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

Association Between Peritoneal Glucose Absorption, Lipid Metabolism, and Cardiovascular Disease Risk in Nondiabetic Patients on Peritoneal Dialysis

BACKGROUND

Excessive sugar intake increases the energy metabolic burden and the risk of cardiovascular disease (CVD). Patients on peritoneal dialysis absorb much more glucose than the World Health Organization recommends, but the link to CVD is unclear.

OBJECTIVE

To identify the association between peritoneal glucose absorption, lipid metabolism, and CVD.

METHODS

We applied generalized additive mixed effects and mixed effects Cox proportional hazard models to evaluate the impact of peritoneal glucose absorption on lipid profiles and CVD risk. We performed subgroup analyses by using protein intake (normalized protein nitrogen appearance [nPNA] and normalized protein catabolic rate [nPCR] were used to assess protein intake) and high-sensitivity C-reactive protein (hs-CRP).

RESULTS

After multivariable adjustment, peritoneal glucose absorption per 10 g/d increase was associated with an increase in cholesterol of 0.145 (95% confidence interval [CI]: 0.086-0.204) mmol/L. No link with the total risk of CVD was observed; however, protein intake and hs-CRP levels affected the relationship between glucose absorption and CVD risk. Patients with values for nPNA and nPCR <1.0 g/(kg·d) were associated with a lower risk of CVD (hazard ratio [HR] 95% CI: 0.68 (0.46-0.98)) with glucose absorption per 10 g/d increase. While patients with hs-CRP levels ≥3 mg/d or values for nPNA or nPCR ≥1.0 g/(kg·d) were associated with a higher risk of CVD (HR 95% CI: 1.32 (1.07-1.63); 1.31 (1.02-1.68)) for glucose absorption per 10 g/d increase.

CONCLUSIONS

Our study found a positive correlation between peritoneal glucose absorption and lipid profiles. Increased glucose absorption was associated with a lower risk of CVD in lower protein intake patients and a higher risk of CVD in higher hs-CRP or protein intake levels in patients on peritoneal dialysis.

Scoping review: Exploring the relationship between chrononutrition and glycemic responses in the adult population

Chrononutrition, an emerging body of evidence on the relationship between biological rhythms and metabolism, has been established to be associated with glycemic responses. However, the available evidence is inconsistent, due to protocol variations. Therefore, this review aims to summarize the findings on chrononutrition characteristics and their association with glycemic responses among adults. Systematic searches were conducted across six databases (PubMed, EBSCO Host, ProQuest Central, MEDLINE & Ovid, Scopus and Web of Science) to identify all relevant studies published from January 2012. Two reviewers independently screened the abstracts and full-text articles based on the inclusion and exclusion criteria. Details about population characteristics, study methods and key findings were extracted following the PRISMA-ScR guideline. The quality of selected studies was evaluated using the mixed methods appraisal tool. The searchers identified 49 studies eligible for analysis. The results showed that meal timing, particularly night-time eating and snacking were associated with glycemic responses. Regarding meal regularity, skipping breakfast may affect glycemic responses, but no clear conclusion was drawn about its effect on insulin. The association between meal frequency and glycemic responses was inconclusive. Night fasting duration and restricted eating window are potentially associated with glycemic responses. The current review extensively investigates the association between chrononutrition factors and glycemic responses in adults. However, more prospective cohort and interventional studies are needed to better understand this causal-effect relationship.

Box A of HMGB1 Maintains the DNA Gap and Prevents DDR-induced Kidney Injury in D-galactose Induction Rats

BACKGROUND/AIM

Disability and mortality rates for renal failure are still increasing. DNA damage and oxidative stress intoxication from body metabolism, high blood glucose, or the environment cause significant kidney damage. Recently, we reported that Box A of HMGB1 (Box A) acts as molecular scissors, producing DNA gaps that prevent DNA damage in kidney cell lines and ultimately reverse aging phenotypes in aging rat models. The present study aimed to demonstrate the potency of Box A in preventing D-galactose (D-gal)-induced kidney injury.

