Association between visit-to-visit HbA1c variability and the risk of cardiovascular disease in patients with type 2 diabetes

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

BACKGROUND

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

METHODS

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

RESULTS

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

CONCLUSIONS

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

2024 Heart Disease and Stroke Statistics: A Report of US and Global Data From the American Heart Association

BACKGROUND

The American Heart Association (AHA), in conjunction with the National Institutes of Health, annually reports the most up-to-date statistics related to heart disease, stroke, and cardiovascular risk factors, including core health behaviors (smoking, physical activity, nutrition, sleep, and obesity) and health factors (cholesterol, blood pressure, glucose control, and metabolic syndrome) that contribute to cardiovascular health. The AHA Heart Disease and Stroke Statistical Update presents the latest data on a range of major clinical heart and circulatory disease conditions (including stroke, brain health, complications of pregnancy, kidney disease, congenital heart disease, rhythm disorders, sudden cardiac arrest, subclinical atherosclerosis, coronary heart disease, cardiomyopathy, heart failure, valvular disease, venous thromboembolism, and peripheral artery disease) and the associated outcomes (including quality of care, procedures, and economic costs).

METHODS

The AHA, through its Epidemiology and Prevention Statistics Committee, continuously monitors and evaluates sources of data on heart disease and stroke in the United States and globally to provide the most current information available in the annual Statistical Update with review of published literature through the year before writing. The 2024 AHA Statistical Update is the product of a full year's worth of effort in 2023 by dedicated volunteer clinicians and scientists, committed government professionals, and AHA staff members. The AHA strives to further understand and help heal health problems inflicted by structural racism, a public health crisis that can significantly damage physical and mental health and perpetuate disparities in access to health care, education, income, housing, and several other factors vital to healthy lives. This year's edition includes additional global data, as well as data on the monitoring and benefits of cardiovascular health in the population, with an enhanced focus on health equity across several key domains.

RESULTS

Each of the chapters in the Statistical Update focuses on a different topic related to heart disease and stroke statistics.

CONCLUSIONS

The Statistical Update represents a critical resource for the lay public, policymakers, media professionals, clinicians, health care administrators, researchers, health advocates, and others seeking the best available data on these factors and conditions.

Correlation between long-term glycemic variability and cognitive function in middle-aged and elderly patients with type 2 diabetes mellitus: a retrospective study

Objective

To investigate the correlation between long-term glycemic variability and cognitive function in middle-aged and elderly patients with type 2 diabetes mellitus (T2DM).

Methods

This retrospective analysis includes 222 patients hospitalized at Second Affiliated Hospital of Anhui Medical University from June 2021 to June 2023. Cognitive function was assessed using the Montreal Cognitive Assessment (MoCA) and Mini-Mental State Examination (MMSE). All patients were categorized into the MCI group and the non-MCI group based on their MoCA score. Long-term blood glucose fluctuations were measured using glycated hemoglobin A1c standard deviation (HbA1c-SD) and fasting plasma glucose standard deviation (FPG-SD). The study compared general clinical data, blood biochemical indicators, and glycemic variability indicators between the two groups. The differences between the groups were compared using t-test, Chi-Square Test, or Mann-Whitney U test. Kendall's correlation analysis, multivariate logistic regression analysis and ROC curve correlation analysis were further used to analyze the correlation and diagnostic power.

Results

The differences in age, MoCA scores, MMSE scores, HOMA-β, HbA1c-M, HbA1c-SD, FPG-M, FPG-SD, eGFR, Smoking, GLP-1RA and SGLT-2i usage were statistically significant between the two groups (P < 0.05). Kendall's correlation analysis showed that age, HbA1c-M, HbA1c-SD, FPG-M, and FPG-SD was negatively correlated with MoCA scores; meanwhile, the HOMA-β, and eGFR was positively correlated with MoCA scores. Multiple logistic regression analysis revealed that HbA1c-SD, FPG-SD and Smoking were risk factors for cognitive dysfunction, while eGFR, GLP-1RA and SGLT-2i usage was a protective effect. The area under the curve (AUC) values for predicting MCI prevalence were 0.830 (95% CI [0.774-0.877], P < 0.001) for HbA1c-SD, 0.791 (95% CI [0.655-0.808], P < 0.001) for FPG-SD, and 0.698 (95% CI [0.633-0.757], P < 0.001) for eGFR. The optimal diagnostic values were 0.91, 1.32, and 74.81 ml/min/1.73 m2 for HbA1c-SD, FPG-SD, and eGFR, respectively.

