Applications of continuous glucose monitoring across settings and populations: Report from the 23rd Hong Kong diabetes and cardiovascular risk factors-East meets west symposium

Continuous glucose monitoring (CGM) is now an integral part of glycaemic management in people with type 1 diabetes and those with insulin-treated type 2 diabetes. Immediate access to information on CGM glucose levels and trends helps to inform food choices, titration and timing of insulin doses and prompts corrective actions in the event of impending hypo- or hyperglycaemia. Although glycated haemoglobin (HbA1c) remains an important measure of the average of glucose, CGM metrics including time-in-range (TIR) and other metrics on glycaemic variability and hypoglycaemia are strongly endorsed by people with diabetes as impacting their daily lives. There is growing consensus on definitions and targets of CGM metrics with an increasing number of studies demonstrating correlations between CGM metrics and incident complications of diabetes. Implementation of new technologies needs to take into consideration factors such as cost-effectiveness, accessibility as well as acceptability of the person with diabetes and healthcare professional. The United Kingdom is one of the few countries that have developed clinical pathways for integrating CGM into the routine care of people with type 1 diabetes. Besides type 1 diabetes, special groups such as people with impaired kidney function and women during pregnancy may derive additional benefits from CGM.

Use of Continuous Glucose Monitoring in the Assessment and Management of Patients With Diabetes and Chronic Kidney Disease

In developed countries, diabetes is the leading cause of chronic kidney disease (CKD) and accounts for 50% of incidence of end stage kidney disease. Despite 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 haemoglobin (HbA_1c), may be biased by abnormalities in blood haemoglobin, use of iron therapy and erythropoiesis-stimulating agents and chronic inflammation due to uraemia. 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 hypoglycaemia 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 hypoglycaemia 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 haemodialysis (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. These undesirable glucose exposure and variability may accelerate 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.

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.

The clinical usefulness of glycated albumin in patients with diabetes and chronic kidney disease: Progress and challenges

Prolonged hyperglycemia leads to a non-enzymatic glycation of proteins, and produces Amadori products, such as glycated albumin (GA) and glycated hemoglobin (HbA1c). The utility of HbA1c in the setting of chronic kidney disease (CKD) may be problematic since altered lifespan of red blood cells, use of iron and/or erythropoietin therapy, uremia and so on. Therefore, as an alternative marker, GA has been suggested as a more reliable and sensitive glycemic index in patients with CKD. In addition to the mean plasma glucose concentration, GA also reflects postprandial plasma glucose and glycemic excursion. Besides, with a half-life of approximately 2-3 weeks, GA may reflect the status of blood glucose more rapidly than HbA1c. GA is also an early precursor of advanced glycation end products (AGEs), which cause alterations in various cellular proteins and organelles. Thus, high GA levels may correlate with adverse outcomes of patients with CKD. In this review, the clinical usefulness of GA was discussed, including a comparison of GA with HbA1c, the utility and limitations of GA as a glycemic index, its potential role in pathogenesis of diabetic nephropathy and the correlations between GA levels and outcomes, specifically in patients with diabetes and CKD.

Serum albumin-adjusted glycated albumin as a better indicator of glycemic control in Type 2 diabetes mellitus patients with short duration of hemodialysis

AIMS

Serum albumin-adjusted glycated albumin (adjusted GA) is reportedly a better predictor of mortality than GA in patients with Type 2 diabetes mellitus (T2DM) on hemodialysis (HD). We compared how accurately GA and adjusted GA reflected glycemic control in these patients.

METHODS

We enrolled 31 patients with T2DM on HD. They were divided into two groups according to duration of HD: ≤6months (short HD group, N=16) and >6months (long HD group, N=15). GA or adjusted GA and parameters of glycemic control obtained by continuous glucose monitoring were measured, and the correlations between these were analyzed.

RESULTS

GA and adjusted GA were significantly correlated with mean glucose levels (r=0.400, P=0.025 and r=0.508, P=0.0037) in all patients. Similar results were obtained in the long HD group (GA: r=0.554, P=0.032; adjusted GA: r=0.604, P=0.017). However, in the short HD group, adjusted GA (r=0.502, P=0.047) but not GA (r=0.340, P=0.20) was significantly correlated with mean glucose levels.

CONCLUSIONS

Adjusted GA may be a better indicator than GA for evaluating glycemic control in T2DM patients with short duration of HD.

Glycated albumin and ratio of glycated albumin to glycated hemoglobin are good indicators of diabetic nephropathy in type 2 diabetes mellitus

BACKGROUND

We investigated the association between serum levels of glycated albumin (GA), glycated hemoglobin (HbA_1c ), and GA:HbA_1c ratio and the presence of diabetic nephropathy (DN) in patients with type 2 diabetes mellitus (T2DM).

