Prognostic impact of visit-to-visit glycemic variability on the risks of major adverse cardiovascular outcomes and hypoglycemia in patients with different glycemic control and type 2 diabetes

The effect of sodium-glucose cotransporter 2 inhibitors as an adjunct to insulin in patients with type 1 diabetes assessed by continuous glucose monitoring: A systematic review and meta-analysis

AIMS

Patients undergoing insulin-based therapy for type 1 diabetes often experience poor glycemic control characterized by significant fluctuations. This study was undertaken to analyze the effect of sodium-glucose cotransporter 2 inhibitors (SGLT2Is), as an adjunct to insulin, on time in range (TIR) and glycemic variability in patients with type 1 diabetes, using continuous glucose monitoring (CGM). In addition, we examined which type of SGLT2I yielded a superior effect compared to others.

METHODS

We conducted a comprehensive search of PubMed, EMBASE, the Cochrane Library, Web of Science, and clinical trial registry websites, retrieving all eligible randomized clinical trials (RCTs) published up until February 2023. We analyzed the mean TIR, mean amplitude of glucose excursions (MAGE), mean daily glucose (MDG), diabetic ketoacidosis (DKA), standard deviation (SD), total insulin dose, and severe hypoglycemia to evaluate the efficacy and safety of SGLT2Is. A random-effects model was also employed.

RESULTS

This study encompassed 15 RCTs. The meta-analysis revealed that the use of SGLT2Is as an adjuvant therapy to insulin led to a significant increase in TIR (MD = 10.78, 95%CI = 9.33-12.23, I2 = 42 %, P < 0.00001) and a decrease in SD (MD = -0.38, 95%CI = -0.50 to -0.26, I2 = 0 %, P < 0.00001), MAGE (MD = -0.92, 95%CI = -1.17 to -0.67, I2 = 19 %, P < 0.00001), MDG(MD = -1.01, 95%CI = -1.32 to -0.70, I2 = 48 %, P < 0.00001), and total insulin dose (MD = -5.81, 95%CI = -7.81 to -3.82, I2 = 32 %, P < 0.00001). No significant increase was observed in the rate of severe hypoglycemia (RR = 1.04, 95 % CI = 0.76-1.43, P = 0.80). However, SGLT2I therapy was associated with increased DKA occurrence (RR = 2.79, 95 % CI = 1.42-5.48; P = 0.003, I2 = 16 %). In addition, the subgroup analyses based on the type of SGLT2Is revealed that dapagliflozin might exhibit greater efficacy compared to other SGLT2Is across most outcomes.

CONCLUSIONS

SGLT2Is exhibited a positive effect on improving blood glucose level fluctuations. Subgroup analysis showed that dapagliflozin appeared to have more advantages. However, giving due consideration to preventing adverse effects, particularly DKA, is paramount.

REGISTRATION

Prospero CRD42023408276.

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

BACKGROUND

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

METHODS

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

RESULTS

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

CONCLUSIONS

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

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

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

In-hospital glycemic variability and all-cause mortality among patients hospitalized for acute heart failure

BACKGROUND

High glycemic variability (GV) is a poor prognostic marker in cardiovascular diseases. We aimed to investigate the association of GV with all-cause mortality in patients with acute heart failure (HF).

METHODS

The Korean Acute Heart Failure registry enrolled patients hospitalized for acute HF from 2011 to 2014. Blood glucose levels were measured at the time of admission, during hospitalization, and at discharge. We included those who had 3 or more blood glucose measurements in this study. Patients were divided into two groups based on the coefficient of variation (CoV) as an indicator of GV. Among survivors of the index hospitalization, we investigated all-cause mortality at 1 year after discharge.

RESULTS

The study analyzed 2,617 patients (median age, 72 years; median left-ventricular ejection fraction, 36%; 53% male). During the median follow-up period of 11 months, 583 patients died. Kaplan-Meier curve analysis revealed that high GV (CoV > 21%) was associated with lower cumulative survival (log-rank P < 0.001). Multivariate Cox proportional analysis showed that high GV was associated with an increased risk of 1-year (HR 1.56, 95% CI 1.26-1.92) mortality. High GV significantly increased the risk of 1-year mortality in non-diabetic patients (HR 1.93, 95% CI 1.47-2.54) but not in diabetic patients (HR 1.19, 95% CI 0.86-1.65, P for interaction = 0.021).

