Identifying pre-disease signals before metabolic syndrome in mice by dynamical network biomarkers

The establishment of new therapeutic strategies for metabolic syndrome is urgently needed because metabolic syndrome, which is characterized by several disorders, such as hypertension, increases the risk of lifestyle-related diseases. One approach is to focus on the pre-disease state, a state with high susceptibility before the disease onset, which is considered as the best period for preventive treatment. In order to detect the pre-disease state, we recently proposed mathematical theory called the dynamical network biomarker (DNB) theory based on the critical transition paradigm. Here, we investigated time-course gene expression profiles of a mouse model of metabolic syndrome using 64 whole-genome microarrays based on the DNB theory, and showed the detection of a pre-disease state before metabolic syndrome defined by characteristic behavior of 147 DNB genes. The results of our study demonstrating the existence of a notable pre-disease state before metabolic syndrome may help to design novel and effective therapeutic strategies for preventing metabolic syndrome, enabling just-in-time preemptive interventions. Gene expression in adipose tissues of three groups of mice---TSOD mice (metabolic syndrome model, untreated), TSNO mice (control), and BTS-treated TSOD mice (metabolic syndrome model with treatment)---were measured at 3, 4, 5, 6, and 7 weeks of age. 3 to 5 biological replicates were included.