Scoping Review—Diabetes Technology for Individuals on Kidney Replacement Therapy (Dialysis): Current Trends and Future Directions

Abstract

Aim:

This review aims to map the existing literature on the use of diabetes technology in people receiving dialysis, with a focus on utilization, accuracy, and effectiveness.

Methods:

A scoping review was conducted using the Joanna Briggs Institute methodology, with systematic searches of Medline, Embase, and CINAHL for studies on diabetes technologies in dialysis populations.

Results:

The search identified 1060 continuous glucose monitoring (CGM) and 1467 continuous subcutaneous insulin infusion or automated insulin delivery (CSII/AID) records, with 64 studies included. Eighteen studies assessed CGM accuracy, reporting mean absolute relative difference (MARD) values ranging from 8.1% to 29%, with over 97% of readings falling within Clarke error grid zones A or B. Thirteen studies compared glycemic markers, finding that HbA_1c underestimated glucose by 7.3 mmol/mol, while glycated albumin showed a stronger correlation (r = 0.508). Four studies reported on dialysis effects, showing that people on automated peritoneal dialysis (APD) had lower mean glucose levels (181 ± 64 mg/dL) compared to continuous ambulatory peritoneal dialysis (CAPD) (238 ± 67 mg/dL; P < .05). Eleven studies evaluating diabetes treatment efficacy using CGM found that dulaglutide significantly reduced glucose CV from 28.1% to 19.8% (P = .003). Twenty-two studies examining glycemic outcomes reported that TIR was lower on dialysis days (80.2%, P = .02). Finally, four AID studies reported TIR improvements of up to 37.6% and a 1.5 mmol/L reduction in glucose (P = .003).

Conclusion:

This review highlights the potential of CGM and AID to improve diabetes outcomes in people on dialysis. While their clinical utility is evident, broader access and further research are needed to optimize their use in this high-risk population.

Keywords

continuous glucose monitoring CGM insulin pump therapy CSII AID diabetes dialysis hemodialysis peritoneal dialysis glycemic control diabetes technology scoping review

Introduction

Diabetes is one of the leading causes of end-stage kidney disease (ESKD) in many countries worldwide.¹ As the incidence of diabetes rises, so too does the number of individuals with both diabetes and ESKD who require kidney replacement therapy (KRT). The limited availability of organs for transplantation, coupled with the cardiovascular comorbidities associated with diabetes, often restricts eligibility for kidney transplantation. Consequently, hemodialysis (HD) or peritoneal dialysis (PD) is frequently the only viable treatment options for many individuals. Despite significant advances in diabetes care, those with diabetes undergoing dialysis experience a reduced quality of life and a high burden of hypoglycemia and hyperglycemia^2
-5 and hypoglycemia unawareness.^6,7

Managing diabetes in individuals with ESKD undergoing KRT presents unique challenges. Traditional glycemic management strategies can be less effective due to altered insulin pharmacokinetics and the dietary restrictions associated with ESKD.⁸ The ESKD is also linked with impaired kidney gluconeogenesis, a weakened counterregulatory hormonal response, malnutrition, insulin resistance, and impaired beta-cell function.⁹ As such, health care providers must navigate a complex landscape to maintain optimal glycemic levels in this population. High glucose levels can lead to increased thirst and fluid retention between dialysis sessions, contributing to fluid overload and higher mortality rates. These increased fluid gains can also affect dialysis tolerance, cause unpleasant hypotensive episodes, and prolong recovery time.^10
-12

Technological advancements in diabetes management, such as continuous glucose monitoring (CGM) systems, insulin pumps or continuous subcutaneous insulin infusion (CSII) or automated insulin delivery (AID), and data management applications, offer promising solutions to improve glycemic levels among people on dialysis. These technologies provide real-time insights into glucose levels and facilitate more tailored insulin delivery. By enabling people with diabetes to respond proactively to glucose fluctuations, these tools meet the dynamic needs of those managing multiple comorbidities. However, integrating diabetes technologies into dialysis settings presents challenges.¹³ Access to advanced diabetes management tools and specialized diabetes services remains limited for many individuals with ESKD.^13,14 As the field of diabetes technology evolves, it is crucial to explore its application within the dialysis population.

Diabetes management in dialysis populations is complex, yet the role of diabetes technologies remains unclear. While previous reviews focus on glycemic outcomes,^15
-17 gaps remain in understanding their accuracy, effectiveness, and real-world use. Given the unique metabolic challenges of KRT, a focused review is essential to optimize care, improve outcomes, and address disparities in access to advanced diabetes technology.

Methods

Aim of the Scoping Review

This review aims to map existing literature on diabetes technology use in people receiving dialysis, focusing on utilization, accuracy in the dialysis population, and effectiveness in diabetes management.

Research Questions

Research Question 1: What diabetes technologies have been studied in individuals on dialysis, and what are their intended purposes?

Research Question 2: How accurate and effective are these technologies in managing glycemia in this population?

Search Strategy and Information Sources

Protocol and registration

This scoping review followed the Joanna Briggs Institute methodology. The reporting of items is in accordance with the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) extension for Scoping Reviews (PRISMA-ScR) reporting guidelines.¹⁸ The protocol was registered with the Open Science Framework at osf.io/tqvzg (https://doi.org/10.17605/OSF.IO/N39BP).

Information sources

The search strategy was developed in collaboration with a research librarian. The databases searched included CINAHL, Medline, and Embase. The search was conducted on May 1, 2024, updated on August 28, 2024, and further updated in May 2025 to identify any subsequent publications. The first author carried out searches using the keywords and Medical Subject Headings (MeSH) terms specified in Supplemental File 1. No restrictions were applied regarding publication date, study design, or country. The search was conducted in Medline (Ovid) and subsequently translated to the other databases. In addition, the reference lists of the included articles were reviewed to identify other potential studies. Our full search strategy is included in the Supplemental File 1.

Study Selection and Eligibility Criteria

Study selection

Articles were uploaded to the online systematic review platform, Covidence, for screening. Duplicates were detected and removed. The titles and abstracts of all studies were then screened by three primary reviewers (HHH-A, JNS, KC) to determine eligibility for full-text review. The primary reviewers conducted full-text assessments to identify studies that met the inclusion criteria. Any disagreements between the primary reviewers were discussed with a fourth, fifth, and sixth reviewer to reach a final decision.

Eligibility criteria

Inclusion criteria

Studies conducted on adult people (age ≥18 years) focused on the use of diabetes technology such as CGM or CSII or AID undergoing dialysis with diabetes:

Research articles, clinical trials, clinical audits, abstracts, conference proceedings, case studies, and observational studies comparing the efficacy of diabetes technology interventions (CGM and/or CSII or AID with usual care in people with diabetes on dialysis).

Studies published in English.

Exclusion criteria

Studies that did not specifically assess the effects of diabetes technology interventions in dialysis population.

Studies that assessed diabetes technology intervention in the kidney transplant population.

Studies evaluating diabetes technology in mixed cohorts of dialysis and transplant population were excluded from this review if data specific to the dialysis subgroup could not be distinctly separated.

Systematic reviews or literature reviews.

Editorials or opinion pieces.

Studies published in languages other than English.

Data Extraction and Synthesis

Charting the data

Three reviewers (HHH-A, JNS, KC) developed the data extraction table, which was reviewed by the senior authors (AF, DCW, JK). HHH-A guided JS and KC in the data extraction process and subsequently verified the extracted data. The senior authors (AF, JK) provided feedback on the final literature tables. The development and completion of these tables allowed us to summarize and synthesize the data effectively.