CONCLUSIONS

High in-hospital GV before discharge was associated with all-cause mortality within 1 year, especially in non-diabetic patients with acute HF.

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

Background

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

Methods

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

Results

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

Conclusions

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

Muscle-to-fat ratio identifies functional impairments and cardiometabolic risk and predicts outcomes: biomarkers of sarcopenic obesity

BACKGROUND

Sarcopenic obesity aims to capture the risk of functional decline and cardiometabolic diseases, but its operational definition and associated clinical outcomes remain unclear. Using data from the Longitudinal Aging Study of Taipei, this study explored the roles of the muscle-to-fat ratio (MFR) with different definitions and its associations with clinical characteristics, functional performance, cardiometabolic risk and outcomes.

METHODS

(1) Appendicular muscle mass divided by total body fat mass (aMFR), (2) total body muscle mass divided by total body fat mass (tMFR) and (3) relative appendicular skeletal muscle mass (RASM) were measured. Each measurement was categorized by the sex-specific lowest quintiles for all study participants. Clinical outcomes included all-cause mortality and fracture.

RESULTS

Data from 1060 community-dwelling older adults (mean age: 71.0 ± 4.8 years) were retrieved for the study. Overall, 196 (34.2% male participants) participants had low RASM, but none was sarcopenic. Compared with those with high aMFR, participants with low aMFR were older (72 ± 5.6 vs. 70.7 ± 4.6 years, P = 0.005); used more medications (2.9 ± 3.3 vs. 2.1 ± 2.5, P = 0.002); had a higher body fat percentage (38 ± 4.8% vs. 28 ± 6.4%, P < 0.001), RASM (6.7 ± 1.0 vs. 6.5 ± 1.1 kg/m² , P = 0.001), and cardiometabolic risk [fasting glucose: 105 ± 27.5 vs. 96.8 ± 18.7 mg/dL, P < 0.001; glycated haemoglobin (HbA1c): 6.0 ± 0.8 vs. 5.8 ± 0.6%, P < 0.001; triglyceride: 122.5 ± 56.9 vs. 108.6 ± 67.5 mg/dL, P < 0.001; high-density lipoprotein cholesterol (HDL-C): 56.2 ± 14.6 vs. 59.8 ± 16 mg/dL, P = 0.010]; and had worse functional performance [Montreal Cognitive Assessment (MoCA): 25.7 ± 4.2 vs. 26.4 ± 3.0, P = 0.143, handgrip strength: 24.7 ± 6.7 vs. 26.1 ± 7.9 kg, P = 0.047; gait speed: 1.8 ± 0.6 vs. 1.9 ± 0.6 m/s, P < 0.001]. Multivariate linear regression showed that age (β = 0.093, P = 0.001), body mass index (β = 0.151, P = 0.046), total percentage of body fat (β = 0.579, P < 0001) and RASM (β = 0.181, P = 0.016) were associated with low aMFR. Compared with those with high tMFR, participants with low tMFR were older (71.7 ± 5.5 vs. 70.8 ± 4.7 years, P = 0.075); used more medications (2.8 ± 3.3 vs. 2.1 ± 2.5, P = 0.006); had a higher body fat percentage (38.1 ± 4.7 vs. 28 ± 6.3%, P < 0.001), RASM (6.8 ± 1.0 vs. 6.5 ± 1.1 kg/m² , P < 0.001), and cardiometabolic risk (fasting glucose: 104.8 ± 27.6 vs. 96.9 ± 18.7 mg/dL, P < 0.001; HbA1c: 6.1 ± 0.9 vs. 5.8 ± 0.6%, P < 0.001; triglyceride: 121.4 ± 55.5 vs. 108.8 ± 67.8 mg/dL, P < 0.001; HDL-C: 56.4 ± 14.9 vs. 59.7 ± 15.9 mg/dL, P = 0.021); and had worse functional performance (MoCA: 25.6 ± 4.2 vs. 26.5 ± 3.0, P = 0.056; handgrip strength: 24.6 ± 6.7 vs. 26.2 ± 7.9 kg, P = 0.017; gait speed: 1.8 ± 0.6 vs. 1.9 ± 0.6 m/s, P < 0.001). Low tMFR was associated with body fat percentage (β = 0.766, P < 0.001), RASM (β = 0.476, P < 0.001) and Mini-Nutritional Assessment (β = -0.119, P < 0.001). Gait speed, MoCA score, fasting glucose, HbA1c and tMFR were significantly associated with adverse outcomes, and the effects of aMFR were marginal (P = 0.074).

