Accuracy of a Factory-Calibrated Continuous Glucose Monitor in Individuals With Diabetes on Hemodialysis

Cardiac arrhythmia and hypoglycaemia among individuals with and without diabetes receiving haemodialysis (the CADDY study): a Danish multicentre cohort study

AIMS/HYPOTHESIS

We aimed to examine arrhythmias and hypoglycaemia among individuals with and without diabetes who are receiving haemodialysis and to investigate the association between arrhythmias and hypoglycaemia, hyperglycaemia and glycaemic variability.

METHODS

This prospective multicentre cohort study included 70 participants on maintenance haemodialysis (35 with diabetes and 35 without diabetes). We employed implantable cardiac monitors for continuous heart rhythm monitoring in combination with periodic use of continuous glucose monitoring. Logistic-regression-type linear mixed models were used to examine associations between arrhythmias and glycaemic measures.

RESULTS

During 18 months of follow-up, clinically significant arrhythmias (bradyarrhythmia and ventricular tachycardia) were identified in 12 (34%) participants with diabetes and 11 (31%) without diabetes. Atrial fibrillation was detected in 13 (37%) participants with diabetes and 14 (40%) without, while other supraventricular tachycardia was detected in seven (20%) and 11 (31%) participants with and without diabetes, respectively. Hypoglycaemia (sensor glucose <3.9 mmol/l) was observed in 27 (77%) participants with diabetes and 32 (91%) without diabetes. Compared with euglycaemia, hypoglycaemia was associated with an increased rate of arrhythmias among participants without diabetes (incidence rate ratio [IRR] 3.13 [95% CI 1.49, 6.55]), while hyperglycaemia (sensor glucose >10.0 mmol/l) was associated with a decreased rate of arrhythmias among participants with diabetes (IRR 0.58 [95% CI 0.37, 0.92]). Glycaemic variability showed no association with arrhythmias regardless of the presence of diabetes.

CONCLUSIONS/INTERPRETATION

Arrhythmias and hypoglycaemia were common in those undergoing haemodialysis regardless of diabetes status. Our data suggest a temporal relationship between arrhythmias and glucose level in both individuals with and without diabetes.

TRIAL REGISTRATION

Clinicaltrials.gov: NCT04841304.

Accuracy of the Dexcom G7 Continuous Glucose Monitoring Sensors in People with Diabetes Undergoing Hemodialysis (ALPHA-2 Study)

The accuracy of the latest generation Dexcom G7 sensors in individuals with diabetes undergoing hemodialysis has not previously been investigated. Participants with diabetes undergoing hemodialysis were recruited, with paired sensor glucose from Dexcom G7 recorded with plasma glucose analyzed in the laboratory, as well as the Freestyle Precision Pro glucometer and EKF Biosen C-Line analyzer. Ten adults (median age 64.0 [58.0-74.5] years) were recruited. Overall percentage (%) mean and median absolute relative differences were 10.4% and 8.5% for matched laboratory pairs, respectively (n = 720). Diabetes Technology Society error grid analysis showed 99.7%, 100%, and 99.9% of pairs within zones A and B for lab, glucometer, and EKF methods, respectively. This, the first Dexcom G7 accuracy study conducted in people on hemodialysis, demonstrates accuracy and safety when compared with lab reference readings. These data support the accessibility of continuous glucose monitoring (CGM) and hybrid closed-loop systems for people with diabetes on hemodialysis.

Glycemia Assessed by Continuous Glucose Monitoring among People Treated with Maintenance Dialysis

Key Points

Background

Kidney failure and its treatments disrupt glucose homeostasis in ways that may promote both hyperglycemia and hypoglycemia. Continuous glucose monitoring (CGM) delineates detailed glycemic profiles, but published studies in kidney failure are limited to small, select groups. We aimed to characterize the spectrum of glycemia and its determinants in a large, diverse maintenance dialysis population.

Methods

We conducted a prospective community-based cohort study of people treated with maintenance dialysis. Each participant wore a Dexcom G6 Pro CGM for approximately 10 days. Outcomes ascertained by CGM included mean blood glucose, time in range (TIR, 70–180 mg/dl), and hypoglycemia events (sustained <70 mg/dl).

