Nonlinear mixed-effects modeling of gastrointestinal glucose absorption for Bayesian digital twin identification

Digital twin (DT) methodologies for type 1 diabetes require personalization of physiological models using continuous glucose monitoring and exogenous insulin data. The imbalance between data information content and model complexity often necessitates Bayesian estimation for model identification, requiring informative a priori parameter distributions that are difficult to obtain from the literature, especially for parameters describing gastrointestinal glucose absorption. This study aims to provide such priors by modeling inter-subject variability of meal glucose rate of appearance (Ra).

Four glucose absorption models used in published DT were formulated within a nonlinear mixed-effects (NLME) framework. Models were identified using Ra derived from gold-standard tracer data collected in 43 adults undergoing either a mixed meal (MTT) or an oral glucose tolerance test (OGTT). A stepwise, parsimony guided, model-building strategy was adopted to define the random-effects structure and incorporate covariate effects. Parameter estimation was performed using likelihood-based methods. Model adequacy was assessed through parameter precision and visual predictive checks.

All models achieved adequate fits of Ra with acceptable parameter precision. Test type and body mass index were the covariates explaining most of the inter-subject variability. While all models well reproduced mixed-meal Ra, differences were observed in their representations of OGTT responses. For each model, fixed effects, random-effects covariance matrices, and covariate relationships were estimated, enabling the construction of ready-to-use prior parameter distributions for Bayesian DT implementations.

The proposed NLME-based framework provides physiologically grounded prior distributions that will support researchers in designing robust and physiologically consistent Bayesian DT algorithms.