Lederer Eleanor D, Sobh Mahmoud M, Brier Michael E, Gaweda Adam E
VA North Texas Health Care Services, Dallas TX, USA.
Department of Medicine and Charles and Jane Pak Center for Mineral Metabolism and Clinical Research, UT Southwestern Medical Center, Dallas, TX, USA.
Clin Kidney J. 2024 Jun 6;17(6):sfae143. doi: 10.1093/ckj/sfae143. eCollection 2024 Jun.
The global derangement of mineral metabolism that accompanies chronic kidney disease (CKD-MBD) is a major driver of the accelerated mortality for individuals with kidney disease. Advances in the delivery of dialysis, in the composition of phosphate binders, and in the therapies directed towards secondary hyperparathyroidism have failed to improve the cardiovascular event profile in this population. Many obstacles have prevented progress in this field including the incomplete understanding of pathophysiology, the lack of clinical targets for early stages of chronic kidney disease, and the remarkably wide diversity in clinical manifestations. We describe in this review a novel approach to CKD-MBD combining mathematical modelling of biologic processes with machine learning artificial intelligence techniques as a tool for the generation of new hypotheses and for the development of innovative therapeutic approaches to this syndrome. Clinicians need alternative targets of therapy, tools for risk profile assessment, and new therapies to address complications early in the course of disease and to personalize therapy to each individual. The complexity of CKD-MBD suggests that incorporating artificial intelligence techniques into the diagnostic, therapeutic, and research armamentarium could accelerate the achievement of these goals.
伴随慢性肾脏病出现的矿物质代谢全面紊乱（CKD-MBD）是肾病患者死亡率加速上升的主要驱动因素。透析治疗、磷结合剂成分以及针对继发性甲状旁腺功能亢进的治疗方法虽有进展，但未能改善该人群的心血管事件情况。该领域进展受阻，存在诸多障碍，包括对病理生理学认识不完整、慢性肾脏病早期缺乏临床靶点以及临床表现差异极大。在本综述中，我们描述了一种针对CKD-MBD的新方法，即将生物过程的数学建模与机器学习人工智能技术相结合，作为生成新假设以及开发针对该综合征创新治疗方法的工具。临床医生需要替代治疗靶点、风险评估工具以及新疗法，以便在疾病进程早期处理并发症并实现个体化治疗。CKD-MBD的复杂性表明，将人工智能技术纳入诊断、治疗和研究手段可加速实现这些目标。
