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

PROTEOMICS, vol. 25, pp. e202400108

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.

Authors 10

1. Marta Belchior Lopes corresponding

   NOVAMath Center for Mathematics and Applications · Universidade Nova de Lisboa

   Affiliation as printed

   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

   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

2. Roberta Coletti

   NOVAMath Center for Mathematics and Applications · Universidade Nova de Lisboa

   Affiliation as printed

   Center for Mathematics and Applications (NOVA Math) NOVA School of Science and Technology (NOVA FCT) Caparica Portugal

   Center for Mathematics and Applications (NOVA Math), NOVA School of Science and Technology (NOVA FCT), Caparica, Portugal

3. Flore Duranton
   Affiliation as printed

   RD‐Néphrologie Montpellier France

4. Griet Glorieux

   Ghent University Hospital

   Affiliation as printed

   Department of Internal Medicine and Pediatrics Nephrology Unit Ghent University Hospital Gent Belgium

   Department of Internal Medicine and Pediatrics, Nephrology Unit, Ghent University Hospital, Gent, Belgium

5. Mayra Alejandra Jaimes Campos Aachen Institute for Molecular Cardiovascular Research

   Universitätsklinikum Aachen · Mosaiques Diagnostics and Therapeutics (Germany) · RWTH Aachen University

   Affiliation as printed

   Department of Biomarker Research Mosaiques Diagnostics GmbH Hannover Germany

   Institute for Molecular Cardiovascular Research University Hospital RWTH Aachen Aachen Germany

   Department of Biomarker Research, Mosaiques Diagnostics GmbH, Hannover, Germany

   Institute for Molecular Cardiovascular Research, University Hospital RWTH Aachen, Aachen, Germany

6. Julie Klein

   Université Toulouse III - Paul Sabatier · Inserm · Institut des Maladies Métaboliques et Cardiovasculaires

   Affiliation as printed

   Institut National de la Santé et de la Recherche Médicale (INSERM) Institute of Cardiovascular and Metabolic Disease Toulouse France

   Université Toulouse III Paul‐Sabatier Toulouse France

7. Matthias Ley

   Medical University of Vienna

   Affiliation as printed

   Delta4 GmbH Vienna Austria

   Division of Pediatric Nephrology and Gastroenterology Department of Pediatrics and Adolescent Medicine Comprehensive Center for Pediatrics Medical University Vienna Vienna Austria

   Delta4 GmbH, Vienna, Austria

   Division of Pediatric Nephrology and Gastroenterology, Department of Pediatrics and Adolescent Medicine, Comprehensive Center for Pediatrics, Medical University Vienna, Vienna, Austria

8. Paul Perco

   Innsbruck Medical University · Universität Innsbruck

   Affiliation as printed

   Delta4 GmbH Vienna Austria

   Department of Internal Medicine IV Medical University Innsbruck Innsbruck Austria

   Delta4 GmbH, Vienna, Austria

   Department of Internal Medicine IV, Medical University Innsbruck, Innsbruck, Austria

9. Alexia Sampri

   University of Cambridge · British Heart Foundation

   Affiliation as printed

   Department of Public Health and Primary Care British Heart Foundation Cardiovascular Epidemiology Unit University of Cambridge Cambridge UK

   Victor Phillip Dahdaleh Heart and Lung Research Institute University of Cambridge Cambridge UK

   Department of Public Health and Primary Care, British Heart Foundation Cardiovascular Epidemiology Unit, University of Cambridge, Cambridge, UK

   Victor Phillip Dahdaleh Heart and Lung Research Institute, University of Cambridge, Cambridge, UK

10. Aviad Tur‐Sinai

    University of Haifa

    Affiliation as printed

    School of Public Health University of Haifa Haifa Israel

    School of Public Health, University of Haifa, Haifa, Israel