Adaptive Metabolic and Inflammatory Responses Identified Using Accelerated Aging Metrics Are Linked to Adverse Outcomes in Severe SARS-CoV-2 Infection
© The Author(s) 2021. Published by Oxford University Press on behalf of The Gerontological Society of America. All rights reserved. For permissions, please e-mail: journals.permissionsoup.com..
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.
| Medienart: |
E-Artikel |
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| Erscheinungsjahr: |
2021 |
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| Erschienen: |
2021 |
| Enthalten in: |
Zur Gesamtaufnahme - volume:76 |
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| Enthalten in: |
The journals of gerontology. Series A, Biological sciences and medical sciences - 76(2021), 8 vom: 13. Juli, Seite e117-e126 |
| Sprache: |
Englisch |
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| Beteiligte Personen: |
Márquez-Salinas, Alejandro [VerfasserIn] |
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| Links: |
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| Themen: |
Biological aging |
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| Anmerkungen: |
Date Completed 29.07.2021 Date Revised 29.07.2021 published: Print Citation Status MEDLINE |
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| doi: |
10.1093/gerona/glab078 |
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| funding: |
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| Förderinstitution / Projekttitel: |
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| 245 | 1 | 0 | |a Adaptive Metabolic and Inflammatory Responses Identified Using Accelerated Aging Metrics Are Linked to Adverse Outcomes in Severe SARS-CoV-2 Infection |
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| 520 | |a © The Author(s) 2021. Published by Oxford University Press on behalf of The Gerontological Society of America. All rights reserved. For permissions, please e-mail: journals.permissionsoup.com. | ||
| 520 | |a 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 | ||
| 520 | |a 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 | ||
| 520 | |a 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 | ||
| 520 | |a 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 | ||
| 650 | 4 | |a Journal Article | |
| 650 | 4 | |a Biological aging | |
| 650 | 4 | |a COVID-19 | |
| 650 | 4 | |a Inflammaging | |
| 650 | 4 | |a PhenoAge | |
| 650 | 4 | |a SARS-CoV2 | |
| 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 | |
| 700 | 1 | |a Roman-Montes, Carla M |e verfasserin |4 aut | |
| 700 | 1 | |a Martinez-Guerra, Bernardo A |e verfasserin |4 aut | |
| 700 | 1 | |a Ponce de Leon, Alfredo |e verfasserin |4 aut | |
| 700 | 1 | |a Sifuentes-Osornio, José |e verfasserin |4 aut | |
| 700 | 1 | |a Gutiérrez-Robledo, Luis Miguel |e verfasserin |4 aut | |
| 700 | 1 | |a Aguilar-Salinas, Carlos A |e verfasserin |4 aut | |
| 700 | 1 | |a Bello-Chavolla, Omar Yaxmehen |e verfasserin |4 aut | |
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