Long-term Glycemic Variability Research Articles

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

To investigate whether long-term glycemic variability (GV) is associated with vascular complication development in type 2 diabetes. In a post hoc FIELD trial analysis, GV was calculated as the standard deviation and coefficient of variation (CV) of glycated hemoglobin A1c (HbA1c) and fasting plasma glucose. Baseline variables were compared across quartiles of on-study variability by chi square and ANOVA. Prospective associations between baseline to 2-year GV and subsequent vascular and mortality outcomes were analyzed using landmark logistic and Cox proportional hazards regression. Baseline factors associated with higher on-study GV included younger age, male gender, longer diabetes duration, and higher pharmacological therapies usage. Both HbA1c and fasting glucose CV were associated with increased risk of microvascular complications (HR 1.02 [95% CI, 1.01-1.03] P < 0.01; and HR 1.01 [95% CI, 1.00-1.01] P < 0.001, respectively). HbA1c and fasting glucose CV were associated with increased cardiovascular disease (HR 1.02 [95% CI, 1.00-1.04]; and HR 1.01 [95% CI, 1.00-1.02], both P < 0.05). HbA1c CV associated with increased stroke (HR 1.03 [95% CI, 1.01-1.06) P < 0.01). Glucose CV associated with increased coronary events (HR 1.01 [95% CI, 1.00-1.02] P < 0.05). Both HbA1c and glucose CV associated with increased total mortality (HR 1.04 [95% CI, 1.02-1.06]; and HR 1.01 [95% CI, 1.01-1.02], both P < 0.001) and noncardiovascular mortality (HR 1.05 [95% CI, (1.03-1.07]; and HR 1.02 [95% CI, 1.01-1.03], both P < 0.001). HbA1c CV associated with coronary mortality (HR 1.04 [95% CI, 1.01-1.07] P < 0.05). Long-term GV was associated with increased risk of vascular outcomes in type 2 diabetes.

Sociodemographic factors associated with HbA1c variability in type 2 diabetes: a prospective exploratory cohort study

BackgroundThe associations between sociodemographic factors and HbA1c variability in type 2 diabetes are not yet established. Examining group differences in HbA1c variability may help identify patient characteristics related to diabetes management. The present study examined differences in baseline HbA1c and HbA1c variability between groups with regard to sex, level of education, civil status, age, and BMI, in a sample of individuals with type 2 diabetes.MethodsThe study was a prospective exploratory cohort study. Differences in HbA1c variability between sociodemographic groups were analyzed in 158 individuals. HbA1c variability was assessed as the standard deviation (SD) and coefficient of variation (CV) over five measured points, and a questionnaire was used to assess sociodemographic factors.ResultsThe results showed significantly higher HbA1c variability in men compared to women (mean difference 1.44 mmol/mol [95% CI: 0.58 to 2.31]), and significantly higher HbA1c variability in individuals with a BMI characterized as obese compared to individuals with a BMI characterized as normal weight (mean difference 1.56 mmol/mol [95% CI: 0.25 to 2.88]). There were no significant associations between HbA1c variability and civil status or education.ConclusionsMen and individuals with obesity may be more vulnerable to future diabetic complications than other groups, since they have greater long-term glycemic variability.

HbA1c variability in adults with type 1 diabetes on continuous subcutaneous insulin infusion (CSII) therapy compared to multiple daily injection (MDI) treatment

ObjectiveTo determine if continuous subcutaneous insulin infusion (CSII) therapy is associated with lower glycatedhaemoglobin (HbA1c) variability (long-term glycaemic variability; GV) relative to multiple daily injection (MDI) treatment in adults with...