Conclusions

Cognitive function in middle-aged and elderly T2DM patients is influenced by long-term blood glucose variability, with poorer cognitive function observed in individuals with higher blood glucose variability. The impact of HbA1c-SD on MCI exhibited a greater magnitude compared to that of PFG-SD and smoking. Additionally, renal function, GLP-1RA and SGLT-2i usage exert positive effects on cognitive function.

Development and validation of a blood biomarker score for predicting mortality risk in the general population

BACKGROUND

Blood biomarkers for multiple pathways, such as inflammatory response, lipid metabolism, and hormonal regulation, have been suggested to influence the risk of mortality. However, few studies have systematically evaluated the combined predictive ability of blood biomarkers for mortality risk.

METHODS

We included 267,239 participants from the UK Biobank who had measurements of 28 blood biomarkers and were free of cardiovascular disease (CVD) and cancer at baseline (2006-2010). We developed sex-specific blood biomarker scores for predicting all-cause mortality risk in a training set of 247,503 participants from England and Wales, and validated the results in 19,736 participants from Scotland. Cox and LASSO regression analyses were performed to identify independent predictors for men and women separately. Discrimination and calibration were evaluated by C-index and calibration plots, respectively. We also assessed mediating effects of the biomarkers on the association between traditional risk factors (current smoking, obesity, physical inactivity, hypertension, diabetes) and mortality.

RESULTS

A total of 13 independent predictive biomarkers for men and 17 for women were identified and included in the score development. Compared to the lowest tertile of the score, the highest tertile showed a hazard ratio of 5.36 (95% confidence interval [CI] 5.04-5.71) in men and 4.23 (95% CI 3.87-4.62) in women for all-cause mortality. In the validation set, the score yielded a C-index of 0.73 (95% CI 0.72-0.75) in men and 0.70 (95% CI 0.68-0.73) in women for all-cause mortality; it was also predictive of CVD (C-index of 0.76 in men and 0.79 in women) and cancer (C-index of 0.70 in men and 0.67 in women) mortality. Moreover, the association between traditional risk factors and all-cause mortality was largely mediated by cystatin C, C-reactive protein, 25-hydroxyvitamin D, and hemoglobin A1c.

CONCLUSIONS

We established sex-specific blood biomarker scores for predicting all-cause and cause-specific mortality in the general population, which hold the potential to identify high-risk individuals and improve targeted prevention of premature death.

Intensified glycemic control by HbA1c for patients with coronary heart disease and Type 2 diabetes: a review of findings and conclusions

The occurrence and development of coronary heart disease (CHD) are closely linked to fluctuations in blood glucose levels. While the efficacy of intensified treatment guided by HbA1c levels remains uncertain for individuals with diabetes and CHD, this review summarizes the findings and conclusions regarding HbA1c in the context of CHD. Our review showed a curvilinear correlation between regulated level of HbA1c and therapeutic effectiveness of intensified glycemic control among patients with type 2 diabetes and coronary heart disease. It is necessary to optimize the dynamic monitoring indicators of HbA1c, combine genetic profiles, haptoglobin phenotypes for example and select more suitable hypoglycemic drugs to establish more appropriate glucose-controlling guideline for patients with CHD at different stage of diabetes.

