Perspective: Multiomics and Artificial Intelligence for Personalized Nutritional Management of Diabetes in Patients Undergoing Peritoneal Dialysis

Abstract

Managing diabetes in patients on peritoneal dialysis (PD) is challenging due to the combined effects of dietary glucose, glucose from dialysate, and other medical complications. Advances in technology that enable continuous biological data collection are transforming traditional management approaches. This review explores how multiomics technologies and artificial intelligence (AI) are enhancing glucose management in this patient population. Continuous glucose monitoring (CGM) offers significant advantages over traditional markers, such as hemoglobin A1c (HbA1c). Unlike HbA1c, which reflects an mean glucose level, CGM provides real-time, dynamic glucose data that allow clinicians to make timely adjustments, leading to better glycemic control and outcomes. Multiomics approaches are valuable for understanding genetic factors that influence susceptibility to diabetic complications, particularly those related to advanced glycation end products (AGEs). Identifying genetic polymorphisms that modify a patient's response to AGEs allows for personalized treatments, potentially reducing the severity of diabetes-related pathologies. Metabolomic analyses of PD effluent are also promising, as they help identify early biomarkers of metabolic dysregulation. Early detection can lead to timely interventions and more tailored treatment strategies, improving long-term patient care. AI integration is revolutionizing diabetes management for PD patients by processing vast datasets from CGM, genetic, metabolic, and microbiome profiles. AI can identify patterns and predict outcomes that may be difficult for humans to detect, enabling highly personalized recommendations for diet, medication, and dialysis management. Furthermore, AI can assist clinicians by automating data interpretation, improving treatment plans, and enhancing patient education. Despite the promise of these technologies, there are limitations. CGM, multiomics, and AI require significant investment in infrastructure, training, and validation studies. Additionally, integrating these approaches into clinical practice presents logistical and financial challenges. Nevertheless, personalized, data-driven strategies offer great potential for improving outcomes in diabetes management for PD patients.

Keywords: artificial intelligence; diabetes; kidney disease; omics; peritoneal dialysis; precision nutrition.

FIGURE 1

Multiomics and AI integration in diabetic PD management. The integration of multiomics strategies and AI in the treatment of diabetes in patients receiving PD treatment aims to enhance personalized treatments and significantly improve patient quality of life and clinical outcomes. The multiomics strategies include CGM for effective glucose management, utilization of genetic testing to identify individuals with a higher risk of susceptibility to tissue damage from AGEs in diabetic complications, metabolomics for identifying PDE biomarkers in DKD, and gut microbiome profiling to develop personalized nutrition strategies. Additionally, AI and ML are employed to implement RPM programs, optimizing healthcare outcomes through a comprehensive and individualized approach for managing diabetes in patients undergoing PD. Abbreviations: AGEs, advanced glycation end products; AI, artificial intelligence; CGM, continuous glucose monitoring; DKD, diabetic kidney disease; PD, peritoneal dialysis; PDE, peritoneal dialysis effluent; ML, machine learning; RPM, remote patient management.