Continuous Glucose Monitoring and Use of Alternative Markers To Assess Glycemia in Chronic Kidney Disease

Application and management of continuous glucose monitoring in diabetic kidney disease

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

Continuous Glucose Monitoring and Reduced Diabetes-Related Hospitalizations in Patients with Type 2 Diabetes and CKD

Key Points

Patients with diabetes and CKD have a heightened risk of glycemic variability, which can lead to severe hypoglycemic or hyperglycemic events, potentially resulting in hospitalization. This study describes the results of a retrospective claims analysis of people with insulin-requiring type 2 diabetes and stage 3–5 CKD who initiated continuous glucose monitoring. Continuous glucose monitoring could help patients with type 2 diabetes and CKD control their glucose and avoid potentially dangerous glycemic events.

Background

There is a heightened risk of glycemic variability in patients with diabetes and CKD. This glycemic variability could lead to hypoglycemic or hyperglycemic crises. We hypothesized that initiation of continuous glucose monitoring (CGM), which provides a glucose measurement every 1–5 minutes, could reduce the incidence of hospitalizations for patients with type 2 diabetes (T2D) and CKD.

Methods

A retrospective analysis of US administrative claims data from the Optum Clinformatics database was conducted. People with T2D, using insulin, not receiving dialysis, and living with stage 3–5 CKD who initiated CGM between January 1, 2016, and March 31, 2022, were identified. National Drug Codes and Healthcare Common Coding Procedure System codes were used to identify CGM device use, and International Classification of Diseases 10th revision codes were used to identify CKD diagnosis and categorize health care encounters. Rates of diabetes-related hospitalizations were obtained, and multivariable logistic regression analyses revealed predictors of hypoglycemic and hyperglycemic encounters.

Results

A total of 8,959 insulin-using patients with T2D and CKD were identified. Most were White (72.3%), had Medicare insurance coverage (82.2%), were using intensive insulin (91.3%), and had stage 3 CKD (86.0%). After CGM initiation, rates of hospitalizations for hyperglycemia or hypoglycemia decreased by 18.2% and 17.0%, respectively (P < 0.0001 for both). The proportion hospitalized with at least one hypoglycemic or hyperglycemic event also significantly decreased after CGM initiation. Significant predictors of both hypoglycemic and hyperglycemic encounters included a previous encounter of that type, age 30–59 years and depression (for hypoglycemia), and age 30–49 years and neuropathy (for hyperglycemia). Use of CGM or glucagon-like peptide-1 receptor agonists was significantly protective against hypoglycemic encounters.

Conclusions

Initiation of CGM was associated with significant reductions in diabetes-related hospitalizations among insulin-using individuals with T2D and moderate-to-severe CKD. CGM could help patients with T2D and CKD control their glucose and avoid potentially dangerous glycemic events.

Primary care diabetes assessment when HbA1c and other measures of glycemia disagree

AIMS

Although diabetes management decisions in primary care are typically based largely on HbA1c, mismatches between HbA1c and other measures of glycemia that are increasingly more available present challenges to optimal management. This study aimed to assess a systematic approach to identify the frequency of mismatches of potential clinical significance amongst various measures of glycemia in a primary care setting.

METHODS

Following screening to exclude conditions known to affect HbA1c interpretation, HbA1c, and fructosamine were obtained and repeated after ∼90 days on 53 adults with prediabetes or type 2 diabetes. A subset of 13 participants with repeat labs wore continuous glucose monitoring (CGM) for 10 days.

RESULTS

As expected, HbA1c and fructosamine only modestly correlated (initial R2 = 0.768/repeat R2 = 0.655). The HbA1c/fructosamine mismatch frequency of ± 0.5% (using the following regression HbA1c = 0.015 *fructosamine + 2.994 calculated from the initial sample) was 27.0%. Of the 13 participants with CGM data, HbA1c and CGM-based Glucose Management Indicator correlated at R2 = 0.786 with a mismatch frequency of ± 0.5% at 46.2% compared to a HbA1c/fructosamine mismatch frequency of ± 0.5% at 30.8%.