How Continuity in Service Impacts Process Variability: Evidence from a Primary Care Setting

Diabetes is a highly prevalent and expensive chronic disease that affects millions of Americans and is associated with multiple comorbidities. Clinical research has found long-term variation in a patient's glycated hemoglobin (A1c) levels to be linked with adverse health outcomes such as increased hospitalizations. Consequently, there is a need for innovative approaches to reduce long-term glycemic variability, and efficient ways to implement them. We draw on the management and healthcare literatures to hypothesize and then show that a key operational lever – continuity of care (CoC) – can be used to reduce glycemic variability, which in turn improves patient health. Additionally, we explore the moderating role of a key demographic characteristic – the patient's marital status – and the mediating role of medication compliance in the relationship between continuity and variability. We use a detailed and comprehensive dataset from the Veterans Health Administration, the largest integrated healthcare delivery system in the United States, which permits us to control for potential sources of heterogeneity. We analyze more than 300,000 patients – over an eleven-year period – with diabetes, a chronic disease whose successful management requires managing glycemic variability. We find that CoC is related to reductions in glycemic variability, more so for patients who are not married. However, this reduction is not linear in continuity; we find evidence of curvilinearity but with a sufficiently high stationary point so that benefits almost always accrue, albeit at a diminishing rate. Additionally, we find that an important mechanism through which CoC may reduce variability is through patients' adherence to medications. We also find evidence of partial mediation for glycemic variability in the CoC-outcomes process chain. Our counterfactual analysis reveals the extent of improvement and cost savings – in the order of tens of millions of dollars – that enhanced continuity can bring, depending on where it is targeted. Our findings are validated by extensive robustness checks and sensitivity analyses. Academically, our study adds to the understanding of the importance of managing variability (via continuity in service) in settings where customers repeatedly interact with service providers. Identifying the process measures through which continuity of care reduces variability is also of interest to practitioners and policymakers as it can help design appropriate policies and pathways, both in terms of processes and staffing/work allocation.

Reconfirmation of newly discovered risk factors of diabetic peripheral neuropathy in patients with type 2 diabetes: A case-control study.

AimsThe aim of the present study was to investigate the major determinants of diabetic peripheral neuropathy (DPN) in patients with type 2 diabetes (T2D), considering the traditional and newly discovered risk factors, including hypoglycaemia and glycemic variability.MethodsThis retrospective case-control study was conducted in a tertiary care hospital in Taiwan. A total of 2,837 patients with T2D were recruited, medical history and biochemical data were obtained, and patients were screened for DPN using the Michigan Neuropathy Screening Instrument (MNSI). DPN was defined as an MNSI exam score > 2. A stepwise selection of variables was used based on the Akaike Information Criterion (AIC) and the Schwarz Criterion (SC). Multivariate analysis was performed using the identified variables obtained from the stepwise selection.ResultsAmong the recruited patients, 604 (21.3%) were found to have DPN. 275 patients with DPN were selected because of longer follow up period before enrollment and complete data of glycemic parameters, and paired with 351 patients with T2D without DPN and matched for age, gender, and diabetes duration. The results of the stepwise selection showed that the presence of moderately and severely increased albuminuria yielded the lowest values of AIC and SC, which indicate the best predictive performance. Multivariate analysis demonstrated that moderately and severely increased albuminuria and greater long-term glycemic variability significantly increased the risk of DPN, with a corresponding odds ratio of 1.85 and 1.61 (95%confidence intervals of 1.25–2.73and1.02–2.55, respectively), after adjusted for hypoglycaemia and types of diabetes treatment.ConclusionsAlbuminuria is a potent predictor of DPN, and greater long-term glycemic variabilityis clearly associated with DPN in adults with T2D. These findings indicate that, in addition to achieve average blood glucose control, screening for albuminuria and reducing blood glucose fluctuations might be useful for improving diabetic microvascular complications.

Effects of long-term glycemic variability on incident cardiovascular disease and mortality in subjects without diabetes

