Glucose variability: where it is important and how to measure it

Evaluation of folliculogenesis and oxidative stress parameters in type 1 diabetes mellitus women with different glycemic profiles

BACKGROUND

Despite enormous advances in diabetes treatment, women with type 1 diabetes mellitus (DM) still experience delayed menarche, menstrual irregularities, fewer pregnancies, and a higher rate of stillbirths compared to women without the disease. Due to the fact that type 1 DM occurs at a young age, the preservation of reproductive health is one of the most important goals of treatment.

AIMS

The aim of this study was to evaluate the relationship between different glycemic profiles and changes in the pro-oxidant-antioxidant balance and ovarian follicular apparatus in reproductive-age patients with type 1 DM.

METHODS

We examined 50 reproductive-age (19-38 years) women with type 1 DM with a disease duration of at least ten years. Carbohydrate metabolism was assessed with the continuous glucose monitoring (CGM) system and glycated hemoglobin (HbA1c) concentration measurement. CGM was performed using the FreeStyle Libre flash glucose monitoring system (Abbott Diabetes Care, Witney, UK). In each patient, malondialdehyde level, catalase activity and 3-nitrotyrosine level in the blood serum were determined. To assess the ovarian function, we measured the ovarian volume, the antral follicle count, and the serum levels of anti-Müllerian hormone and follicle-stimulating hormone. All patients were divided into four groups (glucotypes) based on the CGM results. Group 1 included type 1 DM patients with satisfactory compensation of carbohydrate metabolism; group 2 consisted of patients with frequent hypoglycemic conditions and pathological glucose variability; group 3 included individuals with prolonged hyperglycemic conditions and maximum HbA1c levels; and group 4 comprised patients with the glycemic profile characterized by all the presented types of dysglycemia (intermittent glycemia).

RESULTS

We revealed a negative correlation between serum catalase activity and time of hypoglycemic conditions in patients with type 1 DM based on the CGM results (rs = -0.47, p < 0.01). In group 4 (intermittent glycemia), patients demonstrated the lowest serum catalase activity and increased serum 3-nitrotyrosine level, while in group 3, women with chronic hyperglycemia (HbA1c 8.4 [8.1; 9.9]%; 68 [65; 85] mmol/mol) had a moderate change in antioxidant defense and oxidative stress parameters. Correlation analysis of ovarian volume, the antral follicle count, and the serum anti-Müllerian hormone level in type 1 DM women with different glycemic profiles established a negative relationship (rs = -0.82, p < 0.05) between the antral follicle count and glucose variability in group 1, a positive relationship (rs = 0.68, p < 0.05) between ovarian volume and glucose variability in group 2, and a positive relationship (rs = 0.88, p < 0.05) between ovarian volume and time of hypoglycemic conditions, which, according to the CGM results, amounted to a critical value of 57.5 [40.0; 82.0]%.

CONCLUSIONS

The data obtained indicate the relationship between the ovarian volume, serum anti-Müllerian hormone level, the antral follicle count and oxidative stress parameters not only in patients with hyperglycemia, but also in those with hypoglycemic conditions, as well as with pathological glucose variability.

Continuous glucose monitoring versus conventional glucose monitoring in the ICU: A randomized controlled trial

PURPOSE

This study evaluated the clinical utility of continuous glucose monitoring system (CGMS) in critically ill patients.

METHODS

In this randomized controlled trial, we randomly assigned critically ill participants with diabetes or stress-induced hyperglycemia to the CGMS group (n = 48) or to the conventional point-of-care monitoring (POCM) group (n = 48). The glucose values and clinical outcome were compared between the two group. The primary endpoint was 28-day mortality after intensive care unit admission.

RESULTS

The 28-day mortality was not significantly different between the CGMS and POCM group (20.8% vs 31.3%, P = 0.25). The mean glucose, time-weighted average glucose, glucose standard deviation and time in range (3.9-10.0) were significantly improved in the CGMS group (all P < 0.05).

CONCLUSION

Compared with conventional POCM, CGMS did not decrease the 28-day mortality in critically ill participants with diabetes or stress-induced hyperglycemia. But CGMS may improve the glycemic control and may be increasingly used in critically ill patients.

Potential Benefits of Continuous Glucose Monitoring for Predicting Vascular Outcomes in Type 2 Diabetes: A Rapid Review of Primary Research

(1) Background: Chronic hyperglycaemia is a cause of vascular damage and other adverse clinical outcomes in type 2 diabetes mellitus (T2DM). Emerging evidence suggests a significant and independent role for glycaemic variability (GV) in contributing to those outcomes. Continuous glucose monitoring (CGM) provides valuable insights into GV. Unlike in type 1 diabetes mellitus, the use of CGM-derived GV indices has not been widely adopted in the management of T2DM due to the limited evidence of their effectiveness in predicting clinical outcomes. This study aimed to explore the associations between GV metrics and short- or long-term vascular and clinical complications in T2DM. (2) Methods: A rapid literature review was conducted using the Cochrane Library, MEDLINE, and Scopus databases to seek high-level evidence. Lower-quality studies such as cross-sectional studies were excluded, but their content was reviewed. (3) Results: Six studies (five prospective cohort studies and one clinical trial) reported associations between GV indices (coefficient of variation (CV), standard deviation (SD), Mean Amplitude of Glycaemic Excursions (MAGE), Time in Range (TIR), Time Above Range (TAR), and Time Below Range (TBR)), and clinical complications. However, since most evidence came from moderate to low-quality studies, the results should be interpreted with caution. (4) Conclusions: Limited but significant evidence suggests that GV indices may predict clinical compilations in T2DM both in the short term and long term. There is a need for longitudinal studies in larger and more diverse populations, longer follow-ups, and the use of numerous CGM-derived GV indices while collecting information about all microvascular and macrovascular complications.

Postprandial glucose variability and clusters of sex hormones, liver enzymes, and cardiometabolic factors in a South African cohort of African ancestry

INTRODUCTION

This study aimed to, first, determine the clusters of sex hormones, liver enzymes, and cardiometabolic factors associated with postprandial glucose (PPG) and, second to evaluate the variation these clusters account for jointly and independently with polygenic risk scores (PRSs) in South Africans of African ancestry men and women.

RESEARCH DESIGN AND METHODS

PPG was calculated as the integrated area under the curve for glucose during the oral glucose tolerance test (OGTT) using the trapezoidal rule in 794 participants from the Middle-aged Soweto Cohort. Principal component analysis was used to cluster sex hormones, liver enzymes, and cardiometabolic factors, stratified by sex. Multivariable linear regression was used to assess the proportion of variance in PPG accounted for by principal components (PCs) and type 2 diabetes (T2D) PRS while adjusting for selected covariates in men and women.

RESULTS

The T2D PRS did not contribute to the PPG variability in both men and women. In men, the PCs' cluster of sex hormones, liver enzymes, and cardiometabolic explained 10.6% of the variance in PPG, with PC1 (peripheral fat), PC2 (liver enzymes and steroid hormones), and PC3 (lipids and peripheral fat) contributing significantly to PPG. In women, PC factors of sex hormones, cardiometabolic factors, and liver enzymes explained a similar amount of the variance in PPG (10.8%), with PC1 (central fat) and PC2 (lipids and liver enzymes) contributing significantly to PPG.

CONCLUSIONS

We demonstrated that inter-individual differences in PPG responses to an OGTT may be differentially explained by body fat distribution, serum lipids, liver enzymes, and steroid hormones in men and women.

Factors that determine glucose variability, defined by the coefficient of variation in continuous glucose monitoring values, in a Chinese population with type 2 diabetes

AIMS

To elucidate the clinical determinants of the coefficient of variation (CV) of glucose by analysing the pancreatic β-cell function of subjects with type 2 diabetes mellitus (T2DM).

METHODS

A total of 716 Chinese subjects with T2DM were included. Continuous glucose monitoring (CGM) was used to assess blood glucose, and the CV was calculated. C-peptide concentration at 0, 0.5, 1, 2 and 3 hours (Cp0h, Cp0.5h, Cp1h, Cp2h and Cp3h, respectively) was measured after a standard 100-g steamed bun meal test to assess pancreatic β-cell function. The determinants of glucose variability defined by the CV of CGM values were explored from two perspectives: the CV of qualitative variables and the CV of quantitative variables.

RESULTS

Our data revealed that C-peptide concentration (Cp0h, Cp0.5h, Cp1h, Cp2h, Cp3h), area under the curve for C-peptide concentration at 0.5 and 3 hours (AUC-Cp0.5h and AUC-Cp3h) decreased with increasing CV quartile (P < 0.05). The CV was negatively correlated with homeostatic model assessment of β-cell function index, C-peptide concentration at all timepoints, and AUC-Cp0.5h and AUC-Cp3h (P < 0.001). Quantile regression analysis showed that AUC-Cp0.5h had an overall negative effect on the CV in the 0.05 to 0.95 quartiles, and AUC-Cp3h tended to have a negative effect on the CV in the 0.2 to 0.65 quartiles. After adjusting for confounders, multinomial logistic regression showed that each 1-unit increase in AUC-Cp0.5h was associated with a 31.7% reduction in the risk of unstable glucose homeostasis (CV > 36%; P = 0.036; odds ratio 0.683; 95% confidence interval 0.478-0.976). We also identified the AUC-Cp0.5h (0.735 ng/mL) and AUC-Cp3h (13.355 ng/mL) cut-off values for predicting unstable glucose homeostasis (CV >36%) in T2DM subjects.

CONCLUSION

Our study suggests that impaired pancreatic β-cell function may be a clinical determining factor of CV of glucose in people with T2DM.

Association of Glycemic Variability With Imaging Markers of Vascular Burden, β-Amyloid, Brain Atrophy, and Cognitive Impairment

BACKGROUND AND OBJECTIVE

We aimed to investigate the association between glycemic variability (GV) and neuroimaging markers of white matter hyperintensities (WMH), beta-amyloid (Aβ), brain atrophy, and cognitive impairment.

