Glycemic Variability: How to Measure and Its Clinical Implication for Type 2 Diabetes

Incretin therapy in feline diabetes mellitus - A review of the current state of research

Incretin hormones potentiate the glucose-induced insulin secretion following enteral nutrient intake. The best characterised incretin hormones are glucagon-like peptide-1 (GLP-1) and glucose-dependent insulinotropic polypeptide (GIP) which are produced in and secreted from the gut in response to nutrient ingestion. The property of incretins to enhance endogenous insulin secretion only at elevated blood glucose levels makes them interesting therapeutics for type 2 diabetes mellitus with a better safety profile than exogenous insulin. While incretin therapeutics (especially GLP-1 agonists, and more recently also GLP-1 / GIP dual agonists and other drugs that influence the incretin metabolism (e.g., dipeptidyl peptidase-4 (DPP-4) inhibitors)) are already widely used treatment options for human type 2 diabetes, these drugs are not yet approved for the therapy of feline diabetes mellitus. This review provides an introduction to incretins and feline diabetes mellitus in general and summarises the current study situation on incretins as therapeutics for feline diabetes mellitus to assess their possible future potential in feline medicine. Studies to date on the use of GLP-1 receptor agonists (GLP-1RA) in healthy cats largely confirm their insulinotropic effect known from other species. In diabetic cats, GLP-1RAs appear to significantly reduce glycaemic variability (GV, an indicator for the quality of glycaemic control), which is important for the management of the disease and prevention of long-term complications. However, for widespread use in feline diabetes mellitus, further studies are required that include larger numbers of diabetic cats, and that consider and test a possible need for dose adjustments to overweight and diabetic cats. Also evaluation of the outcome of GLP-1RA monotherapy will be neceessary.

Newer Outpatient Diabetes Therapies and Technologies

New diabetes drugs such as glucagon-like peptide-1 receptor agonists (GLP-1 RAs) and glucose-dependent insulinotropic peptide/GLP-1 RAs have emerged to show hemoglobin A1c (HbA1c) reduction, weight loss, and cardiovascular benefits. Similarly, sodium-glucose cotransporter 2 inhibitors' benefits span from HbA1c decrease to cardiovascular and renoprotective effects. Diabetes technology has expanded to include type 2 diabetes mellitus, with literature supporting its use in T2DM on any insulin regimen. Connected insulin pens and insulin delivery devices have opened new solutions to insulin users and automated insulin delivery systems have become the standard of care therapy for type 1 diabetes mellitus.

Two-week continuous glucose monitoring-derived metrics and degree of hepatic steatosis: a cross-sectional study among Chinese middle-aged and elderly participants

BACKGROUND

Continuous glucose monitoring (CGM) devices provide detailed information on daily glucose control and glycemic variability. Yet limited population-based studies have explored the association between CGM metrics and fatty liver. We aimed to investigate the associations of CGM metrics with the degree of hepatic steatosis.

METHODS

This cross-sectional study included 1180 participants from the Guangzhou Nutrition and Health Study. CGM metrics, covering mean glucose level, glycemic variability, and in-range measures, were separately processed for all-day, nighttime, and daytime periods. Hepatic steatosis degree (healthy: n = 698; mild steatosis: n = 242; moderate/severe steatosis: n = 240) was determined by magnetic resonance imaging proton density fat fraction. Multivariate ordinal logistic regression models were conducted to estimate the associations between CGM metrics and steatosis degree. Machine learning models were employed to evaluate the predictive performance of CGM metrics for steatosis degree.

RESULTS

Mean blood glucose, coefficient of variation (CV) of glucose, mean amplitude of glucose excursions (MAGE), and mean of daily differences (MODD) were positively associated with steatosis degree, with corresponding odds ratios (ORs) and 95% confidence intervals (CIs) of 1.35 (1.17, 1.56), 1.21 (1.06, 1.39), 1.37 (1.19, 1.57), and 1.35 (1.17, 1.56) during all-day period. Notably, lower daytime time in range (TIR) and higher nighttime TIR were associated with higher steatosis degree, with ORs (95% CIs) of 0.83 (0.73, 0.95) and 1.16 (1.00, 1.33), respectively. For moderate/severe steatosis (vs. healthy) prediction, the average area under the receiver operating characteristic curves were higher for the nighttime (0.69) and daytime (0.66) metrics than that of all-day metrics (0.63, P < 0.001 for all comparisons). The model combining both nighttime and daytime metrics achieved the highest predictive capacity (0.73), with nighttime MODD emerging as the most important predictor.

CONCLUSIONS

Higher CGM-derived mean glucose and glycemic variability were linked with higher steatosis degree. CGM-derived metrics during nighttime and daytime provided distinct and complementary insights into hepatic steatosis.

Unmet needs in the treatment of type 1 diabetes: why is it so difficult to achieve an improvement in metabolic control?

The development of advanced diabetes technology has permitted persons with type 1 diabetes mellitus to improve metabolic control significantly, particularly with the development of advanced hybrid closed-loop systems which have improved the quality of life by reducing hypoglycemia, decreasing macroangiopathy and microangiopathy-related complications, ameliorating HbA1c and improving glycemic variability. Despite the progression made over the past few decades, there is still significant margin for improvement to be made in terms of attaining appropriate metabolic control. Various factors are responsible for poor glycemic control including inappropriate carbohydrate counting, repeated bouts of hypoglycemia, hypoglycemia unawareness, cutaneous manifestations due to localized insulin use and prolonged use of diabetes technology, psychosocial comorbidities such as eating disorders or 'diabulimia', the coexistence of insulin resistance among people with type 1 diabetes and the inability to mirror physiological endogenous pancreatic insulin secretion appropriately. Hence, the aim of this review is to highlight and overcome the barriers in attaining appropriate metabolic control among people with type 1 diabetes by driving research into adjunctive treatment for coexistent insulin resistance and developing new advanced diabetic technologies to preserve β cell function and mirror as much as possible endogenous pancreatic functions.

Glycemic Variability as a Predictor of Graft Failure Following Infrainguinal Bypass for Peripheral Arterial Disease: A Retrospective Cohort Study

BACKGROUND

Glycemic variability (GV), measured as the change in visit-to-visit glycated hemoglobin (HbA1c), increases the risk of multiple adverse outcomes. However, the impact of GV on graft patency following infrainguinal bypass (IIB) is unknown. A retrospective cohort study was undertaken to assess the impact of GV on graft patency.

METHODS

A 3-year single-center retrospective case notes analysis of all people undergoing IIB between 2017 and 2019. Rutherford stage, graft conduit, level of bypass, procedure details, baseline demographics, comorbidities, and GV were assessed. Time to reintervention, ipsilateral amputation, or death was recorded to determine primary patency (PP).

RESULTS

One hundred six IIB outcomes were analyzed: mean (± standard deviation) age 68.0 (9.2) years; 69 (65.1%) male, 37 (33.9%), 75 (70.8%) had diabetes mellitus; and 46 (43.4%) underwent elective procedures. GV > 9.1% was associated with significantly lower median PP than GV < 9.1%, 198 (97-753.5) vs. 713 (166.5-1,044.5) days (P = 0.045). On univariate analysis, GV > 9.1% vs. < 9.1% was significantly associated with PP (hazard ratio [HR] 1.85 [confidence interval {CI} 1.091-3.136], P = 0.022). Bypass level was also a univariate predictor, with below knee bypasses (HR 2.31 [CI 1.164-4.564], P = 0.017), and tibial (HR 2.00 [CI 1.022-3.090], P < 0.043) having lower PP than above knee bypasses. On multivariate adjustment, GV > 9.1% and level of bypass remained independent predictors of PP, HR 1.96 (95% CI: 1.12-3.42, P = 0.018) and HR 2.54 (95% CI: 1.24-5.22, P = 0.011), respectively.

CONCLUSIONS

GV is an independent predictor of PP following infrainguinal bypass, thus optimizing GV should be a therapeutic target.

HbA1c variability associated with dementia risk in people with type 2 diabetes

INTRODUCTION

Although poor glycemic control is associated with dementia, it is unknown if variability in glycemic control, even in those with optimal glycosylated hemoglobin A1c (HbA1c) levels, increases dementia risk.

METHODS

Among 171,964 people with type 2 diabetes, we evaluated the hazard of dementia association with long-term HbA1c variability using five operationalizations, including standard deviation (SD), adjusting for demographics and comorbidities.

RESULTS

The mean baseline age was 61 years (48% women). Greater HbA1c SD was associated with greater dementia hazard (adjusted hazard ratio = 1.15 [95% confidence interval: 1.12, 1.17]). In stratified analyses, higher HbA1c SD quintiles were associated with greater dementia hazard among those with a mean HbA1c < 6% (P = 0.0004) or 6% to 8% (P < 0.0001) but not among those with mean HbA1c ≥ 8% (P = 0.42).

DISCUSSION

Greater HbA1c variability is associated with greater dementia risk, even among those with HbA1c concentrations at ideal clinical targets. These findings add to the importance and clinical impact of recommendations to minimize glycemic variability.