Hemoglobin A1c Variability Metrics Predict Coronary Artery Calcium and Cardiovascular Events in Type 1 Diabetes: The CACTI Study

CONTEXT

Interventions that decrease mean glucose have reduced rates of micro- and macrovascular complications in type 1 diabetes (T1D). However, the difference in cardiovascular risk between people with T1D and the general population endures, suggesting that factors beyond hemoglobin A1C (HbA1c) normalization drive cardiovascular outcomes.

OBJECTIVE

To determine whether various HbA1c metrics predict anatomic cardiovascular disease (CVD) risk factors and/or CVD events in people with T1D.

METHODS

We used linear regression to analyze the relationship of several HbA1c metrics to anatomic CVD risk factors and then used Cox regression to model their relationship to incident CVD events in the CACTI Study (ClinicalTrials.gov Identifier: NCT00005754).

RESULTS

In linear regression models adjusted for age, sex, and T1D duration, baseline Hba1c (b = 0.3998, P = 0.0236), mean HbA1c (b = 0.5385, P = 0.0109), and HbA1c SD (b = 1.1521, P = 0.0068) were each positively associated with square root transformed coronary artery calcium volume. Conversely, only mean HbA1c (b = 1.659, P = 0.0048) positively associated with pericardial adipose tissue volume. In survival models adjusted for age, sex, and T1D duration, baseline HbA1c [hazard ratio (HR): 1.471, 95% CI: 1.257-1.721], mean HbA1c (HR: 1.850, 95% CI: 1.511-2.264), time-varying HbA1c (HR: 1.500, 95% CI: 1.236-1.821), and HbA1c SD (HR: 1.665, 95% CI: 1.022-2.711) each independently predicted CVD events over 14.3 ± 5.2 person-years of follow-up.

CONCLUSIONS/INTERPRETATION

We found that various HbA1c metrics positively correlated with CAC volume and independently predicted incident CVD events in the CACTI T1D cohort. These associations with CVD events persisted for baseline HbA1c, mean HbA1c, and time-varying HbA1c even after adjustment for numerous CVD risk factors.

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.

Association between initial in-hospital heart rate and glycemic control in patients with acute ischemic stroke and diabetes mellitus

BACKGROUND

A high resting heart rate (HR) has been associated with an increased risk of diabetes mellitus. This study explored the association between initial in-hospital HR and glycemic control in patients with acute ischemic stroke (AIS) and diabetes mellitus.

METHODS

We analyzed data from 4,715 patients with AIS and type 2 diabetes mellitus enrolled in the Chang Gung Research Database between January 2010 and September 2018. The study outcome was unfavorable glycemic control, defined as glycated hemoglobin (HbA1c) ≥ 7%. In statistical analyses, the mean initial in-hospital HR was used as both a continuous and categorical variable. Odds ratios (ORs) and 95% confidence intervals (CIs) were estimated using multivariable logistic regression analysis. The associations between the HR subgroups and HbA1c levels were analyzed using a generalized linear model.

RESULTS

Compared with the reference group (HR < 60 bpm), the adjusted ORs for unfavorable glycemic control were 1.093 (95% CI 0.786-1.519) for an HR of 60-69 bpm, 1.370 (95% CI 0.991-1.892) for an HR of 70-79 bpm, and 1.608 (95% CI 1.145-2.257) for an HR of ≥ 80 bpm. Even after adjusting for possible confounders, the HbA1c levels after admission and discharge among diabetic stroke patients increased significantly in the subgroups with higher HRs (p < 0.001).

CONCLUSIONS

High initial in-hospital HR is associated with unfavorable glycemic control in patients with AIS and diabetes mellitus, particularly in those with an HR of ≥ 80 bpm, compared with those with an HR of < 60 bpm.

Heart Disease and Stroke Statistics-2023 Update: A Report From the American Heart Association

BACKGROUND

The American Heart Association, in conjunction with the National Institutes of Health, annually reports the most up-to-date statistics related to heart disease, stroke, and cardiovascular risk factors, including core health behaviors (smoking, physical activity, diet, and weight) and health factors (cholesterol, blood pressure, and glucose control) that contribute to cardiovascular health. The Statistical Update presents the latest data on a range of major clinical heart and circulatory disease conditions (including stroke, congenital heart disease, rhythm disorders, subclinical atherosclerosis, coronary heart disease, heart failure, valvular disease, venous disease, and peripheral artery disease) and the associated outcomes (including quality of care, procedures, and economic costs).

