Diabetes, Obesity, and Risk Prediction of Severe COVID-19
Ranganath Muniyappa, Kenneth J. Wilkins
Abstract
he global spread of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) has unleashed health and economic crises in both the developed and the developing world. Differences in demography, prevalence of comorbidities, health care capacity, and the efficacy of risk mitigation measures affect mortality and complications resulting from COVID-19. Severe COVID-19 disproportionately affects older individuals and patients with comorbidities that include diabetes, obesity, hypertension, cardiovascular disease, chronic kidney disease, and chronic lung disease (1, 2). In the United States, a significant portion of hospitalizations, intensive care unit admissions, and deaths occur in individuals older than age 70 years (1). The presence of comorbidities increases the risk of hospitalizations and deaths. The interplay between age and comorbidities in a given population determines the heterogeneity of risk for severe . Indeed, the age-specific prevalence of underlying conditions vary by country. Understanding the interaction between age, comorbidities, and health system capacity is essential for shielding and mitigation strategies (3). Similarly, population-specific models predicting the risk of developing severe COVID-19 that require hospital admission are urgently needed.