scholarly journals Applications of Variability Analysis Techniques for Continuous Glucose Monitoring Derived Time Series in Diabetic Patients

2018 ◽  
Vol 9 ◽  
Author(s):  
Klaus-Dieter Kohnert ◽  
Peter Heinke ◽  
Lutz Vogt ◽  
Petra Augstein ◽  
Eckhard Salzsieder
Keyword(s):  
Time Series ◽  
Continuous Glucose Monitoring ◽  
Glucose Monitoring ◽  
Diabetic Patients ◽  
Variability Analysis ◽  
Analysis Techniques

Continuous Glucose Monitoring Time Series and Hypo/Hyperglycemia Prevention: Requirements, Methods, Open Problems

2008 ◽  
Vol 4 (3) ◽  
pp. 181-192 ◽  
Author(s):  
Giovanni Sparacino ◽  
Andrea Facchinetti ◽  
Alberto Maran ◽  
Claudio Cobelli
Keyword(s):  
Time Series ◽  
Continuous Glucose Monitoring ◽  
Glucose Monitoring ◽  
Open Problems ◽  
Monitoring Time

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

2021 ◽  
Author(s):  
Li Li ◽  
Jie Sun ◽  
Liemin Ruan ◽  
Qifa Song
Keyword(s):  
Time Series ◽  
Time Series Analysis ◽  
Continuous Glucose Monitoring ◽  
Treatment Efficacy ◽  
Treatment Effects ◽  
Glucose Monitoring ◽  
Therapeutic Effects ◽  
Time Series Clustering ◽  
Glucose Lowering ◽  
Series Analysis

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.

scholarly journals Assessment of Hyperglycemia, Hypoglycemia and Inter-Day Glucose Variability Using Continuous Glucose Monitoring among Diabetic Patients on Chronic Hemodialysis

2021 ◽  
Vol 10 (18) ◽  
pp. 4116
Author(s):  
Maria Divani ◽  
Panagiotis I. Georgianos ◽  
Triantafyllos Didangelos ◽  
Vassilios Liakopoulos ◽  
Kali Makedou ◽  
...  
Keyword(s):  
Continuous Glucose Monitoring ◽  
Glycated Albumin ◽  
Glucose Monitoring ◽  
Area Under The Curve ◽  
Glucose Variability ◽  
Hemodialysis Patients ◽  
Chronic Hemodialysis ◽  
Diabetic Patients ◽  
Lower Efficiency ◽  
Target Glucose

Continuous glucose monitoring (CGM) facilitates the assessment of short-term glucose variability and identification of acute excursions of hyper- and hypo-glycemia. Among 37 diabetic hemodialysis patients who underwent 7-day CGM with the iPRO2 device (Medtronic Diabetes, Northridge, CA, USA), we explored the accuracy of glycated albumin (GA) and hemoglobin A1c (HbA1c) in assessing glycemic control, using CGM-derived metrics as the reference standard. In receiver operating characteristic (ROC) analysis, the area under the curve (AUC) in diagnosing a time in the target glucose range of 70–180 mg/dL (TIR70–180) in <50% of readings was higher for GA (AUC: 0.878; 95% confidence interval (CI): 0.728–0.962) as compared to HbA1c (AUC: 0.682; 95% CI: 0.508–0.825) (p < 0.01). The accuracy of GA (AUC: 0.939; 95% CI: 0.808–0.991) in detecting a time above the target glucose range > 250 mg/dL (TAR>250) in >10% of readings did not differ from that of HbA1c (AUC: 0.854; 95% CI: 0.699–0.948) (p = 0.16). GA (AUC: 0.712; 95% CI: 0.539–0.848) and HbA1c (AUC: 0.740; 95% CI: 0.570–0.870) had a similarly lower efficiency in detecting a time below target glucose range < 70 mg/dL (TBR<70) in >1% of readings (p = 0.71). Although the mean glucose levels were similar, the coefficient of variation of glucose recordings (39.2 ± 17.3% vs. 32.0 ± 7.8%, p < 0.001) and TBR<70 (median (range): 5.6% (0, 25.8) vs. 2.8% (0, 17.9)) were higher during the dialysis-on than during the dialysis-off day. In conclusion, the present study shows that among diabetic hemodialysis patients, GA had higher accuracy than HbA1c in detecting a 7-day CGM-derived TIR70–180 < 50%. However, both biomarkers provided an imprecise reflection of acute excursions of hypoglycemia and inter-day glucose variability.

scholarly journals SP434EXPLORING THE ACCURACY OF A CONTINUOUS GLUCOSE MONITORING DEVICE AMONG DIABETIC PATIENTS ON HEMODIALYSIS

