Yavuz Hayrettin, Kumar Manish, Goswami Himanshu Ballav, Erdbrügger Uta, Harris William Thomas, Skopelja-Gardner Sladjana, Graber Martha, Swiatecka-Urban Agnieszka
Department of Pediatrics, University of Virginia School of Medicine, Charlottesville, VA 22903, USA.
Department of Pediatrics, Heersink School of Medicine, University of Alabama at Birmingham, Birmingham, AL 35233, USA.
J Clin Med. 2025 Aug 7;14(15):5585. doi: 10.3390/jcm14155585.
As people with cystic fibrosis (PwCF) live longer, kidney disease is emerging as a significant comorbidity that is increasingly linked to cardiovascular complications and progression to end-stage kidney disease. In our recent review, we proposed the unifying term CF-related kidney disease (CFKD) to encompass the spectrum of kidney dysfunction observed in this population. Early detection of kidney injury is critical for improving long-term outcomes, yet remains challenging due to the limited sensitivity of conventional laboratory tests, particularly in individuals with altered muscle mass and unique CF pathophysiology. Emerging approaches, including novel blood and urinary biomarkers, urinary extracellular vesicles, and genetic risk profiling, offer promising avenues for identifying subclinical kidney damage. When integrated with machine learning algorithms, these tools may enable the development of personalized risk stratification models and targeted therapeutic strategies. This precision medicine approach has the potential to transform kidney disease management in PwCF, shifting care from reactive treatment of late-stage disease to proactive monitoring and early intervention.
随着囊性纤维化患者（PwCF）寿命的延长，肾脏疾病正成为一种重要的合并症，越来越多地与心血管并发症以及终末期肾病的进展相关联。在我们最近的综述中，我们提出了统一术语“囊性纤维化相关肾脏疾病（CFKD）”，以涵盖在该人群中观察到的一系列肾功能障碍。早期发现肾损伤对于改善长期预后至关重要，但由于传统实验室检查的敏感性有限，尤其是在肌肉量改变和具有独特囊性纤维化病理生理学的个体中，早期发现仍然具有挑战性。新兴方法，包括新型血液和尿液生物标志物、尿液细胞外囊泡和遗传风险分析，为识别亚临床肾损伤提供了有前景的途径。当与机器学习算法相结合时，这些工具可能有助于开发个性化风险分层模型和靶向治疗策略。这种精准医学方法有可能改变囊性纤维化患者的肾脏疾病管理，将护理从晚期疾病的被动治疗转变为主动监测和早期干预。
