Deliverables from Metabolomics in Kidney Disease: Adenine, New Insights, and Implication for Clinical Decision-Making

代谢组学 肾脏疾病 医学 疾病 生物标志物 生物信息学 生物标志物发现 计算生物学 内科学 生物 蛋白质组学 遗传学 基因
作者
Nagarjunachary Ragi,Kumar Sharma
出处
期刊:American Journal of Nephrology [S. Karger AG]
卷期号:55 (4): 421-438 被引量:11
标识
DOI:10.1159/000538051
摘要

<b><i>Background:</i></b> Chronic kidney disease (CKD) presents a persistent global health challenge, characterized by complex pathophysiology and diverse progression patterns. Metabolomics has emerged as a valuable tool in unraveling the intricate molecular mechanisms driving CKD progression. <b><i>Summary:</i></b> This comprehensive review provides a summary of recent progress in the field of metabolomics in kidney disease with a focus on spatial metabolomics to shed important insights to enhancing our understanding of CKD progression, emphasizing its transformative potential in early disease detection, refined risk assessment, and the development of targeted interventions to improve patient outcomes. <b><i>Key Message:</i></b> Through an extensive analysis of metabolic pathways and small-molecule fluctuations, bulk and spatial metabolomics offers unique insights spanning the entire spectrum of CKD, from early stages to advanced disease states. Recent advances in metabolomics technology have enabled spatial identification of biomarkers to provide breakthrough discoveries in predicting CKD trajectory and enabling personalized risk assessment. Furthermore, metabolomics can help decipher the complex molecular intricacies associated with kidney diseases for exciting novel therapeutic approaches. A recent example is the identification of adenine as a key marker of kidney fibrosis for diabetic kidney disease using both untargeted and targeted bulk and spatial metabolomics. The metabolomics studies were critical to identify a new biomarker for kidney failure and to guide new therapeutics for diabetic kidney disease. Similar approaches are being pursued for acute kidney injury and other kidney diseases to enhance precision medicine decision-making.
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