CONCLUSIONS

HbA1c is frequently mismatched with fructosamine and CGM data. As each of the measures has strengths and weaknesses, the utilization of multiple different measures of glycemia may be informative for diabetes assessment in the clinical setting.

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

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

Impact of stress hyperglycemia ratio, derived from glycated albumin or hemoglobin A1c, on mortality among ST-segment elevation myocardial infarction patients

BACKGROUND

Stress hyperglycemia ratio (SHR), associated with adverse outcomes in patients with ST-segment elevation myocardial infarction (STEMI), has several definitions. This study aims to assess the prognostic value of SHR, derived from hemoglobin A1c (HbA1c) or glycated albumin (GA), to mortality.

METHODS

The study comprised 1,643 STEMI patients who underwent percutaneous coronary intervention (PCI) in two centers. SHR1 was calculated using fasting blood glucose (FBG)/GA, while SHR2 was calculated using the formula FBG/(1.59*HbA1c-2.59). The primary endpoints were in-hospital death and all-cause mortality, with a median follow-up duration of 1.56 years.

RESULTS

Higher SHR1 and SHR2 values are associated with increased risks of in-hospital death and all-cause mortality. Each standard deviation increase in SHR1 corresponded to a 39% and 22% escalation in in-hospital death and all-cause mortality, respectively. The respective increases for SHR2 were 51% and 26%. Further examinations validated these relationships as linear. Additionally, the areas under the curve (AUC) for in-hospital death were not significantly different between SHR1 and SHR2 (p > 0.05). Incorporating SHR1 or SHR2 into the base model significantly improved the discrimination and risk reclassification for in-hospital and all-cause mortality. A subgroup analysis revealed that the effects of SHR1 and SHR2 were more pronounced in patients with hypercholesteremia.

CONCLUSION

SHR1 and SHR2 have emerged as robust and independent prognostic markers for STEMI patients undergoing PCI. The SHR calculation based on either HbA1c or GA can provide additional predictive value for mortality beyond traditional risk factors, helping to identify high-risk STEMI patients.

Usefulness of glucose management indicator derived from continuous glucose monitoring to assess glycemic condition in hospitalized patients with diabetic kidney disease treated with insulin pumps

BACKGROUND AND OBJECTIVE

This study described the relationship of HbA1c and continuous glucose monitoring (CGM) derived glucose management indicator (CGM-derived GMI) and developed a model to estimate GMI based on clinical parameters (clinical-parameter GMI) for hospitalized DKD treated with insulin pump.

METHODS

This observational study collected clinical data of hospitalized DKD treated with insulin pump between February 2022 to February 2023. According to estimated glomerular filtration rate (eGFR), 156 participants were divided into G1-2 (n = 64), G3 (n = 56) and G4-5 (n = 36). Correlation between HbA1c and CGM-derived GMI was tested. Study population was divided into training and validation set based on ratio of 6:4. In training set, a linear model was established to calculate clinical-parameter GMI. In validation set, paired t-test and residual analysis was used to examine the difference between CGM-derived GMI and clinical-parameter GMI.

RESULTS

With renal function reduced, the correlation of HbA1c and CGM-derived GMI decreased. Meanwhile, as renal function reduced, the discordance between HbA1c and CGM-derived GMI decreased as well (P = 0.009). In training set, based on eGFR stages, anemia, albumin, FBG and HbA1c, formula to clinical-parameter GMI was established. In validation set, the differences between clinical-parameter GMI and CGM-derived GMI was around 0, with 95 % confidence interval of -1.8 % to 1.5 %.

CONCLUSIONS

HbA1c may be less accurate to reflect glycemic condition for DKD with impaired renal function. A easily accessiable model based on clinical parameter to estimate GMI may help assess glycemia for hospitalized DKD treated with insulin pump.

Glycated Albumin Correlates With Time-in-Range Better Than HbA1c or Fructosamine

CONTEXT

Intermediate-term glycemic control metrics may represent a viable alternative to continuous glucose monitoring (CGM) in patients without access to CGM.

OBJECTIVE

This work aimed to compare the relationship between CGM parameters and glycated albumin (GA), glycated hemoglobin A1c (HbA1c), and fructosamine for 24 weeks.