Synthesis of results

The study characteristics, methods, and outcomes were organized in a tabular format. Given the heterogeneity of the included studies, the extracted data were compiled to provide a comprehensive overview of each study, detailing the purpose and use of diabetes technologies and the reported outcomes in a standardized format. This information informed a narrative review that examined key aspects of the identified interventions, including the validation of CGM accuracy in dialysis populations, comparisons between CGM and traditional glycemic markers, the impact of dialysis on glucose profiles, the use of CGM to assess the efficacy of diabetes treatments, CGM-based evaluations of glycemic outcomes, and the application of AID systems in the dialysis population.

Results

Study Characteristics

A systematic search of electronic databases identified a total of 1060 studies on CGM/AID and 1467 studies on insulin infusion (CSII/AID). After the automatic removal of 345 duplicates related to CGM and 310 related to CSII or AID using Covidence review manager, 64 studies were retained. These were selected following a manual screening of titles, abstracts, and full texts by the reviewers. The study screening and selection process is illustrated in the PRISMA diagram (Figure 1).

Figure 1.

Flow diagram of study selection.

All included studies were published between 2003 and 2025, with the majority conducted in Europe (the United Kingdom, Denmark, France, Italy, Spain, Greece, Romania, Poland, Switzerland), Asia (Japan, China, India, Korea, Hong Kong, Bangladesh), North America (the United States of America), South America (Colombia, Brazil), and Australia. Samples ranged from 1 to 153 people, with follow-up durations from 4 hours to 12 months. Most participants were aged between 35 and 85 years, with 60% to 80% being male. Study designs comprised cohort (n = 27), clinical trials (n = 10), post hoc analyses (n = 5), cross-sectional (n = 9), case reports/series (n = 8), and pilot study (n = 5). Most focused on type 2 diabetes (n = 42): eight involved type 1, and 14 included mixed or unspecified types. The HD was the dominant modality (n = 46), followed by PD (n = 16) and both (n = 2). Technologies evaluated included CGMS (Dexcom G6, FreeStyle Libre, iPro2, Guardian Sensor 3) and closed-loop systems. Primary outcomes included device accuracy (Mean Absolute Relative Difference [MARD], Bland-Altman, error grids), glycemic metrics (time in range [TIR], variability, HbA_1c), hypoglycemia detection, and AID safety, feasibility, and acceptability. Table 1 summarizes the study characteristics.

Table 1.

Study Characteristics.