HIGHLIGHTS

We observed a cohort of 171,964 people with type 2 diabetes (mean age 61 years). This cohort was based in Northern California between 1996 and 2018. We examined the association between glycosylated hemoglobin A1c (HbA1c) variability and dementia risk. Greater HbA1c variability was associated with greater dementia hazard. This was most evident among those with normal-low mean HbA1c concentrations.

Short-term Glycemic Variability and Its Association With Macrovascular and Microvascular Complications in Patients With Diabetes

The introduction of continuous glucose monitoring inaugurated a new era in clinical practice by shifting the characterization of glycemic control from HbA1c to novel metrics. The one that gained widespread attention over the past decades was glycemic variability (GV), which typically refers to peaks and nadirs of blood glucose measured over a given time interval. GV can be dichotomized into two main categories: short-term and long-term. Short-term GV reflects within-day and between-day glycemic oscillations, and its contribution to diabetic complications remains an enigma. In this review, we summarize the available data about short-term GV and its possible association with both microvascular and macrovascular complications, evaluating different pathogenic mechanisms and demonstrating nonpharmaceutical, as well as pharmaceutical, therapeutic interventions.

Gluco-regulation & type 2 diabetes: entrenched misconceptions updated to new governing principles for gold standard management

Our understanding of type 2 diabetes (T2D) has evolved dramatically. Advances have upended entrenched dogmas pertaining to the onset and progression of T2D, beliefs that have prevailed from the early era of diabetes research-and continue to populate our medical textbooks and continuing medical education materials. This review article highlights key insights that lend new governing principles for gold standard management of T2D. From the historical context upon which old beliefs arose to new findings, this article outlines evidence and perspectives on beta cell function, the underlying defects in glucoregulation, the remediable nature of T2D, and, the rationale supporting the shift to complication-centric prescribing. Practical approaches translate this rectified understanding of T2D into strategies that fill gaps in current management practices of prediabetes through late type 2 diabetes.

An Integrative Review of Social Determinants of Glycemic Targets Achievement in Adults with Type 2 Diabetes in the United States

Several individual social determinants of health have been identified as significant factors contributing to achieving glycemic targets (glycated hemoglobin < 7). However, it remains unclear how these social variables individually or collectively contribute to glycemic targets among adults with type 2 diabetes (T2D) in the United States (U.S.) The purpose of the current integrative review (IR) was to describe and synthesize findings from studies on social determinants of glycemic target achievement in adults with T2D in the U.S. and integrate them into the United States Department of Health and Human Services Conceptual Framework. The databases searched included PubMed, CINAHL Plus with Full Text, Medline with Full Text [EBSCO], Google Scholar, bibliography, and hand searching. A total of 948 records were identified. After excluding duplicates and irrelevant studies based on inclusion and exclusion criteria through title, abstract, and full-text screening, 13 studies were finally included in this IR. The results revealed that race/ethnicity, economic access and stability, educational access and quality, healthcare access and quality, neighborhood and built environment, and social and community context contribute to glycemic target achievement among adult patients with T2D in the U.S. Integrating findings from key studies on social determinants of glycemic health may contribute to developing interventions aimed at reducing and eventually eradicating health disparities for individuals with and at risk for T2D in the U.S.

Glycemic Variability and the Risk of Diabetic Peripheral Neuropathy: A Meta-Analysis

Glycemic variability (GV) has been related to complications in patients with diabetes. The aim of the systematic review and meta-analysis was to investigate whether GV is also associated with the incidence of diabetic peripheral neuropathy (DPN). A systematic search of Medline, Web of Science, Embase, and Cochrane Library database was conducted to identify relevant observational studies with longitudinal follow-up. The Newcastle-Ottawa Scale was used for study quality evaluation. A random-effects model was utilized to pool the results, accounting for heterogeneity. Ten observational studies including 72 565 patients with diabetes were included. The quality score was 8-9, indicating generally good quality of the included studies. With a mean follow-up duration of 7.1 years, 11 532 patients (15.9%) were diagnosed as DPN. Compared to patients with low GV, patients with high GV were associated with an increased risk incidence of DPN (risk ratio: 1.51, 95% confidence interval: 1.23 to 1.85, p<0.001; I2=78%). In addition, subgroup analysis showed consistent results in patients with type 1 and type 2 diabetes, and in studies evaluating the short-term and long-term GV (p for subgroup difference=0.82 and 0.53). Finally, results of subgroup analysis also suggested that the association between GV and risk of DPN were not significantly affected by study design, follow-up durations, diagnostic methods for DPN, adjustment of mean glycated hemoglobin A1c, or study quality scores (p for subgroup difference all>0.05). A high GV may be associated with an increased incidence of DPN.

Digital technology and healthcare delivery in insulin-treated adults with diabetes: a proposal for analysis of self-monitoring blood glucose patterns using a dedicated platform

PURPOSE

A remote platform for diabetes care (Roche Diabetes® Care Platform, RDCP) has been developed that allows combined face-to-face consultations and remote patient monitoring (RPM).

METHODS

A dedicated flowchart is proposed as a clinical approach to help healthcare professionals in the appropriate interpretation of structured self-monitoring blood glucose data, as visualized on the RDCP during the visits, and in the optimal management of patients using the integrated RDCP-RPM tools.

RESULTS

The platform organizes patterns in different blocks: (i) hypoglycemia; (ii) hyperglycemia; (iii) blood glucose variability; (iv) treatment adherence, which identifies a possible individual pattern according to glycemic control challenges, potential causal factors, and behavioral type patterns. The flowchart proposed for use of the RDCP-RPM is self-explanatory and entails 3 steps: (1) evaluation of quality and quantity of self-monitoring blood glucose data; (2) pattern analysis; (3) personalized suggestions and therapy changes.

CONCLUSION

The main aim of the remote treatment flowchart proposed is to support healthcare professionals in the identification of hypoglycemic and hyperglycemic patterns using the RDCP regardless of the HbA1c value and ongoing treatment, which however, become crucial in combination with pattern analysis in the therapeutical choice.

Implementation and Outcomes of Multidisciplinary Diabetes Management Program Among Type 2 Diabetic Patients: A Comparative Study

BACKGROUND

Current guidelines recommend shifting physician-led care (PLC) for type 2 diabetes mellitus (T2DM) to more effective multidisciplinary health care (MHC). However, few researchers have studied its real-life implementation in Saudi Arabia. Therefore, we aimed to assess the implementation and compare the outcomes of an MDC diabetes management program (DMP) among T2DM patients to a PLC at a general hospital after one year of follow-up in a real-world practice setting.

METHODS

We conducted this comparative patient files review study by analyzing medical records of all T2DM patients at two private care centers. Both were compared for their effectiveness in achieving two outcomes: the glycated hemoglobin (HbA1c) <7% and low-density lipoprotein-cholesterol (LDL-c) <70 mg/dl at the end of the first year. Additionally, we assessed the implementation of the DMP.

RESULTS

Eight hundred thirty-four medical records were reviewed, 537 from DMP, and 279 from the PLC center. The personal health coordination was almost complete (97.8%) in the DMP, but the implementation was incomplete regarding nutrition (65.7%), dental exam (64.8%), and foot care (58.3%). Both care groups were matched for age (p = 0.056), gender (p = 0.085), duration of diabetes (p = 0.217), and basal glycemic control (p = 0.171). The DMP showed a significant net decrease in HbA1c (-0.5 [IQR 1.47%] vs -0.2 [IQR 3.05%], p = 0.0001) and LDL-c (-10 [IQR 50] vs -5 [IQR 60.5] mg/dl, p = 0.004) compared to PLC. A higher percentage of patients achieved glycemic control in the DMP than in the PLC (49.4% vs 38.7%, p = 0.038). However, both programs demonstrated similar outcomes in lipid control (28.7% vs. 30%, p = 0.695).

CONCLUSION

Despite some gaps in implementation, one year of DMP showed better glycemic control among T2DM patients compared to PLC. Both programs were comparable in terms of lipid control. Further studies identifying the gaps in care implementation could improve sustainability, future replication, and generalizability of similar programs to other healthcare systems in Saudi Arabia.

Enhancement of Ambulatory Glucose Profile for Decision Assistance and Treatment Adjustments

The ambulatory glucose profile (AGP) lacks sufficient statistical metrics and insightful graphs; indeed, it is missing important information on the temporal patterns of glucose variations. The AGP graph is difficult to interpret due to the overlapping metrics and fluctuations in glucose levels over 14 days. The objective of this proposed work is to overcome these challenges, specifically the lack of insightful information and difficulty in interpreting AGP graphs, to create a platform for decision assistance. The present work proposes 20 findings built from decision rules that were developed from a combination of AGP metrics and additional statistical metrics, which have the potential to identify patterns and insightful information on hyperglycemia and hypoglycemia. The "CGM Trace" webpage was developed, in which insightful metrics and graphical representations can be used to make inferences regarding the glucose data of any user. However, doctors (endocrinologists) can access the "Findings" tab for a summarized presentation of their patients' glycemic control. The findings were implemented for 67 patients' data, in which the data of 15 patients were collected from a clinical study and the data of 52 patients were gathered from a public dataset. The findings were validated by means of MANOVA (multivariate analysis of variance), wherein a p value of < 0.05 was obtained, depicting a strong significant correlation between the findings and the metrics. The proposed work from "CGM Trace" offers a deeper understanding of the CGM data, enhancing AGP reports for doctors to make treatment adjustments based on insightful information and hidden patterns for better diabetic management.