Increased glycemic variability (GV) is an independent risk factor for cardiovascular complications in patients with diabetes. We evaluated the risk of future development of cardiovascular disease (CVD) and death according to GV in a general population without diabetes.We used the National Health Insurance Service, providing a population-based, nationwide database of Koreans. We included individuals without diabetes who underwent glucose measurement at least 3 times during 2002 to 2006. GV was calculated as standard deviation (SD) of fasting plasma glucose (FPG) levels. We observed development of CVD or all-cause death from 2007 to 2015, and also evaluated the mortality within 1 year after CVD.Among 3,211,319 people, we found 23,374 incident cases of myocardial infarction (MI), 27,705 cases of stroke, and 63,275 deaths during 8.3 years of follow-up. After multivariate adjustment, GV was found to be a significant predictor of MI, stroke and all-cause death for their highest quartile, with corresponding hazard ratios (HR) of 1.08 (95% confidence interval, CI 1.04-1.11), 1.09 (95% CI 1.06-1.13), and 1.12 (95% CI 1.10-1.15), respectively. The risk of death increased more in those who had both impaired fasting glucose and the highest quartile of GV (HR 1.24 [95% CI 1.21-1.28]). Moreover, early death rate after 1 year of CVD was higher in the highest quartile of GV compared to the lowest quartile (HR 1.21 [95% CI 1.03-1.41]).Long-term FPG variation was independently associated with CVD and mortality in a general population without diabetes.

176-LB: Long-Term Glycemic Variability in Diabetic Patients According to Their Glycemic Control in Follow-Up

Long term glycemic variability is linked to a higher rate of chronic complications and mortality in diabetic population and is also regarded a synonymous of poor glycemic control. However, the relationship between glycemic variability and glycemic control has not been studied properly. Aims to describe differences in glycemic variability in diabetic patients according to their trends in glucose level during follow-up. Methods from our database we selected those patients identified as diabetic (fasting glucose ≥ 126 mg/dl and/or Hba1c≥ 6.5%) who attended to our laboratory from January 2010 to august 2016 for a simultaneous Hba1c and glucose determinations. From the whole sample we selected those with at least 3 Hba1c determinations for 2 years. In all cases mean (M), standard deviation (SD) and variation coefficient (VC) were calculated from Hba1c during follow-up. Patients were categorized in four groups according to their first (i) and last determination (f) of Hba1c: group 1: i and f &amp;lt; 7%, group 2: i &amp;lt; 7% and f &amp;gt; 7%, group 3: i &amp;gt; 7% and f &amp;lt; 7% and group 4 i and f &amp;gt; 7%. We compared between groups VC. Results we studied results from 1589 patients (men 52%, age 60.95 (SD 14.83) years, mean number of determinations: 6.65 (95% CI 6.39-6.84). The percent of patients in group 1 to 4 respectively were : 22.7%, 10.2%, 23.5% and 43.6% . The mean (SD) Hba1c i and f were for group 1: 6.33% (0.41), 6.26% (0.43), group 2: 6.30% (0.55), 8.71% (1.99) , group 3: 8.96% (2.01), 6.23% (0.49) and group 4: 9.4% (2.01), 8.78% (1.71) . Mean VC (SD) were from group 1 to 4: 9.16(9.53), 18.36(11.17), 18.37(11.41), 12.68(8.09))(p &amp;lt;0.05 groups 1 vs. 2,3 and 4, group 2 vs. 1, 4, group 3 vs. 1 and 4, group 4 vs. 1, 2 and 3, P NS group 2 vs. 3). Conclusions: long term glycemic variability measured by VC is very similar in groups that migrates from their original glycemic level, no matter if their final Hba1c is within the goal. High VC is not necessarily related to poor metabolic control. Disclosure H. Garcia-Alcala: None.

Clinical Implications of Baroreflex Sensitivity in Type 2 Diabetes

The evaluation of baroreflex sensitivity (BRS), which maintains systemic circulatory homeostasis, is an established tool to assess cardiovascular autonomic neuropathy in type 2 diabetes mellitus (T2DM). As BRS plays an important function in blood pressure regulation, reduced BRS leads to an increase in blood pressure variability, which further leads to reduced BRS. This sequence of events becomes a vicious cycle. The major risk factors for reduced BRS are T2DM and essential hypertension, but many other risk factors have been reported to influence BRS. In recent years, reports have indicated that glycemic variability (GV), such as short- and long-term GV that are considered important risk factors for macrovascular and microvascular complications, is involved in reductions in BRS independently of blood glucose levels. In this review, we discuss reduced BRS in T2DM, its features, and the potential for its reversal.

Visit-to-Visit Glycemic Variability and Risks of Cardiovascular Events and All-Cause Mortality: The ALLHAT Study.