CONCLUSIONS

Older adults identified with low MFR had unfavourable body composition, poor functional performance, high cardiometabolic risk and a high risk for the clinical outcome.

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.

Insulin Degludec Versus Insulin Glargine on Glycemic Variability in Diabetic Patients: A Systematic Review and Meta-Analysis of Randomized Controlled Trials

BACKGROUND/AIMS

Currently, glycemic variability has more deleterious effects than sustained hyperglycemia and is closely associated with acute and chronic complications of diabetes. Reducing glycemic excursion is becoming another vital goal of glycemic control in clinical practice. This study aimed to determine whether insulin degludec (IDeg) or insulin glargine (IGla) was more beneficial for reducing glycemic fluctuations.

MATERIALS AND METHODS

This research was constructed according to the PRISMA guidelines. We searched eight databases and ClinicalTrials.gov from their inception to 30 November 2021. All randomized controlled trials comparing the efficacy of glucose variability between IDeg and IGla in diabetic patients were included.

RESULTS

Fourteen trials with 8,683 participants were included. In patients with T1DM, IDeg was associated with a lower mean (MD: -16.25, 95% CI -29.02 to -3.07, P = 0.01) and standard deviation (P = 0.03) compared to IGla in fasting blood glucose (FBG); in people with T2DM, IDeg was related to a lower mean of FBG versus insulin glargine 100 U/ml (IGla100) (P <0.001) and had a more extended time in the range (TIR) than IGla100 (SMD: 0.15, 95% CI 0.02 to 0.27, P = 0.02) but not longer than insulin glargine 300 U/ml (IGla300). Moreover, IDeg had a lower coefficient of variation of FBG than IGla (P = 0.0254). For other indicators of glycemic variability, namely, standard deviation of blood glucose for 24 h, the mean of 24-h blood glucose, mean amplitude of glycemic excursion, the coefficient of variation for 24 h, the mean of daily differences, area under the glucose curve, and M-value, no significant differences were identified between IDeg and IGla, regardless of T1DM or T2DM.

CONCLUSIONS

Based on the current studies, there was comparable efficacy between IDeg and IGla from multiple aspects of glycemic variability, regardless of T1DM or T2DM. However, IDeg may be superior to IGla in reducing FBG variability in T1DM and T2DM. Nonetheless, due to the limitations of the original studies, it is still unclear whether IDeg is superior to both IGla100 and IGla300. In T2DM, IDeg had more extended TIR than IGla100 but not longer than IGla300. Additionally, more well-designed randomized controlled trials comparing IDeg with IGla300 for different indicators of glycemic variability are still warranted.

SYSTEMATIC REVIEW REGISTRATION

PROSPERO, CRD42021283203.

A retrospective in-depth analysis of continuous glucose monitoring datasets for patients with hepatic glycogen storage disease: Recommended outcome parameters for glucose management

Continuous glucose monitoring (CGM) systems have great potential for real-time assessment of glycemic variation in patients with hepatic glycogen storage disease (GSD). However, detailed descriptions and in-depth analysis of CGM data from hepatic GSD patients during interventions are scarce. This is a retrospective in-depth analysis of CGM parameters, acquired in a continuous, real-time fashion describing glucose management in 15 individual GSD patients. CGM subsets are obtained both in-hospital and at home, upon nocturnal dietary intervention (n = 1), starch loads (n = 11) and treatment of GSD Ib patients with empagliflozin (n = 3). Descriptive CGM parameters, and parameters reflecting glycemic variation and glycemic control are considered useful CGM outcome parameters. Furthermore, the combination of first and second order derivatives, cumulative sum and Fourier analysis identified both subtle and sudden changes in glucose management; hence, aiding assessment of dietary and medical interventions. CGM data interpolation for nocturnal intervals reduced confounding by physical activity and diet. Based on these analyses, we conclude that in-depth CGM analysis can be a powerful tool to assess glucose management and optimize treatment in individual hepatic GSD patients.

