Glycaemic control in type 2 diabetic patients on chronic haemodialysis: use of a continuous glucose monitoring system

Management of hyperglycaemia in people with Type 1 diabetes on dialysis: What are the options? A case report and review of the literature

Type 1 diabetes remains an important cause of end-stage kidney disease. In this report, we describe the use of automated insulin delivery technology to assist a person living with Type 1 diabetes to manage their glucose pre-, peri- and post-renal transplantation. In addition, we review the potential options available for managing such patients, including closed-loop technology, pancreatic and islet cell transplantation.

Glucose monitoring intelligent tracking system for remote glycemic assessment in diabetic dogs: a novel approach

Optimizing glucose control is one of the primary goals of diabetes management. This study assessed the feasibility and accuracy of a remote real-time continuous glucose monitoring system (RT-CGMS) integrated with intelligent tracking in diabetic dogs. Seven Beagle dogs were monitored using interstitial sensors across different configurations: adhesive only, adhesive with protective garments, and garments combined with an innovative glucose monitoring approach for remote transmission. Sensor wear time was slightly longer with garments (8.2 ± 6.7 vs. 5.8 ± 3.1 days; P > 0.05). Valid data acquisition was significantly higher in the remote-monitoring group [95 (84, 96)] compared to Group 1 [67 (47, 78)] and Group 2 [76 (64, 80), P < 0.001 for both]. A strong correlation was found between RT-CGMS and PBGM measurements (r = 0.904). Calibration improved accuracy at glucose levels ≥ 5.5 mmol/L, reducing MARD from 28.5 to 14.5% and increasing Bland-Altman agreement from 48 to 67%. However, MAD slightly increased in the < 5.5 mmol/L range (2.2 to 2.7 mmol/L). Frequent hyperglycemia, high variability, and glucose excursions were observed. In conclusion, RT-CGMS with intelligent tracking improved data continuity and accuracy in diabetic dogs. Future research should focus on improving the system's sensitivity under hypoglycemic conditions and exploring its broader applications, including its role in enhancing in-hospital glucose management, utilizing big data to facilitate online diagnostics and offline follow-up care, providing guidance for daily glucose stabilization, enabling personalized veterinary services, and offering subscription-based health reports for pet owners.

Management of diabetes in people with advanced chronic kidney disease

Diabetes is the commonest cause of end stage kidney disease globally, accounting for almost 40% of new cases requiring renal replacement therapy. Management of diabetes in people with advanced kidney disease on renal replacement therapy is challenging due to some unique aspects of assessment and treatment in this group of patients. Standard glycaemic assessment using glycated haemoglobin may not be valid in such patients due to altered red blood cell turnover or iron/erythropoietin deficiency, leading to changed red blood cell longevity. Therefore, use of continuous glucose monitoring may be beneficial to enable more focussed glycaemic assessment and improved adjustment of therapy. People with advanced kidney disease may be at higher risk of hypoglycaemia due to a number of physiological mechanisms, and in addition, therapeutic options are limited in such patients due to lack of experience or license. Insulin therapy is the basis of treatment of people with diabetes with advanced kidney disease due to many other drugs classes being contraindicated. Targets for glycaemic control should be adjusted according to co-morbidity and frailty, and continuous glucose monitoring should be used in people on dialysis to ensure low risk of hypoglycaemia. Post-transplant diabetes is common amongst people undergoing solid organ transplantation and confers a greater risk of mortality and morbidity in kidney transplant recipients. It should be actively screened for and managed in the post-transplant setting.

Challenges of Glycemic Control in People With Diabetes and Advanced Kidney Disease and the Potential of Automated Insulin Delivery

Diabetes is the leading cause of chronic kidney disease (CKD) and end-stage kidney disease in the world. It is known that maintaining optimal glycemic control can slow the progression of CKD. However, the failing kidney impacts glucose and insulin metabolism and contributes to increased glucose variability. Conventional methods of insulin delivery are not well equipped to adapt to this increased glycemic lability. Automated insulin delivery (AID) has been established as an effective treatment in patients with type 1 diabetes mellitus, and there is emerging evidence for their use in type 2 diabetes mellitus. However, few studies have examined their role in diabetes with concurrent advanced CKD. We discuss the potential benefits and challenges of AID use in patients with diabetes and advanced CKD, including those on dialysis.

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.

Perspective of Continuous Glucose Monitoring-Based Interventions at the Various Stages of Type 2 Diabetes

Continuous glucose monitoring (CGM) is now advocated for the clinical management of individuals with type 1 diabetes (T1D). However, this glucose monitoring strategy is not routinely used in type 2 diabetes (T2D), given the large population, significant cost implications and relatively limited supporting evidence. T2D is a more heterogenous condition compared with T1D with various glucose lowering therapies that do not necessarily require CGM to ensure within target glucose levels. While all individuals with T2D may benefit from CGM at certain time points, the whole T2D population does not necessarily require this technology continuously, which should be prioritized based on patient benefit and cost effectiveness. In this pragmatic opinion piece, we describe the rationale and evidence for CGM use in different subgroups of individuals with T2d, divided according to the stage of the condition, glycemic therapies, presence of diabetes complications, or associated co-morbidities. We discuss a total of 16 T2D subgroups and provide a clinical view on CGM use in each, based on current evidence while also highlighting areas of knowledge gaps. This work provides health care professionals with a simple guide to CGM use in different T2D groups and gives suggestion for future studies to justify expansion of this technology.

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.

Glucose variability in maintenance hemodialysis patients with type 2 diabetes: Comparison of dialysis and nondialysis days

INTRODUCTION

Hemodialysis (HD) induces several physiological changes that can affect plasma glucose levels in patients with diabetes and in turn their glycemic control. Studies using continuous glucose monitoring (CGM) to assess glucose variations on dialysis days compared with nondialysis days report conflicting results. Here, we used CGM to examine glucose variations induced by HD in patients with type 2 diabetes.

METHODS

Patients with type 2 diabetes undergoing maintenance HD were included. CGM (Ipro2®, Medtronic) was performed at baseline and Week 4, 8, 12, and 16 for up to 7 days at each visit. CGM profiles on days where participants received HD were compared with days without HD using a linear mixed model.

FINDINGS

Twenty-seven patients were included. The median number of CGM days performed was 8 (interquartile range [IQR] 6-10) for dialysis days and 16 (IQR 12-17) for nondialysis days. The median sensor glucose was 9.4 (95% confidence interval [CI] 8.8-10.2) mmol/L on dialysis days compared with 9.5 (95% CI 8.9-10.2) mmol/L on nondialysis days (p = 0.58). Nocturnal mean sensor glucose was higher on dialysis days compared with nondialysis days: 8.8 (95% CI 8.0-9.6) mmol/L versus 8.4 (95% CI 7.7-9.2) mmol/L (p = 0.029).