METHODS

Two hundred six consecutive patients with T2DM were enrolled from the endocrinology ward at Shengjing Hospital of China Medical University and classified into 2 groups: DN group (n = 71) and non-diabetic nephropathy (non-DN) group (n = 135).

RESULTS

The DN group showed significantly higher GA and GA:HbA_1c values compared with the non-DN group. After univariate logistic regression, GA, GA:HbA_1c , HbA_1c , age, T2DM duration, systolic blood pressure, diastolic blood pressure, triglycerides, mean blood glucose (from 7 blood glucose checks), and presence of retinopathy were selected for advanced analysis. A multivariate logistic regression analysis was performed to examine the association between the presence of DN and these variables. Glycated albumin, GA:HbA_1c , duration, systolic blood pressure, mean blood glucose, and retinopathy (but not HbA_1c ) were identified as independent variables that predicted the presence of DN. We then fitted the areas under the curve of GA:HbA_1c and GA (0.811 and 0.754, respectively) separately using a receiver operating characteristic curve analysis. Both were better than HbA_1c (0.580). We defined the cutoff value of GA:HbA_1c as 2.71 (sensitivity 0.676, specificity 0.778) and that of GA as 17.5% (sensitivity 0.761, specificity 0.644) for the prediction of DN in patients with T2DM.

CONCLUSIONS

The GA:HbA_1c ratio and GA may be superior to HbA_1c associated with the presence of DN.

Serum albumin-adjusted glycated albumin is a better predictor of mortality in diabetic patients with end-stage renal disease on hemodialysis

AIMS

Glycated albumin (GA) is a marker for monitoring glycemic control in diabetic patients with end-stage renal disease (ESRD). We evaluated whether serum albumin-adjusted GA (adjusted GA) could predict mortality in diabetic patients with ESRD on hemodialysis.

METHODS

Seventy-eight patients with type 2 diabetes treated with regular hemodialysis were enrolled and followed up for 5-years. The adjusted GA was calculated from the regression formula and mean GA. The cut-off values for GA and adjusted GA that predicted mortality risk were determined using receiver operating characteristic curve analysis.

RESULTS

During the follow-up period (median: 36months), 15 patients died. In the Kaplan-Meier analysis, there were no significant differences in the 5-year cumulative survival rate (58.3% [GA ≥19.8%] vs. 88.6% [GA <19.8%], P=0.075). Conversely, this rate was significantly higher in patients with adjusted GA <21.2% than adjusted GA ≥21.2% (86.4 vs. 49.5%, P=0.0068). After adjustment for other confounders, adjusted GA ≥21.2% was an independent predictor for mortality (hazard ratio 3.76, 95% confidence interval 1.12-17.44, P=0.031), but GA ≥19.8% was not (hazard ratio 2.63, 95% confidence interval 0.65-17.69, P=0.19).

CONCLUSIONS

Adjusted GA is a better predictor of mortality than GA in diabetic patients with ESRD on hemodialysis.

Serum albumin-adjusted glycated albumin reflects glycemic excursion in diabetic patients with severe chronic kidney disease not treated with dialysis

OBJECTIVE

Diabetic patients with severe chronic kidney disease (CKD) not treated with dialysis falsely show low levels of HbA1c and GA due to renal anemia and proteinuria, respectively. Recently, we reported that serum albumin-adjusted GA (adjGA) accurately reflects mean plasma glucose (PG) in these patients. It is considered that GA reflects not only mean PG but also glycemic excursion; therefore, in the present study we investigated whether adjGA reflects glycemic excursion in these patients.

METHODS

Thirty type 2 diabetic patients with severe CKD not treated with dialysis were enrolled in the present study. PG was monitored by seven times a day, and indicators of glycemic excursion [SD, max PG, ΔPG, M value, J index, and mean amplitude of glucose excursion (MAGE)] were calculated. The correlations between HbA1c, GA or adjGA and indicators of glycemic excursion were investigated.

RESULTS

HbA1c showed no significant correlation with indicators of glycemic excursion. GA was significantly correlated with them except for ΔPG. adjGA was significantly and strongly associated with them except for MAGE. GA, but not adjGA, showed a significant positive correlation with serum albumin.

CONCLUSION

adjGA was not influenced by serum albumin and showed a significant correlation with indicators of glycemic excursion in diabetic patients with severe CKD not treated with dialysis. These results suggest that adjGA could be useful for indicating glycemic control in diabetic patients with severe CKD not treated with dialysis.