Hypoglycaemia and Cardiovascular Disease Risk in Patients with Diabetes

Hypoglycaemia represents an important side effect of insulin therapy and insulin secretagogues. It can occur in both type 1 and type 2 diabetes mellitus patients. Also, some associations between hypoglycaemia and cardiovascular (CV) risk have been reported. Several mechanisms may be involved, including the sympathoadrenal system, hypokalaemia, endothelial dysfunction, coagulation, platelets, inflammation, atherothrombosis and impaired autonomic cardiac reflexes. This narrative review discusses the associations of hypoglycaemia with CV diseases, including coronary heart disease (CHD), cardiac arrhythmias, stroke, carotid disease and peripheral artery disease (PAD), as well as with dementia. Severe hypoglycaemia has been related to CHD, CV and all-cause mortality. Furthermore, there is evidence supporting an association between hypoglycaemia and cardiac arrhythmias, potentially predisposing to sudden death. The data linking hypoglycaemia with stroke, carotid disease and PAD is limited. Several factors may affect the hypoglycaemia-CV relationships, such as the definition of hypoglycaemia, patient characteristics, co-morbidities (including chronic kidney disease) and antidiabetic drug therapy. However, the association between hypoglycaemia and dementia is bilateral. Both the disorders are more common in the elderly; thus, glycaemic goals should be carefully selected in older patients. Further research is needed to elucidate the impact of hypoglycaemia on CV disease.

Comprehensive elaboration of glycemic variability in diabetic macrovascular and microvascular complications

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

[Insulin therapy is a personalized approach to glycemic management in diabetes]

Type 2 diabetes is characterized by chronic hyperglycemia and varying degrees of insulin resistance and insulinopenia. Achieving targeted glycemic control in diabetic patients is important to reduce the risk of late complications, and many patients with type 2 diabetes ultimately require insulin therapy to maintain adequate glycemic control. Timely administration of insulin can prevent the progression of diabetes, reduce the development of complications, and have fewer side effects. Basal insulin is the preferred option in most cases when glycemic control is not achieved. However, there is considerable therapeutic inertia in clinical practice, both with respect to initiation of insulin therapy and titration of the basal insulin dose. The longer duration of action, reduced glucose variability and a lower risk of hypoglycemia seen with the latest generation of basal insulin analogs compared to the previous generation simplify titration and may increase patient compliance.

Glucose variability and the risks of stroke, myocardial infarction, and all-cause mortality in individuals with diabetes: retrospective cohort study

BACKGROUND

Previous research regarding long-term glucose variability over several years which is an emerging indicator of glycemic control in diabetes showed several limitations. We investigated whether variability in long-term fasting plasma glucose (FG) can predict the development of stroke, myocardial infarction (MI), and all-cause mortality in patients with diabetes.

METHODS

This is a retrospective cohort study using the data provided by the Korean National Health Insurance Corporation. A total of 624,237 Koreans ≥ 20 years old with diabetes who had undergone health examinations at least twice from 2005 to 2008 and simultaneously more than once from 2009 to 2010 (baseline) without previous histories of stroke or MI. As a parameter of variability of FG, variability independent of mean (VIM) was calculated using FG levels measured at least three times during the 5 years until the baseline. Study endpoints were incident stroke, MI, and all-cause mortality through December 31, 2017.

RESULTS

During follow-up, 25,038 cases of stroke, 15,832 cases of MI, and 44,716 deaths were identified. As the quartile of FG VIM increased, the risk of clinical outcomes serially increased after adjustment for confounding factors including duration and medications of diabetes and the mean FG. Adjusted hazard ratios (95% confidence intervals) of FG VIM quartile 4 compared with quartile 1 were 1.20 (1.16-1.24), 1.20 (1.15-1.25), and 1.32 (1.29-1.36) for stroke, MI and all-cause mortality, respectively. The impact of FG variability was higher in the elderly and those with a longer duration of diabetes and lower FG levels.

CONCLUSIONS

In diabetes, long-term glucose variability showed a dose-response relationship with the risk of stroke, MI, and all-cause mortality in this nationwide observational study.

Age-specific associations of glycated haemoglobin variability with cardiovascular disease and mortality in patients with type 2 diabetes mellitus: A 10- year cohort study

AIMS

To investigate the associations of increased variability in glycated haemoglobin (HbA1c) with cardiovascular disease (CVD) and mortality risk in patients with diabetes.

