Adaptive metabolic and inflammatory responses identified using accelerated aging metrics are linked to adverse outcomes in severe SARS-CoV-2 infection

  1. Alejandro Márquez-Salinas
  2. Carlos A. FermĂ­n-MartĂ­nez
  3. NeftalĂ­ Eduardo Antonio-Villa
  4. Arsenio Vargas-Vázquez
  5. Enrique C. Guerra
  6. Alejandro Campos-Muñoz
  7. Lilian Zavala-Romero
  8. Roopa Mehta
  9. Jessica Paola Bahena-LĂłpez
  10. Edgar Ortiz-Brizuela
  11. María Fernanda González-Lara
  12. Carla M. Roman-Montes
  13. Bernardo A. Martinez-Guerra
  14. Alfredo Ponce de Leon
  15. José Sifuentes-Osornio
  16. Luis Miguel Gutiérrez-Robledo
  17. Carlos A. Aguilar-Salinas
  18. Omar Yaxmehen Bello-Chavolla
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Abstract

INTRODUCTION

Chronological age (CA) is a predictor of adverse COVID-19 outcomes; however, CA alone does not capture individual responses to 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.

METHODS

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 (ICU admission, intubation, or death). To explore reproducible patterns which model adaptive responses to SARS-CoV-2 infection, we used k-means clustering using PhenoAge/PhenoAccelAge components.

RESULTS

We included 1068 subjects of whom 401 presented critical illness and 204 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<0.001). Using unsupervised clustering we identified four adaptive responses to SARS-CoV-2 infection: 1) Inflammaging associated with CA, 2) metabolic dysfunction associated with cardio-metabolic comorbidities, 3) unfavorable hematological response, and 4) 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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