Results

We enrolled 420 demographically diverse participants, including 263 with diabetes (of whom 88 were untreated with glucose-lowering medications) and 157 without diabetes. Peritoneal dialysis (PD) was used by 55 participants. Outcomes varied by diabetes status and dialysis modality. Among participants without diabetes, mean blood glucose was higher with PD versus hemodialysis (141 versus 121 mg/dl, P < 0.001). Among participants with untreated diabetes, the mean blood glucose was 162 mg/dl, mean TIR 71%, and only 64% of participants attained TIR ≥70%, while mean hemoglobin A1c (HbA1c) was 5.7%. Among participants with treated diabetes, the mean blood glucose was 214 mg/dl, the mean TIR was 43%, and only 22% of participants attained TIR ≥70%, while the mean HbA1c was 7.0%. In total, 714 unique sustained hypoglycemia events were observed, with highest rates for participants without diabetes. In addition to diabetes and dialysis modality, age, dialysis vintage, insulin use, HbA1c, and serum albumin were significantly associated with mean blood glucose, hypoglycemia, or both.

Conclusions

In maintenance dialysis, CGM frequently identified both hyperglycemia and hypoglycemia that may not be clinically evident. In particular, hyperglycemia was common with PD, patients with untreated diabetes maintained a diabetic glycemic profile, and patients with treated diabetes rarely met contemporary CGM-based treatment targets.

Consensus Report on the Use of Continuous Glucose Monitoring in Chronic Kidney Disease and Diabetes

This report represents the conclusions of 15 experts in nephrology and endocrinology, based on their knowledge of key studies and evidence in the field, on the role of continuous glucose monitors (CGMs) in patients with diabetes and chronic kidney disease (CKD), including those receiving dialysis. The experts discussed issues related to CGM accuracy, indications, education, clinical outcomes, quality of life, research gaps, and barriers to dissemination. Three main goals of management for patients with CKD and diabetes were identified: (1) greater use of CGMs for better glycemic monitoring and management, (2) further research evaluating the accuracy, feasibility, outcomes, and potential value of CGMs in patients with end-stage kidney disease (ESKD) on hemodialysis, and (3) equitable access to CGM technology for patients with CKD. The experts also developed 15 conclusions regarding the use of CGMs in this population related to CGMs' unique delivery of both real-time information that can guide monitoring and management of glycemia and continuous and predictive data in this population, which is at higher risk for hypoglycemia and hyperglycemia. The group noted three major clinical gaps: (1) CGMs are not routinely prescribed for patients with diabetes and CKD; (2) CGMs are not approved by the United States Food and Drug Administration (FDA) for patients with diabetes who are on dialysis; and (3) CGMs are not routinely available to all of those who need them because of structural barriers in the health care system. These gaps can be improved with greater stakeholder collaboration, education, and awareness brought to the use of CGM technology in CKD.

Continuous glucose monitoring with FreeStyle Libre PRO sensor in patients with type 2 diabetes and end-stage renal failure on haemoDIALysis (FSLPRO-DIAL pilot study)

AIMS

For end-stage renal disease (ESRD) patients with diabetes on haemodialysis, diabetes control is difficult to achieve. Hypoglycaemia is a major problem in these frailty subjects. Continuous glucose monitoring (CGM) devices appear therefore to be a good tool to help patients monitor their glycaemic control and to help practitioners optimize treatment. We aimed to compare the laboratory value of Hba1c with the sensor-estimated value of Hba1c (= glucose management indicator, GMI) in ESRD patients with type 2 diabetes (T2D) (with or without insulin treatment) on haemodialysis. Secondly, we aimed to identify CGM-derived monitoring parameters [time in range, time in hypo/hyperglycaemia, glycaemic variability (coefficient of variation, CV)] to identify patients at risk of frequent hypo- or hyperglycaemia.

METHODS

The FSLPRO-DIAL pilot study (NCT04641650) was a prospective monocentric cohort study including 29 subjects with T2D who achieve the protocol. Inclusion criteria were: age ≥ 18 years, haemodialysis duration for at least 3 months, type 2 diabetes with no change in treatment for at least 3 months. Demographic data and blood sample were collected at the day of inclusion. Freestyle Libre pro IQ sensor (blinded CGM) was inserted for 14 days. After this period, all CGMs data were collected and analysed.

