Quantification and clustering of immune states in hepatitis B Cirrhosis

Wei Hou1, Tengxiao Liang2, Fangliang Xing3

  • 1First Department of The Liver Disease Center, Beijing YouAn Hospital, Capital Medical University, Beijing, China.

Immunobiology
|June 29, 2025
PubMed

Insights

Hepatitis B Cirrhosis alters lymphocyte balance, showing an inverse T/NK to B cell relationship. A mathematical model and distinct immune states aid disease staging and personalized treatment.

Area of Science:

  • Immunology
  • Virology
  • Mathematical Modeling

Background:

  • Hepatitis B Cirrhosis is a severe outcome of chronic Hepatitis B infection.
  • Understanding lymphocyte population dynamics is crucial for managing this condition.

Purpose of the Study:

  • To quantify and visualize the relationships among T cells, NK cells, and B cells in Hepatitis B Cirrhosis.
  • To develop a mathematical model for assessing immune status and optimizing treatment.

Main Methods:

  • Peripheral blood samples from 500 Hepatitis B Cirrhosis patients and 500 healthy controls.
  • Sort visualization analysis and 3D numerical fitting to model lymphocyte subset relationships.
  • Self-Organizing Feature Maps (SOFM) for unsupervised clustering of immune states.

Main Results:

  • An inverse relationship was observed: T+NK cell levels decreased as B cell levels increased.
  • SOFM identified three distinct immune state clusters.
  • A mathematical model (T% = -0.9879*B% - 1.041*NK% + 97.66) showed significant differences between patients and controls.
  • Immune states exhibited a cyclical pattern across Child-Pugh grades, correlating with disease progression.

Conclusions:

  • A quantitative model and visual representation of lymphocyte dynamics in Hepatitis B Cirrhosis were established.
  • Distinct immune states linked to disease progression aid in assessing immunological condition.
  • Integrating immunological and clinical data enables precise disease staging and personalized medicine.
Abstract

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