The prognostic value of long-term glycemic variability is incompletely understood. We evaluated the influence of visit-to-visit variability (VVV) of fasting blood glucose (FBG) on incident cardiovascular disease (CVD) and mortality. We conducted a prospective cohort analysis including 4,982 participants in the Antihypertensive and Lipid-Lowering Treatment to Prevent Heart Attack Trial (ALLHAT) who attended the baseline, 24-month, and 48-month visits. VVV of FBG was defined as the SD or variability independent of the mean (VIM) across FBG measurements obtained at the three visits. Participants free of CVD during the first 48 months of the study were followed for incident CVD (coronary heart disease [CHD], stroke, and heart failure [HF]) and all-cause mortality. Over a median follow-up of 5 years, there were 305 CVD events (189 CHD, 45 stroke, and 81 HF) and 154 deaths. The adjusted hazard ratio (HR) comparing participants in the highest versus lowest quartile of SD of FBG (≥26.4 vs. <5.5 mg/dL) was 1.43 (95% CI 0.93-2.19) for CVD and 2.22 (95% CI 1.22-4.04) for all-cause mortality. HR for VIM was 1.17 (95% CI 0.84-1.62) for CVD and 1.89 (95% CI 1.21-2.93) for all-cause mortality. Among individuals without diabetes, the highest quartile of SD of FBG (HR 2.67 [95% CI 0.14-6.25]) or VIM (HR 2.50 [95% CI 1.40-4.46]) conferred a higher risk of death. Greater VVV of FBG is associated with increased mortality risk. Our data highlight the importance of achieving normal and consistent glycemic levels for improving clinical outcomes.

Visit-to-visit HbA1c variability is inversely related to baroreflex sensitivity independently of HbA1c value in type 2 diabetes

BackgroundThe relationship between long-term glycemic variability (GV) represented by visit-to-visit HbA1c variability and baroreflex sensitivity (BRS) in type 2 diabetes mellitus (T2DM) has not been clarified by previous literature. The present study is the first to examine the relationships between visit-to-visit HbA1c variability and BRS.MethodsThis retrospective study initially analyzed data on 94 patients with T2DM. Visit-to-visit HbA1c variability was evaluated using the intrapersonal coefficient of variation (CV), standard deviation (SD), and adjusted SD of 8 or more serial measurements of HbA1c during a 2-year period. The BRS was analyzed using the sequence method. Short-term GV was assessed by measuring the glucose CV during 24-h continuous glucose monitoring (CGM). The primary objective was to determine if there was a relationship between visit-to-visit HbA1c variability (HbA1c CV) and BRS. Secondary objectives were to examine the relationship between other variables and BRS and the respective and combined effects of long-term GV (HbA1c CV) and short-term GV (CGM CV) on BRS.ResultsA total of 57 patients (mean age 67.2 ± 7.7 years, mean HbA1c 7.3 ± 1.0%) who met this study’s inclusion criteria were finally analyzed. In the univariate analysis, HbA1c CV (r = − 0.354, p = 0.007), HbA1c SD (r = − 0.384, p = 0.003), and adjusted HbA1c SD (r = − 0.391, p = 0.003) were significantly related to low levels of BRS. Multiple regression analysis showed that HbA1c CV, HbA1c SD, and adjusted HbA1c SD were inversely related to BRS. Furthermore, although the increase in either long-term GV (HbA1c CV) or short-term GV (CGM CV) as determined by 24-h CGM was inversely correlated with BRS, additional reductions in BRS were not shown in participants with both HbA1c CV and CGM CV values above the median.ConclusionsVisit-to-visit HbA1c variability was inversely related to BRS independently of the mean HbA1c in patients with T2DM. Therefore, visit-to-visit HbA1c variability might be a marker of reduced BRS in T2DM.

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

BackgroundThe 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.MethodsPatients 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.ResultsOf 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.ConclusionsThis 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.

