COVID-19 and cholesterol biosynthesis : towards innovative decision support systems

Kočar, Eva (author), Pušnik, Žiga (author), Skubic, Cene (author), Režen, Tadeja (author), Mraz, Miha (author), Moškon, Miha (author), Rozman, Damjana (author)

With COVID-19 becoming endemic, there is a continuing need to find biomarkers characterizing the disease and aiding in patient stratification. We studied the relation between COVID-19 and cholesterol biosynthesis by comparing 10 intermediates of cholesterol biosynthesis during the hospitalization of 164 patients (admission, disease deterioration, discharge) admitted to the University Medical Center of Ljubljana. The concentrations of zymosterol, 24-dehydrolathosterol, desmosterol, and zymostenol were significantly altered in COVID-19 patients. We further developed a predictive model for disease severity based on clinical parameters alone and their combination with a subset of sterols. Our machine learning models applying 8 clinical parameters predicted disease severity with excellent accuracy (AUC = 0.96), showing substantial improvement over current clinical risk scores. After including sterols, model performance remained better than COVID-GRAM. 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.

• Language: English
• Keywords: assessment in health technology, lipid, bioinformatics
• Work type: Article
• Typology: 1.01 - Original Scientific Article
• Organization: MF - Faculty of Medicine; FRI - Faculty of Computer and Information Science
• Publication status: Published
• Publication version: Version of Record
• Year: 2023
• Number of pages: 17 str.
• Numbering: Vol. 26, iss. 10, art. 107799
• PID: 20.500.12556/RUL-165284
• UDC: 61:60:578
• ISSN on article: 2589-0042
• DOI: 10.1016/j.isci.2023.107799
• COBISS.SI-ID: 165467907
• Publication date in RUL: 29.11.2024

Record is a part of a journal

• Title: iScience
• Publisher: Elsevier
• ISSN: 2589-0042
• COBISS.SI-ID: 24098568

Licences

• License: CC BY-NC-ND 4.0, Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International
• Link: http://creativecommons.org/licenses/by-nc-nd/4.0/
• Description: The most restrictive Creative Commons license. This only allows people to download and share the work for no commercial gain and for no other purposes.

Secondary language

• Language: Slovenian
• Keywords: COVID-19, holesterol, biosinteza, steroli, lipidi, bioinformatika, ocenjevanje v zdravstveni tehnologiji