METHODS

This was a retrospective cohort study that included participants without dementia from a memory clinic. They all had Aβ PET, brain MRI, and standardized neuropsychological tests and had fasting glucose (FG) levels tested more than twice during the study period. We defined GV as the intraindividual visit-to-visit variability in FG levels. Multivariable linear regression and logistic regression were used to identify whether GV was associated with the presence of severe WMH and Aβ uptake with DM, mean FG levels, age, sex, hypertension, and presence of APOE4 allele as covariates. Mediation analyses were used to investigate the mediating effect of WMH and Aβ uptake on the relationship between GV and brain atrophy and cognition.

RESULTS

Among the 688 participants, the mean age was 72.2 years, and the proportion of female participants was 51.9%. Increase in GV was predictive of the presence of severe WMH (coefficient [95% CI] 1.032 [1.012-1.054]; p = 0.002) and increased Aβ uptake (1.005 [1.001-1.008]; p = 0.007). Both WMH and increased Aβ uptake partially mediated the relationship between GV and frontal-executive dysfunction (GV → WMH → frontal-executive; direct effect, -0.319 [-0.557 to -0.080]; indirect effect, -0.050 [-0.091 to -0.008]) and memory dysfunction (GV → Aβ → memory; direct effect, -0.182 [-0.338 to -0.026]; indirect effect, -0.067 [-0.119 to -0.015]), respectively. In addition, increased Aβ uptake completely mediated the relationship between GV and hippocampal volume (indirect effect, -1.091 [-2.078 to -0.103]) and partially mediated the relationship between GV and parietal thickness (direct effect, -0.00101 [-0.00185 to -0.00016]; indirect effect, -0.00016 [-0.00032 to -0.000002]).

DISCUSSION

Our findings suggest that increased GV is related to vascular and Alzheimer risk factors and neurodegenerative markers, which in turn leads to subsequent cognitive impairment. Furthermore, GV can be considered a potentially modifiable risk factor for dementia prevention.

Glycemic variability: Measurement, target, impact on complications of diabetes and does it really matter?

Over the past two decades, there has been continuous advancement in the accuracy and complexity of continuous glucose monitoring devices. Continuous glucose monitoring provides valuable insights into blood glucose dynamics, and can record glucose fluctuations accurately and completely. Glycemic variability (GV) is a straightforward measure of the extent to which a patient's blood glucose levels fluctuate between high peaks and low nadirs. Many studies have investigated the relationship between GV and complications, primarily in the context of type 2 diabetes. Nevertheless, the exact contribution of GV to the development of diabetes complications remains unclear. In this literature review, we aimed to summarize the existing evidence regarding the measurement, target level, pathophysiological mechanisms relating GV and tissue damage, and population-based studies of GV and diabetes complications. Additionally, we introduce novel methods for measuring GV, and discuss several unresolved issues of GV. In the future, more longitudinal studies and trials are required to confirm the exact role of GV in the development of diabetes complications.

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.

Key indices of glycaemic variability for application in diabetes clinical practice

Near normal glycaemic control in diabetes consists to target daily glucose fluctuations and quarterly HbA1c oscillations in addition to overall glucose exposure. Consequently, the prerequisite is to define simple, and mathematically undisputable key metrics for the short- and long-term variability in glucose homeostasis. As the standard deviations (SD) of either glucose or HbA1c are dependent on their means, the coefficient of variation (CV = SD/mean) should be applied instead as it that avoids the correlation between the SD and mean values. A CV glucose of 36% is the most appropriate threshold between those with stable versus labile glucose homeostasis. However, when near normal mean glucose concentrations are achieved a lower CV threshold of <27 % is necessary for reducing the risk for hypoglycaemia to a minimal rate. For the long-term variability in glucose homeostasis, a CVHbA1c < 5 % seems to be a relevant recommendation for preventing adverse clinical outcomes.

Glycemic Variability in Patients with Type 2 Diabetes Mellitus (T2DM): The Role of Melatonin in a Crossover, Double-Blind, Placebo-Controlled, Randomized Study

BACKGROUND

Glycemic variability in patients with type 2 diabetes mellitus (T2DM) may be associated with chronic complications of the disease. Melatonin is a hormone that plays a crucial role in biological rhythms. Previous studies have indicated that individuals with T2DM often exhibit reduced melatonin production. In this study, our objective was to investigate whether nighttime melatonin supplementation could mitigate glycemic variability in these patients.

METHODS

Crossover, double-blind, placebo-controlled, randomized study. A total of 30 patients were enrolled in this study. The study included 15 participants who followed the intervention sequence of placebo (7 days)-washout (7 days)-melatonin (3 mg) (7 days), and another 15 participants who followed the sequence of melatonin (3 mg) (7 days)-washout (7 days)-placebo (7 days). During the final three days of the first and third weeks, the participants measured their pre- and postprandial capillary blood glucose levels. This study was reported according to the CONSORT 2010 statement: extension to randomized crossover trials.

RESULTS

There was a significant absolute difference in the breakfast blood glucose levels (p = 0.016) on Day 7. The use of melatonin determined a greater positive variation between pre- and postprandial glycemia than the placebo. The difference in glycemic amplitude between post-dinner Day 6 and pre-breakfast Day 7 was also significantly higher in the melatonin group (p = 0.032).

CONCLUSIONS

Melatonin increased glycemic variability in individuals with type 2 diabetes mellitus (T2DM). These results can be attributed to the residual daytime effects of melatonin, prospective proximal effects, and damage to the prospective distal effects of exogenous melatonin. Therefore, caution should be exercised when administering melatonin supplementation to patients with T2DM, taking into consideration factors such as dosage, duration of use and genetic considerations.

A study of factors influencing long-term glycemic variability in patients with type 2 diabetes: a structural equation modeling approach

Aim

The present study aims to utilize structural equation modeling (SEM) to investigate the factors impacting long-term glycemic variability among patients afflicted with type 2 diabetes.

Method

The present investigation is a retrospective cohort study that involved the collection of data on patients with type 2 diabetes mellitus who received care at a hospital located in Chengdu, Sichuan Province, over a period spanning from January 1, 2013, to October 30, 2022. Inclusion criteria required patients to have had at least three laboratory test results available. Pertinent patient-related information encompassing general demographic characteristics and biochemical indicators was gathered. Variability in the dataset was defined by standard deviation (SD) and coefficient of variation (CV), with glycosylated hemoglobin variation also considering variability score (HVS). Linear regression analysis was employed to establish the structural equation models for statistically significant influences on long-term glycemic variability. Structural equation modeling was employed to analyze effects and pathways.

Results

Diabetes outpatient special disease management, uric acid variability, mean triglyceride levels, mean total cholesterol levels, total cholesterol variability, LDL variability, baseline glycated hemoglobin, and recent glycated hemoglobin were identified as significant factors influencing long-term glycemic variability. The overall fit of the structural equation model was found to be satisfactory and it was able to capture the relationship between outpatient special disease management, biochemical indicators, and glycated hemoglobin variability. According to the total effect statistics, baseline glycated hemoglobin and total cholesterol levels exhibited the strongest impact on glycated hemoglobin variability.

Conclusion

The factors that have a significant impact on the variation of glycosylated hemoglobin include glycosylated hemoglobin itself, lipids, uric acid, and outpatient special disease management for diabetes. The identification and management of these associated factors can potentially mitigate long-term glycemic variability, thereby delaying the onset of complications and enhancing patients' quality of life.

Insulin therapy and blood glucose management in critically ill patients: a 1-day cross-sectional observational study in 69 French intensive care units

BACKGROUND

Hyperglycaemia is common in critically ill patients, but blood glucose and insulin management may differ widely among intensive care units (ICUs). We aimed to describe insulin use practices and the resulting glycaemic control in French ICUs. We conducted a multicentre 1-day observational study on November 23, 2021, in 69 French ICUs. Adult patients hospitalized for an acute organ failure, severe infection or post-operative care were included. Data were recorded from midnight to 11:59 p.m. the day of the study by 4-h periods.

RESULTS

Two ICUs declared to have no insulin protocol. There was a wide disparity in blood glucose targets between ICUs with 35 different target ranges recorded. In 893 included patients we collected 4823 blood glucose values whose distribution varied significantly across ICUs (P < 0.0001). We observed 1135 hyperglycaemias (> 1.8 g/L) in 402 (45.0%) patients, 35 hypoglycaemias (≤ 0.7 g/L) in 26 (2.9%) patients, and one instance of severe hypoglycaemia (≤ 0.4 g/L). Four hundred eight (45.7%) patients received either IV insulin (255 [62.5%]), subcutaneous (SC) insulin (126 [30.9%]), or both (27 [6.6%]). Among patients under protocolized intravenous (IV) insulin, 767/1681 (45.6%) of glycaemias were above the target range. Among patients receiving insulin, short- and long-acting SC insulin use were associated with higher counts of hyperglycaemias as assessed by multivariable negative binomial regression adjusted for the propensity to receive SC insulin: incidence rate ratio of 3.45 (95% confidence interval [CI] 2.97-4.00) (P < 0.0001) and 3.58 (95% CI 2.84-4.52) (P < 0.0001), respectively.

CONCLUSIONS

Practices regarding blood glucose management varied widely among French ICUs. Administration of short or long-acting SC insulin was not unusual and associated with more frequent hyperglycaemia. The protocolized insulin algorithms used failed to prevent hyperglycaemic events.

