Continuous Glucose Monitoring Predicts Progression to Diabetes in Autoantibody Positive Children

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Abstract

Context Accurate measures are needed for the prediction and diagnosis of type 1 diabetes (T1D) in at-risk persons. Objective The purpose of this study was to explore the value of continuous glucose monitoring (CGM) in predicting T1D onset. Design and Setting The Diabetes Autoimmunity Study in the Young (DAISY) prospectively follows children at increased risk for development of islet autoantibodies (islet autoantibody positive; Ab+) and T1D. Participants We analyzed 23 Ab+ participants with available longitudinal CGM data. Main Outcome Measure CGM metrics as glycemic predictors of progression to T1D. Results Of 23 Ab+ participants with a baseline CGM, 8 progressed to diabetes at a median age of 13.8 years during a median follow-up of 17.7 years (interquartile range, 14.6 to 22.0 years). Compared with nonprogressors, participants who progressed to diabetes had significantly increased baseline glycemic variability (SD, 29 vs 21 mg/dL; P = 0.047), daytime sensor average (122 vs 106 mg/dL; P = 0.02), and daytime sensor area under the curve (AUC, 470,370 vs 415,465; P = 0.047). They spent 24% of time at >140 mg/dL and 12% at >160 mg/dL compared with, respectively, 8% and 3% for nonprogressors (both P = 0.005). A receiver-operating characteristic curve analysis showed an AUC of 0.85 for percentage of time spent at >140 or 160 mg/dL. The cutoff of 18% time spent at >140 mg/dL had 75% sensitivity, 100% specificity, and a 100% positive predictive value for diabetes prediction, although these values could change because some nonprogressors may develop diabetes with longer follow-up. Conclusions Eighteen percent or greater CGM time spent at >140 mg/dL predicts progression to diabetes in Ab+ children.

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APA

Steck, A. K., Dong, F., Taki, I., Hoffman, M., Simmons, K., Frohnert, B. I., & Rewers, M. J. (2019). Continuous Glucose Monitoring Predicts Progression to Diabetes in Autoantibody Positive Children. Journal of Clinical Endocrinology and Metabolism, 104(8), 3337–3344. https://doi.org/10.1210/jc.2018-02196

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