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单细胞技术在研究和理解慢性肾脏病及糖尿病肾病中的进展

Single-Cell Advances in Investigating and Understanding Chronic Kidney Disease and Diabetic Kidney Disease.

作者信息

Bhayana Sagar, Schytz Philip A, Bisgaard Olesen Emma T, Soh Keng, Das Vivek

机构信息

Kidney Biology, Global Drug Development, Novo Nordisk A/S, Søborg, Denmark.

Cardiovascular, Kidney and Alzheimer Disease, Medical and Science, Novo Nordisk A/S, Søborg, Denmark.

出版信息

Am J Pathol. 2025 Jan;195(1):55-68. doi: 10.1016/j.ajpath.2024.07.007. Epub 2024 Aug 2.

Abstract

Chronic kidney disease (CKD) and its subset diabetic kidney disease are progressive conditions that affect >850 million people worldwide. Diabetes, hypertension, and glomerulonephritis are the most common causes of CKD, which is associated with significant patient morbidity and an increased risk of cardiovascular events, such as heart failure, ultimately leading to premature death. Despite newly approved drugs, increasing evidence shows that patients respond to treatment differently given the complexity of disease heterogeneity and complicated pathophysiology. This review article presents an integrative approach to understanding and addressing CKD through the lens of precision medicine and therapeutics. Advancements in single-cell omics technologies and artificial intelligence can be leveraged to explore the intricate cellular mechanisms underlying CKD and diabetic kidney disease pathogenesis. Dissecting the cellular heterogeneity and identifying rare cell populations using single-cell approaches will facilitate uncovering novel therapeutic targets and biomarkers for personalized treatment strategies. Finally, we discuss the potential of artificial intelligence-driven analyses in predicting disease progression and treatment response, thereby paving the way for tailored interventions.

摘要

慢性肾脏病(CKD)及其子集糖尿病肾病是渐进性疾病,全球有超过8.5亿人受其影响。糖尿病、高血压和肾小球肾炎是CKD最常见的病因,CKD与患者的高发病率以及心血管事件(如心力衰竭)风险增加相关,最终导致过早死亡。尽管有新批准的药物,但越来越多的证据显示,鉴于疾病异质性和复杂病理生理学的复杂性,患者对治疗的反应各不相同。这篇综述文章提出了一种综合方法,通过精准医学和治疗学的视角来理解和应对CKD。单细胞组学技术和人工智能的进展可用于探索CKD和糖尿病肾病发病机制背后复杂的细胞机制。使用单细胞方法剖析细胞异质性并识别罕见细胞群体,将有助于发现新的治疗靶点和生物标志物,以制定个性化治疗策略。最后,我们讨论了人工智能驱动的分析在预测疾病进展和治疗反应方面的潜力,从而为量身定制的干预措施铺平道路。

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