Continuous Glucose Monitoring in Prediabetes and Early Dysglycemia: A Narrative Review

Early dysglycemia, including prediabetes, represents heterogeneous and dynamic states that are incompletely captured by fasting plasma glucose, glycosylated hemoglobin, or a single oral glucose tolerance test. Continuous glucose monitoring (CGM) provides a higher-density assessment of glucose behavior under free-living conditions, enabling characterization of fasting, postprandial, nocturnal and activity-related glucose patterns before the onset of diabetes. This narrative review summarizes current evidence on CGM in normoglycemia towards early dysglycemia, with emphasis on conventional CGM metrics, higher-order features derived from raw glucose time series, and emerging artificial intelligence approaches. Existing studies show that individuals with prediabetes have higher mean glucose, greater time above tight glucose ranges, lower time in tight range and greater glycemic variability than normoglycemic comparators. However, absolute differences are small, and distributions overlap substantially. More recent approaches, including the concept of glucotypes, multi-feature classifiers, personalized postprandial response models, glucodensity methods and glucose foundation models, suggest that CGM may capture richer temporal and individualized phenotypes than consensus summary metrics alone, supporting future risk stratification, mechanistic subphenotyping and targeted prevention. However, model validation remains limited, and outcome-linked CGM thresholds are not yet established. At present, CGM should still be viewed as a promising research and phenotyping tool in early dysglycemia.