Skip to main content
Download PDF
Download ePub
  • Original investigation
  • Open access
  • Published:

Interactive effects of a common γ-glutamyltransferase 1 variant and low high-density lipoprotein-cholesterol on diabetic macro- and micro-angiopathy

Cardiovascular Diabetology volume 14, Article number: 49 (2015) Cite this article

Abstract

Background

We investigated the clinical relevance of a common variant, rs4820599, in the γ-glutamyltransferase (GGT)1 gene, associated with the serum GGT level, in Japanese type 2 diabetes mellitus (T2DM) subjects.

Methods

We conducted a retrospective longitudinal study (4.9 ± 2.5 years) including 352 T2DM patients (T2DM subjects) and a cross-sectional study including 796 health screening program participants (general subjects). A real-time TaqMan allelic discrimination assay was used to identify the genotypes. Risk factors for a high brachial-ankle pulse wave velocity (baPWV) (≥1750 cm/sec) or diabetic retinopathy (DR) were determined using a generalized estimating equations approach, receiver operating characteristic (ROC) analysis or Cox proportional hazards model, etc.

Results

The frequency of the GGT1 G allele was 20.8% in the T2DM subjects, and no associations were found between the GGT1 genotype and risk of T2DM. The mean log GGT values in the T2DM and general subjects were significantly higher among G allele carriers than non-carriers. The G allele and a low HDL-C level were identified to be risk factors for a high baPWV in the T2DM subjects [odds ratio (OR) 1.80, P = 0.008; OR 1.71, P = 0.03; respectively), and a significant interactive effect between these factors was found on the risk of a high baPWV and DR. The HDL-C level at baseline was a significant predictor of a high baPWV only in G allele carriers according to the ROC analysis. This result regarding baPWV in the T2DM subjects was replicated in the general population. Meanwhile, the GGT1 genotype was not associated with the risk of DR, although it affected the principal factors involved in the risk of DR, and a low HDL-C level was also found to be a risk factor for DR only in G allele carriers.

Conclusions

We herein describe for the first time the significant interactive effects of the GGT1 G allele and a low HDL-C level on a high baPWV and DR. These findings may encourage future clinical trials comparing the efficacy of agents increasing the HDL-C levels among the GGT1 genotypes. However, well-designed studies in larger cohorts are needed to confirm our results.

Introduction

The level of serum γ-glutamyltransferase (GGT), a widely used index for excessive alcohol consumption and/or liver dysfunction, has been found to be associated with an increased risk of diabetes, metabolic syndrome and cardiovascular disease (CVD), including hypertension, coronary heart disease (CHD) and heart failure (HF), as well as both cardiovascular and all-cause mortality [1-5]. The pro-oxidant effect of GGT is proposed to be the biological mechanism linking GGT to various cardiovascular events, as a strong GGT activity and the GGT-mediated oxidation of low-density lipoprotein cholesterol (LDL-C) have been identified in the core tissue of atheromas, likely influencing plaque evolution and rupture [6-8]. Most serum GGT is bound to lipoproteins, such as anti-atherogenic apolipoprotein (Apo) A-I, contained in high-density lipoprotein cholesterol (HDL-C), and atherogenic Apo B [6,8-11]. Huseby [10] reported that 70% of the total serum GGT activity is detected in association with HDL-C.

Classic ‘diabetic dyslipidemia’ is characterized by the so-called atherogenic lipid triad, consisting of increased levels of small dense LDL particles and triglyceride-rich lipoproteins and a decreased level of HDL-C, associated with an increase in non-HDL cholesterol [12-16]. After the introduction of statins, clinical emphasis first focused on LDL-C lowering as the prime lipid target [12]. However, treated patients remained at high risk of cardiovascular events despite receiving the best standards of care, including high-dose statins [12-14,16,17]. Under such conditions, a raised triglyceride concentration is strongly associated with a low HDL-C level. Hence, the Residual Risk Reduction Initiative defines atherogenic dyslipidemia as the imbalance between Apo B and Apo A-I containing lipoproteins and identified this factor to be a key contributor to residual cardiovascular risks [17]. Next, the effect of potential HDL-C raising dominated research, with less focus on triglycerides [12,13,15-17]. In the Action to Control Cardiovascular Risk in Diabetes (ACCORD) trial, the use of fenofibrate in patients with T2DM receiving simvastatin who were at high risk of cardiovascular disease greatly reduced the triglyceride levels, although it hardly increased the HDL-C levels and did not reduce cardiovascular risks [14]. Nevertheless, the results of this trial suggest that fenofibrate reduces the risk of CHD in patients with high levels of triglycerides and low levels of HDL-C [14]. In 2014, a large meta-analysis of randomized controlled trials of agents that increase the HDL-C level, i.e. niacin, fibrates and cholesteryl ester transfer protein inhibitors, reported that none of these drugs reduce cardiovascular events in patients treated with statins [18]. Additionally, a recent study using an analytical approach that accounts for the potential pleiotropic effects of single-nucleotide polymorphisms (SNPs) on the triglyceride, LDL-C and HDL-C levels and risk of CHD simultaneously in a large sample demonstrated that plasma triglyceride-rich lipoproteins causally influence the risk of CHD rather than low HDL-C [19]. Consequently, the classic HDL hypothesis, defined as the concept that interventions to raise the HDL-C concentration will reduce cardiovascular risks, is gradually being replaced by the triglyceride hypothesis and HDL function hypothesis [12-14,20-22]. On the other hand, in patients with diabetic microvascular complications (MVCs), a large prospective study of newly diagnosed T2DM subjects showed that a 0.03 mmol/L increase in HDL-C is associated with a 1% decrease in the MVC risk and that patients achieving the HDL-C goals (>1.0 mmol/L for males and 1.25 mmol/L for females) have a 11% lower risk of MVC versus non-achievers [23]. The ACCORD trial also demonstrated a large reduction in the progression of diabetic retinopathy (DR) in patients with T2DM [24]. Furthermore, Du et al. [25] identified extravasated Apo A-I and Apo B in human diabetic eyes and revealed that native HDL completely blocks oxidative stress and the apoptosis of retinal pigment epithelial cells induced by heavily oxidized glycated LDL, thereby suggesting an important new role for extravasated and modified plasma lipoproteins in promoting DR.