How to Measure Glycemic Variability? A Literature Review

Optimal glycemic control without the presence of diabetes-related complications is the primary goal for adequate diabetes management. Recent studies have shown that hemoglobin A1c level cannot fully evaluate diabetes management as glycemic fluctuations are demonstrated to have a major impact on the occurrence of diabetes-related micro- and macroangiopathic comorbidities. The use of continuous glycemic monitoring systems allowed the quantification of glycemic fluctuations, providing valuable information about the patients' glycemic control through various indicators that evaluate the magnitude of glycemic fluctuations in different time intervals. This review highlights the significance of glycemic variability by describing and providing a better understanding of common and alternative indicators available for use in clinical practice.

Visit-to-Visit Glucose Variability, Cognition, and Global Cognitive Decline: The Multi-Ethnic Study of Atherosclerosis

CONTEXT

Higher visit-to-visit glucose variability (GV) is associated with dysglycemia and type 2 diabetes (T2D), key risk factors for cognitive decline.

OBJECTIVE

Evaluate the association of GV with cognitive performance and decline in racially/ethnically diverse older populations with and without T2D.

METHODS

We calculated the standard deviation of glucose (SDG), average real variability (ARV), and variability independent of the mean (VIM) among 4367 Multi-Ethnic Study of Atherosclerosis participants over 6 clinical examinations. Participants completed a cognitive assessment at the fifth examination, and a subset completed a second assessment 6 years later. We used multivariable linear regression to estimate the association of intraindividual GV with cognitive test scores after adjustments for cardiovascular risk factors and mean glucose level over the study period.

RESULTS

Two-fold increments in the VIM and SDG were associated with worse Cognitive Abilities Screening Instrument (CASI) performance, while two-fold increments in VIM and ARV were associated with worse Digit Symbol Coding test score. GV measures were not associated with change in CASI performance among 1834 participants with repeat CASI data 6 years later. However, among 229 participants with incident T2D, the SDG and VIM were associated with decline in CASI (-1.7 [95% CI: -3.1, -0.3] and -2.1 [-3.7, -0.5] points, respectively). In contrast, single-timepoint glucose and HbA1c were not associated with CASI decline among participants with or without incident T2D.

CONCLUSION

Higher visit-to-visit GV over 16 to 18 years is associated with worse cognitive performance in the general population, and with modest global cognitive decline in participants with T2D.

Time in range and complications of diabetes: a cross-sectional analysis of patients with Type 1 diabetes

BACKGROUND/ OBJECTIVE

To evaluate the association of CGM parameters and HbA1c with diabetes complications in patients with Type 1 Diabetes (T1D).

METHODS

Patients with T1D using the CGM system Freestyle Libre were included in this analysis. The association of CGM-metrics and HbA1c with diabetes complications (any complication, microvascular complications, or macrovascular complications) was assessed using logistic regression unadjusted and adjusted for age, sex, and diabetes duration (model 1), and further adjusted for hypertension and dyslipidemia (model 2).

RESULTS

One hundred and sixty-one patients with T1D were included. The mean (± SD) age was 37.4 ± 13.4 years old and the median T1D duration was 17.7 ± 10.6 years. Time in range (TIR) was associated with any complication and microvascular complications in the unadjusted model and in the adjusted models. TIR was associated with retinopathy in the unadjusted model as well as in model 1, and was associated with macrovascular complications only in the unadjusted model. HbA1c was associated with any complications, microvascular complications, and retinopathy in the unadjusted model but not in the adjusted models. HbA1c was associated with macrovascular complications in the unadjusted model and in the adjusted model 1.

CONCLUSIONS

In this cross-sectional analysis of patients with T1D using intermittent scanned CGM, TIR, and HbA1c were associated with complications of diabetes. TIR may be a better predictor than HbA1c of any complication and microvascular complications, while HbA1c may be a better predictor of macrovascular complications.

Gut Microbiota Composition and Functionality Are Associated With REM Sleep Duration and Continuous Glucose Levels

CONTEXT

Sleep disruption is associated with worse glucose metabolic control and altered gut microbiota in animal models.

OBJECTIVE

We aimed to evaluate the possible links among rapid eye movement (REM) sleep duration, continuous glucose levels, and gut microbiota composition.

METHODS

This observational, prospective, real-life, cross-sectional case-control study included 118 (60 with obesity), middle-aged (39.1-54.8 years) healthy volunteers recruited at a tertiary hospital. Glucose variability and REM sleep duration were assessed by 10-day continuous glucose monitoring (CGM) (Dexcom G6) and wrist actigraphy (Fitbit Charge 3), respectively. The coefficient of variation (CV), interquartile range (IQR), and SD of glucose variability was assessed and the percentage of time in range (% TIR), at 126-139 mg/dL (TIR2), and 140-199 mg/dL (TIR3) were calculated. Shotgun metagenomics sequencing was applied to study gut microbiota taxonomy and functionality.

RESULTS

Increased glycemic variability (SD, CV, and IQR) was observed among subjects with obesity in parallel to increased % TIR2 and % TIR3. REM sleep duration was independently associated with % TIR3 (β = -.339; P < .001) and glucose variability (SD, β = -.350; P < .001). Microbial taxa from the Christensenellaceae family (Firmicutes phylum) were positively associated with REM sleep and negatively with CGM levels, while bacteria from Enterobacteriacea family and bacterial functions involved in iron metabolism showed opposite associations.

CONCLUSION

Decreased REM sleep duration was independently associated with a worse glucose profile. The associations of species from Christensenellaceae and Enterobacteriaceae families with REM sleep duration and continuous glucose values suggest an integrated picture of metabolic health.

What is Glycaemic Variability and which Pharmacological Treatment Options are Effective? A Narrative Review

Glycated haemoglobin is currently used for diagnosis and follow-up of diabetes mellitus. However, it has important limitations; as it only reflects the average glycaemia over the last 3 months, it does not allow the identification of crucial events, such as episodes of hypoglycaemia and hyperglycaemia. Strict control of hyperglycaemia can result in severe hypoglycaemia that can be life threatening and can have important sequelae. Recently, the concept of glycaemic variability has been developed to provide information about the magnitude of glycaemic excursions and the duration of these fluctuations. This new approach has the potential to improve outcomes, decrease the risk of hypoglycaemia, and decrease cardiovascular risk. This review describes the most commonly prescribed non-insulin anti-diabetic drugs for diabetes management, their mechanism of action, and the existing evidence about their effectiveness in improving glycaemic variability and diabetes control.

Machine Learning-Based Time in Patterns for Blood Glucose Fluctuation Pattern Recognition in Type 1 Diabetes Management: Development and Validation Study

BACKGROUND

Continuous glucose monitoring (CGM) for diabetes combines noninvasive glucose biosensors, continuous monitoring, cloud computing, and analytics to connect and simulate a hospital setting in a person's home. CGM systems inspired analytics methods to measure glycemic variability (GV), but existing GV analytics methods disregard glucose trends and patterns; hence, they fail to capture entire temporal patterns and do not provide granular insights about glucose fluctuations.

OBJECTIVE

This study aimed to propose a machine learning-based framework for blood glucose fluctuation pattern recognition, which enables a more comprehensive representation of GV profiles that could present detailed fluctuation information, be easily understood by clinicians, and provide insights about patient groups based on time in blood fluctuation patterns.

METHODS

Overall, 1.5 million measurements from 126 patients in the United Kingdom with type 1 diabetes mellitus (T1DM) were collected, and prevalent blood fluctuation patterns were extracted using dynamic time warping. The patterns were further validated in 225 patients in the United States with T1DM. Hierarchical clustering was then applied on time in patterns to form 4 clusters of patients. Patient groups were compared using statistical analysis.

RESULTS

In total, 6 patterns depicting distinctive glucose levels and trends were identified and validated, based on which 4 GV profiles of patients with T1DM were found. They were significantly different in terms of glycemic statuses such as diabetes duration (P=.04), glycated hemoglobin level (P<.001), and time in range (P<.001) and thus had different management needs.

CONCLUSIONS

The proposed method can analytically extract existing blood fluctuation patterns from CGM data. Thus, time in patterns can capture a rich view of patients' GV profile. Its conceptual resemblance with time in range, along with rich blood fluctuation details, makes it more scalable, accessible, and informative to clinicians.