MATERIALS AND METHODS

A Box A expression plasmid was constructed and administered to a rat model. D-gal was injected subcutaneously for eight weeks. Serum was collected to study renal function, and white blood cells were collected for DNA gap measurement. Kidney tissue was also collected for γ-H2AX and NF-κB immunostaining; Senescence-associated (SA)-beta-gal staining; and analysis of the mRNA expression of p16INK4A, TNF-α, and IL-6. Moreover, histopathology analysis was performed using hematoxylin & eosin and Masson trichome staining.

RESULTS

Pretreatment with Box A administration prevented the reduction of DNA gaps and the consequences of the DNA damage response, which include elevated serum creatinine; high serum BUN; an increased positive SA-beta-gal staining area; overexpression of p16INK4A, NF-κB and senescence-associated secretory phenotype molecules, including IL-6, TNF-α; and histological alterations, including tubular dilation and collagen accumulation.

CONCLUSION

Box A effectively prevents DNA gap reduction and all D-gal-induced kidney pathological changes at the molecular, histological, and physiological levels. Therefore, Box A administration is a promising novel therapeutic strategy to prevent DNA-damaging agent-induced kidney failure.

Proactive Identification of Patients with Diabetes at Risk of Uncontrolled Outcomes during a Diabetes Management Program: Conceptualization and Development Study Using Machine Learning

BACKGROUND

The growth in the capabilities of telehealth have made it possible to identify individuals with a higher risk of uncontrolled diabetes and provide them with targeted support and resources to help them manage their condition. Thus, predictive modeling has emerged as a valuable tool for the advancement of diabetes management.

OBJECTIVE

This study aimed to conceptualize and develop a novel machine learning (ML) approach to proactively identify participants enrolled in a remote diabetes monitoring program (RDMP) who were at risk of uncontrolled diabetes at 12 months in the program.

METHODS

Registry data from the Livongo for Diabetes RDMP were used to design separate dynamic predictive ML models to predict participant outcomes at each monthly checkpoint of the participants' program journey (month-n models) from the first day of onboarding (month-0 model) up to the 11th month (month-11 model). A participant's program journey began upon onboarding into the RDMP and monitoring their own blood glucose (BG) levels through the RDMP-provided BG meter. Each participant passed through 12 predicative models through their first year enrolled in the RDMP. Four categories of participant attributes (ie, survey data, BG data, medication fills, and health signals) were used for feature construction. The models were trained using the light gradient boosting machine and underwent hyperparameter tuning. The performance of the models was evaluated using standard metrics, including precision, recall, specificity, the area under the curve, the F1-score, and accuracy.

RESULTS

The ML models exhibited strong performance, accurately identifying observable at-risk participants, with recall ranging from 70% to 94% and precision from 40% to 88% across the 12-month program journey. Unobservable at-risk participants also showed promising performance, with recall ranging from 61% to 82% and precision from 42% to 61%. Overall, model performance improved as participants progressed through their program journey, demonstrating the importance of engagement data in predicting long-term clinical outcomes.

CONCLUSIONS

This study explored the Livongo for Diabetes RDMP participants' temporal and static attributes, identification of diabetes management patterns and characteristics, and their relationship to predict diabetes management outcomes. Proactive targeting ML models accurately identified participants at risk of uncontrolled diabetes with a high level of precision that was generalizable through future years within the RDMP. The ability to identify participants who are at risk at various time points throughout the program journey allows for personalized interventions to improve outcomes. This approach offers significant advancements in the feasibility of large-scale implementation in remote monitoring programs and can help prevent uncontrolled glycemic levels and diabetes-related complications. Future research should include the impact of significant changes that can affect a participant's diabetes management.

