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

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.

Edge biomarkers for classification and prediction of phenotypes

“…Thus, how to incorporate dynamical information into biomarkers is a key future direction. DNB theory, which merges nonlinear dynamics and statistics besides exploring the network information, detects early-warning signals of critical transitions to the disease state [7]. Although DNB can in principle determine the critical state and the leading molecules prior to drastic transition from the observed data alone, eliminating the large amount of noise and developing an efficient algorithm for accurately detecting the DNB molecules is required in future.…”

“…This approach fully utilizes the second-order statistics, i.e., the expression deviations and inter-molecular correlations, to detect pre-disease rather than conventional disease states [7]. Here, the pre-disease state is defined as the normal state of an individual immediately before critical transition to the disease state, i.e., the limit of the normal state.…”

“…Here, the pre-disease state is defined as the normal state of an individual immediately before critical transition to the disease state, i.e., the limit of the normal state. Traditional node and edge biomarkers can distinguish between disease and normal states, but usually cannot diagnose the pre-disease state [7] because the molecular expressions of normal and early disease states are not significantly different. DNBs do distinguish between the pre-disease and normal states because they detect the early-warning signals of complex diseases regardless of sample size [7,8].…”

“…Traditional node and edge biomarkers can distinguish between disease and normal states, but usually cannot diagnose the pre-disease state [7] because the molecular expressions of normal and early disease states are not significantly different. DNBs do distinguish between the pre-disease and normal states because they detect the early-warning signals of complex diseases regardless of sample size [7,8]. Identification of DNB molecules (a group of molecules) is based on the following theory: as the system approaches the pre-disease state or the critical transition, (i) the expression of the DNB molecules dramatically deviates from that of the normal state; (ii) the expression correlation between any two DNB molecules increases; (iii) the expression correlation between any molecule in the DNB and any molecule in the non-DNB decreases.…”

“…Chen et al [7] developed a multi-sample-based approach for Class I dynamical network and edge biomarkers. This approach fully utilizes the second-order statistics, i.e., the expression deviations and inter-molecular correlations, to detect pre-disease rather than conventional disease states [7].…”