scholarly journals The Use of HbA1c, Glycated Albumin and Continuous Glucose Monitoring to Assess Glucose Control in the Chronic Kidney Disease Population Including Dialysis

Nephron ◽  
2020 ◽  
Vol 145 (1) ◽  
pp. 14-19
Author(s):  
Tobias Bomholt ◽  
Therese Adrian ◽  
Kirsten Nørgaard ◽  
Ajenthen G. Ranjan ◽  
Thomas Almdal ◽  
...  
Keyword(s):  
Glycemic Control ◽  
Continuous Glucose Monitoring ◽  
Glycated Albumin ◽  
Glucose Monitoring ◽  
Glucose Variability ◽  
Glycated Haemoglobin ◽  
Time Data ◽  
Precise Estimation ◽  
Patients With Diabetes ◽  
Mean Glucose

<b><i>Background:</i></b> Glycated haemoglobin A<sub>1c</sub> (HbA<sub>1c</sub>) has limitations as a glycemic marker for patients with diabetes and CKD and for those receiving dialysis. Glycated albumin is an alternative glycemic marker, and some studies have found that glycated albumin more accurately reflects glycemic control than HbA<sub>1c</sub> in these groups. However, several factors are known to influence the value of glycated albumin including proteinuria. Continuous glucose monitoring (CGM) is another alternative to HbA<sub>1c</sub>. CGM allows one to assess mean glucose, glucose variability, and the time spent in hypo-, normo-, and hyperglycemia. Currently, several different CGM models are approved for use in patients receiving dialysis; CKD (not on dialysis) is not a contraindication in any of these models. Some devices are for blind recording, while others provide real-time data to patients. Small studies suggest that CGM could improve glycemic control in hemodialysis patients, but this has not been studied for individual CKD stages. <b><i>Summary:</i></b> Glycated albumin and CGM avoid the pitfalls of HbA<sub>1c</sub> in CKD and dialysis populations. However, the value of glycated albumin may be affected by several factors. CGM provides a precise estimation of the mean glucose. Here, we discuss the strengths and limitations for using HbA1c, glycated albumin, or CGM in CKD and dialysis population. <b><i>Key Messages:</i></b> Glycated albumin is an alternative glycemic marker but is affected by proteinuria. CGM provides a precise estimation of mean glucose and glucose variability. It remains unclear if CGM improves glycemic control in the CKD and dialysis populations.

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 Glucose Control in the Intensive Care Unit by Use of Continuous Glucose Monitoring: What Level of Measurement Error Is Acceptable?

2014 ◽  
Vol 60 (12) ◽  
pp. 1500-1509 ◽  
Author(s):  
Malgorzata E Wilinska ◽  
Roman Hovorka
Keyword(s):  
Intensive Care Unit ◽  
Intensive Care ◽  
Measurement Error ◽  
Continuous Glucose Monitoring ◽  
Glucose Control ◽  
Glucose Monitoring ◽  
Glucose Variability ◽  
Glucose Measurement ◽  
Target Range ◽  
Mean Glucose

Abstract BACKGROUND Accuracy and frequency of glucose measurement is essential to achieve safe and efficacious glucose control in the intensive care unit. Emerging continuous glucose monitors provide frequent measurements, trending information, and alarms. The objective of this study was to establish the level of accuracy of continuous glucose monitoring (CGM) associated with safe and efficacious glucose control in the intensive care unit. METHODS We evaluated 3 established glucose control protocols [Yale, University of Washington, and Normoglycemia in Intensive Care Evaluation and Surviving Using Glucose Algorithm Regulation (NICE-SUGAR)] by use of computer simulations. Insulin delivery was informed by intermittent blood glucose (BG) measurements or CGM levels with an increasing level of measurement error. Measures of glucose control included mean glucose, glucose variability, proportion of time glucose was in target range, and hypoglycemia episodes. RESULTS Apart from the Washington protocol, CGM with mean absolute relative deviation (MARD) ≤15% resulted in similar mean glucose as with the use of intermittent BG measurements. Glucose variability was also similar between CGM and BG-informed protocols. Frequency and duration of hypoglycemia were not worse by use of CGM with MARD ≤10%. Measures of glucose control varied more between protocols than at different levels of the CGM error. CONCLUSIONS The efficacy of CGM-informed and BG-informed commonly used glucose protocols is similar, but the risk of hypoglycemia may be reduced by use of CGM with MARD ≤10%. Protocol choice has greater influence on glucose control measures than the glucose measurement method.

