Young researcher Eva Kočar and prof. dr. Damjana Rozman of the Centre for Functional Genomics and Biochips, Institute of Biochemistry and Molecular Genetics (UL MF) have published an interdisciplinary research article entitled "COVID-19 and cholesterol biosynthesis: Towards innovative decision support systems" in the journal iScience in collaboration with researchers from the Faculty of Computer and Information Science (UL), researchers from Wageningen University (the Netherlands), and colleagues from the Clinic for Infectious Diseases and Fever Conditions (UKC LJ).
With COVID-19 becoming endemic, there is a continuing need to find biomarkers characterizing the disease and aiding in patient stratification. By combining clinical and biochemical approaches with machine learning methods, researchers have demonstrated the tremendous potential of an interdisciplinary approach in accurately stratifying patients with COVID-19 and the contribution of innovative tools toward improving clinical strategy and standards.
This is the first study to examine cholesterol biosynthesis during COVID-19 and shows that a subset of cholesterol-related sterols is associated with the severity of COVID-19. The research followed 164 patients hospitalized at the Clinic for Infectious Diseases and Fever Conditions (UKC LJ) due to a severe course of COVID-19. The results show that intracellular cholesterol synthesis is more altered in patients with a severe form of COVID-19 than in patients with a mild disease. They also developed predictive models using machine learning algorithms, investigated the predictive power of clinical parameters and sterol intermediates (indicators of cholesterol synthesis), and developed a predictive model with a high confidence level (AUC = 0.96) that outperforms currently established risk tools.
Kočar E., Katz S., Pušnik Ž., Bogovič P., Turel G., Skubic C., Režen T., Strle F., Martins dos Santos V., Mraz M., Moškon M., Rozman D. (2023). COVID-19 and cholesterol biosynthesis: Towards innovative decision support systems. iScience 26. DOI