scholarly journals Comparison of Glucose Area Under the Curve Measured Using Minimally Invasive Interstitial Fluid Extraction Technology with Continuous Glucose Monitoring System in Diabetic Patients

2017 ◽  
Vol 41 (4) ◽  
pp. 265 ◽  
Author(s):  
Mei Uemura ◽  
Yutaka Yano ◽  
Toshinari Suzuki ◽  
Taro Yasuma ◽  
Toshiyuki Sato ◽  
...  
Keyword(s):  
Minimally Invasive ◽  
Monitoring System ◽  
Continuous Glucose Monitoring ◽  
Interstitial Fluid ◽  
Glucose Monitoring ◽  
Area Under The Curve ◽  
Continuous Glucose Monitoring System ◽  
Diabetic Patients ◽  
Fluid Extraction ◽  
Extraction Technology

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.

Peritoneal dialysis – risk factor for glycemic variability assessed by continuous glucose monitoring system

2014 ◽  
Vol 21 (1) ◽  
pp. 47-54 ◽  
Author(s):  
Simona Popa ◽  
Cristina Văduva ◽  
Maria Moţa ◽  
Eugen Moţa
Keyword(s):  
Risk Factor ◽  
Peritoneal Dialysis ◽  
Monitoring System ◽  
Continuous Glucose Monitoring ◽  
Glucose Monitoring ◽  
Glycemic Variability ◽  
Continuous Glucose Monitoring System ◽  
Diabetic Patients ◽  
Type 2 Diabetic

Abstract Background and Aims. Peritoneal dialysis (PD) is accompanied by a multitude of factors that influence glycemic variability, and HbA1c does not detect dynamic glucose changes. In this study we wanted to assess glycemic variability, using a 72-hour continuous glucose monitoring system (CGMS), in 31 patients stratified according to the presence of type 2 diabetes and PD. Materials and Methods. The study included 31 patients (11 type 2 diabetic PD patients, 9 non diabetic PD patients and 11 type 2 diabetic patients without PD). Glycemic variability was assessed on CGM readings by: Mean Amplitude of Glycemic Excursion (MAGE), Mean of Daily Differences (MODD), Fractal Dimensions (FD), Mean Interstitial Glucose (MIG), Area Under glycemia Curve (AUC), M100, % time with glucose >180/<70 mg/dl. Results. The PD diabetic patients presented AUC, MIG and inter-day glycemic variability (MODD) significantly higher than diabetic patients without PD. In PD patients, the type of dialysis fluid in the nocturnal exchange and peritoneal membrane status did not significantly influence glycemic variability. Conclusions. CGMS is more useful than HbA1c in quantifying the metabolic imbalance of PD patients. PD induces inter-day glycemic variability and poor glycemic control, thus being a potential risk factor for chronic complications progression in diabetic patients.

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