The role of cognitive rehabilitation in people with type 2 diabetes: A study protocol for a randomized controlled trial

Today, the prevalence of cognitive dysfunction and the prevalence of diabetes are increasing. Research shows that diabetes increases cognitive impairment risk, and cognitive impairment makes diabetes self-management more challenging. Diabetes self-management, essential to good glycemic control, requires patients to assimilate knowledge about their complex disease and to engage in activities such as glucose self-monitoring and the management of their medications. To test a comprehensive cognitive rehabilitation intervention-the Memory, Attention, and Problem-Solving Skills for Persons with Diabetes (MAPSS-DM) program. Our central hypothesis is that participants who take part in the MAPSS-DM intervention will have improved memory and executive function, increased use of compensatory cognitive skills, and improved self-management. We will also explore the role of glucose variability in those changes. This is a randomized controlled trial. Sixty-six participants with cognitive concerns and type 2 diabetes will be assigned to either the full MAPSS-DM intervention or an active control. Participants will use continuous glucose monitoring pre- and post-intervention to identify changes in glycemic variability. All participants will also be evaluated systematically via questionnaires and neuropsychological tests at three timepoints: baseline, immediately post-intervention, and 3 months post-intervention. This study will fill an important gap by addressing cognitive function in the management of diabetes. Diabetes is related to accelerated cognitive aging, cognitive deficits are related to poorer self-management, and improvements in cognitive performance as a result of cognitive rehabilitation can translate into improved performance in everyday life and, potentially, diabetes self-management. The results of the proposed study will therefore potentially inform strategies to support cognitive function and diabetes self-management, as well as offer new mechanistic insights into cognitive function through the use of continuous glucose monitoring. Trial registration: This study has been registered at ClinicalTrials.gov (NCT04831775).

Individualized diabetes care: Lessons from the real-world experience

Diabetes care is often difficult without a proper collaboration between the patient and the care provider as the disease is mostly self-managed by patients through adjustments in their lifestyles, and medication doses to optimise glycaemic control. Most clinical guidelines on the management of diabetes mellitus (DM) provide only broad principles on diabetes care, and the blind follow-up of such principles without a proper review and consideration of patient characteristics often results in inadequate glycaemic control and diabetes complications consequently. Therefore, a proper understanding of the pathobiology, clinical situation, and comorbidities of the individual case is of paramount importance to tailoring the most appropriate management strategy in real-world diabetes care. With the aid of five unique cases of DM [(1) Medically managed type 2 diabetes mellitus (T2DM) with severe obesity; (2) Management of T2DM with unreliable glycated haemoglobin (HbA1c); (3) Obesity in a patient with type 1 diabetes mellitus (T1DM); and (4) Late diagnosis and subsequent management of monogenic diabetes and 5. Sudden worsening of well-controlled T2DM)] we elaborate on the importance of individualised diabetes care and the practicalities in these situations. The review also provides an evidence update on the management of different forms of DM to guide physicians in optimising the care of their patients in day-to-day clinical practice.

Efficacy of polyethylene glycol loxenatide versus insulin glargine on glycemic control in patients with type 2 diabetes: a randomized, open-label, parallel-group trial

Objective: This trial aimed to evaluate the glycemic control of polyethylene glycol loxenatide measured with continuous glucose monitoring (CGM) in patients with type 2 diabetes mellitus (T2DM), with the hypothesis that participants given PEG-Loxe would spend more time in time-in-range (TIR) than participants were given insulin glargine after 24 weeks of treatment. Methods: This 24-week, randomized, open-label, parallel-group study was conducted in the Department of Endocrine and Metabolic Diseases, Longhu Hospital, Shantou, China. Participants with T2DM, who were ≥45 years of age, HbA1c of 7.0%-11.0%, and treated at least 3 months with metformin were randomized (1:1) to receive PEG-Loxe or insulin glargine. The primary endpoint was TIR (blood glucose range: 3.9-10.0 mmol/L) during the last 2 weeks of treatment (weeks 22-24). Results: From March 2020 to April 2022, a total of 107 participants with T2DM were screened, of whom 78 were enrolled into the trial (n = 39 per group). At the end of treatment (weeks 22-24), participants given PEG-Loxe had a greater proportion of time in TIR compared with participants given insulin glargine [estimated treatment difference (ETD) of 13.4% (95% CI, 6.8 to 20.0, p < 0.001)]. The tight TIR (3.9-7.8 mmol/L) was greater with PEG-Loxe versus insulin glargine, with an ETD of 15.6% (95% CI, 8.9 to 22.4, p < 0.001). The time above range (TAR) was significantly lower with PEG-Loxe versus insulin glargine [ETD for level 1: -10.5% (95% CI: -14.9 to -6.0), p < 0.001; ETD for level 2: -4.7% (95% CI: -7.9 to -1.5), p = 0.004]. The time below range (TBR) was similar between the two groups. The mean glucose was lower with PEG-Loxe versus insulin glargine, with an ETD of -1.2 mmol/L (95% CI, -1.9 to -0.5, p = 0.001). The SD of CGM glucose levels was 1.88 mmol/L for PEG-Loxe and 2.22 mmol/L for insulin glargine [ETD -0.34 mmol/L (95% CI: -0.55 to -0.12), p = 0.002], with a similar CV between the two groups. Conclusion: The addition of once-weekly GLP-1RA PEG-Loxe to metformin was superior to insulin glargine in improving glycemic control and glycemic variability evaluated by CGM in middle-aged and elderly patients with T2DM.

The Relationship Between Dietary Patterns and Glycemic Variability in People with Impaired Glucose Tolerance

BACKGROUND

Diurnal glucose fluctuations are increased in prediabetes and might be affected by specific dietary patterns.

OBJECTIVES

The present study assessed the relationship between glycemic variability (GV) and dietary regimen in people with normal glucose tolerance (NGT) and impaired glucose tolerance (IGT).

METHODS

Forty-one NGT (mean age: 45.0 ± 9.0 y, mean BMI: 32.0 ± 7.0 kg/m2) and 53 IGT (mean age: 48.4 ± 11.2 y, mean BMI: 31.3 ± 5.9 kg/m2) subjects were enrolled in this cross-sectional study. The FreeStyleLibre Pro sensor was used for 14 d, and several parameters of GV were calculated. The participants were provided with a diet diary to record all meals. ANOVA analysis, Pearson correlation, and stepwise forward regression were performed.

RESULTS

Despite no difference in diet patterns between the 2 groups, GV parameters were higher in IGT than in NGT. GV worsened with an increase in overall daily carbohydrate and refined grain consumption and improved with the increase in whole grain intake in IGT. GV parameters were positively related [r = 0.14-0.53; all P < 0.02 for SD, continuous overall net glycemic action 1 (CONGA1), J-index, lability index (LI), glycemic risk assessment diabetes equation, M-value, and mean absolute glucose (MAG)], and low blood glucose index (LBGI) inversely (r = -0.37, P = 0.006) related to the total percentage of carbohydrate, but not to the distribution of carbohydrate between the main meals in the IGT group. A negative relationship existed between total protein consumption and GV indices (r = -0.27 to -0.52; P < 0.05 for SD, CONGA1, J-index, LI, M-value, and MAG). The total EI was related to GV parameters (r = 0.27-0.32; P < 0.05 for CONGA1, J-index, LI, and M-value; and r = -0.30, P = 0.028 for LBGI).

CONCLUSIONS

The primary outcome results showed that insulin sensitivity, calories, and carbohydrate content are predictors of GV in individuals with IGT. Overall, the secondary analyses suggested that carbohydrate and daily consumption of refined grains might be associated with higher GV, whereas whole grains and daily protein intake were related to lower GV in people with IGT.

Developments in the design and delivery of self-management support for children and young people with diabetes: A narrative synthesis of systematic reviews

AIMS

Facilitated self-management support programmes have become central to the treatment of chronic diseases including diabetes. For many children and young people with diabetes (CYPD), the impact on glycated haemoglobin (HbA_1c ) and a range of self-management behaviours promised by these programmes remain unrealised. This warrants an appraisal of current thinking and the existing evidence to guide the development of programmes better targeted at this age group.

METHODS

Create a narrative review of systematic reviews produced in the last 3 years that have explored the impact on CYPD of the four key elements of self-management support programmes: education, instruction and advice including peer support; psychological counselling via a range of therapies; self-monitoring, including diaries and telemetric devices; and telecare, the technology-enabled follow-up and support by healthcare providers.

RESULTS

Games and gamification appear to offer a promising means of engaging and educating CYPD. Psychological interventions when delivered by trained practitioners, appear to improve HbA_1c and quality of life although effect sizes were small. Technology-enabled interactive diaries can increase the frequency of self-monitoring and reduce levels of HbA_1c . Telecare provided synchronously via telephone produced significant improvements in HbA_1c .

CONCLUSIONS

The cost-effective flexibility of increasing the reliance on technology is an attractive proposition; however, there are resource implications for digital connectivity in underserved populations. The need remains to improve the understanding of which elements of each component are most effective in a particular context, and how to optimise the influence and input of families, caregivers and peers.

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.