METHODS

The American Heart Association, through its Epidemiology and Prevention Statistics Committee, continuously monitors and evaluates sources of data on heart disease and stroke in the United States to provide the most current information available in the annual Statistical Update with review of published literature through the year before writing. The 2023 Statistical Update is the product of a full year's worth of effort in 2022 by dedicated volunteer clinicians and scientists, committed government professionals, and American Heart Association staff members. The American Heart Association strives to further understand and help heal health problems inflicted by structural racism, a public health crisis that can significantly damage physical and mental health and perpetuate disparities in access to health care, education, income, housing, and several other factors vital to healthy lives. This year's edition includes additional COVID-19 (coronavirus disease 2019) publications, as well as data on the monitoring and benefits of cardiovascular health in the population, with an enhanced focus on health equity across several key domains.

RESULTS

Each of the chapters in the Statistical Update focuses on a different topic related to heart disease and stroke statistics.

CONCLUSIONS

The Statistical Update represents a critical resource for the lay public, policymakers, media professionals, clinicians, health care administrators, researchers, health advocates, and others seeking the best available data on these factors and conditions.

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.

Long-term glycemic variability and risk of adverse health outcomes in patients with diabetes: A systematic review and meta-analysis of cohort studies

AIMS

To quantify associations of different metrics of long-term glycemic variability (GV) with multiple adverse diabetes-related outcomes.

METHODS

We searched PubMed and Embase from database inception to 23 August 2021. GV was based on measurements of HbA1c or fasting plasma glucose (FPG) and calculated by standard deviation (SD), coefficient of variance (CV) or other metrics. Outcomes included mortality, cardiovascular disease (CVD), renal disease, peripheral neuropathy, retinopathy, dementia and cancer. Random-effects meta-analyses were adopted to pool the relative risks (RRs).

RESULTS

Seventy-five articles with 2,051,701 participants were included. When comparing top with bottom quartiles, HbA1c variabilities were associated with all-cause mortality (RR_CV = 1.63, 95 % CI 1.37-1.92; RR_SD = 1.87, 1.55-2.26), CVD (RR_CV = 1.38, 1.07-1.78; RR_SD = 1.34, 1.12-1.59), renal disease (RR_CV = 1.43, 1.18-1.74; RR_SD = 1.44, 1.24-1.67), and peripheral neuropathy (RR_CV = 1.84, 1.40-2.43; RR_SD = 1.98, 1.51-2.61), but not retinopathy. FPG variabilities were associated with all-cause mortality (RR_CV = 1.59, 1.43-1.78; RR_SD = 1.67, 1.26-2.20), renal disease (RR_CV = 1.77, 1.32-2.38), and retinopathy (RR_CV = 1.92, 1.10-3.35), but not CVD and peripheral neuropathy. Associations of GV with Alzheimer's disease (RR_HbA1c-CV = 1.38, 1.13-1.70; RR_FPG-CV = 1.32, 1.07-1.63) and cancer (RR_HbA1c-SD = 2.19, 1.52-3.17; RR_FPG-CV = 3.64, 2.21-5.98) were each found significant in one study.

CONCLUSIONS

Long-term GV was associated with multiple adverse diabetes-related outcomes, while the strength of associations varied. The findings support the use of long-term GV for diabetes management in clinical practice.

Glycemic control is not related to cerebral small vessel disease in neurologically asymptomatic individuals with type 1 diabetes

AIMS

To determine if medium- and long-term blood glucose control as well as glycemic variability, which are known to be strong predictors of vascular complications, are associated with underlying cerebral small vessel disease (cSVD) in neurologically asymptomatic individuals with type 1 diabetes.

