Utilizing entropy to systematically quantify the resting-condition baroreflex regulation function

TL;DR

This paper introduces PhysioEnt, an entropy-based index to systematically quantify the baroreflex regulation function (BRF), revealing new insights into how aging and obesity differently affect BRF and supporting personalized medicine.

Contribution

It proposes a novel entropy-based index, PhysioEnt, to comprehensively quantify the BRF and its relationship with physiological indexes, advancing beyond traditional measures.

Findings

Aging and obesity influence BRF through different mechanisms.

Men and older individuals' BRF depend more on inter-organ physiological interactions.

PhysioEnt can support individualized medical assessments.

Abstract

Baroreflex is critical to maintain the blood pressure homeostasis, and the quantification of the baroreflex regulation function (BRF) can provide guidance for disease diagnosis, treatment and healthcare. Current quantification of the BRF such as baroreflex sensitivity cannot represent the BRF systematically. From the perspective of complex systems, we regard that the BRF is the emergence result of the diverse states and interactions in the physiological mechanisms. Therefore, the three-layer emergence is constructed in this work, which is from the physiological mechanisms to the physiological indexes and then to the BRF. On this basis, since the entropy in statistical physics macroscopically measures the diversity of the system's states, a new index called the PhysioEnt is proposed to represent the BRF and quantify the physical relationships between the BRF and four physiological indexes, baroreflex sensitivity, heart rate, heart rate variability, and systolic blood pressure. Based on the proposed method, some new findings with medical significance are obtained, including the mechanisms that aging and obesity affect the resting-condition BRF are different, and the resting-condition BRFs of men and older people depend more on the physiological processes among organs/tissues. Based on the measurable indexes, the proposed method would support the individualized medicine prospectively.