Number	Author and country	Study design/period of follow-up	Participants characteristics	Diabetes technology interventions	Outcomes
1.	Avari et al,¹⁹ UK	Design: Cohort study Follow-up: 18 months	Sample: 40 Diabetes type: Any form of diabetes Age: 64.7 Median Sex: 30 Male	Purpose of intervention: Assess CGM accuracy vs reference glucose Type of diabetes technology: DexcomeG6 & FreeStyle Libre 1 Dialysis Modality: HD	Primary: To compare MARD (%) of Dexcom G6 and FreeStyle Libre 1 against reference YSI glucose values during HD
2.	Bally et al,²⁰ UK	Design: Post hoc analysis Follow-up: 15 days	Sample: 17 Diabetes type: T2D Age: 73 ± 8 (Treatment group) 67 ± 10 (Conventional therapy group) Sex: 77% male (Treatment group), 63% male (Conventional therapy group)	Purpose of intervention: Compare closed-loop vs standard insulin therapy Type of diabetes technology: Closed loop: Dana R pump & a model predictive algorithm, with CGM: FreeStyle Navigator II Dialysis modality: HD	Primary: Time spent at glucose level in target glucose range
3.	Bomholt et al,²¹ Denmark	Design: Case-control Study Follow-up: 9 months	Sample: 23 Diabetes type: T2D Age: 69.7 (63.3-73.6) Sex: 18 Male	Purpose of intervention: Evaluate HbA_1c and fructosamine vs CGM Type of diabetes technology: DexcomeG6 Dialysis Modality: PD	Primary: Difference in Mean Glucose values, Glucose variability
4.	Bomholt et al,²² Denmark	Design: Post hoc analysis Follow-up: 3 months	Sample: 24 Diabetes type: T2D Age: Not stated Sex: 21 Male	Purpose of intervention: To evaluate whether liraglutide, compared to placebo treatment Type of diabetes technology: CGM (iPro2) Dialysis Modality: PD &HD	Primary: Time in range; hypoglycemia; HbA_1c
5.	Bomholt et al,²³ Denmark	Design: Case-control study Period of follow-up: 10 months	Sample: 66 (30 HD & 36 Control) Diabetes type: T2D Age: Not stated Sex: Not stated	Purpose of intervention: Compare HbA1c and fructosamine vs CGMType of diabetes technology: CGM iPro2 Dialysis Modality: HD	Primary: Difference in glycemic markers and sensor glucose
6.	Bomholt et al,²⁴ Denmark	Design: Post hoc analysis Follow-up: 7 months	Sample: 27 Diabetes type: T2D Age: 66.4 (±11.5) Sex: 22 Male	Purpose of intervention: Evaluate glucose variation on dialysis vs non-dialysis days using CGM Type of diabetes technology: CGMS iPro2 Dialysis Modality: HD	Primary: Time-in-range, mean glucose, and glucose variability on dialysis vs non-dialysis days
7.	Boughton et al,²⁵ UK	Design: Randomized crossover trial. Follow-up: 20 days	Sample: 26 Diabetes type: T2D Age: Mean 68 ± 11 Sex: 17 Male	Purpose: Assess safety and efficacy of closed-loop vs standard insulin therapy in T2D on dialysis Type of diabetes technology: Cambridge closed-loop system with masked CGM Dialysis modality: PD or HD	Primary: The proportion of time sensor glucose remained within the target range of 5.6 to 10.0 mmol/L.
8.	Chantrel et al,²⁶ France	Design: Cohort observational study Follow-up: 3 months	Sample: 33 Diabetes type: T2D Age: 66 ± 8 years Sex: 19 Male	Purpose of intervention: Investigate impact of dialysis on glucose profiles in diabetes using CGM Type of diabetes technology: CGM Dialysis modality: HD	Primary: CGM parameters compared during and outside dialysis sessions.
9.	Chantrel et al,²⁷ France	Design: Non interventional study Follow-up: 11 days	Sample: 60 Diabetes type: Not specified Age: 70 ± 11 years Sex: Not reported	Purpose of intervention: Describe routine diabetes and glycemic monitoring parameters using CGM Technology: FreeStyle Free Pro Dialysis modality: HD	Primary: Mean blood glucose level, percentage of blood glucose level: < 0.70 g/dL TBR, between 0.70 and 1.80 g/dL TIR, and > 1.80 g/dL TAR
10.	Chaudhry et al,²⁸ UK	Design: Case series Follow-up: ≥ 14 days	Sample: 4 Diabetes type: T1D Age: Mean age 54 years (35–78 years). Sex: 1 Male	Purpose of intervention: Evaluate automated insulin delivery in T1D patients undergoing hemodialysis Type of diabetes technology: Medtronic MiniMed 780G AID and/or CGM Dialysis Modality: HD	Primary: Difference in time-in-range (TIR) defined as 3.9-10 mmol/L (70-180 mg/dL).
11.	Divani et al,²⁹ Greece	Design: Prospective observational study Follow-up: 10 months	Sample: 37 Diabetes type: Not specified Age: 62.0 ± 17.2 Sex: 20 Male	Purpose of intervention: Assess albumin GA and glycated serum protein GSP as alternatives to HbA_1c using CGM as reference Type of diabetes technology: CGM Dialysis Modality: HD	Primary: Evaluation of the accuracy of alternative glycemic biomarkers (such as GA and GSP) compared to HbA_1c based on extensive interstitial glucose measurements obtained over a 7-day CGM period.
12.	Gai et al,³⁰ Italy	Design: Observational study Follow-up: 6 days	Sample: 12 Diabetes type: Not specified Age: 61.8 ± 13.9 years Sex: 9 Male	Purpose of intervention: Assess glycemic control and monitor interstitial glucose over 6 days Type of diabetes technology: CGM iPro Dialysis Modality: HD	Primary: Changes in glycemia by capillary blood glucose and CGM
13.	Genua et al,³¹ Spain	Design: Prospective observational study. Follow-up: 14 days	Sample: 16 Diabetes type: 14 T2D, 1 T1D, and 1 other diabetes Age: 68 ± 13 Sex: 11 Male	Purpose of intervention: Assess FreeStyle Libre accuracy, hydration status correlation, and patient satisfaction Types of diabetes technology: FreeStyle Libre (flash glucose monitor) Dialysis modality: HD	Primary: MARD
14.	Gómez et al,³² Colombia	Design: Cohort interventional study Follow-up: 6-day	Sample: 47 Diabetes type: 4 T1D and 43 T2D Age: 58.57 ± 11.49 Sex: 26 Male	Purpose of intervention: Evaluate effects of education and basal-bolus insulin on glycemic control Type of diabetes technology: CGM: iPro2 Dialysis modality: PD (7 CAPD &40 APD)	Primary: CGM parameters and HbA_1c before and after an educational program and a basal-bolus insulin regimen
15.	Hayashi et al,³³ Japan	Design: Cohort study Follow-up: 48 hours	Sample: 77 Diabetes type: T2D Age: 62 years Sex: 54 Male	Purpose of intervention: Assess glycemic differences between well-controlled vs hypoglycemic patients (TBR ≥4%) Type of diabetes technology: CGMS Dialysis modality: HD	Primary: Glycemic parameters (Time in Range, Time Below Range, Time above range and mean glucose and coefficient of variation).
16.	Hayashi et al,³⁴ Japan	Design: Cross-sectional study Follow-up: 2 days	Sample: 107 Diabetes type: T2D Diabetes type: T2D Age: 62 ± 12 years Sex: 74 Male	Purpose of intervention: To examine associations between TIR, glycemic markers, and diabetic microangiopathy. Type of diabetes technology: CGMS Gold or CGM iPro2 Dialysis modality: HD	Primary: Analyze glycemic markers from CGM data and HbA_1c in relation to history or clinical features of microangiopathies
17.	Hissa et al,³⁵ Brazil	Design: Prospective study Follow-up: 3 weeks	Sample: 12 Diabetes type: T2D Age: 66.8 ± 8.0 Sex: 8 Male	Purpose of intervention: Evaluate FreeStyle Libre accuracy vs SMBG pre- and post-dialysisType of diabetes technology: Free style Libre Dialysis Modality: HD	Primary: To compare pre- and post-dialysis measurements by CGM and SMBG
18.	Horne et al,³⁶ UK	Design: Cohort study Follow-up: 14 days	Sample: 69 Diabetes type: 14 T1D and 55 T2D Age: 64 (33-83) Sex: 42 Male	Purpose of intervention: To validate CGM against capillary and serum glucose in patients with diabetes on HD Type of diabetes technology: CGM: Libre FreeStyle Pro Dialysis modality: HD	Primary: CGM vs SMBG or laboratory glucose
19.	Hu et al,³⁷ China	Design: Non-randomized controlled trial Follow-up: 14 days	Sample: 52 Diabetes type: T2D Age: 60.89 ± 10.57 Sex: 38 Male	Purpose of intervention: Investigate TIR utility in diabetes management and glycemic optimization via flash glucose monitoring system. Type of diabetes technology: CGM: FreeStyle Libre Dialysis modality: HD	Primary: Glucose variability: CV, SD, MAGE, and largest amplitude of excursions
20.	Javherani et al,³⁸ India	Design: A cross-sectional pilot study Follow-up: N/A	Sample: 10 Diabetes type: T2D Age: 60.4 ± 10.5 years Sex: 7 Male	Purpose of intervention: To use ambulatory glucose profile to study patterns before, during, and after dialysis Diabetes technology: Flash glucose monitor Dialysis modality: HD	Primary: Glycemic patterns in subjects with type 2 diabetes mellitus and ESRD undergoing hemodialysis.
21.	Jin et al,³⁹ China	Design: Cohort study Follow-up: 72 hours	Sample: 46 Diabetes type: 36 T2D Age: 62 ± 13 (diabetes group) Sex: 29 Male	Purpose of intervention: To characterize blood fluctuations during HD using CGM Type of diabetes technology: CGM Dialysis modality: HD	Primary: Glycemic parameters (mean, standard deviation, MAGE) including peri-hemodialysis
