rGV

rGV analyzes continuous glucose monitoring system (CGMS) data to compute 16 glycemic variability metrics and quantify glucose fluctuations for research and clinical assessment.

Key Features:

• Glycemic Variability Metrics: Calculates a comprehensive suite of 16 glycemic variability metrics to characterize glucose fluctuations.
• Data format and sensor compatibility: Handles diverse data formats and inputs from various sensor types used in CGMS/CGM datasets.
• Comparative sensitivity to HbA1c: Provides metrics that capture variability beyond standard measures like hemoglobin A1c (HbA1c), addressing biases toward overreporting hyperglycemia.
• Multi-study analysis: Enables analysis of CGM data aggregated from multiple studies to assess glycemic patterns across cohorts.
• Statistical rigor: Employs advanced statistical methods to ensure reliability and accuracy of the computed glycemic variability metrics.

Scientific Applications:

• Non-Diabetic Individuals: Analyses have shown that greater glycemic variability is associated with higher HbA1c values in non-diabetic cohorts.
• Type 2 Diabetes Mellitus (T2DM): In T2DM patients, analyses indicate that high glucose episodes are primary contributors to overall glycemic variability.
• Type 1 Diabetes (T1DM): The package has been used to explore the effects of naltrexone on glycemic variability in T1DM patients, suggesting a potential role for the medication in reducing fluctuations.

Methodology:

Employs advanced statistical methods to compute glycemic variability metrics from CGMS/CGM data and analyzes CGM data from multiple studies.