Abstract：
Chronic kidney disease (CKD) has been increasing over the last years, with a rate between 0.49% to 0.87% new cases per year. Currently, the number of affected people is around 850 million worldwide. CKD is a slowly progressive disease that leads to irreversible loss of kidney function, end-stage kidney disease, and premature death. Therefore, CKD is considered a global health problem, and this sets the alarm for necessary efficient prediction, management, and disease prevention. At present, modern computer analysis, such as in silico medicine (ISM), denotes an emergent data science that offers interesting promise in the nephrology field. ISM offers reliable computer predictions to suggest optimal treatments in a case-specific manner. In addition, ISM offers the potential to gain a better understanding of the kidney physiology and/or pathophysiology of many complex diseases, together with a multiscale disease modeling. Similarly, -omics platforms (including genomics, transcriptomics, metabolomics, and proteomics), can generate biological data to obtain information on gene expression and regulation, protein turnover, and biological pathway connections in renal diseases. In this sense, the novel patient-centered approach in CKD research is built upon the combination of ISM analysis of human data, the use of in vitro models, and in vivo validation. Thus, one of the main objectives of CKD research is to manage the disease by the identification of new disease drivers, which could be prevented and monitored. This review explores the wide-ranging application of computational medicine and the application of -omics strategies in evaluating and managing kidney diseases.
摘要：
慢性肾脏病（CKD）在过去几年中一直在增加，每年新发病例的比率在0.49%至0.87%之间。目前,全球受影响的人数约为8.5亿。CKD是一种缓慢进展的疾病，导致肾脏功能的不可逆转的丧失，终末期肾病,过早死亡。因此,CKD被认为是一个全球性的健康问题，这为必要的有效预测设置了警报，管理,和疾病预防。目前,现代计算机分析,例如硅医学(ISM)，表示新兴的数据科学，在肾脏病学领域提供了有趣的前景。ISM提供可靠的计算机预测，以针对具体病例的方式建议最佳治疗方法。此外,ISM提供了更好地了解许多复杂疾病的肾脏生理学和/或病理生理学的潜力。以及多尺度疾病建模。同样，-组学平台（包括基因组学，转录组学，代谢组学，和蛋白质组学)，可以产生生物数据，以获得有关基因表达和调控的信息，蛋白质周转，和肾脏疾病中的生物学途径连接。在这个意义上,CKD研究中新颖的以患者为中心的方法是建立在对人类数据进行ISM分析的基础上，使用体外模型，和体内验证。因此，CKD研究的主要目标之一是通过识别新的疾病驱动因素来管理疾病，可以预防和监控。这篇综述探讨了计算医学的广泛应用以及-组学策略在评估和管理肾脏疾病中的应用。
