Use of Continuous Glucose Monitoring in Patients with Diabetes Mellitus on Peritoneal Dialysis: Correlation with Glycated Hemoglobin and Detection of High Incidence of Unaware Hypoglycemia

2015 ◽  
Vol 41 (1-3) ◽  
pp. 18-24 ◽  
Author(s):  
Ahad Qayyum ◽  
Tahseen A. Chowdhury ◽  
Elizabeth Ley Oei ◽  
Stanley L. Fan
Keyword(s):  
Peritoneal Dialysis ◽  
Continuous Glucose Monitoring ◽  
Glycated Hemoglobin ◽  
Glucose Monitoring ◽  
Diabetic Control ◽  
Diabetic Patients ◽  
Glucose Levels ◽  
High Incidence ◽  
Patients With Diabetes ◽  
Interstitial Glucose

Introduction: Glycated hemoglobin is used to assess diabetic control although its accuracy in dialysis has been questioned. How does it compare to the Continuous Glucose Monitoring System (CGMS) in peritoneal dialysis (PD) patients? Methods: We conducted a retrospective analysis of 60 insulin-treated diabetic patients on PD. We determined the mean interstitial glucose concentration and the proportion of patients with hypoglycemia (<4 mmol/l) or hyperglycemia (>11 mmol/l). Results: The correlation between HbA1c and glucose was 0.48, p < 0.0001. Three of 15 patients with HbA1c >75 mmol/mol experienced significant hypoglycemia (14-144 min per day). The patients with frequent episodes of hypoglycemia could not be differentiated from those with frequent hyperglycemia by demographics or PD prescription. Conclusion: HbA1c and average glucose levels measured by the CGMS are only weakly correlated. On its own, HbA1c as an indicator of glycemic control in patients with diabetes on PD appears inadequate. We suggest that the CGMS technology should be more widely adopted.

Hemoglobin A1c and Fructosamine Evaluated in Patients with Type 2 Diabetes Receiving Peritoneal Dialysis Using Long-Term Continuous Glucose Monitoring

Nephron ◽  
2021 ◽  
pp. 1-7
Author(s):  
Tobias Bomholt ◽  
Bo Feldt-Rasmussen ◽  
Rizwan Butt ◽  
Rikke Borg ◽  
Mir Hassan Sarwary ◽  
...  
Keyword(s):  
Type 2 Diabetes ◽  
Peritoneal Dialysis ◽  
Continuous Glucose Monitoring ◽  
Plasma Glucose ◽  
Glucose Monitoring ◽  
Control Group ◽  
Glucose Levels ◽  
Sensor Glucose ◽  
Interstitial Glucose

<b><i>Introduction:</i></b> Shortened erythrocyte life span and erythropoietin-stimulating agents may affect hemoglobin A<sub>1c</sub> (HbA<sub>1c</sub>) levels in patients receiving peritoneal dialysis (PD). We compared HbA<sub>1c</sub> with interstitial glucose measured by continuous glucose monitoring (CGM) in patients with type 2 diabetes receiving PD. <b><i>Methods:</i></b> Fourteen days of CGM (Ipro2, Medtronic) were performed in 23 patients with type 2 diabetes receiving PD and in 23 controls with type 2 diabetes and an estimated glomerular filtration rate over 60 mL/min/1.73 m<sup>2</sup>. Patients were matched on gender and age (±5 years). HbA<sub>1c</sub> (mmol/mol), its derived estimate of mean plasma glucose (eMPG<sub>A1c</sub>) (mmol/L), and fructosamine (µmol/L) were measured at the end of the CGM period and compared with the mean sensor glucose (mmol/L) from CGM. <b><i>Results:</i></b> In the PD group, mean sensor glucose was 0.98 (95% con­fidence interval (CI): 0.43–1.54) mmol/L higher than the eMPG<sub>A1c</sub> compared with the control group (<i>p</i> = 0.002) where glucose levels were nearly identical (−0.05 (95% CI: −0.35–0.25) mmol/L). A significant association was found between fructosamine and mean sensor glucose using linear regression with no difference between slopes (<i>p</i> = 0.89) or y-intercepts (<i>p</i> = 0.28). <b><i>Discussion/Conclusion:</i></b> HbA<sub>1c</sub> underestimates mean plasma glucose levels in patients with type 2 diabetes receiving PD. However, the clinical significance of this finding is undetermined. Fructosamine seems to more accurately reflect glycemic status. CGM or fructosamine could complement HbA<sub>1c</sub> to increase the accuracy of glycemic monitoring in the PD population.

scholarly journals Glycemic variability and cardiovascular disease in patients with type 2 diabetes

