Glycemic variability and cardiovascular disease in patients with type 2 diabetes

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.

High sugar consumption for seven days in adult mice increased blood glucose variability, induced an anxiolytic effect and triggered oxidative stress in cerebral cortex

Brain function is highly altered by glucose toxicity related to diabetes. High consumption of sugar in normal conditions is suspected to affect as well brain integrity. The present study investigates the possible effects of short-term exposure to high sugar diet on brain redox homeostasis in healthy mice. Male adult healthy mice were divided into two groups: control (CG) and sugar-exposed group (SG), that was exposed continually to 10% of glucose in drinking water for 7 days and 20% sucrose pellets food. Behavior, blood glucose variability and cerebral cortex oxidative stress biomarkers were measured at the end of exposure. Animals exposed to the high sugar diet expressed a significant increase in blood glucose levels and high glucose variability compared to control. These animals expressed as well anxiolytic behavior as revealed by the plus maze test. Exposure to the sugar diet altered redox homeostasis in the brain cortex as revealed by an increase in lipid peroxidation and the activity of antioxidant enzymes superoxide dismutase (SOD) and glutathione-s-transferase (GST). On the other hand, catalase (CAT) activity was decreased, and reduced glutathione (GSH) level was not altered compared to control. Further studies are required to understand the mechanisms trigging oxidative stress (OS) in the brain in response to short term exposure to high sugar diet and glucose fluctuations.

Hyperglycemic Patterns in Acute Stroke Patients

Background and objective Hyperglycemia following a stroke can independently aggravate the ischemic area. Ensuring adequate glucose management can help avoid complications and minimize mortality and disability in these patients. This study aimed to investigate hyperglycemic patterns in acute stroke patients. Materials and methods We conducted a non-interventional prospective observational study involving acute stroke patients by employing continuous glucose monitoring (CGM) for 72 hours after the onset of stroke symptoms. Admission glucose, patients' total mean glucose (TMG), and time in range (TIR) (70-140 mg/dl) were correlated with the hyperglycemic patterns elicited by the CGM system software. Data were analyzed using SPSS Statistics 26.0 (IBM Corp., Armonk, NY) with descriptive statistics, the Kruskal-Wallis test, and χ2 test. Results Our cohort comprised 105 diabetic and non-diabetic stroke patients. The hyperglycaemic patterns that we observed were as follows: (i) hyperglycemia from 23:00 to 10:00, (ii) 06.00 to 10.00, (iii) at night and after meals, iv) no pattern, v) unspecified patterns. Patients with nocturnal and morning hyperglycemia had admission glucose of 183 mg/dl, mean 72-hour glucose of 156 mg/dl, and TIR of 37%. Patients who did not develop a hyperglycemic pattern either had admission glucose of 131 mg/dl and TIR of 89% or had high admission glucose (197 mg/dl) and a short TIR (14%). Conventional pre-meal capillary glucose tests do not appear to detect these patients' hyperglycemic tendencies. Conclusions These results may indicate the necessity for more intensive measurements during the night or dawn in this patient population. Admission glucose could be considered a predictor of hyperglycemic patterns and contribute to the patient's care plan.

Assessing glycemic variability in critically ill patients: A prospective cohort study comparing insulin infusion therapy with insulin sliding scale

Glycemic variability (GV) has been associated with an increased mortality rate among critically ill patients. The clinical outcomes of having less GV even with slight hyperglycemia are better than those having tight glycemic control but higher GV. Insulin infusion remains the preferred method to control stress hyperglycemia in critically ill patients. However, its impacts on GV and clinical outcomes in critically ill patients still need further investigation. This study intended to evaluate the impact of insulin infusion therapy (IIT) compared to the insulin sliding scale (ISS) on the extent of GV and explore its impact on the clinical outcomes for critically ill patients. A prospective, single-center observational cohort study was conducted at a tertiary academic hospital in Saudi Arabia between March 2021 and November 2021. The study included adult patients admitted to ICUs who received insulin for stress hyperglycemia management. Patients were categorized into two groups based on the regimen of insulin therapy during ICU stay (IIT versus ISS). The primary outcome was the GV between the two groups. Secondary outcomes were ICU mortality, the incidence of hypoglycemia, and ICU length of stay (LOS). A total of 381 patients were screened; out of them, eighty patients met the eligibility criteria. The distribution of patients having diabetes and a history of insulin use was similar between the two groups. The GV was lower in the IIT group compared to the ISS group using CONGA (- 0.65, 95% CI [- 1.16, - 0.14], p-value = 0.01). Compared with ISS, patients who received IIT had a lower incidence of hypoglycemia that required correction (6.8% vs 2.77%; p-value = 0.38). In contrast, there were no significant differences in ICU LOS and ICU mortality between the two groups. Our study demonstrated that the IIT is associated with decreased GV significantly in critically ill patients without increasing the incidence of severe hypoglycemia. There is no survival benefit with the use of the IIT. Further studies with larger sample size are required to confirm our findings and elaborate on IIT's potential effect in reducing ICU complications in critically ill patients.

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.

Blood glucose fluctuation and in-hospital mortality among patients with acute myocardial infarction: eICU collaborative research database

BACKGROUND

To assess the relationship between glycemic variability, glucose fluctuation trajectory and the risk of in-hospital mortality in patients with acute myocardial infarction (AMI).

METHODS

This retrospective cohort study included AMI patients from eICU Collaborative Research Database. In-hospital mortality of AMI patients was primary endpoint. Blood glucose levels at admission, glycemic variability, and glucose fluctuation trajectory were three main study variables. Blood glucose levels at admission were stratified into: normal, intermediate, and high. Glycemic variability was evaluated using the coefficient of variation (CV), divided into four groups based on quartiles: quartile 1: CV≤10; quartile 2: 1030. Univariate and multivariate Cox regression models to assess the relationship between blood glucose levels at admission, glycemic variability, glucose fluctuation trajectory, and in-hospital mortality in patients with AMI.

RESULTS

2590 participants were eventually included in this study. There was a positive relationship between high blood glucose level at admission and in-hospital mortality [hazard ratio (HR) = 1.42, 95%confidence interval (CI): 1.06-1.89]. The fourth quartile (CV>30) of CV was associated with increased in-hospital mortality (HR = 2.06, 95% CI: 1.25-3.40). The findings indicated that only AMI individuals in the fourth quartile of glycemic variability, exhibited an elevated in-hospital mortality among those with normal blood glucose levels at admission (HR = 2.33, 95% CI: 1.11-4.87). Additionally, elevated blood glucose level was a risk factor for in-hospital mortality in AMI patients.

CONCLUSION

Glycemic variability was correlated with in-hospital mortality, particularly among AMI patients who had normal blood glucose levels at admission. Our study findings also suggest early intervention should be implemented to normalize high blood glucose levels at admission of AMI.

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

OBJECTIVE

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

METHODS

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

RESULTS

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

CONCLUSIONS

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

Comparison of admission glycemic variability and glycosylated hemoglobin in predicting major adverse cardiac events among type 2 diabetes patients with heart failure following acute ST-segment elevation myocardial infarction

Background and Objectives

Hyperglycemia is associated with adverse outcomes in patients with acute myocardial infarction (AMI) as well as in patients with heart failure. However, the significance of admission glycemic variability (GV) in predicting outcomes among diabetes patients with heart failure (HF) following acute ST-segment elevation myocardial infarction (ASTEMI) remains unclear. This study aims to explore the prognostic value of admission GV and admission glycosylated hemoglobin (HbA1c) levels in individuals diagnosed with type 2 diabetes and HF following ASTEMI.

