Continuous Glucose Monitoring for Metabolic Phenotyping and Precision Nutrition: Current Evidence, Clinical Applications and Future Perspectives

Continuous glucose monitoring (CGM) has evolved beyond conventional diabetes management into an enabling technology for metabolic phenotyping and precision nutrition by providing continuous, real-world assessment of individual glycemic responses to dietary and lifestyle exposures. This narrative review examines the principles and clinical utility of CGM and its emerging applications in metabolic monitoring and personalized nutrition, with particular emphasis on postprandial glycemic variability, computational prediction, dietary feedback, and behavioral interventions. A literature search was conducted in Web of Science, PubMed, Scopus, and Google Scholar using predefined combinations of terms related to CGM, precision and personalized nutrition, diet, postprandial glycemic responses, lifestyle, glycemic variability, artificial intelligence, machine learning, and computational nutrition, including English-language literature available up to June 2026. Current evidence demonstrates substantial interindividual variability in postprandial glycemic responses and supports the potential integration of CGM with dietary, clinical, anthropometric, behavioral, and microbiome-derived data to characterize individual metabolic responses and inform individualized prediction and nutritional guidance. However, the reproducibility and generalizability of CGM-derived metabolic phenotypes and predictive models remain incompletely established. CGM-supported nutritional and behavioral interventions have demonstrated improvements in glycemic and selected cardiometabolic outcomes, particularly in individuals with type 2 diabetes, whereas evidence of clinical benefit in populations without diabetes remains substantially more limited. Sustained engagement and psychological, technical, and accessibility-related limitations also remain important considerations. Emerging multi-analyte sensing technologies may further broaden continuous metabolic assessment and support more comprehensive, data-driven precision-nutrition strategies.