Development and validation of a clinical prediction model for ischemic stroke recurrence after successful stent implantation in symptomatic intracranial atherosclerotic stenosis

OBJECTIVE

This study aimed to analyze the risk factors for recurrent ischemic stroke in patients with symptomatic intracranial atherosclerotic stenosis (ICAS) who underwent successful stent placement and to establish a nomogram prediction model.

METHODS

We utilized data from a prospective collection of 430 consecutive patients at Jining NO.1 People's Hospital from November 2021 to November 2022, conducting further analysis on the subset of 400 patients who met the inclusion criteria. They were further divided into training (n=321) and validation (n=79) groups. In the training group, we used univariate and multivariate COX regression to find independent risk factors for recurrent stroke and then created a nomogram. The assessment of the nomogram's discrimination and calibration was performed through the examination of various measures including the Consistency index (C-index), the area under the receiver operating characteristic (ROC) curves (AUC), and the calibration plots. Decision curve analysis (DCA) was used to evaluate the clinical utility of the nomogram by quantifying the net benefit to the patient under different threshold probabilities.

RESULTS

The nomogram for predicting recurrent ischemic stroke in symptomatic ICAS patients after stent placement utilizes six variables: coronary heart disease (CHD), smoking, multiple ICAS, systolic blood pressure (SBP), in-stent restenosis (ISR), and fasting plasma glucose. The C-index (0.884 for the training cohort and 0.87 for the validation cohort) and the time-dependent AUC (>0.7) indicated satisfactory discriminative ability of the nomogram. Furthermore, DCA indicated a clinical net benefit from the nomogram.

CONCLUSIONS

The predictive model constructed includes six predictive factors: CHD, smoking, multiple ICAS, SBP, ISR and fasting blood glucose. The model demonstrates good predictive ability and can be utilized to predict ischemic stroke recurrence in patients with symptomatic ICAS after successful stent placement.

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.

Management of Patients With Kidney Disease Undergoing Bariatric Surgery: A Multidisciplinary Approach

Bariatric surgery is increasingly recognized as a safe and effective treatment for obesity in patients with chronic kidney disease (CKD), including stages 4, 5, and 5D (on dialysis). Among the available surgical methods, sleeve gastrectomy (SG) is the most commonly performed weight loss procedure and is mainly done to facilitate kidney transplantation (KT). However, many KT candidates treated with SG remain on the transplant waiting list for months to years, with some never receiving a transplant. Therefore, appropriate candidates for SG must be selected, and post-SG management should address the unique needs of this population, with a focus on sustaining the metabolic benefits of surgery while minimizing potential side effects related to rapid weight loss which may inadvertently lead to muscle and bone catabolism. Multidisciplinary post-SG care in this population may lead to overall better health on the transplant waiting list, resulting in a higher percentage of post-SG patients ultimately receiving KT. To tailor the effective treatment for these patients, clinicians should acknowledge that patients with CKD stage 4-5D have different nutritional needs and are metabolically and psychosocially distinct from the general bariatric surgery population. Sarcopenia is highly prevalent and may be exacerbated by muscle catabolism following SG if not adequately addressed. Blood pressure, glucose, and bone metabolism are all affected by the CKD stage 4-5D, and therefore require distinct diagnostic and management approaches. Long-standing chronic disease, associated comorbidities, and low adherence to medical therapies require ongoing comprehensive psychosocial assessment and support. This paper aims to review and consolidate the existing literature concerning the intersection of CKD stage 4-5D and the consequences of SG. We also suggest future clinical outcome studies examining novel treatment approaches for this medically complex population.

Assessment of Glycemic Control by Continuous Glucose Monitoring, Hemoglobin A1c, Fructosamine, and Glycated Albumin in Patients With End-Stage Kidney Disease and Burnt-Out Diabetes

OBJECTIVE

Patients with diabetes and end-stage kidney disease (ESKD) may experience "burnt-out diabetes," defined as having an HbA1c value <6.5% without antidiabetic therapy for >6 months. We aim to assess glycemic control by continuous glucose monitoring (Dexcom G6 CGM) metrics and glycemic markers in ESKD patients on hemodialysis with burnt-out diabetes.