METHODS

We conducted exploratory comparative analyses of CGM subgroup data from a previously published 24-week prospective study of assay performance in 8 US clinics. Participants included 34 individuals with type 1 (n = 18) and type 2 diabetes (n = 16) undergoing changes to improve glycemic control (n = 22; group 1) or with stable diabetes therapy (n = 12; group 2). Main outcome measures included Pearson correlations between CGM and glycemic indices and receiver operating characteristic (ROC) analysis of glycemic index values predictive of time in range (TIR) greater than 70%.

RESULTS

At weeks 4 and 8, GA correlations with TIR were higher than HbA1c correlations in group 1. In group 2, GA correlations with TIR were statistically significant, whereas HbA1c correlations were not. In both groups over the first 12 weeks, GA correlations with TIR were higher than fructosamine-TIR correlations. In the ROC analysis, GA predicted a TIR greater than 70% during weeks 2 to 24 (area under the curve >0.80); HbA1c was predictive during weeks 12 to 24. Cutoff values for TIR greater than 70% were 17.5% (sensitivity and specificity, 0.88) for GA and 7.3% (0.86) for HbA1c.

CONCLUSION

GA is the most accurate predictor of TIR over 8 weeks compared with other glycemic indices, which may assist in clinical evaluation of changes in treatment where CGM is not possible and it is too early to use HbA1c (NCT02489773).

Glycated serum proteins and albumin but not glycated albumin show negative correlation with BMI in an overweight/obese, diabetic population from the United States

BACKGROUND AND AIMS

Multiple previously published studies have shown a weak to medium, negative correlation between BMI and glycated albumin (GA). However, many of these studies were in populations with a narrow range of BMI. It is unknown whether this trend exists if a wider BMI range is used. This is an important question for proper interpretation of GA levels in obese populations.

MATERIALS AND METHODS

A retrospective analysis of clinical trial data (NCT02519309) was performed. After appropriate exclusions, 334 subjects remained. These included 73.7% with type 2 diabetes (T2D) diagnosis and 26.3% with prediabetes. BMI ranged from 24.8-86.9 kg/m2. Laboratory data were measured in a CLIA-certified laboratory using commercially available, automated methods.

RESULTS

No significant, negative correlation was seen between GA and BMI. However, individual components (glycated serum proteins and albumin) as well as the GA/HbA1c ratio show a weak, negative correlation with BMI for all subjects and those with T2D. The strongest negative correlation was with albumin. Examination by traditional BMI subgroups also showed statistically significant differences for those with T2D, but not for the prediabetic cohort. Correlations between BMI and C-reactive protein were similar in those with diabetes and prediabetes; however, correlation between BMI and insulin was stronger in those with diabetes.

CONCLUSION

Negative correlations between BMI and albumin or BMI and glycated serum proteins persist in diabetic populations that are obese and overweight, even when a statistically significant negative correlation is not observed between BMI and GA. Inflammation or insulin-mediated changes in protein synthesis could be contributors to these negative correlations, but BMI-related changes to the glomerulus could also affect clearance of albumin or glycated proteins and should be examined.

Protein glycation in diabetes mellitus

Diabetes mellitus is the ninth leading cause of mortality worldwide. It is a complex disease that manifests as chronic hyperglycemia. Glucose exposure causes biochemical changes at the proteome level as reflected in accumulation of glycated proteins. A prominent example is hemoglobin A1c (HbA1c), a glycated protein widely accepted as a diabetic indicator. Another emerging biomarker is glycated albumin which has demonstrated utility in situations where HbA1c cannot be used. Other proteins undergo glycation as well thus impacting cellular function, transport and immune response. Accordingly, these glycated counterparts may serve as predictors for diabetic complications and thus warrant further inquiry. Fortunately, modern proteomics has provided unique analytic capability to enable improved and more comprehensive exploration of glycating agents and glycated proteins. This review broadly covers topics from epidemiology of diabetes to modern analytical tools such as mass spectrometry to facilitate a better understanding of diabetes pathophysiology. This serves as an attempt to connect clinically relevant questions with findings of recent proteomic studies to suggest future avenues of diabetes research.

