jessica Paola |e verfasserin |4 aut | |
| 700 | 1 | |a Ortiz-Brizuela, Edgar |e verfasserin |4 aut | |
| 700 | 1 | |a González-Lara, María Fernanda |e verfasserin |4 aut | |
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