Purpose of Review

Diabetic kidney disease (DKD), a leading cause of end-stage kidney disease, is the result of metabolic network alterations in the kidney. Therefore, metabolomics is an effective tool for understanding its pathophysiology, finding key biomarkers, and developing a new treatment strategy. In this review, we summarize the application of metabolomics to DKD research.

Recent Findings

Alterations in renal energy metabolism including the accumulation of tricarboxylic acid cycle and glucose metabolites are observed in the early stage of DKD, and they finally lead to mitochondrial dysfunction in advanced DKD. Mitochondrial fission-fusion imbalance and dysregulated organelle crosstalk might contribute to this process. Moreover, metabolomics has identified several uremic toxins including phenyl sulfate and tryptophan derivatives as promising biomarkers that mediate DKD progression.

Summary

Recent advances in metabolomics have clarified the role of dysregulated energy metabolism and uremic toxins in DKD pathophysiology. Integration of multi-omics data will provide additional information for identifying critical drivers of DKD.

中文翻译：

---

利用代谢组学描述糖尿病肾脏疾病进展的病理生理学

审查目的

糖尿病肾脏疾病（DKD）是终末期肾脏疾病的主要原因，是肾脏代谢网络改变的结果。因此，代谢组学是了解其病理生理学，寻找关键生物标志物以及制定新的治疗策略的有效工具。在这篇综述中，我们总结了代谢组学在DKD研究中的应用。

最近的发现

在DKD的早期阶段，观察到肾脏能量代谢的变化，包括三羧酸循环和葡萄糖代谢产物的积累，最终导致晚期DKD的线粒体功能障碍。线粒体裂变融合失衡和细胞器串扰失调可能是这一过程的原因。此外，代谢组学已经鉴定出几种尿毒症毒素，包括硫酸苯酯和色氨酸衍生物，它们是介导DKD进程的有前途的生物标记。

概括

代谢组学的最新进展阐明了能量代谢失调和尿毒症毒素在DKD病理生理中的作用。多组学数据的集成将提供更多信息，以识别DKD的关键驱动因素。