DISCUSSION

Similar median sensor glucose values were found for days on and off HD. Nocturnal glucose levels were modestly increased on dialysis days. Our findings indicate that antidiabetic treatment does not need to be differentiated on dialysis versus nondialysis days in patients with type 2 diabetes undergoing maintenance HD.

Management of adults with diabetes on dialysis: Summary of recommendations of the Joint British Diabetes Societies guidelines 2022

Diabetes is the commonest cause of end-stage kidney disease in many parts of the world, and many people on dialysis programmes live with diabetes. Such people are vulnerable to complications from their diabetes, and their care may be fragmented due to the many specialists involved. This updated guidance from the Joint British Diabetes Societies aims to review and update the 2016 guidance, with particular emphasis on glycaemic monitoring in the light of recent advances in this area. In addition, the guidance covers clinical issues related to the management of diabetes in people on peritoneal dialysis, along with acute complications such as hypoglycaemia and ketoacidosis, and chronic complications such as foot and eye disease.

Accuracy of Flash Glucose Monitoring in Hemodialysis Patients With and Without Diabetes Mellitus

AIMS

Glucose and insulin metabolism are altered in hemodialysis patients, and diabetes management is difficult in these patients. We aimed to validate flash glucose monitoring (FGM) in hemodialysis patients with and without diabetes mellitus as an attractive option for glucose monitoring not requiring regular self-punctures.

METHODS

We measured interstitial glucose using a FreeStyle Libre device in eight hemodialysis patients with and seven without diabetes mellitus over 14 days and compared the results to simultaneously performed self-monitoring of capillary blood glucose (SMBG).

RESULTS

In 720 paired measurements, mean flash glucose values were significantly lower than self-measured capillary values (6.17±2.52 vs. 7.15±2.41 mmol/L, p=1.3 E-86). Overall, the mean absolute relative difference was 17.4%, and the mean absolute difference was 1.20 mmol/L. The systematic error was significantly larger in patients without vs. with diabetes (- 1.17 vs. - 0.82 mmol/L) and on dialysis vs. interdialytic days (-1.09 vs. -0.90 mmol/L). Compared to venous blood glucose (72 paired measurements), the systematic error of FGM was even larger (5.89±2.44 mmol/L vs. 7.78±7.25 mmol/L, p=3.74E-22). Several strategies to reduce the systematic error were evaluated, including the addition of +1.0 mmol/L as a correction term to all FGM values, which significantly improved accuracy.

CONCLUSIONS

FGM systematically underestimates blood glucose in hemodialysis patients but, taking this systematic error into account, the system may be useful for glucose monitoring in hemodialysis patients with or without diabetes.

Novel clinical relationships between time in range and microangiopathies in people with type 2 diabetes mellitus on hemodialysis

AIMS/HYPOTHESIS

We investigated associations among glucose time in range (TIR, 70-180 mg/dL), glycemic markers and prevalence of diabetic microangiopathy in people with diabetes undergoing hemodialysis (HD).

METHODS

A total of 107 people with type 2 diabetes undergoing HD (HbA_1c 6.4 %; glycated albumin [GA] 20.6 %) using continuous glucose monitoring were analyzed in this observational and cross-sectional study.

RESULTS

HbA_1c and GA levels significantly negatively correlated with TIR, and positively correlated with time rate of hyperglycemia, but not with time rate of hypoglycemia. TIR of 70 % corresponded to HbA_1c of 6.5 % and GA of 21.2 %. The estimated HbA_1c level corresponding to TIR of 70 % in this study was lower than that previously reported in people with diabetes without HD. The prevalence of diabetic neuropathy was not significantly different between people with TIR ≥ 70 % and those with TIR < 70 % (P = 0.1925), but the prevalence of diabetic retinopathy in people with TIR ≥ 70 % was significantly lower than in those with TIR < 70 % (P = 0.0071).

CONCLUSION/INTERPRETATION

TIR correlated with HbA_1c and GA levels in people with type 2 diabetes on HD. Additionally, a higher TIR resulted in a lower rate of diabetic retinopathy.

RESEARCH IN CONTEXT

What is already known about this subject? What is the key question? What are the new findings? How might this impact on clinical practice in the foreseeable future?

Usefulness of continuous glucose monitoring of blood glucose control in patients with diabetes undergoing hemodialysis: A pilot study

Background

Blood glucose stability has recently been considered important in the treatment of diabetes. Both hypoglycemia and hyperglycemia can frequently occur in patients with diabetes undergoing hemodialysis. This study aimed to determine the usefulness of continuous glucose monitoring (CGM) for glycemic control and glycemic variability stabilization in patients with diabetes undergoing hemodialysis.

Materials and methods

Eighteen patients aged ≥18 years with type 1 or 2 diabetes and ≥3 months on hemodialysis at the Eulji Medical Center, Daejeon, Republic of Korea between November 2021 and May 2022 were included. Patients underwent 7 days CGM twice: the baseline study period (T0) and the follow-up study period (T1), at a 12 weeks interval. Physicians modified the treatment strategy according to the T0 results, and then patients conducted T1. As indicators of glycemic control, the mean glucose levels, glycated hemoglobin A1c (HbA1c), and time in range were measured. As indicators of glycemic variability, standard deviation (SD) and % coefficient variation (%CV) were measured.

Results

Data from 18 patients were analyzed. The mean glucose levels, HbA1c, SD, and %CV improved in T1 compared to T0 (P < 0.05). During T0, the mean glucose level was significantly lower on a day with hemodialysis than on a day without (P < 0.05), and SD and %CV were significantly higher on a day with hemodialysis than on a day without (P < 0.05). After the physicians modified the treatment according to the T0 results, there were no differences in the mean glucose levels, SD, and %CV between days with and without hemodialysis during T1.

Conclusion

Continuous glucose monitoring could be a promising tool for individualizing treatment strategies in patients with diabetes undergoing hemodialysis.

Making sense of glucose sensors in end-stage kidney disease: A review

Diabetes mellitus remains the leading cause of end-stage kidney disease worldwide. Inadequate glucose monitoring has been identified as one of the gaps in care for hemodialysis patients with diabetes, and lack of reliable methods to assess glycemia has contributed to uncertainty regarding the benefit of glycemic control in these individuals. Hemoglobin A1c, the standard metric to evaluate glycemic control, is inaccurate in patients with kidney failure, and does not capture the full range of glucose values for patients with diabetes. Recent advances in continuous glucose monitoring have established this technology as the new gold standard for glucose management in diabetes. Glucose fluctuations are uniquely challenging in patients dependent on intermittent hemodialysis, and lead to clinically significant glycemic variability. This review evaluates continuous glucose monitoring technology, its validity in the setting of kidney failure, and interpretation of glucose monitoring results for the nephrologist. Continuous glucose monitoring targets for patients on dialysis have yet to be established. While continuous glucose monitoring provides a more complete picture of the glycemic profile than hemoglobin A1c and can mitigate high-risk hypoglycemia and hyperglycemia in the context of the hemodialysis procedure itself, whether the technology can improve clinical outcomes merits further investigation.

