Cardiometabolic Genetic Variation and COVID-19 Severity
Clinical experience suggested that patients with preexisting diabetes and concomitant cardiometabolic comorbidities are more likely to have poor clinical outcomes from COVID-19. This research examined diabetes-related complications in acute COVID-19 illness, including new and persistent hyperglycemia, diabetic ketoacidosis, and severe insulin resistance.
Aims & Outcomes
Aims
- Evaluate whether cardiometabolic conditions causally affect COVID-19 susceptibility and severity.
- Identify biomarkers of diabetes-related complications in acute COVID-19 illness.
Outcomes
- Higher BMI is a causal risk factor for both COVID-19 susceptibility and severity.
Background: Epidemiological studies report associations of diverse cardiometabolic conditions, including obesity, with COVID-19 illness, but causality had not been established. This study evaluated the associations of 17 cardiometabolic traits with COVID-19 susceptibility and severity using two-sample Mendelian randomization.
Methods and findings: Genetic variants associated with each exposure, including BMI, were selected from genome-wide association studies, and their effects on COVID-19 susceptibility and severity were estimated using summary statistics from the COVID-19 Host Genetics Initiative.
Conclusions: The study found genetic evidence supporting higher BMI as a causal risk factor for both COVID-19 susceptibility and severity.
Background: The high heterogeneity in COVID-19 symptoms and severity makes it challenging to identify high-risk patients early. Cardiometabolic protein biomarkers may provide predictive insight into which patients are most susceptible to severe illness.
Methods: In plasma from 343 patients hospitalized with COVID-19 during the first pandemic wave, 92 circulating protein biomarkers previously implicated in cardiometabolic disease were measured, and predictive models were built and tested against a separate group of 194 patients hospitalized later in the same surge.
Results: A set of seven protein biomarkers predicted admission to the ICU or death within 28 days; two (ADAMTS13 and VEGFD) were associated with lower risk, the rest with higher risk. These models outperformed models built from standard clinical data alone.
Conclusions: Proteomic profiling can inform the early clinical impression of a patient’s likelihood of developing severe COVID-19 outcomes.