A Comparison of Continuous Glucose Monitoring Estimated Hemoglobin A1c in Adults with Type 1 or Type 2 Diabetes

Background: The relationship of mean glucose measured with continuous glucose monitoring (CGM) and hemoglobin A1c (HbA1c) shows considerable variability between individuals with diabetes and may be influenced by race-related factors. Whether the relationship of mean glucose with HbA1c varies according to type 1 diabetes (T1D) or type 2 diabetes (T2D) has not been well evaluated. Methods: Data from 343 participants in four clinical trials (191 with T1D and 152 with T2D) were analyzed. Least squares linear regression models were fit with HbA1c as the dependent variable and mean glucose as the independent variable. Results: Mean age was 57 ± 15 years in the T1D cohort and 58 ± 10 years in the T2D cohort. The T1D cohort was 89% White non-Hispanic, 5% African American, 3% Hispanic, and 3% other or mixed race compared with 52%, 16%, 22%, and 9%, respectively, in the T2D cohort. The relationship between CGM-measured mean glucose and laboratory-measured HbA1c significantly differed between T1D and T2D cohorts, with HbA1c on average being higher with T2D than T1D for the same mean glucose (P = 0.002). However, this difference was largely attributable to the difference in the proportion of African Americans between T1D and T2D; and after stratifying by race, the mean glucose-HbA1c relationship showed only a small difference between T1D non-African Americans and T2D non-African Americans. The mean glucose-HbA1c relationship appeared similar for White non-Hispanic and Hispanic individuals. Conclusion: HbA1c on average was higher in T2D than T1D for a given mean glucose, but after accounting for race, there was no meaningful difference in the mean glucose-HbA1c relationship comparing T1D and T2D. The mean glucose-HbA1c relationship differs in African American compared with White individuals, but does not appear to differ comparing White non-Hispanic to Hispanic individuals. The published glucose management indicator formula appears to be suitable for both T1D and T2D.

Continuous Glucose Monitoring in Dogs and Cats: Application of New Technology to an Old Problem

In recent years, glucose monitoring has been revolutionized by the development of continuous glucose monitoring systems (CGMS), which are wearable non/minimally invasive devices that measure glucose concentration almost continuously for several consecutive d/wk. The Abbott FreeStyle Libre is the CGMS used most commonly. It has adequate clinical accuracy both in dogs and cats, even though the accuracy is lower in the hypoglycemic range. It allows an accurate identification of glycemic excursions occurring throughout the day as well as of glucose variations during consecutive days, enabling the clinician to make a more informed decision about the insulin dose and frequency of administration.

Relationship between glycemic variability and cognitive function in lacune patients with type 2 diabetes

BACKGROUND

Lacunes are the manifestations of lacunar infarction which can lead many patients to the clinical outcome of disability or dementia. However, the relationship between lacune burden, cognitive function and blood glucose fluctuation in patients with type 2 diabetes mellitus (T2DM) complicated with lacunes is not very clear.

AIM

To explore the correlation between glucose variability, lacune burden and cognitive function in patients with lacunes complicated with T2DM.

METHODS

The clinical and imaging data of 144 patients with lacunes combined with T2DM were reviewed retrospectively. 72 h continuous glucose monitoring was performed. The Montreal Cognitive Assessment was used to assess cognitive function. The burden of lacunes was evaluated using magnetic resonance imaging performance. Multifactorial logistic regression analysis was used to study the affecting the lacune load and cognitive impairment in patients. To predict the value of patients' cognitive impairment with lacunes complicated with T2DM, a receiver operating characteristic (ROC) curve and a nomogram prediction model were constructed.

RESULTS

The standard deviation (SD) of the average blood glucose concentration, percentage coefficient of variation (%CV) and time of range (TIR) were significantly different between the low and the high load groups (P < 0.05). The SD, %CV and TIR of the cognitive impairment group and non-cognitive impairment group were significantly different (P < 0.05). SD (odds ratio (OR): 3.558, 95% confidence interval (CI): 1.268-9.978, P = 0.006), and %CV (OR: 1.192, 95%CI: 1.081-1.315, P < 0.05) were the risk factors for an increased infarct burden in lacunes patients complicated with T2DM. TIR (OR: 0.874, 95%CI: 0.833-0.928, P < 0.05) is a protective factor. In addition, an increased SD (OR: 2.506, 95%CI: 1.008-6.23, P = 0.003), %CV (OR: 1.163, 95%CI: 1.065-1.270, P < 0.05) were the risk factors for cognitive impairment in patients with lacunes complicated with T2DM, TIR (OR: 0.957, 95%CI: 0.922-0.994, P < 0.05) is a protective factor. A nomogram prediction model of the risk of cognitive impairment was established based on SD, %CV and TIR. Decision curve analysis and the internal calibration analysis were used for internal verification and showed that the model was clinical benefit. The area under the ROC curves for predicting cognitive impairment in patients with lacunes complicated with T2DM was drawn were %CV: 0.757 (95%CI :0.669-0.845, P < 0.05), TIR: 0.711 (95%CI: 0.623-0.799, P < 0.05).

CONCLUSION

Blood glucose variability is closely associated with the level of lacune burden and cognitive dysfunction in lacune patients combined with T2DM. %CV, TIR have a certain predictive effect in cognitive impairment in lacune patients.

A randomized trial on the effect of transcutaneous electrical nerve stimulator on glycemic control in patients with type 2 diabetes

Transcutaneous electrical nerve stimulator (TENS) has been demonstrated to be beneficial in glycemic control in animal models, but its application in humans has not been well studied. We randomly assigned 160 patients with type 2 diabetes on oral antidiabetic drugs 1:1 to the TENS study device (n = 81) and placebo (n = 79). 147 (92%) randomized participants (mean [SD] age 59 [10] years, 92 men [58%], mean [SD] baseline HbA_1c level 8.1% [0.6%]) completed the trial. At week 20, HbA_1c decreased from 8.1% to 7.9% in the TENS group (- 0.2% [95% CI - 0.4% to - 0.1%]) and from 8.1% to 7.8% in the placebo group (- 0.3% [95% CI - 0.5% to - 0.2%]) (P = 0.821). Glycemic variability, measured as mean amplitude of glycemic excursion (MAGE) at week 20 were significantly different in the TENS group vs. the placebo group (66 mg/dL [95% CI 58, 73] vs. 79 mg/dL [95% CI 72, 87]) (P = 0.009). Our study provides the clinical evidence for the first time in humans that TENS does not demonstrate a statistically significant HbA_1c reduction. However, it is a safe complementary therapy to improve MAGE in patients with type 2 diabetes.

Association Between the Variability of Glycated Hemoglobin and Retinopathy in Patients with Type 2 Diabetes Mellitus: A Meta-Analysis

Visit-to-visit variability of glycated hemoglobin (HbA1c) is a marker of long-term glycemic fluctuation, which has been related to increased risk of macrovascular complications in patients with type 2 diabetes mellitus (T2DM). The association between HbA1c variability and retinopathy in patients with T2DM, however, has been inconsistent in previous studies. In order to fully evaluate the above association, we conducted a meta-analysis. Observational studies related to the aim of the meta-analysis were identified by search of PubMed, Web of Science, and Embase databases. Studies with HbA1c variability evaluated as the standard deviation (SD) and/or the coefficients of variation (CV) of HbA1c were included. The results were analyzed using a random-effects model that incorporated potential heterogeneity between studies. Twelve observational studies involving 44 662 T2DM patients contributed to the meta-analysis. Overall, 5150 (11.5%) patients developed retinopathy. Pooled results showed that compared to patients with lower HbA1c variability, T2DM patients with higher HbA1c-SD (relative risk [RR]: 1.48, 95% confidence interval [CI]: 1.24 to 1.78, p<0.001, I2=34%) and higher HbA1c-CV (RR: 1.29, 95% CI: 1.05 to 1.59, p=0.02, I2=0%) were both associated with higher risk of DR. For studies with HbA1c-SD, the association was not significantly affected by study characteristics such as country, study design, mean age, disease duration, adjustment of mean HbA1c, or quality scores (p for subgroup difference all>0.05). In conclusion, higher HbA1c variability may be associated with an increased risk of retinopathy in patients with T2DM.

Early glycaemic variability increases 28-day mortality and prolongs intensive care unit stay in critically ill patients with pneumonia

OBJECTIVE

This study aimed to evaluate the effect of early glycaemic variability (GV) on 28-day mortality in critically ill patients with pneumonia.

PATIENTS AND METHODS

This single-centre retrospective study included patients admitted to the intensive care unit (ICU) due to pneumonia between 2018 and 2019. A total of 282 patients (mean age, 68.6 years) with blood sugar test (BST) results measured more than three times within 48 h after hospitalization and haemoglobin A1c (HbA1c) levels recorded within 2 months were enrolled. Coefficient of variation (CV) was calculated using the BST values. The effects of GV on 28-day mortality and prolonged ICU stay (>14 days) were also assessed.