2021 ◽  
Vol 9 (1) ◽  
pp. e002032
Author(s):  
Marcela Martinez ◽  
Jimena Santamarina ◽  
Adrian Pavesi ◽  
Carla Musso ◽  
Guillermo E Umpierrez
Keyword(s):  
Type 2 Diabetes ◽  
Blood Glucose ◽  
Glycated Hemoglobin ◽  
Glucose Monitoring ◽  
Glycemic Variability ◽  
Cardiovascular Complications ◽  
Glucose Levels ◽  
Increased Risk ◽  
Patients With Diabetes

Glycated hemoglobin is currently the gold standard for assessment of long-term glycemic control and response to medical treatment in patients with diabetes. Glycated hemoglobin, however, does not address fluctuations in blood glucose. Glycemic variability (GV) refers to fluctuations in blood glucose levels. Recent clinical data indicate that GV is associated with increased risk of hypoglycemia, microvascular and macrovascular complications, and mortality in patients with diabetes, independently of glycated hemoglobin level. The use of continuous glucose monitoring devices has markedly improved the assessment of GV in clinical practice and facilitated the assessment of GV as well as hypoglycemia and hyperglycemia events in patients with diabetes. We review current concepts on the definition and assessment of GV and its association with cardiovascular complications in patients with type 2 diabetes.

P6441New continuous glucose monitoring reveals hypoglycemia risk in both diabetic and nondiabetic patients with acute myocardial infarction

2019 ◽  
Vol 40 (Supplement_1) ◽  
Author(s):  
M Kuroda ◽  
M Kawata ◽  
A Matsuura ◽  
K Adachi ◽  
Y Hirayama ◽  
...  
Keyword(s):  
Myocardial Infarction ◽  
Acute Myocardial Infarction ◽  
Continuous Glucose Monitoring ◽  
Glucose Monitoring ◽  
Mean Amplitude ◽  
Glucose Fluctuation ◽  
Glucose Levels ◽  
Interstitial Glucose ◽  
The Mean ◽  
Artery Disease

Abstract Background There has been growing evidence that the glucose fluctuation is an important contributing factor to the development of coronary artery disease. However, whether large glucose fluctuation, especially hypoglycemia, may be associated with acute myocardial infarction (AMI) remains largely unknown. Aim As new continuous glucose monitoring (CGM) has recently become available to evaluate glucose fluctuation from immediately after an emergency visit, this study sought to investigate glucose fluctuation and the occurrence of hypoglycemia in patients with AMI. Methods In this prospective study, 93 consecutive patients with AMI from April 2017 to November 2018 were enrolled. Subcutaneous interstitial glucose levels were monitored from emergency room to discharge using the CGM System. Based on the CGM data, 24-h mean glucose levels, the time in hyperglycemia and hypoglycemia and the occurrence of hypoglycemia, defined as less than 70 mg/dL, were measured, and the mean amplitude of glycemic excursions (MAGE) were calculated. Results The majority of patients [n=57, 61% (non-DM)] did not have diabetes and 36 patients had diabetes (DM). The occurrence of hypoglycemia within 24 hours after admission was observed in 49 patients [DM: n=11 (30.6%), non-DM: n=38 (66.7%)]. MAGE within 24 hours after admission were 100±47 in DM patients and 67±20 in non-DM patients. The mean time in hypoglycemia within 24 hours after admission was 148 minutes [DM: 100±260 minutes, non-DM: 178±287 minutes]. The occurrence of hypoglycemia during a hospital stay (mean 11.5 days) was detected in 76 patients [DM: n=28 (77.8%), non-DM: n=48 (84.2%)]. Representative case of hypoglycemia Conclusion Not only in DM patients but also in non-DM patients with AMI, large glucose fluctuation and high incidence of hypoglycemia were observed using new CGM system. Further investigations should address the rationale for the early detection and control of glucose fluctuation for AMI patients.

scholarly journals Correlation between short- and mid-term hemoglobin A1c and glycemic control determined by continuous glucose monitoring

2021 ◽  
Vol 13 (1) ◽  
Author(s):  
Jen-Hung Huang ◽  
Yung-Kuo Lin ◽  
Ting-Wei Lee ◽  
Han-Wen Liu ◽  
Yu-Mei Chien ◽  
...  
Keyword(s):  
Blood Glucose ◽  
Glycemic Control ◽  
Continuous Glucose Monitoring ◽  
Hemoglobin A1c ◽  
Glucose Monitoring ◽  
Mean Amplitude ◽  
Glucose Levels ◽  
Blood Glucose Levels ◽  
Before And After ◽  
Patients With Diabetes

