Continuous Glucose Monitoring and Personalized Nutrition in Type 2 Diabetes - A Scoping Review

Continuous Glucose Monitoring (CGM) is increasingly applied to personalized nutrition in Type 2 Diabetes (T2D), yet evidence is scattered. This scoping review mapped CGM-based nutrition interventions, classified models, summarized outcomes, and identified gaps. Following JBI and PRISMA-ScR guidelines, five databases and Google Scholar were searched (2020-2025) for studies of adults with T2D using real-time or intermittently scanned CGM to guide diet. Forty-five studies were included, mostly randomized trials, with additional pilot and observational designs. Interventions included CGM-guided nutrition, AI-enabled prediction, CGM-AI hybrid/digital-twin models, and telehealth coaching. Measured outcomes focused on HbA1c, Time in Range, and weight, while behavioral, cardiovascular, and microbiome measures were rarely assessed. Overall, CGM-enabled nutrition shows promise but remains methodologically inconsistent, with gaps in participant reporting, outcome diversity, and AI-driven approaches. Larger, long-term studies are needed to advance precision nutrition in diabetes care using continuous glucose monitoring.