RESULTS

The mean age was 60.6 years (male to female ratio, 2.5:1). The 28-day mortality rate was 31.6% (n = 89) and was not different according to the presence of diabetes (DM vs. non-DM) or HbA1c levels (≥7.5 vs. <7.5%; both p > .05). However, the mortality rate was significantly higher in patients with high GV (CV ≥ 36%) than in those with low GV (CV < 36%; 37.5 vs. 25.4%, p = .028). The risk of mortality in patients with high GV was prominent in the subgroups with DM or low HbA1c levels. Among the surviving patients (n = 193), 44 remained in the ICU for more than 14 days. Compared to low GV, high GV was associated with a higher rate of prolonged ICU stay, although not statistically significant (27.8 vs. 18.5%, p = .171). After adjusting for the severity of illness and treatment strategy, CV was an independent risk factor for 28-day mortality (hazard ratio [HR], 1.01, p = .04) and prolonged ICU stay (odds ratio, 1.02; p = .04).

CONCLUSIONS

High GV within 48 h of ICU admission was associated with an increased 28-day mortality risk and prolonged ICU stay. Early phase GV should be carefully managed in critically ill patients with pneumonia.KEY MESSAGESThe presence of diabetes or HbA1c alone is insufficient to predict 28-day mortality and prolonged ICU stay in critically ill patients with pneumonia.High glycaemic variability (GV) within 48 h of ICU admission increases 28-day mortality and prolongs ICU stay, which is consistent after adjusting for severity of illness and treatment strategy.Patients with high GV, especially those with DM or low HbA1c levels (<7.5%) should be more carefully treated to reduce mortality.

Effects of break in sedentary behaviour on blood glucose control in diabetic patients. Systematic review

INTRODUCTION

One of the main goals of prescribing physical activity for people with type 2 diabetes is to reduce hyperglycaemia, as it is a risk factor for the development of chronic complications. As less time spent each day in sedentary behaviour would lead to higher glucose consumption by skeletal muscle tissue, this could have significant positive effects on blood glucose control parameters. For this reason, the aim of this study was to analyse the information from different protocols for breaking sedentary behaviour and the association with blood glucose control parameters in patients with type 2 diabetes.

MATERIAL AND METHODS

A systematic search was carried out for randomised controlled studies on this topic published in the scientific literature. The following databases were considered: PubMed, Cochrane, EBSCO, WoS, ScienceDirect and Medline.

RESULTS

24 studies were identified and analysed using the COVIDENCE platform. Seven articles were selected for the final analysis, comprising 138 patients. The results show that breaks in sedentary behaviour with light physical activity in people with type 2 diabetes are effective in reducing insulin resistance, the area under the glucose curve, fasting and postprandial blood glucose, and blood glucose variability.

CONCLUSIONS

Acute interruption of sedentary behaviour, through light-intensity and short-duration exercise, can improve blood glucose indicators in patients with type 2 diabetes, including short term blood glucose variability.

Glucose variability is a marker for COVID-19 severity and mortality

Objective

We aimed to investigate the association between glucose coefficient of variation (CV) and mortality and disease severity in hospitalized patients with coronavirus disease-19 (COVID-19).

Subjects and Methods

Retrospective cohort study in a tertiary center of patients with COVID-19 admitted to designated departments between March 11^th, 2020, and November 2^nd, 2020. We divided patients based on quartiles of glucose CV after stratification to those with and without diabetes mellitus (DM). Main outcomes were length of stay and in-hospital mortality.

Results

The cohort included 565 patients with a mean age of 67.71 ± 15.45 years, and 62.3% were male. Of the entire cohort, 44.4% had DM. The median glucose CV was 32.8% and 20.5% in patients with and without DM, respectively. In patients with DM, higher glucose CV was associated with a longer hospitalization in the unadjusted model (OR = 2.7, 95% CI [1.3,5.6] for Q4), and when adjusted for age, sex, comorbidities, and laboratory markers, this association was no longer statistically significant (OR = 1.3, 95% CI [0.4,4.5] for Q4). In patients with and without DM, higher glucose CV was associated with higher rates of in-hospital mortality in the unadjusted model, but adjustment for comorbidities and laboratory markers eliminated the association (OR = 0.5, 95% CI [0.1,3.4] for Q4 in patients with DM).

Conclusion

Higher glucose CV was associated with increased in-hospital mortality and length of stay, but this association disappeared when the adjustment included laboratory result data. Glucose CV can serve as a simple and cheap marker for mortality and severity of disease in patients with COVID-19.

Comparison of the Impact of Insulin Degludec U100 and Insulin Glargine U300 on Glycemic Variability and Oxidative Stress in Insulin-Naive Patients With Type 2 Diabetes Mellitus: Pilot Study for a Randomized Trial

Background

There is an ongoing discussion about possible differences between insulin degludec (IDeg-100) and glargine U300 (IGlar-300). There is little data and head-to-head comparison of IDeg-100 and IGlar-300 regarding their simultaneous impact on glycemic variability and oxidative stress in patients with type 2 diabetes mellitus (T2DM).

Objective

In our randomized, open-label, crossover study, we compared the impact of IDeg-100 and IGlar-300 on glycemic variability and oxidative stress in insulin-naive patients with T2DM.

Methods

We recruited a total of 25 adult patients with T2DM (7 females) whose diabetes was uncontrolled (HbA1c ≥7.5%) on two or more oral glucose-lowering drugs; a total of 22 completed the study. Mean age was 57.3 (SD 6.99) years and duration of diabetes was 9.94 (SD 5.01) years. After the washout period, they were randomized alternately to first receive either IDeg-100 or IGlar-300 along with metformin. Each insulin was administered for 12 weeks and then switched. At the beginning and end of each phase, biochemical and oxidative stress parameters were analyzed. On 3 consecutive days prior to each control point, patients performed a 7-point self-monitoring of blood glucose profile. Oxidative stress was assessed by measuring thiol groups and hydroperoxides (determination of reactive oxygen metabolites test) in serum.

Results

IGlar-300 reduced mean glucose by 0.02-0.13 mmol/L, and IDeg-100 reduced glucose by 0.10-0.16 mmol/L, with no significant difference. The reduction of the coefficient of glucose variation also did not show a statistically significant difference. IGlar-300 increased thiols by 0.08 µmol/L and IDeg-100 increased thiols by 0.15 µmol/L, with no significant difference (P=.07) between them. IGlar-300 reduced hydroperoxides by 0.040 CARR U and IDeg-100 increased hydroperoxides by 0.034 CARR U, but the difference was not significant (P=.12).

Conclusions

The results of our study do not show a significant difference regarding glycemic variability between patients receiving either insulin IDeg-100 or IGlar-300, although IGlar-300 showed greater dispersion of data. No significant difference in oxidative stress was observed. In a larger study, doses of insulins should be higher to achieve significant impact on glycemic parameters and consequently on glycemic variability and oxidative stress.

Trial Registration

ClinicalTrials.gov, NCT04692415; https://clinicaltrials.gov/ct2/show/NCT04692415

Endothelial Dysfunction and Platelet Hyperactivation in Diabetic Complications Induced by Glycemic Variability

The development and progression of the complications of chronic diabetes mellitus are attributed not only to increased blood glucose levels but also to glycemic variability. Therefore, a deeper understanding of the role of glycemic variability in the development of diabetic complications may provide more insight into targeted clinical treatment strategies in the future. Previously, the mechanisms implicated in glycemic variability-induced diabetic complications have been comprehensively discussed. However, endothelial dysfunction and platelet hyperactivation, which are two newly recognized critical pathogenic factors, have not been fully elucidated yet. In this review, we first evaluate the assessment of glycemic variability and then summarise the roles of endothelial dysfunction and platelet hyperactivation in glycemic variability-induced complications of diabetes, highlighting the molecular mechanisms involved and their interconnections.

Acute Glycemic Variability and Functional Outcome in Patients with Acute Ischemic Stroke: A Meta-Analysis

Dysglycemia are involved in the development of functional impairment after acute ischemic stroke (AIS). The aim of the study was to evaluate the association between acute glycemic variability and functional outcome in patients with AIS. Cohort studies were obtained by search Medline, Web of Science, Embase, Wanfang, and China National Knowledge Infrastructure databases from inception to November, 2021. A random-effect model which incorporates the intra-study heterogeneity was chosen to pool the results. Ten cohort studies including 3038 patients were included, and 1319 (43.4%) had poor functional outcome (modified Rankin Scale >2) up to three months after disease onset. Pooled results showed that higher acute GV was associated with an increased risk of poor functional outcome, as evidenced by GV evaluated by the standard deviation of blood glucose (SDBG, OR: 1.91, 95% CI: 1.38 to 2.65, I2=60%, p<0.001), the coefficient of variation of blood glucose (OR: 2.03, 95% CI: 1.15 to 3.58, I2=17%, p=0.02), the range of glucose (OR: 1.43, 95% CI: 1.11 to 1.83, I2=22%, p=0.005), and the mean amplitude of glycemic excursion (OR: 1.59, 95% CI: 1.10 to 2.31, I2=0%, p=0.01). Subgroup analyses did not support that difference in study design, treatments for AIS, mean age of the patients, duration for GV measuring, or study quality would significantly affect the association between SDBG and functional outcome after AIS. In conclusion, higher acute glycemic variability may predict poor functional outcome within 3 months after AIS.

