Insulin Resistance and Metabolic Syndrome as Risk Factors for Hospitalization in Patients with COVID-19: Pilot Study on the Use of Machine Learning.

Aim: Conditions linked to metabolic syndrome, such as obesity, hypertension, insulin resistance, and dyslipidemia, are common in patients with severe coronavirus disease 2019 (COVID-19). These conditions can act synergistically to contribute to negative outcomes. We describe and analyze the relationship between metabolic syndrome and COVID-19 severity in terms of risk of hospitalization. Methods: We designed a retrospective, cross-sectional study, including patients with confirmed COVID-19 diagnosis. Clinical and laboratory parameters regarding metabolic syndrome were collected. The Homeostatic Model Assessment of Insulin Resistance (HOMA-IR) was used to assess insulin resistance. The outcome was needed for hospitalization. Logistic regression was used to calculate odds ratios, and to determine the association between variables and risk of hospitalization. Advanced approaches using machine learning were also used to identify and interpret the effects of predictors on the proposed outcome. Results: We included 2716 COVID-19 patients with a mean age of 61.8 years. Of these, 48.9% were women, 28.9% had diabetes, and 50.6% were diagnosed with metabolic syndrome. Overall, 212 patients required hospitalization. Patients with metabolic syndrome had a 58% greater chance of hospitalization if they were men, 32% if they had metabolic syndrome, and 23% if they were obese. Machine learning methods identified body mass index, metabolic syndrome, systolic blood pressure, and HOMA-IR as the most relevant features for our predictive model. Conclusion: Metabolic syndrome and its related biomarkers increase the odds for a severe clinical course of COVID-19 and the need for hospitalization. Machine learning methods can aid understanding of the effects of single features when assessing risks for a given outcome.