Long-Term Glycemic Variability and Vascular Complications in Type 2 Diabetes: Post Hoc Analysis of the FIELD Study

Association between glycated hemoglobin A1c levels, control status, and cognitive function in type 2 diabetes: a prospective cohort study

Cognitive impairment (CI) is a common complication in patients with type 2 diabetes mellitus (T2DM), but its relationship with long-term glycemic control remains unclear. This study aimed to investigate the associations between mean hemoglobin A1c (HbA1c) levels, HbA1c control status, HbA1c fluctuations, and CI in Chinese adults aged 45 years and older with T2DM using data from the China Health and Retirement Longitudinal Study (CHARLS). A total of 797 participants with HbA1c measurements from 2011 to 2015 and cognitive function assessments in 2018 were included. Logistic regression models and restricted cubic spline (RCS) analysis were applied, adjusting for potential confounders. Higher mean HbA1c levels (≥ 9%) were significantly associated with an increased risk of CI, particularly in global cognition and episodic memory (OR 4.03 (1.45-11.20) for global cognition; OR 2.92 (1.02-8.38) for episodic memory). RCS analysis revealed a U-shaped relationship between mean HbA1c and CI, indicating that both excessively low and high HbA1c levels elevate CI risk. Uncontrolled HbA1c levels (≥ 8%) were also linked to higher CI risk compared to stable HbA1c levels. Maintaining HbA1c levels below 8% may significantly reduce CI risk in T2DM patients, highlighting the importance of personalized glycemic management.

The Relation of Diabetes Complications to a New Interpretation of Glycaemic Variability from Continuous Glucose Monitoring in People with Type 1 Diabetes

INTRODUCTION

Microvascular and macrovascular complications in type 1 diabetes (T1D) may be linked to endothelial stress due to glycaemic variability. Continuous glucose monitoring systems (CGMs) provide new opportunities to quantify this variability, utilising the amplitude of glucose change summated over time. The aim of this study was to examine whether this determination of glucose variability (GV) is associated with microvascular clinical sequelae.

METHODS

Continuous glucose monitoring values were downloaded for 89 type 1 diabetes mellitus (T1D) individuals for up to 18 months from 2021 to 2023. Data for patient demographics was also taken from the patient record which included Sex, Date of Birth, and Date of Diagnosis. The recorded laboratory glycated haemoglobin (HbA1c) test results were also recorded. The glucose management index (GMI) was calculated from average glucose readings for 18 months using the formula GMI (%) = (0.82-(Average glucose/100)). This was then adjusted to give GMI (mmol/mol) = 10.929 * (GMI (%) - 2.15). Average Glucose Fluctuation (AGF) was calculated by adding up the total absolute change value between all recorded results over 18 months and dividing by the number of results minus one. The % Above Critical Threshold (ACT) was calculated by summing the total number of occurrences for each result value. A cumulative 95% limit was then applied to identify the glucose value that only 5% of results exceeded in the overall population. Using this value, we estimated the percentage of total tests that were above the Critical Threshold (ACT).

RESULTS

Results for the 89 individuals (44 men and 45 women) were analysed over 18 months. The mean age of participants was 43 years and the mean duration of diabetes was 18 years. A total of 3.22 million readings were analysed, giving an average of 10.3 mmol/L blood glucose. Those with the largest change in glucose from reading to reading, summated over time, showed the greatest change in eGFR of 3.12 ml/min/1.73 m2 (p = 0.007). People with a higher proportion of glucose readings > 18 mmol/L showed a fall in eGFR of 2.8 ml/min/1.73 m2 (p = 0.009) and experienced higher rates of sight-threatening retinopathy (44% of these individuals) (p = 0.01) as did 39% of individuals in the highest tertile of glucose levels (p = 0.008).

CONCLUSION

Those individuals with T1D in the highest tertile of reading-to-reading glucose change showed the greatest change in eGFR. Those with a higher proportion of glucose readings > 18 mmol/L also showed a fall in eGFR and experienced higher rates of sight-threatening retinopathy, as did people with higher mean glucose. Discussions with T1D individuals could reflect on how the percentage recorded glucose above a critical level and degree of change in glucose are important in avoiding future tissue complications.

The clinical importance of measuring glycaemic variability: Utilising new metrics to optimise glycaemic control

With the widespread use of continuous glucose monitoring (CGM), glycaemic variability (GV) is a glucose metric that has been gaining increasing attention. However, unlike other glucose metrics that are easily defined and have clear targets, GV has a large number of different measures given the complexity involved in assessment. While variabilities in HbA1c, fasting and postprandial glucose have been incorporated under the GV banner, short-term variability in glucose, within day and between days, is more in keeping with the correct definition of GV. This review is focused on short-term GV, as assessed by CGM data, although studies calculating GV from capillary glucose testing are also mentioned as appropriate. The different measures of GV are addressed, and their potential role in microvascular and macrovascular complications, as well as patient-related outcomes, discussed. It should be noted that the independent role of GV in vascular pathology is not always clear, given the inconsistent findings in different populations and the close association between GV and hypoglycaemia, itself an established risk factor for adverse outcomes. Therefore, this review attempts, where possible, to disentangle the contribution of GV to diabetes complications from other glycaemic parameters, particularly hypoglycaemia. Evidence to date strongly suggests an independent role for GV in vascular pathology but future large-scale outcome studies are required to fully understand the exact contribution of this metric to vascular complications. This can be followed by setting appropriate GV measures and targets in different diabetes subgroups, in order to optimise glycaemic management and limit the risk of complications.