Comparison of Glycemic Markers in Chronic Hemodialysis Using Continuous Glucose Monitoring

2017 ◽  
Vol 47 (1) ◽  
pp. 21-29 ◽  
Author(s):  
Maria Divani ◽  
Panagiotis I. Georgianos ◽  
Triantafyllos Didangelos ◽  
Fotios Iliadis ◽  
Areti Makedou ◽  
...  
Keyword(s):  
Glycemic Control ◽  
Continuous Glucose Monitoring ◽  
Glycated Albumin ◽  
Glucose Monitoring ◽  
Standard Of Care ◽  
Hemodialysis Patients ◽  
Chronic Hemodialysis ◽  
Poor Glycemic Control ◽  
Area Under Roc Curve ◽  
Average Glucose

Background: Glycated hemoglobin A1c (HbA1c) among diabetic hemodialysis patients continues to be the standard of care, although its limitations are well recognized. This study evaluated glycated albumin (GA) and glycated serum protein (GSP) as alternatives to HbA1c in detecting glycemic control among diabetic hemodialysis patients using continuous-glucose-monitoring (CGM)-derived glucose as reference standard. Methods: A CGM system (iPRO) was applied for 7 days in 37 diabetic hemodialysis patients to determine glycemic control. The accuracy of GA and GSP versus HbA1c in detecting a 7-day average glucose ≥184 mg/dL was evaluated via receiver-operating-characteristic (ROC) analysis. Results: CGM-derived glucose exhibited strong correlation (r = 0.970, p < 0.001) and acceptable agreement with corresponding capillary glucose measurements obtained by the patients themselves in 1,169 time-points over the 7-day-long CGM. The area under ROC curve (AUC) for GA, GSP, and HbA1c to detect poor glycemic control was 0.976 (0.862–1.000), 0.682 (0.502–0.862), and 0.776 (0.629–0.923) respectively. GA levels >20.3% had 90.9% sensitivity and 96.1% specificity in detecting a 7-day average glucose ≥184 mg/dL. The AUC for GA was significantly higher than the AUC for GSP (difference between areas: 0.294, p < 0.001) and the AUC for HbA1c (difference between areas: 0.199, p < 0.01). Conclusion: Among diabetic hemodialysis patients, GA is a stronger indicator of poor glycemic control assessed with 7-day-long CGM when compared to GSP and HbA1c.

scholarly journals Advances in Understanding Glucose Variability and the Role of Continuous Glucose Monitoring

2010 ◽  
Vol 06 ◽  
pp. 53 ◽  
Author(s):  
Klaus-Dieter Kohnert ◽  
Lutz Vogt ◽  
◽  
Keyword(s):  
Continuous Glucose Monitoring ◽  
Glucose Monitoring ◽  
Clinical Importance ◽  
Mean Amplitude ◽  
Glucose Variability ◽  
Glycated Haemoglobin ◽  
Data Sets ◽  
Temporal Aspects ◽  
Chronic Hyperglycaemia ◽  
The Impact

Recent data have suggested that glucose variability may add to or modify the risk of diabetes complications. Glycated haemoglobin (HbA1c), an integrated measure of sustained chronic hyperglycaemia, fails to reflect glucose variability and the risks associated with extreme glucose swings. Thus, whether glucose variability should become an integral part of assessing glucose control in clinical practice remains unknown. Since the establishment of continuous glucose monitoring (CGM) systems, various indices of glucose variability and quality of glycaemic control such as the mean amplitude of glycaemic excursions (MAGE) and the Glycaemic Risk Assessment Diabetes Equation (GRADE) can now be precisely computed from CGM data sets. Analysis of CGM data, including the impact of glucose variability and its temporal aspects, has clinical importance and should be incorporated into use in clinical trials and the design of optimal antidiabetes therapies.

