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suck abstract from ncbi


10.1093/gerona/glab060

http://scihub22266oqcxt.onion/10.1093/gerona/glab060
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33684206!7989601!33684206
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suck abstract from ncbi

pmid33684206      J+Gerontol+A+Biol+Sci+Med+Sci 2021 ; 76 (8): e133-e141
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  • Biological Aging Predicts Vulnerability to COVID-19 Severity in UK Biobank Participants #MMPMID33684206
  • Kuo CL; Pilling LC; Atkins JL; Masoli JAH; Delgado J; Tignanelli C; Kuchel GA; Melzer D; Beckman KB; Levine ME
  • J Gerontol A Biol Sci Med Sci 2021[Jul]; 76 (8): e133-e141 PMID33684206show ga
  • BACKGROUND: 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. METHODS: 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. RESULTS: 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 x 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 x 10-8) and at the early pandemic (ORM = 1.21, 95% CI: 1.04-1.40 per 5 years, p = .011) decreased the association. CONCLUSIONS: 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.
  • |*Biological Specimen Banks[MESH]
  • |*Severity of Illness Index[MESH]
  • |Aged[MESH]
  • |Aging/*physiology[MESH]
  • |Biomarkers[MESH]
  • |COVID-19 Testing/*statistics & numerical data[MESH]
  • |COVID-19/*epidemiology[MESH]
  • |Chronic Disease[MESH]
  • |Humans[MESH]
  • |Middle Aged[MESH]
  • |Models, Statistical[MESH]
  • |Mortality/*trends[MESH]
  • |Preexisting Condition Coverage/statistics & numerical data[MESH]
  • |SARS-CoV-2/isolation & purification[MESH]
  • |Time Factors[MESH]


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