Methods

We measured GV and HbA1c upon admission in 484 consecutive patients diagnosed with type 2 diabetes and HF following ASTEMI. GV, indicated as the mean amplitude of glycemic excursions (MAGE), was assessed utilizing a continuous glucose monitoring system (CGMS). admission MAGE values were categorized as < 3.9 or ≥ 3.9 mmol/L, while HbA1c levels were classified as < 6.5 or ≥ 6.5%. Participants were followed up prospectively for 12 months. The relationship of admission MAGE and HbA1c to the major adverse cardiac event (MACE) of patients with type 2 diabetes and HF following ASTEMI was analyzed.

Results

Among the 484 enrolled patients, the occurrence of MACE differed significantly based on MAGE categories (< 3.9 vs. ≥ 3.9 mmol/L), with rates of 13.6% and 25.3%, respectively (P = 0.001). While MACE rates varied by HbA1c categories (< 6.5 vs. ≥ 6.5%) at 15.7% and 21.8%, respectively (P = 0.086). Patients with higher MAGE levels exhibited a notably elevated risk of cardiac mortality and an increased incidence of HF rehospitalization. The Kaplan-Meier curves analysis demonstrated a significantly lower event-free survival rate in the high MAGE level group compared to the low MAGE level group (log-rank test, P < 0.001), while HbA1c did not exhibit a similar distinction. In multivariate analysis, high MAGE level was significantly associated with incidence of MACE (hazard ratio 3.645, 95% CI 1.287-10.325, P = 0.015), whereas HbA1c did not demonstrate a comparable association (hazard ratio 1.075, 95% CI 0.907-1.274, P = 0.403).

Conclusions

Elevated admission GV emerges as a more significant predictor of 1-year MACE in patients with type 2 diabetes and HF following ASTEMI, surpassing the predictive value of HbA1c.

Continuous glucose monitoring metrics in pregnancy with type 1 diabetes mellitus

Managing diabetes during pregnancy is challenging, given the significant risk it poses for both maternal and foetal health outcomes. While traditional methods involve capillary self-monitoring of blood glucose level monitoring and periodic HbA1c tests, the advent of continuous glucose monitoring (CGM) systems has revolutionized the approach. These devices offer a safe and reliable means of tracking glucose levels in real-time, benefiting both women with diabetes during pregnancy and the healthcare providers. Moreover, CGM systems have shown a low rate of side effects and high feasibility when used in pregnancies complicated by diabetes, especially when paired with continuous subcutaneous insulin infusion pump as hybrid closed loop device. Such a combined approach has been demonstrated to improve overall blood sugar control, lessen the occurrence of preeclampsia and neonatal hypoglycaemia, and minimize the duration of neonatal intensive care unit stays. This paper aims to offer a comprehensive evaluation of CGM metrics specifically tailored for pregnancies impacted by type 1 diabetes mellitus.

Cardiovascular protection significantly depends on HbA1c improvement with GLP-1RAs but not with SGLT2 is in type 2 diabetes: A narrative review

BACKGROUND

Glucagon-like peptide-1 receptor agonists (GLP-1RAs) and sodium-glucose cotransporter 2 inhibitors (SGLT2is), while developed as antihyperglycaemic medications for the treatment of type 2 diabetes, have proven to reduce major cardiovascular adverse events (MACEs) and hospitalization for heart failure (especially for SGLT2is) in dedicated cardiovascular outcome trials. The contribution of the glucose-lowering effect in the cardiovascular protection is uncertain and may differ between the two drug classes.

METHODS

This narrative review compares the relative effects of glycated hemoglobin (HbA1c) reduction on the cardiovascular protection provided by GLP-1RAs and SGLT2is in placebo-controlled cardiovascular outcome trials by using the results of either post-hoc mediation analyses or meta-regression studies.

RESULTS

Both mediation and meta-regression analyses suggest that the lower cardiovascular risk with GLP-1RAs partially but substantially tracks with their glucose-lowering effect, especially when considering the reduction in nonfatal strokes. In contrast, similar analyses fail to demonstrate any significant contribution of the glucose-lowering effect with SGLT2is, not only on MACEs but also on heart failure issues.

CONCLUSION

The contribution of improved glucose control in cardiovascular protection is limited, but is much greater for GLP-1RAs than for SGLT2is. Of note, such mediation or meta-regression analyses are exploratory and can only be viewed as hypothesis generating.

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.

Glycemic variability evaluated by HbA1c rather than fasting plasma glucose is associated with adverse cardiovascular events

Background

Although studies have shown that glycemic variability is positively associated with an increased risk of cardiovascular disease, few studies have compared hemoglobin A1c (HbA1c) and fasting plasma glucose (FPG) variability with adverse cardiovascular events in patients with type 2 diabetes mellitus (T2DM).

Methods

This was a post hoc analysis of the Action to Control Cardiovascular Risk in Diabetes (ACCORD) study. Cox proportional hazards models were used to explore the relationship between HbA1c or FPG variability and the incidence of major adverse cardiovascular events (MACEs).

Results

In total, 9,547 patients with T2DM were enrolled in this study. During the median 4.6 ± 1.5 years follow-up period, 907 patients developed MACEs. The risk of MACEs increased in the HbA1c variability group in each higher quartile of HbA1c variability (P < 0.01). Compared with those in the first quartile of HbA1c variability, patients in the fourth quartile had a hazard ratio of 1.37 (Model 2, 95% confidence interval: 1.13-1.67) for MACEs. Higher FPG variability was not associated with a higher risk of MACEs in patients with T2DM (P for trend=0.28). A U-shaped relationship was observed between HbA1c and FPG variability, and MACEs. Glucose control therapy modified the relationship between HbA1c and MACEs; participants with higher HbA1c variability receiving intensive glucose control were more likely to develop MACEs (P for interaction <0.01).

Conclusion

In adults with T2DM, the relationship between glycemic variability evaluated using HbA1c and FPG was U-shaped, and an increase in HbA1c variability rather than FPG variability was significantly associated with MACEs. The relationship between HbA1c variability and MACEs was affected by the glucose control strategy, and a higher HbA1c variability was more strongly associated with MACEs in patients receiving an intensive glucose control strategy.

The double burden: type 1 diabetes and heart failure-a comprehensive review

Heart failure (HF) is increasing at an alarming rate, primary due to the rising in aging, obesity and diabetes. Notably, individuals with type 1 diabetes (T1D) face a significantly elevated risk of HF, leading to more hospitalizations and increased case fatality rates. Several risk factors contribute to HF in T1D, including poor glycemic control, female gender, smoking, hypertension, elevated BMI, and albuminuria. However, early and intensive glycemic control can mitigate the long-term risk of HF in individuals with T1D. The pathophysiology of diabetes-associated HF is complex and multifactorial, and the underlying mechanisms in T1D remain incompletely elucidated. In terms of treatment, much of the evidence comes from type 2 diabetes (T2D) populations, so applying it to T1D requires caution. Sodium-glucose cotransporter 2 inhibitors have shown benefits in HF outcomes, even in non-diabetic populations. However, most of the information about HF and the evidence from cardiovascular safety trials related to glucose lowering medications refer to T2D. Glycemic control is key, but the link between hypoglycemia and HF hospitalization risk requires further study. Glycemic variability, common in T1D, is an independent HF risk factor. Technological advances offer the potential to improve glycemic control, including glycemic variability, and may play a role in preventing HF. In summary, HF in T1D is a complex challenge with unique dimensions. This review focuses on HF in individuals with T1D, exploring its epidemiology, risk factors, pathophysiology, diagnosis and treatment, which is crucial for developing tailored prevention and management strategies for this population.

