Impact of glycemic variability on cardiovascular outcomes beyond glycated hemoglobin. Evidence and clinical perspectives

doi:10.1016/j.numecd.2012.03.006

Nutrition, Metabolism and Cardiovascular Diseases

Volume 22, Issue 9, September 2012, Pages 691-696

https://doi.org/10.1016/j.numecd.2012.03.006 Get rights and content

Abstract

Aims

The aim of this review is to focus on intra-day glucose variability (GV), specifically reviewing its correlation with HbA1c, the methods currently available to measure it, and finally the relationship between GV and cardiovascular outcomes, in type 1 and type 2 diabetic patients, and in the non-diabetic population.

Data synthesis

The term GV has been used in the literature to express many different concepts; in the present review, we focus our attention on intra-day GV. In particular, we try to assess whether GV provides additional information on glycemic control beyond HbA1c, since GV seems to be incompletely expressed by HbA1c, particularly in patients with good metabolic control. Many different indexes have been proposed to measure GV, however at the moment no “gold standard” procedure is available. Evidence in vitro, in experimental settings and in animal studies, shows that fluctuating glucose levels display a more deleterious effect than constantly high glucose exposure. However, these findings are not completely reproducible in human settings. Moreover, the relationship between GV and cardiovascular events is still controversial.

Conclusions

The term GV should be reserved to indicate intra-day variability and different indexes of GV should be used, depending on the metabolic profile of the population studied and the specific issue to be investigated. Self glucose monitoring or continuous glucose monitoring should be used for assessing glucose variability.

Introduction

In recent years, several papers and reviews have focused on the possible effects of glucose variability (GV) on cardiovascular diseases, and, in fact, when searching the term “glucose variability” on pubmed, 2891 results are found. However, going through the titles of these papers, it becomes immediately clear that the term “glucose variability” implies many different concepts.

The first concept relates to the day-to-day variability of fasting glucose, the second meaning refers to post-prandial spikes, the third to glycated hemoglobin (HbA1c) variability and, finally, the last concerns the intra-day GV. Further, in this last meaning, two different kinds of measurements are included: self-monitoring blood glucose (SMBG) or continuous glucose monitoring (CGM).

The labyrinth becomes even more intricate, when trying to address the impact of GV on cardiovascular mortality, as recently extensively discussed in the paper by Standl et al. [1], the only evidence being available for day-to-day variability of fasting blood glucose (FBG) [2] and for post-prandial glucose (PPG) [3], [4]. Only two papers investigated the relationship between HbA1c variability and cardiovascular mortality [5], [6]. Lastly, the role of intra-day GV on cardiovascular outcomes is still controversial [7], [8].

It should also be noticed that most of the available data have been collected in type 1 and type 2 diabetic patients, whilst in non-diabetic population, no data on the predictive role of GV on cardiovascular outcomes are available, possibly due to the paucity of data collected by CGM or SMBG in subjects without overt glycemic abnormalities [9].

The aim of this review is to focus on intra-day GV, specifically reviewing its correlation with HbA1c, the methods currently available to measure it, and finally the relationship between GV and cardiovascular outcomes, in type 1 and type 2 diabetic patients, and in non-diabetic population.

Section snippets

Relationship between glucose variability and HbA1c

Since evidence from the Diabetes Control and Complications Trial (DCCT) [10] has linked the reduction of HbA1c to lower incidence and progression of micro-vascular complications, current glycemic management mainly relies on HbA1c measurement. The DCCT investigators observed that ‘total glycemic exposure’ (HbA1c and duration of diabetes) only explained about 11% of the variation in retinopathy risk in the complete DCCT cohort, meaning that factors independent of HbA1c must presumably explain the

Methods to measure glucose variability

Many different indexes have been proposed to assess GV, however, at the moment no “gold standard” procedure is available. The introduction of CGM into clinical practice has resulted in a more accurate assessment of glycemic profile. Recently, Hill et al. [19] have proposed the normal reference ranges for mean blood glucose and GV derived from CGM for subjects without diabetes in different ethnic groups. The most commonly used indices of intra-day GV are hereinafter described.

a.
Standard deviation

Evidence in vitro

Glycemic fluctuations are more deleterious for endothelial cells than constantly high glucose concentrations [30]. Apoptosis is significantly higher in human umbilical vein endothelial cells incubated for 14 days in media containing a daily alternating 5 or 20 mmol/l glucose versus stable high glucose. Moreover, Quagliaro et al. [31] showed that intermittent high glucose levels enhance, in endothelial cells, the formation of nitrotyrosine and 8-hydroxydeoxyguanosine (8-OHdG), markers of