CGMap: Characterizing continuous glucose monitor data in thousands of non-diabetic individuals

Despite its rising prevalence, diabetes diagnosis still relies on measures from blood tests. Technological advances in continuous glucose monitoring (CGM) devices introduce a potential tool to expand our understanding of glucose control and variability in people with and without diabetes. Yet CGM data have not been characterized in large-scale healthy cohorts, creating a lack of reference for CGM data research. Here we present CGMap, a characterization of CGM data collected from over 7,000 non-diabetic individuals, aged 40-70 years, between 2019 and 2022. We provide reference values of key CGM-derived clinical measures that can serve as a tool for future CGM research. We further explored the relationship between CGM-derived measures and diabetes-related clinical parameters, uncovering several significant relationships, including associations of mean blood glucose with measures from fundus imaging and sleep monitoring. These findings offer novel research directions for understanding the influence of glucose levels on various aspects of human health.

The association between fasting plasma glucose variability and incident eGFR decline: evidence from two cohort studies

BACKGROUND

Glycemic variability (GV) is developing as a marker of glycemic control, which can be utilized as a promising predictor of complications. To determine whether long-term GV is associated with incident eGFR decline in two cohorts of Tehran Lipid and Glucose Study (TLGS) and Multi-Ethnic Study of Atherosclerosis (MESA) during a median follow-up of 12.2 years.

METHODS

Study participants included 4422 Iranian adults (including 528 patients with T2D) aged ≥ 20 years from TLGS and 4290 American adults (including 521 patients with T2D) aged ≥ 45 years from MESA. The Multivariate Cox proportional hazard models were used to assess the risk of incident eGFR decline for each of the fasting plasma glucose (FPG) variability measures including standard deviation (SD), coefficient of variation (CV), average real variability (ARV), and variability independent of the mean (VIM) both as continuous and categorical variables. The time of start for eGFR decline and FPG variability assessment was the same, but the event cases were excluded during the exposure period.

RESULTS

In TLGS participants without T2D, for each unit change in FPG variability measures, the hazards (HRs) and 95% confidence intervals (CI) for eGFR decline ≥ 40% of SD, CV, and VIM were 1.07(1.01-1.13), 1.06(1.01-1.11), and 1.07(1.01-1.13), respectively. Moreover, the third tertile of FPG-SD and FPG-VIM parameters was significantly associated with a 60 and 69% higher risk for eGFR decline ≥ 40%, respectively. In MESA participants with T2D, each unit change in FPG variability measures was significantly associated with a higher risk for eGFR decline ≥ 40%.Regarding eGFR decline ≥ 30% as the outcome, in the TLGS, regardless of diabetes status, no association was shown between FPG variability measures and risk of eGFR decline in any of the models; however, in the MESA the results were in line with those of GFR decline ≥ 40%.Using pooled data from the two cohorts we found that generally FPG variability were associated with higher risk of eGFR decline ≥ 40% only among non-T2D individuals.

CONCLUSIONS

Higher FPG variability was associated with an increased risk of eGFR decline in the diabetic American population; however, this unfavorable impact was found only among the non-diabetic Iranian population.

Software Packages and Tools for the Analysis of Continuous Glucose Monitoring Data

The advancement of technology in the field of glycemic control has led to the widespread use of continuous glucose monitoring (CGM), which can be nowadays obtained from wearable devices equipped with a minimally invasive sensor, that is, transcutaneous needle type or implantable, and a transmitter that sends information to a receiver or smart device for data storage and display. This work aims to review the currently available software packages and tools for the analysis of CGM data. Based on the purposes of this work, 12 software packages have been identified from the literature, published until December 2021, namely: GlyCulator, EasyGV (Easy Glycemic Variability), CGM-GUIDE^© (Continuous Glucose Monitoring Graphical User Interface for Diabetes Evaluation), GVAP (Glycemic Variability Analyzer Program), Tidepool, CGManalyzer, cgmanalysis, GLU, CGMStatsAnalyser, iglu, rGV, and cgmquantify. Comparison of available software packages and tools has been done in terms of main characteristics (i.e., publication year, presence of a graphical user interface, availability, open-source code, number of citations, programming language, supported devices, supported data format and organization of the data structure, documentation, presence of a toy example, video tutorial, data upload and download, measurement-units conversion), preprocessing procedures, data display options, and computed metrics; also, each of the computed metrics has been analyzed in terms of its adherence to the American Diabetes Association (ADA) 2017 international consensus on CGM data analysis and the ADA 2019 international consensus on time in range. Eventually, the agreement between metrics computed by different software and tools has been investigated. Based on such comparison, usability and complexity of data management, as well as the possibility to perform customized or patients-group analyses, have been discussed by highlighting limitations and strengths, also in relation to possible different user categories (i.e., patients, clinicians, researchers). The information provided could be useful to researchers interested in working in the diabetic research field as to clinicians and endocrinologists who need tools capable of handling CGM data effectively.

Comparison of glucose control by added liraglutide to only insulin infusion in diabetic patient undergoing cardiac surgery: A preliminary randomized-controlled trial

Background

Liraglutide, glucagon-like peptide-1 (GLP-1) receptor agonist, has been investigated for safety and effectiveness for blood glucose (BG) control in a surgical setting. However, there are only a few studies specific to cardiac surgery patients.

Aims

To primarily compare perioperative 1) BG and 2) glycemic variability (GV) between added liraglutide and only insulin infusion in diabetes mellitus (DM) patients undergoing cardiac surgery.

Setting and Design

A randomized control trial was conducted in DM patients undergoing cardiac surgery with cardiopulmonary bypass (CPB). Inclusion criteria were age 20-80 years and DM Type 2.

Material and Methods

: The recruited patients were randomly assigned to Group 1 (added liraglutide with insulin infusion) and Group 2 (insulin infusion). Insulin infusion was based on institutional protocol. Point of care testing (POCT) glucose was used for the adjustment of insulin and BG analysis. Continuous glucose monitor (CGM) was for GV analysis (using Standard deviation: SD).

Statistics

: t-test, Chi-square or Fisher-exact test, or Mann-Whitney U test.

Results

: Finally, 60 patients were in our study (Group 1 = 32 vs Group 2 = 28). Perioperative mean BG levels of Group 1 were significantly lower than Group 2 with a mean difference of 15.9 mg/dL. Nine patients (18.7% vs 10.7%, P = 0.384) had BG of 60-70 with mean BGs (109.1 vs 147.9, P = 0.001) in the morning. Thirteen patients (9.4% vs 35.7%, P = 0.025) had BG >180 mg/dL at the 1^st operative hour. SDs were increasing, but lower SD of Group 1 were observed at the postoperative period. Mean of SDs at postoperative day 2 were 23.65 vs 32.79 mg/dL, P = 0.018.

Conclusions

: Liraglutide added with insulin infusion can attenuate perioperative BG and is beneficial in the aspect of lowering GV together with BG at the postoperative period in DM patients. Liraglutide can be applied in cardiac surgery but a rearrangement of time and dosage should be further investigated.

WiSER: Robust and scalable estimation and inference of within-subject variances from intensive longitudinal data

The availability of vast amounts of longitudinal data from electronic health records (EHRs) and personal wearable devices opens the door to numerous new research questions. In many studies, individual variability of a longitudinal outcome is as important as the mean. Blood pressure fluctuations, glycemic variations, and mood swings are prime examples where it is critical to identify factors that affect the within-individual variability. We propose a scalable method, within-subject variance estimator by robust regression (WiSER), for the estimation and inference of the effects of both time-varying and time-invariant predictors on within-subject variance. It is robust against the misspecification of the conditional distribution of responses or the distribution of random effects. It shows similar performance as the correctly specified likelihood methods but is 103 ∼ 105 times faster. The estimation algorithm scales linearly in the total number of observations, making it applicable to massive longitudinal data sets. The effectiveness of WiSER is evaluated in extensive simulation studies. Its broad applicability is illustrated using the accelerometry data from the Women's Health Study and a clinical trial for longitudinal diabetes care.

Glycemic variability and all-cause mortality in a large prospective southern European cohort of patients with differences in glycemic status

Background

Few studies have analyzed the relationship between glucose variability (GV) and adverse health outcomes in patients with differences in glycemic status. The present study tests the hypothesis that GV predicts all-cause mortality regardless of glycemic status after simple adjustment (age and sex) and full adjustment (age, sex, cardiovascular disease, hypertension, use of aspirin, statins, GLP-1 receptor agonists, SGLT-2 inhibitors and DPP-4 inhibitors, baseline FPG and average HbA1c).

Methods

Prospective cohort study with 795 normoglycemic patients, 233 patients with prediabetes, and 4,102 patients with type 2 diabetes. GV was measured using the coefficient of variation of fasting plasma glucose (CV-FPG) over 12 years of follow-up. The outcome measure was all-cause mortality.

Results

A total of 1,223 patients (657 men, 566 women) died after a median of 9.8 years of follow-up, with an all-cause mortality rate of 23.35/1,000 person-years. In prediabetes or T2DM patients, the fourth quartile of CV-FPG exerted a significant effect on all-cause mortality after simple and full adjustment. A sensitivity analysis excluding participants who died during the first year of follow-up revealed the following results for the highest quartile in the fully adjusted model: overall, HR (95%CI) = 1.54 (1.26–1.89); dysglycemia (prediabetes and T2DM), HR = 1.41 (1.15–1.73); T2DM, HR = 1.36 (1.10–1.67).

Conclusion

We found CV-FPG to be useful for measurement of GV. It could also be used for the prognostic stratification of patients with dysglycemia.

Association Between Glycemic Gap and Mortality in Critically Ill Patients with Diabetes

OBJECTIVES

Dysglycemia is associated with poor outcomes in critically ill patients,which is uncertain in patients with diabetes regarding to the situation of glucose control before hospitalization. This study was aimed to investigate the effect of the difference between the level of blood glucose during ICU stay and before admission to ICU upon the outcomes of critically ill patients with diabetes.