RESULTS

Data were available for 27 patients. Mean age was 73 ± 10, mean BMI 27.2 kg/m2, mean duration of diabetes 16.9 years and mean dialysis duration 2.9 years. Twenty-four subjects were treated with insulin. Mean HbA1c was 6.6% (SD 1.2), and mean GMI was 6.7% (SD 0.9) (no significant difference, p = 0.3). Twelve subjects (44.4%) had a discordance between HbA1c and GMI of < 0.5%, 11 (40.8%) had a discordance between 0.5 and 1%, and only 4 (14.8%) had a discordance of > 1%. Mean time in range (70-180 mg/dl) was 71.9%, mean time below range (< 70 mg/dl) was 5.6%, and mean time above range (> 180 mg/dl) was 22.1%. Mean CV was 31.8%. For 13 out of 27 patients, we reduced antidiabetic treatment by stopping treatments or reducing insulin doses.

CONCLUSION

In this pilot study, there was no global significant difference between HbA1c and GMI in this particular cohort with very well-controlled diabetes. However, the use of the sensor enabled us to identify an excessive time in hypoglycemia in this fragile population and to adapt their treatment.

Accuracy of Continuous Glucose Monitoring in Hemodialysis Patients With Diabetes

OBJECTIVE

In the general population, continuous glucose monitoring (CGM) provides convenient and less-invasive glucose measurements than conventional self-monitored blood glucose and results in reduced hypoglycemia and hyperglycemia and increased time in target glucose range. However, accuracy of CGM versus blood glucose is not well established in hemodialysis patients.

RESEARCH DESIGN AND METHODS

Among 31 maintenance hemodialysis patients with diabetes hospitalized from October 2020 to May 2021, we conducted protocolized glucose measurements using Dexcom G6 CGM versus blood glucose, with the latter measured before each meal and at night, plus every 30-min during hemodialysis. We examined CGM-blood glucose correlations and agreement between CGM versus blood glucose using Bland-Altman plots, percentage of agreement, mean and median absolute relative differences (ARDs), and consensus error grids.

RESULTS

Pearson and Spearman correlations for averaged CGM versus blood glucose levels were 0.84 and 0.79, respectively; Bland-Altman showed the mean difference between CGM and blood glucose was ∼+15 mg/dL. Agreement rates using %20/20 criteria were 48.7%, 47.2%, and 50.2% during the overall, hemodialysis, and nonhemodialysis periods, respectively. Mean ARD (MARD) was ∼20% across all time periods; median ARD was 19.4% during the overall period and was slightly lower during nonhemodialysis (18.2%) versus hemodialysis periods (22.0%). Consensus error grids showed nearly all CGM values were in clinically acceptable zones A (no harm) and B (unlikely to cause significant harm).

CONCLUSIONS

In hemodialysis patients with diabetes, although MARD values were higher than traditional optimal analytic performance thresholds, error grids showed nearly all CGM values were in clinically acceptable zones. Further studies are needed to determine whether CGM improves outcomes in hemodialysis patients.

Utilising continuous glucose monitoring for glycemic control in diabetic kidney disease

In this editorial, we comment on the article by Zhang et al. Chronic kidney disease (CKD) presents a significant challenge in managing glycemic control, especially in diabetic patients with diabetic kidney disease undergoing dialysis or kidney transplantation. Conventional markers like glycated haemoglobin (HbA1c) may not accurately reflect glycemic fluctuations in these populations due to factors such as anaemia and kidney dysfunction. This comprehensive review discusses the limitations of HbA1c and explores alternative methods, such as continuous glucose monitoring (CGM) in CKD patients. CGM emerges as a promising technology offering real-time or retrospective glucose concentration measure-ments and overcoming the limitations of HbA1c. Key studies demonstrate the utility of CGM in different CKD settings, including hemodialysis and peritoneal dialysis patients, as well as kidney transplant recipients. Despite challenges like sensor accuracy fluctuation, CGM proves valuable in monitoring glycemic trends and mitigating the risk of hypo- and hyperglycemia, to which CKD patients are prone. The review also addresses the limitations of CGM in CKD patients, emphasizing the need for further research to optimize its utilization in clinical practice. Altogether, this review advocates for integrating CGM into managing glycemia in CKD patients, highlighting its superiority over traditional markers and urging clinicians to consider CGM a valuable tool in their armamentarium.