Glycated haemoglobin variability and risk of renal function decline in type 2 diabetes mellitus: An updated systematic review and meta-analysis

OBJECTIVE

To assess the association between glycated haemoglobin (HbA1c) variability and risk of renal function decline in type 2 diabetes mellitus (T2DM).

RESEARCH DESIGN AND METHODS

A comprehensive search was carried out in PubMed, Embase, Web of Science and the Cochrane Library (until 12 March 2024). The Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) statement guidelines were followed for this meta-analysis. HbA1c variability was presented as indices of the standard deviation (SD), coefficient of variation (CV), HbA1c variability score (HVS) and haemoglobin glycation index (HGI). This meta-analysis was performed using random-effect models.

RESULTS

Eighteen studies met the objectives of this meta-analysis. The analyses showed positive associations between HbA1c variability and kidney function decline, with hazard ratio (HR) 1.26 (95% confidence interval [CI] 1.15-1.38) for high versus low SD groups, HR 1.47 (95% CI 1.30-1.65) for CV groups, HR 1.32 (95% CI 1.10-1.57) for HVS groups and HR 1.53 (95% CI 1.05-2.23) for HGI groups. In addition, each 1% increase in SD and CV was linked to kidney function decline, with HR 1.26 (95% CI 1.17-1.35), and 1.13 (95% CI 1.03-1.23), respectively. Also, each 1-SD increase in SD of HbA1c was associated with deterioration in renal function, with HR 1.17 (95% CI 1.07-1.29).

CONCLUSIONS

The four HbA1c variability indicators were all positively associated with renal function decline progression; therefore, HbA1c variability might play an important and promising role in guiding glycaemic control targets and predicting kidney function decline progression in T2DM.

Observational study on stability of within-day glycemic variability of type 2 diabetes inpatients treated with decoctions of traditional Chinese medicine

Background

Within-day glycemic variability (GV), characterized by frequent and significant fluctuations in blood glucose levels, is a growing concern in hospitalized patients with type 2 diabetes mellitus (T2DM). It is associated with an increased risk of hypoglycemia and potentially higher long-term mortality rates. Robust clinical evidence is needed to determine whether traditional Chinese medicine (TCM) decoctions can be a beneficial addition to the management of within-day GV in this patient population.

Methods

This retrospective cohort study utilized data from adult inpatients diagnosed with T2DM admitted to the Traditional Chinese Medicine Hospital of Kaifeng. The primary outcome investigated was the association between the use of TCM decoctions and improved stability of within-day GV. Blood glucose variability was assessed using the standard deviation of blood glucose values (SDBG). For each patient, the total number of hospitalization days with SDBG below 2 mmol/L was calculated to represent within-day GV stability. Hospitalization duration served as the secondary outcome, compared between patients receiving TCM decoctions and those who did not. The primary analysis employed a multivariable logistic regression model, with propensity score matching to account for potential confounding variables.

Results

A total of 1,360 patients were included in the final analysis. The use of TCM decoctions was significantly associated with enhanced stability of within-day GV (OR = 1.77, 95% CI: 1.34-2.33, P < 0.01). This association was most prominent in patients with a diagnosis of deficiency syndrome (predominantly qi-yin deficiency, accounting for 74.8% of cases) and a disease duration of less than 5 years (OR = 2.28, 95% CI: 1.21-4.29, P = 0.03). However, TCM decoctions did not exert a statistically significant effect on hospitalization duration among patients with T2DM (OR = 0.96, 95% CI: 0.91-1.01, P = 0.22).

Conclusion

This study suggests that TCM decoctions may be effective in improving within-day GV stability in hospitalized patients with T2DM. This effect appears to be most pronounced in patients diagnosed with deficiency syndrome, particularly those with qi-yin deficiency and a shorter disease course. Further investigation is warranted to confirm these findings and elucidate the underlying mechanisms.

Management of Type 2 Diabetes Mellitus: Synopsis of the Department of Veterans Affairs and Department of Defense Clinical Practice Guideline

The US Department of Veterans Affairs (VA) and the US Department of Defense (DoD) approved a joint clinical practice guideline for the management of type 2 diabetes. This was the product of a multidisciplinary guideline development committee composed of clinicians from both the VA and the DoD and was overseen by the VA/DoD Evidence Based Practice Work Group. The development process conformed to the standards for trustworthy guidelines as established by the National Academy of Medicine. The guideline development committee developed 12 key questions to guide an evidence synthesis. An independent third party identified relevant randomized controlled trials and systematic reviews that were published from January 2016 through April 2022. This evidence synthesis served as the basis for drafting recommendations. Twenty-six recommendations were generated and rated by the GRADE (Grading of Recommendations Assessment, Development and Evaluation) system. Two algorithms were developed to guide clinical decision-making. This synopsis summarizes key aspects of the VA/DoD Clinical Practice Guideline for diabetes in 5 areas: prediabetes, screening for co-occurring conditions, diabetes self-management education and support, glycemic treatment goals, and pharmacotherapy. The guideline is designed to help clinicians and patients make informed treatment decisions to optimize health outcomes and quality of life and to align with patient-centered goals of care.

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.

