Sorelli Michele, Hutson T Noah, Iasemidis Leonidas, Bocchi Leonardo
European Laboratory for Non-Linear Spectroscopy, University of Florence, Florence, Italy.
Department of Physics and Astronomy, University of Florence, Florence, Italy.
Front Netw Physiol. 2022 Mar 8;2:840829. doi: 10.3389/fnetp.2022.840829. eCollection 2022.
In this study, we explored the possibility of developing non-invasive biomarkers for patients with type 1 diabetes (T1D) by quantifying the directional couplings between the cardiac, vascular, and respiratory systems, treating them as interconnected nodes in a network configuration. Towards this goal, we employed a linear directional connectivity measure, the directed transfer function (DTF), estimated by a linear multivariate autoregressive modelling of ECG, respiratory and skin perfusion signals, and a nonlinear method, the dynamical Bayesian inference (DBI) analysis of bivariate phase interactions. The physiological data were recorded concurrently for a relatively short time period (5 min) from 10 healthy control subjects and 10 T1D patients. We found that, in both control and T1D subjects, breathing had greater influence on the heart and perfusion with respect to the opposite coupling direction and that, by both employed methods of analysis, the causal influence of breathing on the heart was significantly decreased ( < 0.05) in T1D patients compared to the control group. These preliminary results, although obtained from a limited number of subjects, provide a strong indication for the usefulness of a network-based multi-modal analysis for the development of biomarkers of T1D-related complications from short-duration data, as well as their potential in the exploration of the pathophysiological mechanisms that underlie this devastating and very widespread disease.
在本研究中，我们将心脏、血管和呼吸系统视为网络结构中相互连接的节点，通过量化它们之间的定向耦合，探索为1型糖尿病（T1D）患者开发非侵入性生物标志物的可能性。为实现这一目标，我们采用了一种线性定向连接性测量方法，即直接传递函数（DTF），它通过对心电图、呼吸和皮肤灌注信号进行线性多元自回归建模来估计；还采用了一种非线性方法，即对双变量相位相互作用进行动态贝叶斯推理（DBI）分析。从10名健康对照受试者和10名T1D患者中，在相对较短的时间段（5分钟）内同时记录生理数据。我们发现，在对照受试者和T1D受试者中，相对于相反的耦合方向，呼吸对心脏和灌注的影响更大，并且通过两种分析方法均发现，与对照组相比，T1D患者中呼吸对心脏的因果影响显著降低（<0.05）。这些初步结果尽管来自有限数量的受试者，但为基于网络的多模态分析在从短时间数据开发T1D相关并发症生物标志物方面的有用性，以及它们在探索这种毁灭性且非常普遍的疾病背后的病理生理机制方面的潜力，提供了有力的指示。
