Use Of Continuous Glucose Monitoring in The Assessment And Management Of Patients With Diabetes And Chronic Kidney Disease Ⅱ

In developed countries, diabetes is the leading cause of chronic kidney disease (CKD) and accounts for 50% of the incidence of end-stage kidney disease. Despite the declining prevalence of micro- and macrovascular complications, there are rising trends in renal replacement therapy in diabetes. Optimal glycemic control may reduce risk of progression of CKD and related death. However, assessing glycemic control in patients with advanced CKD and on dialysis (G4-5) can be challenging. Laboratory biomarkers, such as glycated hemoglobin (HbA1c), may be biased by abnormalities in blood hemoglobin, use of iron therapy and erythropoiesis-stimulating agents and chronic inflammation due to uremia. Similarly, glycated albumin and fructosamine may be biased by abnormal protein turnover. Patients with advanced CKD exhibited heterogeneity in glycemic control ranging from severe insulin resistance to 'burnt-out' beta-cell function. They also had high risk of hypoglycemia due to reduced renal gluconeogenesis, frequent use of insulin and dysregulation of counterregulatory hormones. Continuous glucose monitoring (CGM) systems measure glucose in interstitial fluid every few minutes and provide an alternative and more reliable method of glycemic assessment, including asymptomatic hypoglycemia and hyperglycaemic excursions. Recent international guidelines recommended use of CGM-derived Glucose Management Index (GMI) in patients with advanced CKD although data are scarce in this population. Using CGM, patients with CKD were found to experience marked glycemic fluctuations with hypoglycemia due to loss of glucose and insulin during hemodialysis (HD) followed by hyperglycemia in the post-HD period. On the other hand, during peritoneal dialysis, patients may experience glycemic excursions with influx of glucose from dialysate solutions. This undesirable glucose exposure and variability may accelerate the decline of residual renal function. Although CGM may improve the quality of glycemic monitoring and control in populations with CKD, further studies are needed to confirm the accuracy, optimal mode and frequency of CGM as well as their cost-effectiveness and user-acceptability in patients with advanced CKD and dialysis.

Keywords: continuous glucose monitoring, end-stage kidney disease (ESKD), dialysis, diabetes, type 2 (non-insulin-dependent) diabetes mellitus, diabetic kidney disease, diabetic nephropathy

INTRODUCTION 

Diabetic kidney disease (DKD) is now the leading cause of chronic kidney disease (CKD) and end-stage kidney disease (ESKD) in many countries. In 2014, DKD accounted for 50% of patients with ESKD in the developed world (1). Data from the United States (US) suggested a slower decline in ESKD incidence compared with other diabetic complications including cardiovascular disease. The US Renal Registry reported a steady increase in the incidence of ESKD due to diabetes up to 47% in 2017, compared with 15% in 1985 (2). In the Hong Kong Renal Registry, diabetes was the cause of ESKD in 50% of patients which had replaced glomerulonephritis as the leading cause of renal replacement therapy since 1998 (3).

Patients with diabetes and CKD have an increased risk of morbidity and premature mortality than those without renal complications. In the Hong Kong Diabetes Register, patients with CKD had 63% higher risk in all-cause mortality than their non-CKD counterparts, after adjusting for factors such as age, body mass index (BMI), blood pressure and use of oral glucose-lowering drugs (OGLDs) (4). Patients with CKD had high risk of cardiovascular events which accounted for 40-50% of mortality in those with estimated glomerular filtration rate (eGFR) < 30 ml/min/1.73m2 . This excess risk could not be explained by comorbid factors such as hypertension and dyslipidemia (5) and might be attributed to additional factors such as vascular calcification, chronic inflammation and myocardial fibrosis (6). Patients with CKD are at increased risk and more vulnerable to hypoglycaemic episodes (4). In a cohort of over 30,000 US veterans with diabetes transitioning to dialysis, the frequency of hypoglycemia-related hospitalizations was associated with higher post-ESKD mortality in a dose-dependent manner (7).