22.	Joubert et al,⁴⁰ France	Design: Pilot study Follow-up: 12 weeks	Sample: 15 Diabetes type: 2 T1D, 9 T2D, 4 secondary diabetes Age: 60.9 ± 14.8 Sex: 8 Male	Purpose of intervention: To evaluate the impact of repeated CGM sessions on glycemic levels in dialysis patients with diabetes. Type of diabetes technology: CGM iPro2 Dialysis modality: HD	Primary: Mean CGM glucose value at the end of each period, compared to baseline.
23.	Jung et al,⁴¹ Korea	Design: Cross-sectional observational study Follow-up: 6 days	Sample: 9 Diabetes type: T2D Age: 67 ± 9 years Sex: 3 Male	Purpose of intervention: To describe glucose profiles and hypoglycemia associated with HD in diabetes patients using a CGM system. Diabetes technology: CGM Dialysis modality: HD	Primary: Glucose profiles on/off HD; episodes of hypoglycemia
24.	Kaminski et al,⁴² USA	Design: Pilot prospective study Follow-up: Not reported	Sample: 40 Diabetes type: 20 with “burnt-out” diabetes Age: Not reported Sex: Not reported	Purpose of intervention: To assess glycemic control by CGM metrics and glycemic markers in patients on hemodialysis with burnt-out diabetes Diabetes technology: Dexcom G6 CGM. Dialysis modality: HD	Primary: Glycemic control by measured by CGM metrics and glycemic markers
25.	Kashem et al,⁴³ Bangladesh	Design: Cohort study Follow-up: 4 hours	Sample: 14 Diabetes type: Not specified Age: Not reported Sex: Not reported	Purpose of intervention: To validate CGMS during dialysis comparing readings to laboratory blood levels intra-dialysis with samples taken every 15 minutes. Type of diabetes technology: CGMS (Dexcom G4) Dialysis modality: HD	Primary: CGM data and laboratory blood glucose measurements.
26.	Kazempour-Ardebili et al⁴⁴ UK	Design: Cross-sectional study. Follow-up: 48 hours	Sample: 17 Diabetes type: T2D Age: 61.5 ± 8.8 years (42–79 years) Sex: 13 Male	Purpose of intervention: To compare 48-hour CGM glucose profiles on vs off dialysis, explore associations with self-reported food intake, and assess the utility of 48-hour CGM for glycemic monitoring. Diabetes technology: GlucoDay CGM Dialysis modality: HD	Primary: Glucose profiles from days on and off dialysis using 48-hour CGM in type 2 patients with diabetes
27.	Kepenekian et al,⁴⁵ France	Design: Pilot prospective multicenter study Follow-up: 12 months	Sample: 28 Diabetes type: T2D Age: Not reported Sex: Not reported	Purpose of intervention: To evaluate the contribution of CGM to the management of insulin regimen. Diabetes technology: CGM (Navigator) Dialysis modality: HD	Primary: HbA_1c levels and mean CGM glucose values
28.	Lee et al,⁴⁶ South Korea	Design: Prospective study Follow-up: 13 weeks	Sample: 18 Diabetes type: 1 T1D Age: 62.0 ± 11.2 Sex: 13 Male	Purpose of intervention: To assess CGM usefulness for glycemic control and variability stabilization. Type of diabetes technology: CGM iPro2 & Enlite glucose sensors Dialysis Modality: HD	Primary: Changes in glycemia markers and CGM metrics, glycemic variability, standard deviation (SD), and % coefficient variation (%CV)
29.	Lee et al,⁴⁷ Taiwan	Design: Non- interventional study Follow-up: 3 days	Sample: 25 Diabetes type: Not specified Age: 59 ± 13 years Sex: 13 Male	Purpose of intervention: To test accuracy of HbA_1c, fasting glucose, fructosamine, AlbF, and GA as markers. Diabetes technology: CGMS Dialysis modality: PD	Primary: Mean values of various glycemic control parameters
30.	Ling et al,⁴⁸ Hong Kong	Design: Exploratory analysis Follow-up: 14 days	Sample: 30 Diabetes type: T2D Age: 64.7 ± 5.6 years, Sex: 23 Male	Purpose of intervention: Investigate CGM accuracy correlation with body composition and Hb in CAPD patients Diabetes technology: CGM Guardian Sensor 3 Dialysis modality: PD (CAPD)	Primary: The mean absolute relative difference (MARD) between CGM–plasma YSI glucose pairs during the in-clinic session
31.	Mambelli et al,⁴⁹ Italy	Design: Cohort study Follow-up: 12 weeks	Sample: 31 Diabetes type: 20 T2D Age: 18-85 years (63.3 ± 2.8 for T2DM and 63.7 ± 4.5 for non-diabetics) Sex: 22 Male	Purpose of intervention: To compare Flash glucose monitoring to SMBG Diabetes technology: Flash glucose monitoring Dialysis Modality: HD	Primary: Relationship between Flash glucose monitoring and SMBG readings
32.	Marshall et al,⁵⁰ UK	Design: Cohort study Follow-up: 3 days	Sample: 8 Diabetes type: 5 T2D & 3 T1D Age: 52 years (Mean) (Range = 42-72). Sex: Not reported	Purpose of intervention: To assess glycemic control under various peritoneal dialysis solutions Diabetes technology: CGMS Dialysis modality: PD (CAPD)	Primary: The effect of three differing peritoneal dialysis regimes on glycemic control
33.	Matoba et al,⁵¹ Japan	Design: Cohort study Follow-up: 14 days	Sample: 13 Diabetes type: T2D Age: 61.6 ± 11.9 Sex: 9 Male	Purpose of intervention: To compare flash glucose monitoring, CGM, and SMBG in T2D on HD Diabetes technology: FreeStyle LibrePro, CGM iPro2 Dialysis modality: HD	Primary: Correlations between SMBG and flash glucose monitoring and CGM
34.	Mayeda et al,⁵² USA	Design: Cohort study Period of follow-up: 10 days	Sample: 153 (132 HD, 21 PD). Diabetes type: Not specified Age: 61 ± 14 Sex: Not reported	Purpose of intervention: To examine the prevalence, causes, and outcomes of dysglycemia in people dialysis. Type of diabetes technology: Dexcom G6 CGM Dialysis modality: PD or HD	Primary: Glycemic metrics (mean glucose, GMI, TIR, high/very high/low/very low)
35.	Meyer et al,⁵³ UK	Study design: Cross- sectional study Follow-up: 54 hours	Sample: 23 Diabetes type: 22 T2D and 1 T1D Age: 65.7 ± 9.4 Sex: 13 Male	Purpose of intervention: To explore the relationship between CGM, HbA_1c, glycated albumin, and other markers Diabetes technology: CGM Dialysis modality: HD	Primary: Relationship between HbA_1c, glycated albumin, and other markers in persons with diabetes treated with hemodialysis
36.	Mirani et al,⁵⁴ Italy	Design: Cohort study Follow-up: 2 days	Sample: 12 Diabetes type: T2D Age: 62.5 ± 9.9 years Sex: 7 Male	Purpose of intervention: To assess short-term variation in glucose metabolism in T2D patients on HD Diabetes technology: CGM (GlucoDay) Dialysis modality: HD	Primary: Inter-day glycemic variability in insulin-treated T2D on HD
37.	Mori et al,⁵⁵ Japan	Design: Cohort study Follow-up: 3 days	Sample: 10 Diabetes type: Not specified Sex: Not reported Age: Not reported	Purpose of intervention: To determine diurnal variations in blood glucose in PD. Diabetes technology: CGM iPro2 Dialysis modality: PD	Primary: Diurnal variation; standard deviation of blood glucose vs dialysate-to-plasma ratio of creatinine and end-to-initial dialysate
38.	Mori et al,⁵⁶ Japan	Design: Case report Follow-up: 2 days	Sample: 1 Diabetes type: T2D Age: 68 years Sex: 1 Male	Purpose of intervention: Visualize glucose fluctuation pre, during, and post dialysis Diabetes Technology: CGM Dialysis modality: HD	Primary: Blood glucose levels before, during and after HD
39.	Munch et al,⁵⁷ France	Design: Randomized Control trial Follow-up: 12 weeks	Sample: 65 Diabetes type: T2D Age: 70.5 ± 8.5 years Sex: 31 Male	Purpose of intervention: To assess the effect of adding vildagliptin to insulin therapy using CGM Diabetes technology: CGM iPro2 Dialysis modality: HD	Primary: Change in CGM glucose profiles and metabolic control
40.	Narasaki et al,⁵⁸ USA	Design: Case report Follow-up: N/A	Sample: 1 Diabetes type: Not specified Age: 49 years Sex: 1 Male	Purpose of intervention: To describe a patient with diabetes on HD in whom CGM improved glycemic control and reduced treatment burden Diabetes technology: CGM Dexcom G6 Dialysis modality: HD	Primary: Change in glycemic control and patient-burden.
41.	Ng et al,⁵⁹ Hong Kong	Design: Cohort study Follow-up: 14 days	Sample: 30 Diabetes type: T2D Age: 64.7 ± 5.6 Sex: 23 Male	Purpose of intervention: To evaluate the performance of a real-time CGM in individuals with diabetes on PD. Diabetes technology: CGM Guardian Sensor 3 Dialysis modality: PD (CAPD)	Primary: MARD between CGM-plasma Yellow Springs Instrument (YSI) glucose
42.	Oei et al,⁶⁰ UK	Design: Case reports Follow-up s: N/A	Sample: 3 Diabetes type: 2 T2D & 1 T1D Age: 56-60 (Range) Sex: 3 Males	Purpose of intervention: To report differing CGM profiles despite similar HbA_1c levels in patients on PD. Diabetes technology: CGM Dexcom G4 Dialysis modality: PD (CAPD)	Primary: HbA_1c levels vs CGM-derived glucose variability
43.	Okada et al,⁶¹ Japan	Design: Cohort study. Follow-up: 72 hours	Sample: 20 Diabetes type: Age: 55 ± 10 years Sex: 16 Male	Purpose of intervention: To compare glucose variability in APD vs CAPD Diabetic technology: CGMS Dialysis modality: PD (APD and CAPD)	Primary: Differences in glycemic variability between APD and CAPD