The serum level of GGT is influenced by both environmental and genetic factors, and its heritability has been estimated to be 52% [26]. Furthermore, genome-wide association studies (GWAS) have identified an association between the GGT1 gene loci and the GGT level in different ethnic groups [27-29]. However, the association of these loci with diabetes and/or CVD has not yet been reported. We herein present for the first time the significant interactive effects of a GGT1 variant, rs4820599, and low HDL-C on a high pulse wave velocity (PWV), a surrogate marker of arterial stiffness, and DR in Japanese subjects with type 2 diabetes mellitus (T2DM).

Methods

We conducted a retrospective longitudinal study including 352 patients with T2DM (231 males and 121 females, 60.0 ± 11.0 years: T2DM subjects) who had been treated at the Jinnouchi Clinic, Diabetes Care Center for more than six months between February 2002 and January 2011 and a cross-sectional study including 796 subjects (517 males and 279 females, 62.1 ± 12.4 years: general subjects) who had participated in a health screening program at the Japanese Red Cross Kumamoto Hospital Health Center. Subjects with a positive serological test for hepatitis viruses or hepatobiliary diseases other than fatty liver disease were excluded, whereas those with a history of CHD, 37 cases (10.5%) among the T2DM subjects and 37 cases (4.6%) among the general subjects, were included. The study protocol was approved by the institutional ethics committees, and written informed consent was obtained from each subject. The study was performed in accordance with the Declaration of Helsinki.

Height and weight were measured using standard protocols, and the body mass index (BMI) was calculated. Blood pressure (BP) was measured after the subjects rested in the sitting position. Hypertension was defined as a systolic BP of ≥ 140 mmHg, diastolic BP of ≥ 90 mmHg or history of hypertension. The brachial-ankle PWV (baPWV) was measured using an automated waveform analyzer (Omron-Colin, Tokyo, Japan). Pneumatic cuffs were wrapped around both upper arms and ankles and connected to a plethysmographic sensor to determine the volume pulse waveform. The highest value of baPWV measured on either side of each patient was used for the analysis. The value of baPWV at each follow-up visit was dichotomized by 1,750 cm/sec, which has been reported as the cut-off value for predicting the onset of stroke or cardiovascular disease [30].

Fatty liver was diagnosed based on the following four criteria: a diffuse hyperechoic echo texture (bright liver), increased echo texture in comparison to the kidneys, vascular blurring and deep attenuation [31]. DR was diagnosed by a professional ophthalmologist using direct ophthalmoscopy or fundus fluorescein angiography. DR was staged as no retinopathy, nonproliferative DR (NPDR) or proliferative DR (PDR) according to the criteria determined at the third national ophthalmology conference held in 1985. The occurrence of DR was defined as having no sign of DR signs in both eyes at baseline with the development of NPDR or PDR in either eye during the follow-up period.

Laboratory tests were performed using the standard methods of the Japan Society of Clinical Chemistry. Dyslipidemia was defined as a triglyceride level of ≥ 1.7 mmol/L, HDL-C level of < 1.0 mmol/L, LDL-C level of ≥ 3.6 mmol/L or history of dyslipidemia. Diabetes was defined as a fasting plasma glucose level of ≥ 7.0 mmol/L or casual or two-hour glucose level of ≥ 11.1 mmol/L after a 75 g oral glucose load test and a hemoglobin A1c (HbA1c) level of ≥ 6.5% or history of diabetes. Patients with type 1 diabetes were excluded based on their clinical manifestations. Information regarding smoking habits and alcohol intake was obtained via face-to-face interviews with health care providers.