Optimal glycemic control had been shown to delay the progression of CKD and reduce death rate in diabetes. In the Diabetes Control and Complication Trial, 1441 patients with type 1 diabetes (T1D) were randomized to receive intensive or conventional insulin treatment. The risk of microalbuminuria was reduced by 34% in the intensive treatment group after at least four years of follow-up (8). The Action in Diabetes and Vascular Disease: Preterax and Diamicron MR Controlled Evaluation (ADVANCE) trial enrolled high-risk patients with long duration of type 2 diabetes, (T2D), many of whom had prior history of complications. The in-trial reductions in the risk of ESKD was maintained during a total follow-up period of 9.9 years with a hazard ratio of 0.54 (29 events in the intensive treatment group and 53 events in the usual treatment group) (9). In a randomized controlled study of Japanese patients with 110 T2D lasting for 8 years, intensive insulin therapy reduced the rate of progression in nephropathy compared with conventional treatment (10). In the Dialysis Outcomes and Practice Pattern Study (DOPPS) including 9201 patients on dialysis with either T1D or T2D, there was a U-shaped relationship between HbA1c and all-cause mortality. Using HbA1c 7 – 8% as a reference, there was 38% increased risk of mortality in patients with HbA1c ≥9% and 21% for those with HbA1c <7% (11). Based on the available evidence, The Kidney Disease Improving Global Outcome (KDIGO) 2020 guideline recommended an optimal HbA1c target range of 6.5-8.0% for patients with diabetes and CKD, with emphasis on individualization of targets taking age, comorbidities, life expectancy, and hypoglycemia risks into consideration (12).

Optimal glycemic management in patients with diabetes and CKD can be challenging, particularly in those with advanced CKD. Reasons include a progressive decline in beta-cell function and an increase in insulin resistance along with the increased risk of severe hypoglycemia and limited choices of OGLDs. Indeed, the heterogeneity in glycemic control amongst patients with CKD represents inter- and intra-individual variations amongst multiple interacting factors including insulin secretion, insulin resistance, renal clearance of insulin, renal gluconeogenesis and renal function. Increased insulin resistance in early CKD may be triggered by metabolic acidosis, uremic toxins, and chronic inflammation associated with reduced kidney function (13–16). With the progression of CKD, the prolonged glucose-lowering effects of oral glucose drugs (OGLD) including insulin, together with reduced renal gluconeogenesis, shift the balance toward an increased risk of hypoglycemia (17, 18). In patients with ESKD, around 30% had "burn-out diabetes" and required reduction or discontinuation of insulin treatment and OGLDs (18). In these patients, initiation of dialysis may remove uremic toxins with restoration of insulin sensitivity. Patients with "burnt-out diabetes' often require only low-dose insulin treatment (19). On the other hand, the dialysis regimen and glucose content of dialysates can significantly influence day-to-day glucose profiles.

One of the greatest challenges in optimizing glycemic management is the accurate assessment of glucose control. Conventional markers such as glycated hemoglobin (HbA1c), fructosamine or glycated albumin may be less reliable in in advanced CKD and ESKD. With the emergence of continuous glucose monitoring (CGM), this might be a helpful alternative in assessing and managing diabetes patients with advanced CKD and ESKD. The aim of this narrative review is to summarise current clinical evidence on the accuracy and utility of CGM in CKD patients. We have reviewed the literature on clinical reports, observational studies and clinical trials of use of CGM in CKD. Due to potential issues of sensor performance and the impact of dialysis regimens, we have devoted special attention to use of CGM in patients on hemodialysis and peritoneal dialysis, a challenging group who are prone to both hypoglycemic and hyperglycemic excursions.

CHALLENGES IN GLYCEMIC ASSESSMENT IN CKD

The monitoring of glycemic status in patients with diabetes and CKD including ESKD is challenging. HbA1c, the gold standard as a laboratory glycemic marker, can be influenced by multiple factors in CKD. The formation of HbA1c is dependent on the intensity and duration of non-enzymatic interaction between blood glucose and hemoglobin. At any one time, patients may have a mixture of erythrocytes with different ages and varying degrees of exposure to glucose. Therefore, agents that alter erythropoiesis and the lifespan of red blood cells will affect HbA1c. For example, HbA1c can be biased towards high values by iron or vitamin B12 deficiency due to reduced synthesis of red blood cells with the increased relative amount of HbA1c. On the other hand, HbA1c can be biased towards low values by iron therapy and use of erythropoietin stimulating agents (ESA) with increased turnover of red blood cells (20, 21). The uremic environment in patients with advanced CKD can stimulate carbamylation of hemoglobin which may interfere with HbA1c assays using the ion-exchange method, but this can be avoided by using other methods such as high-pressure liquid chromatography (22).