[Alternative strategies to the use of glycosylated hemoglobin in monitoring the glycemic status of diabetic patients with end-stage renal disease]

BACKGROUND

Diabetes mellitus (DM) is one of the leading causes of end-stage renal disease. Glycosylated hemoglobin (HbA1c) is the recommended glycemic marker to achieve an optimal glycemic control that is essential to prevent comorbidities associated with the disease. However, in patients on haemodialysis (HD) this marker has important limitations, this reason has led us to search alternative markers such as glycosylated albumin (AG), labile fraction of glycosylated hemoglobin (LHbA1c) or glycation indices.

PATIENTS AND METHODS

We enrolled 47 patients in HD, 23 with DM, obtaining samples for the determination of de AG, HbA1c y LHbA1c. Glycation indices, which allow estimated the HbA1c using glucose, AG or LHbA1c, were calculated including a control group composed of 75 diabetic patients without kidney disease.

RESULTS

Diabetic patients in HD had significantly higher mean values than patients without DM for glucose [160 (44) vs 96 (12)mg/dL], HbA1c [6,4 (1,0) vs 4,9 (0,3)%], AG [16,0 (5,1) vs 12,9 (1,6)%] and LHbA1c [2,0 (0,3) vs 1,7 (0,2)%]. HbA1c calculated using glycation indices was significantly higher than measured in all HD patients, regardless of the marker used for the estimation.

CONCLUSIONS

The glycemic markers evaluated (glucose, AG and LHbA1c), could reflect a possible underestimation of the real glycemic state by HbA1c because of the limitations of this marker in HD patients. The use of alternative markers, knowing their limitations, could improve the monitoring of patients on HD and, therefore, reduce the risk of developing DM2 complications.

Within-Person and Between-Sensor Variability in Continuous Glucose Monitoring Metrics

BACKGROUND

The within-person and between-sensor variability of metrics from different interstitial continuous glucose monitoring (CGM) sensors in adults with type 2 diabetes not taking insulin is unclear.

METHODS

Secondary analysis of data from 172 participants from the Hyperglycemic Profiles in Obstructive Sleep Apnea randomized clinical trial. Participants simultaneously wore Dexcom G4 and Abbott Libre Pro CGM sensors for up to 2 weeks at baseline and again at the 3-month follow-up visit.

RESULTS

At baseline (up to 2 weeks of CGM), mean glucose for both the Abbott and Dexcom sensors was approximately 150 mg/dL (8.3 mmol/L) and time in range (70180 mg/dL [3.910.0 mmol/L]) was just below 80. When comparing the same sensor at 2 different time points (two 2-week periods, 3 months apart), the within-person coefficient of variation (CVw) in mean glucose was 17.4 (Abbott) and 14.2 (Dexcom). CVw for percent time in range: 20.1 (Abbott) and 18.6 (Dexcom). At baseline, the Pearson correlation of mean glucose from the 2 sensors worn simultaneously was r 0.86, root mean squared error (RMSE), 13 mg/dL (0.7 mmol/L); for time in range, r 0.88, RMSE, 8 percentage points.

CONCLUSIONS

Substantial variation was observed within sensors over time and across 2 different sensors worn simultaneously on the same individuals. Clinicians should be aware of this variability when using CGM technology to make clinical decisions.ClinicalTrials.gov Identifier: NCT02454153.

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

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

Diabetes Management and Outcomes among Patients with Type 2 Diabetes Attending a Renal Service

Background

Patients with comorbid type 2 diabetes mellitus (T2DM) and renal disease, particularly those treated with insulin, often require complex pharmacological treatment and management of other diabetes complications.

Aims

To assess the achievement of metabolic targets and compare the current management of renal service attenders with insulin- and noninsulin-treated T2DM.

Methods

Single-centre retrospective cross-sectional study involving medical record review of patients with T2DM aged ≥18 years who visited a metropolitan renal outpatient clinic in 2017. Univariable analysis and multivariable logistic regression were used to identify factors associated with insulin treatment.

