Quantification and Clustering of Immune States in Lymphocyte Populations of Hepatitis B Cirrhosis

Abstract Background: In Hepatitis B Cirrhosis, a severe progression of chronic Hepatitis B infection, understanding the interplay among lymphocyte populations like T cells, NK cells, and B cells is crucial. This insight aids in assessing immune status, diagnosing the condition, and optimizing treatment strategies, offering significant potential benefits. Methods: Peripheral blood samples were collected from 200 patients diagnosed with Hepatitis B Cirrhosis. Correlation analysis and three-dimensional numerical fitting were performed to establish a mathematical model describing the relationships among lymphocyte subsets. Self-Organizing Feature Maps (SOFM) were employed for unsupervised clustering to identify distinct immune states. Results: Analysis revealed that the percentages of T cells, NK cells, and B cells are constrained within a three-dimensional plane, as described by the equation: T percentage = -0.9879 ×B percentage - 1.041 ×NK percentage + 97.66, yielding a prediction error (RMSE) of 2.5%. Additionally, another equation was identified: Lymphocytes = 1.2 × T cells + 1.072 × B cells + 18.59, with an RMSE of 3.7%. SOFM clustering further identified four distinct immune states associated with the progression of the disease. Conclusions: This study provides a quantitative mathematical model describing the states of lymphocyte populations in Hepatitis B Cirrhosis. The identification of distinct immune states associated with disease progression facilitate the assessment of immunological condition and the optimization of treatment strategies.