Early time-restricted eating improves markers of cardiometabolic health but has no impact on intestinal nutrient absorption in healthy adults

Early time-restricted eating (eTRE) improves aspects of cardiometabolic health. Although the circadian system appears to regulate nutrient absorption, little is known about the effects of eTRE on intestinal absorption. In this randomized crossover trial, 16 healthy adults follow a controlled, weight maintenance diet for 9 days, consuming all calories between 0800 and 1400 (eTRE schedule) or 0800 and 2000 (control schedule). We measure the energy content of the diet, stool, and urine with bomb calorimetry and calculate intestinal energy absorption. The eTRE schedule is more effective than the control eating schedule for improving markers of cardiometabolic health, including 24-h mean glucose concentrations and glycemic variability, assessed as the mean amplitude of glycemic excursions. However, eTRE has no effect on intestinal energy and macronutrient absorption, gastrointestinal transit time, colonic hydrogen gas production, or stool microbial composition, suggesting eTRE does not impact gastrointestinal function. This trial is registered (ClinicalTrials.gov: NCT04877262).

Perioperative Glycemic Management in Cardiac Surgery: A Narrative Review

Diabetes and hyperglycemic events in cardiac surgical patients are associated with postoperative morbidity and mortality. The causes of dysglycemia, the abnormal fluctuations in blood glucose concentrations, in the perioperative period include surgical stress, surgical techniques, medications administered perioperatively, and patient factors. Both hyperglycemia and hypoglycemia lead to poor outcomes after cardiac surgery. While trying to control blood glucose concentration tightly for better postoperative outcomes, hypoglycemia is the main adverse event. Currently, there is no definite consensus on the optimum perioperative blood glucose concentration to be maintained in cardiac surgical patients. This review provides an overview of perioperative glucose homeostasis, the pathophysiology of dysglycemia, factors that affect glycemic control in cardiac surgery, and current practices for glycemic control in cardiac surgery.

Factors associated with nocturnal and diurnal glycemic variability in patients with type 2 diabetes: a cross-sectional study

PURPOSE

There is little information on factors that influence the glycemic variability (GV) during the nocturnal and diurnal periods. We aimed to examine the relationship between clinical factors and GV during these two periods.

METHODS

This cross-sectional study included 134 patients with type 2 diabetes. 24-h changes in blood glucose were recorded by a continuous glucose monitoring system. Nocturnal and diurnal GV were assessed by standard deviation of blood glucose (SDBG), coefficient of variation (CV), and mean amplitude of glycemic excursions (MAGE), respectively. Robust regression analyses were performed to identify the factors associated with GV. Restricted cubic splines were used to determine dose-response relationship.

RESULTS

During the nocturnal period, age and glycemic level at 12:00 A.M. were positively associated with GV, whereas alanine aminotransferase was negatively associated with GV. During the diurnal period, homeostatic model assessment 2-insulin sensitivity (HOMA2-S) was positively associated with GV, whereas insulin secretion-sensitivity index-2 (ISSI2) was negatively associated with GV. Additionally, we found a J-shape association between the glycemic level at 12:00 A.M. and MAGE, with 9.0 mmol/L blood glucose level as a cutoff point. Similar nonlinear associations were found between ISSI2 and SDBG, and between ISSI2 and MAGE, with ISSI2 value of 175 as a cutoff point.

CONCLUSION

Factors associated with GV were different between nocturnal and diurnal periods. The cutoff points we found in this study may provide the therapeutic targets for beta-cell function and pre-sleep glycemic level in clinical practice.

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.

Interaction between haptoglobin genotype and glycemic variability on diabetic macroangiopathy: a population-based cross-sectional study

PURPOSE

Haptoglobin (Hp) is a hemoglobin-binding protein that functions as an antioxidant in human plasma. It is reported that glycemic variability (GV) plays a key role in diabetes-related complications associated with impaired glucose metabolism and oxidative stress. Here we aim to investigate whether the effect of GV on diabetic macroangiopathy depends on Hp genotype in type 2 diabetes.

METHODS

A number of 860 Chinese patients with type 2 diabetes was genotyped and assigned to two Hp subgroups (Hp 2-2 and Hp 1 carriers). Glycemic variability (GV) was assessed by using a retrospective continuous glucose monitoring system for three consecutive days, and it was measured using the glucose coefficient of variation (%CV), which is calculated as the ratio of glucose standard deviation to glucose mean. Clinical features, history of cardiac surgery, and vascular imaging tests were utilized to diagnose macroangiopathy. We evaluated the interaction between Hp genotypes and %CV on diabetic macroangiopathy. Furthermore, serum concentration of 8-hydroxy-2'-deoxyguanosine (8-OHdG) was measured using an enzyme-linked immunosorbent assay as a biomarker of oxidative stress.

RESULTS

Serum 8-OHdG levels were positively correlated with %CV in Hp 1 carriers (r = 0.117; p = 0.021). Patients in the highest %CV tertile were associated with a higher prevalence of diabetic macroangiopathy than those in the lowest %CV tertile in Hp 1 carriers (OR = 2.461 [95% CI, 1.183-5.121], p = 0.016), but not in those with Hp 2-2 genotype (OR = 0.540 [95% CI, 0.245-1.191], p = 0.127). A significant interactive effect of Hp genotypes and %CV on diabetic macroangiopathy was found (p interaction = 0.008).

CONCLUSION

Hp genotype modifies the effect of GV on diabetic macroangiopathy among Chinese patients with type 2 diabetes.

Hypoglycemia: Turning the tide on crashing blood glucose

ABSTRACT

Primary care NPs are central to the management of diabetes mellitus, which carries with it the risk of hypoglycemia. Fully understanding risk factors, prevention strategies, and treatment assist in reducing hypoglycemic events. This article details hypoglycemia, risk factors for hypoglycemia, prevention strategies, and appropriate treatment plans.

Optimized glycemic control of type 2 diabetes with reinforcement learning: a proof-of-concept trial

The personalized titration and optimization of insulin regimens for treatment of type 2 diabetes (T2D) are resource-demanding healthcare tasks. Here we propose a model-based reinforcement learning (RL) framework (called RL-DITR), which learns the optimal insulin regimen by analyzing glycemic state rewards through patient model interactions. When evaluated during the development phase for managing hospitalized patients with T2D, RL-DITR achieved superior insulin titration optimization (mean absolute error (MAE) of 1.10 ± 0.03 U) compared to other deep learning models and standard clinical methods. We performed a stepwise clinical validation of the artificial intelligence system from simulation to deployment, demonstrating better performance in glycemic control in inpatients compared to junior and intermediate-level physicians through quantitative (MAE of 1.18 ± 0.09 U) and qualitative metrics from a blinded review. Additionally, we conducted a single-arm, patient-blinded, proof-of-concept feasibility trial in 16 patients with T2D. The primary outcome was difference in mean daily capillary blood glucose during the trial, which decreased from 11.1 (±3.6) to 8.6 (±2.4) mmol L-1 (P < 0.01), meeting the pre-specified endpoint. No episodes of severe hypoglycemia or hyperglycemia with ketosis occurred. These preliminary results warrant further investigation in larger, more diverse clinical studies. ClinicalTrials.gov registration: NCT05409391 .

Glucose variability: a new risk factor for cardiovascular disease

AIMS AND DATA SYNTHESIS

Glucose variability (GV) is increasingly considered an additional index of glycemic control. Growing evidence indicates that GV is associated with diabetic vascular complications, thus being a relevant point to address in diabetes management. GV can be measured using various parameters, but to date, a gold standard has not been identified. This underscores the need for further studies in this field also to identify the optimal treatment.

CONCLUSIONS

We reviewed the definition of GV, the pathogenetic mechanisms of atherosclerosis, and its relationship with diabetic complications.

