Greater HbA1c variability is associated with increased cardiovascular events in type 2 diabetes patients with preserved renal function, but not in moderate to advanced chronic kidney disease

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.

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.

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.

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 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.

Long-term glycemic variability and the risk of mortality in diabetic patients receiving peritoneal dialysis

Background

The large amount of glucose in the dialysate used in peritoneal dialysis (PD) likely affects the glycemic control. The aim of this study was to investigate the association between HbA1c variability, as a measure of long-term glycemic variability, and the risk of all-cause mortality in diabetic patients with PD.

Methods

325 patients with diabetes and ESRD were followed (2008–2018) in the Swedish Renal Registry. Patients were separated in seven groups according to level of HbA1c variability. The group with the lowest variability was denoted the reference. The ratio of the standard deviation (SD) to the mean of HbA1c, HbA1c (SD)/HbA1c (mean), i.e. the coefficient of variation (CV), was defined as HbA1c variability. Hazard ratios (HR) and 95% confidence intervals (CI) were examined using Cox regression analyses.

Results

During follow-up, 170 (52%) deaths occurred. The highest mortality was among patients with the second highest HbA1c variability, CV≥2.83 [n = 44 of which 68% patients died]. In the multivariate analyses where lowest HbA1c variability (CV≤0.51) was used as the reference group, HbA1c CV 2.83–4.60 (HR 3.15, 95% CI 1.78–5.55; p<0.001) and CV> 4.6 (HR 2.48, 95% CI 1.21–5.11; p = 0.014) were associated with increased risk of death.

Conclusion

The high risk of all-cause mortality in patients with diabetes and PD increased significantly with elevated HbA1c variability, as measure of long-term glycemic control. This indicates that stable glycemia is associated with an improvement of survival; whereas more severe glycemic fluctuations, possibly caused by radical changes in dialysis regimes or peritonitis, are associated with a higher risk of mortality in diabetic patients with PD.

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.

Is the insulin necessary for the struggle against oxidative stress in diabetes mellitus type 2: A pilot study

Background/Aim. Hyperglycaemia has a detrimental effect on the progress of micro/macrovascular complications in patients with diabetes mellitus type 2 (T2DM). Additionally, all known complications in T2DM are coupled with oxidative stress developed from different metabolic pathways. The aim of this study was to estimate the quality of glucoregulation and the degree of oxidative stress in T2DM patients depending on the applied therapeutic protocol and assess their correlation with clinical data and crucial biochemical parameters important for the development of diabetes complications. Methods. All included patients were divided into two groups: those treated with oral antidiabetic drugs (OAD) and those treated with oral antidiabetic drugs and insulin (OA-DINS). Thiobarbituric acid reactive substances (TBARS), total sulfhydryl groups (TSH), the activity of superoxide dismutase (SOD), total nitrites (NOx), vascular endothelial growth factor (VEGF), and activities of matrix metalloproteinase 9 (MMP9) were measured, together with lipid profile and routine biochemical parameters. All subjects were analyzed for demographic characteristics and detailed medical history as well as smoking habits and calculated for body mass index (BMI). Results. All patients were uniformly poor glucoregulated and dyslipidemic. SOD activity was decreased, and lipid peroxidation was increased in the OAD group compared to OADINS. Deficient glucoregulation in both the OAD and the OADINS groups did not associate with an oxidative state outcome. In both of these groups, the concentrations of VEGF and MMP9 were significantly higher than in controls. Conclusion. The better antioxidative outcome, expressed with a normalized concentration of TBARS, preserved TSH, and normalized SOD activity in T2DM patients treated with OADINS compared to those treated exclusively with OAD, suggests the need for more careful consideration of earlier insulin introduction into T2DM therapy in order to prevent the development of complications.

HbA1C variability among type 2 diabetic patients: a retrospective cohort study

BACKGROUND

Studies have found that HbA1C variability is an independent risk factor for diabetic complications in type 2 diabetic patients. This study aims to find factors contributing to higher HbA1C variability in the community.

METHODS

The study was conducted in the southern district of Israel, in Clalit Health Services (CHS). The study population was type 2 diabetic individuals aged 40-70 years in 2005, with a follow-up period of 11 years, until 2015. The definition of HbA1C variability was done by the standard deviation from the average HbA1C value of the entire study period, which was calculated for each participant. The study population was divided into two groups, "variability group" with HbA1C SD > 1.2, and "comparison group" of participants with HbA1C SD ≤ 1.2. In the univariate analysis we used X² or Fisher test for categorical variables and independent t-test for numeric continuous variables. In the multivariate analysis we used logistic regression as well as assessing for possible interactions. Statistical analysis was ascribed for p < 0.05. All the data was drawn from the computerized medical system used by all primary care physicians and nurses in CHS working in the community.

