Abstract
Context
There is a challenge to predict treatment effects in patients with T2DM.
Objective
To assess and predict treatment effects in patients with T2DM through time-series analysis of continuous glucose monitoring (CGM) measurements.
Design
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.
Intervention
The patients underwent CGM measurement for 14 days at the beginning of glucose-lowering treatment.
Main Outcome Measures
HbA1c and FPG were obtained at the beginning and 6-month of treatment.
Results
111 patients each had 960 –1344 CGM measurements for 14 days at 96 measurements per day. The patients were classified into three 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 six-month glucose-lowering treatment, FPG declined from 10.2 to 6.8 mmol/L (a decline of 3.5 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). The changes of HbA1c were 2.2%, 0.2%, and 0.9% for the three groups (P<0.01), respectively.
Conclusions
Clustering of the trend components of CGM data generates representative CGM profiles that are predictive of six-month therapeutic effects for T2DM.
CGMTSA: An R package for continuous glucose monitoring time series data analysis
