Time-Series Analysis of Continuous Glucose Monitoring Data to Predict Treatment Efficacy in Patients with T2DM
Li Li, Jie Sun, Liemin Ruan, Qifa Song
Abstract
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.