RESEARCH DESIGN AND METHODS

In this pilot prospective study, glycemic control was assessed by continuous glucose monitoring (CGM), HbA1c measures, and glycated albumin and fructosamine measurements in patients with burnt-out diabetes (n = 20) and without a history of diabetes (n = 20).

RESULTS

Patients with burnt-out diabetes had higher CGM-measured daily glucose levels, lower percent time in the range 70-180 mg/dL, higher percent time above range (>250 mg/dL), and longer duration of hyperglycemia >180 mg/dL (hours/day) compared with patients without diabetes (all P < 0.01). HbA1c and fructosamine levels were similar; however, patients with burnt-out diabetes had higher levels of glycated albumin than did patients without diabetes.

CONCLUSIONS

The use of CGM demonstrated that patients with burnt-out diabetes have significant undiagnosed hyperglycemia. CGM and glycated albumin provide better assessment of glycemic control than do values of HbA1c and fructosamine in patients with ESKD.

Supporting Type 1 and Type 2 Diabetes Care in the Hemodialysis Unit: A Quality-improvement Initiative Throughout the COVID-19 Pandemic

OBJECTIVE

People living with diabetes mellitus (DM) and chronic kidney disease can have difficulty attending multiple appointments to receive DM care. We developed and studied the utility of a DM outreach program to offer in the hemodialysis (HD) unit.

METHODS

We conducted a quality improvement project in a satellite HD unit in London, Ontario, Canada, between August 1, 2019, and July 31, 2022. We assessed for baseline gaps in DM care among those with DM, performed root-cause analysis with key stakeholders to identify critical drivers of gaps, and conceptualized a certified diabetes educator-led outreach program to offer in the HD unit. We aimed to improve DM self-monitoring, hypo- and hyperglycemia, and DM-related screening. We used run and control charts to track outcome measures over time and modified our outreach program iteratively.

RESULTS

Fifty-eight persons with DM receiving HD participated in our program. Support spanned multiple waves of the COVID-19 pandemic. With 4 tests of change, we observed improvement in DM self-monitoring with a modest decline in self-reported hyperglycemia. There were no adverse consequences, and satisfaction with our program was high.

CONCLUSIONS

Although we did not meet all measures of success during the pandemic, outreach DM support in the HD unit appeared to improve self-monitoring and self-reported hyperglycemia. Similar programs could be modified and implemented in other centres.

Risk prediction of the progression of chronic kidney disease stage 1 based on peripheral blood samples: construction and internal validation of a nomogram

Patients with chronic kidney disease (CKD) have high morbidity and mortality, and the disease progression has a significant impact on their survival and living standards. This research aims to analyze risk factors for CKD stage 1 and provide a reference for clinical decision making. The clinical data and peripheral blood samples of 300 patients with CKD stage 1 were collected retrospectively. Patients were randomly assigned into a training set (n = 210) and a validation set (n = 90). Patients' baseline characteristic levels were subjected to statistical tests for difference. Univariate and multivariate Cox regression analyses were utilized to identify risk factors influencing disease progression. Subsequently, a prediction model for disease progression was developed using a nomogram, and the model's accuracy was assessed using the C-index and calibration curve. The results revealed that hypertension, diabetes, and urinary albumin were essential factors in the progression of CKD stage 1. The nomogram was constructed and then the C-index was calculated. The calibration curve was utilized to assess the risk model. The C-index of the training set was 0.75, and the C-index of the validation set was 0.73, suggesting a good predictive ability of the model. The risk model accurately predicted the progression of CKD stage 1, which is of great significance to developing personalized treatment for patients in clinical practice.