Acute glycemic variability and mortality of patients with acute stroke: a meta-analysis

BACKGROUND

Increased glucose fluctuation has been related to poor prognosis in patients with critical illnesses, while its prognostic role in patients with acute stroke remains unknown. The meta-analysis aimed to evaluate the association between the acute glycemic variation (GV) and mortality risk in patients with acute stroke.

METHODS

Cohort studies were obtained by searching Medline, Web of Science, Embase, Wanfang and CNKI databases. A random-effect model which incorporates the intra-study heterogeneity was chosen to pool the results.

RESULTS

Ten cohort studies with 1433 patients were included, and 280 (19.5%) of them died within 90 days of disease onset. Results of the meta-analyses showed that a higher acute GV was associated with an increased risk of early mortality in patients with acute stroke, as indicated by GV measured with the coefficient of variation of blood glucose (CVBG, odds ratio [OR]: 2.24, 95% CI 1.40 to 3.58, p < 0.001, I2 = 73%), the standard deviation of blood glucose (SDBG, OR: 2.31, 95% CI 1.70 to 3.13, p < 0.001, I2 = 50%), and the mean amplitude of glycemic excursion (OR: 3.57, 95% CI 1.44 to 8.85, p = 0.006, I2 = 23%). For acute GV measured with CVBG and SDBG, subgroup analyses showed consistent results in patients with acute ischemic and hemorrhagic stroke, and for studies reporting 28-day and 90-day all-cause mortality (p for subgroup analysis all > 0.05).

CONCLUSIONS

Higher acute GV may be an independent risk factor of early mortality in patients with acute stroke.

Acute glycemic variability and risk of mortality in patients with sepsis: a meta-analysis

BACKGROUND

Acute glycemic variability (GV) has been correlated with the severity of sepsis. The aim of the study was to evaluate the potential association between acute GV and mortality risk in patients with sepsis.

METHODS

Cohort studies comparing the risk of death within 3 months between septic patients with higher versus lower acute GV were retrieved by systematic search of Medline, Embase, Web of Science, Wanfang and CNKI databases. We used a random-effect model to pool the data by incorporating the between-study heterogeneity. Sensitivity analyses were performed to evaluate the stability of the findings.

RESULTS

Ten studies including 4296 patients were available for the meta-analysis. Pooled results showed that septic patients with higher acute GV had significantly increased mortality risk compared to those with lower acute GV, as evidenced by results using different parameters including standard deviation of blood glucose (SDBG, risk ratio [RR]: 1.74, 95% confidence interval [CI] 1.36-2.24, p < 0.001; I2 = 0%), coefficient of variation of blood glucose (RR: 1.91, 95% CI 1.57-2.31, p < 0.001; I2 = 0%), mean amplitude of glycemic excursion (RR: 1.81. 95% CI 1.36-2.40, p < 0.001; I2 = 0%), and glycemic lability index (RR: 2.52, 95% CI 1.72-3.68, p < 0.001; I2 = 0%). Sensitivity analyses by excluding one study at a time did not significantly affect the results (p all < 0.05).

CONCLUSIONS

Higher acute GV may be a predictor of mortality risk in patients with sepsis.

Increased glycated albumin levels in patients with acromegaly related to glucose fluctuation caused by growth hormone excess but not albumin metabolism

Acromegaly is often complicated by impaired glucose tolerance. The accuracy of glycated hemoglobin (HbA_1c) and glycated albumin (GA) levels in representing glycemic profiles in patients with endocrine disorders, such as acromegaly, is unclear. This retrospective study reviewed data from patients whose GA levels had been recorded. 14 patients with acromegaly without diabetes mellitus (DM) (the acromegaly group), 15 patients with severe adult GH deficiency without DM (the growth hormone deficiency (GHD) group), and 55 nondiabetic patients (the control group) were included in this study. GA levels were significantly increased in the acromegaly group compared with the control and GHD groups, but no significant differences were observed between the control and GHD groups. The three groups were matched using propensity score matching (13 patients with acromegaly, 13 with GHD, and 13 control patients). Nonetheless, the results after matching were the same as those before matching. GA levels in the acromegaly group were significantly associated with plasma glucose (PG) levels at 0, 30, and 120 min after a 75-g oral glucose tolerance test (OGTT). Further, GH levels at 120 min after a 75-g OGTT in the acromegaly group were significantly correlated with GA levels and the difference in PG levels at baseline and 30 min. Our findings suggest that increases in PG levels attributable to excess GH after glucose loading are related to increases in GA levels in patients with acromegaly without DM. Hence, both HbA_1c and GA should be checked to accurately assess impaired glucose tolerance in patients with acromegaly.

Impact of COVID-19 Lockdown on the Metabolic Control Parameters in Patients with Diabetes Mellitus: A Systematic Review and Meta-Analysis

BACKGROUND

Abrupt implementation of lockdowns during the coronavirus disease 2019 (COVID-19) pandemic affected the management of diabetes mellitus in patients worldwide. Limited access to health facilities and lifestyle changes potentially affected metabolic parameters in patients at risk. We conducted a meta-analysis to determine any differences in the control of metabolic parameters in patients with diabetes, before and during lockdown.

METHODS

We performed searches of five databases. Meta-analyses were carried out using random- or fixed-effect approaches to glycaemic control parameters as the primary outcome: glycosylated hemoglobin (HbA1c), random blood glucose (RBG), fasting blood glucose (FBG), time-in-range (TIR), time-above-range (TAR), time-below-range (TBR). Mean difference (MD), confidence interval (CI), and P value were calculated. Lipid profile was a secondary outcome and is presented as a descriptive analysis.

RESULTS

Twenty-one studies enrolling a total of 3,992 patients with type 1 or type 2 diabetes mellitus (T1DM or T2DM) were included in the study. Patients with T1DM showed a significant improvement of TIR and TAR (MD=3.52% [95% CI, 0.29 to 6.74], I2=76%, P=0.03; MD=-3.36% [95% CI, -6.48 to -0.25], I2=75%, P=0.03), while FBG among patients with T2DM significantly worsened (MD=3.47 mg/dL [95% CI, 1.22 to 5.73], I2=0%, P<0.01). No significant difference was found in HbA1c, RBG, and TBR. Use of continuous glucose monitoring in T1DM facilitated good glycaemic control. Significant deterioration of lipid parameters during lockdown, particularly triglyceride, was observed.

CONCLUSION

Implementation of lockdowns during the COVID-19 pandemic did not worsen glycaemic control in patients with diabetes. Other metabolic parameters improved during lockdown, though lipid parameters, particularly triglyceride, worsened.

Inhibition of SGLT1 Alleviates the Glycemic Variability-Induced Cardiac Fibrosis via Inhibition of Activation of Macrophage and Cardiac Fibroblasts

Glycemic variability has been considered one of the predictors of diabetes complications in patients with diabetes mellitus (DM). In this work, we evaluated whether glycemic variability induces cardiac fibrosis through regulating cardiac fibroblast activation and macrophage polarization. Moreover, we determined whether glucose transporter sodium-glucose cotransporter 1 (SGLT1) plays an important role in this process. Glycemic variability-induced mice were established using DM mice (GVDM mice), and intermittent high-glucose (IHG) treatment was used to simulate glycemic variability in RAW264.7 macrophages and cardiac fibroblasts. The short hairpin RNA for SGLT1 was used to knock down SGLT1. The results showed that glycemic variability aggravated the cardiac fibrosis in GVDM mice. Additionally, glycemic variability promoted the expression of fibrogenic cytokine and the extracellular matrix proteins in left ventricular tissues and cardiac fibroblasts. GVDM mice showed a higher incidence of macrophage infiltration and M1 polarization in left ventricular tissues. Moreover, IHG-promoted RAW264.7 macrophages tended to differentiate to M1 phenotype. SGLT1 knockdown alleviated cardiac fibrosis in GVDM mice and inhibited activations of cardiac fibroblast and macrophage M1 polarization. Our results indicated that glycemic variability aggravates cardiac fibrosis through activating cardiac fibroblast and macrophage M1 polarization, which could be partially inhibited by SGLT1 knockdown.

Risk for Imbalanced Blood Glucose Pattern: Construct Analysis and Nursing Diagnosis Proposal

To identify a clinical judgment of susceptibility referring to the development of glycemic fluctuations in adults with Diabetes Mellitus undergoing treatment. Theoretical study with construct analysis. The exploration of the phenomena of glycemic variations provided clues for the description of the blood glucose pattern construct and the proposal of a new risk Nursing diagnosis as a judgment of susceptibility to the balance of this pattern. The risk factors for "Risk for Imbalanced Blood Glucose Pattern" are cognitive dysfunction; excessive alcohol consumption; excessive daily exercise; inadequate follow through with treatment regimen; increased frequency of self-monitoring of blood glucose; inadequate knowledge of disease process; inadequate management of amount of food; inadequate regularity of meal consumption; obesity; overweight; smoking; underweight. The elaboration of "Risk for Imbalanced Blood Glucose Pattern" Nursing diagnosis contributes to the advancement in the Nursing classifications and to the elaboration of planning actions and specific interventions.