HbA1c variability and diabetic peripheral neuropathy in type 2 diabetic patients

BackgroundDiabetic complications may be associated with impaired time-dependent glycemic control. Therefore, long-term glycemic variability, assessed by variations in haemoglobin A1c (HbA1c), may be a potential risk factor for microvascular complications, such as diabetic peripheral neuropathy (DPN). We investigated the association of HbA1c variability with DPN in patients with type 2 diabetes.MethodsIn this cross-sectional study, 563 type 2 diabetic patients who had been screened for DPN and undergone quarterly HbA1c measurements during the year preceding enrolment were recruited. DPN was confirmed in patients displaying both clinical manifestations of neuropathy and abnormalities in a nerve conduction evaluation. HbA1c variability was assessed by the coefficient of variation of HbA1c (CV-HbA1c), and the mean of HbA1c (M-HbA1c) was calculated. In addition, medical history and clinical data were collected.ResultsAmong the recruited patients, 18.1% (n = 102) were found to have DPN, and these patients also presented with a higher CV-HbA1c than the patients without DPN (p < 0.001). The proportion of patients with DPN increased significantly from 6.9% in the first to 19.1% in the second and 28.5% in the third tertile of CV-HbA1c (p for trend < 0.001). After adjusting for initial HbA1c, M-HbA1c and other clinical factors via multiple logistic regression analysis, the odds ratios (ORs) for DPN in the second and third versus those in the first CV-HbA1c tertile were 3.61 (95% CI 1.62–8.04) and 6.48 (2.86–14.72), respectively. The area under the receiver operating characteristic (ROC) curve of CV-HbA1c was larger than that of M-HbA1c, at 0.711 (95% CI 0.659–0.763) and 0.662 (0.604–0.721), respectively. ROC analysis also revealed that the optimal cutoff value of CV-HbA1c to indicate DPN was 15.15%, and its corresponding sensitivity and specificity were 66.67% and 65.73%, respectively.ConclusionsIncreased HbA1c variability is closely associated with DPN in type 2 diabetic patients and could be considered as a potent indicator for DPN in these patients.

Type 2 Diabetes and Glycemic Variability: Various Parameters in Clinical Practice

It has become possible to measure blood glucose levels continuously from 24 h to approximately 2 weeks due to the recent development of relevant devices such as continuous glucose monitoring and flash glucose monitoring systems. This has enabled not only medical professionals but also patients to monitor details of glycemic variability (GV) which was not possible in the past. Details of GV for both short and intermediate periods can now be obtained, and it is important in patient care to appropriately evaluate the data obtained. Types of GV in terms of time frame vary from short-term to long-term. Several studies reported that long-term GV was related to micro- and macro-angiopathies in patients with type 2 diabetes mellitus (T2DM). However, there are still unknown aspects regarding the relationships of various durations of GV with prognosis. Further clinical trials are required to examine the mechanism of GV and to evaluate whether GV can be a valuable therapeutic target in treatment of patients with T2DM.

The association of long-term glycaemic variability versus sustained chronic hyperglycaemia with heart rate-corrected QT interval in patients with type 2 diabetes.

ObjectivesProlonged heart rate-corrected QT(QTc) interval is related to ventricular arrhythmia and cardiovascular mortality, with considerably high prevalence of type 2 diabetes. Additionally, long-term glycaemic variability could be a significant risk factor for diabetic complications in addition to chronic hyperglycaemia. We compared the associations of long-term glycaemic variability versus sustained chronic hyperglycaemia with the QTc interval among type 2 diabetes patients.MethodsIn this cross-sectional study, 2904 type 2 diabetes patients were recruited who had undergone at least four fasting plasma glucose (FPG) and 2-hour postprandial plasma glucose (PPG) measurements (at least once for every 3 months, respectively) during the preceding year. Long-term glycaemic variabilities of FPG and 2-hour PPG were assessed by their standard deviations (SD-FPG and SD-PPG, respectively), and chronic fasting and postprandial hyperglycaemia were assessed by their means (M-FPG and M-PPG, respectively). HbA1c was also determined upon enrolment to assess current overall glycaemic control. QTc interval was estimated from resting 12-lead electrocardiograms, and more than 440 ms was considered abnormally prolonged.ResultsPatients with prolonged QTc interval (≥440 ms) had greater M-FPG, M-PPG, SD-PPG and HbA1c than those with normal QTc interval but comparable SD-FPG. QTc interval was correlated with M-FPG, M-PPG, SD-PPG and HbA1c (r = 0.133, 0.153, 0.245 and 0.207, respectively, p = 0.000) but not with SD-FPG (r = 0.024, p = 0.189). After adjusting for metabolic risk factors via multiple linear regression analysis, SD-PPG, M-PPG and HbA1c (t = 12.16, 2.69 and 10.16, respectively, p = 0.000) were the major independent contributors to the increased QTc interval. The proportion of prolonged QTc interval increased significantly from 10.9% to 14.2% to 26.6% for the first (T1) to second (T2) to third (T3) tertiles of SD-PPG. After adjusting via multiple logistic regression analysis, the odd ratios of prolonged QTc interval of the T2 and T3 versus the T1 of SD-PPG were 1.15 (95% CI, 0.82–1.60) and 2.62 (1.92–3.57), respectively.ConclusionsIncreased long-term variability of PPG is a strong independent risk factor for prolonged QTc interval in type 2 diabetes patients, in addition to long-term postprandial hyperglycaemia and current HbA1c.

