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Updated: Sep 9, 2025

Simple Continuous Glucose Monitoring in Freely Moving Mice
Published on: February 24, 2023
Identifying high glucose variability using non-glycemic factors in low continuous glucose monitoring use settings
Suresh Rama Chandran1, Ming Ming Teh1, Hong Chang Tan1
1Department of Endocrinology, Singapore General Hospital, 20 College Road, 169856, Singapore.
Aims:
Identifying non-glycemic factors associated with high Glucose variability (GV).
Methods:
A cross-sectional observational study recruited people with type 2 diabetes, who wore a Freestyle Libre Pro CGM.
Independent Variables:
Age, sex, BMI, diabetes medication, diabetes duration, HbA1c and estimated glomerular filtration rate (eGFR). CGM-derived variables calculated included Time-in-Range (TIR, 70-180 mg/dl), below-range 1 (TBR1, <70 mg/dl), -below-range 2 (TBR2, <54 mg/dl) and -above-range (TAR, >180 mg/dl), coefficient of variation (%CV). A logistic regression model examined independent variables associated with high GV (CV ≥36 %). All analysis was done on R version 4.3.1 RESULTS: T2D cohort (n = 403), 46 % women, had median age of 61 y, BMI of 26.5 kg/m2, diabetes duration 14 y, HbA1c 7.8 %(62 mmol/mol) and creatinine of 75 µmol/L. Using sulphonylurea, premixed or basal-bolus insulin had an odds ratio (OR) of 4.7 - 5.2 for CV ≥ 36 %. Longer diabetes duration [OR 1.2], and lower eGFR [OR 1.2] were associated with higher odds and older age [OR 0.8]and higher BMI [0.8] were associated with lower odds of CV≥ 36 %. Sex and HbA1c had no association with high GV.
Conclusion:
Nonglycemic-factors like medication type, diabetes duration and eGFR can aid in identification of high GV even in low-CGM use settings.
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