The risk factors for mental health disorders in patients with type 2 diabetes: An umbrella review of systematic reviews with and without meta-analysis

Background

Patients with type 2 diabetes have a nearly twofold higher rate of diagnosed mental disorders than those without diabetes. The association between type 2 diabetes and mental disorders is well established in the literature and recognized as a bidirectional relationship. This study aims to conduct an umbrella review of risk and protective factors for mental health disorders in patients with type 2 diabetes and assess the credibility of the evidence for the association between each factor and mental health disorders.

Methods

A comprehensive search was conducted of Medline via PubMed, Web of Science, EMBASE, CINHAL, and PsycINFO from inception to November 17, 2022, to identify systematic reviews with and without meta-analyses examining associations of factors with mental health disorders in patients with type 2 diabetes. For each association, we recalculated the summary effect size and 95% confidence intervals using random-effects models. We also reported the 95% prediction interval and between-group heterogeneity.

Results

The study included 11 systematic reviews that met the inclusion criteria, comprising eight meta-analyses and three without meta-analyses. This involved approximately 489,930 participants and encompassed 26 unique factors. Six factors were rated as having suggestive evidence at the Class III level. These factors were obesity (n = 18,456, OR 1.75 [1.2 to 2.59], I2 97.7%), neuropathy (n = 3898, OR 2.01 [1.60 to 2.54], I2 44.5%), diabetes complications (n = 1769, OR 1.90 [1.53 to 2.36], I2 39.3%), peripheral blood concentrations of CRP (n = 1742, SMD 0.31 [0.16 to 0.45], I2 84.1%), female sex (n = 35,162, OR 1.36 [1.19 to 1.54], I2 64.5%), and social support (n = 3151, OR 2.02 [1.51 to 2.70], I2 87.2%).

Conclusions

Several factors associated with mental health disorders in patients with type 2 diabetes were identified with varying degrees of supporting evidence. Significantly, obesity, neuropathy, complications, peripheral blood CRP concentrations, female sex, and social support emerged with suggestive evidence. An investigation of these factors should be conducted to target interventions accordingly. It may be helpful to prioritize patients who have these risk factors as high-risk groups and to implement plans and policies to enhance support before mental health disorders occur.

Glycemic Variability As a Prognostic Factor for Mortality in Patients With Critical Illness: A Systematic Review and Meta-Analysis

OBJECTIVES

To perform a systematic review and meta-analysis to evaluate the association of various measures of glycemic variability, including time-domain and complexity-domain, with short-term mortality in patients with critical illness.

DATA SOURCES

We searched Embase Classic +, MEDLINE, and the Cochrane Database of Systematic Reviews from inception to November 3, 2023.

STUDY SELECTION

We included English language studies that assessed metrics of glycemic variation or complexity and short-term mortality in patients admitted to the ICU.

DATA EXTRACTION

Two authors performed independent data abstraction and risk-of-bias assessments. We used a random-effects model to pool binary and continuous data and summarized estimates of effect using odds ratios and mean difference. We used the Quality in Prognosis Studies tool to assess risk of bias and the Grading of Recommendations, Assessment, Development and Evaluations to assess certainty of pooled estimates.

DATA SYNTHESIS

We included 41 studies (n = 162,259). We demonstrate that increased sd, coefficient of variance, glycemic lability index, and decreased time in range are probably associated with increased mortality in critically ill patients (moderate certainty) and that increased mean absolute glucose, mean amplitude of glycemic excursion, and detrended fluctuation analysis may be associated with increased mortality (low certainty).

CONCLUSIONS

We found a consistent association between increased measures of glycemic variability and higher short-term mortality in patient with critical illness. Further research should focus on standardized measurements of glycemic variation and complexity, along with their utility as therapeutic targets and prognostic markers.

Efficacy and safety of teneligliptin in patients with type 2 diabetes mellitus: a Bayesian network meta-analysis

Background

As a popular antidiabetic drug, teneligliptin has been used for over 10 years, but its efficacy and safety have rarely been systematically evaluated. Therefore, a Bayesian network meta-analysis was conducted to evaluate the efficacy and safety of teneligliptin in patients with type 2 diabetes mellitus (T2DM).

Methods

We systematically searched PubMed, Web of Science, Embase, Cochrane Central Register of Controlled Trials, and ClinicalTrials.gov. Randomized controlled trials (RCTs) comparing teneligliptin with placebo or active comparators in T2DM patients for at least 12 weeks were included in the study. Data analysis was performed using R 4.2.3 and Stata 17.0 software. Each outcome was presented as a mean difference (MD) or an odds ratio (OR) along with 95% confidence interval (CI) and the surface under the cumulative ranking curve value (SUCRA).

