Integrating Age, Obesity, Inflammation, Metabolism, and Tissue Injury to Understand Postacute Sequelae of SARS-CoV-2 Infection

Introduction: The etiology of sequelae in adult and pediatric survivors of SARS-CoV-2 infection are different but cluster with obesity, suggesting that age and obesity may make significant and divergent contributions to the development of post-acute sequelae of SARS-CoV-2 infection (PASC). We hypothesize that overlapping but distinct inflammatory and metabolic profiles during acute SARS-CoV-2 infection in adults and children: (1) results in differential tissue injury, (2) is exacerbated by obesity, and (3) can be exploited to predict the development of PASC.

Objective: Our goal is to produce a clinically predictive model to forecast PASC.

Methods: We will use electronic medical records and RECOVER databases to clinically phenotype participants in adult and pediatric cohorts. To create cohort studies that reflect a broad spectrum of disease severity in both children and adults, each cohort (adult subjects >18 years old and pediatric subjects <18 years old) will be divided into 5 distinct groups with 4 groups (uninfected control subjects, subjects with other viral infections, acutely infected with SARS-CoV-2 (non-PASC), and subjects who have recovered from SARS-CoV-2 infection (non-PASC)) that overlap between the pediatric and adult cohorts. The non-overlapping fifth group represents the spectrum of PASC, with multi-inflammatory syndrome in children (MIS-C) being specific to pediatric subjects. Each group will be divided equally into lean and obese clades to assess if obesity changes the molecular response to SARS-CoV-2 infection and worsens outcomes within groups. We will interrogate sera samples from pediatric and adult clinical cohorts to generate multiomics datasets that will be integrated to identify biomarkers and tissue injury patterns that can be correlated to patient phenotypes. We have assembled a team of technical experts to analyze proteomic, metabolomic, cell free DNA profiling, and clinical datasets.

Results: Pending.

Conclusion/Discussion: This model can be used to design medical interventions that identify, prevent, or restore disease-induced tissue injury to protect the health of the millions of people who have and will survive SARS-CoV-2 infection. It will serve as an adaptable modeling approach to assess the molecular underpinnings of disease in response to future viral infections that challenge human health.

Key Topics:

• Biomarker, in-depth phenotyping assays and in vitro studies using tissue and other biospecimens
• Long COVID and other chronic conditions
• Long COVID in special populations