Delrue Charlotte, Speeckaert Marijn M
Department of Nephrology, Ghent University Hospital, 9000 Ghent, Belgium.
Research Foundation-Flanders (FWO), 1000 Brussels, Belgium.
J Pers Med. 2024 Dec 19;14(12):1157. doi: 10.3390/jpm14121157.
Chronic kidney disease (CKD) is a major worldwide health concern because of its progressive nature and complex biology. Traditional diagnostic and therapeutic approaches usually fail to account for disease heterogeneity, resulting in low efficacy. Precision medicine offers a novel approach to studying kidney disease by combining omics technologies such as genomics, transcriptomics, proteomics, metabolomics, and epigenomics. By identifying discrete disease subtypes, molecular biomarkers, and therapeutic targets, these technologies pave the way for personalized treatment approaches. Multi-omics integration has enhanced our understanding of CKD by revealing intricate molecular linkages and pathways that contribute to treatment resistance and disease progression. While pharmacogenomics offers insights into expected responses to personalized treatments, single-cell and spatial transcriptomics can be utilized to investigate biological heterogeneity. Despite significant development, challenges persist, including data integration concerns, high costs, and ethical quandaries. Standardized data protocols, collaborative data-sharing frameworks, and advanced computational tools such as machine learning and causal inference models are required to address these challenges. With the advancement of omics technology, nephrology may benefit from improved diagnostic accuracy, risk assessment, and personalized care. By overcoming these barriers, precision medicine has the potential to develop novel techniques for improving patient outcomes in kidney disease treatment.
慢性肾脏病(CKD)因其渐进性本质和复杂生物学特性,成为全球主要的健康问题。传统的诊断和治疗方法通常无法考虑疾病的异质性,导致疗效不佳。精准医学通过整合基因组学、转录组学、蛋白质组学、代谢组学和表观基因组学等组学技术,为研究肾脏疾病提供了一种新方法。通过识别离散的疾病亚型、分子生物标志物和治疗靶点,这些技术为个性化治疗方法铺平了道路。多组学整合通过揭示导致治疗抵抗和疾病进展的复杂分子联系和途径,增强了我们对CKD的理解。虽然药物基因组学为个性化治疗的预期反应提供了见解,但单细胞和空间转录组学可用于研究生物异质性。尽管取得了重大进展,但挑战依然存在,包括数据整合问题、高成本和伦理困境。需要标准化的数据协议、协作数据共享框架以及机器学习和因果推断模型等先进计算工具来应对这些挑战。随着组学技术的进步,肾脏病学可能会受益于提高的诊断准确性、风险评估和个性化护理。通过克服这些障碍,精准医学有可能开发出改善肾脏疾病治疗患者预后的新技术。