Results

A total of 18 RCTs with 3,290 participants with T2DM were included in this study. Generally, compared to placebo, sitagliptin, vildagliptin, metformin, and bromocriptine, 20 mg of teneligliptin showed better efficacy in reducing HbA1c (MD [95% CI], -0.78 [-0.86 to -0.70], -0.08 [-0.36 to 0.19], -0.04 [-0.72 to 0.60], -0.12 [-0.65 to 0.42], and -0.50 [-0.74 to -0.26], respectively) and fasting plasma glucose (FPG) (MD [95% CI], -18.02 [-20.64 to -15.13], 1.17 [-9.39 to 11.70], -8.06 [-30.95 to 14.35], -2.75 [-18.89 to 13.01], and -34.23 [-45.93 to -22.96], respectively), and 40 mg of teneligliptin also showed better efficacy in reducing HbA1c (MD [95% CI], -0.84 [-1.03 to -0.65], -0.15 [-0.49 to 0.19], -0.10 [-0.81 to 0.57], -0.18 [-0.76 to 0.39], and -0.56 [-0.88 to -0.26], respectively) and FPG (MD [95% CI], -20.40 [-26.07 to -14.57], -1.20 [-13.21 to 10.38], -10.43 [-34.16 to 12.65], -5.13 [-22.21 to 11.66], and -36.61 [-49.33 to -24.01], respectively). Compared to placebo, 20 mg of teneligliptin showed no significant difference in incidences of hypoglycemia and gastrointestinal adverse events (OR [95% CI], 1.30 [0.70 to 2.19] and 1.48 [0.78 to 2.98], respectively), and 40 mg of teneligliptin showed no significant difference in incidence of hypoglycemia (OR [95% CI], 2.63 [0.46 to 8.10]). Generally, antidiabetic effect and hypoglycemia risk of teneligliptin gradually increased as its dose increased from 5 mg to 40 mg. Compared to 20 mg of teneligliptin, 40 mg of teneligliptin showed superior efficacy and no-inferior safety, which was considered as the best option in reducing HbA1c, FPG, and 2h PPG and increasing proportion of the patients achieving HbA1c < 7% (SUCRA, 85.51%, 84.24%, 79.06%, and 85.81%, respectively) among all the included interventions.

Conclusion

Compared to sitagliptin, vildagliptin, metformin, bromocriptine, and placebo, teneligliptin displayed favorable efficacy and acceptable safety in treating T2DM. Twenty milligrams or 40 mg per day was the optimal dosage regimen of teneligliptin. The results of this study will provide important evidence-based basis for rational use of teneligliptin and clinical decision-making of T2DM medication.

Glycated hemoglobin variability and the risk of cardiovascular events in patients with prediabetes and type 2 diabetes mellitus: A post-hoc analysis of a prospective and multicenter study

AIMS/INTRODUCTION

High glycated hemoglobin (HbA1c) variability has been reported to be linked with cardiovascular events in type 2 diabetes patients. Only a few studies have been carried out on Asian patients. This study aimed to investigate the association of prediabetes and type 2 diabetes in Asian patients by performing a post-hoc analysis of a multicenter, prospective, observational study.

MATERIALS AND METHODS

Data for prediabetes and type 2 diabetes patients were retrieved from a multicenter national registry entitled "CORE-Thailand study." The primary outcome was 4P-MACE (major adverse cardiovascular events, including non-fatal myocardial infarction, heart failure hospitalization, non-fatal stroke and all-cause death). Patients were stratified according to quartiles of HbA1c standard deviation. The Cox proportional hazards regression model was used to estimate the association of HbA1c variability with incident cardiovascular disease.

RESULTS

A total of 3,811 patients with prediabetes and type 2 diabetes were included. The median follow-up duration was 54 months. In the fully adjusted model, the highest quartile of HbA1c variability showed a statistically significant association with 4P-MACE (hazard ratio [HR] 2.77, 95% confidence interval [CI] 1.77-4.35), fatal and non-fatal myocardial infarction (HR 6.91, 95% CI 1.90-25.12), hospitalization for heart failure (HR 3.34, 95% CI 1.20-9.26) and all-cause death (HR 3.10, 95% CI 1.72-5.57). All these outcomes were statistically significantly different among four quartiles of HbA1c (log-rank P-value <0.05). Fatal and non-fatal stroke showed no statistically significant association with high HbA1c variability.

CONCLUSION

High HbA1c variability in the highest quartile showed a statistically significant association with multiple adverse cardiovascular events in an Asian population. Minimizing HbA1c fluctuation during long-term follow up should be another important objective for type 2 diabetes patients.

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.

Signaling pathways and intervention for therapy of type 2 diabetes mellitus

Type 2 diabetes mellitus (T2DM) represents one of the fastest growing epidemic metabolic disorders worldwide and is a strong contributor for a broad range of comorbidities, including vascular, visual, neurological, kidney, and liver diseases. Moreover, recent data suggest a mutual interplay between T2DM and Corona Virus Disease 2019 (COVID-19). T2DM is characterized by insulin resistance (IR) and pancreatic β cell dysfunction. Pioneering discoveries throughout the past few decades have established notable links between signaling pathways and T2DM pathogenesis and therapy. Importantly, a number of signaling pathways substantially control the advancement of core pathological changes in T2DM, including IR and β cell dysfunction, as well as additional pathogenic disturbances. Accordingly, an improved understanding of these signaling pathways sheds light on tractable targets and strategies for developing and repurposing critical therapies to treat T2DM and its complications. In this review, we provide a brief overview of the history of T2DM and signaling pathways, and offer a systematic update on the role and mechanism of key signaling pathways underlying the onset, development, and progression of T2DM. In this content, we also summarize current therapeutic drugs/agents associated with signaling pathways for the treatment of T2DM and its complications, and discuss some implications and directions to the future of this field.

Higher glucose fluctuation is associated with a higher risk of cardiovascular disease: Insights from pooled results among patients with diabetes

BACKGROUND

The relationship between glucose fluctuation and the risk of cardiovascular disease (CVD) in patients with diabetes remains elusive. Glycated hemoglobin (HbA1c) variability is a key parameter of glucose fluctuation.