44.	Osonoi et al,⁶² Japan	Design: Pilot study Follow-up: 3 days	Sample: 10 Diabetes type: T2D Age: 65.8 ±9.3 years Sex: 9 Male	Purpose of intervention: To assess if liraglutide dose adjustment is needed in people with diabetes on HD using CGM to evaluate pharmacokinetics, efficacy, and safety Diabetic technology: CGM Dialysis modality: HD	Primary: Efficacy of liraglutide in controlling blood glucose levels
45.	Popa et al,⁶³ Romania	Design: Cross-sectional study Follow-up: 3 days	Sample: 31 comprising of PD+DM+(n = 11); PD+DM-(n = 9); PD-DM+(n = 11) Diabetes type: Age: 65.8 ± 10.2; 60.2 ± 8.3; 57.6 ± 9.5 Sex: 12 Male	Purpose of intervention: To assess CGM-based glycemic variability and the impact of glucose load and transport status Diabetes technology: CGM Dexcom 7 Dialysis modality: PD	Primary: To compare glycemic variability among diabetic PD patients (PD+DM+), non-diabetic PD patients (PD+DM-), and patients with diabetes without PD (PD-DM+)
46.	Qayyum et al,⁶⁴ UK	Design: Cohort study Follow-up: Not stated	Sample: 60 Diabetes type: 5 T1D & 55 T2D Age: 60.2 ± 10.9 years Sex: Not stated	Purpose of intervention: To compare glycated hemoglobin with CGM data in individuals on PD. Diabetic technology: CGMS Dexcom G4 Dialysis modality: PD (CAPD and APD)	Primary: Correlation between glycated hemoglobin and CGM
47.	Ramos et al,⁶⁵ Brazil	Design: Cohort study Follow-up: 42 days	Sample: 11 Diabetes type: T2D Age: 67.1 ± 6.7 Sex: 9 Male	Purpose of intervention: To assess glycemic variability and hypoglycemia in individuals on HD using basal insulin Diabetes technology: CGMS Dialysis modality: HD	Primary: Glycemic variability
48.	Riveline et al,⁶⁶ France	Design: Non-randomized comparative study Follow-up: 4 days	Sample: 58 (19 HD; 39 non-HD) Diabetes type: T2D Age: HD T2: 64 ± 10; non-HD T2: 65 ± 6 Sex: 33 Male	Purpose of intervention: To evaluate the clinical performance of CGM in individuals with T2D on HD. Diabetes technology: CGMS MiniMed Dialysis modality: HD	Primary: Accuracy of CGMS in measuring glucose variability
49.	Rossi et al,⁶⁷ Italy	Design: Case report Follow-up: 1 year	Sample: 1 Diabetes type: T1D Age: 77 years Sex: 1 Male	Purpose: Describe benefits of automated insulin delivery in complex cases. Type of diabetes technology: Hybrid Closed Loop (Medtronic MiniMed 670G) Dialysis modality: PD	Primary: Time in range (TIR)
50.	Savu et al,⁶⁸ Romania	Design: Prospective study comparing two non-randomized groups Follow-up: 5 days	Sample: 14 (7 on Glargine and 7 on Detemir) Diabetes type: T2D Age: 62.7 ± 3.2 (Glargine) 70.8. ± 3.3 (Detemir) Sex: 8 Male	Purpose of intervention: To analyze the impact of basal insulin analogues on glucose variability in T2D during renal replacement therapy Diabetes technology: CGMS Dexcom 7 Dialysis modality: HD	Primary: Glucose variability
51.	Schwing et al,⁶⁹ USA	Design: Cross-sectional study Follow-up: 3 days	Sample: 7 Diabetes type: T2D & T1D Age: Not provided Sex: Not provided	Purpose of intervention: To assess minute-to-minute effect of high-dextrose peritoneal dialysate Diabetes technology: CGMS MiniMed Dialysis modality: PD	Primary: Glycemic outcome
52.	Skubala et al,⁷⁰ Poland	Design: Non-randomized clinical trial Follow-up: 3 months	Sample: 43 Diabetes type: 30 unspecified diabetes Age: study group 57.46 ± 12.46, control group 32.3 ± 9.21 Sex: 17 Male	Purpose of intervention: To assess the impact of glucose-rich peritoneal fluids on daily glycemic profiles using CGM. Diabetes technology: CGMS MiniMed Dialysis modality: PD (CAPD)	Primary: Glycemic outcomes
53.	Toyoda et al,⁷¹ Japan	Study design: Cohort study Follow-up: 14 days	Sample: 41 Diabetes type: T2D Age: 67.4 (± 11.5) Sex: 28 Male	Purpose of intervention: To evaluate the accuracy of FreeStyle Libre in individuals with T2D undergoing HD. Diabetes technology: FreeStyle Libre Dialysis modality: HD	Primary: Accuracy of FreeStyle Libre as measured by the MARD over 14 days
54.	Ugamura et al,⁷² Japan	Design: Exploratory study Follow-up: 6 days	Sample: 12 Diabetes type: T2D Age: 70.0 years (Median age) Sex: 9 Male	Purpose of intervention: To evaluate the efficacy and safety of dulaglutide in insulin-treated individuals with T2D on HD. Diabetes technology: CGM iPro 2 Dialysis modality: HD	Primary: Changes from baseline over 24 weeks in mean and SD of blood glucose, MAGE (6-day CGM), glycated albumin, HbA_1c, pre-dialysis glucose, and daily insulin dose
55.	Ushigome et al,⁷³ Japan	Design: Case report Follow-up: 1 week	Sample: 18 Diabetes type: T2D Age: 46-76 (range) Sex: 10 Male	Purpose of intervention: To investigate whether estimated HbA_1c from flash CGM reflects glycemic control in hemodialysis people with diabetes. Diabetes technology: Flash Glucose Monitoring System Dialysis modality: HD	Primary: Estimated hemoglobin A1c (eHbA_1c) levels
56.	Villard et al,⁷⁴ USA	Design—Pilot study Follow-up:181 days	Sample: 20 Diabetes type: Not specified Age: 61.2 ± 11.6 Sex: 14 Male	Purpose of intervention: To assess the accuracy of a factory-calibrated CGM in undergoing HD. Diabetes technology: CGM Dexcom G6 Dialysis modality: HD	Primary: MARD in blood glucose as measured by CGMS
57.	Wada et al,⁷⁵ Japan	Design: Non-randomized clinical trial Follow-up: 4 weeks	Sample: 10 Diabetes type: T2D Age: 68.0 Mean Sex: 8 Male	Purpose of intervention: To evaluate the efficacy of teneligliptin using CGM in people with T2D on HD. Diabetes technology: CGM Dialysis modality: HD	Primary: Improvement of glycemic control evaluated by area under the curve (AUC)
58.	Wang et al,⁷⁶ Australia	Design: Cross-Sectional analysis Follow-up: Not reported	Sample: 10 Diabetes type: T1D Age: 44 (Median age)	Purpose of intervention: To assess CGM accuracy in adults with T1D on HD during acute hospital stays, addressing limitations of traditional glucose monitoring. Diabetes technology: FreeStyle Libre2 or Dexcom G6 Dialysis modality: HD	Primary: CGM accuracy versus reference blood glucose in hospitalized T1D patients on HD
59.	Weber et al,⁷⁷ Switzerland	Design: Non-randomized clinical trial Follow-up: 14 days	Sample: 15 Diabetes type: 8 unspecified diabetes Age: 71 ± 11 years Sex: 11 Male	Purpose of intervention: To validate flash glucose monitoring as a convenient glucose monitoring option for people on HD with and without diabetes Diabetes technology: FreeStyle Libre Dialysis modality: HD	Primary: Mean flash glucose values Mean absolute relative difference
60.	Williams et al,⁷⁸ UK	Design: Case-control study Follow-up: 72 hours	Sample: 15 (9 automated PD [APD] and 6 continuous ambulatory PD [CAPD]) and 16 CKD-5 controls Diabetes type: Not specified Age: PD: 74 [69–79]; Controls: 65 [54–70.5] Sex: 21 Male	Purpose of intervention: To describe 24-hour glycemic profiles in people on PD without diabetes and compare them to non-dialysis CKD-5 controls. Diabetes technology: FreeStyle Libre Dialysis modality: PD	Primary: 24-hour average glycemic profiles
61.	Yajima et al,⁷⁹ Japan	Design: Pre-post study Follow-up: 24 weeks	Sample: 21 Diabetes type: T2D Age: 70.4 ± 8.0 years Sex: 16 Male	Purpose of intervention: To evaluate the efficacy and safety of adding teneligliptin to insulin therapy in people with T2D on HD Diabetes technology: CGMS iPro2 Dialysis modality: HD	Primary: Efficacy and safety of adding teneligliptin to insulin therapy
62.	Yajima et al,⁸⁰ Japan	Design: Non-randomized trial Follow-up: 4 days	Sample: 31 (HD: ≤6 months [short HD group, N = 16] and >6 months [long HD group, N = 15]). Diabetes type: T2D Age: 70.8 ± 8.1 Sex: 24 Male	Purpose of intervention: To evaluate the accuracy of serum albumin–adjusted glycated albumin and GA for assessing glycemic control in people with T2D on HD Diabetes technology: CGMS iPro2 Dialysis modality: HD	Primary: Accuracy of adjusted GA and GA in evaluating glycemic control
63.	Yajima et al,⁸¹ Japan	Design: Cohort study Follow-up: 7 days	Sample: 15 Diabetes type: T2D Age: 72 (66–79) years Sex: 13 Male	Purpose of intervention: To evaluate the efficacy and safety of dulaglutide added to insulin therapy in T2D on HD. Diabetes technology: CGM iPro2 Dialysis modality: HD	Primary: Efficacy and safety of adding dulaglutide to insulin therapy in type 2 diabetes on HD.
64.	Yajima et al,⁸² Japan	Design: Cross-sectional study Follow-up: Not reported	Sample: 13 Diabetes type: T2D Age: 63.5 ± 11.3 Sex: 11 Male	Purpose of intervention: To assess flash glucose monitoring and CGM accuracy versus capillary glucose in T2D on HD. Diabetes technology: Flash glucose monitoring FreeStyle Libre Pro; CGM iPro2 and SMBG. Dialysis modality: HD	Primary: Accuracy of flash glucose monitoring in people with T2D undergoing HD

