Continuous Glucose Monitoring (CGM) Research

This systematic review evaluated the potential of continuous glucose monitoring for cardiovascular prevention in individuals without diabetes. The research examines how CGM-guided interventions might reduce cardiovascular disease risk through improved glucose control and lifestyle modifications.

This systematic review examined the evidence on continuous glucose monitoring (CGM) use in people without diabetes for cardiovascular risk prevention. The authors searched multiple databases and included studies that assessed CGM-guided lifestyle interventions in normoglycemic or prediabetic populations, focusing on outcomes related to cardiovascular health markers.

The review found that CGM provided actionable real-time feedback that helped participants make meaningful dietary and exercise modifications. Individuals using CGM were able to identify foods and meal patterns that triggered excessive postprandial glucose spikes, and adjust their habits accordingly. These behavioral changes led to measurable reductions in glycemic variability, a metric increasingly linked to endothelial dysfunction and early atherosclerosis risk.

The authors concluded that CGM shows promise as a preventive cardiology tool in non-diabetic populations, enabling personalized lifestyle interventions that target glucose dysregulation before it progresses to overt metabolic disease. However, they noted that more long-term randomized trials are needed to confirm whether CGM-guided changes translate into reduced cardiovascular events.

This systematic review and meta-analysis investigated whether glycemic variability (GV) measured by continuous glucose monitoring is associated with cardiometabolic risk markers in people without diabetes. The authors pooled data from observational and interventional studies that used CGM in normoglycemic or prediabetic populations.

The meta-analysis found significant associations between higher glycemic variability metrics (such as MAGE, CV, and SD of glucose) and adverse cardiometabolic profiles. Participants with greater glucose fluctuations tended to have higher BMI, larger waist circumference, elevated triglycerides, and other markers of metabolic dysfunction, even in the absence of a diabetes diagnosis.

These findings suggest that CGM-derived glycemic variability captures meaningful metabolic information beyond what fasting glucose or HbA1c alone provide. The results support the concept that glucose instability may be an early and independent contributor to cardiometabolic risk, strengthening the rationale for CGM use as a screening and monitoring tool in non-diabetic populations seeking to optimize metabolic health.

This systematic review and meta-analysis of randomized controlled trials evaluated the effectiveness of continuous glucose monitoring as a behavioral change intervention across populations with and without diabetes. The authors identified RCTs where CGM was used specifically as a tool to drive lifestyle modifications rather than purely for clinical glucose management.

The meta-analysis found that CGM-based interventions led to statistically significant reductions in HbA1c compared to control groups, with meaningful effects observed in both diabetic and non-diabetic populations. Additionally, CGM users achieved greater reductions in body weight, suggesting that the real-time biofeedback from CGM motivated sustained dietary and physical activity changes.

Importantly, the study demonstrated that CGM value extends well beyond its traditional use in diabetes management. When framed as a behaviour change tool, CGM provides the kind of immediate, personalized feedback that supports self-regulation and goal-directed health behaviours. The authors concluded that CGM represents a promising intervention for improving metabolic health outcomes across a broad population, though they called for more research on long-term adherence and cost-effectiveness.

This comprehensive review examines the emerging role of continuous glucose monitoring in people without diabetes. The authors identify specific populations who may benefit and establish frameworks for clinical and wellness applications of CGM technology.

This Stanford study used continuous glucose monitoring to characterize glucose patterns in non-diabetic individuals. The research discovered that glucose regulation is highly variable between individuals, leading to the identification of distinct "glucotypes" that predict metabolic health better than standard testing.