METHODS

PubMed, Cochrane Library, Web of Science, and Embase were searched up to 1 July 2022. Studies reporting associations of HbA1c variability (HbA1c-SD), coefficient of variation of HbA1c (HbA1c-CV), and HbA1c variability score [HVS] with the risk of CVD among patients with diabetes were included. We used three different insights (a high-low value meta-analysis, a study-specific meta-analysis, and a non-linear dose-response meta-analysis) to explore the relationship between HbA1c variability and CVD risk. A subgroup analysis was also performed to screen the potential confounding factors.

RESULTS

A total of 14 studies with 254 017 patients with diabetes were eligible. The highest HbA1c variability was significantly associated with increased risks of CVD (HbA1c-SD, risk ratio [RR] 1.45; HbA1c-CV, RR 1.74; HVS, RR 2.46; all p < .001) compared to the lowest HbA1c variability. The RRs of CVD for per HbA1c variability were significantly >1 (all p < .001). The subgroup analysis for per HbA1c-SD found a significant exposure-covariate interaction in the types of diabetes (p = .003 for interaction). The dose-response analysis showed a positive association between HbA1c-CV and CVD risk (P for nonlinearity <.001).

CONCLUSIONS

Our study suggests that the higher glucose fluctuation is significantly associated with the higher CVD risk in diabetes patients based on HbA1c variability. The CVD risk associated with per HbA1c-SD might be higher among patients type 1 diabetes than patients with type 2 diabetes.

Oscillating Glucose Induces the Increase in Inflammatory Stress through Ninjurin-1 Up-Regulation and Stimulation of Transport Proteins in Human Endothelial Cells

Clinical data implicate fluctuations of high levels of plasma glucose in cardiovascular diseases. Endothelial cells (EC) are the first cells of the vessel wall exposed to them. Our aim was to evaluate the effects of oscillating glucose (OG) on EC function and to decipher new molecular mechanisms involved. Cultured human ECs (EA.hy926 line and primary cells) were exposed to OG (5/25 mM alternatively at 3 h), constant HG (25 mM) or physiological concentration (5 mM, NG) for 72 h. Markers of inflammation (Ninj-1, MCP-1, RAGE, TNFR1, NF-kB, and p38 MAPK), oxidative stress (ROS, VPO1, and HO-1), and transendothelial transport proteins (SR-BI, caveolin-1, and VAMP-3) were assessed. Inhibitors of ROS (NAC), NF-kB (Bay 11-7085), and Ninj-1 silencing were used to identify the mechanisms of OG-induced EC dysfunction. The results revealed that OG determined an increased expression of Ninj-1, MCP-1, RAGE, TNFR1, SR-B1, and VAMP-3 andstimulated monocyte adhesion. All of these effects were induced bymechanisms involving ROS production or NF-kB activation. NINJ-1 silencing inhibited the upregulation of caveolin-1 and VAMP-3 induced by OG in EC. In conclusion, OG induces increased inflammatory stress, ROS production, and NF-kB activation and stimulates transendothelial transport. To this end, we propose a novel mechanism linking Ninj-1 up-regulation to increased expression of transendothelial transport proteins.

Long-term HbA1c variability and macro-/micro-vascular complications in type 2 diabetes mellitus: a meta-analysis update

AIMS

The aim of the present study was to evaluate, by means of a meta-analysis approach, whether new available data, appeared on qualified literature, can support the effectiveness of an association of HbA1c variability with the risk of macro- and/or micro-vascular complications in type 2 diabetes mellitus (T2DM).

METHODS

The meta-analysis was conducted according to PRISMA Statement guidelines and considered published studies on T2DM, presenting HbA1c variability as standard deviation (SD) or its derived coefficient of variation (CV). Literature search was performed on PubMed in the time range 2015-July 2022, with no restrictions of language.

RESULTS

Twenty-three selected studies fulfilled the aims of the present investigation. Overall, the analysis of the risk as hazard ratios (HR) indicated a significant association between the HbA1c variability, expressed either as SD or CV, and the complications, except for neuropathy. Macro-vascular complications were all significantly associated with HbA1c variability, with HR 1.40 (95%CI 1.31-1.50, p < 0.0001) for stroke, 1.30 (95%CI 1.25-1.36, p < 0.0001) for transient ischaemic attack/coronary heart disease/myocardial infarction, and 1.32 (95%CI 1.13-1.56, p = 0.0007) for peripheral arterial disease. Micro-vascular complications yielded HR 1.29 (95%CI 1.22-1.36, p < 0.0001) for nephropathy, 1.03 (95%CI 0.99-1.08, p = 0.14) for neuropathy, and 1.15 (95%CI 1.08-1.24, p < 0.0001) for retinopathy. For all-cause mortality, HR was 1.33 (95%CI 1.27-1.39, p < 0.0001), and for cardiovascular mortality 1.25 (95%CI 1.17-1.34, p < 0.0001).

CONCLUSIONS

Our meta-analysis on HbA1c variability performed on the most recent published data since 2015 indicates positive association between HbA1c variability and macro-/micro-vascular complications, as well as mortality events, in T2DM, suggesting that this long-term glycaemic parameter merits further attention as a predictive, independent risk factor for T2DM population.

