Institute of Molecular Cardiovascular Research (Z.W., J.J.), Rheinisch-Westfälische Technische Hochschule Aachen University, Germany.
Department of Radiology (C.K.), Rheinisch-Westfälische Technische Hochschule Aachen University, Germany.
Circ Res. 2023 Apr 14;132(8):1084-1100. doi: 10.1161/CIRCRESAHA.123.321765. Epub 2023 Apr 13.
The identification of mediators for physiologic processes, correlation of molecular processes, or even pathophysiological processes within a single organ such as the kidney or heart has been extensively studied to answer specific research questions using organ-centered approaches in the past 50 years. However, it has become evident that these approaches do not adequately complement each other and display a distorted single-disease progression, lacking holistic multilevel/multidimensional correlations. Holistic approaches have become increasingly significant in understanding and uncovering high dimensional interactions and molecular overlaps between different organ systems in the pathophysiology of multimorbid and systemic diseases like cardiorenal syndrome because of pathological heart-kidney crosstalk. Holistic approaches to unraveling multimorbid diseases are based on the integration, merging, and correlation of extensive, heterogeneous, and multidimensional data from different data sources, both -omics and nonomics databases. These approaches aimed at generating viable and translatable disease models using mathematical, statistical, and computational tools, thereby creating first computational ecosystems. As part of these computational ecosystems, systems medicine solutions focus on the analysis of -omics data in single-organ diseases. However, the data-scientific requirements to address the complexity of multimodality and multimorbidity reach far beyond what is currently available and require multiphased and cross-sectional approaches. These approaches break down complexity into small and comprehensible challenges. Such holistic computational ecosystems encompass data, methods, processes, and interdisciplinary knowledge to manage the complexity of multiorgan crosstalk. Therefore, this review summarizes the current knowledge of kidney-heart crosstalk, along with methods and opportunities that arise from the novel application of computational ecosystems providing a holistic analysis on the example of kidney-heart crosstalk.
在过去的 50 年中,人们广泛研究了单一器官(如肾脏或心脏)内生理过程、分子过程甚至病理生理过程的介质识别,以回答特定的研究问题,采用以器官为中心的方法。然而,很明显这些方法并不能很好地相互补充,并且显示出扭曲的单一疾病进展,缺乏整体多层次/多维相关性。由于病理性心脏-肾脏相互作用,整体方法在理解和揭示多器官和全身性疾病(如心肾综合征)的病理生理学中不同器官系统之间的高维相互作用和分子重叠方面变得越来越重要。整体方法旨在通过整合、合并和相关来自不同数据源的广泛、异质和多维数据来解开多疾病,这些数据源包括组学和非组学数据库。这些方法旨在使用数学、统计和计算工具生成可行和可转化的疾病模型,从而创建第一个计算生态系统。作为这些计算生态系统的一部分,系统医学解决方案侧重于分析单一器官疾病中的组学数据。然而,为了解决多模态和多病性的复杂性而提出的数据科学要求远远超出了当前的可用范围,需要多阶段和横断面方法。这些方法将复杂性分解为小而可理解的挑战。这种整体计算生态系统涵盖了数据、方法、流程和跨学科知识,以管理多器官相互作用的复杂性。因此,本综述总结了目前关于肾心相互作用的知识,以及从计算生态系统的新应用中出现的方法和机会,以肾心相互作用为例进行整体分析。