METHOD

Patients with diabetes expected to stay for more than 24hs were enrolled, HbA1c was converted to A1C-derived average glucose (ADAG) by the equation: ADAG  =  [ (HbA1c * 28.7) - 46.7 ] * 18^-1, blood glucose were measured four times a day during the first 7 days after admission, the mean glucose level(MGL) and SOFA (within 3, 5, and 7days) were calculated for each person, GAP_adm and GAP_mean was calculated as admission blood glucose and MGL minus ADAG, the incidence of moderate hypoglycemia(MH), severe hypoglycemia (SH), total dosage of glucocorticoids and average daily dosage of insulin, duration of renal replacement therapy(RRT), ventilator-free hours, and non-ICU days were also collected. Patients were divided into survival group and nonsurvival group according to survival or not at 28-day, the relationship between GAP and mortality were analyzed.

RESULTS

431 patients were divided into survival group and nonsurvival group. The two groups had a comparable level of HbA1c, the nonsurvivors had greater APACHE II, SOFA, GAP_adm, GAP_mean-3, GAP_mean-5, GAP_mean-7 and higher MH and SH incidences. Less duration of ventilator-free, non-ICU stay and longer duration of RRT were recorded in the nonsurvival group. GAP_mean-5 had the greatest predictive power with an AUC of 0.807(95%CI: 0.762-0.851), the cut-off value was 3.6 mmol/L (sensitivity 77.7% and specificity 76.6%). The AUC was increased to 0.852(95%CI: 0.814-0.889) incorporated with SOFA₅ (NRI  =  11.34%).

CONCLUSION

Glycemic GAP between the MGL within 5 days and ADAG was independently associated with 28-day mortality of critically ill patients with diabetes. The predictive power was optimized with addition of SOFA₅.

Large-Scale Data Analysis for Glucose Variability Outcomes with Open-Source Automated Insulin Delivery Systems

Open-source automated insulin delivery (AID) technologies use the latest continuous glucose monitors (CGM), insulin pumps, and algorithms to automate insulin delivery for effective diabetes management. Early community-wide adoption of open-source AID, such as OpenAPS, has motivated clinical and research communities to understand and evaluate glucose-related outcomes of such user-driven innovation. Initial OpenAPS studies include retrospective studies assessing high-level outcomes of average glucose levels and HbA1c, without in-depth analysis of glucose variability (GV). The OpenAPS Data Commons dataset, donated to by open-source AID users with insulin-requiring diabetes, is the largest freely available diabetes-related dataset with over 46,070 days’ worth of data and over 10 million CGM data points, alongside insulin dosing and algorithmic decision data. This paper first reviews the development toward the latest open-source AID and the performance of clinically approved GV metrics. We evaluate the GV outcomes using large-scale data analytics for the n = 122 version of the OpenAPS Data Commons. We describe the data cleaning processes, methods for measuring GV, and the results of data analysis based on individual self-reported demographics. Furthermore, we highlight the lessons learned from the GV outcomes and the analysis of a rich and complex diabetes dataset and additional research questions that emerged from this work to guide future research. This paper affirms previous studies’ findings of the efficacy of open-source AID.

Hypoglycemic agents and glycemic variability in individuals with type 2 diabetes: A systematic review and network meta-analysis

While hemoglobin A1c (HbA1c) is commonly used to monitor therapy response in type 2 diabetes (T2D), GV is emerging as an essential additional metric for optimizing glycemic control. Our goal was to learn more about the impact of hypoglycemic agents on HbA1c levels and GV in patients with T2D. A systematic review and network meta-analysis (NMA) of randomized controlled trials were performed to assess the effects of glucagon-like peptide 1 receptor agonists (GLP-1 RAs), sodium-glucose cotransporter (SGLT)-2 inhibitors, dipeptidyl peptidase (DPP)-4 inhibitors, sulfonylurea and thiazolidinediones on Mean Amplitude of Glycemic Excursions (MAGE) and HbA1c. Searches were performed using PubMed and EMBASE. A random-effect model was used in the NMA, and the surface under the cumulative ranking was used to rank comparisons. All studies were checked for quality according to their design and also for heterogeneity before inclusion in this NMA. The highest reduction in MAGE was achieved by GLP-1 RAs (SUCRA 0.83), followed by DPP-4 inhibitors (SUCRA: 0.72), and thiazolidinediones (SUCRA: 0.69). In terms of HbA1c reduction, GLP-1 RAs were the most effective (SUCRA 0.81), followed by DPP-4 inhibitors (SUCRA 0.72) and sulfonylurea (SUCRA 0.65). Our findings indicated that GLP-1 RAs have relatively high efficacy in terms of HbA1c and MAGE reduction when compared with other hypoglycemic agents and can thus have clinical application. Future studies with a larger sample size and appropriate subgroup analyses are warranted to completely understand the glycemic effects of these agents in various patients with T2D. The protocol for this systematic review was registered with the International Prospective Register of Systematic Reviews (CRD42021256363).

The Reproducibility and Usefulness of Estimated Average Glucose for Hyperglycemia Management during Health Checkups: A Retrospective Cross-Sectional Study

HbA1c reflects average glucose levels over 3 months, but it does not measure glycemic variability. This study aimed to determine the reproducibility and usefulness of HbA1c-derived estimated average glucose (eAG) and to analyze the factors associated with eAG during health checkups. This cross-sectional retrospective study consecutively selected subjects who had undergone health checkups at 16 health-promotion centers in 13 Korean cities in 2020. The subjects comprised 182,848 healthy subjects with normoglycemia, 109,555 with impaired fasting glucose (IFG), and 35,632 with diabetes. eAG was calculated using Nathan’s regression equation. In all subjects, fasting plasma glucose (FPG) was found to be fairly strongly correlated with eAG (r = 0.811). When the subjects were divided into FPG subgroups, the strength of the correlation decreased among those with normoglycemia and IFG (p < 0.001). Higher eAG levels were associated with older age, females, higher FPG, and lower HDL-C and triglycerides (p < 0.05). The proportion of subjects with a higher value of FPG than eAG was 46.3% in poorly controlled diabetic patients, compared with only 1.5% in normoglycemic subjects. This suggests eAG could help patients to understand their glycemic variability intuitively and healthcare providers to identify patients who might worsen in hyperglycemia control through measuring the difference between eAG and FPG.

Glycaemia dynamics in gestational diabetes mellitus

Pregnant women may develop gestational diabetes mellitus (GDM), a disease of pregnancy characterised by maternal and fetal hyperglycaemia with hazardous consequences to the mother, the fetus, and the newborn. Maternal hyperglycaemia in GDM results in fetoplacental endothelial dysfunction. GDM-harmful effects result from chronic and short periods of hyperglycaemia. Thus, it is determinant to keep glycaemia within physiological ranges avoiding short but repetitive periods of hyper or hypoglycaemia. The variation of glycaemia over time is defined as 'glycaemia dynamics'. The latter concept regards with a variety of mechanisms and environmental conditions leading to blood glucose handling. In this review we summarized the different metrics for glycaemia dynamics derived from quantitative, plane distribution, amplitude, score values, variability estimation, and time series analysis. The potential application of the derived metrics from self-monitoring of blood glucose (SMBG) and continuous glucose monitoring (CGM) in the potential alterations of pregnancy outcome in GDM are discussed.

Glycaemic Variability and Hyperglycaemia as Prognostic Markers of Major Cardiovascular Events in Diabetic Patients Hospitalised in Cardiology Intensive Care Unit for Acute Heart Failure

(1) Background: Hyperglycaemia and hypoglycaemia are both emerging risk factors for cardiovascular disease. Nevertheless, the potential effect of glycaemic variability (GV) on mid-term major cardiovascular events (MACE) in diabetic patients presenting with acute heart failure (AHF) remains unclear. This study investigates the prognostic value of GV in diabetic patients presenting with acute heart failure (AHF). (2) Methods: this was an observational study including consecutive patients with diabetes and AHF between January 2015 and November 2016. GV was calculated using standard deviation of glycaemia values during initial hospitalisation in the intensive cardiac care unit. MACE, including recurrent AHF, new-onset myocardial infarction, ischaemic stroke and cardiac death, were recorded. The predictive effects of GV on patient outcomes were analysed with respect to baseline characteristics and cardiac status. (3) Results: In total, 392 patients with diabetes and AHF were enrolled. During follow-up (median (interquartile range) 29 (6−51) months), MACE occurred in 227 patients (57.9%). In total, 92 patients died of cardiac causes (23.5%), 107 were hospitalised for heart failure (27.3%), 19 had new-onset myocardial infarction (4.8%) and 9 (2.3%) had an ischaemic stroke. Multivariable logistic regression analysis showed that GV > 50 mg/dL (2.70 mmol/L), age > 75 years, reduced left ventricular ejection fraction (LVEF < 30%) and female gender were independent predictors of MACE: hazard ratios (HR) of 3.16 (2.25−4.43; p < 0.001), 1.54 (1.14−2.08; p = 0.005), 1.47 (1.06−2.07; p = 0.02) and 1.43 (1.05−1.94; p = 0.03), respectively. (4) Conclusions: among other well-known factors of HF, a GV cut-off value of >50 mg/dL was the strongest independent predictive factor for mid-term MACE in patients with diabetes and AHF.

The Gut Microbiome of Adults With Type 1 Diabetes and Its Association With the Host Glycemic Control

OBJECTIVE

Previous studies have demonstrated an association between gut microbiota composition and type 1 diabetes (T1D) pathogenesis. However, little is known about the composition and function of the gut microbiome in adults with longstanding T1D or its association with host glycemic control.

RESEARCH DESIGN AND METHODS

We performed a metagenomic analysis of the gut microbiome obtained from fecal samples of 74 adults with T1D, 14.6 ± 9.6 years following diagnosis, and compared their microbial composition and function to 296 age-matched healthy control subjects (1:4 ratio). We further analyzed the association between microbial taxa and indices of glycemic control derived from continuous glucose monitoring measurements and blood tests and constructed a prediction model that solely takes microbiome features as input to evaluate the discriminative power of microbial composition for distinguishing individuals with T1D from control subjects.