Observational assessments of the relationship of dietary and pharmacological treatment on continuous measures of dysglycemia over 24 hours in women with gestational diabetes

Objectives

Studies that use continuous glucose monitoring (CGM) to monitor women with gestational diabetes (GDM), highlight the importance of managing dysglycemia over a 24-hour period. However, the effect of current treatment methods on dysglycemia over 24-hrs are currently unknown. This study aimed to characterise CGM metrics over 24-hrs in women with GDM and the moderating effect of treatment strategy.

Methods

Retrospective analysis of CGM data from 128 women with GDM in antenatal diabetes clinics. CGM was measured for 7-days between 30-32 weeks gestation. Non-parametric tests were used to evaluate differences of CGM between periods of day (morning, afternoon, evening, and overnight) and between treatment methods (i.e., diet alone or diet+metformin). Exploratory analysis in a subgroup of 34 of participants was performed to investigate the association between self-reported macronutrient intake and glycaemic control.

Results

Glucose levels significantly differed during the day (i.e., morning to evening; P<0.001) and were significantly higher (i.e., mean blood glucose and area under the curve [AUC]) and more variable (i.e., SD and CV) than overnight glucose levels. Morning showed the highest amount of variability (CV; 8.4% vs 6.5%, P<0.001 and SD; 0.49 mmol/L vs 0.38 mmol/L, P<0.001). When comparing treatment methods, mean glucose (6.09 vs 5.65 mmol/L; P<0.001) and AUC (8760.8 vs 8115.1 mmol/L.hr; P<0.001) were significantly higher in diet+metformin compared to diet alone. Finally, the exploratory analysis revealed a favourable association between higher protein intake (+1SD or +92 kcal/day) and lower mean glucose (-0.91 mmol/L p, P=0.02) and total AUC (1209.6 mmol/L.h, P=0.021).

Conclusions

Glycemia varies considerably across a day, with morning glycemia demonstrating greatest variability. Additionally, our work supports that individuals assigned to diet+metformin have greater difficulty managing glycemia and results suggest that increased dietary protein may assist with management of dysglycemia. Future work is needed to investigate the benefit of increased protein intake on management of dysglycemia.

Type 2 diabetes

Type 2 diabetes accounts for nearly 90% of the approximately 537 million cases of diabetes worldwide. The number affected is increasing rapidly with alarming trends in children and young adults (up to age 40 years). Early detection and proactive management are crucial for prevention and mitigation of microvascular and macrovascular complications and mortality burden. Access to novel therapies improves person-centred outcomes beyond glycaemic control. Precision medicine, including multiomics and pharmacogenomics, hold promise to enhance understanding of disease heterogeneity, leading to targeted therapies. Technology might improve outcomes, but its potential is yet to be realised. Despite advances, substantial barriers to changing the course of the epidemic remain. This Seminar offers a clinically focused review of the recent developments in type 2 diabetes care including controversies and future directions.

Telomeres do not always shorten over time in individuals with type 1 diabetes

AIMS

We aimed to determine how white blood cell (WBC) telomeres and telomere length change over time are associated with health status in type 1 diabetes.

METHODS

Relative telomere length (rTL) was measured in WBC DNA from two time-points (median 6.8 years apart) in 618 individuals from the Finnish Diabetic Nephropathy Study by quantitative PCR, with interassay CV ≤ 4%.

RESULTS

Baseline rTL correlated inversely with age and was shorter in men. Individuals in the shortest vs. longest rTL tertile had adverse cardiometabolic profiles, worse renal function, and were prescribed more antihypertensive and lipid-lowering drugs. While overall rTL tended to decrease during the median 6.8-years of follow-up, telomeres shortened in 55.3% of subjects, lengthened in 40.0%, and did not change in 4.7%. Baseline rTL correlated inversely with rTL change. Telomere lengthening was associated with higher HDL-Cholesterol (HDL-C), HDL-C/ApoA1, and with antihypertensive drug and (inversely) with lipid-lowering drug commencement during follow-up. Correlates of rTL percentage change per-annum (adjusted model) were baseline BMI, eGFR, previous retinal laser treatment, HDL-C, and HDL-C/ApoA1.

CONCLUSIONS

Telomere length measurements may facilitate the treatment and monitoring of the health status of individuals with type 1 diabetes.

Determinant of Osteopontin Levels in Microvascular Complications in Patients with Diabetes

Background

Osteopontin (OPN) is a 44-kDa multifunctional protein and has a diverse role in biomineralization, tissue remodeling, and chronic inflammation. However, its role in type 2 diabetes (T2D) patients with microvascular complications is not clear. Therefore, the present study aimed to investigate the role of OPN in T2D patients with microvascular complications.

Methods

A total of 324 type 2 diabetes patients in the age group of 38-66 years were included in this study; 249 T2D patients were diagnosed with microvascular complications. OPN was measured using an enzyme-linked immunosorbent assay kit. Clinical data, such as age, gender, diabetes duration, systolic blood pressure, diastolic blood pressure, were measured. Correlation between OPN levels with different clinical parameters was evaluated.

Results

In patients with microvascular complications, OPN levels were significantly higher than those without microvascular complications (p < 0.05). Moreover, OPN levels were positively associated with systolic blood pressure (SBP), C-reactive protein, and albumin creatinine ratio (ACR). Multiple linear regression analysis showed that OPN levels were independently associated with C-reactive protein (p < 0.045).