Genomic DNA was isolated from EDTA-preserved blood samples using an automated DNA isolation system (NA-3000) (Kurabo, Osaka, Japan). A real-time TaqMan allelic discrimination assay was used to identify the GGT1 (rs4820599, assay ID: C_2446754_10) and aldehyde dehydrogenase 2 (ALDH2) (rs671, assay ID: C_11703892_10) polymorphisms, and genotyping was performed according to the manufacturer’s protocol (Applied Biosystems, Tokyo, Japan). To ensure the quality of the genotyping, we included DNA samples as internal controls, with hidden samples of known genotypes and negative controls (water).

The data are presented as the mean ± standard deviation or proportion for categorical variables. Student’s t-test or a one-way analysis of variance and Fisher's exact test were used for comparisons of continuous and categorical variables, respectively. Factors influencing the GGT level were determined using a univariate linear regression analysis. Independent models were used for the analysis of the risk of a high baPWV (≥1,750 cm/sec), with calculation of the odds ratio (OR) and 95% confidence interval (95%CI) as well as the analysis of the baPWV values obtained during the observation period, with calculation of the adjusted partial regression coefficient (Β). We developed multiple regression models by incorporating potentially confounding factors that showed a P value of < 0.10 in the univariate models. Receiver operating characteristic (ROC) curves were determined to evaluate the predictive performance of these factors for detecting a high baPWV, with calculation of the area under the curve (AUC). We also determined the cut-off values for these factors as the point with the shortest distance from the left upper corner of the graph. Multivariate-adjusted hazard ratios (HRs) and 95%CIs for the cumulative incidence of the development of DR were examined using a Cox proportional hazards model. In addition, the interactive effects of the GGT1 genotypes and potentially confounding factors on the baPWV or incidence of DR were assessed. A P value of < 0.05 was considered to be statistically significant. All statistical analyses were performed using the SPSS software package (version 17.0, IBM Japan Inc., Tokyo, Japan).

Results

The frequency of the GGT1 G allele was 20.8% and 22.1% in the T2DM patients and general subjects, respectively. The observed genotype frequency distributions in both populations were consistent with the Hardy-Weinberg equilibrium. No associations were found between the GGT1 genotype and the risk of T2DM.

The mean follow-up duration in the T2DM subjects was 4.9 ± 2.5 years. The clinical characteristics of the T2DM subjects at baseline stratified by the GGT1 genotype are shown in Table 1. The GGT1 genotypes were not significantly associated with any of the parameters. However, the mean log GGT value was significantly higher in GGT1 G allele carriers than in non-carriers (1.52 ± 0.30 IU/L vs. 1.45 ± 0.30 IU/L, P = 0.04). The other parameters did not differ between G allele carriers and non-carriers.

Table 1 Clinical characteristics of T2DM stratified by the GGT1 genotype at baseline
Full size table

Among the general subjects, the mean log GGT values were also significantly higher in G allele carriers than in non-carriers (1.47 ± 0.29 IU/L vs. 1.43 ± 0.29 IU/L, P = 0.03). Factors associated with the serum GGT level stratified by the GGT1 genotype in the general subjects are shown in Table 2. Fatty liver, dyslipidemia, ever-smoking, drinking, the ALDH2*2 allele and the BMI, transaminase, HbA1c, BP and triglyceride values were significantly associated with the GGT levels, irrespective of the GGT1 genotype, while the fasting plasma glucose, HDL-C and LDL-C levels were significantly associated with the GGT levels only in G allele carriers. In the T2DM subjects, ever-smoking, drinking, the ALDH2*2 allele and the transaminase, diastolic BP and triglyceride levels were significantly associated with the GGT levels, irrespective of the GGT1 genotype, while the HbA1c level was significantly associated with the GGT level only in non-carriers (Additional file 1: Table S1).

Table 2 Factors associated with the serum GGT levels stratified by the GGT1 genotype in the general subjects in the univariate regression model
Full size table

The GGT1 G allele was significantly associated with the risk of a high baPWV (≥1,750 cm/sec) in the T2DM subjects (OR 1.80, P = 0.008), in addition to age, hypertension, the diabetes duration, low HDL-C (<1.0 mmol/L) and drinking in the univariate models (Table 3). According to the multiple regression models, age and hypertension were found to be independent risk factors for a high baPWV irrespective of the GGT1 genotype (Table 4)(Additional file 1: Table S2). A significant interactive effect of the GGT1 genotype and the HDL-C level on the risk of a high baPWV was observed in the generalized estimating equations approach (P = 0.04). Notably, the risk of a high baPWV was significantly greater in G allele carriers with a low HDL-C level (OR 2.49, P = 0.04) than in non-carriers with a normal HDL-C level (Table 5). The baPWV values were also higher in GGT1 G allele carriers with a low HDL-C level than in non-carriers with a normal HDL-C level (B: 150.84 cm/sec, P = 0.06). Moreover, the HDL-C level at baseline was found to be a significant predictor of a high baPWV at the last point of follow-up only in G allele carriers according to the ROC curve analysis (AUC 0.63, 95%CI 0.52 - 0.74, P = 0.02) (Figure 1a and b). The optimal cut-off value for the HDL-C level for predicting a high baPWV in G allele carriers was 1.1 mmol/L (sensitivity: 46.3%; specificity: 74.5%).