Results

Among 268 patients (45.5% insulin-treated), mean HbA1c was higher in insulin-treated vs. noninsulin-treated patients (8.0 ± 1.8% (64 mmol/mol) vs. 6.8 ± 1.2% (51 mmol/mol), p < 0.001). Significantly fewer insulin-treated patients had HbA1c ≤ 7.0% (53 mmol/mol; 31.8% vs. 69.3%, p < 0.001). More insulin-treated patients had ischaemic heart disease (46.7% vs. 33.6%, p = 0.028), diabetic foot disease (15.6% vs. 4.8%, p = 0.003), retinopathy (40.2% vs. 11.0%, p < 0.001), and emergency attendance for severe hypoglycaemia (3.8% vs. 0% p = 0.042). Insulin treatment was more associated with chronic kidney disease stages 4-5 (adjusted odds ratio (aOR) 2.41, 95% CI 1.07-5.43), retinopathy (aOR 3.10, 95% CI 1.04-9.27), and podiatry review (aOR 5.06, 95% CI 1.20-21.38). Only 38 (14.2%) individuals were seen by a colocated public multidisciplinary diabetes service in 2017.

Conclusions

Renal clinic attenders with T2DM, particularly if insulin-treated, remained at increased risk of diabetes-related complications, including severe hypoglycaemia, with limited input from the colocated hospital diabetes team. Approaches to increase coordination of diabetes care among renal patients should be investigated.

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.

Assessment of glycemia in chronic kidney disease

Reliable assessment of glycemia is central to the management of diabetes. The kidneys play a vital role in maintaining glucose homeostasis through glucose filtration, reabsorption, consumption, and generation. This review article highlights the role of the kidneys in glucose metabolism and discusses the benefits, pitfalls, and evidence behind the glycemic markers in patients with chronic kidney disease. We specifically highlight the role of continuous glucose monitoring as an emerging minimally invasive technique for glycemic assessment.

Continuous Glucose Monitoring metrics in the Assessment of Glycemia in Moderate-to-Advanced Chronic Kidney Disease (CKD) in Diabetes

Introduction

Glycated hemoglobin A1c (HbA1c) has reduced reliability in advanced chronic kidney disease (CKD) owing to factors influencing red cell turnover. Recent guidelines support the use of continuous glucose monitoring (CGM) in glycemic assessment in these patients. We evaluated relationships between HbA1c and CGM metrics of average glycemia and glucose variability (GV) in moderate-to-advanced CKD.

Methods

There were a total of 90 patients with diabetes in CKD stages G3b (n = 33), G4 (n = 43), and G5 (nondialysis) (n = 14) (age [mean ± SD] 65.4 ± 9.0 years, estimated glomerular filtration rate [eGFR] 26.1 ± 9.6 ml/min per 1.73 m², and HbA1c 7.4 ± 0.8%). CGM metrics were estimated from blinded CGM (Medtronic Ipro2 with Enlite sensor) and compared with HbA1c in the same week.

Results

Correlations between glucose management indicator (GMI) and HbA1c attenuated with advancing CKD (G3b [r = 0.68, P < 0.0001], G4 [r = 0.52, P < 0.001], G5 [r = 0.22, P = 0.44], P = 0.01 for CKD stage). In G3b and G4, HbA1c correlated significantly with time-in-range (TIR) (3.9–10.0 mmol/l) (r = −0.55 and r = −0.54, respectively) and % time > 13.9 mmol/l (r = 0.53 and r = 0.44, respectively), but not in G5. HbA1c showed no correlation with % time <3.0 mmol/l (r = −0.045, P = 0.67) or % coefficient of variation (CV) (r = −0.05, P = 0.64) in any CKD stage. Only eGFR was a significant determinant of bias for the difference between GMI and HbA1c (difference −0.28%, 95% CI [−0.52 to −0.03] per 15 ml/min per 1.73 m² decrement, P = 0.03).

Conclusion

CGM-derived indices might serve as an adjunct to HbA1c monitoring to guide glycemic management, especially in those with eGFR <30 ml/min per 1.73 m². Time in hypoglycemia and glycemic variability are relevant glycemic targets for optimization not reflected by HbA1c.