RESULTS

The study population included 2866 participants, the average age was 58.6 years, 43.3% men and 56.7% women. Each participant had an average of 20.9 HbA1C measures in their computerized medical record during the 11 years of follow up. The mean HbA1C value was 7.8%. We found 632 patients (22%) with a high variability, whereas 2234 (78%) had a low variability of HbA1C. In the "variability group" there was a higher percentage of smokers, BMI ≥ 30 and a higher rate of visits to diabetic clinics compared to the "no variability" group. In the "variability group" we found a much higher use of insulin and ACE inhibitors. The highest frequency of variability was between HbA1c values of 8.1-8.5. The multivariate analysis showed that HbA1C variability was associated with insulin use (OR = 4.1, p < 0.001), with age (OR = 0.939, p < 0.001), and Ischemic heart disease (OR = 1.258, p = 0.03). BMI ≥ 30 was almost statistically significant (OR = 1.206, p = 0.063). Gender was statistically insignificant.

CONCLUSIONS

In conclusion, HbA1C variability might be used as an additional marker in Diabetes Mellitus type 2, reflecting the disease complexity characteristics and the patient's lifestyle profile.

Effect of mean HbA1c on the association of HbA1c variability and all-cause mortality in patients with type 2 diabetes

AIM

To evaluate the effect of mean HbA1c on the correlation between HbA1c variability and all-cause mortality, and the risks associated with different levels of HbA1c and glycaemic control status in patients with type 2 diabetes.

MATERIALS AND METHODS

Patients with type 2 diabetes and at least three HbA1c measurements within 12-24 months were included. HbA1c variability score, coefficient of variation (CV) and standard deviation (SD) were used to evaluate variability. A variability score of 50 was set as a cutoff to define low and high variability.

RESULTS

A total of 4216 patients were included, of whom 1196 died during the observation period (11.1 ± 3.2 years). All-cause mortality increased with HbA1c variability score and the quartiles of HbA1c CV and SD. The strength of this association was attenuated after adjustment for mean HbA1c, and the risks associated with HbA1c variability and glycaemic control status were similar. The highest associated risk was observed with an HbA1c variability score of >50 and mean HbA1c of ≥7.5%. Mortality risk was significantly higher with a mean HbA1c of ≤6.0% and >8.5% and of ≤6.0% and >8.0% for low and high HbA1c variability, respectively.

CONCLUSIONS

Mean HbA1c contributed to the correlation between HbA1c variability and all-cause mortality. The risks associated with HbA1c variability and glycaemic control status were similar. The relationship between mean HbA1c and mortality presented a J-shaped distribution for both low and high HbA1c variability.

Mean and variability of annual haemoglobin A1c are associated with high-risk peripheral artery disease

BACKGROUND

Glucose variability is predictive of cardiovascular events and all-cause mortality. However, the association between peripheral artery disease and glucose variability has not been thoroughly investigated. Therefore, the standard deviation of annual haemoglobin A1c was assessed in patients with type 2 diabetes for evaluating the different risks of peripheral artery disease.

METHODS

A total of 4144 patients underwent an evaluation for the ankle-brachial index and the percentage of mean arterial pressure at the ankle. The first haemoglobin A1c record was retrospectively collected from each year until the ankle-brachial index measurement.

RESULTS

The standard deviation of annual haemoglobin A1c was higher in patients with ankle-brachial index ⩽0.90 than in those with ankle-brachial index >0.90 (1.1 ± 0.9% vs 1.0 ± 0.8%, p = 0.009) and was higher in patients with percentage of mean arterial pressure ⩾45% than in those with percentage of mean arterial pressure <45% (1.1 ± 0.8% vs 1.0 ± 0.8%, p = 0.007). A high standard deviation and mean of annual haemoglobin A1c are associated with high-risk peripheral artery disease, which is defined as a combination of ankle-brachial index ⩽0.90, percentage of mean arterial pressure ⩾45% or both (odds ratio = 1.306; 95% confidence interval = 1.057-1.615; p = 0.014).

CONCLUSION

Fluctuation in the haemoglobin A1c value indicates higher risk for peripheral artery disease in patients with type 2 diabetes and poor glucose control.

Fasting plasma glucose variability and HbA1c are associated with peripheral artery disease risk in type 2 diabetes

BACKGROUND

This study investigated whether visit-to-visit fasting plasma glucose (FPG) variability, as measured by the coefficient of variation (CV), increased peripheral artery disease (PAD) risk.

METHODS

Individuals with type 2 diabetes from the National Diabetes Care Management Program during the period 2002-2004, ≥ 30 years of age, and free of PAD (n = 30,932) were included and monitored until 2011. Cox proportional hazards regression models were implemented to analyze related determinants of PAD.