Table 3 Association between a high baPWV (≥1,750 cm/sec) and the covariates in the T2DM subjects
Full size table
Table 4 Interactive effects between the GGT1 genotype and serum HDL-C levels on the baPWV in the T2DM subjects
Full size table
Table 5 Association between the risk of DR and the covariates stratified by the GGT1 genotype in the T2DM subjects in the multiple regression model
Full size table
Figure 1
figure 1

The ROC curves of HDL-C for high baPWV (≥1750 cm/sec) in the GGT1 G allele carriers (a) and non-carriers (b) in T2DM subjects. The HDL-C level at baseline was found to be a significant predictor of a high baPWV at the last point of follow-up only in G allele carriers. The optimal cut-off value for the HDL-C level for a high baPWV in G allele carriers was 1.1 mmol/L (sensitivity: 46.3%; specificity: 74.5%). AUC, area under the curve; CI, confidence interval; ROC, receiver operating characteristic; HDL-C, high-density lipoprotein cholesterol; baPWV, brachial-ankle pulse wave velocity; T2DM, type 2 diabetes mellitus.

Full size image

In the general subjects, a significant interactive effect of the GGT1 genotype and dyslipidemia on the baPWV values was observed (P = 0.02). The mean baPWV values in G allele carriers were significantly higher among the subjects with dyslipidemia than those without dyslipidemia (1,790.8 ± 370.4 cm/sec vs. 1,617.8 ± 347.8 cm/sec, P = 0.01), while the values did not differ in non-carriers. Similarly, the mean baPWV values in G allele carriers were higher among the subjects with a low HDL-C level than those with a normal HDL-C level, although the difference did not reach statistical significance (1,988.3 ± 381.4 cm/sec vs. 1,705.1 ± 367.8 cm/sec, P = 0.19) (Additional file 1: Table S3). The HDL-C level was also significantly associated with a high baPWV only in G allele carriers among the general subjects (AUC 0.63, 95%CI 0.52 - 0.74, P = 0.02) (Additional file 2: Figure S1a and b).

Forty-five (21.0%) patients were newly diagnosed with DR during the observation period. The HbA1c level was found to be the only risk factor for the incidence of DR in the univariate regression model. When stratified by the GGT1 genotype, the HbA1c level, a female gender and age were identified to be significantly associated with the incidence of DR in non-carriers, whereas the diabetes duration, ALDH2*2 allele and HDL-C level were significantly associated with the incidence of DR in G allele carriers (Table 5). A significant interactive effect of the GGT1 genotype and HDL-C level on the risk of DR was also observed (P = 0.02).

Discussion

The novel findings of this study are as follows. First, a GGT1 variant, rs4820599, the G allele and a low HDL-C level were identified to be risk factors for a high baPWV in Japanese subjects with T2DM in the longitudinal analysis. Interestingly, a significant interactive effect of the GGT1 genotype and low HDL-C level on the risks of both a high baPWV and DR was found. Second, the HDL-C level at baseline was identified to be a significant predictor of a high baPWV only in G allele carriers according to the ROC analysis. This result in the T2DM patients was also noted in the general population. Third, the GGT1 genotype was not associated with the risk of DR, although it affected the principal factors involved in the risk of DR. Fourth, the associations between the levels of GGT and fasting plasma glucose, HDL-C and LDL-C and between dyslipidemia and a high baPWV were significant only in G allele carriers among the general subjects. In this study, the triglyceride levels were positively associated with the serum GGT levels but not the risk of a high baPWV or DR, and the LDL-C levels were positively associated with the serum GGT levels only in the general subjects with the G allele.

The mean serum log GGT level was significantly higher in G allele carriers than in non-carriers among both the T2DM patients and the general population. The expression of GGT increases as an adaptive response to oxidative stress, although the mechanism underlying GGT regulation in response to oxidative stress remains unclear [9,32]. Human GGT is a multigene family consisting of at least seven GGT genes or pseudogenes, in which a major GGT1 and minor GGT5 have been found to encode proteins exhibiting GGT activity [32]. A variant, rs4820599, on the GGT1 gene is predicted to be located at a transcription factor binding site, which can result in differential gene transcription, according to FuncPred (http://snpinfo.niehs.nih.gov/snpinfo/snpfunc.htm, accessed 3/31/2015). This fact may explain the findings of the present study and previous observations obtained using GWAS [27-29]. In addition to GGT1, however, population-based GWAS have revealed evidence of an association between the serum GGT level and a series of SNPs in candidate genes, i.e. HNF1A in various ethnic groups [27-29], C14orf73 and RORA in subjects of European descent [29], MYL2, C12olf51 and OAS1 in individuals of East Asian descent [28] and ALDH2 in Japanese [33]. A pathway analysis of the SNP associations showed significant overlap between genes affecting the GGT level and those affecting common metabolic and inflammatory diseases under the control of the Hepatic Nuclear family [29]. Therefore, our results provide only the first glimpse into the association between one of these SNPs and the GGT level, factors affecting the GGT level and the risk of vascular disease resulting from metabolic and inflammatory diseases, as represented by T2DM.