Evaluation of the relationship between hemodialysis-related glycemic variability and hormonal profiles in patients with type 2 diabetes on hemodialysis: a pilot study

Background

The number of dialysis patients with diabetes is currently increasing in Japan and a similar proportion worldwide. It was suggested that approximately 20% of these patients had hypoglycemia after dialysis session and most of these hypoglycemia were unconscious. Furthermore, it was suggested that glucose variabilities induced by hemodialysis may be related to insulin and insulin-counter hormones, such as glucagon, adrenocorticotropic hormone (ACTH), and cortisol and growth hormone, but conclusive evidence has not still been obtained.

Methods

We investigated in detail the glucose and hormonal profiles in 7 patients with type 2 diabetes on hemodialysis (all male, HbA1c 6.8 ± 2.1%, glycated albumin 24.7 ± 10.2%). All participants were attached continuous glucose monitoring (iPro2®). Blood glucose level, C-peptide immunoreactivity, plasma glucagon, ACTH, cortisol and growth hormone were measured by 7 points blood tests at before breakfast, after breakfast (predialysis), 2 h and 4 h after starting dialysis, after lunch and before/after dinner on the dialysis day and 6 points at before/after each meal on the non-dialysis day, and these relationship with blood glucose dynamics were examined. The meal contents were set to the indicated energy amount, and the same menu was served daily for breakfast, lunch, and dinner on dialysis and non-dialysis days of this study period. In addition, the start time of lunch on non-dialysis day was the same as the start time of lunch on the dialysis day.

Results

Serum C-peptide level was significantly increased by taking breakfast and lunch on the hemodialysis day, significantly decreased during hemodialysis, and was significantly lower before and after lunch on the hemodialysis day than on the non-hemodialysis day. Plasma glucagon level significantly decreased during hemodialysis and that before lunch on hemodialysis day was significantly lower than on non-hemodialysis day. ACTH, cortisol, and growth hormone did not show any changes related to hemodialysis.

Conclusions

It was suggested that C-peptide and glucagon play an important role in hemodialysis-related glycemic variabilities in patients with type 2 diabetic hemodialysis.

Trial registration UMIN Clinical Trial Registry (Registration Number UMIN000018707). Registered 18 August 2015, https://center6.umin.ac.jp/cgi-open-bin/ctr/ctr.cgi?function=brows&action=brows&type=summary&language=J&recptno=R000021647.

Targeting Predialysis Glucose up to 180 mg/dl Reduces Glycemic Variability in End Stage Diabetic Nephropathy

Context

Glycemic variability plays a major role in the development as well as the progression of cardiovascular disease in diabetes.

Aims

We compared the mean plasma glucose and glycemic variability (GV) parameters on and off hemodialysis (HD) in patients with End-Stage Diabetic Nephropathy (ESDN) and End-Stage Renal Disease (ESRD).

Settings and Design

Cross-sectional study.

Methods and Material

We included 23 ESDN and 6 ESRD patients who underwent continuous glucose monitoring (CGM) (iPro2) for 6 days and a glucose-free dialysate for 4 hours thrice weekly. EasyGV software was used to calculate the variability parameters {mean glucose, Time in range (TIR), Time above and below range (TAR/TBR), CV (Coefficient of Variation) and MAGE}.

Statistical Analysis Used

The quantitative data variables were expressed by using mean and SD. Unpaired t-test was used to compare the two groups. P value <0.05 was considered significant.

Results

In the ESDN group, TIR was significantly lower whereas TAR and TBR were significantly higher on HD day. MAGE (101.88 ± 40.5 v/s 89.46 ± 30.0, P < 0.007) and CV (29.41% v/s 21.67%) were higher on HD day. Subjects with pre-HD glucose values ≥180 mg/dl (Group B, n = 24) had a rapid drop with a delayed higher rise in glucose values than those with pre-HD glucose values <180 mg/dl (Group A, n = 27). Ten patients had 13 episodes of hypoglycemia. The CGM parameters were not different in the ESRD group.

Conclusions

Targeting a pre- HD glucose value <180 mg/dl could be a good strategy to prevent larger fluctuation during and post HD.

Blood Glucose Fluctuation in Older Adults with Diabetes Mellitus and End-Stage Renal Disease on Maintenance Hemodialysis: An Observational Study

INTRODUCTION

Patients with diabetes mellitus and end-stage renal disease are at a high risk of developing coronary, cerebrovascular, and peripheral vascular diseases. This study aimed to characterize hypoglycemia and blood glucose fluctuations associated with maintenance hemodialysis in older adult patients with diabetes mellitus and end-stage renal disease using a continuous glucose monitoring system.

METHODS

Seven patients were enrolled in this study, and 13 pairs of continuous glucose monitoring system data were collected. Each pair included data of 1 dialysis-on day and 1 dialysis-off day. Information on basic patient characteristics, including age, diabetes mellitus duration, hemodialysis duration, and proportions of hemoglobin A1c and glycated albumin, were collected. Differences in blood glucose fluctuation were compared between dialysis-on days and dialysis-off days.

RESULTS

The mean blood glucose on dialysis-on days (6.96 ± 2.57 mmol/L) was significantly lower than that on dialysis-off days (7.68 ± 2.31 mmol/L; P < 0.05). In contrast, the following parameters had significantly higher values (all P < 0.05) on dialysis-on days compared to dialysis-off days: large amplitude of glycemic excursion level (5.82 ± 2.86 mmol/L versus 4.21 ± 1.71 mmol/L), large amplitude of glycemic excursion level from 8 a.m. to 2 p.m. (3.6 ± 1.74 mmol/L versus 2.8 ± 1.33 mmol/L), mean amplitude of glycemic excursion level (4.78 ± 1.68 mmol/L versus 3.89 ± 1.67 mmol/L), mean amplitude of glycemic excursion level from 8 a.m. to 2 p.m. (4.01 ± 1.03 mmol/L versus 3.12. ± 0.97 mmol/L), standard deviation of blood glucose (1.55 ± 0.89 mmol/L versus 1.03 ± 0.4 mmol/L), and time below a target glucose range of less than 3.9 mmol/L (8.27% versus 4.25%).

