51. Identifying pre-disease signals before metabolic syndrome in mice by dynamical network biomarkers
- Author
-
Akiko Inujima, Keiichi Koizumi, Naotoshi Shibahara, Kazuyuki Tobe, Luonan Chen, Kazuyuki Aihara, Shigeru Saito, Makoto Kadowaki, Makito Oku, Yoshiko Igarashi, and Shusaku Hayashi
- Subjects
0301 basic medicine ,lcsh:Medicine ,Disease ,Bioinformatics ,Predictive markers ,Models, Biological ,Article ,Theory based ,03 medical and health sciences ,Behavioral traits ,Mice ,0302 clinical medicine ,Medicine ,Animals ,Humans ,lcsh:Science ,Dynamical network ,Metabolic Syndrome ,Multidisciplinary ,business.industry ,lcsh:R ,Computational Biology ,medicine.disease ,Phenotype ,030104 developmental biology ,Disease Progression ,Biomarker (medicine) ,lcsh:Q ,Neural Networks, Computer ,DNA microarray ,Metabolic syndrome ,Symptom Assessment ,business ,030217 neurology & neurosurgery ,Biomarkers - Abstract
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.
- Published
- 2018