The Need to Pair Molecular Monitoring Devices with Molecular Imaging to Personalize Health

By enabling the non-invasive monitoring and quantification of biomolecular processes, molecular imaging has dramatically improved our understanding of disease. In recent years, non-invasive access to the molecular drivers of health versus disease has emboldened the goal of precision health, which draws on concepts borrowed from process monitoring in engineering, wherein hundreds of sensors can be employed to develop a model which can be used to preventatively detect and diagnose problems. In translating this monitoring regime from inanimate machines to human beings, precision health posits that continual and on-the-spot monitoring are the next frontiers in molecular medicine. Early biomarker detection and clinical intervention improves individual outcomes and reduces the societal cost of treating chronic and late-stage diseases. However, in current clinical settings, methods of disease diagnoses and monitoring are typically intermittent, based on imprecise risk factors, or self-administered, making optimization of individual patient outcomes an ongoing challenge. Low-cost molecular monitoring devices capable of on-the-spot biomarker analysis at high frequencies, and even continuously, could alter this paradigm of therapy and disease prevention. When these devices are coupled with molecular imaging, they could work together to enable a complete picture of pathogenesis. To meet this need, an active area of research is the development of sensors capable of point-of-care diagnostic monitoring with an emphasis on clinical utility. However, a myriad of challenges must be met, foremost, an integration of the highly specialized molecular tools developed to understand and monitor the molecular causes of disease with clinically accessible techniques. Functioning on the principle of probe-analyte interactions yielding a transducible signal, probes enabling sensing and imaging significantly overlap in design considerations and targeting moieties, however differing in signal interpretation and readout. Integrating molecular sensors with molecular imaging can provide improved data on the personal biomarkers governing disease progression, furthering our understanding of pathogenesis, and providing a positive feedback loop toward identifying additional biomarkers and therapeutics. Coupling molecular imaging with molecular monitoring devices into the clinical paradigm is a key step toward achieving precision health.

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.

Use of continuous glucose monitoring system in patients with type 2 mellitus diabetic during hemodialysis treatment

BACKGROUND

Studies highlight the inaccuracy of glycated hemoglobin (HbA1c) for the assessment of glycemic control in dialysis diabetics and suggest the use of continuous glucose monitoring (CGM) as an alternative. Of the CGMs, FreeStyle Libre^® is the most used in worldwide, but there is still no consensus on its use in dialysis.

METHOD

A 3-week prospective study was performed with 12 patients comparing capillary and interstitial glucose during dialysis.

RESULTS

Comparing capillary and interstitial measurements, similar values were observed in pre-dialysis in the 1st week (184.1 ± 69.5 mg/dl and 173.1 ± 78.9 mg/dl, respectively, p = 0.303), in patients with body mass index less than 24.9 kg/m² (214.2 ± 72.2 mg/dl and 201.3 ± 77.0 mg/dl respectively, p = 0.466), in those dialysis fluid loss less than 2 l (185.5 ± 82.6 mg/dl and 183.1 ± 94.0 mg/dl respectively and p = 0.805) and in those with hemoglobin greater than 12 g/dl (152.0 ± 35, 5 mg/dl and 129.5 ± 47.4 mg/dl respectively, p = 0.016). In the correlation of the capillary measurement with the interstitial sensor, it was observed that the proportions in the Clarke Error Grid of zone A, zone B, zone C, zone D and zone E were 62.5%, 27.1%, 0.0%, 10.4% and 0.0% respectively and in the Parkes error grid in zone A, zone B, zone C, zone D and zone E were 80.6%, 9.7%, 9.7% 0.0% and 0.0%, respectively.

CONCLUSION

The mean absolute relative difference in dialysis patients is higher than the general population without end-stage renal disease. However, clinical decision-making based on the values measured by the system can be made with a good margin based on the correlation between interstitial and capillary measurements.