MATERIALS AND METHODS

This prospective cohort study included 147 811 patients aged 45 to 84 years with type 2 diabetes mellitus, without CVD and with at least three HbA1c values recorded before baseline in the period 2008 to 2010. HbA1c variability was evaluated using a mixed effects model to reduce regression dilution bias. Age-specific associations (45- 54, 55- 64, 65- 74 and 75- 84 years) between HbA1c variability and risk of CVD and mortality were assessed by Cox regression, adjusted for patient characteristics and usual HbA1c.

RESULTS

After a median follow-up of 7.4 years(1.02 million person-years), an overall incidence of 40 785 events including CVD (incidence 27  793) and all-cause mortalities (incidence 23 175) were identified. Positive log-linear associations between HbA1c variability and CVD and mortality were identified in all age groups. The hazard ratios (HRs) for the composite of CVD and all-cause mortality showed that age was inversely associated with HbA1c variability, with a 28% higher risk per 1% increase in HbA1c variability in the age group 45 to 54 years (all composite outcomes: HR 1.28, 95% confidence interval [CI] 1.21, 1.35), whereas only a 14% higher risk in the 75- 84 age group (all composite outcomes: HR 1.14, 95% CI 1.11, 1.17). Subgroup analysis showed the risk in patients with usual HbA1c <53mmol/mol was about eight times higher than in those with usual HbA1c ≥64mmol/mol.

CONCLUSIONS

HbA1c variability was strongly related to CVD and mortality in patients with diabetes across all age groups. Whilst pursuing optimal HbA1c targets, attention should be given to patients with high HbA1c variability, especially younger patients with good HbA1c control.

Glycemic variability: adverse clinical outcomes and how to improve it?

Glycemic variability (GV), defined as an integral component of glucose homoeostasis, is emerging as an important metric to consider when assessing glycemic control in clinical practice. Although it remains yet no consensus, accumulating evidence has suggested that GV, representing either short-term (with-day and between-day variability) or long-term GV, was associated with an increased risk of diabetic macrovascular and microvascular complications, hypoglycemia, mortality rates and other adverse clinical outcomes. In this review, we summarize the adverse clinical outcomes of GV and discuss the beneficial measures, including continuous glucose monitoring, drugs, dietary interventions and exercise training, to improve it, aiming at better addressing the challenging aspect of blood glucose management.

Hypoglycemia in Older Patients

Hypoglycemia is a serious problem in older patients with diabetes mellitus. This chapter discusses risk factors associated with hypoglycemia and approaches to mitigation of the risk for hypoglycemia. Specific considerations include selection of glucose lowering agents, comorbid conditions and the implications of declining cognitive dysfunction.

Prognostic Significance of Long-term HbA1c Variability for All-Cause Mortality in the ACCORD Trial

OBJECTIVE

The association between high glycemic variability and all-cause mortality has been widely investigated in epidemiological studies but rarely validated in glucose-lowering clinical trials. We aimed to identify the prognostic significance of visit-to-visit HbA_1c variability in treated patients in the Action to Control Cardiovascular Risk in Diabetes (ACCORD) trial population.

RESEARCH DESIGN AND METHODS

We studied the risk of all-cause mortality in relation to long-term visit-to-visit HbA_1c variability, expressed as coefficient of variation (CV), variability independent of the mean (VIM), and average real variability (ARV), from the 8th month to the transition from intensive to standard glycemic therapy. Multivariable Cox proportional hazards models were used to estimate adjusted hazard ratio (HR) and 95% CI.

RESULTS

Compared with the standard therapy group (n = 4,728), the intensive therapy group (n = 4,755) had significantly lower mean HbA_1c (6.6% [49 mmol/mol] vs. 7.7% [61 mmol/mol], P < 0.0001) and lower CV, VIM, and ARV (P < 0.0001). In multivariate adjusted analysis, all three HbA_1c variability indices were significantly associated with total mortality in all patients as well as in the standard- and intensive-therapy groups analyzed separately. The hazard ratios for a 1-SD increase in HbA_1c variability indices for all-cause mortality were 1.19 and 1.23 in intensive and standard therapy, respectively. Cross-tabulation analysis showed the third tertile of HbA_1c mean and VIM had significantly higher all-cause mortality (HR 2.05; 95% CI 1.17-3.61; P < 0.01) only in the intensive-therapy group.

CONCLUSIONS

Long-term visit-to-visit HbA_1c variability was a strong predictor of all-cause mortality. HbA_1c VIM combined with HbA_1c mean conferred an increased risk for all-cause mortality in the intensive-therapy group.

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.