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 Real-World Hypoglycemia Avoidance With a Predictive Low Glucose Alert Does Not Depend on Frequent Screen Views

2019 ◽  
Vol 14 (1) ◽  
pp. 83-86 ◽  
Author(s):  
Sarah Puhr ◽  
Mark Derdzinski ◽  
Andrew Scott Parker ◽  
John B. Welsh ◽  
David A. Price
Keyword(s):  
Continuous Glucose Monitoring ◽  
Glucose Monitoring ◽  
Mobile App ◽  
Convenience Sample ◽  
Minute Interval ◽  
Patients With Diabetes ◽  
Low Glucose ◽  
Device Interaction ◽  
Mean Glucose ◽  
Monitoring Devices

Background: Frequent real-time continuous glucose monitoring (rtCGM) data viewing has been associated with reduced mean glucose and frequent scanning of an intermittently scanned continuous glucose monitoring (isCGM) system has been associated with reduced hypoglycemia for patients with diabetes. However, requiring patients to frequently interact with their glucose monitoring devices to detect actual or impending hypoglycemia is burdensome. We hypothesized that a predictive low glucose alert, which forecasts glucose ≤55 mg/dL within 20 minutes and is included in a new rtCGM system, could mitigate hypoglycemia without requiring frequent device interaction. Methods: We analyzed estimated glucose values (EGVs) from an anonymized convenience sample of 15,000 patients who used Dexcom G6 (Dexcom, Inc, San Diego, CA, USA) and its mobile app for at least 30 days with or without the “Urgent Low Soon” alert (ULS) enabled. Screen view frequency was determined as the frequency with which the trend screen was accessed on the app. Multiple screen views within any 5-minute interval were counted as one. Hypoglycemia exposure for patients in the top and bottom quartiles of screen view frequency (>8.25 and <3.30 per day, respectively) was calculated as the percentage of EGVs below various thresholds. Results: Over 93% of users enabled the ULS alert; its use was associated with significantly reduced hypoglycemia <55 and <70 mg/dL, independent of screen view frequency. Conclusion: Use of the G6 ULS alert may disencumber rtCGM users by promoting significant reductions in hypoglycemia without requiring frequent device interactions.

scholarly journals The Bihormonal Bionic Pancreas Improves Glycemic Control in Individuals With Hyperinsulinism and Postpancreatectomy Diabetes— A Pilot Study

2021 ◽  
Author(s):  
Arpana Rayannavar ◽  
Lauren M. Mitteer ◽  
Courtney A. Balliro ◽  
Firas H. El-Khatib ◽  
Katherine L. Lord ◽  
...  
Keyword(s):  
Pilot Study ◽  
Glycemic Control ◽  
Usual Care ◽  
Continuous Glucose Monitoring ◽  
Glucose Concentration ◽  
Glucose Monitoring ◽  
Congenital Hyperinsulinism ◽  
Open Label ◽  
Mean Glucose

<i>Objective:</i> To determine if the bihormonal bionic pancreas (BHBP) improves glycemic control and reduces hypoglycemia in individuals with congenital hyperinsulinism (HI) and post-pancreatectomy diabetes (PPD) compared with usual care (UC). <p><i>Methods</i>: Ten subjects with HI and PPD completed this open-label, crossover pilot study. Co-primary outcomes were mean glucose concentration and time with continuous glucose monitoring (CGM) glucose concentration <3.3 mmol/L.</p> <p><i>Results</i>: Mean (SD) CGM glucose concentration was 8.3 mmol/L (0.7) in the BHBP period vs. 9 mmol/L (1.8) in the UC period (p=0.13). Mean (SD) time with CGM glucose concentration <3.3 mmol/L was 0% (0.002) in the BHBP period vs. 1.3% (0.018) in the UC period (p=0.11). </p> <p><i>Conclusion</i>: Relative to UC, the BHBP resulted in comparable glycemic control in our population. </p>

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