Kočar Eva, Katz Sonja, Pušnik Žiga, Bogovič Petra, Turel Gabriele, Skubic Cene, Režen Tadeja, Strle Franc, Martins Dos Santos Vitor A P, Mraz Miha, Moškon Miha, Rozman Damjana
Centre for Functional Genomics and Bio-Chips, Institute of Biochemistry and Molecular Genetics, Faculty of Medicine, University of Ljubljana, Zaloška cesta 4, SI-1000 Ljubljana, Slovenia.
LifeGlimmer GmbH, Markelstraße 38, 12163 Berlin, Germany.
iScience. 2023 Aug 31;26(10):107799. doi: 10.1016/j.isci.2023.107799. eCollection 2023 Oct 20.
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.
随着新冠病毒成为地方性流行病毒，持续需要找到能够表征该疾病并有助于患者分层的生物标志物。我们通过比较卢布尔雅那大学医学中心收治的164例患者（入院、疾病恶化、出院）住院期间胆固醇生物合成的10种中间产物，研究了新冠病毒与胆固醇生物合成之间的关系。新冠患者中，酵母甾醇、24-脱氢羊毛甾醇、链甾醇和酵母甾烯醇的浓度发生了显著变化。我们进一步基于临床参数及其与部分甾醇的组合，开发了一种疾病严重程度预测模型。我们应用8个临床参数的机器学习模型以极高的准确率预测了疾病严重程度（AUC = 0.96），相较于当前临床风险评分有显著改善。纳入甾醇后，模型性能仍优于COVID-GRAM。这是第一项研究新冠病毒感染期间胆固醇生物合成的研究，表明部分与胆固醇相关的甾醇与新冠病毒感染的严重程度相关。
