Dynamical network biomarker analysis using multi-organ RNA sequencing in metabolic syndrome model mice

Dynamical network biomarker (DNB) theory has emerged as a powerful framework for detecting early warning signals of pre-disease states. Based on our previous work demonstrating its utility in adipose tissue of metabolic syndrome model mice, we conducted a comprehensive DNB analysis using RNA sequencing data across 13 organs and 14 to 16 time points in high-fat diet-fed mice. Our findings revealed organ-specific variation in the timing of early warning signals, suggesting heterogeneous inter-organ dynamics during the pre-disease state of metabolic syndrome. These results highlight the potential of DNB theory for elucidating systemic early-stage pathophysiology in complex metabolic disorders. Male C57BL/6J mice were fed high-fat diet (HFD) from eight weeks of age. From zero days (HFD onset) to 12 weeks, RNA-seq was conducted for 13 organs: epididymal white adipose tissue (eWAT), inguinal white adipose tissue (iWAT), interscapular brown adipose tissue (BAT), pancreas, liver, small intestine, soleus muscle, gastrocnemius muscle, hypothalamus, pituitary gland, neocortex, hippocampus, and olfactory bulb. At 16 and 20 weeks, RNA-seq was conducted only for eWAT. Six mice were measured per time point.