Predicting Cardiovascular Disease Mortality: Leveraging Machine Learning for Comprehensive Assessment of Health and Nutrition Variables

Cardiovascular disease (CVD) is one of the primary causes of death around the world. This study aimed to identify risk factors associated with CVD mortality using data from the National Health and Nutrition Examination Survey (NHANES). We created three models focusing on dietary data, non-diet-related health data, and a combination of both. Machine learning (ML) models, particularly the random forest algorithm, demonstrated robust consistency across health, nutrition, and mixed categories in predicting death from CVD. Shapley additive explanation (SHAP) values showed age, systolic blood pressure, and several other health factors as crucial variables, while fiber, calcium, and vitamin E, among others, were significant nutritional variables. Our research emphasizes the importance of comprehensive health evaluation and dietary intake in predicting CVD mortality. The inclusion of nutrition variables improved the performance of our models, underscoring the utility of dietary intake in ML-based data analysis. Further investigation using large datasets with recurring dietary recalls is necessary to enhance the effectiveness and interpretability of such models.

Low Blood Glucose Index Associated with Cardiovascular Events in Diabetic Hemodialysis Patients

INTRODUCTION

Blood glucose monitoring was vitally important in diabetic kidney disease (DKD) patients for preventing complications and improving survival rates. The associations between glycemic variability and blood biochemical indicators were underestimated in patients with DKD undergoing hemodialysis. Therefore, we primarily aimed to investigate the glycemic variability and 1-year risk of cardiovascular disease events in diabetic hemodialysis patients. And we secondarily aimed to explore the association between glycemic variability and blood biochemical indicators.

METHODS

In total, 27 patients were included in the final analysis. Continuous glucose monitoring (CGM) was used to evaluate glucose variability for 14 days. Patients were divided into two groups by the cutoff level of time in range (TIR; >70% or ≤70%). The three-point major adverse cardiovascular event (3P MACE) was recorded within 1 year.

RESULTS

After 1 year of follow-up, 4 patients in the high-TIR group and 3 patients in the low-TIR group had 3p MACE. Higher low blood glucose index (LBGI) level in diabetic hemodialysis patients increased the risk of 3p MACE outcomes (HR = 2.37, p = 0.018). And the level of albumin was positively associated with LBGI (β = 0.51, p = 0.036). The plasma levels of albumin, glycosylated hemoglobin, and hemoglobin were positively associated with other CGM parameters.

CONCLUSION

LBGI during 14 days was positively associated with the risk of cardiovascular events in diabetic hemodialysis patients.

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.

Effects of a low-carbohydrate diet in adults with type 1 diabetes management: A single arm non-randomised clinical trial

Public interest in low-carbohydrate (LC) diets for type 1 diabetes (T1D) management has increased. This study compared the effects of a healthcare professional delivered LC diet compared to habitual diets higher in carbohydrates on clinical outcomes in adults with T1D. Twenty adults (18-70 yrs) with T1D (≥6 months duration) with suboptimal glycaemic control (HbA1c>7.0% or >53 mmol/mol) participated in a 16-week single arm within-participant, controlled intervention study involving a 4-week control period following their habitual diets (>150 g/day of carbohydrates) and a 12-week intervention period following a LC diet (25-75 g/day of carbohydrates) delivered remotely by a registered dietitian. Glycated haemoglobin (HbA1c -primary outcome), time in range (blood glucose: 3.5-10.0 mmol/L), frequency of hypoglycaemia (<3.5 mmol/L), total daily insulin, and quality of life were assessed before and after the control and intervention periods. Sixteen participants completed the study. During the intervention period, there were reductions in total dietary carbohydrate intake (214 to 63 g/day; P<0.001), HbA1c (7.7 to 7.1% or 61 to 54 mmol/mol; P = 0.003) and total daily insulin use (65 to 49 U/day; P<0.001), increased time spent in range (59 to 74%; P<0.001), and improved quality of life (P = 0.015), with no significant changes observed during the control period. Frequency of hypoglycaemia episodes did not differ across timepoints, and no episodes of ketoacidosis or other adverse events were reported during the intervention period. These preliminary findings suggest that a professionally supported LC diet may lead to improvements in markers of blood glucose control and quality of life with reduced exogenous insulin requirements and no evidence of increased hypoglycaemia or ketoacidosis risk in adults with T1D. Given the potential benefits of this intervention, larger, longer-term randomised controlled trials are warranted to confirm these findings. Trial Registration: https://www.anzctr.org.au/ACTRN12621000764831.aspx.

Type 2 diabetes and in-hospital sudden cardiac arrest in ST-elevation myocardial infarction in the US

Aims

We aimed to assess the impact of diabetes on sudden cardiac arrest (SCA) in US patients hospitalized for ST-elevation myocardial infarction (STEMI).

Methods

We used the National Inpatient Sample (2005-2017) data to identify adult patients with STEMI. The primary outcome was in-hospital SCA. Secondary outcomes included in-hospital mortality, ventricular tachycardia (VT), ventricular fibrillation (VF), cardiogenic shock (CS), acute renal failure (ARF), and the revascularization strategy in SCA patients.

Results

SCA significantly increased from 4% in 2005 to 7.6% in 2018 in diabetes patients and from 3% in 2005 to 4.6% in 2018 in non-diabetes ones (p < 0.001 for both). Further, diabetes was associated with an increased risk of SCA [aOR = 1.432 (1.336-1.707)]. In SCA patients with diabetes, the mean age (SD) decreased from 68 (13) to 66 (11) years old, and mortality decreased from 65.7% to 49.3% during the observation period (p < 0.001). Compared to non-diabetes patients, those with T2DM had a higher adjusted risk of mortality, ARF, and CS [aOR = 1.72 (1.62-1.83), 1.52 (1.43-1.63), 1.25 (1.17-1.33); respectively] but not VF or VT. Those patients were more likely to undergo revascularization with CABG [aOR = 1.197 (1.065-1.345)] but less likely to undergo PCI [aOR = 0.708 (0.664-0.754)].

Conclusion

Diabetes is associated with an increased risk of sudden cardiac arrest in ST-elevation myocardial infarction. It is also associated with a higher mortality risk in SCA patients. However, the recent temporal mortality trend in SCA patients shows a steady decline, irrespective of diabetes.

Telemonitoring With a Connected Glucose Meter Improves Glycemia Among People With Insulin-Treated Type 2 Diabetes

BACKGROUND

Delayed initiation and inadequate titration remain critical challenges to optimizing insulin therapy in type 2 diabetes (T2D). We aimed to study whether hemoglobin A1c (HbA1c) can be lowered in people with insulin-treated T2D using telemonitoring.

METHODS

This single-center study recruited adults with greater than or equal to six months of diabetes, greater than or equal to three months of insulin therapy, HbA1c ≥8.5% and ≤12.5%, and body mass index (BMI) ≤40 kg/m2. All participants received a connected glucose meter and the accompanying smartphone application. Participants sent weekly blood glucose (BG) diary to their primary endocrinologist via email. Adjustments in insulin doses were communicated to the participants. HbA1c, proportion of BG readings in range (70-180 mg/dL, PIR), below range (<70 mg/dL, PBR) and above range (>180 mg/dL, PAR), and glycemic variability as the coefficient of variation (% CV) were measured at baseline, week 12, and week 24 and compared using repeated-measures analysis of variance (ANOVA) or Friedman's ANOVA.

RESULTS

We recruited 40 people (55% women). Mean age was 57.9 years, BMI 27.8 kg/m2, and baseline HbA1c 9.8% (83.7 mmol/mol). Mean HbA1c improved by 1.7%, % CV reduced from 32.9% to 30.7%, PIR increased from 58.8% to 67.1% (all P <.01) by week 24, without any change in PBR. This was achieved with a 0.04 U/kg/d median increase in total daily dose of insulin and 0.9 kg weight gain over 24 weeks.