The baPWV is used in epidemiological and clinical research as an index of arterial stiffness and atherosclerosis and is indicated to be an independent predictor of CVD [30,34-36]. A few epidemiological studies have examined the association between the GGT level and baPWV, although the findings are inconsistent [35,36]. In the present study, the GGT1 G allele was identified to be a risk factor for a high baPWV, while the GGT level was found to be a risk factor in non-carriers. GGT is expressed on the cell surface membrane of most cell types, although only liver GGT is detected in the serum, and the relationship between the cellular GGT and serum GGT expressions is unknown [6,37]. The serum GGT level appears to be associated with the risk of CVD from the early stage of arterial stiffness through the onset of CVD events [2,4,6,7,35,36,38]. This is because oxidative stress, vascular inflammation and endothelial dysfunction play a central role in the pathogenesis of atherosclerosis [39,40], and because the serum GGT level is proposed to be an early and sensitive marker of oxidative stress [7,9,32]. A key question is whether the serum GGT level is an simply innocent marker or whether its activity is causally involved in the development of arterial stiffness and atherogenesis, as shown in atheroma plaques [6,7]. Cellular GGT is known to be involved in the generation of reactive oxygen species in the presence of transition metals and serum GGT is speculated to promote the oxidation of circulating lipoproteins [6,7,9,41].

The serum HDL-C level has been reported to be inversely related to the baPWV in a general Chinese population (50 to 90 years of age) and the aortic PWV in other populations [42]. An isolated low HDL-C level is a dyslipidemic phenotype that appears to be more prevalent among Asian populations, in whom low HDL-C levels are strongly associated with an increased CHD risk [43]. Several potential mechanisms may explain the association between the serum HDL-C level and CVD [15,20-22,42-44]. HDL has a role in reverse cholesterol transport and other direct actions on numerous cell types, thereby reducing cardiovascular risks. In endothelial cells and their progenitors, HDL prevents apoptosis and stimulates proliferation and migration. HDL also has diverse anti-inflammatory actions in both endothelial cells and leukocytes. In vascular smooth muscles, HDL attenuates proinflammatory, promigratory and degradative processes. HDL has an antithrombotic effect via its actions on the endothelium and platelets. In addition, HDL protects pancreatic β-cells from apoptosis, decreases the white adipose tissue mass, increases energy expenditures and promotes the production of adiponectin, which possesses its own vascular protective properties. However, the classic HDL (concentration) hypothesis is gradually being replaced by the triglyceride hypothesis and the HDL function hypothesis due to the failure of clinical trials and negative results for human genetics [12-14,18-22]. The classic entity of ‘diabetic dyslipidemia’ is characterized by the so-called atherogenic lipid triad, consisting of an increase in small dense LDL particles and triglyceride-rich lipoproteins and a decrease in HDL-C [12-17,22]. In this study, the triglyceride levels were positively associated with the serum GGT levels, but not any of the risk factors for a high baPWV or DR in both the T2DM patients and general subjects. In order to prove that a low HDL-C level, rather than a high triglyceride level, is a risk factor for a high PWV, we applied a multivariate model including triglycerides on purpose despite the insignificance of this parameter as a covariate. Most serum GGT is bound to lipoproteins, more dominantly to Apo A than to Apo B, although the ratio may change depending on the serum lipid profile [6,8-11,41]. All things considered, the serum GGT level is associated with cardiovascular risks, including dyslipidemia, more greatly in G allele carriers than in non-carriers, and coexisting low HDL-C may lead to increased LDL-associated GGT and GGT-dependent LDL oxidation followed by the possible development of significant arterial stiffness in this population.

The interactive effect of the GGT1 genotype and a low HDL-C level on the risk of DR was also significant in the current study, and a low HDL-C level was found to be an independent risk factor for DR only in G allele carriers. Oxidative stress plays a pivotal role in the development of DR as well as macrovascular diseases [45-49]. The metabolic abnormalities associated with diabetes cause mitochondrial superoxide overproduction in the endothelial cells of both small and large vessels [45,48,49]. This increased superoxide production causes the activation of major pathways involved in the pathogenesis of various complications, such as polyol pathway flux as well as the increased formation of advanced glycation end products, activation of protein kinase C and over-activity of the hexosamine pathway [45,48,49]. The overexpression of superoxide dismutase in transgenic diabetic mice prevents DR, nephropathy and cardiomyopathy [48,49]. In addition to two principal and reversible risk factors for DR, blood glucose and BP, a low HDL-C level is indicated to be a risk factor for DR [16,23,25,46,49]. Many of the pathogenic effects of lipoproteins occur after these particles leak from the circulation [16,25,49]. Du et al. [25] identified extravasated Apo A1 and Apo B in human diabetic eyes and showed that native HDL completely blocks oxidative stress and the apoptosis of retinal pigment epithelial cells induced by heavily oxidized glycated LDL, thereby suggesting an important new role for extravasated and modified plasma lipoproteins in promoting DR. We speculate that GGT may co-localize with extravasated HDL and LDL and possibly take part in the pathogenesis of DR in G allele carriers with a low HDL-C level. In the present study, among G allele carriers, the diabetes duration and ALDH2*2 allele were other independent risk factors for DR. We previously reported a significant association between the ALDH2*2 allele and DR, as follows: the incidence of DR is significantly higher in ALDH2*2 allele carriers with a high GGT level (>37 IU/L for males and > 26 IU/L for females) than in non-carriers with a high or low GGT level; a high GGT level in non-carriers is most significantly associated with drinking habits, while that in ALDH2*2 allele carriers is significantly associated with multiple cardiovascular risk factors; and ALDH2 therefore may protect the vasculature against reactive aldehydes generated under conditions of sustained oxidative stress [50]. In non-carriers, a female gender, age and the HbA1c level, which is associated with a high GGT level, were found to be independent risk factors for DR.