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Cited by (36)

Glycemic variability in diagnosis of gestational diabetes as predictor of pharmacological treatment
2024, Endocrinologia, Diabetes y Nutricion
Establecer si determinados parámetros de variabilidad glucémica (VG) al diagnóstico de diabetes mellitus gestacional (DMG) podrían ayudar a predecir la probabilidad de precisar tratamiento farmacológico y analizar su relación con el desarrollo de complicaciones materno-fetales.
Estudio prospectivo en 87 mujeres con DMG a las que se realizó una monitorización continua de glucosa (MCG) retrospectiva entre las semanas 26 y 32 de gestación durante 6 días, tras el diagnóstico. Se analizan glucemia media y parámetros de VG y su asociación con complicaciones materno-fetales y necesidad de tratamiento farmacológico. Se elaboran curvas ROC para estimar la validez para detectar la necesidad de tratamiento farmacológico.
Las pacientes con glucemia media más elevada (p < 0,001) y solapamiento continuo de la acción de la glucosa en un periodo de n horas (CONGA) (p = 0,001) precisaron tratamiento farmacológico. A partir de las curvas ROC se obtuvieron los puntos de corte de 98,81 mg/dl para glucemia media y de 86,70 mg/dl para CONGA, con una sensibilidad del 83,3% y una especificidad del 67,8% para ambos parámetros. No se observó una relación entre los distintos parámetros de VG y el desarrollo de complicaciones materno-fetales.
El uso de la MCG al diagnóstico de la DMG permite identificar a aquellas pacientes que se beneficiarían de una vigilancia más estrecha durante la gestación, lo que facilita el inicio de un tratamiento farmacológico precoz. No obstante, son necesarios estudios prospectivos con un mayor número de pacientes y que evalúen los costes asociados para poder recomendar el uso de la MCG tras el diagnóstico de DMG.
To establish whether glycemic variability (GV) parameters used when gestational diabetes mellitus (GDM) has been diagnosed could help predict the probability that a patient will need pharmacological treatment, and to analyze the link of these parameters to the development of maternal-fetal complications.
A prospective study of 87 women with GDM who underwent retrospective continuous glucose monitoring (CGM) for 6 days between weeks 26 and 32 of gestation, following diagnosis. The mean glycemia levels and GV variables were analyzed together with their link to maternal-fetal complications, and the need for pharmacological treatment. ROC (receiver operating characteristic) curves were developed to determine validity to detect the need for pharmacological treatment.
Patients with higher mean glycemia (P < 0.001) and continuous overlapping of net glycemic action in a period of n-hours (CONGAn) (P = 0.001) required pharmacological treatment. The ROC curves showed cut-off points of 98.81 mg/dl for mean glycemia, and 86.70 mg/dl for CONGAn, with 83.3% sensitivity and 67.8% specificity for both parameters. No relation between the GV parameters and development of maternal-fetal complications was observed.
The use of CGM, once GDM is diagnosed, enables us to identify those patients who would benefit from closer monitoring during gestation, and facilitate a speedier take-up of pharmacological treatment. However, prospective studies involving a higher number of patients are needed, as well as a cost assessment for recommending the use of CGM following GDM diagnosis.
Effect of the GSTM1 gene deletion on glycemic variability, sympatho-vagal balance and arterial stiffness in patients with metabolic syndrome, but without diabetes
2018, Diabetes Research and Clinical Practice
Citation Excerpt :
To calibrate CGM, GlucoCard G+ meter blood glucose monitoring system (Menarini, Neuss, Germany) was used. The following Glycemic Variability indexes (GV) were calculated: mean (MEAN), mean amplitude of glucose excursion (MAGE); J-index (JINDEX); mean absolute glucose (MAG); continuous overall net glycemic action (CONGA1, 2, 3, and 4); Coefficient of Variation (COEFF_VAR), low blood glucose (LBGI), high blood glucose index (HBGI), lability index (LI) and M value (M_VALUE) [9]. A 24-h Ambulatory Blood Pressure Measurement (ABPM) set to measure every 15 min during the day and every 30 min during nighttime was used.
An increased rate of cerebrovascular complications in patients with metabolic syndrome (MetS) has been reported. Previous studies demonstrated an association between glycemic variability (GV) and cerebrovascular reactivity (CRV) in MetS, thus suggesting a putative role of GV on cerebrovascular events. Although the pathophysiological mechanism linking GV to damage is still to be elucidated, evidence suggests oxidative stress plays a crucial role. Since functional variants in glutathione S-transferases (GST) genes modulate the cellular detoxification processes, the aim of this study was to elucidate the involvement of GSTs in MetS and investigating the correlation with GV, arterial stiffness, and sympatho-vagal (SV) balance.
