Anna R Kahkoska, Linda A Adair, Allison E Aiello, Kyle S Burger, John B Buse, Jamie Crandell, David M Maahs, Crystal T Nguyen, Michael R Kosorok, Elizabeth J Mayer-Davis
Journal: Pediatric diabetes 2020;20(5):556-566
PMID: 30972889
Young people with type 1 diabetes often experience large changes in their blood sugar levels throughout the day. Some adolescents struggle to keep their blood sugar within the recommended range, which can lead to high levels of haemoglobin A1c (HbA1c), a measure of average blood sugar over several months. Understanding different patterns of blood sugar control may help healthcare professionals provide more personalised treatment for young people with diabetes. This study aimed to identify different patterns of blood sugar control, including time spent in high blood sugar (hyperglycaemia), low blood sugar (hypoglycaemia), and overall glucose variability through the use of a continuous glucose monitor (CGM).
The results showed that adolescents could be grouped into distinct patterns of blood sugar control, with some experiencing mainly high blood sugar levels, others showing greater blood sugar fluctuations, and some having combinations of both high and low blood sugar episodes. These patterns highlight that not all young people with elevated HbA1c experience the same type of glucose control problems.
In conclusion, identifying different blood glucose patterns using CGM data may help healthcare professionals better understand individual glucose control challenges. This information could support more personalised diabetes management strategies for adolescents with type 1 diabetes who have difficulty maintaining stable blood sugar levels.
BACKGROUND/OBJECTIVE
To identify and characterize subgroups of adolescents with type 1 diabetes (T1D) and elevated hemoglobin A1c (HbA1c) who share patterns in their continuous glucose monitoring (CGM) data as "dysglycemia phenotypes."
METHODS
Data were analyzed from the Flexible Lifestyles Empowering Change randomized trial. Adolescents with T1D (13-16 years, duration >1 year) and HbA1c 8% to 13% (64-119 mmol/mol) wore blinded CGM at baseline for 7 days. Participants were clustered based on eight CGM metrics measuring hypoglycemia, hyperglycemia, and glycemic variability. Clusters were characterized by their baseline features and 18 months changes in HbA1c using adjusted mixed effects models. For comparison, participants were stratified by baseline HbA1c (≤/>9.0% [75 mmol/mol]).
RESULTS
The study sample included 234 adolescents (49.8% female, baseline age 14.8 ± 1.1 years, baseline T1D duration 6.4 ± 3.7 years, baseline HbA1c 9.6% ± 1.2%, [81 ± 13 mmol/mol]). Three Dysglycemia Clusters were identified with significant differences across all CGM metrics (P < .001). Dysglycemia Cluster 3 (n = 40, 17.1%) showed severe hypoglycemia and glycemic variability with moderate hyperglycemia and had a lower baseline HbA1c than Clusters 1 and 2 (P < .001). This cluster showed increases in HbA1c over 18 months (p-for-interaction = 0.006). No other baseline characteristics were associated with Dysglycemia Clusters. High HbA1c was associated with lower pump use, greater insulin doses, more frequent blood glucose monitoring, lower motivation, and lower adherence to diabetes self-management (all P < .05).
CONCLUSIONS
There are subgroups of adolescents with T1D for which glycemic control is challenged by different aspects of dysglycemia. Enhanced understanding of demographic, behavioral, and clinical characteristics that contribute to CGM-derived dysglycemia phenotypes may reveal strategies to improve treatment.
© 2019 John Wiley & Sons A/S. Published by John Wiley & Sons Ltd.
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