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Identification of clinically relevant dysglycemia phenotypes based on continuous glucose monitoring data from youth with type 1 diabetes and elevated hemoglobin A1c
Author(s) -
Kahkoska Anna R.,
Adair Linda A.,
Aiello Allison E.,
Burger Kyle S.,
Buse John B.,
Crandell Jamie,
Maahs David M.,
Nguyen Crystal T.,
Kosorok Michael R.,
MayerDavis Elizabeth J.
Publication year - 2019
Publication title -
pediatric diabetes
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.678
H-Index - 75
eISSN - 1399-5448
pISSN - 1399-543X
DOI - 10.1111/pedi.12856
Subject(s) - medicine , glycemic , hypoglycemia , type 1 diabetes , diabetes mellitus , hemoglobin , type 2 diabetes , hemoglobin a , cluster (spacecraft) , blood glucose self monitoring , insulin , continuous glucose monitoring , endocrinology , computer science , programming language
Abstract 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.