DDLA: a double deep latent autoencoder for diabetic retinopathy diagnose based on continuous glucose sensors

The current diagnosis of diabetic retinopathy is based on fundus images and clinical experience. However, considering the ineffectiveness and non-portability of medical devices, we aimed to develop a diagnostic model for diabetic retinopathy based on glucose series data from the wearable continuous glucose monitoring system. Therefore, this study developed a novel method, i.e., double deep latent autoencoder, for exploring glycemic variability influence from multi-day glucose data for diabetic retinopathy. Specifically, the model proposed in this research could encode continuous glucose sensor data with non-continuous and variable length via the integration of a data reorganization module and a novel encoding module with fragmented-missing-wise objective function. Additionally, the model implements a double deep autoencoder, which integrated convolutional neural network, long short-term memory, to jointly capturing the inter-day and intra-day glucose latent features from glucose series. The effectiveness of the proposed model is evaluated through a cross-validation method to clinical datasets of 765 type 2 diabetes patients. The proposed method achieves the highest accuracy value (0.89), precision value (0.88), and F1 score (0.73). The results suggest that our model can be used to remotely diagnose and screen for diabetic retinopathy by learning potential features of glucose series data collected by wearable continuous glucose monitoring systems.

The use of technology in type 2 diabetes and prediabetes: a narrative review

The increasing incidence of type 2 diabetes, which represents 90% of diabetes cases globally, is a major public health concern. Improved glucose management reduces the risk of vascular complications and mortality; however, only a small proportion of the type 2 diabetes population have blood glucose levels within the recommended treatment targets. In recent years, diabetes technologies have revolutionised the care of people with type 1 diabetes, and it is becoming increasingly evident that people with type 2 diabetes can also benefit from these advances. In this review, we describe the current knowledge regarding the role of technologies for people living with type 2 diabetes and the evidence supporting their use in clinical practice. We conclude that continuous glucose monitoring systems deliver glycaemic benefits for individuals with type 2 diabetes, whether treated with insulin or non-insulin therapy; further data are required to evaluate the role of these systems in those with prediabetes (defined as impaired glucose tolerance and/or impaired fasting glucose and/or HbA1c levels between 39 mmol/mol [5.7%] and 47 mmol/mol [6.4%]). The use of insulin pumps seems to be safe and effective in people with type 2 diabetes, especially in those with an HbA1c significantly above target. Initial results from studies exploring the impact of closed-loop systems in type 2 diabetes are promising. We discuss directions for future research to fully understand the potential benefits of integrating evidence-based technology into care for people living with type 2 diabetes and prediabetes.

Diabetes technology in people with diabetes and advanced chronic kidney disease

Diabetes is the leading cause and a common comorbidity of advanced chronic kidney disease. Glycaemic management in this population is challenging and characterised by frequent excursions of hypoglycaemia and hyperglycaemia. Current glucose monitoring tools, such as HbA1c, fructosamine and glycated albumin, have biases in this population and provide information only on mean glucose exposure. Revolutionary developments in glucose sensing and insulin delivery technology have occurred in the last decade. Newer factory-calibrated continuous glucose monitors provide real-time glucose data, with predictive alarms, allowing improved assessment of glucose excursions and preventive measures, particularly during and between dialysis sessions. Furthermore, integration of continuous glucose monitors and their predictive alerts with automated insulin delivery systems enables insulin administration to be decreased or stopped proactively, leading to improved glycaemic management and diminishing glycaemic fluctuations. While awaiting regulatory approval, emerging studies, expert real-world experience and clinical guidelines support the use of diabetes technology devices in people with diabetes and advanced chronic kidney disease.

