Network Physiology: From Neural Plasticity to Organ Network Interactions

Metabolic homeostasis emerges from the interplay between several feedback systems that regulate the physiological variables related to energy expenditure and energy availability, maintaining them within a certain range. Although it is well known how each individual physiological system functions, there is little research focused on how the integration and adjustment of multiple systems results in the generation of metabolic health. The aim here was to generate an integrative model of metabolism, seen as a physiological network, and study how it changes across the human lifespan. We used data from a transverse, community-based study of an ethnically and educationally diverse sample of 2572 adults. Each participant answered an extensive questionnaire and underwent anthropometric measurements (height, weight, and waist), fasting blood tests (glucose, HbA1c, basal insulin, cholesterol HDL, LDL, triglycerides, uric acid, urea, and creatinine), along with vital signs (axillar temperature, systolic, and diastolic blood pressure). The sample was divided into 6 groups of increasing age, beginning with less than 25 years and increasing by decades up to more than 65 years. In order to model metabolic homeostasis as a network, we used these 15 physiological variables as nodes and modeled the links between them, either as a continuous association of those variables, or as a dichotomic association of their corresponding pathological states. Weight and overweight emerged as the most influential nodes in both types of networks, while high betweenness parameters, such as triglycerides, uric acid and insulin, were shown to act as gatekeepers between the affected physiological systems. As age increases, the loss of metabolic homeostasis is revealed by changes in the network's topology that reflect changes in the system−wide interactions that, in turn, expose underlying health stages. Hence, specific structural properties of the network, such as weighted transitivity, i.e., the density of triangles in the network, can provide topological indicators of health that assess the whole state of the system. Overall, our findings show the importance of visualizing health as a network of organs and/or systems, and highlight the importance of triglycerides, insulin, uric acid and glucose as key biomarkers in the prevention of the development of metabolic disorders.

show abstract

“…Networks that consider links associated with correlations in time have also been considered inIvanov et al (2017) andLin et al (2020).…”

mentioning

confidence: 99%

Metabolic Physiological Networks: The Impact of Age

et al. 2020

View full text Add to dashboard Cite

show abstract

“…The difference in complexity of the system control mechanisms demonstrated that the human body works as a complex system (as hypothesized by the Central Integrative Regulator model [2] and the Network Physiology models [20]- [22]). This complexity may be due to the complex interaction of the biological processes that occurred between the central systems and the peripheral systems.…”

Section: Discussionmentioning

confidence: 99%

“…The emerging field network physiology aims to address these fundamental questions [9], [11], [20], [20]- [24]. Recent developments in this field [9], [20]- [22] address similar questions of how integrated function and transition across states emerge from the network of physiological interactions [21]. The focus of the field is to get insights of how homeostasis and its counterpart of homeostenosis emerge as different physiological states from the interaction between many different physiological variables, with an objective to improve diagnosis, prognosis and preventive medicine, and to better understand physiological function [8], [9], [11], [20], [20]- [24].…”

Section: Introductionmentioning

confidence: 99%

Investigation of Complexity and Regulatory Role of Physiological Activities During a Pacing Exercise

et al. 2019

View full text Add to dashboard Cite

Existing physiological control fatigue models propose that there may be a regulator in the central nervous system which modulates our daily physical activity. Within limits, this regulator ensures physical activity is completed without physiological system failure through interactive communications between the peripheral systems and the central systems. The ability of the central nervous system to regulate exercise is vital to optimise sport performance when severe intensity exercise might be required for prolonged or frequent periods. Based on mathematical models, this investigation explores the complex relationship between some of the mechanisms controlling physical activity and behaviour. In order to analyse the system control mechanisms, heart rate, volume of oxygen consumption and power output were measured for a well-trained male cyclist. Using power spectrum analysis, fractal analysis and continuous wavelet transforms, we show that the system control mechanisms regulating physiological systems, have distinct complexity. Moreover, the potential central controller uses specific frequency bands simultaneously to control and communicate with the various physiological systems. We show that pacing trials are regulated by different physiological systems. INDEX TERMS Pacing, fractal analysis, wavelet analysis, complexity, exercise dynamics. MAIA ANGELOVA received the Ph.D. degree from the University of Sofia. She was a Lecturer in physics with the Somerville College, University of Oxford, and a Professor of mathematical physics with Northumbria University, U.K., until 2016. She is currently a Professor of data analytics and machine learning with the

show abstract

“…Besides the diversity in natural frequencies, heterogeneity in the coupling strength is also an important characteristic that may dominate in many real systems [10,11], inducing the emergence of various coherent states in the route to synchronization, such as cardio-respiratory synchronization [12,13] and network physiology studying networks between different organ systems [14][15][16]. Furthermore, non-local coupling is known as relevant for the formation of chimera states [17], and Kuramoto models with both positive and negative coupling can exhibit π states, travelling-wave states, standing-wave states, and glass states, among others [18,19].…”

Section: Introductionmentioning

confidence: 99%

Universal phase transitions to synchronization in Kuramoto-like models with heterogeneous coupling

Boccaletti

Zheng

et al. 2019

New J. Phys.

View full text Add to dashboard Cite

We reveal a class of universal phase transitions to synchronization in Kuramoto-like models with both in-and out-coupling heterogeneity. By analogy with metastable states, an oscillatory state occurs as a high-order coherent phase accompanying explosive synchronization in the system. The critical points of synchronization transition and the stationary solutions are obtained analytically, by the use of mean-field theory. In particular, the stable conditions for the emergence of phase-locked states are determined analytically, consistently with the analysis based on the Ott-Antonsen manifold. We demonstrate that the in-or out-coupling heterogeneity have influence on both the dynamical properties (eigen'spectrum) and the synchronizability of the system.

show abstract

Network Physiology: From Neural Plasticity to Organ Network Interactions

Cited by 34 publications

References 42 publications

Metabolic Physiological Networks: The Impact of Age

Metabolic Physiological Networks: The Impact of Age

Investigation of Complexity and Regulatory Role of Physiological Activities During a Pacing Exercise

Universal phase transitions to synchronization in Kuramoto-like models with heterogeneous coupling

Contact Info

Product

Resources

About