Abbreviations: AID, automated insulin delivery; APD, automated peritoneal dialysis; AUC, area under the curve; CAPD, continuous ambulatory peritoneal dialysis; CGM, continuous glucose monitoring; CGMS, continuous glucose monitoring system; CKD-5, chronic kidney disease stage 5; CV, coefficient of variation; eHbA_1c, Estimated HbA_1c; GA, glycated albumin; GMI, glucose management indicator; HD, hemodialysis; MAGE, mean amplitude of glycemic excursions; MARD, mean absolute relative difference; PD, peritoneal dialysis; SD, standard deviation; SMBG, self-monitoring of blood glucose; T1D, type 1 diabetes; T2D, type 2 diabetes; TAR, time above range; TBR, time below range; TIR, time in range; YSI, Yellow Springs Instrument.

Findings

The extracted data from the studies were categorized into six key themes: the validation of CGM accuracy in dialysis populations, comparisons between CGM and traditional glycemic markers, the impact of dialysis on glucose profiles, the use of CGM to assess the efficacy of diabetes treatments, CGM-based evaluations of glycemic outcomes, and the application of AID systems in the dialysis population. Tables 2 to 7 in Supplemental File 1 provide an overview of the outcomes assessed in the included studies and show how each study contributed to these thematic categories.

Validation studies: accuracy of CGM in dialysis populations

The 18 studies^{19,31,35,36,39,43,44,48
-51,59,66,71,74,76,77,82} reviewed included a total of 483 people undergoing dialysis, examining the accuracy and clinical utility of CGM and flash glucose monitoring devices in this population, with reference to capillary blood glucose or laboratory glucose measurements (see Table 2 in Supplemental File). Accuracy measures, such as the MARD, ranged from 8.1% to 29%,^{19,31,35,48,59,71,74,76,77,82} with most studies reporting MARD values between 10% and 20%. Correlation coefficients between CGM/flash glucose monitoring readings and capillary or laboratory glucose were generally strong (r² ranging from 0.77 to 0.936). Accuracy was frequently reduced during or immediately after dialysis (P < .05), with statistically significant differences observed between dialysis and non-dialysis days.⁷¹ Error grid analyses consistently showed that over 97% of glucose readings fell within clinically acceptable zones A and B, indicating good clinical reliability of these monitoring systems in dialysis population.^{19,36,49,59,74,76}

Comparisons between continuous glucose monitoring and traditional glycemic markers in dialysis populations

Thirteen studies^{21,23,29,47,53
-55,60,63,64,66},^73,80 involving a total of approximately 397 people on dialysis, consistently show that HbA_1c underestimates true glycemic level (see Table 3 in Supplemental File). Bomholt et al²³ reported a mean underestimation of 7.3 mmol/mol (95% confidence interval [CI] = –10.0 to –4.7, P < .001) in individuals on HD when comparing HbA_1c with CGM. Similar findings were reported by Yajima et al,⁸⁰ who observed a moderate correlation between HbA_1c and CGM values (r = 0.508, P = .0037). The CGM also identified poor glycemic control more accurately than HbA_1c, as shown by Divani et al²⁹ (area under the curve [AUC] = 0.878 vs 0.682, P < .01). Moderate correlations between HbA_1c and CGM glucose levels have been observed in people on dialysis, as reported by Qayyum et al⁶⁴ and Popa et al⁶³ with the latter reporting a correlation of approximately r ≈ 0.48 (P < .0001). However, glycemic variability remains an issue. Factors such as body mass index and hemoglobin also contribute to discrepancies between HbA_1c and glucose monitoring in this population.⁷³

Impact of dialysis on glucose profiles

Four studies involving approximately 103 participants investigated the effects of dialysis on glucose profiles in people with diabetes using CGM (see Table 4 in Supplemental File).^26,61,69,70 Among those undergoing PD, individuals on automated peritoneal dialysis (APD) exhibited lower average blood glucose levels (181 ± 64 mg/dL) and less variability (SD = 36.3 ± 14.5 mg/dL) compared to those on continuous ambulatory peritoneal dialysis (CAPD), who recorded higher levels (238 ± 67 mg/dL; SD = 49.2 ± 14.1 mg/dL) (P < .05).⁶¹ The use of high-glucose peritoneal fluids was associated with increased 24-hour glucose exposure, elevated peaks (up to 320 mg/dL), and more pronounced fluctuations, particularly among individuals with T1D (type 1 diabetes).^69,70 In HD, mean glucose levels were significantly lower during dialysis compared to post-dialysis (7.5 ± 2.5 vs 9.4 ± 1.9 mmol/L; P < .001), with reduced variability but a higher frequency of hypoglycemia (4.4% vs 2.1%; P < .001).²⁶