Accuracy of a Real-Time Continuous Glucose Monitor in Pediatric Diabetic Ketoacidosis Admissions

Objective: Continuous glucose monitoring (CGM) devices are integral in the outpatient care of people with type 1 diabetes, although they lack inpatient labeling. Food and Drug Administration began allowing inpatient use during the coronavirus disease 2019 (COVID-19) pandemic, with some accuracy data now available, primarily from adult hospitals. Pediatric inpatient data remain limited, particularly during diabetic ketoacidosis (DKA) admissions and for patients receiving intravenous (IV) insulin. Design and Methods: This retrospective chart review compared point-of-care glucose values to personal Dexcom G6 sensor data during pediatric hospitalizations. Accuracy was assessed using mean absolute relative difference (MARD), Clarke Error Grids, and the percentage of values within 15/20/30% if glucose value >100 mg/dL and 15/20/30 mg/dL if glucose value ≤100 mg/dL. Results: Matched paired glucose values (N = 612) from 36 patients (median age 14 years, 58.3% non-Hispanic White, 47.2% male) and 42 inpatient encounters were included in this subanalysis of DKA admissions. The MARDs for DKA and non-DKA admissions (N = 503) were 11.8% and 11.7%, with 97.6% and 98.6% of pairs falling within A and B zones of the Clarke Error Grid, respectively. Severe DKA admissions (pH <7.15 and/or bicarbonate <5 mmol/L) had a MARD of 8.9% compared to 14.3% for nonsevere DKA admissions. The MARD during administration of IV insulin (N = 266) was 13.4%. Conclusions: CGM accuracy is similar between DKA and non-DKA admissions and is maintained in severe DKA and during IV insulin administration, suggesting potential usability in pediatric hospitalizations. Further study on the feasibility of implementation of CGM in the hospital is needed.

Application and management of continuous glucose monitoring in diabetic kidney disease

Diabetic kidney disease (DKD) is a common complication of diabetes mellitus that contributes to the risk of end-stage kidney disease (ESKD). Wide glycemic var-iations, such as hypoglycemia and hyperglycemia, are broadly found in diabetic patients with DKD and especially ESKD, as a result of impaired renal metabolism. It is essential to monitor glycemia for effective management of DKD. Hemoglobin A1c (HbA1c) has long been considered as the gold standard for monitoring glycemia for > 3 months. However, assessment of HbA1c has some bias as it is susceptible to factors such as anemia and liver or kidney dysfunction. Continuous glucose monitoring (CGM) has provided new insights on glycemic assessment and management. CGM directly measures glucose level in interstitial fluid, reports real-time or retrospective glucose concentration, and provides multiple glycemic metrics. It avoids the pitfalls of HbA1c in some contexts, and may serve as a precise alternative to estimation of mean glucose and glycemic variability. Emerging studies have demonstrated the merits of CGM for precise monitoring, which allows fine-tuning of glycemic management in diabetic patients. Therefore, CGM technology has the potential for better glycemic monitoring in DKD patients. More research is needed to explore its application and management in different stages of DKD, including hemodialysis, peritoneal dialysis and kidney transplantation.

Continuous glucose monitoring in people with diabetes and end-stage kidney disease-review of association studies and Evidence-Based discussion

Diabetic nephropathy is currently the leading cause of end-stage kidney disease. The present methods of assessing diabetes control, such as glycated hemoglobin or self-monitoring of blood glucose, have limitations. Over the past decade, the field of continuous glucose monitoring has been greatly improved and expanded. This review examines the use of continuous glucose monitoring in people with end-stage kidney disease treated with hemodialysis (HD), peritoneal dialysis (PD), or kidney transplantation. We assessed the use of both real-time continuous glucose monitoring and flash glucose monitoring technology in terms of hypoglycemia detection, glycemic variability, and efficacy, defined as an improvement in clinical outcomes and diabetes control. Overall, the use of continuous glucose monitoring in individuals with end-stage kidney disease may improve glycemic control and detection of hypoglycemia. However, most of the published studies were observational with no control group. Moreover, not all studies used the same assessment parameters. There are very few studies involving subjects on peritoneal dialysis. The small number of studies with limited numbers of participants, short follow-up period, and small number of manufacturers of continuous glucose monitoring systems are limitations of the review. More studies need to be performed to obtain more reliable results.