A hundred metabolic syndrome patients without diabetes underwent GST gene polymorphism analysis and a sub-sample 36 patients were randomly selected to investigate the correlation between GST gene polymorphisms and GV, and sympatho-vagal (SV) balance and arterial stiffness.
GSTM1 showed a significant association with several GV, arterial stiffness, and SV balance indexes. In particular, the GSTM1 deletion positively correlates with lower values of these indexes when compared to the presence of the gene.
Therefore, we suggested a global influence of GSTM1 deletion on the GV, arterial stiffness, and SV balance pathways in MetS patients, probably also interacting with AMP-activated protein kinase (AMPK) regulation.
Our novel findings indicate GSTM1 could be a risk locus in MetS development and shed light novel scenarios on the role of glucose fluctuations in neurological impairments.
Diabetes remission after bariatric surgery is characterized by high glycemic variability and high oxidative stress
2017, Nutrition, Metabolism and Cardiovascular Diseases
Citation Excerpt :
The dynamics of glucose fluctuations are well depicted by the measure of GV and the assessment of its two most important components, i.e., the amplitude of glucose excursions and the time a person spent within a certain range of blood glucose. At present, there are consistent data from pathophysiological and clinical studies that high GV may be involved in the pathogenesis of diabetic vascular complications via activation of inflammatory pathways, increased oxidative stress and endothelial dysfunction [15,18,20–22]. In addition, GV is reported to impact depression, quality of life and other mental health outcomes in diabetic individuals [12].
To evaluate glycemic variability (GV) and oxidative stress in patients who achieved type 2 diabetes (T2DM) remission after bariatric surgery (BS).
Twenty-two patients (M/F10/12, age 50 ± 9 years, BMI 31 ± 6 kg/m²) who were in remission of T2DM (T2DM remitters) after BS since at least 1 year and 22 age-, sex- and BMI-matched control subjects were studied. Of the BS group, eleven subjects had undergone Roux-en-Y gastric bypass (RYGB) and eleven subjects sleeve gastrectomy (SG). Oral glucose tolerance test (OGTT), 7 days-continuous glucose monitoring, 24-h urinary excretion of 8-isoprostaglandin F2α (8-isoPGF2α) and dietary intake evaluation were performed. According to general linear model for repeated measures, glucose and insulin response during OGTT were significantly different in T2DM remitter than in control subjects (p < 0.001, for both). All measures of GV (standard deviation, coefficient of variation and mean amplitude of glucose excursions) were significantly higher in T2DM remitters than in controls, (p < 0.001 for all). These indexes were higher among RYGB than SG patients (p < 0.05). The time spent out of the 60–160 mg/dl range was significantly longer in T2DM remitters undergoing RYGB than in controls (p < 0.02). Mean 24-h urinary 8-isoPGF2α excretion was significantly higher in T2DM remitters than that of control subjects (p = 0.04). All GV indexes were directly correlated with blood glucose levels at 30 and 60 min during OGTT (p < 0.05–0.001).
Remission of T2DM after BS is characterized by high GV and high oxidative stress in the face of fasting blood glucose and HbA1c within the normal range.
The potential effect of ultra-long insulin degludec on glycemic variability
2017, Diabetes Research and Clinical Practice
Citation Excerpt :
MAGE has been proposed as the “gold-standard” system for evaluating GV by some authors, but it presents limitations, such as a bias towards hyperglycemic peaks over hypoglycemic nadirs. CONGA has the benefits of being reproducible and it is unaffected by asymmetry of the glycemic profile [15]. Although MODD is not ideal to evaluate the glucose variability within a day, it reflects the day-to-day glucose variability and therefore is a very important index to evaluate the variability of the action of basal insulins.
Despite the therapeutic advances in the treatment of diabetes, metabolic control instability due to glycemic variability (GV) is frequently observed in patients with diabetes on intensive insulin therapy and is associated with hyperglycemic peaks and hypoglycemic episodes. Hyperglycemia associated with GV has been implicated in the development of chronic complications due to its pro-oxidative consequences. On the other hand, hypoglycemia can be associated with increased cardiovascular risk secondarily to adrenergic activation. The ultra-long-acting insulin analogue, insulin degludec (IDeg), presents a flat and stable glucose-lowering effect both in Type 1 and Type 2 diabetes patients. In pharmacodynamic studies, IDeg has been associated with a lower variability in its insulin action than other alternatives for basal insulin, which might have clinical advantages for the stability of the glycemic control. The main objective of this review is to present pharmacological and clinical data regarding the efficacy and safety of IDeg for the treatment of diabetes focusing on its effects on GV and on hypoglycemia frequency.