RESULTS

Adults with T1D had a distinct microbial signature that separated them from control subjects when using prediction algorithms on held-out subjects (area under the receiver operating characteristic curve = 0.89 ± 0.03). Linear discriminant analysis showed several bacterial species with significantly higher scores in T1D, including Prevotella copri and Eubacterium siraeum, and species with higher scores in control subjects, including Firmicutes bacterium and Faecalibacterium prausnitzii (P < 0.05, false discovery rate corrected for all). On the functional level, several metabolic pathways were significantly lower in adults with T1D. Several bacterial taxa and metabolic pathways were associated with the host's glycemic control.

CONCLUSIONS

We identified a distinct gut microbial signature in adults with longstanding T1D and associations between microbial taxa, metabolic pathways, and glycemic control indices. Additional mechanistic studies are needed to identify the role of these bacteria for potential therapeutic strategies.

Intraoperative Glycemic Variability and Mean Glucose are Predictors for Postoperative Delirium After Cardiac Surgery: A Retrospective Cohort Study

Purpose

Postoperative delirium (POD) is a common but serious complication after cardiac surgery and is associated with various short- and long-term outcomes. In this study, we investigated the effects of intraoperative glycemic variability (GV) and other glycemic variables on POD after cardiac surgery.

Patients and Methods

A retrospective single-center cohort analysis was conducted using data from electronic medical record from 2018 to 2020. A total of 705 patients undergoing coronary artery bypass graft surgery and/or valve surgery, and/or aortic replacement surgery were included in the analysis. Intraoperative GV was assessed with a coefficient of variation (CV), which was defined as the standard deviation of five intraoperative blood glucose measurements divided by the mean. POD assessment was performed three times a day in the ICU and twice a day in the ward until discharge by trained medical staff. POD was diagnosed if any of the Confusion Assessment Method for the Intensive Care Unit was positive in the ICU, and the Confusion Assessment Method was positive in the ward. Multivariable logistic regression was used to identify associations between intraoperative GV and POD.

Results

POD occurred in 306 (43.4%) patients. When intraoperative glycemic CV was compared as a continuous variable, the delirium group had higher intraoperative glycemic CV than the non-delirium group (22.59 [17.09, 29.68] vs 18.19 [13.00, 23.35], p < 0.001), and when intraoperative glycemic CV was classified as quartiles, the incidence of POD increased as intraoperative glycemic CV quartiles increased (first quartile 29.89%; second quartile 36.67%; third quartile 44.63%; and fourth quartile 62.64%, p < 0.001). In the multivariable logistic regression model, patients in the third quartile of intraoperative glycemic CV were 1.833 times (OR 1.833, 95% CI: 1.132–2.967, p = 0.014), and patients in the fourth quartile of intraoperative glycemic CV were 3.645 times (OR 3.645, 95% CI: 2.235–5.944, p < 0.001) more likely to develop POD than those in the first quartile of intraoperative glycemic CV.

Conclusion

Intraoperative blood glucose fluctuation, manifested by intraoperative GV, is associated with POD after cardiac surgery. Patients with a higher intraoperative GV have an increased risk of POD.

Influence of Fasting Plasma Glucose Targets on Glycemic Variability in Chinese Participants With Type 2 Diabetes: A Post Hoc Analysis of the FPG GOAL Trial (BEYOND III)

INTRODUCTION

This post hoc analysis examines the relationship between glycemic variability (GV) and fasting plasma glucose (FPG) targets used to achieve glycated hemoglobin (HbA1c) < 7%, and HbA1c levels after 24 weeks of treatment with insulin glargine and oral antidiabetic drugs (OADs) in Chinese participants with type 2 diabetes mellitus (T2DM) from the BEYOND III FPG GOAL trial (NCT02545842).

METHODS

Participants were randomized for three FBG targets (≤ 5.6 mmol/L, ≤ 6.1 mmol/L, and ≤ 7.0 mmol/L) receiving insulin glargine 100 U/mL were analyzed for mean change from baseline to 24 weeks in postprandial glucose (PPG) excursion and FPG coefficient of variation (FPG-CV). The study analyzed change from baseline in HbA1c and the proportion of participants who achieved HbA1c < 7% at 24 weeks, according to their baseline FPG-CV and change from baseline in PPG excursion.

RESULTS

The change in PPG excursion and FPG-CV from baseline to 24 weeks was not significantly different between the three groups stratified by randomization or by 24-week FPG levels. While the change in HbA1c from baseline to 24 weeks was slightly higher among participants with baseline FPG-CV < 33.3% (vs. > 66.7%; P = 0.023), a higher proportion of participants with baseline FPG-CV < 33.3% achieved HbA1c < 7% (P = 0.021).

CONCLUSIONS

GV was not associated with either target FPG levels or HbA1c < 7.0% after 24 weeks of treatment with insulin glargine and OADs.

TRIAL REGISTRATION

Clinicaltrials.gov identifier NCT02545842.

Insulin Degludec Versus Insulin Glargine on Glycemic Variability in Diabetic Patients: A Systematic Review and Meta-Analysis of Randomized Controlled Trials

BACKGROUND/AIMS

Currently, glycemic variability has more deleterious effects than sustained hyperglycemia and is closely associated with acute and chronic complications of diabetes. Reducing glycemic excursion is becoming another vital goal of glycemic control in clinical practice. This study aimed to determine whether insulin degludec (IDeg) or insulin glargine (IGla) was more beneficial for reducing glycemic fluctuations.

MATERIALS AND METHODS

This research was constructed according to the PRISMA guidelines. We searched eight databases and ClinicalTrials.gov from their inception to 30 November 2021. All randomized controlled trials comparing the efficacy of glucose variability between IDeg and IGla in diabetic patients were included.

RESULTS

Fourteen trials with 8,683 participants were included. In patients with T1DM, IDeg was associated with a lower mean (MD: -16.25, 95% CI -29.02 to -3.07, P = 0.01) and standard deviation (P = 0.03) compared to IGla in fasting blood glucose (FBG); in people with T2DM, IDeg was related to a lower mean of FBG versus insulin glargine 100 U/ml (IGla100) (P <0.001) and had a more extended time in the range (TIR) than IGla100 (SMD: 0.15, 95% CI 0.02 to 0.27, P = 0.02) but not longer than insulin glargine 300 U/ml (IGla300). Moreover, IDeg had a lower coefficient of variation of FBG than IGla (P = 0.0254). For other indicators of glycemic variability, namely, standard deviation of blood glucose for 24 h, the mean of 24-h blood glucose, mean amplitude of glycemic excursion, the coefficient of variation for 24 h, the mean of daily differences, area under the glucose curve, and M-value, no significant differences were identified between IDeg and IGla, regardless of T1DM or T2DM.

CONCLUSIONS

Based on the current studies, there was comparable efficacy between IDeg and IGla from multiple aspects of glycemic variability, regardless of T1DM or T2DM. However, IDeg may be superior to IGla in reducing FBG variability in T1DM and T2DM. Nonetheless, due to the limitations of the original studies, it is still unclear whether IDeg is superior to both IGla100 and IGla300. In T2DM, IDeg had more extended TIR than IGla100 but not longer than IGla300. Additionally, more well-designed randomized controlled trials comparing IDeg with IGla300 for different indicators of glycemic variability are still warranted.

SYSTEMATIC REVIEW REGISTRATION

PROSPERO, CRD42021283203.

Association of long-term visit-to-visit variability of HbA1c and fasting glycemia with hypoglycemia in type 2 diabetes mellitus

BACKGROUND

Self-management of blood glucose levels to avoid hypoglycemia is vital for patients with type 2 diabetes mellitus (T2DM). The association between specific metrics of glycemic variability (glycosylated hemoglobin A1c [HbA1c] and fasting plasma glucose [FPG]) and severe hypoglycemia has not been fully studied in patients with T2DM.

METHODS

In this post hoc analysis, patients with established T2DM with a high risk of cardiovascular disease were included in the Action to Control Cardiovascular Risk in Diabetes (ACCORD) study. The Cox proportional hazards model was used to investigate the relationship between glycemic variability and hypoglycemia requiring medical assistance (HMA) and hypoglycemia requiring any third-party assistance (HAA). The prognostic value of HbA1c/FPG variability for our predefined outcomes was compared using Harrell's C method.

RESULTS

After adjusting for confounders, each increase in HbA1c variability of 1 standard deviation (SD) indicated a higher risk of HAA (hazard ratio [HR]: 1.10; 95% confidence interval [CI]: 1.03-1.16; P < 0.01) and HMA events (HR: 1.11; 95% CI: 1.03-1.20; P < 0.01). Meanwhile, each increase in FPG variability of 1 SD increased the risk of HAA (HR: 1.40; 95% CI: 1.31-1.49; P < 0.01) and HMA events (HR: 1.46; 95% CI: 1.35-1.57; P < 0.01). Meanwhile, models, including FPG variability, had better prognostic value for our predefined outcomes than HbA1c variability (P < 0.01).

CONCLUSIONS

Increased visit-to-visit variability in HbA1c and fasting glycemia is associated with a greater risk of severe hypoglycemic events in T2DM patients. FPG variability is a more sensitive indicator than HbA1c variability.

TRIAL REGISTRATION

http://www.clinicaltrials.gov. Unique identifier: NCT00000620.

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.

Effect of glycemic gap upon mortality in critically ill patients with diabetes

AIMS/INTRODUCTION

Hyperglycemia, hypoglycemia, and blood glucose fluctuation are associated with the outcome in critically ill patients, but the target of blood glucose control is debatable especially in patients with diabetes regarding the status of blood glucose control before admission to ICU. This study aimed to investigate the association between the glycemic gap which is calculated as the mean blood glucose level during the first 7 days after admission to ICU minus the A1C-derived average glucose and the outcome of critically ill patients with diabetes.

