Biological Aging Predicts Vulnerability to COVID-19 Severity in UK Biobank Participants
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- Chia-Ling Kuo
- Connecticut Convergence Institute for Translation in Regenerative Engineering, University of Connecticut Health, Farmington, USA
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- Luke C Pilling
- University of Connecticut Center on Aging, School of Medicine, Farmington, USA
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- Janice L Atkins
- College of Medicine and Health, University of Exeter, UK
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- Jane A H Masoli
- College of Medicine and Health, University of Exeter, UK
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- João Delgado
- College of Medicine and Health, University of Exeter, UK
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- Christopher Tignanelli
- Department of Surgery, University of Minnesota, Minneapolis, USA
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- George A Kuchel
- University of Connecticut Center on Aging, School of Medicine, Farmington, USA
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- David Melzer
- University of Connecticut Center on Aging, School of Medicine, Farmington, USA
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- Kenneth B Beckman
- Institute for Health Informatics, University of Minnesota, Minneapolis, USA
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- Morgan E Levine
- Department of Pathology, Yale School of Medicine, New Haven, Connecticut, USA
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- Anne B Newman
- editor
Description
<jats:title>Abstract</jats:title> <jats:sec> <jats:title>Background</jats:title> <jats:p>Age and disease prevalence are the 2 biggest risk factors for Coronavirus disease 2019 (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.</jats:p> </jats:sec> <jats:sec> <jats:title>Methods</jats:title> <jats:p>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 2 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 preexisting diseases/conditions.</jats:p> </jats:sec> <jats:sec> <jats:title>Results</jats:title> <jats:p>Six hundred and thirteen 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 = 4.7 × 10−13) adjusting for demographics including age at the pandemic. Further adjustment for preexisting diseases/conditions at baseline (ORM = 1.50, 95% CI: 1.30–1.73 per 5 years, p = 3.1 × 10−8) and at the early pandemic (ORM = 1.21, 95% CI: 1.04–1.40 per 5 years, p = .011) decreased the association.</jats:p> </jats:sec> <jats:sec> <jats:title>Conclusions</jats:title> <jats:p>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.</jats:p> </jats:sec>
Journal
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- The Journals of Gerontology: Series A
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The Journals of Gerontology: Series A 76 (8), e133-e141, 2021-03-04
Oxford University Press (OUP)
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Details 詳細情報について
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- CRID
- 1360013170370680704
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- ISSN
- 1758535X
- 10795006
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- Data Source
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- Crossref