Glycemic variability assessed using continuous glucose monitoring in individuals without diabetes and associations with cardiometabolic risk markers: A systematic review and meta-analysis.

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Continuous glucose monitors are widely available. They could help nutritionists and nutritional therapists to personalise nutrition plans and reduce risk factors for cardiovascular disease and type 2 diabetes when working with a qualified health care practitioner.

Introduction

Glycaemic variability (GV) has been associated with increased risk of cardiovascular disease (CVD) in individuals with type 2 diabetes (T2D). It is not known whether there are similar risks for individuals without T2D. Continuous blood glucose monitors (CGM) measure short-term GV and may be a potential tool for assessing these risks.

Methods

• 71 worldwide studies with diverse populations were included in this systematic review and meta-analysis. Most studies were cross sectional and included CGM use for 24 hours or longer.
• Measurement data included: standard deviation (SD) and coefficient of variation (CV) of GV, mean amplitude of glycaemic excursions (MAGE), mean of daily differences (MODD), continuous overlapping net glycaemic action (CONGA), M-value, lability index (L-index), J-index or glycaemic risk assessment in diabetes equation (GRADE).
• Outcome measurements were any associated with cardiometabolic risk markers.

Results

• Adults with prediabetes had greater SD (p <0.0001), CV (p =0.008) and MAGE (p<0.0001) values. SD, MODD, and MAGE were also higher in individuals with normal glucose tolerance (NGT) and a previous history of gestational diabetes.
• SD was higher in children and adolescents with prediabetes. SD and CV were also higher in adolescents with cystic fibrosis. An inverse association was found in adolescents for MAGE and soluble receptor of advanced glycation end-products (sRAGE) (P=<0.05).
• 6 studies found measures of beta-cell function were inversely associated with GV.
• Higher levels of MAGE were positively associated with a higher incidence of cardiovascular events (p=0.004), higher C-reactive protein and PAI-1 (p<0.001).
• No differences were found in GV between obese, overweight and normal weight individuals, nor correlations with body composition for all populations (p>0.05 for all).

Conclusion

This study found that GV is elevated in adults with prediabetes compared to individuals with NGT and may be linked with beta-cell dysfunction. The evidence for children and adolescents was less clear. GV was also positively associated with the development of atherosclerosis and an increased risk of cardiovascular events. GV may therefore be an effective proxy for cardiovascular risk in adults without diabetes.

• There is a large variability in postprandial response between individuals consuming the same foods.
• HbA1C does not include short term variability in blood glucose levels.
• CGMs are widely available and easily accessible and could help nutritionists and nutritional therapists to provide personalised nutrition plans.
• This study found that changes in GV were not associated with HbA1c, fasting glucose, homeostatic model assessment of insulin resistance or oral glucose tolerance test-derived measures.
• GV was also not associated with adiposity, blood pressure, blood fatty liver disease, blood lipid profile or oxidative stress.

• Limitations of this study were the inclusion of mainly cross-sectional data as well as the heterogeneity between outcome measures, study durations, populations and sample sizes.
• Further prospective studies are needed in healthy individuals.
• Future studies should focus on measurements that specifically assess GV and cardiometabolic risk markers.