Baseline glycemia exhibits non-random, history-dependent variation across repeated meals

TL;DR

This study reveals that pre-meal glucose baselines in individuals are not fixed but change systematically with repeated meals, influenced by prior responses, indicating history-dependent glycemic regulation beyond random fluctuations.

Contribution

The paper demonstrates that glycemic baselines evolve over repeated meals and are influenced by previous postprandial responses, challenging the notion of a stable glucose set point.

Findings

Baseline glucose levels change systematically across repeated meals.

Displacements in baseline levels exceed short-term fluctuations.

Baseline shifts are positively related to previous post-meal responses.

Abstract

Glycemic regulation is often described as maintaining glucose levels near a stable baseline. However, continuous glucose monitoring after meals displays intra-individual variability even under controlled conditions, suggesting intrinsic system dynamics beyond sensor noise, measurement error or short-term variability around a fixed set point. Therefore, we estimated pre-meal glucose baselines, tracking their changes across repeated identical meal challenges within individuals. The baseline was defined as the median glucose level in a pre-meal window, while successive displacements were computed between consecutive repetitions. Using a publicly available dataset of normoglycemic subjects, we observed systematic changes in baseline levels across repeated exposures. These displacements exceeded short-term fluctuations within the same pre-meal interval and were robust to alternative baseline definitions. Moreover, the magnitude of each baseline shifted is positively related to the size of the preceding postprandial response. This association persisted under permutation testing, indicating that it cannot be explained by random temporal ordering. Overall, these findings suggest that glycemic dynamics cannot be fully described as independent fluctuations around a fixed baseline. Instead, baseline levels evolve across repeated perturbations through history-dependent adjustments, such that each perturbation influences subsequent system states. Potential applications include refined interpretation of continuous glucose monitoring data and development of models that incorporate temporal dependence in glucose dynamics.