CONCLUSION

Telemonitoring and titration of insulin using a connected glucose meter resulted in significant improvements in glycemia, characterized by a reduction in HbA1c, increase in PIR, and reduction in glycemic variability without any increase in hypoglycemia.

Time in Range Estimation in Patients with Type 2 Diabetes is Improved by Incorporating Fasting and Postprandial Glucose Levels

INTRODUCTION

Time in range (TIR) as assessed by continuous glucose monitoring (CGM) measures an individual's glucose fluctuations within set limits in a time period and is increasingly used together with HbA1c in patients with diabetes. HbA1c indicates the average glucose concentration but provides no information on glucose fluctuation. However, before CGM becomes available for patients with type 2 diabetes (T2D) worldwide, especially in developing nations, fasting plasma glucose (FPG) and postprandial plasma glucose (PPG) are still the common biomarkers used for monitoring diabetes conditions. We investigated the importance of FPG and PPG to glucose fluctuation in patients with T2D. We used machine learning to provide a new estimate of TIR based on the HbA1c, together with FPG and PPG.

METHODS

This study included 399 patients with T2D. (1) Univariate and (2) multivariate linear regression models and (3) random forest regression models were developed to predict the TIR. Subgroup analysis was performed in the newly diagnosed T2D population to explore and optimize the prediction model for patients with different disease history.

RESULTS

Regression analysis suggests that FPG was strongly linked to minimum glucose, while PPG was strongly correlated with maximum glucose. After FPG and PPG were incorporated into the multivariate linear regression model, the prediction performance of TIR was improved compared with the univariate correlation between HbA1c and TIR, and the correlation coefficient (95% CI) increased from 0.62 (0.59, 0.65) to 0.73 (0.72, 0.75) (p < 0.001). The random forest model significantly outperformed the linear model (p < 0.001) in predicting TIR through FPG, PPG and HbA1c, with a stronger correlation coefficient 0.79 (0.79, 0.80).

CONCLUSIONS

The results offered a comprehensive understanding of glucose fluctuations through FPG and PPG compared to HbA1c alone. Our novel TIR prediction model based on random forest regression with FPG, PPG, and HbA1c provides a better prediction performance than the univariate model with solely HbA1c. The results indicate a nonlinear relationship between TIR and glycaemic parameters. Our results suggest that machine learning may have the potential to be used in developing better models for understanding patients' disease status and providing necessary interventions for glycaemic control.

A Comparative Cross-Sectional Study of Cardiac Autonomic Status by Five Minute Heart Rate Variability among Type 2 Diabetics, Hypertensives and Normotensive-Nondiabetics

Background

Diabetes and hypertension are known to co-exist frequently as adverse cardiovascular risk factors. Both can produce cardiac autonomic neuropathythat can be measured by ECG RR interval-based heart rate variability (HRV). We compared 5 minutes HRV in four groups based on diabetes and hypertension.

Methodology

A cross sectional study was done on 203 participants divided into four groups- diabetics, hypertensives, diabetic-hypertensives and normotensive-nondiabetics. They were evaluated for current disease control and five minutes HRV was done in supine condition following standard protocols by Variowin HR Software. HRV parameters of time domain, frequency domain and Poincare plot were compared between groups and associated with gender, glycaemic control and blood pressure control. Statistical significance was set at p<0.05.

Results

Three diseasedgroups had mean age in mid-fifties, mean duration of disease > 6 years, comparable BMI, poor glycaemic and blood pressure control. As compared to normal groups, three diseased groups exhibit reduced HRV with respect to all three domains of HRV with varying statistical significance. Among diseased groups, HRV was associated with blood pressure control better than glycaemic control but not with gender. LF /HF ratio was the most consistent HRV parameter showing statistical significance in tests.

Conclusion

HRV is reduced in both diabetics more than hypertensives; related to blood pressure control more than glycaemic control. It points altered cardiac autonomic balance, and possibility of cardiovascular risk and early detection of it with timely intervention. It also calls for investigation of same for reinforcement of our observations and further exploration.

Trends in Passive IoT Biomarker Monitoring and Machine Learning for Cardiovascular Disease Management in the U.S. Elderly Population

It is predicted that the growth in the U.S. elderly population alongside continued growth in chronic disease prevalence will further strain an already overburdened healthcare system and could compromise the delivery of equitable care. Current trends in technology are demonstrating successful application of artificial intelligence (AI) and machine learning (ML) to biomarkers of cardiovascular disease (CVD) using longitudinal data collected passively from internet-of-things (IoT) platforms deployed among the elderly population. These systems are growing in sophistication and deployed across evermore use-cases, presenting new opportunities and challenges for innovators and caregivers alike. IoT sensor development that incorporates greater levels of passivity will increase the likelihood of continued growth in device adoption among the geriatric population for longitudinal health data collection which will benefit a variety of CVD applications. This growth in IoT sensor development and longitudinal data acquisition is paralleled by the growth in ML approaches that continue to provide promising avenues for better geriatric care through higher personalization, more real-time feedback, and prognostic insights that may help prevent downstream complications and relieve strain on the healthcare system overall. However, findings that identify differences in longitudinal biomarker interpretations between elderly populations and relatively younger populations highlights the necessity that ML approaches that use data from newly developed passive IoT systems should collect more data on this target population and more clinical trials will help elucidate the extent of benefits and risks from these data driven approaches to remote care.

Hyperglycemia and Glycemic Variability Associated with Glucocorticoids in Women without Pre-Existing Diabetes Undergoing Neoadjuvant or Adjuvant Taxane Chemotherapy for Early-Stage Breast Cancer

Glucocorticoids, which are administered with chemotherapy, cause hyperglycemia. Glycemic variability among breast cancer patients without diabetes is not well known. A retrospective cohort study was conducted involving early-stage breast cancer patients without diabetes who received dexamethasone prior to neoadjuvant or adjuvant taxane chemotherapy between August 2017-December 2019. Random blood glucose levels were analyzed, and steroid-induced hyperglycemia (SIH) was defined as a random glucose level of >140 mg/dL. A multivariate proportional hazards model was used to identify the risk factors of SIH. Out of 100 patients, the median age was 53 years (IQR: 45-63.5). A total of 45% of patients were non-Hispanic White, 28% Hispanic, 19% Asian, and 5% African American. The incidence of SIH was 67%, and glycemic fluctuations were highest in those with glucose levels of >200 mg/dL. Non-Hispanic White patients represented a significant predictor for time to SIH, with a hazard ratio of 2.5 (95% CI: 1.04, 5.95, p = 0.039). SIH was transient in over 90% of the patients, and only seven patients remained hyperglycemic after glucocorticoid and chemotherapy completion. Pretaxane dexamethasone-induced hyperglycemia was observed in 67% of the patients, with the greatest glycemic lability in those patients with blood glucose levels of >200 mg/dL. The non-Hispanic White patients had a higher risk of developing SIH.

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.

Bariatric surgery normalizes diabetes risk index by one month post-operation

AIM

The Diabetes risk index (DRI) is a composite of NMR-measured lipoproteins and branched chain amino acids predictive of diabetes mellitus development. Bariatric surgery is indicated in patients with severe obesity, many of whom are at high-risk for developing diabetes. Substantial weight loss occurs following bariatric surgery and sustained weight loss likely contributes to reductions in the development of diabetes and cardiovascular disease. However, some evidence suggests that bariatric surgical procedures themselves may contribute to reducing risk of these conditions independent of weight loss. We aimed to investigate DRI and its association with reductions in body weight and adiposity over one year following bariatric surgery.