Conclusion

The findings in the present study showed that OPN level was more positively associated with C-reactive protein than that with glucose metabolism in patients with microvascular complications. Thus, OPN might serve as a marker in predicting vascular disease.

Role of diabetes in stroke: Recent advances in pathophysiology and clinical management

The increasing prevalence of diabetes and stroke is a major global public health concern. Specifically, acute stroke patients, with pre-existing diabetes, pose a clinical challenge. It is established that diabetes is associated with a worse prognosis after acute stroke and the various biological factors that mediate poor recovery profiles in diabetic patients is unknown. The level of association and impact of diabetes, in the setting of reperfusion therapy, is yet to be determined. This article presents a comprehensive overview of the current knowledge of the role of diabetes in stroke, therapeutic strategies for primary and secondary prevention of cardiovascular disease and/or stroke in diabetes, and various therapeutic considerations that may apply during pre-stroke, acute, sub-acute and post-stroke stages. The early diagnosis of diabetes as a comorbidity for stroke, as well as tailored post-stroke management of diabetes, is pivotal to our efforts to limit the burden. Increasing awareness and involvement of neurologists in the management of diabetes and other cardiovascular risk factors is desirable towards improving stroke prevention and efficacy of reperfusion therapy in acute stroke patients with diabetes.

HbA1c variability predicts cardiovascular complications in type 2 diabetes regardless of being at glycemic target

Background

HbA1c variability has emerged as risk factor for cardiovascular diseases in diabetes. However, the impact of HbA1c variability on cardiovascular diseases in subjects within the recommended HbA1c target has been relatively unexplored.

Methods

Using data from a large database, we studied 101,533 people with type 2 diabetes without cardiovascular diseases. HbA1c variability was expressed as quartiles of the standard deviation of HbA1c during three years (exposure phase). The primary composite outcome included non-fatal myocardial infarction, non-fatal stroke, all-cause mortality and was assessed during five years following the first three years of exposure to HbA1c variability (longitudinal phase). An expanded composite outcome including non-fatal myocardial infarction, non-fatal stroke, coronary revascularization/reperfusion procedures, peripheral revascularization procedures, and all-cause mortality was also considered, as well as a series of specific cardiovascular complications. Cox models were adjusted for a large range of risk factors and results were expressed as adjusted hazard ratios.

Results

An association between HbA1c variability and all the outcomes considered was found. The correlation between HbA1c variability and cardiovascular complications development was confirmed in both the subgroups of subjects with a mean HbA1c ≤ 53 mmol/mol (recommended HbA1c target) or > 53 mmol/mol during the exposure phase. The risk related to HbA1c variability was higher in people with mean HbA1c ≤ 53 mmol/mol for the primary outcome (p for interaction 0.004), for the expanded secondary outcome (p for interaction 0.001) and for the stroke (p for interaction 0.001), even though HbA1c remained at the target during the follow-up.

Conclusions

These findings suggest that HbA1c variability may provide additional information for an optimized management of diabetes, particularly in people within the target of HbA1c.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12933-022-01445-4.

Long-term glycemic variability and risk of stroke in patients with diabetes: a meta-analysis

OBJECTIVES

Long-term glycemic variability has been related to increased risk of vascular complication in patients with diabetes. However, the association between parameters of long-term glycemic variability and risk of stroke remains not fully determined. We performed a meta-analysis to systematically evaluate the above association.

METHODS

Medline, Embase, and Web of Science databases were searched for longitudinal follow-up studies comparing the incidence of stroke in diabetic patients with higher or lower long-term glycemic variability. A random-effect model incorporating the potential heterogeneity among the included studies were used to pool the results.

RESULTS

Seven follow-up studies with 725,784 diabetic patients were included, and 98% of them were with type 2 diabetes mellitus (T2DM). The mean follow-up duration was 7.7 years. Pooled results showed that compared to those with lowest category of glycemic variability, diabetic patients with the highest patients had significantly increased risk of stroke, as evidenced by glycemic variability analyzed by fasting plasma glucose coefficient of variation (FPG-CV: risk ratio [RR] = 1.24, 95% confidence interval [CI] 1.11 to 1.39, P < 0.001; I² = 53%), standard deviation of FPG (FPG-SD: RR = 1.16, 95% CI 1.02 to 1.31, P = 0.02; I² = 74%), HbA1c coefficient of variation (HbA1c-CV: RR = 1.88, 95% CI 1.61 to 2.19 P < 0.001; I² = 0%), and standard deviation of HbA1c (HbA1c-SD: RR = 1.73, 95% CI 1.49 to 2.00, P < 0.001; I² = 0%).

CONCLUSIONS

Long-term glycemic variability is associated with higher risk of stroke in T2DM patients.

Glycaemic management in diabetes: old and new approaches

HbA_1c is the most used parameter to assess glycaemic control. However, evidence suggests that the concept of hyperglycaemia has profoundly changed and that different facets of hyperglycaemia must be considered. A modern approach to glycaemic control should focus not only on reaching and maintaining optimal HbA_1c concentrations as early as possible, but to also do so by reducing postprandial hyperglycaemia, glycaemic variability, and to extend as much as possible the time in range in near-normoglycaemia. These goals should be achieved while avoiding hypoglycaemia, which, should it occur, should be reverted to normoglycaemia. Modern technology, such as intermittently scanned glucose monitoring and continuous glucose monitoring, together with new drug therapies (eg, ultra-fast insulins, SGLT2 inhibitors, and GLP-1 receptor agonists), could help to change the landscape of glycaemia management based on HbA_1c in favour of a more holistic approach that considers all the different aspects of this commonly oversimplified pathophysiological feature of diabetes.