METHODS

A total of 189 individuals (47.1% men; median age 40.0, IQR 33.0-45.2 years) with type 1 diabetes (median diabetes duration of 21.7, IQR 18.3-30.7 years) were enrolled in a cross-sectional retrospective study, as part of the Finnish Diabetic Nephropathy (FinnDiane) Study. Glycated hemoglobin (HbA_1c) values were collected over the course of ten years before the visit including a clinical examination, biochemical sampling, and brain magnetic resonance imaging. Markers of glycemic control, measured during the visit, included HbA_1c, fructosamine, and glycated albumin.

RESULTS

Signs of cSVD were present in 66 (34.9%) individuals. Medium- and long-term glucose control and glycemic variability did not differ in individuals with signs of cSVD compared to those without. Further, no difference in any of the blood glucose variables and cSVD stratified for cerebral microbleeds (CMBs) or white matter hyperintensities were detected. Neither were numbers of CMBs associated with the studied glucose variables. Additionally, after dividing the studied variables into quartiles, no association with cSVD was observed.

CONCLUSIONS

We observed no association between glycemic control and cSVD in neurologically asymptomatic individuals with type 1 diabetes. This finding was unexpected considering the large number of signs of cerebrovascular pathology in these people after two decades of chronic hyperglycemia and warrants further studies searching for underlying factors of cSVD.

Long-Term Glycemic Variability and Risk of Cardiovascular Events in Type 2 Diabetes: A Meta-Analysis

Long-term glycemic fluctuation has been associated with cardiovascular risk in patients with type 2 diabetes mellitus (T2DM). However, the findings are inconsistent. We performed a meta-analysis to summarize the association between parameters of long-term glycemic variability and risk of cardiovascular events in T2DM patients. Medline, Embase, and Web of Science databases were searched for longitudinal follow-up studies comparing the incidence of cardiovascular events in T2DM patients with higher or lower long-term glycemic variability. A random-effect model incorporating the potential heterogeneity among the included studies was used to pool the results. Twelve follow-up studies with 146 653 T2DM patients were included. The mean follow-up duration was 4.9 years. Pooled results showed that compared to those with the lowest glycemic variability, patients with the highest glycemic variability had significantly increased risk of cardiovascular events, as evidenced by the standard deviation of glycated hemoglobin [HbA1c-SD: relative risk (RR)=1.44, 95% confidence interval (CI): 1.23 to 1.69, p<0.001; I²=70%], HbA1c coefficient of variation (HbA1c-CV: RR=1.46, 95% CI: 1.19 to 1.79. p<0.001; I²=83%), standard deviation of fasting plasma glucose (FPG-SD: RR=1.33, 95% CI: 1.07 to 1.65, p=0.009; I²=0%), and FPG coefficient of variation (FPG-CV: RR=1.29, 95% CI: 1.01 to 1.64, p=0.04; I²=47%). In conclusion, increased long-term glycemic variability may be an independent risk factor for cardiovascular events in T2DM patients.

The Scope of HbA1c Variability and Risk of Vascular Complications Among Patients with Type 2 Diabetes: A Systematic Review and Meta-Analysis of Prospective Studies

Glycated hemoglobin (HbA1c) variability is emerging as an indicator of long-term glycemic control, which may play a significant role during vascular complications. We conducted a systematic review and meta-analysis to assess the association between the scope of HbA1c variability and vascular complications in patients with type 2 diabetes mellitus. PubMed and Embase were searched for studies that evaluated the association of HbA1c variability with vascular complications in patients with type 2 diabetes. Two reviewers independently completed data extraction. Random-effects meta-analysis was conducted with stratification according to the type of vascular complications. Nine studies were eligible for inclusion in our systematic review and meta-analysis. Six studies evaluated the impact of the standard deviation of HbA1c (HbA1c-SD) on cardiovascular events and showed an association of HbA1c-SD with cardiovascular events (HR: 1.25, 95% CI 1.18-1.32, 5 studies). Six studies evaluated renal disease associated with HbA1c-SD and showed that HbA1c-SD was correlated with an increased risk of renal disease (HR: 1.19, 95% CI 1.13-1.24). Two studies evaluated HbA1c-SD and the risk of retinopathy and showed that no significant association was found between retinopathy and HbA1c-SD (HR 1.08, 95% CI 0.92-125). For HbA1c-SD ranging from 0.6 to 0.8%, HbA1c-SD was associated with an increased risk of cardiovascular events (HR: 1.25, 95% CI 1.15-1.35) and renal disease (HR: 1.16, 95% CI 1.11-1.22). For individuals with index HbA1c variability greater than or equal to 0.6%, HbA1c variability was significantly associated with vascular complications in patients with type 2 diabetes mellitus.

