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A case study of glucose levels during sleep using multilevel fast function on scalar regression inference
Renat Sergazinov1, Andrew Leroux2, Erjia Cui3
1Department of Statistics, Texas A&M University, College Station, Texas, USA.
Biometrics
|May 16, 2023
Summary
Biguanide medication and sleep apnea severity significantly impact glucose levels during sleep. These effects on glucose trajectories were found to be time-invariant in individuals with type II diabetes mellitus.
Area of Science:
- Biostatistics
- Endocrinology
- Sleep Medicine
Background:
- Continuous glucose monitors (CGMs) are vital for diabetes management.
- Analyzing CGM data during sleep presents statistical challenges due to data complexity.
Purpose of the Study:
- To quantify the effects of diabetes medications and sleep apnea severity on glucose levels during sleep.
- To compare statistical methods for analyzing complex CGM data.
Main Methods:
- Utilized continuous glucose monitoring (CGM) data from 174 participants with type II diabetes mellitus over an average of 10 nights.
- Evaluated and compared Fast Univariate Inference (FUI) and Functional Additive Mixed Models (FAMMs).
- Extended FUI to test hypotheses regarding covariate effects and time invariance.
Main Results:
- Biguanide medication significantly affects glucose trajectories during sleep.
- Sleep apnea severity is also a significant factor influencing glucose levels during sleep.
- The identified effects of medication and sleep apnea on glucose were time-invariant.
Conclusions:
- Biguanide medication and sleep apnea severity have a significant, time-invariant impact on glucose regulation during sleep.
- The study highlights the need for advanced statistical methods to analyze complex CGM data.
- Further methodological development for FAMMs is recommended.
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