Impact of Body Composition and Anemia on Accuracy of a Real-Time Continuous Glucose Monitor in Diabetes Patients on Continuous Ambulatory Peritoneal Dialysis

Continuous glucose monitoring (CGM) is proposed as an alternative for glycemic assessment in peritoneal dialysis, but volume overload and anemia may affect sensor accuracy. This is an exploratory analysis of a study of Guardian Connect™ with Guardian Sensor™ 3 in 30 participants with diabetes on continuous ambulatory peritoneal dialysis (CAPD) (age [mean ± standard deviation] 64.7 ± 5.6 years, 23 men, body mass index [BMI] 25.4 ± 3.9 kg/m2, blood hemoglobin [Hb] 10.7 ± 1.3 g/dL). The mean absolute relative difference (MARD) was calculated between paired sensor and YSI 2300 STAT venous glucose readings (n = 941) during an 8-h in-clinic session with glucose challenge. Body composition was evaluated using bioimpedance. The overall MARD was 10.4% (95% confidence interval 9.6-11.7). There were no correlations between BMI, extracellular water, relative hydration index, and lean or fat mass with MARD. No correlations were observed between MARD and Hb (r = 0.016, P > 0.05). In summary, this real-time CGM demonstrated good accuracy in CAPD with minimal influence from body composition and anemia.

The Accuracy of Continuous Glucose Sensors in People with Diabetes Undergoing Haemodialysis (ALPHA Study)

OBJECTIVES

Real-time and intermittently scanned continuous glucose monitoring are increasingly used for glucose monitoring in people with diabetes requiring renal replacement therapy, with limited data reporting their accuracy in this cohort. We evaluated the accuracy of Dexcom G6 and Abbott Freestyle Libre 1 glucose monitoring systems in people with diabetes undergoing haemodialysis.

METHODS

Participants on haemodialysis with diabetes (on insulin or sulfonylureas) were recruited. Paired sensor glucose from Dexcom G6 and Freestyle Libre 1 were recorded with plasma glucose analysed using the YSI (Yellow Springs Instrument) method at frequent intervals during haemodialysis. Analysis of accuracy metrics included mean absolute relative difference (MARD), Clarke Error Grid (CEG) analysis and proportion of CGM values within 15 and 20% or 15 and 20mg/dL of YSI reference values for blood glucose >100 mg/dL or ≤100 mg/dL, respectively (% 15/15, % 20/20).

RESULTS

Forty adults (median age 64.7 (60.2-74.4) years) were recruited. Overall MARD for Dexcom G6 was 22.7% (2,656 matched glucose pairs), and 11.3% for Libre 1 (n=2,785). The proportions of readings meeting %15/15 and %20/20 were 29.1% and 45.4% for Dexcom G6, respectively, and 73.5% and 85.6% for Libre 1. CEG analysis showed 98.9% of all values in zones A and B for Dexcom G6 and 99.8% for Libre 1.

CONCLUSIONS

Our results indicate Freestyle Libre 1 is a reliable tool for glucose monitoring in adults on haemodialysis. Further studies are required to evaluate Dexcom G6 accuracy in people on haemodialysis. Small molecule interferents may affect electrochemical glucose sensors in end-stage kidney disease.

Continuous Glucose Monitoring to Optimize Management of Diabetes in Patients with Advanced CKD

Treatment of patients with diabetes and CKD includes optimizing glycemic control using lifestyle modifications and drugs that safely control glycemia and improve clinical kidney and cardiovascular disease outcomes. However, patients with advanced CKD, defined as eGFR <30 ml/min per 1.73 m2 or kidney disease treated with dialysis, have limitations to the use of some preferred glucose-lowering medications, are often treated with insulin, and experience high rates of severe hypoglycemia. Moreover, hemoglobin A1c accuracy decreases as GFR deteriorates. Hence, there is a need for better glycemic monitoring tools. Continuous glucose monitoring allows for 24-hour glycemic monitoring to understand patterns and the effects of lifestyle and medications. Real-time continuous glucose monitoring can be used to guide the administration of insulin and noninsulin therapies. Continuous glucose monitoring can overcome the limitations of self-monitored capillary glucose testing and hemoglobin A1c and has been shown to prevent hypoglycemic excursions in some populations. More data are needed to understand whether similar benefits can be obtained for patients with diabetes and advanced CKD. This review provides an updated approach to management of glycemia in advanced CKD, focusing on the role of continuous glucose monitoring in this high-risk population.