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 Fermín-Martínez
  3. Neftalí 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-López
  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

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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  1. Version published to 10.1093/gerona/glab078
  2. Version published to 10.1101/2020.11.03.20225375v2 on medRxiv
  3. ScreenIT

    SciScore for 10.1101/2020.11.03.20225375: (What is this?)

    Please note, not all rigor criteria are appropriate for all manuscripts.

    Table 1: Rigor

    Institutional Review Board StatementIRB: All proceedings were approved by the INCMNSZ Research and Ethics Committee, written informed consent was waived due to the retrospective nature of the study.
    Consent: All proceedings were approved by the INCMNSZ Research and Ethics Committee, written informed consent was waived due to the retrospective nature of the study.
    Randomizationnot detected.
    Blindingnot detected.
    Power Analysisnot detected.
    Sex as a biological variablenot detected.

    Table 2: Resources

    No key resources detected.

    Results from OddPub: Thank you for sharing your code.

    Results from LimitationRecognizer: We detected the following sentences addressing limitations in the study:
    Our study has certain limitations, such as the inclusion of a non-representative population composed only of hospitalized patients with severe COVID-19; moreover, we were not able to study the effects of longitudinal PhenoAge trajectories on clinical course due to the lack of repeated measurements over time. Due to the fact that PhenoAge and PhenoAgeAccel were estimated at admission, it remains unclear the role that PhenoAge values prior to the disease and its longitudinal changes may have on reduced physiological reserve, diminished intrinsic capacity or frailty in the setting of COVID-19 [31–33]. Lastly, we only used PhenoAge and PhenoAgeAccel to estimate adaptive responses potentially linked to aging; however, it is well known that different biological age estimations may illustrate distinct points of view of the aging process [34]. Prospective studies assessing aging measures before, during and after the infection are necessary to further elucidate the impact of premature aging on the clinical course of COVID-19 patients; additionally, other parameters should be taken into account, such as imaging features, immunophenotyping and histopathological findings. Finally, to examine whether the identified adaptive responses to SARS-CoV-2 infection have distinguishable pathophysiological differences, in-depth phenotyping studies are still required. In conclusion, we propose that PhenoAge and PhenoAgeAccel may be better predictors for adverse COVID-19 outcomes and lethality compar...

    Results from TrialIdentifier: No clinical trial numbers were referenced.

    Results from Barzooka: We did not find any issues relating to the usage of bar graphs.

    Results from JetFighter: We did not find any issues relating to colormaps.

    Results from rtransparent:
    • Thank you for including a conflict of interest statement. Authors are encouraged to include this statement when submitting to a journal.
    • Thank you for including a funding statement. Authors are encouraged to include this statement when submitting to a journal.
    • No protocol registration statement was detected.

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  4. Version published to 10.1101/2020.11.03.20225375v1 on medRxiv