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.

The Association of Postprandial Triglyceride Variability with Renal Dysfunction and Microalbuminuria in Patients with Type 2 Diabetic Mellitus: A Retrospective and Observational Study

OBJECTIVE

We examined whether or not day-to-day variations in lipid profiles, especially triglyceride (TG) variability, were associated with the exacerbation of diabetic kidney disease.

METHODS

We conducted a retrospective and observational study. First, 527 patients with type 2 diabetes mellitus (DM) who had had their estimated glomerular filtration rate (eGFR) checked every 6 months since 2012 for over 5 years were registered. Variability in postprandial TG was determined using the standard deviation (SD), SD adjusted (Adj-SD) for the number of measurements, and maximum minus minimum difference (MMD) during the first three years of follow-up. The endpoint was a ≥40% decline from baseline in the eGFR, initiation of dialysis or death. Next, 181 patients who had no micro- or macroalbuminuria in February 2013 were selected from among the 527 patients for an analysis. The endpoint was the incidence of microalbuminuria, initiation of dialysis, or death.

RESULTS

Among the 527 participants, 110 reached a ≥40% decline from baseline in the eGFR or death. The renal survival was lower in the higher-SD, higher-Adj-SD, and higher-MMD groups than in the lower-SD, lower-Adj-SD, and lower-MMD groups, respectively (log-rank test p = 0.0073, 0.0059, and 0.0195, respectively). A lower SD, lower Adj-SD, and lower MMD were significantly associated with the renal survival in the adjusted model (hazard ratio, 1.62, 1.66, 1.59; 95% confidence intervals, 1.05-2.53, 1.08-2.58, 1.04-2.47, respectively). Next, among 181 participants, 108 developed microalbuminuria or death. The nonincidence of microalbuminuria was lower in the higher-SD, higher-Adj-SD, and higher-MMD groups than in the lower-SD, lower-Adj-SD, and lower-MMD groups, respectively (log-rank test p = 0.0241, 0.0352, and 0.0474, respectively).

CONCLUSIONS

Postprandial TG variability is a novel risk factor for eGFR decline and the incidence of microalbuminuria in patients with type 2 DM.

Predictive Ability of Visit-to-Visit Variability of HbA1c Measurements for the Development of Diabetic Kidney Disease: A Retrospective Longitudinal Observational Study

AIMS

This study is aimed at clarifying the relationship between visit-to-visit variability of glycated hemoglobin (HbA1c) and the risk of diabetic kidney disease (DKD) and to identifying the most useful index of visit-to-visit variability of HbA1c.

METHODS

This clinic-based retrospective longitudinal study included 699 Japanese type 2 diabetes mellitus patients. Visit-to-visit variability of HbA1c was calculated as the internal standard deviation of HbA1c (HbA1c-SD), the coefficient of variation of HbA1c (HbA1c-CV), the HbA1c change score (HbA1c-HVS), and the area under the HbA1c curve (HbA1c-AUC) with 3-year serial HbA1c measurement data, and the associations between these indices and the development/progression of DKD were examined.

RESULTS

Cox proportional hazards models showed that the HbA1c-SD and HbA1c-AUC were associated with the incidence of microalbuminuria, independently of the HbA1c level. These results were verified and replicated in propensity score (PS) matching and bootstrap analyses. Moreover, the HbA1c-SD and HbA1c-AUC were also associated with oxidized human serum albumin (HSA), an oxidative stress marker.