Abstract Background Glucose monitoring is vital for glycemic control in patients with diabetes mellitus (DM). Continuous glucose monitoring (CGM) measures whole-day glucose levels. Hemoglobin A1c (HbA1c) is a vital outcome predictor in patients with DM. Methods This study investigated the relationship between HbA1c and CGM, which remained unclear hitherto. Data of patients with DM (n = 91) who received CGM and HbA1c testing (1–3 months before and after CGM) were retrospectively analyzed. Diurnal and nocturnal glucose, highest CGM data (10%, 25%, and 50%), mean amplitude of glycemic excursions (MAGE), percent coefficient of variation (%CV), and continuous overlapping net glycemic action were compared with HbA1c values before and after CGM. Results The CGM results were significantly correlated with HbA1c values measured 1 (r = 0.69) and 2 (r = 0.39) months after CGM and 1 month (r = 0.35) before CGM. However, glucose levels recorded in CGM did not correlate with the HbA1c values 3 months after and 2–3 months before CGM. MAGE and %CV were strongly correlated with HbA1c values 1 and 2 months after CGM, respectively. Diurnal blood glucose levels were significantly correlated with HbA1c values 1–2 months before and 1 month after CGM. The nocturnal blood glucose levels were significantly correlated with HbA1c values 1–3 months before and 1–2 months after CGM. Conclusions CGM can predict HbA1c values within 1 month after CGM in patients with DM.

scholarly journals Noninvasive, wearable, and tunable electromagnetic multisensing system for continuous glucose monitoring, mimicking vasculature anatomy

2020 ◽  
Vol 6 (24) ◽  
pp. eaba5320
Author(s):  
Jessica Hanna ◽  
Moussa Bteich ◽  
Youssef Tawk ◽  
Ali H. Ramadan ◽  
Batoul Dia ◽  
...  
Keyword(s):  
Continuous Glucose Monitoring ◽  
Artificial Pancreas ◽  
Time Lag ◽  
Life Quality ◽  
Glucose Monitoring ◽  
Physical Parameters ◽  
Diabetic Patients ◽  
Glucose Levels ◽  
Glycemic Monitoring ◽  
Animal Models Of Diabetes

Painless, needle-free, and continuous glucose monitoring sensors are needed to enhance the life quality of diabetic patients. To that extent, we propose a first-of-its-kind, highly sensitive, noninvasive continuous glycemic monitoring wearable multisensor system. The proposed sensors are validated on serum, animal tissues, and animal models of diabetes and in a clinical setting. The noninvasive measurement results during human trials reported high correlation (>0.9) between the system’s physical parameters and blood glucose levels, without any time lag. The accurate real-time responses of the sensors are attributed to their unique vasculature anatomy–inspired tunable electromagnetic topologies. These wearable apparels wirelessly sense hypo- to hyperglycemic variations with high fidelity. These components are designed to simultaneously target multiple body locations, which opens the door for the development of a closed-loop artificial pancreas.

scholarly journals A Multicenter Evaluation of the Performance and Usability of a Novel Glucose Monitoring System in Chinese Adults With Diabetes

2016 ◽  
Vol 11 (2) ◽  
pp. 290-295 ◽  
Author(s):  
Linong Ji ◽  
Xiaohui Guo ◽  
Lixin Guo ◽  
Qian Ren ◽  
Nan Yu ◽  
...  
Keyword(s):  
Glucose Monitoring ◽  
Relative Difference ◽  
Glucose Sensing ◽  
Chinese Patients ◽  
Chinese Adults ◽  
Flash Glucose Monitoring ◽  
Glucose Levels ◽  
Patients With Diabetes ◽  
Interstitial Glucose ◽  
Error Grid

Objective: Flash glucose monitoring is a new glucose sensing technique that measures interstitial glucose levels for up to 14 days and does not require any calibration. The aim of this study is to evaluate the performance of the new system in Chinese patients with diabetes. Methods: A multicenter, prospective, masked study was performed in a total of 45 subjects with diabetes. Subjects wore 2 sensors at the same time, for up to 14 days. The accuracy was evaluated against capillary blood glucose (BG) and venous Yellow Springs Instrument (YSI; Yellow Springs, OH) measurements. During all 14 days, subjects were asked to perform at least 8 capillary BG tests per day. Each subject attended 3 days of 8-hour clinic sessions to measure YSI and sensor readings every 15 minutes. Results: Forty subjects had evaluable glucose readings, with 6687 of 6696 (99.9%) sensor and capillary BG pairs within consensus error grid zones A and B, including 5824 (87.0%) in zone A. The 6969 sensor and venous YSI pairs resulted in 6965 (99.9%) pairs within zones A and B, including 5755 (82.6%) in zone A. The sensor pairs with BG and YSI result in mean absolute relative difference (MARD) of 10.0% and 10.7%, respectively. Overall between-sensor coefficient of variation (CV) was 8.0%, and the mean lag time was 3.1 (95% confidence interval 2.54 to 4.29) minutes. Conclusions: The system works well for people with diabetes in China, and it is easy to wear and use.

scholarly journals Self-measurement of Blood Glucose and Continuous Glucose Monitoring – Is There Only One Future?

