Peritoneal dialysis effluent biomarkers from a multi-omics and artificial intelligence perspective: advances and challenges

Long-term peritoneal dialysis (PD) treatment can lead to the destruction of peritoneal structure and function, which can lead to PD failure or even a poor prognosis. However, validated early biomarkers for patients undergoing PD are lacking. PD effluent (PDE) is rich in various biological components, such as nucleic acids, proteins, and metabolites, and is now an important source of noninvasive biomarkers for the dynamic monitoring of disease progression. In recent studies, a variety of histological techniques have provided unprecedented depth and breadth to PD biomarker research, and are becoming key tools in the early diagnosis, prognosis, and therapeutic monitoring of PD patients. Correspondingly, artificial intelligence (AI) approaches, which can flexibly handle data and excel at mining nonlinear and high-dimensional relationships in multimodal data, have moved from theory to practice. AI-based multi-omics analysis has not only greatly improved the understanding of the pathophysiological mechanisms of PD-associated fibrosis (PF) but has also contributed to the development of new biomarkers and novel targets. This review provides a comprehensive summary of recent advances in the development of PDE biomarkers using AI-based multi-omics approaches. We highlight the application of AI-based multi-omics techniques for early diagnosis, evaluation of peritoneal injury, assessment of peritoneal function, and prediction of prognosis. Finally, we discuss the challenges and limitations of PDE biomarkers from the perspectives of multi-omics and AI. In conclusion, AI-based multi-omics analysis holds great promise for the development of PDE biomarkers, which are expected to significantly improve the prognosis of PD patients and ultimately facilitate precision medicine.