Mahdavi Sara, Anthony Nicole M, Sikaneta Tabo, Tam Paul Y
Department of Nutrition, Harvard T. H. Chan School of Public Health, Boston, MA, United States; Department of Nutritional Sciences, University of Toronto, 6 Queen's Park Cres, Toronto, Ontario, Canada; Department of Nephrology, the Scarborough Health Network, Toronto, Ontario, Canada.
Department of Nutritional Sciences, University of Toronto, 6 Queen's Park Cres, Toronto, Ontario, Canada.
Adv Nutr. 2025 Mar;16(3):100378. doi: 10.1016/j.advnut.2025.100378. Epub 2025 Jan 20.
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.
由于饮食中的葡萄糖、透析液中的葡萄糖以及其他医学并发症的综合影响，对接受腹膜透析（PD）的患者进行糖尿病管理具有挑战性。能够实现连续生物数据收集的技术进步正在改变传统的管理方法。本综述探讨了多组学技术和人工智能（AI）如何改善这一患者群体的血糖管理。连续血糖监测（CGM）相较于传统指标，如糖化血红蛋白（HbA1c），具有显著优势。与反映平均血糖水平的HbA1c不同，CGM提供实时、动态的血糖数据，使临床医生能够及时进行调整，从而实现更好的血糖控制和治疗效果。多组学方法对于理解影响糖尿病并发症易感性的遗传因素非常有价值，尤其是那些与晚期糖基化终产物（AGEs）相关的因素。识别可改变患者对AGEs反应的基因多态性有助于进行个性化治疗，可能降低糖尿病相关病理状况的严重程度。对PD流出液进行代谢组学分析也很有前景，因为它们有助于识别代谢失调的早期生物标志物。早期检测能够带来及时的干预措施和更具针对性的治疗策略，改善患者的长期护理。通过处理来自CGM、遗传、代谢和微生物组谱的大量数据集，AI的整合正在彻底改变PD患者的糖尿病管理。AI可以识别出人类难以察觉的模式并预测结果，从而为饮食、药物治疗和透析管理提供高度个性化的建议。此外，AI可以通过自动解读数据、改进治疗方案和加强患者教育来协助临床医生。尽管这些技术前景广阔，但也存在局限性。CGM、多组学和AI需要在基础设施、培训和验证研究方面进行大量投资。此外，将这些方法整合到临床实践中存在后勤和财务方面的挑战。尽管如此，个性化的、数据驱动的策略在改善PD患者糖尿病管理结果方面具有巨大潜力。