Diabetes and hypertension promote adverse changes throughout the vascular tree, eliciting both macrovascular and microvascular complications [34,38,45,46]. Increased arterial stiffness and microvascular remodeling are the most prevalent and earliest forms of organ damage in these diseases [34,38,45,46]. Therefore, diabetic subjects with a high PWV and/or MVCs appear to be particularly prone to developing accelerated atherosclerosis and premature death [38,46,47]. A significant positive association between the presence of DR and baPWV has been reported in Japanese T2DM patients without macrovascular complications [34]. The association between a high baPWV and DR is likely to exist in G allele carriers, although this finding did not reach statistical significance in the present study due to the small sample size (data not shown). Although fibrates fail to reduce cardiovascular events in patients treated with statins in general [14,18], fenofibrate has received major attention as a novel medical treatment for DR and other diabetes-induced MCVs [24]. This is because well-designed clinical trials, i.e. the Fenofibrate Intervention and Event Lowering in Diabetes (FIELD) and ACCORD trials, demonstrated large reductions in the progression of DR and the subsequent need for laser intervention, in addition to reductions in adverse renal and neurological outcomes, in patients with T2DM [24]. Fenofibrate regulates the expression of many different genes, with a range of beneficial effects for lipid control, inflammation, angiogenesis and cell apoptosis [24,51]. Interestingly, treatment with 200 mg/day of fenofibrate for 48 weeks in patients with non-alcoholic fatty liver disease was recently shown to improve metabolic syndrome, in addition to glucose and liver parameters, including a reduction in the serum GGT level of 39% [51]. These findings and the current results suggest that the early detection and treatment of low HDL-C levels in populations at high risk for MVC and CVD, such as T2DM patients with arterial stiffness and/or DR, may be an attractive therapeutic target for prevention [6,7,15-17,24,34,38,45,46,51]. The GGT1 G allele is a novel candidate risk factor in this population and we hope to reanalyze the findings of previous clinical trials of agents used to increase the HDL-C levels, stratifying the outcome according to the GGT1 genotype.

The strength of this study is that significant interactive effects between the GGT1 genotype and a low HDL-C level were observed on both a high baPWV and DR in two different populations and different analyses. The crucial limitations of this study include the retrospective study design, small sample size and lack of information on factors such as the effects of drug therapy, the HDL-C fraction and function and specific biomarkers of oxidative stress.

We herein presented for the first time the significant interactive effects of the GGT1 G allele and a low HDL-C level on a high baPWV and DR. These findings may suggest a common pathological mechanism in case of diabetic macro- and micro-angiopathy and prompt repeat-analyses of previous trials according to the GGT1 genotype as well as the development of future clinical trials comparing the efficacy of agents increasing the HDL-C level in improving angiopathy between GGT1 genotypes. However, well-designed studies in larger cohorts are still needed to further confirm our results.

Abbreviations

ACCORD:

Action to Control Cardiovascular Risk in Diabetes

ACE:

Angiotensin converting enzyme

ALDH2:

Aldehyde dehydrogenase 2

ALT:

Alanine aminotransferase

Apo:

Apolipoprotein

ARB:

Angiotensin II receptor blocker

AST:

Aspartate aminotransferase

AUC:

Area under the curve

B:

Partial regression coefficient

baPWV:

brachial-ankle pulse wave velocity

BMI:

Body mass index

BP:

Blood pressure

CAD:

Coronary artery disease

CI:

Confidence interval

CVD:

Cardiovascular disease

DR:

Diabetic retinopathy

FIELD:

Fenofibrate Intervention and Event Lowering in Diabetes

GGT:

γ-glutamyltransferase

GWAS:

Genome-wide association study

HbA1c:

Hemoglobin A1c

HDL-C:

High-density lipoprotein cholesterol

HF:

Heart failure

HR:

hazard ratio

LDL-C:

Low-density lipoprotein cholesterol

MVC:

Microvascular complication

NPDR:

Nonproliferative diabetic retinopathy

OR:

Odds ratio

PDR:

Proliferative diabetic retinopathy

ROC:

Receiver operating characteristic

SNP:

Single-nucleotide polymorphism

T2DM:

Type 2 diabetes mellitus

References

  1. McKillop AM, Flatt PR. Emerging applications of metabolomic and genomic profiling in diabetic clinical medicine. Diabetes Care. 2011;34(12):2624–30.

