Lopes Marta B, Coletti Roberta, Duranton Flore, Glorieux Griet, Jaimes Campos Mayra Alejandra, Klein Julie, Ley Matthias, Perco Paul, Sampri Alexia, Tur-Sinai Aviad
Center for Mathematics and Applications (NOVA Math), NOVA School of Science and Technology (NOVA FCT), Caparica, Portugal.
UNIDEMI, Department of Mechanical and Industrial Engineering, NOVA School of Science and Technology (NOVA FCT), Caparica, Portugal.
Proteomics. 2025 Jan 10:e202400108. doi: 10.1002/pmic.202400108.
Chronic kidney disease (CKD) poses a significant and growing global health challenge, making early detection and slowing disease progression essential for improving patient outcomes. Traditional diagnostic methods such as glomerular filtration rate and proteinuria are insufficient to capture the complexity of CKD. In contrast, omics technologies have shed light on the molecular mechanisms of CKD, helping to identify biomarkers for disease assessment and management. Artificial intelligence (AI) and machine learning (ML) could transform CKD care, enabling biomarker discovery for early diagnosis and risk prediction, and personalized treatment. By integrating multi-omics datasets, AI can provide real-time, patient-specific insights, improve decision support, and optimize cost efficiency by early detection and avoidance of unnecessary treatments. Multidisciplinary collaborations and sophisticated ML methods are essential to advance diagnostic and therapeutic strategies in CKD. This review presents a comprehensive overview of the pipeline for translating CKD omics data into personalized treatment, covering recent advances in omics research, the role of ML in CKD, and the critical need for clinical validation of AI-driven discoveries to ensure their efficacy, relevance, and cost-effectiveness in patient care.
慢性肾脏病（CKD）对全球健康构成了重大且日益严峻的挑战，因此早期检测和减缓疾病进展对于改善患者预后至关重要。传统的诊断方法，如肾小球滤过率和蛋白尿检测，不足以全面反映CKD的复杂性。相比之下，组学技术揭示了CKD的分子机制，有助于识别用于疾病评估和管理的生物标志物。人工智能（AI）和机器学习（ML）可以变革CKD的治疗，实现用于早期诊断和风险预测的生物标志物发现以及个性化治疗。通过整合多组学数据集，AI可以提供实时、针对个体患者的见解，改善决策支持，并通过早期检测和避免不必要的治疗来优化成本效益。多学科合作和先进的ML方法对于推进CKD的诊断和治疗策略至关重要。本综述全面概述了将CKD组学数据转化为个性化治疗的流程，涵盖了组学研究的最新进展、ML在CKD中的作用，以及对AI驱动的发现进行临床验证的迫切需求，以确保其在患者护理中的有效性、相关性和成本效益。
