Detecting early-warning signals for sudden deterioration of complex diseases by dynamical network biomarkers

TLDR

Complex disease progression can involve abrupt deteriorations that trigger critical transitions at tipping points. The authors develop a model‑free method to detect early‑warning signals of such critical transitions using only a small number of samples. They theoretically derive an index based on a dynamical network biomarker and apply it to microarray data from three diseases, validating its relevance with experimental data and functional analysis. The method can predict sudden transitions from small samples when many measurements per sample are available, and its relevance was confirmed by experimental data and functional analysis.

Abstract

Considerable evidence suggests that during the progression of complex diseases, the deteriorations are not necessarily smooth but are abrupt, and may cause a critical transition from one state to another at a tipping point. Here, we develop a model-free method to detect early-warning signals of such critical transitions, even with only a small number of samples. Specifically, we theoretically derive an index based on a dynamical network biomarker (DNB) that serves as a general early-warning signal indicating an imminent bifurcation or sudden deterioration before the critical transition occurs. Based on theoretical analyses, we show that predicting a sudden transition from small samples is achievable provided that there are a large number of measurements for each sample, e.g., high-throughput data. We employ microarray data of three diseases to demonstrate the effectiveness of our method. The relevance of DNBs with the diseases was also validated by related experimental data and functional analysis.