Time-Series Analysis of Continuous Glucose Monitoring Data to Predict Treatment Efficacy in Patients with T2DM.

CONTEXT There is a challenge to predict treatment effects in patients with type 2 diabetes mellitus (T2DM). OBJECTIVE To assess and predict treatment effects in patients with T2DM through time-series analysis of continuous glucose monitoring (CGM) measurements. METHOD We extracted and clustered the trend components of CGM measurements to generate representative time-series profiles, which were used as a predictor of treatment effects in groups of patients. SETTING AND PARTICIPANTS We recruited 111 outpatients with T2DM at Ningbo City First Hospital, China. INTERVENTION The patients underwent CGM measurement for 14 days at the beginning of glucose-lowering treatment. MAIN OUTCOME MEASURES Hemoglobin A1c (HbA1c) and fasting plasma glucose (FPG) were obtained at the beginning and after 6 months of treatment. RESULTS 111 patients each had 960 to 1344 CGM measurements for 14 days at 96 measurements per day. The patients were classified into 3 groups according to the profiles of trend components of CGM observed values by time-series clustering method, including decreasing (47 patients), increasing (26 patients), and unchanged (38 patients) profiles. After 6 months of glucose-lowering treatment, FPG declined from 10.2 to 6.8 mmol/L (a decline of 3.4 mmol/L) in the decreasing group, from 8.9 to 9.2 mmol/L (a rise of 0.3 mmol/L) in the increasing group, and from 8.4 to 7.5 mmol/L (a decline of 0.9 mmol/L) in the unchanged group. The changes of HbA1c were 2.3%, 0.2%, and 0.9% for the 3 groups (P < 0.01), respectively. CONCLUSIONS Clustering of the trend components of CGM data generates representative CGM profiles that are predictive of 6-month therapeutic effects for T2DM.