Continuous glucose monitoring in people without diabetes: real-time biological feedback and precision metabolic subphenotyping

Abstract

The identification of dysglycemia through single-point glucose measurements —fasting plasma glucose, 2-hour post-OGTT glucose, or HbA1c— has defined the diagnostic paradigm for intermediate states of impaired glucose metabolism. The clinical category of "prediabetes," which clusters together pathophysiologically heterogeneous entities, represents an imprecise synthesis that may induce in patients a misleading perception of relative metabolic normality, thereby delaying timely therapeutic interventions.

Metwally et al. (Nature Biomedical Engineering, 2025) demonstrated that the morphology of the glucose curve during a home-based OGTT measured by CGM allows identification, through machine learning algorithms, of distinct metabolic subphenotypes in individuals without diabetes or with prediabetes: muscular or hepatic insulin resistance (34%; AUC 0.88–0.95) and beta-cell dysfunction with impaired incretin action (40%; AUC 0.84). These subphenotypes do not significantly correlate with conventional biomarkers such as HbA1c, HOMA-IR, HOMA-B, or polygenic risk score, demonstrating that the dynamic information provided by CGM outperforms static measurements in characterizing the individual pathophysiological phenotype.

CGM also offers a therapeutic dimension through real-time biological feedback. Richardson et al. (International Journal of Behavioral Nutrition and Physical Activity, 2024), in a meta-analysis of 25 randomized controlled trials (n=2,996, including populations with obesity without diabetes), demonstrated that CGM as a behavioral intervention reduces HbA1c by 0.28% (95% CI: 0.15–0.42; p<0.001) and increases time in range by 7.4% (95% CI: 2.0–12.8; p=0.008). The real-time visualization of how diet, physical activity, and other behavioral determinants modulate glycemia constitutes a highly effective behavior modification mechanism, particularly when integrated with longitudinal tracking of health-related habits.

From a precision public health perspective, CGM transcends its diagnostic role to become a functional metabolic characterization tool. Individualization based on the patient's "glucotype" —the dynamic glycemic profile that defines their individual metabolic response— (Mao et al., Health Data Science, 2022) allows the targeting of personalized preventive interventions before the establishment of overt hyperglycemia, with potential applications in both the prevention of type 2 diabetes and the optimization of metabolic and athletic performance in disease-free populations.