Department of Internal Medicine and Biomedical Research Institute, Pusan National University Hospital, Busan, Korea.
Department of Internal Medicine, Pusan National University School of Medicine, Yangsan, Korea.
Korean J Intern Med. 2024 Nov;39(6):898-905. doi: 10.3904/kjim.2024.111. Epub 2024 Oct 22.
Diabetic nephropathy (DN), a leading cause of chronic kidney disease and end-stage kidney disease (ESKD), poses global health challenges given its increasing prevalence. DN increases the risk of mortality and cardiovascular events. Early identification and appropriate DN management are crucial. However, current diagnostic methods rely on general traditional markers, highlighting the need for DN-specific diagnostics. Metabolomics, the study of small molecules produced by metabolic activity, promises to identify specific biomarkers that distinguish DN from other kidney diseases, decode the underlying disease mechanisms, and predict the disease course. Profound changes in metabolic pathways are apparent in individuals with DN, alterations in the tricarboxylic acid cycle and amino acid and lipid metabolism, suggestive of mitochondrial dysfunction. Metabolomics aids prediction of chronic kidney disease progression; several metabolites serve as indicators of renal functional decline and the risk of ESKD. Integration of such information with other omics data will further enhance our understanding of DN, paving the way to personalized treatment. In summary, metabolomics and multi-omics offer valuable insights into DN and are promising diagnostic and prognostic tools.
糖尿病肾病(DN)是慢性肾脏病和终末期肾病(ESKD)的主要病因,由于其患病率不断增加,给全球健康带来了挑战。DN 增加了死亡和心血管事件的风险。早期识别和适当的 DN 管理至关重要。然而,目前的诊断方法依赖于一般的传统标志物,这突出了对 DN 特异性诊断的需求。代谢组学是研究代谢活动产生的小分子的科学,有望识别出区分 DN 与其他肾脏疾病的特异性生物标志物,揭示潜在的疾病机制,并预测疾病进程。DN 患者的代谢途径发生了深刻的变化,三羧酸循环以及氨基酸和脂质代谢发生改变,提示线粒体功能障碍。代谢组学有助于预测慢性肾脏病的进展;一些代谢物可作为肾功能下降和 ESKD 风险的指标。将此类信息与其他组学数据相结合,将进一步加深我们对 DN 的理解,为个性化治疗铺平道路。总之,代谢组学和多组学为 DN 提供了有价值的见解,是有前途的诊断和预后工具。