Real-World Studies Support Use of Continuous Glucose Monitoring in Type 1 and Type 2 Diabetes Independently of Treatment Regimen

Numerous randomized controlled trials (RCTs) have demonstrated the glycemic benefits of continuous glucose monitoring (CGM) in management of type 1 diabetes (T1D) and type 2 diabetes. Although RCTs remain the gold standard clinical study design, findings from these trials do not necessarily reflect the effectiveness of CGM or reveal the feasibility and wider applications for use in broader real-life settings. This review evaluates recent real-world evidence (RWE) demonstrating the value of CGM to improve clinical outcomes, such as avoidance of severe hypoglycemic and hyperglycemic crises, and improved measures of psychological health and quality of life. Additionally, this review considers recent RWE for the role of CGM to enhance health care resource utilization, including prediction of T1D and applications in gestational diabetes, chronic kidney disease, and monitoring during surgery.

Evaluation of Serum Glucose and Kidney Disease Progression Among Patients With Diabetes

IMPORTANCE

The appropriate target of glycemic control in diabetic kidney disease is unclear.

OBJECTIVE

To investigate optimal on-treatment glycemic levels associated with slowing of diabetic kidney disease progression.

DESIGN, SETTING, AND PARTICIPANTS

This retrospective cohort study was conducted using nationwide Korean cohorts from the National Health Information Database from 2005 to 2019. Included individuals were adults with diabetes using antihyperglycemic agents with and without chronic kidney disease (CKD) identified from participants aged 40 to 74 years in a nationwide health screening survey conducted from 2009 to 2010. Data were analyzed from October 2020 through March 2021.

EXPOSURE

On-treatment fasting blood glucose (FBG) level.

MAIN OUTCOMES AND MEASURES

The primary outcome was a composite of doubling of serum creatinine, end-stage kidney disease, or death from CKD.

RESULTS

Among 183 049 adults with diabetes using antihyperglycemic agents (mean [SD] age, 61.7 [8.4] years; 99 110 [54.1%] men), there were 131 401 individuals with dipstick albuminuria or an estimated glomerular filtration rate (eGFR) of 15 to 59 mL/min/1.73 m2 (mean [SD] age, 62.4 [8.3] years; 71 280 [54.2%] men) and 51 648 individuals with no CKD (mean [SD] age, 59.6 [8.4] years; 27 830 [53.9%] men). During 9 years of follow-up, the primary outcome occurred among 13 802 individuals with CKD (10.5%) and 1421 individuals with no CKD (2.8%). On-treatment FBG level had a J-shaped hazard ratio (HR) curve for the primary outcome. Among patients with albuminuria, FBG levels of 126 mg/dL to less than 140 mg/dL (HR, 0.87; 95% CI, 0.81-0.94) and 140 mg/dL to less than 160 mg/dL (HR, 0.90; 95% CI, 0.84-0.96) were associated with decreased risk and levels of 160 mg/dL to less than 180 mg/dL were associated with increased risk (HR, 1.10; 95% CI, 1.03-1.18) compared with FBG levels of 110 mg/dL to less than 126 mg/dL. Among patients with decreased eGFR, FBG levels of 80 mg/dL to less than 100 mg/dL (HR, 1.30; 95% CI, 1.20-1.42) and levels of 160 mg/dL to less than 180 mg/dL (HR, 1.13; 95% CI, 1.04-1.23) were associated with increased risk of the primary outcome compared with FBG levels of 110 mg/dL to less than 126 mg/dL. Among patients with no CKD, FBG levels of 80 mg/dL to less than 100 mg/dL (HR, 1.29; 95% CI, 1.01-1.65) and levels of 126 mg/dL to less than 140 mg/dL (HR, 1.23; 95% CI, 1.03-1.47) were associated with increased risk compared with FBG levels of 110 mg/dL to less than 126 mg/dL. Among patients with no albuminuria at baseline, FBG levels of 140 mg/dL to less than 160 mg/dL (HR, 1.14; 95% CI, 1.09-1.20) were associated with increased risk of new-onset albuminuria, while levels of 100 mg/dL to less than 110 mg/dL were not associated with increased risk compared with FBG levels of 110 mg/dL to less than 126 mg/dL. For all-cause mortality, while FBG levels of 160 mg/dL to less than 180 mg/dL (HR, 1.20; 95% CI, 1.12-1.28) were associated with increased risk among patients with albuminuria, FBG levels of 140 mg/dL to less than 160 mg/dL were associated with increased risk among patients with decreased eGFR (HR, 1.10; 95% CI, 1.04-1.16) and those with no CKD (HR, 1.10; 95% CI, 1.00-1.21) compared with levels of 110 mg/dL to less than 126 mg/dL.