Assessing the efficacy of diabetes treatments using continuous glucose monitoring

Eleven studies involving 257 participants with T2D (type 2 diabetes) on HD evaluated interventions including glucagon-like peptide-1 receptor agonists (GLP-1 RAs) (liraglutide, dulaglutide), dipeptidyl peptidase-4 (DPP-4) inhibitors (vildagliptin, teneligliptin), basal-bolus insulin, and basal insulin analogues (see Table 5 in Supplemental File).^{22,32,45,57,62,65,68,72,75,79,81} Bomholt et al²² reported a non-significant change with liraglutide (7.5-7.4%, P = .81), whereas Gómez et al³² observed a significant reduction of −0.74% (P < .0001). Similarly, Képénékian et al⁴⁵ reported a decrease from 8.4% to 7.6% following CGM-guided adjustments (P < .01), and Munch et al⁵⁷ found a 0.6% reduction with vildagliptin plus insulin (P < .01).

Improvements in glycemic variability were also reported. Yajima et al⁸¹ demonstrated a reduction in the coefficient of variation (CV) from 28.1% to 19.8% (P = .003), while Savu et al⁶⁸ noted significant decreases on both HD and non-HD days (P = .0001 and P = .0011, respectively). Insulin requirements declined significantly in some studies. For instance, insulin doses were reduced from 18 to 6 units with teneligliptin (P < .0001)⁷⁹ and by 15 units per day with dulaglutide (P = .002).⁷² While liraglutide was associated with an increase in mild hypoglycemia (P = .02),²² most studies reported no significant rise in hypoglycemic episodes.

Continuous glucose monitoring-based evaluation of glycemic outcomes in the dialysis population

Twenty-two studies involving 768 people with diabetes on dialysis reported glycemic outcomes (see Table 6 in Supplemental File).^24,27,30,32,^{33,34,37,38,40
-42,46,52,54}-^{56,58,63,65,68,78} Glucose levels typically fell during dialysis and rebounded afterwards, with notable nocturnal variation, eg, drop from 8.8 to 8.4 mmol/L (P = .029).²⁴ The TIR was generally lower on dialysis days but improved following interventions, eg, Chantrel et al²⁷ (TIR = 80.2%, P = .02) and Lee et al⁴⁶ post-intervention improvement (P < .05). Glycemic variability increased on dialysis days (P < .05).⁵⁴ Hypoglycemia risk varied: some studies showed no significant increase (P = .77),³² while others reported extended hyperglycemia mean Time Above Range (TAR) (49%) and Time Below Range (TBR) (2%).⁵² Educational and insulin adjustment programs reduced HbA_1c by up to 0.74% (P < .0001)³² and improved glycemic control.

Automated insulin delivery in the dialysis population

Four studies involving 48 participants with diabetes undergoing dialysis (T1D, T2D) evaluated the safety and efficacy of AID systems (see Table 7 in Supplemental File).^20,25,28,67 All studies reported significant improvements in glycemic levels, particularly in TIR. Bally et al²⁰ found that users of closed-loop systems spent 69.0% of the time within the target range, compared with 31.5% in the control group, a difference of 37.6% (95% CI = 24.4-50.8; P < .001). Boughton et al²⁵ reported an increase in TIR from 37.7 to 52.8% (P < .001), while Rossi et al⁶⁷ observed a rise from 63% at baseline to 72% at three months and 74% at one year. Chaudhry et al²⁸ recorded an increase from 43.5 to 64.8% (P = .015).

Mean glucose levels also declined. Boughton et al²⁵ reported a reduction from 11.6 to 10.1 mmol/L (P = .003), while Chaudhry et al²⁸ noted a non-significant drop of 2.13 mmol/L (P = .06). Bally et al²⁰ found no increase in hypoglycemia with closed-loop use, whereas Boughton et al²⁵ observed a significant reduction (0.1% vs 0.2%; P = .040).

Additional outcomes included lower glycemic variability; Rossi et al⁶⁷ reported a reduction in the CV to 24% at one year and a decrease in HbA_1c to 55 mmol/mol (7.2%). Chaudhry et al²⁸ observed a significant decrease in TAR, from 55.5% to 34.8% (P = .03), although glycemic variability remained unchanged.

Discussion

This review highlights the potential of CGM and AID technologies to enhance diabetes management in the dialysis population. Evidence from a range of studies underscores their effectiveness in addressing the distinct challenges associated with both HD and PD. Individuals receiving dialysis are particularly susceptible to hypoglycemia, hyperglycemia, and glycemic variability, largely due to dialysis-induced metabolic changes.

Validation studies included in the review confirm that CGM systems offer acceptable accuracy in both HD and PD settings, with MARD values typically between 10% and 23%. These results align with findings in non-dialysis populations^83
-88 and show strong correlations with laboratory glucose and self-monitoring of blood glucose (SMBG), supporting CGM as a reliable monitoring tool for people on dialysis.⁸⁹ However, accuracy often declines during and shortly after HD, likely due to physiological changes and sensor lag,^90,91 requiring cautious interpretation. Despite this, CGM provides valuable insights such as trend data and alarms. In contrast, SMBG, while accurate, provides only snapshot measurements and lacks the continuous glucose profile offered by CGM. Furthermore, Clarke and Parkes Consensus Error Grid analyses across the included studies demonstrated high clinical accuracy of CGM and flash glucose monitoring devices in dialysis populations. Specifically, 97%–99% of glucose readings fell within Zones A and B on the Clarke Error Grid, and 98%–100% on the Parkes Consensus Error Grid, indicating no or minimal clinical risk. These results are comparable to findings in non-dialysis populations, supporting the reliability of CGM in the dialysis setting.

Traditional markers such as HbA_1c consistently underestimate glycemic levels in dialysis populations due to altered red blood cell lifespan, erythropoietin use, and the uremic environment. This review reaffirms that HbA_1c is less reliable in this context. Glycated albumin shows stronger correlation with CGM-derived glucose values and provides a more accurate measure of glycemic control in people receiving dialysis,⁹² highlighting the need to consider alternative or additional markers. The TIR has been proposed as a complementary biomarker, offering several advantages over HbA_1c. It reflects glycemic variability, captures hypoglycemia and hyperglycemia, responds quickly to treatment changes, and is not affected by pathological factors that may affect HbA_1c levels.⁹⁰

The dynamic impact of dialysis on glucose profiles was evident. Differences in glucose patterns between APD and CAPD modalities further illustrate the complexity of glycemic management in this population, particularly due to the sustained hyperglycemia associated with high-glucose dialysate solutions. Moreover, mean glucose levels were significantly lower during dialysis sessions and higher post-dialysis, accompanied by increased glucose variability and a higher incidence of hypoglycemia during dialysis. These glycemic fluctuations present a clinical challenge and underscore the value of CGM in providing real-time insights into glucose trends and enabling timely interventions.