CONCLUSION

Fluctuations in blood glucose levels were larger on dialysis-on days, particularly from the start of hemodialysis to 2 h post-hemodialysis, than on dialysis-off days. Hypoglycemia, as indicated by the time below a target glucose range of less than 3.9 mmol/L, occurred more frequently on dialysis-on days than on dialysis-off days.

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

INTRODUCTION

Shortened erythrocyte life span and erythropoietin-stimulating agents may affect hemoglobin A1c (HbA1c) levels in patients receiving peritoneal dialysis (PD). We compared HbA1c with interstitial glucose measured by continuous glucose monitoring (CGM) in patients with type 2 diabetes receiving PD.

METHODS

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 m2. Patients were matched on gender and age (±5 years). HbA1c (mmol/mol), its derived estimate of mean plasma glucose (eMPGA1c) (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.

RESULTS

In the PD group, mean sensor glucose was 0.98 (95% con-fidence interval (CI): 0.43-1.54) mmol/L higher than the eMPGA1c compared with the control group (p = 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 (p = 0.89) or y-intercepts (p = 0.28).

DISCUSSION/CONCLUSION

HbA1c 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 HbA1c to increase the accuracy of glycemic monitoring in the PD population.

The Accuracy of Hemoglobin A1c and Fructosamine Evaluated by Long-Term Continuous Glucose Monitoring in Patients with Type 2 Diabetes Undergoing Hemodialysis

INTRODUCTION

The accuracy of hemoglobin A1c (HbA1c) as a glycemic marker in patients with type 2 diabetes (T2D) receiving hemodialysis (HD) remains unknown. To assess accuracy, we compared HbA1c and fructosamine levels with interstitial glucose measured by continuous glucose monitoring (CGM) in patients with T2D receiving HD.

METHODS

Thirty patients in the HD group and 36 patients in the control group (T2D and an estimated glomerular filtration rate >60 mL/min/1.73 m2) completed the study period of 17 weeks. CGM (Ipro2®, Medtronic) was performed 5 times for periods of up to 7 days (with 4-week intervals) during a 16-week period. HbA1c (mmol/mol), the estimated mean plasma glucose from HbA1c (eMPGA1c [mmol/L]) and fructosamine (μmol/L) was measured at week 17 and compared with mean sensor glucose levels from CGM.

FINDINGS

In the HD group, mean sensor glucose was 1.4 mmol/L (95% confidence interval [CI]: 1.0-1.8) higher than the eMPGA1c, whereas the difference for controls was 0.1 mmol/L (95% CI: -0.1-[0.4]; p < 0.001). Adjusted for mean sensor glucose, HbA1c was lower in the HD group (-7.3 mmol/mol, 95% CI: -10.0-[-4.7]) than in the control group (p < 0.001), with no difference detected for fructosamine (p = 0.64).

DISCUSSION

HbA1c evaluated by CGM underestimates plasma glucose levels in patients receiving HD. The underestimation represents a clinical challenge in optimizing glycemic control in the HD population. Fructosamine is unaffected by the factors affecting HbA1c and appears to be more accurate for glycemic monitoring. CGM or fructosamine could thus complement HbA1c in obtaining more accurate glycemic control in this patient group.

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

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 (TIR^70–180) 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 TIR^70–180 < 50%. However, both biomarkers provided an imprecise reflection of acute excursions of hypoglycemia and inter-day glucose variability.

Continuous glucose monitoring in patients with type 2 diabetes on hemodialysis

Diabetic kidney disease is the leading cause of end-stage kidney disease in high-income countries. The strict control of glycemic oscillations is the principal therapeutic target, but this could be hard to achieve in uremic patients due to their unpredictable insulin sensitivity. Currently, the evaluation of the glycemic profile relies on serum markers (glycated hemoglobin HbA1c, glycated albumin, and fructosamine), capillary glucose blood control (self-monitoring of blood glucose), and interstitial glucose control (continue glucose monitoring). We conducted a systematic review of published articles on continue glucose monitoring in hemodialysis patients with type 2 diabetes, which included 12 major articles. Four studies found significant fluctuations in glucose levels during hemodialysis sessions. All studies reported a higher mean amplitude of glucose variations on the hemodialysis day. Three studies agreed that continue glucose monitoring is better than glycated hemoglobin in detecting these abnormalities. Moreover, continue glucose monitoring was more accurate and perceived as easier to use by patients and their caregivers. In patients with type 2 diabetes on hemodialysis, glucose levels show different variation patterns than the patients on hemodialysis without diabetes. Considering manageability, accuracy, and cost-effectiveness, continue glucose monitoring could be the ideal diagnostic tool for the patient with diabetes on hemodialysis.

Hemodialysis-Related Glycemic Disarray Proven by Continuous Glucose Monitoring; Glycemic Markers and Hypoglycemia

OBJECTIVE

There is a high risk of asymptomatic hypoglycemia associated with hemodialysis (HD) using glucose-free dialysate; therefore, the inclusion of glucose in the dialysate is believed to prevent intradialytic hypoglycemia. However, the exact glycemic fluctuation profiles and frequency of asymptomatic hypoglycemia using dialysates containing >100 mg/dL glucose have not been determined.

RESEARCH DESIGN AND METHODS

We evaluated the glycemic profiles of 98 patients, 68 of whom were men, with type 2 diabetes undergoing HD (HbA_1c 6.4 ± 1.2%; glycated albumin 20.8 ± 6.8%) with a dialysate containing 100, 125, or 150 mg/dL glucose using continuous glucose monitoring.

RESULTS

Sensor glucose level (SGL) showed a sustained decrease during HD, irrespective of the dialysate glucose concentration, and reached a nadir that was lower than the dialysate glucose concentration in 49 participants (50%). Twenty-one participants (21%) presented with HD-related hypoglycemia, defined by an SGL <70 mg/dL during HD and/or between the end of HD and their next meal. All these hypoglycemic episodes were asymptomatic. Measures of glycemic variability calculated using the SGL data (SD, coefficient of variation, and range of SGL) were higher and time below range (<70 mg/dL) was lower in participants who experienced HD-related hypoglycemia than in those who did not, whereas time in range between 70 and 180 mg/dL, time above range (>180 mg/dL), HbA_1c, and glycated albumin of the two groups were similar.

CONCLUSIONS

Despite the use of dialysate containing 100-150 mg/dL glucose, patients with diabetes undergoing HD experienced HD-related hypoglycemia unawareness frequently. SGL may fall well below the dialysate glucose concentration toward the end of HD.

Glucose tolerance in patients with and without type 2 diabetes mellitus during hemodialysis

AIMS

The disposal of a glucose bolus was studied to identify glucose metabolism in patients with and without type 2 diabetes mellitus (T2DM) during their regular hemodialysis (HD) treatment.