RESULTS

A total of 894 incident cases of PAD were identified during an average 8.2 years of follow-up, resulting in a crude incidence rate of 3.53 per 1000 person-years. Both FPG-CV and HbA1c were significantly associated with PAD after multivariate adjustment, with corresponding hazard ratios of 1.24 [95% confidence interval (CI) 1.04-1.47] for FPG-CV in the third tertile and 1.50 (95% CI 1.10-2.04) for HbA1c ≥ 10%. The findings of the sensitivity analysis remained consistent after excluding potential confounders, demonstrating the consistency of the results.

CONCLUSIONS

The associations between HbA1c, variability in FPG-CV, and PAD suggest a linked pathophysiological mechanism, suggesting the crucial role of glycemic variability in clinical management and therapeutic goals in preventing PAD in type 2 diabetes.

Impact of HbA1c variability on subclinical left ventricular remodeling and dysfunction in patients with type 2 diabetes mellitus

BACKGROUND

Glycemic instability confers a risk of poor prognosis in patients with type 2 diabetes mellitus (T2DM). This study aimed to investigate whether HbA1c variability provided additional value over mean HbA1c for predicting subclinical left ventricular remodeling and dysfunction in T2DM patients.

METHODS

A total of 466 T2DM patients with normal cardiac structure and function were recruited and prospectively followed up for a median of 4.7 y. HbA1c was measured quarterly. The intrapersonal mean and standard deviation (SD) of HbA1c measurements were calculated, and SD-HbA1c was considered as a measure of HbA1c variability. All participants underwent transthoracic echocardiography at baseline and after follow-up.

RESULTS

In multivariable regression analyses, SD-HbA1c was independently associated with annualized changes in left ventricular end diastolic diameter, interventricular septum, left ventricular posterior wall, left ventricular mass index, left ventricular ejection fraction, E/e' ratio, and E/A ratio (P < 0.001). Subgroup analysis based on mean HbA1c levels (<7.0%, 7.0-7.5%, and ≥7.5%) further confirmed that SD-HbA1c was associated with most of the above parameters regardless of mean HbA1c levels.

CONCLUSION

This study indicates that HbA1c variability adds to the mean value in predicting subclinical left ventricular remodeling and dysfunction in T2DM patients.

Predicting short- and long-term glycated haemoglobin response after insulin initiation in patients with type 2 diabetes mellitus using machine-learning algorithms

AIM

To assess the potential of supervised machine-learning techniques to identify clinical variables for predicting short-term and long-term glycated haemoglobin (HbA1c) response after insulin treatment initiation in patients with type 2 diabetes mellitus (T2DM).

MATERIALS AND METHODS

We included patients with T2DM from the Groningen Initiative to Analyse Type 2 diabetes Treatment (GIANTT) database who started insulin treatment between 2007 and 2013 and had a minimum follow-up of 2 years. Short- and long-term responses at 6 (±2) and 24 (±2) months after insulin initiation, respectively, were assessed. Patients were defined as good responders if they had a decrease in HbA1c ≥ 5 mmol/mol or reached the recommended level of HbA1c ≤ 53 mmol/mol. Twenty-four baseline clinical variables were used for the analysis and an elastic net regularization technique was used for variable selection. The performance of three traditional machine-learning algorithms was compared for the prediction of short- and long-term responses and the area under the receiver-operating characteristic curve (AUC) was used to assess the performance of the prediction models.

RESULTS

The elastic net regularization-based generalized linear model, which included baseline HbA1c and estimated glomerular filtration rate, correctly classified short- and long-term HbA1c response after treatment initiation, with AUCs of 0.80 (95% CI 0.78-0.83) and 0.81 (95% CI 0.79-0.84), respectively, and outperformed the other machine-learning algorithms. Using baseline HbA1c alone, an AUC = 0.71 (95% CI 0.65-0.73) and 0.72 (95% CI 0.66-0.75) was obtained for predicting short-term and long-term response, respectively.

CONCLUSIONS

Machine-learning algorithm performed well in the prediction of an individual's short-term and long-term HbA1c response using baseline clinical variables.

Patient clusters based on HbA1c trajectories: A step toward individualized medicine in type 2 diabetes

Aims

To identify clinically meaningful clusters of patients with similar glycated hemoglobin (HbA1c) trajectories among patients with type 2 diabetes.

Methods

A retrospective cohort study using unsupervised machine learning clustering methodologies to determine clusters of patients with similar longitudinal HbA1c trajectories. Stability of these clusters was assessed and supervised random forest analysis verified the clusters’ reproducibility. Clinical relevance of the clusters was assessed through multivariable analysis, comparing differences in risk for a composite outcome (macrovascular and microvascular outcomes, hypoglycemic events, and all-cause mortality) at HbA1c thresholds for each cluster.