Impact of long-term glycemic variability on development of atrial fibrillation in type 2 diabetic patients.

Objective:It is well known that patients with type 2 diabetes mellitus (T2DM) have a high risk of atrial fibrillation (AF). The current study was designed to determine the relationship between long-term glycemic variability and incidence of new-onset AF in T2DM patients.Methods:Between January 2008 and December 2009, we conducted a retrospective cohort study in patients with T2DM referred to our hospital. In 505 consecutive patients without any medical history of AF at baseline, the relationship between hemoglobin A1c (HbA1c) variability and future AF incidence was evaluated, with adjustments for other possible confounding factors. HbA1c variability was determined by standard deviation (SD) and coefficient of variation (CV).Results:Over a median of 6.9-year follow-up period, 48 patients (9.5%) developed incident AF. Multiple cox regression revealed that higher HbA1c-SD (HR: 1.726, 95% CI: 1.104–1.830, p=0.001) or HbA1c-CV (HR: 1.241, 95% CI: 1.029–1.497, p=0.024) remained the remarkable predictor of new-onset AF after adjusting for age, body mass index, left ventricular mass index, and left atrium diameter. Receiver operating curve analysis identified thresholds for HbA1c-SD (0.665%, sensitivity 71.4%, specificity 54.9%) and HbA1c-CV (8.970%, sensitivity 73.8%, specificity 47.1%) to detect new-onset AF development.Conclusion:In patients with T2DM, higher HbA1c variability is significantly associated with future AF development.

Response: Cardiovascular Disease Predicts Severe Hypoglycemia in Patients with Type 2 Diabetes (Diabetes Metab J 2015;39:498-506)

We sincerely appreciate the interests and comments on our study, disease predicts severe hypoglycemia in patients with type 2 which was published in Diabetes & Metabolism Journal [1]. Recent guidelines emphasize that glycemic management should be individualized according to multiple patients factors [2]. Hypoglycemia is regarded as one of the major barriers to achieve glycemic targets, and early detection of hypoglycemia risk is clinically important [3]. In this study, we have found that a history of cardiovascular disease was an independent risk factor for the development of severe hypoglycemia (SH) after adjusting for other SH-related factors (including cardiovascular autonomic dysfunction) in type 2 diabetes patients without renal dysfunction. We agree with Dr. Kim's opinion that glycemic variability is one of the important factors that may influence the development of SH. In this study, 62 patients experienced a total of 78 SH episodes during the 10-year follow-up period. Two SH events occurred in eight patients, and three SH events occurred in four patients. We routinely checked the patients' glycosylated hemoglobin (HbA1c) level every 6 months and calculated the mean and standard deviation (SD) of HbA1c during the follow-up period. As a result, we found that there were no differences in the mean HbA1c (8.34%±1.31% vs. 8.52%±1.47%, P=0.684) and SD of HbA1c (1.10±0.58 vs. 1.14±0.61, P= 0.855) between the group that experienced one SH event and those that experienced recurrent SH events. Also, there were no differences in mean HbA1c (8.37%±1.34% vs. 8.24%± 1.35%, P=0.460) and SD of HbA1c (1.11±0.58 vs. 1.23±0.75, P=0.228) between the group who experienced SH and those who did not experienced SH, although the group with SH showed borderline significant difference in baseline HbA1c when compared with the group without SH. Further regression model adjusted for SD of HbA1c did not affect the outcome (hazard ratio, 2.40; 95% confidence interval, 1.11 to 5.21; P= 0.027). Thus, we confirmed that long-term glycemic variability did not influence the main result of this study. In addition, there were some studies that evaluated peripheral neuropathy as a risk factor for SH. In Action to Control Cardiovascular Risk in Diabetes (ACCORD) trial, peripheral neuropathy conferred a modest but significantly increased risk of SH [4]. Other studies showed that peripheral neuropathy was related to the risk of recurrent SH [5,6,7], as Dr. Kim pointed out. The precise mechanism for the relationship between peripheral neuropathy and SH remained obscure. Unfortunately, baseline examinations of peripheral neuropathy were not performed in all patients in this study. Future analyses will include factors such as nerve conduction study [8] as well as additional data. We would like to appreciate Dr. Kim for the interest in our study and the thoughtful comments.