METHODS

Plasma glucose, insulin, and c-peptide concentrations were measured during a 60 min observation phase following a rapid glucose infusion (0.5 g/kg dry weight). Glucose disposition and elimination rates were determined from kinetic analysis, and insulinogenic index was calculated. Insulin resistance (R_HOMA) was determined by homeostatic model assessment (HOMA).

RESULTS

35 HD patients (14 with T2DM) distinguished by a higher age (median: 70 vs. 55 y, p < 0.01) in T2DM patients were studied. Glucose kinetic data showed only small differences between patients with or without T2DM, but as R_HOMA measured in all patients increased, a larger fraction of glucose was removed by the extracorporeal system (r = 0.430, p = 0.01). One hour after glucose bolus injection the glucose level was not different from that before HD also in patients with T2DM (p = 0.115).

CONCLUSIONS

The larger glucose amount recovered in dialysate in patients with increasing R_HOMA indicates that impaired glucose disposal could be measured during HD using a non-invasive dialysis quantification approach without blood sampling. Glucose infusion during HD is safe also in patients with T2DM.

Monitoring and management of hyperglycemia in patients with advanced diabetic kidney disease

Diabetes mellitus is the leading cause of end-stage renal disease, and uncontrolled hyperglycemia is directly related to the increased mortality in this setting. As kidney function decreases, it becomes more challenging to control blood glucose since the risk of hypoglycemia increases. Decreased appetite, changes in glycaemia homeostasis, along with reduced renal excretion of anti-hyperglycemic drugs tend to facilitate the occurrence of hypoglycemia, despite the paradoxical occurrence of insulin resistance in advanced kidney disease. Thus, in patients using insulin and/or oral anti-hyperglycemic agents, dynamic adjustments with drug dose reduction or drug switching are often necessary. Furthermore, in addition to consider these pharmacokinetics alterations, it is of utmost importance to choose drugs with proven cardio-renal benefits in this setting, such as sodium-glucose co-transporter 2 inhibitors and glucagon-like peptide 1 receptor agonists. In this review, we summarize the indications and contraindications, titration of doses and side effects of the available anti-hyperglycemic agents in the presence of advanced diabetic kidney disease (DKD) and dialysis, highlighting the risks and benefits of the different agents. Additionally, basic renal function assessment and monitoring of glycemic control in DKD will be evaluated in order to guide the use of drugs and define the glycemic targets to be achieved.

Assessment of the accuracy of an intermittent-scanning continuous glucose monitoring device in patients with type 2 diabetes mellitus undergoing hemodialysis (AIDT2H) study

FreeStyle Libre has been approved for use in patients undergoing hemodialysis (HD) in Japan, unlike Europe and the United States; however, evidence regarding its accuracy in such patients is sparse. Forty‐one participants with type 2 diabetes undergoing HD were recruited. The overall mean absolute relative difference and mean absolute difference were 23.4% and 33.9 mg/dL, respectively. Sensor glucose levels and capillary glucose levels were significantly correlated (r = 0.858, P < .01), although the sensor glucose levels were significantly lower than the capillary glucose levels. The accuracy of FreeStyle Libre in patients undergoing HD became deteriorated with the days of usage. The percentage of sensor results in Zones A and B in the consensus error grid analysis and in the Clarke error grid analysis were 99.7% and 99.0%, respectively. Its insufficient accuracy necessitates adjunct usage of FreeStyle Libre with self‐monitoring of blood glucose in patients undergoing HD.

Reduced erythrocyte lifespan measured by chromium-51 in patients with type 2 diabetes undergoing long-term hemodialysis

INTRODUCTION

A reduced erythrocyte lifespan potentially explains the low hemoglobin A1c values found in hemodialysis patients. However, data supporting this notion in patients with type 2 diabetes is unclear. We evaluated the erythrocyte lifespan in patients with type 2 diabetes undergoing long-term hemodialysis and investigated potential predictors of erythrocyte lifespan.

METHODS

Long-term hemodialysis patients with type 2 diabetes and type 2 diabetes patients without nephropathy (estimated glomerular filtration rate > 60 mL/min/1.73 m² ) were included. The erythrocyte lifespan was measured using chromium-51 (⁵¹ Cr)-labeled erythrocytes. Blood radiotracer activity was measured six to nine times over a period of 3-5 weeks to determine the erythrocyte lifespan of each patient. Biochemical markers were obtained five times over 16 weeks and associated with the erythrocyte lifespan.

FINDINGS

Type 2 diabetes patients undergoing hemodialysis (N = 13) had a significantly shorter median erythrocyte lifespan of 49.7 (interquartile range [IQR] = 44.1-58.6) days compared with 64.2 (IQR = 62.6-83.5) days in the control group (N = 10) (P ˂ 0.001) with a difference between medians of 14.5 (95% confidence interval = 8.1-38.8) days. In the hemodialysis group, no association could be detected between the erythrocyte lifespan and markers of hemolysis, level of inflammation, or urea.

DISCUSSION

A reduced erythrocyte lifespan was detected in type 2 diabetes patients undergoing long-term hemodialysis. This may contribute to the reduced hemoglobin A1c values observed in the type 2 diabetic hemodialysis population. An association could not be detected between the erythrocyte lifespan and biochemical markers of hemolysis or inflammation.

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

BACKGROUND

Glycated haemoglobin A1c (HbA1c) 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 HbA1c 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 HbA1c. 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.

SUMMARY

Glycated albumin and CGM avoid the pitfalls of HbA1c 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. Key Messages: 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.

Effects of DPP-4 Inhibitors on Blood Glucose Variability in Japanese Patients with Type 2 Diabetes on Maintenance Hemodialysis: A Prospective Observational Exploratory Study

INTRODUCTION

The precise blood glucose (BG) profile of hemodialysis patients is unclear, as is the effectiveness of dipeptidyl peptidase-4 (DPP-4) inhibitors in hemodialysis patients with type 2 diabetes. Here, we used continuous glucose monitoring (CGM) to evaluate BG variability in these patients and to assess the efficacy of DPP-4 inhibitors, particularly during hemodialysis sessions and at nighttime (UMIN000012638).

METHODS

We examined BG profiles using CGM in 31 maintenance hemodialysis patients with type 2 diabetes. Differences between patients with and without DPP-4 inhibitors (n = 15 and 16, respectively) were analyzed using a linear mixed-effects model to assess changes in glucose levels in 5-min intervals.