CONCLUSIONS AND RELEVANCE

These findings suggest that intensive vs standard glucose control may not be associated with increased protection for the progression of established diabetic kidney disease and that timely control is important for prevention. However, careful glycemic control would still be associated with improved overall outcomes among patients with CKD, particularly those with no albuminuria.

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.

Management of type 2 diabetes in chronic kidney disease

The management of patients with type 2 diabetes and chronic kidney disease (CKD) encompasses lifestyle modifications, glycemic control with individualized HbA1c targets, and cardiovascular disease risk reduction. Metformin and sodium-glucose cotransporter-2 inhibitors are first-line agents. Glucagon-like peptide-1 receptor agonists are second-line agents. The use of other antidiabetic agents should consider patient preferences, comorbidities, drug costs, and the risk of hypoglycemia. Renin-angiotensin-aldosterone system inhibitors are strongly recommended for patients with diabetes, hypertension, and albuminuria. Non-steroidal mineralocorticoid receptor antagonists, which pose less risk of hyperkalemia than steroidal agents, are undergoing further evaluation among patients with diabetic kidney disease. Here, we discuss important advancements in the management of patients with type 2 diabetes and CKD.

Making sense of glucose metrics in diabetes: linkage between postprandial glucose (PPG), time in range (TIR) & hemoglobin A1c (A1C)

While A1C is the standard diagnostic test for evaluating long-term glucose management, additional glucose data, either from fingerstick blood glucose testing, or more recently, continuous glucose monitoring (CGM), is necessary for safe and effective management of diabetes, especially for individuals treated with insulin. CGM technology and retrospective pattern-based management using various CGM reports have the potential to improve glycemic management beyond what is possible with fingerstick blood glucose monitoring. CGM software can provide valuable retrospective data on Time-in-Ranges (above, below, within) metrics, the Ambulatory Glucose Profile (AGP), overlay reports, and daily views for persons with diabetes and their healthcare providers. This data can aid in glycemic pattern identification and evaluation of the impact of lifestyle factors on these patterns. Time-in-Ranges data provide an easy-to-define metric that can facilitate goal setting discussions between clinicians and persons with diabetes to improve glycemic management and can empower persons with diabetes in self-management between clinic consultation visits. Here we discuss multiple real-life scenarios from a primary care clinic for the application of CGM in persons with diabetes. Optimizing the use of the reports generated by CGM software, with attention to time in range, time below range, and postprandial glucose-induced time above range, can improve the safety and efficacy of ongoing glucose management.

Executive summary of the 2020 KDIGO Diabetes Management in CKD Guideline: evidence-based advances in monitoring and treatment

THE KIDNEY DISEASE

Improving Global Outcomes (KDIGO) Clinical Practice Guideline for Diabetes Management in Chronic Kidney Disease represents the first KDIGO guideline on this subject. The guideline comes at a time when advances in diabetes technology and therapeutics offer new options to manage the large population of patients with diabetes and chronic kidney disease (CKD) at high risk of poor health outcomes. An enlarging base of high-quality evidence from randomized clinical trials is available to evaluate important new treatments offering organ protection, such as sodium-glucose cotransporter-2 inhibitors and glucagon-like peptide-1 receptor agonists. The goal of the new guideline is to provide evidence-based recommendations to optimize the clinical care of people with diabetes and CKD by integrating new options with existing management strategies. In addition, the guideline contains practice points to facilitate implementation when insufficient data are available to make well-justified recommendations or when additional guidance may be useful for clinical application. The guideline covers comprehensive care of patients with diabetes and CKD, glycemic monitoring and targets, lifestyle interventions, antihyperglycemic therapies, and self-management and health systems approaches to management of patients with diabetes and CKD.