Personalized Care in CKD: Moving Beyond Traditional Biomarkers.

Abstract

Background: Traditional biomarkers, such as estimated glomerular filtration rate (eGFR) and urine albumin-to-creatinine ratio (uACR), have long been central to chronic kidney disease (CKD) diagnosis and management, leading to a standardized CKD classification system. However, these biomarkers are non-specific and fail to capture the heterogeneity within CKD and the nuances of an individual's disease mechanism, limiting personalized treatment approaches. There is an increasing need for novel biomarkers that reflect the diverse pathophysiological processes underlying CKD progression, enabling more precise risk prediction and treatment strategies.

Summary: This review examines the limitations of current CKD biomarkers and classification systems, highlighting the need for a precision medicine approach. While traditional markers like eGFR and uACR are foundational, they inadequately capture CKD's complexity. Emerging biomarkers offer insights into specific disease processes, such as inflammation, oxidative stress, fibrosis, and tubular injury, which are crucial for personalized care. The article discusses the potential benefits of integrating these novel biomarkers into clinical practice, including more accurate risk prediction, tailored treatments, and personalized clinical trial designs, as well as the barriers to their implementation. Furthermore, advancements in multi-omics and high-throughput techniques offer opportunities to identify novel causative proteins with druggable targets, pushing CKD care towards greater precision.

Key messages: Current CKD classification systems, based on non-specific biomarkers, fail to capture CKD's heterogeneity. Incorporating biomarkers reflecting diverse pathophysiological mechanisms can enhance risk prediction, customized treatments, and personalized clinical trials. High-throughput multi-omic techniques present a promising path towards precision medicine in nephrology.