RESULTS

The model revealed that DPP-4 inhibitor use was significantly associated with suppression of a rapid drop in glucose levels, both with and without adjustment for BG levels at the start of hemodialysis. Moreover, the model revealed that the two groups differed significantly in the pattern of changes in BG levels from 0:00 to 6:55 am. DPP-4 inhibitors suppressed the tendency for subsequent nocturnal hypoglycemia.

CONCLUSIONS

This prospective observational exploratory study showed that DPP-4 inhibitors could suppress BG variability during hemodialysis sessions as well as subsequent nocturnal changes in patients with type 2 diabetes.

TRIAL REGISTRATION

ClinicalTrials.gov identifier, UMIN000012638.

Diabetes mellitus in chronic kidney disease: Biomarkers beyond HbA1c to estimate glycemic control and diabetes-dependent morbidity and mortality

Diabetes mellitus (DM) is the leading cause of chronic kidney disease (CKD). Optimal glycemic control contributes to improved outcomes in patients with DM, particularly for microvascular damage, but blood glucose levels are too variable to provide an accurate assessment and instead markers averaging long-term glycemic load are used. The most established glycemic biomarker of long-term glycemic control is HbA1c. Nevertheless, HbA1c has pitfalls that limit its accuracy to estimate glycemic control, including the presence of altered red blood cell survival, hemoglobin glycation and suboptimal performance of HbA1c assays. Alternative methods to evaluate glycemic control in patients with DM include glycated albumin, fructosamine, 1-5 anhydroglucitol, continuous glucose measurement, self-monitoring of blood glucose and random blood glucose concentration measurements. Accordingly, our aim was to review the advantages and pitfalls of these methods in the context of CKD.

Comparison of accuracy between flash glucose monitoring and continuous glucose monitoring in patients with type 2 diabetes mellitus undergoing hemodialysis

INTRODUCTION

We evaluated the accuracy and clinical utility of flash glucose monitoring (FGM) in comparison with continuous glucose monitoring (CGM) and self-monitoring blood glucose (SMBG) in patients with type 2 diabetes (T2D) undergoing hemodialysis (HD).

METHODS

Simultaneous FGM (FreeStyle LibrePro), CGM (iPro2) and SMBG were performed on 13 T2D research subjects.

RESULTS

There were good overall correlations between SMBG and FGM (64.7% and 30.8% within the A and B of Parkes Error Grid, respectively) and between SMBG and CGM (87.9% and 11.0% within the A and B, respectively). However, during HD, correlations between SMBG and FGM were only 49.7% and 37.2% within the A and B, respectively, while correlations of SMBG and CGM were 72.8% and 22.2% within the A and B, respectively. The percentage of FGM not in Zone A + B was more than 4 times higher than for CGM. The overall mean absolute relative difference (MARD) for FGM was 18.2%, this significantly higher than 11.2% for CGM. During HD, MARD for FGM was 22.8%, significantly higher than 15.0% for CGM.

CONCLUSION

FGM has good clinical agreement in T2D patients undergoing HD. However, the accuracy of FGM relative to SMBG was worse than that of CGM.

Glycemic Monitoring and Management in Advanced Chronic Kidney Disease

Glucose and insulin metabolism in patients with diabetes are profoundly altered by advanced chronic kidney disease (CKD). Risk of hypoglycemia is increased by failure of kidney gluconeogenesis, impaired insulin clearance by the kidney, defective insulin degradation due to uremia, increased erythrocyte glucose uptake during hemodialysis, impaired counterregulatory hormone responses (cortisol, growth hormone), nutritional deprivation, and variability of exposure to oral antihyperglycemic agents and exogenous insulin. Patients with end-stage kidney disease frequently experience wide glycemic excursions, with common occurrences of both hypoglycemia and hyperglycemia. Assessment of glycemia by glycated hemoglobin (HbA1c) is hampered by a variety of CKD-associated conditions that can bias the measure either to the low or high range. Alternative glycemic biomarkers, such as glycated albumin or fructosamine, are not fully validated. Therefore, HbA1c remains the preferred glycemic biomarker despite its limitations. Based on observational data for associations with mortality and risks of hypoglycemia with intensive glycemic control regimens in advanced CKD, an HbA1c range of 7% to 8% appears to be the most favorable. Emerging data on the use of continuous glucose monitoring in this population suggest promise for more precise monitoring and treatment adjustments to permit fine-tuning of glycemic management in patients with diabetes and advanced CKD.

Eating during the Hemodialysis Session: A Practice Improving Nutritional Status or a Risk Factor for Intradialytic Hypotension and Reduced Dialysis Adequacy?

Historically, eating during the hemodialysis treatment has been associated with increased risk for adverse intradialytic symptoms and events, risks that have resulted in the implementation of restrictive in-center nutrition policies. Recent studies, however, have recorded a shift in clinical practice with a higher proportion of physicians following the view that administration of intradialytic meals and supplements represents a simple and effective approach to enhance caloric intake and improve nutritional status among patients on hemodialysis. This shift towards less restrictive in-center nutrition practices is mainly supported by evidence from observational studies associating intradialytic nutritional supplementation with improvements in protein-energy wasting, inflammatory state, and health-related quality of life. In sharp contrast, earlier and recent interventional studies have documented that feeding during the hemodialysis treatment provokes a rapid postprandial decline in blood pressure and raises the incidence of symptomatic intradialytic hypotension. Furthermore, other studies have shown that postprandial redistribution in intravascular volume and enhanced blood supply to the gastrointestinal circulation may interfere with the adequacy of the delivered hemodialysis. Those who defend the position that intradialytic nutritional support is beneficial do not dispute the physiology of postprandial hemodynamic response, but they argue against its clinical significance. In this article, we provide an overview of studies that explored the effect of eating during the hemodialysis treatment on intradialytic hemodynamic stability and adequacy of the delivered hemodialysis. We reason that these risks have important clinical implications that are not counteracted by anticipated benefits of this strategy on caloric intake and nutritional status.

When ESKD complicates the management of diabetes mellitus

Given the increased incidence and prevalence of ESKD (end-stage kidney disease) attributed to diabetes mellitus, it is important to consider the physiological and global sociodemographic factors that give rise to unique challenges in providing excellent care to this population. The individual with diabetes and ESKD faces alterations of glucose homeostasis that require close therapeutic attention, as well as the consideration of safe and effective means of maintaining glycemic control. Implementation of routine monitoring of blood glucose and thoughtful alteration of the individual's hypoglycemic drug regimen must be employed to reduce the risk of neurological, cardiovascular, and diabetes-specific complications that may arise as a result of ESKD. Titration of insulin therapy may become quite challenging, as kidney replacement therapy often significantly impacts insulin requirements. New medications have significantly improved the ability of the clinician to provide effective therapies for the management of diabetes, but have also raised an equal amount of uncertainty with respect to their safety and efficacy in the ESKD population. Additionally, the clinician must consider the challenges related to the delivery of kidney replacement therapy, and how inter-modality differences may impact glycemic control, diabetes, and ESKD-related complications, and issues surrounding dialysis vascular access creation.