Time-in-range and frequency of continuous glucose monitoring: Recommendations for South Asia

BACKGROUND AND AIM

The prevalence of diabetes is on its rise and South Asia bears a huge burden. Several factors such as heterogeneity in genetics, socio-economic factors, diet, and sedentary behavior contribute to the heightened risk of developing diabetes, its rapid progression, and the development of complications in this region. Even though there have been considerable advances in glucose monitoring technologies, diabetes treatments and therapeutics, glycemic control in South Asia remains suboptimal. The successful implementation of treatment interventions and metrics for the attainment of glycemic goals depends on appropriate guidelines that accord with the characteristics of the diabetes population.

METHOD

The data were collected from studies published for more than the last ten years in the electronic databases PubMed and Google Scholar on the various challenges in the assessment and achievement of recommended TIR targets in the SA population using the keywords: Blood glucose, TIR, TAR, TBR, HbA1c, hypoglycemia, CGM, Gestational diabetes mellitus (GDM), and diabetes.

RESULTS

The objective of this recommendation is to discuss the limitations in considering the IC-TIR Expert panel recommendations targets and to propose some modifications in the lower limit of TIR in older/high-risk population, upper limit of TAR, and flexibility in the percentage of time spent in TAR for pregnant women (GDM, T2DM) for the South Asian population.

CONCLUSION

The review sheds insights into some of the major concerns in implementing the IC-TIR recommendations in South Asian population where the prevalence of diabetes and its complications are significantly higher and modifications to the existing guidelines for use in routine clinical practice.

Glycemic Variability in Early Pregnancy May Predict a Subsequent Diagnosis of Gestational Diabetes

PURPOSE

This study examined the prospective association between CGM-derived glycemic variability (GV) and glycemic control (GC) parameters in the first and second trimester, with subsequent diagnosis of GDM in the early third trimester.

METHODS

In a longitudinal observational study, 60 study participants in the first trimester (9-13 weeks' gestation), and 53 participants (18-23 weeks' gestation) in the second trimester of pregnancy had CGM data extracted after a minimum of 8 days' wear time (up to 14 days). At 24-31 weeks' gestation, participants underwent a 75 g, 2-hour oral glucose-tolerance test as per IADPSG criteria to diagnose GDM. GV parameters examined in both first and second trimesters were mean amplitude of glycemic excursion (MAGE), standard deviation (SD), mean glucose, and coefficient of variation (CV). GC parameters measured were J-Index and percentage of time spent in glucose target ranges.

RESULTS

The first trimester SD and MAGE were significantly higher in participants subsequently diagnosed with GDM (SD adjusted median 1.31 [interquartile range 1.2-1.3] mmol/L; MAGE 3.26 [3.2-3.3] mmol/L) than those who were not (SD 1.01 [0.9-1.0] mmol/L, MAGE 2.59 [2.4-2.6] mmol/L; p<0.05). Similarly, second trimester SD and MAGE were also significantly higher in participants subsequently diagnosed with GDM (SD 1.35 [1.3-1.4] mmol/L; MAGE 3.32 (3.31-3.41) mmol/L) than those who were not (SD 0.99 [0.98-1.01] mmol/L, MAGE 2.42 [2.42-2.55] mmol/L; p<0.05). Associations between SD and MAGE with GDM outcomes were adjusted for prepregnancy BMI and ethnicity. There were nonsignificant trends of higher J-Index scores in the first and second trimester, higher CV in the first trimester only, and higher mean in the second trimester only in participants diagnosed with GDM. Other study parameters measured were not significantly different between groups (p>0.003).

CONCLUSION

Our study suggests the potential value of CGM-derived SD and MAGE in early pregnancy as potential predictors of subsequent GDM diagnosis.

Association Between Long-Term HbA1c Variability and Functional Limitation in Individuals Aged Over 50 Years: A Retrospective Cohort Study

BACKGROUND

As mean HbA1c provides incomplete information regarding glycemic variability, there has been considerable interest in the emerging association between glycemic variability and macrovascular events and with microvascular complications and mortality in adults with and without diabetes. However, the association between long-term glycemic variability, represented by visit-to-visit HbA1c variability, and functional limitations has not been clarified in previous literature. The present study aimed to explore the longitudinal association between long-term glycemic variability, represented by visit-to-visit HbA1c variability and functional limitations.

METHODS

This cohort study included adults aged over 50 years who participated in the 2006 to 2016 waves of the Health and Retirement Study. Physical functions, including mobility, large muscle function, activities of daily living (ADLs), and instrumental ADLs (IADLs), were assessed at baseline and every 2 years, and HbA1c levels were assessed at baseline and every 4 years. Visit-to-visit HbA1c variability was calculated using the HbA1c variability score (HVS) during the follow-up period. Generalized estimating equation models were used to evaluate the longitudinal association between HbA1c variability and functional limitations with adjustment for a series of confounders.

RESULTS

A total of 5,544 participants having three HbA1c measurements from 2006 to 2016, having two or more physical function measures (including one at baseline), and age over 50 years were included in this analysis. The mean age at baseline was 66.13 ± 8.39 years. A total of 916 (16.5%) participants had an HVS = 100, and 35.1% had an HVS = 50. The highest HVS category (HVS =100) was associated with increased functional status score (β = 0.093, 95% CI: 0.021-0.165) in comparison with the lowest HVS category (HVS = 0). Sensitivity analyses using the CV and SD of HbA1c as measures of variability showed similar associations between HbA1c variability and functional limitation. An incremental increase in HbA1c-CV (β = 0.630, 95% CI: 0.127-1.132) or HbA1c-SD (β = 0.078, 95% CI: 0.006-0.150) was associated with an increase in functional limitation in the fully adjusted model.

CONCLUSIONS

HbA1c variability was associated with heightened difficulty in performing functional activities over time after adjusting for mean HbA1c levels and multiple demographics and comorbidities. This study provides further evidence regarding the detrimental effect of HbA1c variability and highlights the significance of steady glycemic control.

Perspectives of glycemic variability in diabetic neuropathy: a comprehensive review

Diabetic neuropathy is one of the most prevalent chronic complications of diabetes, and up to half of diabetic patients will develop diabetic neuropathy during their disease course. Notably, emerging evidence suggests that glycemic variability is associated with the pathogenesis of diabetic complications and has emerged as a possible independent risk factor for diabetic neuropathy. In this review, we describe the commonly used metrics for evaluating glycemic variability in clinical practice and summarize the role and related mechanisms of glycemic variability in diabetic neuropathy, including cardiovascular autonomic neuropathy, diabetic peripheral neuropathy and cognitive impairment. In addition, we also address the potential pharmacological and non-pharmacological treatment methods for diabetic neuropathy, aiming to provide ideas for the treatment of diabetic neuropathy.

Zhang et al. describe metrics for evaluating glycaemic variability (GV) in clinical practice and summarize the role and related mechanisms of GV in diabetic neuropathy, including cardiovascular autonomic neuropathy, diabetic peripheral neuropathy and cognitive impairment. They aim to stimulate ideas for the treatment of diabetic neuropathy.

Glucose variability predicts 6-month mortality in patients hospitalized with acute heart failure

In diabetes mellitus (DM), glycaemic fluctuations associate with higher oxidative stress than sustained chronic hyperglycaemia and glucose variability increases the risk of chronic diabetic complications. Our hypothesis was that higher glucose variability would associate with mortality after an acute heart failure (HF) episode. We retrospectively analysed patients with DM hospitalized with acute HF between 2009 and 2010. Patients with < 2 point-of-care glucose values/day were excluded. Glucose coefficient of variation (GCV) was defined as (glucose standard deviation/mean glucose) × 100. Patients were categorized according GCV ≤ 30.0 and > 30.0%. Follow-up: 6-months. Endpoint: all-cause mortality. A Cox-regression analysis was used to study the association of glucose variability with 6-month mortality. We studied 214 diabetic patients with acute HF, 49.1% male, mean age 76 years. Mean glycaemia during hospitalization was 187 ± 50 mg/dL, hypoglycaemia (< 70 mg/dL) was reported in 21 patients and mean GCV was 28.3 ± 7.6%. Patients with GCV > 30.0% had higher mean glycaemia, more hypoglycaemic episodes and higher HbA1c; they were also more often treated with insulin. Patients were similar concerning age, gender, comorbidities, left ventricular systolic dysfunction and ischemic heart disease. During the 6-month follow-up, 38 (17.8%) patients died. Patients with GCV > 30.0% had a HR of 6-month mortality of 2.21 (95% CI: 1.16-4.21), p = 0.02. This association with more than twofold higher short-term mortality was independent of main confounders. Elevated glycaemic variability in acute HF admissions of patients with DM predicts short-term mortality. Patients with GCV > 30.0% have an independent more than twofold higher risk of 6-month death after an acute HF hospitalization.