    Article  PubMed Central  CAS  PubMed  Google Scholar 

  2. Onat A, Can G, Ornek E, Cicek G, Ayhan E, Dogan Y. Serum gamma-glutamyltransferase: independent predictor of risk of diabetes, hypertension, metabolic syndrome, and coronary disease. Obesity. 2012;20(4):842–8.

    Article  CAS  PubMed  Google Scholar 

  3. Wang Y, Tuomilehto J, Jousilahti P, Salomaa V, Li B, Antikainen R, et al. Serum gamma-glutamyltransferase and the risk of heart failure in men and women in Finland. Heart. 2013;99(3):163–7.

    Article  CAS  PubMed  Google Scholar 

  4. Ruttmann E, Brant LJ, Concin H, Diem G, Rapp K, Ulmer H, et al. Gamma-glutamyltransferase as a risk factor for cardiovascular disease mortality: an epidemiological investigation in a cohort of 163,944 Austrian adults. Circulation. 2005;112(14):2130–7.

    Article  CAS  PubMed  Google Scholar 

  5. Du G, Song Z, Zhang Q. Gamma-glutamyltransferase is associated with cardiovascular and all-cause mortality: a meta-analysis of prospective cohort studies. Prev Med. 2013;57(1):31–7.

    Article  PubMed  Google Scholar 

  6. Emdin M, Pompella A, Paolicchi A. Gamma-glutamyltransferase, atherosclerosis, and cardiovascular disease: triggering oxidative stress within the plaque. Circulation. 2005;112(14):2078–80.

    Article  PubMed  Google Scholar 

  7. Turgut O, Tandogan I. Gamma-glutamyltransferase to determine cardiovascular risk: shifting the paradigm forward. J Atheroscler Thromb. 2011;18(3):177–81.

    Article  CAS  PubMed  Google Scholar 

  8. Dominici S, Paolicchi A, Lorenzini E, Maellaro E, Comporti M, Pieri L, et al. Gamma-glutamyltransferase-dependent prooxidant reactions: a factor in multiple processes. Biofactors. 2003;17(1–4):187–98.

    Article  CAS  PubMed  Google Scholar 

  9. Whitfield JB. Gamma glutamyl transferase. Crit Rev Clin Lab Sci. 2001;38(4):263–355.

    Article  CAS  PubMed  Google Scholar 

  10. Huseby NE. Multiple forms of serum gamma-glutamyltransferase. Association of the enzyme with lipoproteins. Clin Chim Acta. 1982;124(1):103–12.

    Article  CAS  PubMed  Google Scholar 

  11. Artur Y, Wellman-Bednawska M, Jacquier A, Siest G. Associations between serum gamma-glutamyltransferase and apolipoproteins: relationships with hepatobiliary diseases. Clin Chem. 1984;30(8):1318–21.

    CAS  PubMed  Google Scholar 

  12. Nordestgaard BG, Varbo A. Triglycerides and cardiovascular disease. Lancet. 2014;384(9943):626–35.

    Article  CAS  PubMed  Google Scholar 

  13. Tenenbaum A, Klempfner R, Fisman EZ. Hypertriglyceridemia: a too long unfairly neglected major cardiovascular risk factor. Cardiovasc Diabetol. 2014;13(1):159.

    Article  PubMed Central  PubMed  Google Scholar 

  14. Saely CH, Rein P, Drexel H. Combination lipid therapy in type 2 diabetes. N Engl J Med. 2010;363(7):692. author reply 694–695.

    Article  CAS  PubMed  Google Scholar 

  15. Hausenloy DJ, Yellon DM. Targeting residual cardiovascular risk: raising high-density lipoprotein cholesterol levels. Heart. 2008;94(6):706–14.

    Article  CAS  PubMed  Google Scholar 

  16. Wu M, Lyons TJ. Treatment approaches for diabetes and dyslipidemia. Horm Res Paediatr. 2011;76 Suppl 1:76–80.

    Article  PubMed Central  CAS  PubMed  Google Scholar 

  17. Fruchart JC, Davignon J, Hermans MP, Al-Rubeaan K, Amarenco P, Assmann G, et al. Residual macrovascular risk in 2013: what have we learned? Cardiovasc Diabetol. 2014;13:26.

    Article  PubMed Central  PubMed  Google Scholar 

  18. Keene D, Price C, Shun-Shin MJ, Francis DP. Effect on cardiovascular risk of high density lipoprotein targeted drug treatments niacin, fibrates, and CETP inhibitors: meta-analysis of randomised controlled trials including 117,411 patients. BMJ. 2014;349:g4379.

    Article  PubMed Central  PubMed  Google Scholar 

  19. Do R, Willer CJ, Schmidt EM, Sengupta S, Gao C, Peloso GM, et al. Common variants associated with plasma triglycerides and risk for coronary artery disease. Nat Genet. 2013;45(11):1345–52.