2017 ◽  
Vol 32 (suppl_3) ◽  
pp. iii269-iii269
Author(s):  
Maria Divani ◽  
Panagiotis Georgianos ◽  
Fotios Iliadis ◽  
Triantafyllos Didangelos ◽  
Areti Makedou ◽  
...  
Keyword(s):  
Continuous Glucose Monitoring ◽  
Glucose Monitoring ◽  
Diabetic Patients ◽  
Monitoring Device

scholarly journals Electrochemically Activated Conductive Ni-Based MOFs for Non-enzymatic Sensors Toward Long-Term Glucose Monitoring

2020 ◽  
Vol 8 ◽  
Author(s):  
Yating Chen ◽  
Yulan Tian ◽  
Ping Zhu ◽  
Liping Du ◽  
Wei Chen ◽  
...  
Keyword(s):  
Continuous Glucose Monitoring ◽  
Glucose Sensor ◽  
Glucose Monitoring ◽  
Glucose Sensors ◽  
Blood Glucose Monitoring ◽  
Diabetic Patients ◽  
Potential Range ◽  
Potential Applications ◽  
Naoh Solution

Continuous intensive monitoring of glucose is one of the most important approaches in recovering the quality of life of diabetic patients. One challenge for electrochemical enzymatic glucose sensors is their short lifespan for continuous glucose monitoring. Therefore, it is of great significance to develop non-enzymatic glucose sensors as an alternative approach for long-term glucose monitoring. This study presented a highly sensitive and selective electrochemical non-enzymatic glucose sensor using the electrochemically activated conductive Ni3(2,3,6,7,10,11-hexaiminotriphenylene)2 MOFs as sensing materials. The morphology and structure of the MOFs were investigated by scanning SEM and FTIR, respectively. The performance of the activated electrode toward the electrooxidation of glucose in alkaline solution was evaluated with cyclic voltammetry technology in the potential range from 0.2 V to 0.6 V. The electrochemical activated Ni-MOFs exhibited obvious anodic (0.46 V) and cathodic peaks (0.37 V) in the 0.1 M NaOH solution due to the Ni(II)/Ni(III) transfer. A linear relationship between the glucose concentrations (ranging from 0 to 10 mM) and anodic peak currents with R2 = 0.954 was obtained. It was found that the diffusion of glucose was the limiting step in the electrochemical reaction. The sensor exhibited good selectivity toward glucose in the presence of 10-folds uric acid and ascorbic acid. Moreover, this sensor showed good long-term stability for continuous glucose monitoring. The good selectivity, stability, and rapid response of this sensor suggests that it could have potential applications in long-term non-enzymatic blood glucose monitoring.

Decreased complexity of glucose dynamics in diabetes: evidence from multiscale entropy analysis of continuous glucose monitoring system data

2014 ◽  
Vol 307 (2) ◽  
pp. R179-R183 ◽  
Author(s):  
Jin-Long Chen (陳錦龍) ◽  
Pin-Fan Chen (陳品汎) ◽  
Hung-Ming Wang (王鴻銘)
Keyword(s):  
Monitoring System ◽  
Continuous Glucose Monitoring ◽  
Glucose Monitoring ◽  
Continuous Glucose Monitoring System ◽  
Multiscale Entropy ◽  
Diabetic Patients ◽  
Control Subjects ◽  
Complexity Index ◽  
The Mean ◽  
Glucose Dynamics

Parameters of glucose dynamics recorded by the continuous glucose monitoring system (CGMS) could help in the control of glycemic fluctuations, which is important in diabetes management. Multiscale entropy (MSE) analysis has recently been developed to measure the complexity of physical and physiological time sequences. A reduced MSE complexity index indicates the increased repetition patterns of the time sequence, and, thus, a decreased complexity in this system. No study has investigated the MSE analysis of glucose dynamics in diabetes. This study was designed to compare the complexity of glucose dynamics between the diabetic patients ( n = 17) and the control subjects ( n = 13), who were matched for sex, age, and body mass index via MSE analysis using the CGMS data. Compared with the control subjects, the diabetic patients revealed a significant increase ( P < 0.001) in the mean (diabetic patients 166.0 ± 10.4 vs. control subjects 93.3 ± 1.5 mg/dl), the standard deviation (51.7 ± 4.3 vs. 11.1 ± 0.5 mg/dl), and the mean amplitude of glycemic excursions (127.0 ± 9.2 vs. 27.7 ± 1.3 mg/dl) of the glucose levels; and a significant decrease ( P < 0.001) in the MSE complexity index (5.09 ± 0.23 vs. 7.38 ± 0.28). In conclusion, the complexity of glucose dynamics is decreased in diabetes. This finding implies the reactivity of glucoregulation is impaired in the diabetic patients. Such impairment presenting as an increased regularity of glycemic fluctuating pattern could be detected by MSE analysis. Thus, the MSE complexity index could potentially be used as a biomarker in the monitoring of diabetes.

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