GLU

GLU derives standardized summary variables from continuous glucose monitoring (CGM) data and quantifies glucose dynamics for epidemiological analysis.

Key Features:

• Consistent summary variables: Generates a standardized set of summary variables from CGM data to enable replication and comparison across studies.
• Quality control measures: Performs per-sample quality control that manages missing data and assesses data reliability.
• Diverse summary metrics: Computes multiple metrics across six domains, including Area Under the Curve (AUC) and proportion of time in hypo-, normo-, and hyperglycemic ranges.
• Derived outputs: Produces derived summary variables and accompanying quality-control information for further analysis.

Scientific Applications:

• Epidemiological studies: Analysis of CGM data to investigate glucose patterns and population-level associations.
• Glucose metabolism and health outcomes: Studying associations between glucose dynamics and epidemiological factors or health outcomes.

Methodology:

Implemented in R; applies per-sample quality control including handling of missing data and computes summary metrics such as AUC and time-in-range proportions from CGM data.