METHOD

This study was undertaken in two intensive care units (ICUs) with a total of 30 beds. Patients with diabetes who were expected to stay for more than 24 h were enrolled, the HbA1c was tested within 3 days after admission and converted to the A1C-derived average glucose (ADAG) by the equation: ADAG = [(HbA1c * 28.7) - 46.7 ] * 18-1 , arterial blood glucose measurements were four per day routinely during the first 7 days after admission, the APACHE II score within the first 24 h, the mean blood glucose level (MGL), standard deviation (SD), and coefficient of variation (CV) during the first 7 days were calculated for each person, the GAPadm and GAPmean were calculated as the admission blood glucose and MGL minus the ADAG, respectively, the incidence of moderate hypoglycemia (MH) and severe hypoglycemia (SH), the total dosage of glucocorticoids and average daily dosage of insulin within 7 days, the duration of renal replacement therapy (RRT), ventilator-free hours, and non-ICU stay days within 28 days were also collected. The enrolled patients were divided into a survival group and a nonsurvival group according to survival or not at 28 days and 1 year after admission, and the relationship between parameters derived from blood glucose and mortality in the enrolled critically ill patients was explored.

RESULTS

Five hundred and two patients were enrolled and divided into a survival group (n = 310) and a nonsurvival group (n = 192). It was shown that the two groups had a comparable level of HbA1c, the nonsurvivors had a greater APACHE II, MGL, SD, CV, GAPadm , GAPmean , and a higher incidence of hypoglycemia. A lesser duration of ventilator-free, non-ICU stay, and a longer duration of RRT were recorded in the nonsurvival group, who received a lower carbohydrate intake, a higher daily dosage of insulin and glucocorticoid. GAPmean had the greatest predictive power with an AUC of 0.820 (95%CI: 0.781-0.850), the cut-off value was 3.60 mmol/L (sensitivity 78.2% and specificity 77.3%). Patients with a low GAPmean tended to survive longer than the high GAPmean group 1 year after admission.

CONCLUSIONS

Glycemic GAP between the mean level of blood glucose within the first 7 days after admission to ICU and the A1C-derived average glucose was independently associated with a 28 day mortality of critically ill patients with diabetes, the predictive power extended to 1 year. The incidence of hypoglycemia was associated with mortality either.

Association of glycemic variability and hypoglycemia with distal symmetrical polyneuropathy in adults with type 1 diabetes

Previous studies exploring the influence of glycemic variability (GV) on the pathogenesis of distal symmetrical polyneuropathy (DSPN) in type 1 diabetes (T1DM) produced conflicting results. The aim of this study was to assess the relationship between GV and DSPN in T1DM. Adults with T1DM were included in this cross-sectional study and asked to undergo 3-day CGM. GV quantified by coefficient of variation (CV) and mean amplitude of glucose excursions (MAGE) were obtained from CGM. Clinical characteristics and biochemical assessments were collected for analysis. The study comprised 152 T1DM patients (53.9% males) with mean age of 44.2 year. Higher levels of age and duration of diabetes and lower levels of total cholesterol, LDL, fasting C-peptide and postprandial C-peptide were observed in DSPN subjects. DSPN groups displayed a higher blood glucose between 00:00 and 12:59 according to the CGM profile. Higher MAGE and CV were associated with increased risk of DSPN in the fully adjusted model. Meanwhile, a significant association between measurements of hypoglycemia, especially nocturnal hypoglycemia, and DSPN was found after multiple tests. CGM parameters describing the glycemic variability and hypoglycemia were potential risk factors for DSPN in adults with T1DM.

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.

Pharmacogenetics of sulfonylurea-induced hypoglycemia in Type 2 diabetes patients: the SUCLINGEN study

Aim: This study investigated the incidence of sulfonylurea-induced hypoglycemia and its predictors in Type 2 diabetes (T2D) patients. Patients & methods: In this prospective, observational study, T2D patients on maximal sulfonylurea-metformin therapy >1 year were enrolled. Hypoglycemia was defined as having symptoms or a blood glucose level <3.9 mmol/l. Results: Of the 401 patients, 120 (29.9%) developed sulfonylurea-induced hypoglycemia during the 12-month follow-up. The ABCC8 rs757110, KCNJ11 rs5219, CDKAL1 rs7756992 and KCNQ1 rs2237892 gene polymorphisms were not associated with sulfonylurea-induced hypoglycemia (p > 0.05). Prior history of hypoglycemia admission (odds ratio = 16.44; 95% CI: 1.74-154.33, p = 0.014) independently predicted its risk. Conclusion: Sulfonylurea-treated T2D patients who experienced severe hypoglycemia are at increased risk of future hypoglycemia episodes.

Sleep quality and glycaemic variability in a real-life setting in adults with type 1 diabetes

AIMS/HYPOTHESIS

Suboptimal subjective sleep quality is very common in adults with type 1 diabetes. Reducing glycaemic variability is a key objective in this population. To date, no prior studies have examined the associations between objectively measured sleep quality and overnight glycaemic variability in adults with type 1 diabetes. We aimed to test the hypothesis that poor sleep quality would be associated with greater overnight glycaemic variability.

METHODS

Data were collected in the home setting from 20 adults (ten male and ten female participants) with type 1 diabetes who were current insulin pump users. Simultaneous assessments of objective sleep quality (Zmachine Insight+) and continuous glucose monitoring (CGM) were performed over multiple nights (up to 15 nights) in each participant. Due to the real-life nature of this study, the participants kept their usual CGM alerts for out-of-range glucose values. Sleep quality was categorised as 'good' or 'poor' using a composite of objective sleep features (i.e. sleep efficiency, wake after sleep onset and number of awakenings) based on the National Sleep Foundation's consensus criteria. Glycaemic variability was quantified using SD and CV of overnight glucose values based on overnight CGM profiles.

RESULTS

A total of 170 nights were analysed. Overall, 86 (51%) nights were categorised as good sleep quality, and 84 (49%) nights were categorised as poor sleep quality. Using linear mixed-effects models, poor sleep quality was significantly associated with greater glycaemic variability as quantified by SD (coefficient: 0.39 [95% CI 0.10, 0.67], p = 0.009) and CV (coefficient: 4.35 [95% CI 0.8, 7.9], p = 0.02) of overnight glucose values, after accounting for age, sex, BMI and overnight insulin dose. There was large inter- and intra-individual variability in sleep and glycaemic characteristics. Both pooled and individual-level data revealed that the nights with poor sleep quality had larger SDs and CVs, and, conversely, the nights with good sleep quality had smaller SDs and CVs. No associations were found between sleep quality and time spent in the target glucose range, or above or below the target glucose range, where CGM alarms are most likely to occur.

CONCLUSIONS/INTERPRETATION

Objectively measured sleep quality is associated with overnight glycaemic variability in adults with type 1 diabetes. These findings highlight an important role of sleep quality in overnight glycaemic control in type 1 diabetes. They also provide a strong incentive to both patients and healthcare providers for considering sleep quality in personalised type 1 diabetes glycaemic management plans. Future studies should investigate the mechanisms linking sleep quality to glycaemic variability.

Glycemic Variability Impacted by SGLT2 Inhibitors and GLP 1 Agonists in Patients with Diabetes Mellitus: A Systematic Review and Meta-Analysis

To investigate the effect of sodium-glucose cotransporter 2 (SGLT-2) inhibitors and glucagon-like peptide 1 (GLP-1) agonists on glycemic variability (GV), the mean amplitude of glucose excursion (MAGE), mean blood glucose (MBG) levels, and percentage of time maintaining euglycemia were evaluated. Randomized controlled trials evaluating the efficacy of SGLT-2 inhibitors and GLP-1 agonists for treating people with diabetes were selected through searches of PubMed, EMBASE, and other databases. Sixteen studies were finally analyzed. There were no differences in the reductions in MAGE after treatment with SGLT-2 inhibitors or GLP-1 agonists (standardized mean difference (SMD) = -0.59, 95% CI = -0.82 to -0.36 vs. SMD = -0.43, 95% CI = -0.51 to -0.35, respectively), and treatment with SGLT-2 inhibitors was associated with an increased reduction in MBG levels (SMD = -0.56, 95% CI = -0.65 to -0.48, p < 0.00001). Monotherapy and add-on therapy with medications were correlated with MAGE and MBG level reductions. In conclusion, SGLT-2 inhibitors and GLP-1 agonists were associated with a reduction in GV and could be alternatives for treating people with diabetes.

Sexual dysfunctions and short-term glucose variability in young men with type 1 diabetes

PURPOSE

Erectile dysfunction (ED) and premature ejaculation (PE) are common sexual disorders in people with diabetes. Glucose variability (GV) has been recognized as a predictor of microvascular complications. The aim of this study was to investigate the relationship between glucose variability and sexual dysfunctions in young men with type 1 diabetes.

METHODS

One hundred and twelve patients with type 1 diabetes, aged 18-30 years, were enrolled. Patients were divided into two groups according to glucose variability [group 1 (high GV with coefficient of variation ≥ 36%)] and group 2 (low GV with coefficient of variation < 36%)). The presence of sexual dysfunctions was investigated with validated questionnaires.

RESULTS

ED and PE prevalence rates in group 1 were 26% and 13%, respectively. Similarly, in group 2, the prevalence of ED was 24%, and the prevalence of PE was 13%. In both groups, no significant associations between sexual dysfunctions and parameters of glucose variability were found. Multiple regression analysis identified age and depression as independent predictors of ED and PE.

CONCLUSION

Young male patients affected by type 1 diabetes with high or low glucose variability show a similar prevalence of sexual dysfunctions. ED is the most common sexual dysfunction in diabetic men. Age and depression were the only independent predictive factors for sexual dysfunctions in this population.