Results

Among 60,423 patients, three clusters of HbA1c trajectories were generated: stable (n = 45,679), descending (n = 6,084), and ascending (n = 8,660) trends, which were reproduced with 99.8% accuracy using a random forest model. In the clinical relevance assessment, HbA1c levels demonstrated a J-shape association with the risk for outcomes. HbA1c level thresholds for minimizing outcomes’ risk differed by cluster: 6.0–6.4% for the stable cluster, <8.0% for the descending cluster, and <9.0 for the ascending cluster.

Conclusions

By applying unsupervised machine learning to longitudinal HbA1c trajectories, we have identified clusters of patients who have distinct risk for diabetes-related complications. These clusters can be the basis for developing individualized models to personalize glycemic targets.

Prognostic impact of HbA1c variability on long-term outcomes in patients with heart failure and type 2 diabetes mellitus

Background

The prognostic impact of long-term glycemic variability on clinical outcomes in patients with heart failure (HF) and type 2 diabetes mellitus (T2DM) remains unclear. We determined and compared hemoglobin A1c (HbA1c) variability and clinical outcomes for patients with HF with preserved ejection fraction (HFpEF), HF with mid-range ejection fraction (HFmrEF) and HF with reduced ejection fraction (HFrEF) in a prospective longitudinal study.

Methods

Patients with HF and T2DM, undergone 3 or more HbA1c determinations during the first 18 months, were then followed for 42 months. The primary outcome was death from any cause. Secondary outcome was composite endpoints with death and HF hospitalization. Cox proportional hazards models were used to compare outcomes for patients with HFpEF, HFmrEF and HFrEF.

Results

Of 902 patients enrolled, 32.2% had HFpEF, 14.5% HFmrEF, and 53.3% HFrEF. During 42 months of follow-up, 270 (29.9%) patients died and 545 (60.4%) patients experienced composite endpoints of death and HF readmission. The risk of all-cause death or composite endpoints was lower for HFpEF than HFrEF. Moreover, higher HbA1c variability was associated with higher all-cause mortality or composite endpoints and HbA1c variability was an independent predictor of all-cause mortality or composite endpoints, regardless of EF.

Conclusions

This prospective longitudinal study showed that the all-cause death and composite events was lower for HFpEF than HFrEF. HbA1c variability was independently and similarly predictive of death or combined endpoints in the three HF phenotypes.

Electronic supplementary material

The online version of this article (10.1186/s12933-018-0739-3) contains supplementary material, which is available to authorized users.

Long-term visit-to-visit glycemic variability as predictor of micro- and macrovascular complications in patients with type 2 diabetes: The Rio de Janeiro Type 2 Diabetes Cohort Study

Background

Long-term visit-to-visit glycemic variability is an additional measure of glycemic control. We aimed to evaluate the prognostic value of several measures of glycemic variability for the occurrence of micro- and macrovascular complications, and all-cause mortality in patients with type 2 diabetes.

Methods

654 individuals were followed-up over a median of 9.3 years. Glycemic variability (SDs and coefficients of variation of HbA_1c and fasting glycaemia) was measured during the first 12- and 24-months. Multivariate Cox analysis, adjusted for risk factors and mean HbA_1c and fasting glycaemia levels, examined the associations between glycemic variability and the occurrence of microvascular (retinopathy, microalbuminuria, renal function deterioration, peripheral neuropathy) and macrovascular complications [total cardiovascular events (CVE), major adverse CVEs (MACE) and cardiovascular mortality], and of all-cause mortality.

Results

During follow-up, 128 patients had a CVE (96 MACE), and 158 patients died (67 from cardiovascular diseases); 152 newly-developed or worsened diabetic retinopathy, 183 achieved the renal composite outcome (89 newly developed microalbuminuria and 91 deteriorated renal function), and 96 newly-developed or worsened peripheral neuropathy. Glycemic variability, particularly the 24-month parameters either estimated by HbA_1c or by fasting glycemia, predicted all endpoints, except for retinopathy and peripheral neuropathy development/progression, and was a better predictor than mean HbA_1c. Glycemic variability predicted retinopathy development/progression in patients with good glycemic control (HbA_1c ≤ 7.5%, 58 mmol/mol) and predicted new-incident peripheral neuropathy.

Conclusions

Long-term visit-to-visit glycemic variability is an additional and frequently a better glycemic parameter than mean HbA_1c levels for assessing the risk of future development of micro- and macrovascular complications in patients with type 2 diabetes.