Determination of adipose tissue beiging from passive electrical and optical properties

Introduction Adipose tissue plays a central role in mammalian energy homeostasis and metabolic regulation, and this study aims to characterize functionally distinct adipose depots and their dynamic adaptations. Methods Electrical Impedance Spectroscopy (EIS), Dielectric Relaxation Spectroscopy (DRS), and Near-Infrared Spectroscopy (NIRS) were used to analyze different types of adipose tissue, which were then classified using machine learning (ML), alongside extraction and validation of dielectric properties to estimate water content. Results The results show that while inguinal white adipose tissue (iWAT) is morphologically closer to peri-gonadal white adipose tissue (gWAT), iWAT and brown adipose tissue (BAT) share more similar electrical and optical properties; moreover, ML methods applied to DRS measurements achieved excellent classification accuracy ($>99%$), and dielectric analysis enabled reliable estimation of depot-specific water content. Discussion This approach provides a promising non-destructive method for studying adipose tissue adaptations and remodeling, including monitoring of beiging and the transdifferentiation of white adipocytes into highly metabolic beige adipocytes.