A practical approach to the clinical challenges in initiation of basal insulin therapy in people with type 2 diabetes

Initiating insulin therapy with a basal insulin analogue has become a standard of care in the treatment of type 2 diabetes mellitus (T2DM). Despite increasing choices in pharmacological approaches, intensified glucose monitoring and improvements in quality of care, many patients do not achieve the desired level of glycaemic control. Although insulin therapy, when optimized, can help patients reach their glycaemic goals, there are barriers to treatment initiation on both the side of the patient and provider. Providers experience barriers based on their perceptions of patients' capabilities and concerns. They may lack the confidence to solve the practical problems of insulin therapy and avoid decisions they perceive as risky for their patients. In this study, we review recommendations for basal insulin initiation, focussing on glycaemic targets, titration, monitoring, and combination therapy with non-insulin anti-hyperglycaemic medications. We provide practical advice on how to address some of the key problems encountered in everyday clinical practice and give recommendations where there are gaps in knowledge or guidelines. We also discuss common challenges faced by people with T2DM, such as weight gain and hypoglycaemia, and how providers can address and overcome them.

Acute glucose fluctuation induces inflammation and neurons apoptosis in hippocampal tissues of diabetic rats

It is well-recognized that glycemic disorders are leading causes of diabetic complications and acute fluctuation of blood glucose and reported more likely being related to oxidative stress, vasculopathy, and other diabetic complications than continuous hyperglycemia in patients with diabetic and animal models. To explore the hypothesis that acute glucose fluctuation (GF) aggravates inflammatory lesions and neuron apoptosis in the hippocampus of diabetic rats. Twenty female GK rats were randomly allocated into a glucose fluctuating group (GK-GF) and a continuous hyperglycemia group (GK-CHG) and 10 age-matched female Wistar rats served as controls. GF was induced in the GK-GF group by injection with glucose and insulin at different periods of time per day for 6 weeks. Body weight was determined weekly. At the end of the study, blood hemoglobin A1c (HbA1c) and serum lipids were measured. Serum and hippocampus interleukin 1β (IL-1β), IL-6, IL-8, and tumor necrosis factor-α (TNF-α) were measured by enzyme-linked immunosorbent assay and real-time quantitative reverse transcription-polymerase chain reaction (qRT-PCR). Hippocampus Bcl-2, Bax, Pten, fas, and myc were quantified by qRT-PCR and Western blot analysis and Mirror Water Maze (MWM) test was performed. We successfully established an animal model with daily GF and a control model with CHG using GK diabetic rats. The GF and CHG rats showed lower weight gain during the 6-week experimental period with no significant difference in the levels of serum lipids such as total triglyceride, total cholesterol, high-density lipoprotein cholesterol, and low-density lipoprotein cholesterol compared with the control rats at the end of the study. Meanwhile, the GF and CHG rats showed higher blood HbA1c levels than that of control rats. MWM trainings tests detected that glucose disorders in GF and CHG rats tend to present longer latencies, more cross times and longer path length compared with those of the control rats, indicating impaired the hippocampus-regulated behavioral function such as spatial orientating and memory. Importantly, it was found that GF promoted the expression of TNF-α and IL-1β in the hippocampus of the GF rats while continuous hyperglycemia in CHG rats had little effect on that. Furthermore, both GF and CHG diabetic rats had abnormal expression of apoptosis-associated genes in the hippocampus compared with control Wistar rats and neurons apoptosis in GF rats appears to be more severe than CHG rats. Overall, this study confirmed that GF is a more critical factor that would promote the neuron apoptosis and induce inflammation in the hippocampus than continuous hyperglycemia in diabetic animals, which shed new light on the importance of monitoring and administration of blood glucose in the prevention and therapy for diabetes.

Glucose Regulation Beyond HbA1c in Type 2 Diabetes Treated With Insulin: Real-World Evidence From the DIALECT-2 Cohort

OBJECTIVE

To investigate glucose variations associated with glycated hemoglobin (HbA1c) in insulin-treated patients with type 2 diabetes.

RESEARCH DESIGN AND METHODS

Patients included in Diabetes and Lifestyle Cohort Twente (DIALECT)-2 (n = 79) were grouped into three HbA1c categories: low, intermediate, and high (≤53, 54-62, and ≥63 mmol/mol or ≤7, 7.1-7.8, and ≥7.9%, respectively). Blood glucose time in range (TIR), time below range (TBR), time above range (TAR), glucose variability parameters, day and night duration, and frequency of TBR and TAR episodes were determined by continuous glucose monitoring (CGM) using the FreeStyle Libre sensor and compared between HbA1c categories.

RESULTS

CGM was performed for a median (interquartile range) of 10 (7-12) days/patient. TIR was not different for low and intermediate HbA1c categories (76.8% [68.3-88.2] vs. 76.0% [72.5.0-80.1]), whereas in the low category, TBR was higher and TAR lower (7.7% [2.4-19.1] vs. 0.7% [0.3-6.1] and 8.2% [5.7-17.6] vs. 20.4% [11.6-27.0], respectively, P < 0.05). Patients in the highest HbA1c category had lower TIR (52.7% [40.9-67.3]) and higher TAR (44.1% [27.8-57.0]) than the other HbA1c categories (P < 0.05), but did not have less TBR during the night. All patients had more (0.06 ± 0.06/h vs. 0.03 ± 0.03/h; P = 0.002) and longer (88.0 [45.0-195.5] vs. 53.4 [34.4-82.8] minutes; P < 0.001) TBR episodes during the night than during the day.

CONCLUSIONS

In this study, a high HbA1c did not reduce the occurrence of nocturnal hypoglycemia, and low HbA1c was not associated with the highest TIR. Optimal personalization of glycemic control requires the use of newer tools, including CGM-derived parameters.

Glycemic Control, Diabetic Complications, and Risk of Dementia in Patients With Diabetes: Results From a Large U.K. Cohort Study

OBJECTIVE

Type 2 diabetes is an established risk factor for dementia. However, the roles of glycemic control and diabetic complications in the development of dementia have been less well substantiated. This large-scale cohort study aims to examine associations of longitudinal HbA_1c levels and diabetic complications with the risk of dementia incidence among patients with type 2 diabetes.

RESEARCH DESIGN AND METHODS

Data of eligible patients with diabetes, aged ≥50 years in the U.K. Clinical Practice Research Datalink from 1987 to 2018, were analyzed. Time-varying Cox regressions were used to estimate adjusted hazard ratios (HRs) and 95% CIs for dementia risk.

RESULTS

Among 457,902 patients with diabetes, 28,627 (6.3%) incident dementia cases were observed during a median of 6 years' follow-up. Patients with recorded hypoglycemic events or microvascular complications were at higher risk of dementia incidence compared with those without such complications (HR 1.30 [95% CI 1.22-1.39] and 1.10 [1.06-1.14], respectively). The HbA_1c level, modeled as a time-varying exposure, was associated with increased dementia risk (HR 1.08 [95% CI 1.07-1.09] per 1% HbA_1c increment) among 372,287 patients with diabetes with postdiagnosis HbA_1c records. Similarly, a higher coefficient of variation of HbA_1c during the initial 3 years of follow-up was associated with higher subsequent dementia risk (HR 1.03 [95% CI 1.01-1.04] per 1-SD increment).

CONCLUSIONS

Higher or unstable HbA_1c levels and the presence of diabetic complications in patients with type 2 diabetes are associated with increased dementia risk. Effective management of glycemia might have a significant role in maintaining cognitive health among older adults with diabetes.

Defining the target value of the coefficient of variation by continuous glucose monitoring in Chinese people with diabetes

AIMS/INTRODUCTION

To define the target value for the percentage coefficient of variation for glucose (%CV) as a measure of glycemic variability (GV) in Chinese diabetes patients.

MATERIALS AND METHODS

This retrospective study included 3,007 diabetes patients who underwent continuous glucose monitoring for 3 days. Type 2 diabetes was divided into groups according to the received treatment: group 1, non-insulinotropic agent (n = 138); group 2, insulinotropic agent (n = 761); group 3, basal insulin therapy (n = 100); group 4, premixed insulin (n = 784); and group 5, intensive insulin therapy (n = 612). Type 1 diabetes patients were included as group 6 (n = 612). %CV and percentage of time per day within, below (3.9mmol/L; TBR3.9 ) and above (10.0 mmol/L) the target glucose range (3.9-10.0 mmol/L) were computed. TBR3.9 ≥4% was defined as excessive hypoglycemia.

RESULTS

Type 2 diabetes with a premixed or intensive insulin regimen had an increased %CV compared with those receiving oral therapy or basal insulin. The upper limit of %CV in group 1 was 33%, which was adopted as the threshold to define excessive GV. For each treatment group, the percentage of people with TBR3.9 ≥4% was significantly greater in the subgroup with %CV >33% than ≤33% (P < 0.001). In participants who achieved TBR3.9 <4%, the time per day spent within the target glucose range of 3.9-10.0 mmol/L > 70% and time per day above 10.0 mmol/L <25%, the 95th percentile of %CV was 32.70%. Further receiver operating characteristic curve analysis showed that the cut-off values of %CV for predicting TBR3.9 ≥4% varied by the type of diabetes and glycated hemoglobin categories.

CONCLUSIONS

A %CV of 33% was set as the threshold for excess glucose variability in Chinese diabetes patients. Meanwhile, glycated hemoglobin and the type of diabetes should be considered for the goal-setting of %CV.

Association of specialized enteral nutrition with glycemic control and clinical outcomes in critically ill patients: A meta-analysis of randomized controlled trials

OBJECTIVE

To evaluate the association of glycemic-control formulae (GCF) with measurements of glycemic control and clinical outcomes compared to standard enteral formulae (SF) in critically ill patients.

