Complications of Diabetes and Metrics of Glycemic Management Derived From Continuous Glucose Monitoring

Michael Yapanis, Steven James, Maria E Craig, David O'Neal, Elif I Ekinci, Michael Yapanis, Steven James, Maria E Craig, David O'Neal, Elif I Ekinci

Abstract

Context: Although glycated hemoglobin A1c is currently the best parameter used clinically to assess risk for the development of diabetes complications, it does not provide insight into short-term fluctuations in glucose levels. This review summarizes the relationship between continuous glucose monitoring (CGM)-derived metrics of glycemic variability and diabetes-related complications.

Evidence acquisition: PubMed and Embase databases were searched from January 1, 2010 to August 22, 2020, using the terms type 1 diabetes, type 2 diabetes, diabetes-related microvascular and macrovascular complications, and measures of glycaemic variability. Exclusion criteria were studies that did not use CGM and studies involving participants who were not diabetic, acutely unwell (post stroke, post surgery), pregnant, or using insulin pumps.

Evidence synthesis: A total of 1636 records were identified, and 1602 were excluded, leaving 34 publications in the final review. Of the 20 852 total participants, 663 had type 1 diabetes (T1D) and 19 909 had type 2 diabetes (T2D). Glycemic variability and low time in range (TIR) showed associations with all studied microvascular and macrovascular complications of diabetes. Notably, higher TIR was associated with reduced risk of albuminuria, retinopathy, cardiovascular disease mortality, all-cause mortality, and abnormal carotid intima-media thickness. Peripheral neuropathy was predominantly associated with standard deviation of blood glucose levels (SD) and mean amplitude of glycemic excursions (MAGE).

Conclusion: The evidence supports the association between diabetes complications and CGM-derived measures of intraday glycemic variability. TIR emerged as the most consistent measure, supporting its emerging role in clinical practice. More longitudinal studies and trials are required to confirm these associations, particularly for T1D, for which there are limited data.

Keywords: continuous glucose monitoring; diabetes complications; glycemic variability; time-in-range; type 1 diabetes mellitus; type 2 diabetes mellitus.

© The Author(s) 2022. Published by Oxford University Press on behalf of the Endocrine Society.

Figures

Figure 1.
Figure 1.
High vs low glycemic variability. Glucose profiles of 2 individuals showing identical glycated hemoglobin A1c (6.3%) over a 5-day monitoring period but vastly different variability.
Figure 2.
Figure 2.
PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) flowchart.
Figure 3.
Figure 3.
Time in range (TIR) vs glycemic variability. Glucose profiles of 2 individuals highlighting the difference between glycemic variability and TIR.
Figure 4.
Figure 4.
Limitation of short continuous glucose monitoring (CGM) periods. Most studies included in this review have CGM periods of 2 to 3 days. This diagram demonstrates how the data from this period may not be representative of the participants’ overall glycemic management.
Figure 5.
Figure 5.
Observational vs longitudinal studies. Thirty out of the 34 papers included in this review used cross-sectional study designs. Diabetes complications are the results of years of altered glycemia. This diagram further illustrates how data from a single point in time (as in a cross-sectional study) may misrepresent the preceding months of data that are causative of the disease outcome. Longitudinal studies may be able to provide a more comprehensive analysis of the associations between different metrics of glycemia and the risk of diabetes complications.

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