Machine learning based study of longitudinal HbA1c trends and their association with all-cause mortality: Analyses from a National Diabetes Registry

OBJECTIVE

The association of long-term HbA1c variability with mortality has been previously suggested. However, the significance of HbA1c variability and trends in different age and HbA1c categories is unclear.

RESEARCH DESIGN AND METHODS

Data on patients with diabetes listed in the Israeli National Diabetes Registry during years 2012-2016 (observation period) were collected. Patients with >4 HbA1c measurements, type 1 diabetes, eGFR < 30mg/ml/min, persistent HbA1c < 6% or malignancy were excluded. Utilizing machine learning methods, patients were classified into clusters according to their HbA1c trend (increasing, stable, decreasing). Mortality risk during 2017-2019 was calculated in subgroups defined by age (35-54, 55-69, 70-89 years) and last HbA1c (≤7% and >7%) at end of observation period. Models were adjusted for demographic, clinical and laboratory measurements including HbA1c, standard deviation (SD) of HbA1c and HbA1c trend.

RESULTS

This historical cohort study included 293,314 patients. Increased HbA1c variability (high SD) during the observation period was an independent predictor of mortality in patients aged more than 55 years (p < 0.01). The HbA1c trend was another independent predictor of mortality. Patients with a decreasing versus stable HbA1c trend had a greater mortality risk; this association persisted in all age groups in patients with HbA1c > 7% at the end of the observation period (p = 0.02 in age 35-54; p < 0.01 in aged >55). Patients with an increasing versus stable HbA1c trend had a greater mortality risk only in the elderly group (>70), yet in both HbA1c categories (p < 0.01).

CONCLUSIONS

HbA1c variability and trend are important determinants of mortality risk and should be considered when adjusting glycaemic targets.

Development of a predictive risk model for all-cause mortality in patients with diabetes in Hong Kong

INTRODUCTION

Patients with diabetes mellitus are risk of premature death. In this study, we developed a machine learning-driven predictive risk model for all-cause mortality among patients with type 2 diabetes mellitus using multiparametric approach with data from different domains.

RESEARCH DESIGN AND METHODS

This study used territory-wide data of patients with type 2 diabetes attending public hospitals or their associated ambulatory/outpatient facilities in Hong Kong between January 1, 2009 and December 31, 2009. The primary outcome is all-cause mortality. The association of risk variables and all-cause mortality was assessed using Cox proportional hazards models. Machine and deep learning approaches were used to improve overall survival prediction and were evaluated with fivefold cross validation method.

RESULTS

A total of 273 678 patients (mean age: 65.4±12.7 years, male: 48.2%, median follow-up: 142 (IQR=106-142) months) were included, with 91 155 deaths occurring on follow-up (33.3%; annualized mortality rate: 3.4%/year; 2.7 million patient-years). Multivariate Cox regression found the following significant predictors of all-cause mortality: age, male gender, baseline comorbidities, anemia, mean values of neutrophil-to-lymphocyte ratio, high-density lipoprotein-cholesterol, total cholesterol, triglyceride, HbA1c and fasting blood glucose (FBG), measures of variability of both HbA1c and FBG. The above parameters were incorporated into a score-based predictive risk model that had a c-statistic of 0.73 (95% CI 0.66 to 0.77), which was improved to 0.86 (0.81 to 0.90) and 0.87 (0.84 to 0.91) using random survival forests and deep survival learning models, respectively.

CONCLUSIONS

A multiparametric model incorporating variables from different domains predicted all-cause mortality accurately in type 2 diabetes mellitus. The predictive and modeling capabilities of machine/deep learning survival analysis achieved more accurate predictions.

Glycemic variability and cardiovascular disease in patients with type 2 diabetes

Glycated hemoglobin is currently the gold standard for assessment of long-term glycemic control and response to medical treatment in patients with diabetes. Glycated hemoglobin, however, does not address fluctuations in blood glucose. Glycemic variability (GV) refers to fluctuations in blood glucose levels. Recent clinical data indicate that GV is associated with increased risk of hypoglycemia, microvascular and macrovascular complications, and mortality in patients with diabetes, independently of glycated hemoglobin level. The use of continuous glucose monitoring devices has markedly improved the assessment of GV in clinical practice and facilitated the assessment of GV as well as hypoglycemia and hyperglycemia events in patients with diabetes. We review current concepts on the definition and assessment of GV and its association with cardiovascular complications in patients with type 2 diabetes.

Comprehensive elaboration of glycemic variability in diabetic macrovascular and microvascular complications

Diabetes mellitus is the major risk factor for the development of macrovascular and microvascular complications. It is increasingly recognized that glycemic variability (GV), referring to oscillations in blood glucose levels and representing either short-term or long-term GV, is involved in the pathogenesis of diabetic complications and has emerged as a possible independent risk factor for them. In this review, we summarize the metrics and measurement of GV in clinical practice, as well as comprehensively elaborate the role and related mechanisms of GV in diabetic macrovascular and microvascular complications, aiming to provide the mechanism-based therapeutic strategies for clinicians to manage diabetes mellitus.