METHODS

We examined 51 severely obese premenopausal women without diabetes. DRI, BMI, body weight and waist measurements were made before and at 1, 6 and 12 months after Roux-en-Y Gastric Bypass (RYGB) or Sleeve Gastrectomy. Values were compared to healthy women with normal BMI (18.5-24.9 kg/m2; n = 15).

RESULTS

Non-diabetic women with severe obesity (BMI 44.7 ± 6.2 kg/m2) exhibited significantly elevated DRI scores prior to surgery versus controls (35 [26, 39] vs 12 [1, 20]; p < 0.0001). At 1 month after surgery, BMI decreased 5.1 ± 1.1 kg/m2, but DRI decreased so that it no longer differed from that of normal BMI controls (1.9 [1, 17] vs control 12 [1, 20]; p = 0.35). Subjects continued to lose weight, whereas DRI remained similar, throughout follow-up with DRI 1.0 [1, 7] at 12 months. Changes in DRI did not correlate with changes in BMI, body weight or waist circumference at any time during follow-up. There was no difference in change in DRI between surgical procedures or pre-operative metabolic syndrome status.

CONCLUSIONS

Our analysis of DRI scores supports the capacity of bariatric surgery to reduce risk of developing diabetes in severely obese individuals. Our findings suggest that bariatric surgical techniques may have inherent effects that improve cardiometabolic risk independent of reductions in body weight or adiposity.

Longitudinal HbA1c trajectory modelling reveals the association of HbA1c and risk of hospitalization for heart failure for patients with type 2 diabetes mellitus

BACKGROUND

Inconsistent conclusions in past studies on the association between poor glycaemic control and the risk of hospitalization for heart failure (HHF) have been reported largely due to the analysis of non-trajectory-based HbA1c values. Trajectory analysis can incorporate the effects of HbA1c variability across time, which may better elucidate its association with macrovascular complications. Furthermore, studies analysing the relationship between HbA1c trajectories from diabetes diagnosis and the occurrence of HHF are scarce.

METHODS

This is a prospective cohort study of the SingHealth Diabetes Registry (SDR). 17,389 patients diagnosed with type 2 diabetes mellitus (T2DM) from 2013 to 2016 with clinical records extending to the end of 2019 were included in the latent class growth analysis to extract longitudinal HbA1c trajectories. Association between HbA1c trajectories and risk of first known HHF is quantified with the Cox Proportional Hazards (PH) model.

RESULTS

5 distinct HbA1c trajectories were identified as 1. low stable (36.1%), 2. elevated stable (40.4%), 3. high decreasing (3.5%), 4. high with a sharp decline (10.8%), and 5. moderate decreasing (9.2%) over the study period of 7 years. Poorly controlled HbA1c trajectories (Classes 3, 4, and 5) are associated with a higher risk of HHF. Using the diabetes diagnosis time instead of a commonly used pre-defined study start time or time from recruitment has an impact on HbA1c clustering results.

CONCLUSIONS

Findings suggest that tracking the evolution of HbA1c with time has its importance in assessing the HHF risk of T2DM patients, and T2DM diagnosis time as a baseline is strongly recommended in HbA1c trajectory modelling. To the authors' knowledge, this is the first study to identify an association between HbA1c trajectories and HHF occurrence from diabetes diagnosis time.

Vitamin C supplementation for diabetes management: A comprehensive narrative review

Growing evidence suggests that vitamin C supplementation may be an effective adjunct therapy in the management of people with diabetes. This paper critically reviews the current evidence on effects of vitamin C supplementation and its potential mechanisms in diabetes management. Evidence from meta-analyses of randomized controlled trials (RCTs) show favourable effects of vitamin C on glycaemic control and blood pressure that may be clinically meaningful, and mixed effects on blood lipids and endothelial function. However, evidence is mostly of low evidence certainty. Emerging evidence is promising for effects of vitamin C supplementation on some diabetes complications, particularly diabetic foot ulcers. However, there is a notable lack of robust and well-designed studies exploring effects of vitamin C as a single compound supplement on diabetes prevention and patient-important outcomes (i.e. prevention and amelioration of diabetes complications). RCTs are also required to investigate potential preventative or ameliorative effects of vitamin C on gestational diabetes outcomes. Oral vitamin C doses of 500-1000 mg per day are potentially effective, safe, and affordable for many individuals with diabetes. However, personalisation of supplementation regimens that consider factors such as vitamin C status, disease status, current glycaemic control, vitamin C intake, redox status, and genotype is important to optimize vitamin C's therapeutic effects safely. Finally, given a high prevalence of vitamin C deficiency in patients with complications, it is recommended that plasma vitamin C concentration be measured and monitored in the clinic setting.

Reconsidering the role of glycaemic control in cardiovascular disease risk in type 2 diabetes: A 21st century assessment

It is well known that the multiple factors contributing to the pathogenesis of type 2 diabetes (T2D) confer an increased risk of developing cardiovascular disease (CVD). Although the relationship between hyperglycaemia and increased microvascular risk is well established, the relative contribution of hyperglycaemia to macrovascular events has been strongly debated, particularly owing to the failure of attempts to reduce CVD risk through normalizing glycaemia with traditional therapies in high-risk populations. The debate has been further fuelled by the relatively recent discovery of the cardioprotective properties of glucagon-like peptide-1 receptor agonists and sodium-glucose cotransporter-2 inhibitors. Further, as guidelines now recommend individualizing glycaemic targets, highlighting the importance of achieving glycated haemoglobin (HbA1c) goals safely, the previously observed negative influences of intensive therapy on CVD risk might not present if trials were repeated using current-day treatments and individualized HbA1c goals. Emerging longitudinal data illuminate the overall effect of excess glucose, the impacts of magnitude and duration of hyperglycaemia on disease progression and risk of CVD complications, and the importance of glycaemic control at or early after diagnosis of T2D for prevention of complications. Herein, we review the role of glucose as a modifiable cardiovascular (CV) risk factor, the role of microvascular disease in predicting macrovascular risk, and the deleterious impact of therapeutic inertia on CVD risk. We reconcile new and old data to offer a current perspective, highlighting the importance of effective, early treatment in reducing latent CV risk, and the timely use of appropriate therapy individualized to each patient's needs.

Comparison of the effects of triglyceride variability and exposure estimate on clinical prognosis in diabetic patients

BACKGROUND

Hypertriglyceridemia is an important feature of dyslipidemia in type 1 and type 2 diabetic patients and associated with the development of atherosclerotic cardiovascular disease. Recently, variability of lipid profile has been suggested as a residual risk factor for cardiovascular disease. This study compared the clinical impact of serum triglyceride variability, and their cumulative exposure estimates on cardiovascular prognosis in diabetic patients.

METHODS

A total of 25,933 diabetic patients who had serum triglyceride levels measured at least 3 times and did not have underlying malignancy, myocardial infarction (MI), and stroke during the initial 3 years (modeling phase) were selected from three tertiary hospitals. They were divided into a high/low group depending on their coefficient of variation (CV) and cumulative exposure estimate (CEE). Incidence of major adverse event (MAE), a composite of all-cause death, MI, and stroke during the following 5 years were compared between groups by multivariable analysis after propensity score matching.

RESULTS

Although there was a slight difference, both the high CV group and the high CEE group had a higher cardiovascular risk profile including male-dominance, smoking, alcohol, dyslipidemia, and chronic kidney disease compared to the low groups. After the propensity score matching, the high CV group showed higher MAE incidence compared to the low CV group (9.1% vs 7.7%, p = 0.01). In contrast, there was no significant difference of MAE incidence between the high CEE group and the low CEE group (8.6% vs 9.1%, p = 0.44). After the multivariable analysis with further adjustment for potential residual confounding factors, the high CV was suggested as an independent risk predictor for MAE (HR 1.19 [95% CI 1.03-1.37]).