CONCLUSIONS

Visit-to-visit variability of HbA1c was an independent risk factor of microalbuminuria in association with oxidative stress among type 2 diabetes mellitus patients. HbA1c-AUC, a novel index of HbA1c variability, may be a potent prognostic indicator in predicting the risk of microalbuminuria.

Impact of glycemic control on the association of endothelial dysfunction and coronary artery disease in patients with type 2 diabetes mellitus

Background

We investigated whether glycemic control affects the relation between endothelial dysfunction and coronary artery disease in patients with type 2 diabetes mellitus (T2DM).

Methods

In 102 type 2 diabetic patients with stable angina, endothelial function was evaluated using brachial artery flow-mediated dilation (FMD) with high-resolution ultrasound, and significant stenosis of major epicardial coronary arteries (≥ 50% diameter narrowing) and degree of coronary atherosclerosis (Gensini score and SYNTAX score) were determined. The status of glycemic control was assessed by blood concentration of glycated hemoglobin (HbA1c).

Results

The prevalence of significant coronary artery stenosis (67.9% vs. 37.0%, P = 0.002) and degree of coronary atherosclerosis (Gensini score: 48.99 ± 48.88 vs. 15.07 ± 21.03, P < 0.001; SYNTAX score: 15.88 ± 16.36 vs. 7.28 ± 10.54, P = 0.003) were higher and FMD was lower (6.03 ± 2.08% vs. 6.94 ± 2.20%, P = 0.036) in diabetic patients with poor glycemic control (HbA1c ≥ 7.0%; n = 56) compared to those with good glycemic control (HbA1c < 7.0%; n = 46). Multivariate regression analysis revealed that tertile of FMD was an independent determinant of presence of significant coronary artery stenosis (OR = 0.227 95% CI 0.056–0.915, P = 0.037), Gensini score (β =  − 0.470, P < 0.001) and SYNTAX score (β =  − 0.349, P = 0.004) in diabetic patients with poor glycemic control but not for those with good glycemic control (P > 0.05).

Conclusion

Poor glycemic control negatively influences the association of endothelial dysfunction and coronary artery disease in T2DM patients.

Visit-to-visit variability of glycated albumin was associated with incidence or progression of lower extremity atherosclerotic disease

BACKGROUND

The aim of this study was to investigate the association of visit-to-visit variability of hemoglobin A1c (HbA1c) and glycated albumin (GA) with the risk of lower extremity atherosclerotic disease (LEAD).

METHOD

We performed a prospective cohort study of 436 patients with type 2 diabetes (258 men and 178 women) with at least 3 measurements of HbA1c and GA prior to baseline investigation from the Department of Endocrinology and Metabolism, Shanghai Sixth People's Hospital. Different HbA1c and GA variability markers were calculated. Multivariable Cox proportional hazard regression models were used to demonstrate the association between visit-to-visit HbA1c and GA variability and the risk of incident or progressive LEAD.

RESULTS

During a mean follow-up period of 3.77 years, 112 participants developed LEAD. Multivariate-adjusted hazard ratios (HRs) of LEAD across tertiles of GA-CV values were 1.00, 1.06 (95% confidence interval [CI] 0.65-1.75), and 1.71 (95% CI 1.07-2.73) (P for trend = 0.042), respectively. When we used GA-VIM and GA-ARV values as exposures, similar positive associations with the risk of LEAD primary were found. Multivariate-adjusted HRs of LEAD for each 1 unit increase in GA-CV, GA-VIM and GA-ARV were 1.03 (95% CI 1.01-1.06), 1.32 (95% CI 1.03-1.69), and 1.07 (95%CI 1.01-1.15), respectively. However, there was no significant association between visit-to-visit variability of HbA1c and the risk of LEAD.

CONCLUSIONS

Visit-to-visit variability of GA may be an optimal biomarker in relation to LEAD risk among patients with type 2 diabetes.