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The Omics-Driven Machine Learning Path to Cost-Effective Precision Medicine in Chronic Kidney Disease
Marta B Lopes1,2, Roberta Coletti1, Flore Duranton3
1Center for Mathematics and Applications (NOVA Math), NOVA School of Science and Technology (NOVA FCT), Caparica, Portugal.
Insights
Omics technologies and artificial intelligence (AI) offer new ways to detect chronic kidney disease (CKD) early and personalize treatment. Integrating these approaches can improve patient outcomes and healthcare efficiency.
Area of Science:
- Nephrology
- Bioinformatics
- Computational Biology
Background:
- Chronic kidney disease (CKD) is a major global health issue with diagnostic limitations.
- Current methods like glomerular filtration rate and proteinuria lack comprehensive insights into CKD complexity.
- Omics technologies reveal molecular mechanisms, aiding biomarker identification for CKD assessment.
Purpose of the Study:
- To provide a comprehensive overview of translating omics data into personalized CKD treatment.
- To highlight the role of artificial intelligence (AI) and machine learning (ML) in advancing CKD care.
- To emphasize the need for clinical validation of AI-driven discoveries in CKD.
Main Methods:
- Review of recent advances in omics research for CKD.
- Analysis of AI and ML applications in biomarker discovery, early diagnosis, and risk prediction for CKD.
- Discussion on integrating multi-omics datasets for patient-specific insights and decision support.
Main Results:
- Omics technologies provide deeper insights into CKD molecular mechanisms.
- AI and ML can enhance early CKD detection, risk stratification, and personalized treatment strategies.
- Integration of multi-omics data with AI offers real-time, patient-specific clinical decision support.
Conclusions:
- AI and omics integration hold significant potential to transform CKD diagnosis and management.
- Multidisciplinary collaboration and advanced ML methods are crucial for progress.
- Clinical validation is essential to ensure the efficacy, relevance, and cost-effectiveness of AI-driven CKD solutions.
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.