CONCLUSION

Visit-to-visit variability of triglyceride rather than their cumulative exposure is more strongly related to the incidence of MAE in diabetic patients.

Association of visit-to-visit HbA1c variability with cardiovascular diseases in type 2 diabetes within or outside the target range of HbA1c

Background

Although a growing attention has been recently paid to the role of HbA1c variability in the risk of diabetic complications, the impact of HbA1c variability on cardiovascular diseases (CVD) in type 2 diabetes is still debated. The aim of the study is to investigate the association of HbA1c variability with CVD in individuals within or outside the target range of HbA1c.

Methods

Using data from Action in Diabetes and Vascular disease: preterAx and diamicroN-MR Controlled Evaluation (ADVANCE), we enrolled 855 patients with type 2 diabetes in China. The primary outcomes included major macrovascular events and major microvascular events. Visit-to-visit HbA1c variability was expressed as the coefficient of variation (CV) of five measurements of HbA1c taken 3-24 months after treatment. Cox proportional hazard models were used to estimate adjusted hazard ratios (aHR).

Results

Among 855 patients in the intensive glucose treatment group, 563 and 292 patients were assigned to the group of "within the target range of HbA1c" (WTH) (updated mean HbA1c ≤ 7.0%) and "outside the target range of HbA1c" (OTH) (updated mean HbA1c > 7.0%), respectively. HbA1c variability was positively associated with the risk of major microvascular events in all patients and both the subgroups during a median follow-up period of 4.8 years. Particularly, the risk related to HbA1c variability was higher in patients in WTH group for the new or worsening nephropathy [aHR: 3.35; 95% confidence interval (CI): 1.05-10.74; P = 0.042].

Conclusions

This retrospective cohort study confirmed the positive correlation between HbA1c variability and major microvascular events, especially in subjects in WTH or OTH.

Association of glycated albumin to hemoglobin A1c ratio with all-cause and cardiovascular mortality among US adults: A population-based cohort study

AIMS

To investigate the association of glycated albumin to hemoglobin A1c (GA/HbA1c) ratio, an indicator of blood glucose fluctuations, with all-cause and cardiovascular mortality among US adults.

METHODS

This cohort study used data from the National Health and Nutrition Examination Survey 1999-2004. Participants were linked to National Death Index mortality data through December 31, 2015. Cox proportional hazards model was used to calculate hazard ratios (HRs) and 95% confidence intervals (CIs), and restricted cubic spline (RCS) regression was conducted.

RESULTS

A total of 11,508 US adults (weighted mean age, 43.9 years; 5748 males [weighted, 48.9 %]) were included. During a median follow‑up of 13.6 years, 1963 total deaths occurred, including 383 cardiovascular deaths. After multivariable adjustments, a higher GA/HbA1c ratio was associated with a higher risk of all-cause (tertiles: P for trend < 0.001; continuous: HR 1.49 [95 % CI 1.32-1.69]) and cardiovascular (tertiles: P for trend = 0.048; continuous: HR 1.65 [95 % CI 1.27-2.14]) mortality. RCS revealed a linear relationship of GA/HbA1c ratio to mortality.

CONCLUSIONS

In the nationally representative cohort of US adults, GA/HbA1c ratio was significantly associated with the risk of all-cause and cardiovascular mortality. These findings suggest that GA/HbA1c ratio may serve as an effective indicator for identifying high-risk individuals.

Longitudinal Adherence to Diabetes Quality Indicators and Cardiac Disease: A Nationwide Population-Based Historical Cohort Study of Patients With Pharmacologically Treated Diabetes

Background Evidence of the cardiovascular benefits of adherence to quality indicators in diabetes care over a period of years is lacking. Methods and Results We conducted a population-based, historical cohort study of 105 656 people aged 45 to 80 with pharmacologically treated diabetes and who were free of cardiac disease in 2010. Data were retrieved from electronic medical records of the 4 Israeli health maintenance organizations. The association between level of adherence to national quality indicators (2006-2010: adherence assessment) and incidence of cardiac outcome; ischemic heart disease or heart failure (2011-2016: outcome assessment) was estimated using Cox proportional hazards models. During 529 551 person-years of follow-up, 19 246 patients experienced cardiac disease. An inverse dose-response association between the level of adherence and risk of cardiac morbidity was shown for most of the quality indicators. The associations were modified by age, with stronger associations among younger patients (<65 years). Low adherence to low-density lipoprotein cholesterol testing (≤2 years) during the first 5 years was associated with 41% increased risk of cardiac morbidity among younger patients. Patients who had uncontrolled low-density lipoprotein cholesterol in all first 5 years had hazard ratios of 1.60 (95% CI, 1.49-1.72) and 1.23 (95% CI, 1.14-1.32), among patients aged <65 and ≥65 years, respectively, compared with those who achieved target level. Patients who failed to achieve target levels of glycated hemoglobin or blood pressure had an increased risk (hazard ratios, 1.50-1.69) for cardiac outcomes. Conclusions Longitudinal adherence to quality indicators in diabetes care is associated with reduced risk of cardiac morbidity. Implementation of programs that measure and enhance quality of care may improve the health outcomes of people with diabetes.

Role of continuous glucose monitoring in diabetic patients at high cardiovascular risk: an expert-based multidisciplinary Delphi consensus

Background

Continuous glucose monitoring (CGM) shows in more detail the glycaemic pattern of diabetic subjects and provides several new parameters (“glucometrics”) to assess patients’ glycaemia and consensually guide treatment. A better control of glucose levels might result in improvement of clinical outcome and reduce disease complications. This study aimed to gather an expert consensus on the clinical and prognostic use of CGM in diabetic patients at high cardiovascular risk or with heart disease.

Methods

A list of 22 statements concerning type of patients who can benefit from CGM, prognostic impact of CGM in diabetic patients with heart disease, CGM use during acute cardiovascular events and educational issues of CGM were developed. Using a two-round Delphi methodology, the survey was distributed online to 42 Italian experts (21 diabetologists and 21 cardiologists) who rated their level of agreement with each statement on a 5-point Likert scale. Consensus was predefined as more than 66% of the panel agreeing/disagreeing with any given statement.

Results

Forty experts (95%) answered the survey. Every statement achieved a positive consensus. In particular, the panel expressed the feeling that CGM can be prognostically relevant for every diabetic patient (70%) and that is clinically useful also in the management of those with type 2 diabetes not treated with insulin (87.5%). The assessment of time in range (TIR), glycaemic variability (GV) and hypoglycaemic/hyperglycaemic episodes were considered relevant in the management of diabetic patients with heart disease (92.5% for TIR, 95% for GV, 97.5% for time spent in hypoglycaemia) and can improve the prognosis of those with ischaemic heart disease (100% for hypoglycaemia, 90% for hyperglycaemia) or with heart failure (87.5% for hypoglycaemia, 85% for TIR, 87.5% for GV). The experts retained that CGM can be used and can impact the short- and long-term prognosis during an acute cardiovascular event. Lastly, CGM has a recognized educational role for diabetic subjects.

Conclusions

According to this Delphi consensus, the clinical and prognostic use of CGM in diabetic patients at high cardiovascular risk is promising and deserves dedicated studies to confirm the experts’ feelings.