2018 ◽  
Vol 14 (2) ◽  
pp. 24 ◽  
Author(s):  
Lutz Heinemann ◽  
Andreas Stuhr
Keyword(s):  
Blood Glucose ◽  
Continuous Glucose Monitoring ◽  
Subcutaneous Tissue ◽  
Glucose Monitoring ◽  
Relative Difference ◽  
Management Options ◽  
Measurement Quality ◽  
Glucose Levels ◽  
Wide Range ◽  
Patients With Diabetes

Monitoring glycaemic control in patients with diabetes has evolved dramatically over the past decades. The introduction of easy-to-use systems for self-monitoring of blood glucose (SMBG) utilising capillary blood samples has resulted in the availability of a wide range of systems, providing different measurement quality. Systems for continuous glucose monitoring (CGM) – used mainly in patients with type 1 diabetes (T1D) – were made possible by the development of glucose sensors that measure glucose levels in the interstitial fluid (ISF) in the subcutaneous tissue of the skin. CGM readings might not correspond exactly to SMBG measurement results taken at the same time, especially during rapid changes in either blood glucose or ISF glucose levels. The mean absolute relative difference is the most popular method used for characterising the measurement performance of CGM systems. Unlike the International Organization for Standardization 15197:2013 criteria for SMBG systems, no accuracy standards for CGM systems exist. Measurement quality of CGM systems can vary based on several factors, limiting their safety and effective use in managing diabetes. Patients have to be trained adequately to make safe and efficient use of CGM systems (like with SMBG systems). Also, systems for CGM must be evaluated in terms of patient safety and the ability to provide accurate measurements regardless of the fluctuation of glucose levels. As new technological advancements in glucose monitoring are essential for improved management options of diabetes, such as automated insulin dosing systems, there is a need for a critical view of all such developments. It is likely that both, SMBG and CGM systems, will play important future roles in the treatment of diabetes.

scholarly journals Outcome of Patients with ST-T Changes in Non STSegment Elevation Myocardial Infarction

2021 ◽  
Vol 1 (1) ◽  
Author(s):  
Mohammad Arifur Rahman
Keyword(s):  
Myocardial Infarction ◽  
Continuous Glucose Monitoring ◽  
Hemoglobin A1c ◽  
Glycated Hemoglobin ◽  
Glucose Monitoring ◽  
Measurement Methods ◽  
Glucose Levels ◽  
Sensor Device ◽  
Sensor Glucose ◽  
Average Glucose

This article discusses the fundamental characteristics of measured glucose levels and predicted glycated hemoglobin A1c (HbA1c) values among three sets of collected data, measured finger-piercing and continuous glucose monitoring (CGM) sensor device collected glucose levels at 15-minute (15-min) and 5-minute (5-min) intervals. The average glucose (in milligram per deciliter-mg/dL) is listed below: Finger glucose: 109 mg/dL (100%) Sensor at 15-min: 120 mg/dL (109%) Sensor at 5-min: 117 mg/dL (107%) Using candlestick chart, the comparison of average glucoses during this period between two sensor glucose (mg/dL) data (15-min/5-min) are as follows: Open glucose: 108/111 Close glucose: 115/115 Maximum (max) glucose: 170 /175 Minimum (min) glucose: 85/83 Average glucose: 120/117 Additional analysis of time above range (TAR)≥140 mg/dL for hyperglycemia, time within the range (TIR) from 70-140 mg/dL for normal, time below range (TBR)≤70 mg/dL for hypoglycemia based on two sensor candlesticks revealing the following information in a specific format of TAR%/ TIR%/TBR%. 15-min:18.3%, 80.5%, 1.2% 5-min: 17.0%, 81.9%, 1.1% By evaluating the results of the TIR analysis, the 5-min glucose levels appear to be marginally healthier (1.4%) than the 15-min ones. During the coronavirus pandemic (COVID 19) quarantine period, the author lived a rather unique lifestyle which is extremely calm with regular routines, such as eating home-cooked meals and exercising on a regular basis. As a result, his HbA1c has decreased from 6.6% to 6.3% with an average A1c of 6.4% without taking any diabetes medications. However, these three different measurement methods still provide three different sets of glucoses levels which are within a 10% margin of differences, while the HbA1c values are particularly close to each other between the finger-piercing and CGM 15-min.

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