The Omics-Driven Machine Learning Path to Cost-Effective Precision Medicine in Chronic Kidney Disease

Abstract

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.

Keywords: artificial intelligence; chronic kidney disease; cost‐effectiveness; machine learning; multi‐omics.

FIGURE 1

The multi‐omics ML pipeline in chronic kidney disease (CKD). Achieving early disease detection, risk prediction, and personalized medicine in CKD requires an interdisciplinary approach. Integrating available omics datasets from various experimental and clinical studies is essential. ML is promising to extract insights from complex omics datasets, but external validation is necessary to ensure clinical relevance and assess cost‐effectiveness. Optimization of the entire pipeline (data availability, standardized disease, and preclinical and clinical validation) is of utmost importance to allow developing novel tools and improving patient outcomes in CKD. Created with BioRender.com.

FIGURE 2

Hypothetical graph illustrating key topological features of a network. The blue node indicates a high degree of centrality, while the red node represents high betweenness centrality. Given a disease‐associated node A, and a predicted target B, the shortest path between nodes A and B is highlighted in green, while the longest path is shown in yellow. The dashed purple lines indicate sample clusters.