Association between serum complements and kidney function in patients with diabetic kidney disease

Objective

We aimed to explore the association between serum complements and kidney function of diabetic kidney disease (DKD) in Chinese patients.

Methods

This is a retrospective study involving 2,441 participants. DKD was diagnosed according to the Kidney Disease: Improving Global Outcomes (KDIGO) categories. Participants were classified as stages G1-G5 by KDIGO glomerular filtration rate (GFR) categories. Effect sizes are expressed as odds ratio (OR) with 95% confidence interval (CI).

Results

After balancing age, gender, systolic blood pressure (SBP), hemoglobin A1c (HbA1C), serum triglyceride (TG), and urinary albumin-to-creatinine ratio (UACR) between the G2-G5 and control groups, per 0.1 g/L increment in serum complement C3 was significantly associated with a 27.8% reduced risk of DKD at G5 stage (OR, 95% CI, P: 0.722, 0.616-0.847, <0.001) relative to the G1 stage. Conversely, per 0.1 g/L increment in serum complement C4 was associated with an 83.0-177.6% increased risk of G2-G5 stage (P<0.001). Serum complement C1q was not statistically significant compared to controls at all stages prior to or after propensity score matching.

Conclusions

Our results indicate that high concentrations of serum C4 were associated with the significantly elevated risk of kidney function deterioration across all stages, and reduced serum C3 levels with an increased risk of DKD stage G5.

Epidermal Wearable Biosensors for the Continuous Monitoring of Biomarkers of Chronic Disease in Interstitial Fluid

Healthcare technology has allowed individuals to monitor and track various physiological and biological parameters. With the growing trend of the use of the internet of things and big data, wearable biosensors have shown great potential in gaining access to the human body, and providing additional functionality to analyze physiological and biochemical information, which has led to a better personalized and more efficient healthcare. In this review, we summarize the biomarkers in interstitial fluid, introduce and explain the extraction methods for interstitial fluid, and discuss the application of epidermal wearable biosensors for the continuous monitoring of markers in clinical biology. In addition, the current needs, development prospects and challenges are briefly discussed.

Accuracy of Continuous Glucose Monitoring in an Insulin-Treated Population Requiring Haemodialysis

BACKGROUND

Continuous glucose monitoring (CGM) is revolutionizing diabetes care by giving both patients and the healthcare professionals unprecedented insights into glucose variability and patterns. It is established in National Institute for Health and Care Excellence (NICE) guidance as a standard of care for type 1 diabetes and diabetes in pregnancy under certain conditions. Diabetes mellitus (DM) is recognized as an important risk factor for chronic kidney disease (CKD). Around a third of patients receiving in-center haemodialysis as renal replacement therapy (RRT) have diabetes, either as a direct cause of renal failure or as an additional co-morbidity. Evidence of poor compliance with the current standard of care (self-monitoring of blood glucose [SMBG]) and overall greater morbidity and mortality, suggests this patient population as an ideal target group for CGM. However, there exists no strong published evidence showing the validity of CGM devices in insulin-treated diabetes patients requiring haemodialysis.

METHODS

We applied a Freestyle Libre Pro sensor to 69 insulin-treated diabetes haemodialysis (HD) patients on a dialysis day. Interstitial glucose levels were obtained, and time matched within 7 minutes to capillary blood glucose testing and any plasma blood glucose levels sent. Data cleansing techniques were applied to account for rapidly correcting hypoglycaemia and poor SMBG technique.

RESULTS

Clarke-error grid analysis showed 97.9% of glucose values in an acceptable range of agreement (97.3% on dialysis days and 99.1% on non-dialysis days).

CONCLUSIONS

We conclude that the Freestyle Libre sensor is accurate in measuring glucose levels when compared to glucose as measured by capillary SMBG testing and laboratory obtained serum glucose in patients on HD.

