Márquez-Salinas Alejandro, Fermín-Martínez Carlos A, Antonio-Villa Neftalí Eduardo, Vargas-Vázquez Arsenio, Guerra Enrique C, Campos-Muñoz Alejandro, Zavala-Romero Lilian, Mehta Roopa, Bahena-López Jessica Paola, Ortiz-Brizuela Edgar, González-Lara María Fernanda, Roman-Montes Carla M, Martinez-Guerra Bernardo A, Ponce de Leon Alfredo, Sifuentes-Osornio José, Gutiérrez-Robledo Luis Miguel, Aguilar-Salinas Carlos A, Bello-Chavolla Omar Yaxmehen
Research Division, Instituto Nacional de Geriatría, Mexico City, Mexico.
MD/PhD (PECEM), Faculty of Medicine, Universidad Nacional Autónoma de México, Mexico City, Mexico.
J Gerontol A Biol Sci Med Sci. 2021 Jul 13;76(8):e117-e126. doi: 10.1093/gerona/glab078.
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.
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.
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.
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.
实足年龄（CA）是2019冠状病毒病（COVID-19）不良结局的一个预测指标；然而，仅实足年龄并不能反映个体对严重急性呼吸综合征冠状病毒2（SARS-CoV-2）感染的反应。在此，我们评估了衰老指标PhenoAge和PhenoAgeAccel对预测COVID-19不良结局的影响。此外，我们试图利用个体PhenoAge成分对严重SARS-CoV-2感染的适应性代谢和炎症反应进行建模。
在这项回顾性队列研究中，我们评估了墨西哥城一家COVID-19参考中心收治的病例。入院时的实验室检查值用于估算PhenoAge和PhenoAgeAccel。采用Cox比例风险模型来估算COVID-19致死率和不良结局（入住重症监护病房、插管或死亡）的风险。为了探索对SARS-CoV-2感染的适应性反应的可重复模式，我们使用PhenoAge成分进行k均值聚类。
我们纳入了1068名受试者，其中222人出现危重症，218人死亡。与实足年龄和血氧饱和度相比，PhenoAge是不良结局和致死率的更好预测指标，并且其预测能力在所有年龄组中均保持不变。与PhenoAgeAccel>0相关反应的患者比PhenoAgeAccel值较低的患者有更高的死亡和危重症风险（对数秩检验p<0.001）。通过无监督聚类，我们确定了对SARS-CoV-2感染的4种适应性反应：（i）与实足年龄相关的炎症衰老，（ii）与心脏代谢合并症相关的代谢功能障碍，（iii）不良血液学反应，以及（iv）与良好结局相关的反应。
与加速衰老指标相关的适应性反应与COVID-19不良结局相关，且具有独特且可区分的特征。与实足年龄相比，PhenoAge是不良结局的更好预测指标。
