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J Clin Endocrinol Metab. 2019 Aug 1;104(8):3337-3344. doi: 10.1210/jc.2018-02196.

Continuous Glucose Monitoring Predicts Progression to Diabetes in Autoantibody Positive Children.

Author information

1
Barbara Davis Center for Childhood Diabetes, University of Colorado School of Medicine, Aurora, Colorado.

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.

PMID:
30844073
PMCID:
PMC6589073
[Available on 2020-03-07]
DOI:
10.1210/jc.2018-02196

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