Does Aerobic plus Machine-Assisted Resistance Training Improve Vascular Function in Type 2 Diabetes? A Systematic Review and Meta-Analysis of Randomized Controlled Trials with Trial Sequential Analysis

Type 2 diabetes mellitus (T2DM) is a chronic disease characterized by hyperglycemia, insulin resistance, and pancreatic B cell dysfunction. Hyperglycemia can cause several complications, including nephrological, neurological, ophthalmological, and vascular complications. Many modalities, such as medication, physical therapies, and exercise, are developed against vascular disorders. Among all exercise forms, aerobic plus machine-assisted resistance training is widely applied. However, whether this intervention can significantly improve vascular conditions remains controversial. In this study, an electronic search was processed for the Pubmed, Embase, and Cochrane libraries for randomized controlled trials (RCTs) comparing the efficacy of aerobic plus machine-assisted resistance training with no exercise (control) on patients with T2DM. Pulse wave velocity (PWV), the index of arterial stiffness, was chosen as primary outcome. The reliability of the pooled outcome was tested by trial sequential analysis (TSA). Secondary outcomes included systolic blood pressure (SBP) and hemoglobin A1c (HbA1c). Finally, five RCTs with a total of 328 patients were included. Compared with control, aerobic plus machine-assisted resistance training failed to provide significant improvement on PWV (MD −0.54 m/s, 95% CI [−1.69, 0.60], p = 0.35). On the other hand, TSA indicated that this results till needs more verifications. Additionally, this training protocol did not significantly decrease SBP (MD −1.05 mmHg, 95% CI [−3.71, 1.61], p = 0.44), but significantly reduced the level of HbA1c (MD −0.55%, 95% CI [−0.88, −0.22], p = 0.001). In conclusion, this meta-analysis failed to detect a direct benefit of aerobic plus machine-assisted resistance training on vascular condition in T2DM population. Yet the improvement in HbA1c implied a potential of this training method in mitigating vascular damage. More studies are needed to verify the benefit.

Impact of short-term glycemic variability on risk of all-cause mortality in type 2 diabetes patients with well-controlled glucose profile by continuous glucose monitoring: A prospective cohort study

AIMS

To investigate the association between short-term glycemic variability (GV) and all-cause mortality in type 2 diabetes with well-controlled glucose profile by continuous glucose monitoring (CGM).

METHODS

In this prospective study, 1839 diabetes patients who reached percentage of time in the target glucose range of 3.9-10 mmol/L > 70%, percentage of time above range of 10 mmol/L < 25% and percentage of time below range of 3.9 mmol/L < 4% on CGM were enrolled and were classified into five groups by coefficient of variation for glucose (%CV) level: ≤20%, 20-25%, 25-30%, 30-35%, and > 35%. Cox proportional hazard models were used to estimate hazard ratios (HRs) of all-cause mortality risk associated with the different %CV categories.

RESULTS

At baseline, participants had mean age of 60.9 years and mean HbA_1c of 7.3% (56 mmol/mol). A total of 165 deaths were identified during a median follow-up of 6.9 years. In multivariate Cox regression analysis, HRs associated with %CV categories were 1.00, 1.16 (95% CI 0.78-1.73), 1.38 (95% CI 0.89-2.15), 1.33 (95% CI 0.77-2.29) and 2.26 (95% CI 1.13-4.52) for all-cause mortality.

CONCLUSIONS

Greater %CV was associated with increased risk for all-cause mortality even among patients with seemingly well-controlled glucose status.

Adaptation and Psychometric Evidence of the ARABIC Version of the Diabetes Self-Management Questionnaire (A-DSMQ)

(1) Background: Diabetic patients must engage in self-care practices in order to maintain optimal glycemic control, hence reducing the likelihood of developing complications, and enhance the overall quality of their lives. The Diabetes Self-care Management Questionnaire (DSMQ) is a tool for assessing self-management habits that may be used to predict glycemic control in people with diabetes. However, no Arabic language version of the instrument has been found. Therefore, we adapted an Arabic language version of the instrument in Tunisia. The purpose of the current research aimed to assess the psychometric features of the Tunisian version of the DSMQ in patients with type 2 diabetes. (2) Method: Two samples including both genders, one exploratory (n = 208, mean age 53.2 ± 8.3) and one confirmatory (n = 441, mean age 53.4 ± 7.4), completed an adapted Arabic language version of the DSMQ, a sociodemographic questionnaire and information about their HbA1C levels. (3) Results: The exploratory factor analysis revealed that the 15 items of the A-DSMQ fit well with the data. Likewise, the alpha coefficients for the A-DSMQ factors were above 0.80: for “Glucose Management” (GM), “Dietary Control” (DC), “Physical Activity” (PA), and “Heath-Care Use” (HU). The fit indices for the CFA were good, and the four-factor solution was confirmed. The Average Variance Extracted values and Fornell–Larcker criterion established the convergent and discriminant validity, respectively. The concurrent validity of the tool was established through the statistically significant negative relationships between the A-DSMQ factors and HbA1C, in addition to its positive association with the practice of physical activity measured by the IPAQ. (4) Conclusions: Given the high EFA factor loadings, the CFA fit indices, the correlation matrix, the sensitivity analysis, the convergent validity, and the excellent internal consistency of the A-DSMQ, it can be concluded that the A-DSMQ is an effective psychometric tool for diabetes self-management in Tunisia.

Diabetes mellitus and perioperative outcomes: a scoping review of the literature

BACKGROUND

Diabetes mellitus (DM) is frequently encountered in the perioperative period. DM may increase the risk of adverse perioperative outcomes owing to the potential vascular complications of DM. We conducted a scoping review to examine the association between DM and adverse perioperative outcomes.

METHODS

A systematic search strategy of the published literature was built and applied in multiple databases. Observational studies examining the association between DM and adverse perioperative outcomes were included. Abstract screening determined full texts suitable for inclusion. Core information was extracted from each of the included studies including study design, definition of DM, type of DM, surgical specialties, and outcomes. Only primary outcomes are reported in this review.

RESULTS

The search strategy identified 2363 records. Of those, 61 were included and 28 were excluded with justification. DM was mostly defined by either haemoglobin A1c (HbA1c) or blood glucose values (19 studies each). Other definitions included 'prior diagnosis' or use of medication. In 17 studies the definition was unclear. Type 2 DM was the most frequently studied subtype. Five of seven studies found DM was associated with mortality, 5/13 reported an association with 'complications' (as a composite measure), and 12/17 studies found DM was associated with 'infection'. Overall, 33/61 studies reported that DM was associated with the primary outcome measure.

CONCLUSION

Diabetes mellitus is inconsistently defined in the published literature, which limits the potential for pooled analysis. Further research is necessary to determine which cohort of patients with DM are most at risk of adverse postoperative outcomes, and how control influences this association.

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.

Magnolol and Luteolin Inhibition of α-Glucosidase Activity: Kinetics and Type of Interaction Detected by In Vitro and In Silico Studies

Magnolol and luteolin are two natural compounds recognized in several medicinal plants widely used in traditional medicine, including type 2 diabetes mellitus. This research aimed to determine the inhibitory activity of magnolol and luteolin on α-glucosidase activity. Their biological profile was studied by multispectroscopic methods along with inhibitory kinetic analysis and computational experiments. Magnolol and luteolin decreased the enzymatic activity in a concentration-dependent manner. With 0.075 µM α-glucosidase, the IC₅₀ values were similar for both compounds (~ 32 µM) and significantly lower than for acarbose (815 μM). Magnolol showed a mixed-type antagonism, while luteolin showed a non-competitive inhibition mechanism. Thermodynamic parameters suggested that the binding of magnolol was predominantly sustained by hydrophobic interactions, while luteolin mainly exploited van der Waals contacts and hydrogen bonds. Synchronous fluorescence revealed that magnolol interacted with the target, influencing the microenvironment around tyrosine residues, and circular dichroism explained a rearrangement of the secondary structure of α-glucosidase from the initial α-helix to the final conformation enriched with β-sheet and random coil. Docking studies provided support for the experimental results. Altogether, the data propose magnolol, for the first time, as a potential α-glucosidase inhibitor and add further evidence to the inhibitory role of luteolin.