Alternative glycemic indicators such as glycated albumin (GA) and fructosamine have their own limitations in CKD. Extracellular GA is more susceptible to glycation than intracellular hemoglobin (23). Also, GA is unaffected by factors such as iron therapy and ESA frequently used in patients with CKD which can affect HbA1c (21). Due to the shorter half-life of albumin, GA reflects recent glycemic control lasting for 2-3 weeks. However, GA can be affected by albumin metabolism. In patients with low albumin state or increased protein turnover due to chronic inflammation, GA can be falsely low or high (24). In patients treated with peritoneal dialysis (PD) with increased protein loss, GA value may underestimate true glycemia (25). Although GA can be corrected for serum albumin to reflect the true distribution (26), GA can be affected by oxidative and uremic environments, as well as reduced renal clearance of advanced glycation end products, resulting in positive bias (27).

Fructosamines are ketoamines formed by the glycation of albumin and other less abundant serum proteins (28). Although this biomarker involves a wider spectrum of glycated proteins, fructosamine suffers a similar bias as GA due to abnormal albumin metabolism and increased protein loss in patients with CKD. In patients with diabetes without CKD and normal serum albumin level, increased albuminuria was associated with low fructosamine value. Besides, fructosamine is sensitive to the fluctuation of serum levels of immunoglobulins and low-molecular-weight molecules (29). In patients with CKD, the uremic environment with altered immunoglobulin levels may affect fructosamine levels (30).

OVERVIEW OF CGM 

The introduction of continuous glucose monitoring (CGM) offers an alternative for more reliable and comprehensive glycemic evaluation in patients with CKD. Adherence to self-monitoring of blood glucose (SMBG) is often poor due to the inconvenience of finger-pricking. In a survey conducted in China, only 40% of patients adhered to the recommended SMBG frequencies (31). Most commercially available CGM devices are minimally invasive by inserting a small filament into subcutaneous tissue for the measurement of glucose in interstitial fluid. There is a dynamic equilibrium between interstitial glucose and blood glucose due to diffusion dependent on concentration gradient. The interstitial glucose is absorbed into the filament of the CGM device by capillary action. The concentration of interstitial glucose is determined by electrochemical reaction in the sensor (32). Minute-to-minute interstitial glucose readings are transmitted to and displayed in a mobile device, either a reader or a smartphone app.

In general, CGM systems can be classified into three categories based on their principles of operation and clinical usage. For professional CGM devices, readings are principally used for glycemic assessment by healthcare professionals in clinical trial settings which may be blinded or unblinded to the user. Real-time CGM (rt-CGM) devices display readings to the user continuously and can incorporate hypoglycemic or hyperglycemic alerts and trend prediction. The intermittentlyscanned or flash CGM devices display readings to user only when the user scans the transmitter (33). Real-time CGM and flash CGM are gaining popularity to facilitate self-monitoring in diabetes. In some countries, CGM devices are reimbursed or funded by public health systems for patients with T1D, including those on dialysis, and some patients with T2D receiving intensive insulin therapy (34).

PERFORMANCE OF CGM SENSORS IN ADVANCED CKD AND DIALYSIS 

The performance of the CGM sensor is dependent on the enzymatic electrochemical reactions which may be subject to multiple interferences (Figure 1). In early CGM devices, interstitial glucose was detected by glucose oxidase-peroxidase method (36). This method continues to be used by some CGM systems due to the small size and rapid response time of the sensor. However, the electrodes often require pretreatment to attach to the enzyme surface. Prolonged chemical reactions may pollute the surface of the transducer and affect the electrochemical response (37). Both endogenous and exogenous substances may cause interference of the electrochemical sensing of the oxidaseperoxidase reaction.

In patients with advanced CKD, hypoxia or hyperoxia can give rise to false sensor glucose values by changing the oxygen concentration at the initiation of the glucose oxidase chain reaction (38). There had been reports on the effects of hematocrit in altering glucose readings of glucometers that use glucose-dehydrogenase or glucose-oxidase methods (39). Endogenous substances such as uric acid and uremia may affect sensor performance. Ogawa et al. demonstrated significant interference of uric acid, a reducing agent, on glucometers using the glucose oxidase method compared with laboratory glucose hexokinase reference (40) However, uric acid did not significantly interfere with the sensor performance of a microdialysis-based CGM system (41). There are no dedicated studies evaluating the effect of pH on CGM sensor performance in ESKD. In critically ill patients, extreme pH <6.95 may affect the performance of point-of-care glucometers but not within pH range 6.97-7.84 (42). One study evaluated the effect of pH on the accuracy of CGM in a group of pediatric intensive-care patients and did not observe any significant effect (43). It is unknown whether fluid status might affect CGM performance in CKD patients due to lack of dedicated studies, however, a small study comparing hospitalized diabetes patients with and without congestive heart failure showed no differences in sensor accuracy (44).