Transcriptomic analysis identifies novel candidates in cardiorenal pathology mediated by chronic peritoneal dialysis

Peritoneal dialysis (PD) is associated with increased cardiovascular (CV) risk. Studies of PD-related CV pathology in animal models are lacking despite the clinical importance. Here we introduce the phenotypic evaluation of a rat model of cardiorenal syndrome in response to chronic PD, complemented by a rich transcriptomic dataset detailing chronic PD-induced changes in left ventricle (LV) and kidney tissues. This study aims to determine how PD alters CV parameters and risk factors while identifying pathways for potential therapeutic targets. Sprague Dawley rats underwent Sham or 5/6 nephrectomy (5/6Nx) at 10 weeks of age. Six weeks later an abdominal dialysis catheter was placed in all rats before random assignment to Control or PD (3 daily 1-h exchanges) groups for 8 days. Renal and LV pathology and transcriptomic analysis was performed. The PD regimen reduced circulating levels of BUN in 5/6Nx, indicating dialysis efficacy. PD did not alter blood pressure or cardiovascular function in Sham or 5/6Nx rats, though it attenuated cardiac hypertrophy. Importantly PD increased serum triglycerides in 5/6Nx rats. Furthermore, transcriptomic analysis revealed that PD induced numerous changed transcripts involved with inflammatory pathways, including neutrophil activation and atherosclerosis signaling. We have adapted a uremic rat model of chronic PD. Chronic PD induced transcriptomic changes related to inflammatory signaling that occur independent of 5/6Nx and augmented circulating triglycerides and predicted atherosclerosis signaling in 5/6Nx LV tissues. The changes are indicative of increased CV risk due to PD and highlight several pathways for potential therapeutic targets.

Comprehensive analysis of endoplasmic reticulum stress-related mechanisms in type 2 diabetes mellitus

BACKGROUND

The endoplasmic reticulum (ER) is closely related to a wide range of cellular functions and is a key component to maintain and restore metabolic health. Type 2 diabetes mellitus (T2DM) is a serious threat to human health, but the ER stress (ERS)-related mechanisms in T2DM have not been fully elucidated.

AIM

To identify potential ERS-related mechanisms and crucial biomarkers in T2DM.

METHODS

We conducted gene set enrichment analysis (GSEA) and gene set variation analysis (GSVA) in myoblast and myotube form GSE166502, and obtained the differentially expressed genes (DEGs). After intersecting with ERS-related genes, we obtained ERS-related DEGs. Finally, functional analyses, immune infiltration, and several networks were established.

RESULTS

Through GSEA and GSVA, we identified several metabolic and immune-related pathways. We obtained 227 ERS-related DEGs and constructed several important networks that help to understand the mechanisms and treatment of T2DM. Finally, memory CD4⁺ T cells accounted for the largest proportion of immune cells.

CONCLUSION

This study revealed ERS-related mechanisms in T2DM, which might contribute to new ideas and insights into the mechanisms and treatment of T2DM.

Narrative Review of glycaemic management in people with diabetes on peritoneal dialysis

There is an increasing number of people with diabetes on peritoneal dialysis (PD) worldwide. However, there is a lack of guidelines and clinical recommendations for managing glucose control in people with diabetes on PD. The aim of this review is to provide a summary of the relevant literature and highlight key clinical considerations with practical aspects in the management of diabetes in people undergoing PD. A formal systematic review was not conducted because of the lack of sufficient and suitable clinical studies. A literature search was performed using PubMed, MEDLINE, Central, Google Scholar and ClinicalTrials.gov., from 1980 through February 2022. The search was limited to publications in English. This narrative review and related guidance have been developed jointly by diabetologists and nephrologists, who reviewed all available current global evidence regarding the management of diabetes in people on PD.We focus on the importance of individualized care for people with diabetes on PD, the burden of hypoglycemia, glycemic variability in the context of PD and treatment choices for optimizing glucose control. In this review, we have summarized the clinical considerations to guide and inform clinicians providing care for people with diabetes on PD.

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

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