Dialysis-associated hyperglycemia: manifestations and treatment

PURPOSE

Dialysis-associated hyperglycemia (DAH), is associated with a distinct fluid and electrolyte pathophysiology. The purpose of this report was to review the pathophysiology and provide treatment guidelines for DAH.

METHODS

Review of published reports on DAH. Synthesis of guidelines based on these reports.

RESULTS

The following fluid and solute abnormalities have been identified in DAH: (a) hypoglycemia: this is a frequent complication of insulin treatment and its prevention requires special attention. (b) Elevated serum tonicity. The degree of hypertonicity in DAH is lower than in similar levels of hyperglycemia in patients with preserved renal function. Typically, correction of hyperglycemia with insulin corrects the hypertonicity of DAH. (c) Extracellular volume abnormalities ranging from pulmonary edema associated with osmotic fluid shift from the intracellular into the extracellular compartment as a consequence of gain in extracellular solute (glucose) to hypovolemia from osmotic diuresis in patients with residual renal function or from fluid losses through extrarenal routes. Correction of DAH by insulin infusion reverses the osmotic fluid transfer between the intracellular and extracellular compartments and corrects the pulmonary edema, but can worsen the manifestations of hypovolemia, which require saline infusion. (d) A variety of acid-base disorders including ketoacidosis correctable with insulin infusion and no other interventions. (e) Hyperkalemia, which is frequent in DAH and is more severe when ketoacidosis is also present. Insulin infusion corrects the hyperkalemia. Extreme hyperkalemia at presentation or hypokalemia developing during insulin infusion require additional measures.

CONCLUSIONS

In DAH, insulin infusion is the primary management strategy and corrects the fluid and electrolyte abnormalities. Patients treated for DAH should be monitored for the development of hypoglycemia or fluid and electrolyte abnormalities that may require additional treatments.

Hypoglycemia in People with Type 2 Diabetes and CKD

BACKGROUND AND OBJECTIVES

Among people with diabetes mellitus, CKD may promote hypoglycemia through altered clearance of glucose-lowering medications, decreased kidney gluconeogenesis, and blunted counter-regulatory response. We conducted a prospective observational study of hypoglycemia among 105 individuals with type 2 diabetes treated with insulin or a sulfonylurea using continuous glucose monitors.

DESIGN, SETTING, PARTICIPANTS & MEASUREMENTS

We enrolled 81 participants with CKD, defined as eGFR<60 ml/min per 1.73 m², and 24 control participants with eGFR≥60 ml/min per 1.73 m² frequency-matched on age, duration of diabetes, hemoglobin A1c, and glucose-lowering medications. Each participant wore a continuous glucose monitor for two 6-day periods. We examined rates of sustained level 1 hypoglycemia (<70 mg/dl) and level 2 hypoglycemia (<54 mg/dl) among participants with CKD. We then tested differences compared with control participants as well as a second control population (n=73) using Poisson and linear regression, adjusting for age, sex, and race.

RESULTS

Over 890 total days of continuous glucose monitoring, participants with CKD were observed to have 255 episodes of level 1 hypoglycemia, of which 68 episodes reached level 2 hypoglycemia. Median rate of hypoglycemic episodes was 5.3 (interquartile range, 0.0-11.7) per 30 days and mean time spent in hypoglycemia was 28 (SD 37) minutes per day. Hemoglobin A1c and the glucose management indicator were the main clinical correlates of time in hypoglycemia (adjusted differences 6 [95% confidence interval, 2 to 10] and 13 [95% confidence interval, 7 to 20] fewer minutes per day per 1% higher hemoglobin A1c or glucose management indicator, respectively). Compared with control populations, participants with CKD were not observed to have significant differences in time in hypoglycemia (adjusted differences 4 [95% confidence interval, -12 to 20] and -12 [95% confidence interval, -29 to 5] minutes per day).

CONCLUSIONS

Among people with type 2 diabetes and moderate to severe CKD, hypoglycemia was common, particularly with tighter glycemic control, but not significantly different from groups with similar clinical characteristics and preserved eGFR.

Management of adults with diabetes on the haemodialysis unit: summary of guidance from the Joint British Diabetes Societies and the Renal Association

Diabetic nephropathy remains the principal cause of end-stage renal failure in the UK and its prevalence is set to increase. People with diabetes and end-stage renal failure on maintenance haemodialysis are highly vulnerable, with complex comorbidities, and are at high risk of adverse cardiovascular outcomes, the leading cause of mortality in this population. The management of people with diabetes receiving maintenance haemodialysis is shared between diabetes and renal specialist teams and the primary care team, with input from additional healthcare professionals providing foot care, dietary support and other aspects of multidisciplinary care. In this setting, one specialty may assume that key aspects of care are being provided elsewhere, which can lead to important components of care being overlooked. People with diabetes and end-stage renal failure require improved delivery of care to overcome organizational difficulties and barriers to communication between healthcare teams. No comprehensive guidance on the management of this population has previously been produced. These national guidelines, the first in this area, bring together in one document the disparate needs of people with diabetes on maintenance haemodialysis. The guidelines are based on the best available evidence, or on expert opinion where there is no clear evidence to inform practice. We aim to provide clear advice to clinicians caring for this vulnerable population and to encourage and improve education for clinicians and people with diabetes to promote empowerment and self-management.

Glycated Albumin Versus HbA1c in the Evaluation of Glycemic Control in Patients With Diabetes and CKD

INTRODUCTION

It is inaccurate to assess blood glucose with glycated hemoglobin (HbA1c) in patients with diabetes and chronic kidney disease (CKD), and whether glycated albumin (GA) is better than HbA1c in these patients remains unclear.

METHODS

We searched PubMed, Embase, Web of Science, Scopus, the Cochrane Library, and MEDLINE to July 2017 for studies that investigated the correlation between GA or HbA1c and the average glucose levels (AG) relevant to this theme. Statistical analysis was performed using RevMan5.3 and Stata12.0. The outcome was the correlation coefficient between GA or HbA1c and AG. For the first time, we made a comparison of GA and HbA1c in different CKD stages.

RESULTS

A total of 24 studies with 3928 patients were included. Early stages of CKD refer to CKD stage 1 to 3. Advanced CKD refer to CKD stage 4 and 5 including patients receiving dialysis. The meta-analysis suggested that in early stages of CKD, the pooled R between GA and AG was 0.61 (95% CI = 0.49-0.73) and 0.71 (95% CI = 0.55-0.87) for HbA1c (P > 0.05). In advanced CKD patients, the pooled R between GA and AG was 0.57 (95% CI = 0.52-0.62), and 0.49 (95% CI = 0.45-0.52) for HbA1c (P = 0.0001).

