Coupled Physiological Sensor Models for Continuous Glucose Monitoring in Diabetes

Abstract

Continuous glucose monitoring (CGM) has transformed diabetes care, yet its accuracy remains constrained by the coupled physics of glucose transport in subcutaneous tissue and enzyme-based sensor transduction. Prevailing approaches represent physiology as a fixed blood–interstitial lag, the sensor as a static or first-order calibration block, and filtering as an independent signal-processing layer. Because delay and drift arise from a shared tissue state, including perfusion, inflammation, and fibrotic encapsulation, such decoupled architectures cannot uniquely separate their contributions. This review presents a systematic and critical synthesis of fully coupled physiological–sensor models for CGM. We first examine the anatomy and transport physiology of the vascular–interstitial–sensor interface and quantify the resulting effective lags, which range from seconds in healthy tissue to more than 20 min in the presence of fibrotic encapsulation. We then formulate interstitial transport as a diffusion–reaction partial differential equation (PDE) domain and sensor electrochemistry as an ordinary differential equation (ODE) domain, and unify them in a canonical PDE–ODE framework in which the sensor surface acts as a dynamic interface, supported by IMEX and operator-splitting solvers together with sensitivity and identifiability analysis. Calibration and state estimation are addressed through dual estimation, Kalman-family, Bayesian, particle, and adaptive filters, evaluated using MARD, error grids, and time-in-range. A stepwise roadmap from data preprocessing to hierarchical validation (in silico, in vitro, animal, and human) is provided, together with a comparative benchmark that decomposes error into physiological, sensor, and filtering components. Finally, we discuss digital twins, physics–machine learning hybrids, model-order reduction for embedded devices, and open challenges in identifiability, fibrosis, and long-term drift. Explicit coupling offers an interpretable, physically consistent foundation for next-generation CGM design, calibration, and clinical translation.

Keywords: Continuous glucose monitoring; Coupled physiological–sensor model; PDE–ODE framework; Interstitial glucose transport; Electrochemical biosensor; Sensor drift and lag; Kalman filtering and state estimation; Digital twin