TY - JOUR
T1 - The Omics-Driven Machine Learning Path to Cost-Effective Precision Medicine in Chronic Kidney Disease
AU - Lopes, Marta B.
AU - Coletti, Roberta
AU - Duranton, Flore
AU - Glorieux, Griet
AU - Jaimes Campos, Mayra Alejandra
AU - Klein, Julie
AU - Ley, Matthias
AU - Perco, Paul
AU - Sampri, Alexia
AU - Tur-Sinai, Aviad
N1 - Publisher Copyright: © 2025 The Author(s). Proteomics published by Wiley-VCH GmbH.
PY - 2025/1/10
Y1 - 2025/1/10
N2 - 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.
AB - 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.
KW - artificial intelligence
KW - chronic kidney disease
KW - cost-effectiveness
KW - machine learning
KW - multi-omics
UR - http://www.scopus.com/inward/record.url?scp=85214435788&partnerID=8YFLogxK
U2 - https://doi.org/10.1002/pmic.202400108
DO - https://doi.org/10.1002/pmic.202400108
M3 - Review article
C2 - 39790049
SN - 1615-9853
JO - Proteomics
JF - Proteomics
ER -