Prediction of features of the course of chronic hepatitis C using Bayesian networks

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Abstract

Materials and methods. 253 patients with chronic hepatitis C (CHC) and liver cirrhosis were included in the study. Assessment of gene polymorphisms of genes involved in inflammatory reactions and antiviral immunity (IL-1β-511C/T, IL-10 -1082G/A, IL28B C/T, IL28B T/G, TNF-α -238G/A, TGF-β -915G/C, IL-6 -174G/C), activators of local hepatic fibrosis (AGT G-6A, AGT 235 M/T, ATR1 1166 A/C), hemochro-matosis (HFE C282Y, HFE H63D), platelet receptors (ITGA2 807 C/T, ITGB3 1565 T/C), coagulation proteins and endothelial dysfunction (FII 20210 G/A, FV 1691G/A, FVII 10976 G/A, FXIII 103 G/T, eNOS 894 G/T, CYBA 242 C/T, FBG -455 G/A, PAI-675 5G/4G, MTHFR 677 C/T) was carried. Using Bayesian networks we studied the predictor value of clinical and laboratory factors for the following conditions - end points (EP): development of cirrhosis (EP1), fibrosis rate (EP2), presence of portal hypertension (EP3) and cryoglobulins (EP4). Results and discussion. In addition to traditional factors we have shown the contribution of the following mutations. Predicting EP1- liver cirrhosis - HFE H63D, C282Y, CYBA 242 C/T, AGT G-6G, ITGB31565 T/C gene mutations were significant. We also found a link between the rate of progression of liver fibrosis and gene polymorphisms of AGT G-6G, AGT M235T, FV 1691G/A, ITGB31565 T/C. Among the genetic factors associated with portal hypertension there are gene polymorphisms of PAI-I-675 5G/4G, FII 20210 G/A, CYBA 242 C/T, HFE H63D and Il-6 174GC. Cryoglobulins and cryoglobuliemic vasculitis (EP4) are associated with gene mutations MTHFR C677T, ATR A1166C and HFE H63D. Conclusion. The results obtained allow to detect the major pathophysiological and genetic factors which determine the status of the patient and the outcome of the disease, to clarify their contribution, and to reveal the significance of point mutations of genes that control the main routes of HCV course and progression.

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Samokhodskaya, L. M., Starostina, E. E., Sulimov, A. V., Krasnova, N., Rosina, T. P., Avdeev, V. G., … Sadovnichii, V. A. (2019). Prediction of features of the course of chronic hepatitis C using Bayesian networks. Terapevticheskii Arkhiv, 91(2), 32–39. https://doi.org/10.26442/00403660.2019.02.000076

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