DATA SOURCES

MEDLINE, EMBASE, Scopus and the Cochrane Central Register of Controlled Trials were searched from inception up to January, 2021.

STUDY SELECTION

RCTs that assessed the effects of GCF relative to SF in adult critically ill patients.

DATA EXTRACTION

Measurements of glycemic control were the primary outcomes. Secondary outcomes included insulin requirements, mechanical ventilation (MV), length of intensive care unit (ICU) stay and mortality. Two authors independently extracted data and assessed risk of bias using the Cochrane's RoB 2 tool and the GRADE approach was used to assess the quality of evidence.

DATA SYNTHESIS

Ten studies (12 reports, 685 patients) were included. The use of GCFs was associated with lower blood glucose (WMD, -16.06 mg/dL; 95% CI -23.48 to -8.63; I² = 47%) and lower daily administered insulin (WMD, -7.20 IU; 95% CI -13.92 to -0.48; I² = 53%). Glycemic variability, measured by the coefficient of variation, was also associated with the use of GCFs (WMD, -6.84%; 95% CI, -13.57 to -0.11; I² = 95%). In contrast, analyses for length of ICU stay (WMD, -0.12, 95% CI -1.77 to 1.52; I² = 0%), duration of MV (WMD, -0.34 days; 95% CI, -1.72 to 1.04; I² = 0%) and mortality (RR, 1.13; 95% CI 0.82 to 1.56; I² = 0%) were not statistically significant. Quality of evidence ranged from low to very low, and only one study was judged as at low risk of bias.

CONCLUSIONS

In this meta-analysis, GCFs were significantly associated with lower insulin requirements and improved glycemic control. Although results for clinical outcomes were not statistically significant, there is insufficient evidence to confirm or exclude important differences due to serious imprecision in the effect estimates and overall low quality of evidence. The effects of GCFs on clinical outcomes require confirmation in larger randomized trials.

Biomarker monitoring and long-acting insulin treatment in a stress model to facilitate personalized diabetic control

Diabetes is a chronic disease involving elevated blood glucose levels. Controlled insulin delivery is an ideal method to achieve glycemic control. However, glucose levels vary as a result of changes in the body's physiological conditions resulting from different daily events, such as meals, exercise and stress. It is essential to develop and to monitor appropriate biomarkers for these natural physiological perturbations in order to achieve a holistic understanding of metabolic changes in diabetic patients to facilitate optimal glucose management and make possible personalized medicine for this very debilitating disease. The novel aspects of the current research include the combination of the tail suspension stress animal model and microdialysis for stress biomarker monitoring. A tail suspension test was optimized and used as a stress inducer for different groups of model rats (normal, untreated diabetic and insulin-treated diabetic rats). The levels of multiple metabolic analytes in the subcutaneous tissue were monitored continuously using subcutaneous microdialysis analysis. Different analytes, including pH, glucose and lactate were monitored and analyzed. Another important and original aspect of the reported work is that revelation that the pH, lactate and glucose levels increased under the stress conditions and the changes tended to be more pronounced in diabetic rats (both untreated and long-acting insulin-treated) compared to normal rats. Long-acting insulin treatment was shown to shorten the duration of the pH and glucose changes in response to stress when compared to the untreated diabetic rats. An additional novel aspect of the research is that subcutaneous pH was shown to be a sensitive stress biomarker for diabetic rats as pH changes rapidly after stress initiation. This research provides insight into the importance of accurate glycemic control and diabetes management for individual patients through highlighting stress management and appropriate biomarker monitoring, especially the value of combining pH and lactate monitoring with typical glucose monitoring. This research will pave the way for individualized medicine for diabetic patients through helping to obtain a holistic understanding of the various important biomarkers, rather than solely depending on glucose monitoring to determine appropriate insulin dosing.

Identifying Glycemic Variability in Diabetes Patient Cohorts and Evaluating Disease Outcomes

Glycemic variability (GV) is an obstacle to effective blood glucose control and an autonomous risk factor for diabetes complications. We, therefore, explored sample data of patients with diabetes mellitus who maintained better amplitude of glycemic fluctuations and compared their disease outcomes with groups having poor control. A retrospective study was conducted using electronic data of patients having hemoglobin A1C (HbA1c) values with five recent time points from Think Whole Person Healthcare (TWPH). The control variability grid analysis (CVGA) plot and coefficient of variability (CV) were used to identify and cluster glycemic fluctuation. We selected important variables using LASSO. Chi-Square, Fisher’s exact test, Bonferroni chi-Square adjusted residual analysis, and multivariate Kruskal–Wallis tests were used to evaluate eventual disease outcomes. Patients with very high CV were strongly associated (p < 0.05) with disorders of lipoprotein (p = 0.0014), fluid, electrolyte, and acid–base balance (p = 0.0032), while those with low CV were statistically significant for factors influencing health status such as screening for other disorders (p = 0.0137), long-term (current) drug therapy (p = 0.0019), and screening for malignant neoplasms (p = 0.0072). Reducing glycemic variability may balance alterations in electrolytes and reduce differences in lipid profiles, which may assist in strategies for managing patients with diabetes mellitus.

Dapagliflozin as an Adjunct Therapy to Insulin in Patients with Type 1 Diabetes Mellitus: Efficacy and Safety of this Combination

The prevalence of type 1 diabetes (T1D) is increasing worldwide. T1D reduces life expectancy due to complications including cardiovascular disease. Sodium-glucose co-transporter (SGLT) inhibitors are a new class of drugs developed to treat type 2 diabetes (T2D), and now they can be used as an adjunct to insulin in T1D. In clinical trials, they have been shown to improve glycaemic control and decrease body weight without the risk of increased hypoglycaemia and with a reduction in insulin dose. Four SGLT2 inhibitors have been approved in Europe for the treatment of T2D, while only dapagliflozin and sotagliflozin, a dual SGLT1 and SGLT2 inhibitor approved in 2019, have been approved for the treatment of T1D. Both can be used as an adjunct therapy in combination with insulin in adults with a body mass index (BMI) of ≥27 kg/m², inadequately controlled with insulin. In Europe, dapagliflozin is the only currently available SGLT2 inhibitor indcated as adjunct therapy for patients with T1D. The subgroup of patients with a BMI of ≥27 kg/m² from the DEPICT-1 and -2 trials (Dapagliflozin Evaluation in Patients with Inadequately Controlled Type 1 diabetes) showed similar reduction in hyperglycaemia and body weight but no significant increased risk of diabetic ketoacidosis (DKA) than the overall trial population. The risk of DKA has been shown to increase in patients with T1D treated with adjunct therapy with SGLT2 inhibitors, and studies on sotagliflozin and empagliflozin have suggested a dose response. Thus, it is important to educate patients and doctors how to recognize symptoms of upcoming DKA and mitigate it. An independent DKA education programme has recently been developed to instruct patients with T1D being treated with SGLT inhibitor therapies with and without insulin pumps to prevent, identify and treat DKA. Despite these considerations, clinical trials support the use of SGLT2 inhibitors in the management of T1D. The benefits and potential risks of dapagliflozin as an adjunct therapy to insulin in adults with T1D should be considered in each individual case. Here we discuss the efficacy and safety of dapagliflozin as adjunct therapy in patients with T1D.

Low-carbohydrate, high-fat enteral formulas for managing glycemic control in patients who are critically ill: A review of the evidence

Hyperglycemia is associated with increased morbidity and mortality. Low-carbohydrate, high-fat (LCHF) enteral formulas are marketed to improve glycemic control; however, given the multifactorial mechanisms contributing to hyperglycemia in patients who are critically ill, the effect that LCHF formulas may have on improving glycemic control in this patient population is unclear. Current guidelines for the use of LCHF formulas among patients who are critically ill are limited by a lack of evidence. This review explores recent research published in the past 7 years to determine whether LCHF enteral formulas improve glycemic control compared with standard enteral formulas in patients who are critically ill. Four randomized controlled trials met the inclusion criteria for this review. Their results suggest that LCHF formulas may improve glycemic control in patients who are critically ill with diabetes mellitus and/or who are hyperglycemic. Further large-scale randomized controlled trials are warranted to validate these findings among different subgroups of patients with critical illness. The potential benefits of LCHF formulas need to be weighed against specific limitations, including that LCHF formulas typically do not contain sufficient protein to meet the recommended needs of patients who are critically ill.

Spousal Influence on Diabetes Self-care: Moderating Effects of Distress and Relationship Quality on Glycemic Control

BACKGROUND

Spouses often attempt to influence patients' diabetes self-care. Spousal influence has been linked to beneficial health outcomes in some studies, but to negative outcomes in others.

PURPOSE

We aimed to clarify the conditions under which spousal influence impedes glycemic control in patients with type 2 diabetes. Spousal influence was hypothesized to associate with poorer glycemic control among patients with high diabetes distress and low relationship quality.

METHODS

Patients with type 2 diabetes and their spouses (N = 63 couples) completed self-report measures before patients initiated a 7-day period of continuous glucose monitoring. Mean glucose level and coefficient of variation (CV) were regressed on spousal influence, diabetes distress, relationship quality, and their two- and three-way interactions.

RESULTS

The three-way interaction significantly predicted glucose variability, but not mean level. Results revealed a cross-over interaction between spousal influence and diabetes distress at high (but not low) levels of relationship quality, such that spousal influence was associated with less variability among patients with low distress, but more among those with high distress. Among patients with high distress and low relationship quality, a 1 SD increase in spousal influence predicted a difference roughly equivalent to the difference between the sample mean CV and a CV in the unstable glycemia range.

CONCLUSIONS

This was the first study to examine moderators of the link between spousal influence and glycemic control in diabetes. A large effect was found for glucose variability, but not mean levels. These novel results highlight the importance of intimate relationships in diabetes management.