Carotid Intima-Media Thickness and Visit-to-Visit HbA1c Variability Predict Progression of Chronic Kidney Disease in Type 2 Diabetic Patients with Preserved Kidney Function

Background/Aims. Subclinical atherosclerosis and long-term glycemic variability have been reported to predict incident chronic kidney disease (CKD) in the general population. However, these associations have not been investigated in patients with type 2 diabetes with preserved kidney function. Methods. We prospectively followed up 162 patients with type 2 diabetes (mean age, 62.3 years; 53.6% men) and assessed whether carotid intima-media thickness (IMT) measured by B-mode ultrasound and visit-to-visit HbA1c variability are associated with deterioration of CKD (incident CKD defined as estimated GFR [eGFR] < 60 mL/min/1.73 m2 and progression of CKD stages) over a median follow-up of 6.0 years. At baseline, 25 patients (15.4%) had CKD. Cox proportional hazards regression models were used for identifying associated factors of CKD deterioration. Results. Estimated GFR decreased from 75.8 ± 16.3 to 67.4 ± 18.2 mL/min/1.73 m2 (p < 0.01). Of 162 patients, 32 developed CKD and 8 made a progression of CKD stages. Multivariate Cox regression analysis revealed that carotid IMT (HR: 4.0, 95% CI: 1.1–14.226.7, and p = 0.03) and coefficient of variation of HbA1c (HR: 1.12, 95%: 1.04–1.21, and p = 0.003) were predictors of deterioration of CKD independently of age, mean HbA1c, urinary albumin/creatinine ratio, baseline eGFR, uric acid, and leucocyte count. Conclusions. Subclinical atherosclerosis and long-term glycemic variability predict deterioration of chronic kidney disease (as defined by incident or worsening CKD) in type 2 diabetic patients with preserved kidney function.

Long-term Glycemic Variability and Risk of Adverse Outcomes: A Systematic Review and Meta-analysis.

Glycemic variability is emerging as a measure of glycemic control, which may be a reliable predictor of complications. This systematic review and meta-analysis evaluates the association between HbA1c variability and micro- and macrovascular complications and mortality in type 1 and type 2 diabetes. Medline and Embase were searched (2004-2015) for studies describing associations between HbA1c variability and adverse outcomes in patients with type 1 and type 2 diabetes. Data extraction was performed independently by two reviewers. Random-effects meta-analysis was performed with stratification according to the measure of HbA1c variability, method of analysis, and diabetes type. Seven studies evaluated HbA1c variability among patients with type 1 diabetes and showed an association of HbA1c variability with renal disease (risk ratio 1.56 [95% CI 1.08-2.25], two studies), cardiovascular events (1.98 [1.39-2.82]), and retinopathy (2.11 [1.54-2.89]). Thirteen studies evaluated HbA1c variability among patients with type 2 diabetes. Higher HbA1c variability was associated with higher risk of renal disease (1.34 [1.15-1.57], two studies), macrovascular events (1.21 [1.06-1.38]), ulceration/gangrene (1.50 [1.06-2.12]), cardiovascular disease (1.27 [1.15-1.40]), and mortality (1.34 [1.18-1.53]). Most studies were retrospective with lack of adjustment for potential confounders, and inconsistency existed in the definition of HbA1c variability. HbA1c variability was positively associated with micro- and macrovascular complications and mortality independently of the HbA1c level and might play a future role in clinical risk assessment.

