Abstract Background Traditional medical paradigms view disease progression as gradual processes, yet mounting evidence suggests that health-to-disease transitions may exhibit critical transition phenomena characterized by sudden shifts between stable states. Understanding the dynamical signatures preceding these transitions could revolutionize early disease detection and intervention strategies. Methods We conducted a comprehensive dynamical systems analysis of longitudinal clinical data from 360 patients across three distinct disease phenotypes: thyroid nodules, abdominal aortic aneurysms, and brain gliomas, spanning 120 months of follow-up. We computed multiple early warning indicators including variance, autocorrelation, recovery time, state flickering, and critical slowing down. Phase space reconstruction was performed using time-delay embedding to characterize system dynamics through Lyapunov exponents, correlation dimension, and phase space entropy. Dynamic network analysis captured time-varying correlations among physiological indicators. Machine learning models were developed using random forest algorithms to predict health state transitions. Results Our analysis identified 5,385 critical transition points across all patients, averaging 44.88 transitions per individual. Variance (importance score: 0.2263) and autocorrelation (0.1405) emerged as the most predictive early warning indicators. Lyapunov exponent analysis revealed chaotic dynamics in 97.2% of cases (mean: 0.6298), indicating inherent system instability near transition points. Dynamic network analysis demonstrated non-linear evolution patterns with network density fluctuating between 0.2000-1.0000 (mean: 0.8018) during disease progression. The integrated prediction model achieved 77.41% accuracy, with 83% precision for healthy state identification. Critical transition detection provided early warning signals 9.3 ± 8.6 time points before actual health state changes. Conclusions Critical transition theory effectively identifies early signatures of health-disease state transitions, with variance increase and autocorrelation enhancement serving as the most sensitive indicators. This approach provides a theoretical foundation for developing personalized disease risk assessment and early intervention strategies, potentially transforming precision medicine approaches to disease prevention and management.
Ma et al. (Wed,) studied this question.