Diurnal glycemic fluctuation is associated with severity of coronary artery disease in prediabetic patients: Possible role of nitrotyrosine and glyceraldehyde-derived advanced glycation end products
2017, Journal of Cardiology
Citation Excerpt :
The mean amplitude of glycemic excursions (MAGE) was calculated using these data. The MAGE represents fluctuations in blood glucose levels over a 24-h period and was calculated by measuring the arithmetic mean of the differences between consecutive peaks and nadirs, provided that the differences were >1 standard deviation (SD) of the mean glucose value [16]. Blood samples were collected from an antecubital vein at admission and in the morning following an overnight fast.
Glucose fluctuation (GF) is a risk factor for coronary artery disease (CAD). However, it remains unknown whether specific indices of GF are risk factors for CAD. Therefore, we evaluated the relationship between GF, as determined by a continuous glucose monitoring system (CGMS) or the glucose level at 2 h after a 75-g oral glucose tolerance test (75 g OGTT 120), and the severity of CAD in prediabetic patients. We also evaluated whether nitrotyrosine (NT) and glyceraldehyde-derived advanced glycation end-products (Glycer-AGE) were induced by GF.
Twenty-eight prediabetic patients underwent coronary angiography (CAG), and the Gensini score and the SYNTAX score were evaluated as the severity of CAD, while the mean amplitude of glycemic excursions (MAGE) by CGMS and 75 g OGTT 120 were evaluated. Serum NT and Glycer-AGE were measured.
The MAGE was closely associated with the Gensini score (r = 0.742, p < 0.001) and the SYNTAX score (r = 0.776, p < 0.001), respectively. The 75 g OGTT 120 was not associated with the Gensini score (r = 0.36, p = 0.06), but it was significantly associated with the SYNTAX score (r = 0.413, p = 0.036). Multiple linear regression analysis showed that the MAGE was the only independent determinant for the severity of CAD. The levels of NT and Glycer-AGE were significantly higher in the high MAGE group than in the low MAGE group.
Diurnal GF is associated with the severity of CAD, even in prediabetic patients. GF, NT, and Glycer-AGE may play a pathological role in the progression of CAD.
Glycemic control and variability in association with body mass index and body composition over 18 months in youth with type 1 diabetes
2016, Diabetes Research and Clinical Practice
Citation Excerpt :
While BMI is strongly correlated with body fat, it is an indirect measure, and does not reflect body fat distribution [17], which may be differentially associated with glycemic control. Similarly, while HbA1c is the clinical benchmark for assessing chronic glycemic control, the measure does not reflect short-term glucose excursions shown to predict diabetes complications [18]. Complementary measures of overall glycemic control include 1,5-anhydroglucitol (1,5-AG), which is sensitive to recent glucose excursions in moderately well-controlled type 1 diabetes [19–22], and continuous glucose monitoring, which can be used to assess glycemic variability.
The impact of adiposity on glycemic control in type 1 diabetes patients has important implications for preventing complications. This study examined associations of glycemic outcomes with body mass index (BMI, kg/m²) and body composition in youth with type 1 diabetes.
This is a secondary analysis of an 18-month randomized controlled dietary intervention trial (N = 136, baseline age = 12.3 ± 2.5 y, HbA1c = 8.1 ± 1.0% (65 ± 11 mmol/mol)). Measured height and weight every 3 months were abstracted from medical records. Body composition was assessed by dual energy X-ray absorptiometry (DXA) at baseline, 12 and 18 months. Glycated hemoglobin (HbA1c) and glycemic variability assessed by masked 3-day continuous blood glucose monitoring (CGM) were obtained every 3 months. 1,5-Anhydroglucitol (1,5-AG) was assessed every 6 months. Adjusted random effects models for repeated measures estimated associations of time-varying BMI and body composition with time-varying glycemic outcomes.
There was no treatment effect on glycemic outcomes. HbA1c was not associated with BMI or body composition indicators. 1,5-AG was inversely associated with BMI and adiposity indicators (%fat, trunk fat mass and trunk %fat), adjusting for developmental covariates. Adiposity indicators were positively associated with %glucose >180 mg/dL and >126 mg/dL when adjusting for developmental covariates, and %glucose >126 mg/dL when additionally adjusting for diabetes-related covariates. Fewer consistent relationships were observed for 3-day mean glucose and %glucose <70.2 mg/dL. BMI and body composition variables were not associated with standard deviation of glycemic values or mean amplitude of glycemic excursions.
The role of greater BMI and adiposity in diabetes management in youth with type 1 diabetes may relate specifically to increased hyperglycemic excursions.

