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Biological Aging Predicts Vulnerability to COVID-19 Severity in UK Biobank Participants

Age and disease prevalence are the two biggest risk factors for COVID-19 symptom severity and death. We therefore hypothesized that increased biological age, beyond chronological age, may be driving disease-related trends in COVID-19 severity. Using the UK Biobank England data, we tested whether a b...

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Autores principales: Kuo, Chia-Ling, Pilling, Luke, Atkins, Janice, Masoli, Jane, Delgado, Joao, Kuchel, George, Melzer, David, Levine, Morgan
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Oxford University Press 2021
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8679949/
http://dx.doi.org/10.1093/geroni/igab046.1285
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author Kuo, Chia-Ling
Pilling, Luke
Atkins, Janice
Masoli, Jane
Delgado, Joao
Kuchel, George
Melzer, David
Levine, Morgan
author_facet Kuo, Chia-Ling
Pilling, Luke
Atkins, Janice
Masoli, Jane
Delgado, Joao
Kuchel, George
Melzer, David
Levine, Morgan
author_sort Kuo, Chia-Ling
collection PubMed
description Age and disease prevalence are the two biggest risk factors for COVID-19 symptom severity and death. We therefore hypothesized that increased biological age, beyond chronological age, may be driving disease-related trends in COVID-19 severity. Using the UK Biobank England data, we tested whether a biological age estimate (PhenoAge) measured more than a decade prior to the COVID-19 pandemic was predictive of two COVID-19 severity outcomes (inpatient test positivity and COVID-19 related mortality with inpatient test-confirmed COVID-19). Logistic regression models were used with adjustment for age at the pandemic, sex, ethnicity, baseline assessment centers, and pre-existing diseases/conditions. 613 participants tested positive at inpatient settings between March 16 and April 27, 2020, 154 of whom succumbed to COVID-19. PhenoAge was associated with increased risks of inpatient test positivity and COVID-19 related mortality (ORMortality=1.63 per 5 years, 95% CI: 1.43-1.86, p=4.7x10E-13) adjusting for demographics including age at the pandemic. Further adjustment for pre-existing disease s/conditions at baseline (OR_M=1.50, 95% CI: 1.30-1.73 per 5 years, p=3.1x10E-8) and at the early pandemic (OR_M=1.21, 95% CI: 1.04-1.40 per 5 years, p=0.011) decreased the association. PhenoAge measured in 2006-2010 was associated with COVID-19 severity outcomes more than 10 years later. These associations were partly accounted for by prevalent chronic diseases proximate to COVID-19 infection. Overall, our results suggest that aging biomarkers, like PhenoAge may capture long-term vulnerability to diseases like COVID-19, even before the accumulation of age-related comorbid conditions.
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spelling pubmed-86799492021-12-17 Biological Aging Predicts Vulnerability to COVID-19 Severity in UK Biobank Participants Kuo, Chia-Ling Pilling, Luke Atkins, Janice Masoli, Jane Delgado, Joao Kuchel, George Melzer, David Levine, Morgan Innov Aging Abstracts Age and disease prevalence are the two biggest risk factors for COVID-19 symptom severity and death. We therefore hypothesized that increased biological age, beyond chronological age, may be driving disease-related trends in COVID-19 severity. Using the UK Biobank England data, we tested whether a biological age estimate (PhenoAge) measured more than a decade prior to the COVID-19 pandemic was predictive of two COVID-19 severity outcomes (inpatient test positivity and COVID-19 related mortality with inpatient test-confirmed COVID-19). Logistic regression models were used with adjustment for age at the pandemic, sex, ethnicity, baseline assessment centers, and pre-existing diseases/conditions. 613 participants tested positive at inpatient settings between March 16 and April 27, 2020, 154 of whom succumbed to COVID-19. PhenoAge was associated with increased risks of inpatient test positivity and COVID-19 related mortality (ORMortality=1.63 per 5 years, 95% CI: 1.43-1.86, p=4.7x10E-13) adjusting for demographics including age at the pandemic. Further adjustment for pre-existing disease s/conditions at baseline (OR_M=1.50, 95% CI: 1.30-1.73 per 5 years, p=3.1x10E-8) and at the early pandemic (OR_M=1.21, 95% CI: 1.04-1.40 per 5 years, p=0.011) decreased the association. PhenoAge measured in 2006-2010 was associated with COVID-19 severity outcomes more than 10 years later. These associations were partly accounted for by prevalent chronic diseases proximate to COVID-19 infection. Overall, our results suggest that aging biomarkers, like PhenoAge may capture long-term vulnerability to diseases like COVID-19, even before the accumulation of age-related comorbid conditions. Oxford University Press 2021-12-17 /pmc/articles/PMC8679949/ http://dx.doi.org/10.1093/geroni/igab046.1285 Text en © The Author(s) 2021. Published by Oxford University Press on behalf of The Gerontological Society of America. https://creativecommons.org/licenses/by/4.0/This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0/ (https://creativecommons.org/licenses/by/4.0/) ), which permits unrestricted reuse, distribution, and reproduction in any medium, provided the original work is properly cited.
spellingShingle Abstracts
Kuo, Chia-Ling
Pilling, Luke
Atkins, Janice
Masoli, Jane
Delgado, Joao
Kuchel, George
Melzer, David
Levine, Morgan
Biological Aging Predicts Vulnerability to COVID-19 Severity in UK Biobank Participants
title Biological Aging Predicts Vulnerability to COVID-19 Severity in UK Biobank Participants
title_full Biological Aging Predicts Vulnerability to COVID-19 Severity in UK Biobank Participants
title_fullStr Biological Aging Predicts Vulnerability to COVID-19 Severity in UK Biobank Participants
title_full_unstemmed Biological Aging Predicts Vulnerability to COVID-19 Severity in UK Biobank Participants
title_short Biological Aging Predicts Vulnerability to COVID-19 Severity in UK Biobank Participants
title_sort biological aging predicts vulnerability to covid-19 severity in uk biobank participants
topic Abstracts
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8679949/
http://dx.doi.org/10.1093/geroni/igab046.1285
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