AI Article Synopsis
- The study investigates how aging metrics PhenoAge and PhenoAgeAccel can better predict negative outcomes in COVID-19 patients compared to just using chronological age (CA).
- In a cohort of over 1,000 patients, researchers found that PhenoAge was more accurate in predicting severe illness and death, especially highlighting different aging-related responses in patients.
- The findings show that those with higher PhenoAgeAccel scores faced greater risks, and four distinct adaptive responses to SARS-CoV-2 infection were identified, suggesting a link between accelerated aging and poor COVID-19 outcomes.
Article Abstract
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.
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Source |
|---|---|
| http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7989655 | PMC |
| http://dx.doi.org/10.1093/gerona/glab078 | DOI Listing |
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