The association between glycemic variability and diabetic cardiovascular autonomic neuropathy in patients with type 2 diabetes.

BackgroundIt is presently unclear whether glycemic variability is associated with diabetic cardiovascular autonomic neuropathy (CAN). The aim of this study was to examine whether short- and/or long-term glycemic variability (GV) contribute to CAN.MethodsA total of 110 patients with type 2 diabetes who underwent three-day continuous glucose monitoring (CGM) completed five standardized autonomic neuropathy tests. Short-term GV was measured by the standard deviation (SD), coefficient of variation (CV) of glucose, and the mean amplitude of glycemic excursions (MAGE) in CGM. HbA1c variability was calculated from the intrapersonal SD, adjusted SD, and CV of serial HbA1c over 2-year period. CAN was defined as the presence of at least two abnormal parasympathetic function tests. The severity of CAN was evaluated by total scores of five autonomic function tests.ResultsIn univariate analysis, not only SD and CV in CGM but also all parameters of HbA1c variability were significantly higher in the patients with CAN (n = 47, 42.7 %) than in those without CAN. In multivariate analysis, CV (Odds ratio [OR] 1.07, 95 % confidence interval [CI] 1.01–1.13; p = 0.033), but neither SD nor MAGE in CGM, independently correlated with the presence of CAN. All parameters of HbA1c variability, such as SD of HbA1c (OR 12.10 [95 % CI 2.29–63.94], p = 0.003), adjusted SD of HbA1c (OR 17.02 [95 % CI 2.66–108.86], p = 0.003), and log CV of HbA1c (OR 24.00 [95 % CI 3.09–186.48], p = 0.002), were significantly associated with the presence of CAN. The patients with higher HbA1c variability had an increased risk of advanced CAN.ConclusionCV in CGM and all parameters of HbA1c variability were independently associated with the presence of CAN in patients with inadequately controlled type 2 diabetes requiring CGM.Electronic supplementary materialThe online version of this article (doi:10.1186/s12933-015-0233-0) contains supplementary material, which is available to authorized users.

Glycemic control, inflammation, and cognitive function in older patients with type 2 diabetes.

Glycated hemoglobin (HbA1c) and C-reactive protein (CRP) have been associated with cognitive impairment independently. However, it is unclear if their combination exacerbates poor cognitive function. We assessed whether long-term glycemic level and glycemic variability modulate the association of systemic inflammation with cognitive function, in a sample of cognitively normal older people with type 2 diabetes. A retrospective cohort study of 777 randomly selected participants from ~11,000 patients in the Maccabi Healthcare Services Diabetes Registry, as part of the Israel Diabetes and Cognitive Decline study. Subjects averaged 18 (±9.4) HbA1c measures in the Maccabi Healthcare Services Registry, which were used to calculate long-term glycemic level (HbA1c-mean) and glycemic variability (HbA1c-standard deviation (SD)). Linear regression models assessed the interactions of CRP, a marker of systemic inflammation, with HbA1c-mean and HbA1c-SD on subjects' performance in tests of Memory, Executive Functions, Attention, and Semantic Categorization. Quadratic interactions of CRP with HbA1c-SD approached significance for executive functions and overall cognition. However, after Bonferroni adjustment, none of the interactions of CRP with HbA1c were statistically significant. In partial correlations according to HbA1c-SD tertiles, CRP was weakly correlated in the middle tertile with decreased performance in the domains of semantic categorization (r = -0.166, p = 0.011), executive functions (r = -0.136, p = 0.038), and overall cognition (r = -0.157, p = 0.016). Glycated hemoglobin does not substantially modulate the association of CRP with cognition in a sample of cognitively normal, community dwelling older people with relatively well-managed type 2 diabetes.