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Kuo CL, Pilling LC, Atkins JL, Masoli JAH, Delgado J, Tignanelli C, Kuchel GA, Melzer D, Beckman KB, Levine ME. COVID-19 severity is predicted by earlier evidence of accelerated aging. MEDRXIV : THE PREPRINT SERVER FOR HEALTH SCIENCES 2020:2020.07.10.20147777. [PMID: 32676624 PMCID: PMC7359549 DOI: 10.1101/2020.07.10.20147777] [Citation(s) in RCA: 16] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Indexed: 01/06/2023]
Abstract
With no known treatments or vaccine, COVID-19 presents a major threat, particularly to older adults, who account for the majority of severe illness and deaths. The age-related susceptibility is partly explained by increased comorbidities including dementia and type II diabetes [1]. While it is unclear why these diseases predispose risk, we hypothesize that increased biological age, rather than chronological age, may be driving disease-related trends in COVID-19 severity with age. To test this hypothesis, we applied our previously validated biological age measure (PhenoAge) [2] composed of chronological age and nine clinical chemistry biomarkers to data of 347,751 participants from a large community cohort in the United Kingdom (UK Biobank), recruited between 2006 and 2010. Other data included disease diagnoses (to 2017), mortality data (to 2020), and the UK national COVID-19 test results (to May 31, 2020) [3]. Accelerated aging 10-14 years prior to the start of the COVID-19 pandemic was associated with test positivity (OR=1.15 per 5-year acceleration, 95% CI: 1.08 to 1.21, p=3.2×10-6) and all-cause mortality with test-confirmed COVID-19 (OR=1.25, per 5-year acceleration, 95% CI: 1.09 to 1.44, p=0.002) after adjustment for demographics including current chronological age and pre-existing diseases or conditions. The corresponding areas under the curves were 0.669 and 0.803, respectively. Biological aging, as captured by PhenoAge, is a better predictor of COVID-19 severity than chronological age, and may inform risk stratification initiatives, while also elucidating possible underlying mechanisms, particularly those related to inflammaging.
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Affiliation(s)
- Chia-Ling Kuo
- Connecticut Convergence Institute for Translation in Regenerative Engineering, University of Connecticut Health, Farmington, Connecticut, USA
- University of Connecticut Center on Aging, School of Medicine, Farmington, Connecticut, USA
| | - Luke C. Pilling
- University of Connecticut Center on Aging, School of Medicine, Farmington, Connecticut, USA
- College of Medicine and Health, University of Exeter, UK
| | | | - Jane AH Masoli
- College of Medicine and Health, University of Exeter, UK
| | - João Delgado
- College of Medicine and Health, University of Exeter, UK
| | | | - George A Kuchel
- University of Connecticut Center on Aging, School of Medicine, Farmington, Connecticut, USA
| | - David Melzer
- University of Connecticut Center on Aging, School of Medicine, Farmington, Connecticut, USA
- College of Medicine and Health, University of Exeter, UK
| | - Kenneth B Beckman
- Institute for Health Informatics, University of Minnesota, Minneapolis, MN, USA
| | - Morgan E. Levine
- Department of Pathology, Yale School of Medicine, New Haven, Connecticut, USA
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