Amongst exogenous substances, ascorbic acid, paracetamol, xylose, and ethanol have the potential to interfere with glucose oxidase sensors (45, 46) Other metabolites of icodextrin, such as maltose, also interfere with glucose dehydrogenase-based detectors using pyrroloquinoline quinone (GDH-PQQ) due to lack of selectivity on glucose (47). Use of GDH-PQQ glucometers can result in falsely elevated glucose readings in patients with PD using icodextrin dialysate. On the other hand, glucose-oxidase-based capillary blood glucometers are mostly unaffected by icodextrin (35). Most commercially available CGM system use glucose-oxidase sensors although interference of CGM sensors by icodextrin had not been explored.

The performance of commercially available enzyme-based CGM systems have been validated in small numbers of patients on dialysis. For example, Yajima et al. evaluated the accuracy of two CGM systems, the Freestyle Libre Pro and Medtronic iPro2™with Enlite™ sensor versus capillary blood glucose in patients undergoing HD. For Freestyle Libre, 49% of readings fell within the Parkes Error Grid zone A and 51% in zone B. The Medtronic Ipro2™ sensor exhibited smaller deviations with 93% of readings within zone A and 6.3% in zone B which are regarded as clinically acceptable. Mean absolute relative difference (MARD) was 19.5% ± 13.2% for Freestyle Libre versus 8.1% ± 7.6% for Medtronic iPro2 (48). In a three-week study comparing the accuracy of Freestyle Libre versus capillary blood glucose in 12 patients on hemodialysis, the MARD was found to be higher than people without ESKD (49). Only one study had evaluated the accuracy of Medtronic iPro2™ with Enlite™ sensor in 40 patients on PD. When compared with capillary blood glucose, MARD was 14%-19% (50). The accuracy of Dexcom sensors in hemodialysis is being investigated in ongoing trials (NCT04217161). Larger evaluation studies of sensor glucose against values measured by standard laboratory analyzers are needed in patients on different dialysis regimens.

USE OF CGM METRICS IN GLYCEMIC ASSESSMENT IN CKD 

Several studies analyzed the correlation between HbA1c, fructosamine, GA, and average sensor glucose across different CKD stages (Table 2). In general, the correlation between HbA1c and mean sensor glucose values tends to fall in CKD stage G4-5, in part confounded by differences in use of iron and ESA and blood hemoglobin. Lo and colleagues reported good correlation of mean CGM-glucose with HbA1c (r= 0.79) in patients with eGFR 30-59 ml/min/1.73m2 but fell (r=0.34) in participants (n=43) with eGFR below 30 ml/min/1.73m2 (51). In another study involving 25 patients with diabetes, the authors reported a weak correlation (r=0.38) between mean CGM-glucose and HbA1c in patients with eGFR <30ml/min/1.73m2 (52).

Nathan et al. first estimated HbA1c by linearly regressing mean sensor glucose with HbA1c in intensively treated patients with T1D in the Diabetes Control and Complication Trial (DCCT) (53). Bergenstal et al. later proposed the use of a glucose management index (GMI) to reflect the relationship between CGM glucose and HbA1c (54). However, these equations were derived predominantly from T1D and T2D patients with normal renal function and the reliability of the current GMI equation is unknown in patients with CKD (55). In one cohort, Zelnick and colleagues reported similar correlations between GMI and HbA1c of 0.78 in patients with eGFR >30 ml/ min/1.73m2 (n=80) and 0.76 in those with <30 ml/min/1.73m2 (n=24) (56). Nevertheless, the 2020 KDIGO guideline suggested GMI might be an alternative index for guiding treatment in patients with CKD G4-5 or dialysis where HbA1c were less reliable (12). (Table 1).

Of equal if not greater importance is the use of time-in-ranges which describes the proportion of time the patient spent in the hyperglycemia or hypoglycaemia range. In 2019, at the Advanced Technology and Treatment for Diabetes (ATTD) Conference, there was consensus on using a series of CGM-derived metrics as clinical targets for glycemic management. The recommended target in an adult patient with T2D and without complications was >70% Time in range (TIR, % time sensor glucose >3.9 and <10 mmol/L), <25% time in Time above range reflecting significant hyperglycemia (TAR, % time sensor glucose >10 mmol/L), <5% time below target suggesting hypoglycemia (TBR, % time sensor glucose <3.9 mmol/L) with a Coefficient of Variation < 36% (%CV = SD (standard deviation) of sensor glucose/mean sensor glucose) (57). However, the validity of TIR targets and the prognostic values of CGM-derived metrics on complications and death need to be confirmed in clinical trials involving patients with advanced CKD and dialysis (12).

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