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ReviewImpact of glycemic variability on cardiovascular outcomes beyond glycated hemoglobin. Evidence and clinical perspectives

Abstract

Aims

Data synthesis

Conclusions

Introduction

Section snippets

Relationship between glucose variability and HbA1c

Methods to measure glucose variability

Evidence in vitro

Diabetes Res Clin Pract

Diabetes Metab

J Thromb Haemost

Diabetes Res Clin Pract

Postprandial hyperglycemia and glycemic variability - should we care

Diabetes Care

Fasting plasma glucose variability predicts 10-year survival of type 2 diabetic patients: the Verona Diabetes Study

Diabetes Care

Postprandial blood glucose is a stronger predictor of cardiovascular events than fasting blood glucose in type 2 diabetes mellitus, particularly in women: lessons from the San Luigi Gonzaga Diabetes Study

J Clin Endocrinol Metab

Postprandial blood glucose predicts cardiovascular events and all-cause mortality in type 2 diabetes in a 14-year follow-up: lessons from the San Luigi Gonzaga Diabetes Study

Diabetes Care

HbA1(c) and mean blood glucose show stronger associations with cardiovascular disease risk factors than do postprandial glycaemia or glucose variability in persons with diabetes: the A1C-Derived Average Glucose (ADAG) study

Diabetologia

A1C variability predicts incident cardiovascular events, microalbuminuria, and overt diabetic nephropathy in patients with type 1 diabetes

Diabetes

Glycaemic variability and complications in patients with diabetes mellitus: evidence from a systematic review of the literature

Diabetes Obes Metab

The effect of glucose variability on the risk of microvascular complications in type 1 diabetes

Diabetes Care

Glucose fluctuations in subjects with normal glucose tolerance, impaired glucose regulation and newly-diagnosed type 2 diabetes mellitus

Clin Endocrinol (Oxf)

The effect of intensive treatment of diabetes on the development and progression of long-term complications in insulin dependent diabetes mellitus

N Engl J Med

The effect of glycemic exposure on the risk of microvascular complications in the diabetes control and complications trial – revisited

Diabetes

Associations between features of glucose exposure and A1C The A1C-Derived Average Glucose (ADAG) study

Diabetes

Contributions of fasting and postprandial plasma glucose increments to the overall diurnal hyperglycemia of type 2 diabetic patients: variations with increasing levels of HbA1c

Diabetes Care

Integrating glycaemic variability in the glycaemic disorders of type 2 diabetes: a move toward a unified glucose tetrad concept

Diabetes Metab Res Rev

The loss of postprandial glycemic control precedes stepwise deterioration of fasting with worsening diabetes

Diabetes Care

Glucose tolerance and cardiovascular mortality: comparison of fasting and 2-hour diagnostic criteria

Arch Intern Med

Arguments for and against the role of glucose variability in the development of diabetes complications

Diabetes Sci Technol

Normal reference range for mean tissue glucose and glycemic variability derived from continuous glucose monitoring for subjects without diabetes in different ethnic groups

Diabetes Technol Ther

“J”-index. A new proposition of the assessment of current glucose control in diabetic patients

Horm Metab Res

Measuring glycaemic variation

Curr Diabetes Rev

The M-value, an index of blood sugar control in diabetics

Acta Med Scand

Quantifying temporal glucose variability in diabetes via continuous glucose monitoring: mathematical methods and clinical application

Diabetes Technol Ther

Review
Impact of glycemic variability on cardiovascular outcomes beyond glycated hemoglobin. Evidence and clinical perspectives