Barajas-Martínez Antonio, Ibarra-Coronado Elizabeth, Fossion Ruben, Toledo-Roy Juan Claudio, Martínez-Garcés Vania, López-Rivera Juan Antonio, Tello-Santoyo Geraldine, Lavin Rusland D, Gómez José Luis, Stephens Christopher R, Aguilar-Salinas Carlos A, Estañol Bruno, Torres Nimbe, Tovar Armando R, Resendis-Antonio Osbaldo, Hiriart Marcia, Frank Alejandro, Rivera Ana Leonor
Doctorado en Ciencias Biomédicas, Universidad Nacional Autónoma de México, Mexico City, Mexico.
Centro de Ciencias de la Complejidad, Universidad Nacional Autónoma de México, Mexico City, Mexico.
Front Physiol. 2021 May 11;12:678507. doi: 10.3389/fphys.2021.678507. eCollection 2021.
Within human physiology, systemic interactions couple physiological variables to maintain homeostasis. These interactions change according to health status and are modified by factors such as age and sex. For several physiological processes, sex-based distinctions in normal physiology are present and defined in isolation. However, new methodologies are indispensable to analyze system-wide properties and interactions with the objective of exploring differences between sexes. Here we propose a new method to construct complex inferential networks from a normalization using the clinical criteria for health of physiological variables, and the correlations between anthropometric and blood tests biomarkers of 198 healthy young participants (117 women, 81 men, from 18 to 27 years old). Physiological networks of men have less correlations, displayed higher modularity, higher small-world index, but were more vulnerable to directed attacks, whereas networks of women were more resilient. The networks of both men and women displayed sex-specific connections that are consistent with the literature. Additionally, we carried out a time-series study on heart rate variability (HRV) using Physionet's Fantasia database. Autocorrelation of HRV, variance, and Poincare's plots, as a measure of variability, are statistically significant higher in young men and statistically significant different from young women. These differences are attenuated in older men and women, that have similar HRV distributions. The network approach revealed differences in the association of variables related to glucose homeostasis, nitrogen balance, kidney function, and fat depots. The clusters of physiological variables and their roles within the network remained similar regardless of sex. Both methodologies show a higher number of associations between variables in the physiological system of women, implying redundant mechanisms of control and simultaneously showing that these systems display less variability in time than those of men, constituting a more resilient system.
在人体生理学中,系统相互作用将生理变量耦合在一起以维持体内平衡。这些相互作用会根据健康状况而变化,并受到年龄和性别等因素的影响。对于一些生理过程,正常生理学中存在基于性别的差异,并且是单独定义的。然而,新的方法对于分析全系统的特性和相互作用以探索性别差异是必不可少的。在此,我们提出一种新方法,该方法基于生理变量健康的临床标准进行归一化,并利用198名健康年轻参与者(117名女性,81名男性,年龄在18至27岁之间)的人体测量指标与血液检测生物标志物之间的相关性来构建复杂的推理网络。男性的生理网络相关性较低,表现出更高的模块化、更高的小世界指数,但更容易受到定向攻击,而女性的网络则更具弹性。男性和女性的网络都显示出与文献一致的性别特异性连接。此外,我们使用Physionet的Fantasia数据库对心率变异性(HRV)进行了时间序列研究。作为变异性的一种度量,HRV的自相关性、方差和庞加莱图在年轻男性中具有统计学显著更高的值,并且与年轻女性在统计学上有显著差异。这些差异在年龄较大的男性和女性中减弱,他们具有相似的HRV分布。网络方法揭示了与葡萄糖稳态、氮平衡、肾功能和脂肪储存相关的变量之间关联的差异。无论性别如何,生理变量簇及其在网络中的作用都保持相似。两种方法都表明女性生理系统中变量之间的关联数量更多,这意味着存在冗余的控制机制,同时表明这些系统在时间上的变异性比男性的系统小,构成了一个更具弹性的系统。