Preliminary modeling study on the identification of "pre-disease" state in traditional Chinese medicine based on the theory of critical slowing down
10.3969/j.issn.1006-2157.2024.03.004
- VernacularTitle:基于临界慢化理论的中医"未病"状态识别建模研究初探
- Author:
Shiyao WANG
1
;
Kangle SHI
;
Cong LEI
;
Fangyan YANG
;
Qinggang MENG
Author Information
1. 北京中医药大学 北京 100029
- Keywords:
pre-disease;
traditional Chinese medicine;
critical slowing down;
complex system;
model framework;
dynamic network biomarkers
- From:
Journal of Beijing University of Traditional Chinese Medicine
2024;47(3):312-319
- CountryChina
- Language:Chinese
-
Abstract:
The "pre-disease" theory of traditional Chinese medicine focuses on the dynamic and continuous evolution from health to disease, and emphasizes early identification and intervention in the complex and gradual process of evolution from health to disease. The "pre-disease" theory and complexity science share the same perspective on health and disease from the standpoint of features of the dynamic evolution and holism, i. e., life is considered as a complex system with ongoing dynamic changes, which exhibit the nonlinear features of " homeostasis-destabilization-phase transition-another homeostasis". In this paper, from the perspective of nonlinear dynamics in complexity science, we explain the scientific connotation of the evolution law of "pre-disease"-disease based on the theory of critical slowing down in traditional Chinese medicine. Based on the theory of critical slowing down and the dynamic network biomarker method generated by its development, combined with the macro signs of comprehensive analysis of data gained by four diagnostic method and the micro features including transcriptomics and the microbiomics, this paper proposes to integrate macro and micro multi-hierarchy information to construct a "pre-disease" critical slowing down identification model with the characteristics of traditional Chinese medicine diagnosis and treatment, which provides a new perspective and method for the early warning of complex diseases.