CONCLUSION

GA is superior to HbA1c in assessing blood glucose control in diabetes patients with advanced CKD.

Is the management of diabetes different in dialysis patients?

Diabetes is highly and increasingly prevalent in the dialysis population and negatively impacts both quality and quantity of life. Nevertheless, the best approach to these patients is still debatable. The question of whether the management of diabetes should be different in dialysis patients does not have a clear yes or no answer but is divided into too many sub-issues that should be carefully considered. In this review, lifestyle, cardiovascular risk, and hyperglycemia management are explored, emphasizing the possible pros and cons of a similar approach to diabetes in dialysis patients compared to the general population.

Managing Diabetes and Cardiovascular Risk in Chronic Kidney Disease Patients

We discuss mechanisms of increased cardiovascular disease (CVD) in patients with chronic kidney disease (CKD) and strategies for managing cardiovascular (CV) risk in these patients. Our focus was mainly on decreasing CV events and progression of microvascular complications by reducing levels of glucose and lipids. We searched PubMed with no limit on the date of the article. All articles were discussed among all authors. We chose pertinent articles, and searched their references in turn for additional relevant publications.

Comparison of Glycemic Markers in Chronic Hemodialysis Using Continuous Glucose Monitoring

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.

Therapeutic Considerations for Antihyperglycemic Agents in Diabetic Kidney Disease

Diabetic kidney disease is among the most frequent complications of diabetes, with approximately 50% of patients with ESRD attributed to diabetes in developed countries. Although intensive glycemic management has been shown to delay the onset and progression of increased urinary albumin excretion and reduced GFR in patients with diabetes, conservative dose selection and adjustment of antihyperglycemic medications are necessary to balance glycemic control with safety. A growing body of literature is providing valuable insight into the cardiovascular and renal safety and efficacy of newer antihyperglycemic medications in the dipeptidyl peptidase-4 inhibitor, glucagon-like peptide-1 receptor agonist, and sodium-glucose cotransporter 2 inhibitor classes of medications. Ongoing studies will continue to inform future use of these agents in patients with diabetic kidney disease.

Role of continuous glucose monitoring for type 2 in diabetes management and research

The advent of continuous glucose monitoring (CGM) is a significant stride forward in our ability to better understand the glycemic status of our patients. Current clinical practice employs two forms of CGM: professional (retrospective or "masked") and personal (real-time) to evaluate and/or monitor glycemic control. Most studies using professional and personal CGM have been done in those with type 1 diabetes (T1D). However, this technology is agnostic to the type of diabetes and can also be used in those with type 2 diabetes (T2D). The value of professional CGM in T2D for physicians, patients, and researchers is derived from its ability to: (1) to discover previously unknown hyper- and hypoglycemia (silent and symptomatic); (2) measure glycemic control directly rather than through the surrogate metric of hemoglobin A1C (HbA1C) permitting the observation of a wide variety of metrics that include glycemic variability, the percent of time within, below and above target glucose levels, the severity of hypo- and hyperglycemia throughout the day and night; (3) provide actionable information for healthcare providers derived by the CGM report; (4) better manage patients on hemodialysis; and (5) effectively and efficiently analyze glycemic effects of new interventions whether they be pharmaceuticals (duration of action, pharmacodynamics, safety, and efficacy), devices, or psycho-educational. Personal CGM has also been successfully used in a small number of studies as a behavior modification tool in those with T2D. This comprehensive review describes the differences between professional and personal CGM and the evidence for the use of each form of CGM in T2D. Finally, the opinions of key professional societies on the use of CGM in T2D are presented.

The angiotensin-I converting enzyme gene I/D variation contributes to end-stage renal disease risk in Chinese patients with type 2 diabetes receiving hemodialysis

Whether the DD genotype of the angiotensin-I converting enzyme (ACE) I/D variation contributes to end-stage renal disease (ESRD) risk in type 2 diabetes mellitus (T2DM) remains controversial. Differences in study design, case and control definition, sample size and ethnicity may contribute to the discrepancies reported in association studies. We performed a case-control study to evaluate the association of the ACE I/D variation with ESRD risk in Chinese patients with T2DM receiving hemodialysis and analyzed the genotype-phenotype interaction. Unrelated Chinese patients (n = 432) were classified into the non-diabetic nephropathy (DN) control group (n = 222, duration of diabetes >10 years, no signs of renal involvement) and the DN-ESRD group (n = 210; ESRD due to T2DM, receiving hemodialysis). Polymerase chain reaction was used to genotype ACE I/D for all 432 subjects. The frequencies of the ID + DD genotypes were higher in the DN-ESRD group than non-DN control group (65.2 vs. 50.9 %; adjusted OR 1.98 (95 % CI, 1.31-3.00; P = 0.001). In the DN-ESRD group, the DD genotypic subgroup had significantly elevated HbA1c and diastolic blood pressure (DBP) compared to the II subgroup (both P < 0.05). The DD genotype of the ACE I/D variation may be associated with more elevated blood pressure and HbA1c, and therefore may predict the development, progression and severity of DN-ESRD in Chinese patients with T2DM undergoing hemodialysis.

Population pharmacodynamic analysis of hemoglobin A1c-lowering effects by adding treatment of DPP-4 inhibitors (sitagliptin) in type 2 diabetes mellitus patients based on electronic medical records

AIM

To develop a population pharmacodynamic (PPD) model describing the time course for the hemoglobin A1c (HbA1c)-lowering effects of adding treatment of DPP-4 inhibitors and to assess the efficacy of combination therapy in type 2 diabetes mellitus patients based on electronic medical records.

METHODS

Information on patients was collected retrospectively from electronic medical records. Of the 4 DPP-4 inhibitors used, we focused on sitagliptin as it had the best time-response relationships. A physiological indirect response model was developed to describe changes in HbA1c levels.

RESULTS

An indirect response model, based on the 1300 HbA1c levels of 160 patients, described the time course for the HbA1c-lowering effects of adding sitagliptin. The combination with pioglitazone decreased the HbA1c synthesis rate by 7.74% relative to without pioglitazone. Bayesian forecasting based on the final PDD model using the first two HbA1c observations, before and within 30days after the addition of sitagliptin, gave a precise prediction of HbA1c-lowering effects individually.

CONCLUSIONS

Our PPD model quantitatively described the beneficial effects of combination therapy with pioglitazone and sitagliptin. The proposal methodology is also expected to be applicable to other medicines based on electronic medical records in clinical practice.