Wearable Continuous Glucose Monitoring–Derived Glycemic Variability As A Predictor Of Microvascular Complications In Type 2 Diabetes Mellitus: A Prospective Cohort Study

Background: Continuous glucose monitoring (CGM) provides detailed metrics of glycemic variability (GV) that may be associated with microvascular complications in type 2 diabetes.

Objectives: To evaluate the association between GV indices derived from CGM and the development of microvascular complications in adults with type 2 diabetes.

Methods: In this prospective cohort study, 50 adults with type 2 diabetes were followed for 12 months. Participants underwent CGM at baseline, and intraoperative and postoperative parameters were recorded. GV indices including coefficient of variation (CV%) and mean amplitude of glycemic excursions (MAGE) were calculated. The incidence of microvascular complications (neuropathy, nephropathy, and retinopathy) was assessed. Group comparisons, logistic regression, and receiver operating characteristic (ROC) curve analysis were performed.

Results: The mean age of participants was 55.2 ± 7.5 years, 64% were male, mean diabetes duration was 7.9 ± 2.8 years, and baseline HbA1c was 8.7 ± 1.1%. At enrollment, 6% had pre-existing complications, while 62% developed new complications during follow-up. Those who developed complications had higher GV, with CV% (21.2 ± 2.6% vs. 16.5 ± 2.7%, p < 0.001) and MAGE (66.2 ± 11.7 vs. 51.5 ± 10.3 mg/dL, p = 0.002) elevated compared with those who remained complication-free. Logistic regression showed a strong trend for CV% (OR 1.36 per 1%, 95% CI 0.98–1.89) and MAGE (OR 0.94 per mg/dL, 95% CI 0.87–1.01) as predictors. ROC analysis demonstrated that CV% ≥ 19.8% and MAGE ≥ 70.7 mg/dL identified complications with fair accuracy (AUC: CV% 0.725; MAGE 0.719), and the adjusted model achieved an AUC of 0.858, outperforming HbA1c (AUC 0.715).

Conclusion: Higher GV, reflected by elevated CV% and MAGE, was associated with increased risk of microvascular complications in type 2 diabetes. These findings suggest CGM-derived variability metrics may complement HbA1c in risk stratification, though larger studies are needed to confirm their predictive value.