Studies evaluating the use of CGM to guide diabetes treatment have demonstrated its effectiveness in monitoring therapeutic impact on glycemic control. These findings underscore the value of CGM in optimizing medication regimens for people on dialysis by enhancing efficacy and reducing the risk of hypoglycemia. Intervention studies further support the potential of CGM-guided adjustments to improve glycemic outcomes in this population. In non-dialysis populations, CGM is also a feasible tool for comparing treatment approaches.⁹³ However, uncertainty regarding regulatory acceptance of CGM data may hinder wider adoption. Clear guidance from authorities such as the Conformité Européenne (CE) and Food and Drug Administration (FDA) would help establish its role in the evaluation of new therapies.⁹³

This review further highlights the utility of CGM in managing glycemia and glycemic variability in people on dialysis. The CGM effectively identifies intradialytic glucose lows and post-dialytic highs, supporting tailored insulin adjustments and improved glycemic level without increasing the risk of hypoglycemia. It also captures glycemic differences between dialysis and non-dialysis days, providing insights into metabolic fluctuations. These findings are supported by previous systematic reviews,^15
-17,⁹⁴ further reinforcing the potential of CGM in this population.⁸⁹

The AID systems have proven effective in non-dialysis populations, improving TIR, reducing hypoglycemia, and enhancing glycemic variability compared to standard insulin therapy.^95,96 Emerging evidence in dialysis people shows similar benefits despite metabolic fluctuations during HD or PD. Closed-loop insulin delivery offers a person-centered approach by lessening the need for frequent monitoring and insulin adjustments. However, altered insulin clearance, slow insulin pharmacokinetics, and potential CGM inaccuracies during dialysis present challenges.⁸⁹ Still, studies in T1D and T2D people on dialysis report improved glucose control and safety with fully closed-loop systems. Although current data are limited, larger, longer-term trials are needed to confirm these benefits and support wider use in this complex group.

Despite growing evidence and endorsement from national and international guidelines supporting the use of diabetes technologies in dialysis settings,^8,97 their utilization remains limited in this vulnerable population. Historically, and in line with manufacturer guidance, the use of CGM devices (including Dexcom G5/G6/G7 and FreeStyle Libre 1/2/3) has not been recommended for people on dialysis due to limited accuracy data in this group. However, as the evidence base grows, manufacturers will need to reconsider their current guidance.

Laursen et al⁹⁸ explored the experiences of people on HD using CGM, identifying six key themes: improved management of hypo- and hyperglycemia, informed decision-making, reduced practical challenges, enhanced confidence and safety, greater effectiveness with health care professional support, and mixed opinions on alarms. Overall, participants viewed CGM as a valuable tool for diabetes management during dialysis. However, additional health care professional support or tailored education may be needed to maximize CGM use and alleviate psychosocial challenges such as anxiety related to alarms or device burden, thereby enabling more sustained use.

Both CGM and AID offer significant benefits for glycemic management in this population. Yet, barriers including cost, limited clinical capacity, and workforce readiness continue to hinder routine integration.¹³ Persistent inequities in access further restrict utilization and limit optimization of diabetes care for those at greatest risk.^89,99 To overcome these challenges, targeted staff training, adequate funding, and clear care pathways are essential. These measures would support effective adoption, provide ongoing clinical support, and promote equitable access to diabetes technologies in dialysis settings. Further research on implementation strategies is needed to refine treatment approaches and improve care for people with diabetes undergoing dialysis. While CGM and AID systems show promise, their high costs, lack of universal reimbursement, and limited access, particularly in low- and middle-income countries, pose significant barriers. Given the complex medical needs of this population, it is essential to raise awareness and promote equitable access to these technologies wherever feasible.

Strengths and Weaknesses

This review provides a comprehensive overview of the potential benefits of CGM and AID in dialysis populations, drawing on studies from various dialysis modalities. It offers valuable insights into efficacy, safety, and glycemic outcomes but is limited by small sample sizes, varying follow-up durations, and the inclusion of lower-level evidence such as cross-sectional analyses, audits, and case reports, which may affect consistency and weaken conclusions. While scoping reviews typically do not include formal quality appraisal, the predominance of observational data and variability in study quality should be considered when interpreting the findings. Most studies were conducted in Europe, Asia, and the Americas, with limited representation from low- and middle-income countries, potentially restricting the generalizability of the results.

Conclusion

This review highlights the promising role of CGM and AID in improving diabetes management in dialysis populations. The CGM reliably monitors glucose, offering valuable insights to guide treatment, while AID improves glucose level and safety with a personalized approach. Integrating these technologies into routine care could enhance outcomes and reduce diabetes burden in people on dialysis. Wider adoption requires clear clinical pathways, dedicated funding, and health care professional training. Further research is needed to optimize implementation and ensure optimal care.

Supplemental Material

sj-docx-1-dst-10.1177_19322968251353811 – Supplemental material for Scoping Review—Diabetes Technology for Individuals on Kidney Replacement Therapy (Dialysis): Current Trends and Future Directions

Supplemental material, sj-docx-1-dst-10.1177_19322968251353811 for Scoping Review—Diabetes Technology for Individuals on Kidney Replacement Therapy (Dialysis): Current Trends and Future Directions by Hellena Hailu Habte-Asres, Joseph Ngmenesegre Suglo, Khuram Chaudhry, Angus Forbes, David C. Wheeler and Janaka Karalliedde in Journal of Diabetes Science and Technology

Footnotes

Acknowledgements

Nil.

Abbreviations

AID, automated insulin delivery; APD, automated peritoneal dialysis; AUC, area under the curve; CAPD, continuous ambulatory peritoneal dialysis; CE, Conformité Européenne; CGM, continuous glucose monitoring; CINAHL, Cumulative Index to Nursing and Allied Health Literature; CSII, continuous subcutaneous insulin infusion; CV, coefficient of variation; DPP-4 inhibitors, dipeptidyl peptidase-4 inhibitors; ESKD, end-stage kidney disease; FDA, Food and Drug Administration; GLP-1 RA, Glucagon-Like Peptide-1 Receptor Agonists; HbA_1c, glycated hemoglobin; HD, hemodialysis; KRT, kidney replacement therapy; MARD, mean absolute relative difference; MeSH, medical subject headings; mg/dL, milligrams per deciliter; PD, peritoneal dialysis; PRISMA, preferred reporting items for systematic reviews and meta-analyses; SD, standard deviation; SMBG, self-monitoring of blood glucose; T1D, type 1 diabetes; T2D, type 2 diabetes; TAR, time above range; TBR, time below range; TIR, time in range.

Declaration of Conflicting Interests

The author(s) declared the following potential conflicts of interest with respect to the research, authorship, and/or publication of this article: HHH-A received speaker honoraria from AstraZeneca and Bayer. JNS, KC, and AF have declared no conflicts of interest. DCW has an ongoing consultancy contract with AstraZeneca and has received payments for consultancy and/or speaking activities from multiple companies, including Amgen, Astellas, Bayer, Boehringer Ingelheim, Eledon, GSK, Galderma, Gilead, Janssen, Mundipharma, Menarini, MSD, NovoNordisk, Pharmacosmos, Tricida, and Vifor. JK has received honoraria for delivering educational meetings and/or attending advisory boards from Boehringer Ingelheim, AstraZeneca, Sanofi, and NAPP and research grants from AstraZeneca and Sanofi.

Funding

The author(s) received no financial support for the research, authorship, and/or publication of this article.

ORCID iD

Hellena Hailu Habte-Asres

Supplemental Material

Supplemental material for this article is available online.

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