    Article  PubMed Central  CAS  PubMed  Google Scholar 

  20. Rader DJ, Hovingh GK. HDL and cardiovascular disease. Lancet. 2014;384(9943):618–25.

    Article  CAS  PubMed  Google Scholar 

  21. Rohatgi A, Khera A, Berry JD, Givens EG, Ayers CR, Wedin KE, et al. HDL cholesterol efflux capacity and incident cardiovascular events. N Engl J Med. 2014;371(25):2383–93.

    Article  CAS  PubMed  Google Scholar 

  22. Von Eckardstein A, Kardassis D. High density lipoproteins: from biological understanding to clinical exploitation. Springer International Publishing; 2015. p. 337-421.

  23. Toth PP, Simko RJ, Palli SR, Koselleck D, Quimbo RA, Cziraky MJ. The impact of serum lipids on risk for microangiopathy in patients with type 2 diabetes mellitus. Cardiovasc Diabetol. 2012;11:109.

    Article  PubMed Central  CAS  PubMed  Google Scholar 

  24. Noonan JE, Jenkins AJ, Ma JX, Keech AC, Wang JJ, Lamoureux EL. An update on the molecular actions of fenofibrate and its clinical effects on diabetic retinopathy and other microvascular end points in patients with diabetes. Diabetes. 2013;62(12):3968–75.

    Article  PubMed Central  CAS  PubMed  Google Scholar 

  25. Du M, Wu M, Fu D, Yang S, Chen J, Wilson K, et al. Effects of modified LDL and HDL on retinal pigment epithelial cells: a role in diabetic retinopathy? Diabetologia. 2013;56(10):2318–28.

    Article  CAS  PubMed  Google Scholar 

  26. Whitfield JB, Zhu G, Nestler JE, Heath AC, Martin NG. Genetic covariation between serum gamma-glutamyltransferase activity and cardiovascular risk factors. Clin Chem. 2002;48(9):1426–31.

    CAS  PubMed  Google Scholar 

  27. Yuan X, Waterworth D, Perry JR, Lim N, Song K, Chambers JC, et al. Population-based genome-wide association studies reveal six loci influencing plasma levels of liver enzymes. Am J Hum Genet. 2008;83(4):520–8.

    Article  PubMed Central  CAS  PubMed  Google Scholar 

  28. Kim YJ, Go MJ, Hu C, Hong CB, Kim YK, Lee JY, et al. Large-scale genome-wide association studies in East Asians identify new genetic loci influencing metabolic traits. Nat Genet. 2011;43(10):990–5.

    Article  CAS  PubMed  Google Scholar 

  29. Middelberg RP, Benyamin B, de Moor MH, Warrington NM, Gordon S, Henders AK, et al. Loci affecting gamma-glutamyl transferase in adults and adolescents show age x SNP interaction and cardiometabolic disease associations. Hum Mol Genet. 2012;21(2):446–55.

    Article  PubMed Central  CAS  PubMed  Google Scholar 

  30. Kawai T, Ohishi M, Onishi M, Ito N, Takeya Y, Maekawa Y, et al. Cut-off value of brachial-ankle pulse wave velocity to predict cardiovascular disease in hypertensive patients: a cohort study. J Atheroscler Thromb. 2013;20(4):391–400.

    Article  PubMed  Google Scholar 

  31. Jimba S, Nakagami T, Takahashi M, Wakamatsu T, Hirota Y, Iwamoto Y, et al. Prevalence of non-alcoholic fatty liver disease and its association with impaired glucose metabolism in Japanese adults. Diabet Med. 2005;22(9):1141–5.

    Article  CAS  PubMed  Google Scholar 

  32. Zhang H, Forman HJ. Redox regulation of gamma-glutamyl transpeptidase. Am J Respir Cell Mol Biol. 2009;41(5):509–15.

    Article  PubMed Central  CAS  PubMed  Google Scholar 

  33. Kamatani Y, Matsuda K, Okada Y, Kubo M, Hosono N, Daigo Y, et al. Genome-wide association study of hematological and biochemical traits in a Japanese population. Nat Genet. 2010;42(3):210–5.

    Article  CAS  PubMed  Google Scholar 

  34. Ogawa O, Hiraoka K, Watanabe T, Kinoshita J, Kawasumi M, Yoshii H, et al. Diabetic retinopathy is associated with pulse wave velocity, not with the augmentation index of pulse waveform. Cardiovasc Diabetol. 2008;7:11.

    Article  PubMed Central  PubMed  Google Scholar 

  35. Kweon SS, Shin MH, Nam HS, Jeong SK, Park KS, Choi JS, et al. Dose–response relationship between serum gamma-glutamyltransferase and arterial stiffness in Korean adults: the Namwon Study. J Epidemiol. 2014;24(1):7–14.

    Article  PubMed Central  PubMed  Google Scholar 

  36. Lin Y, Xu Y, Chen G, Huang B, Yao J, Chen Z, et al. Serum gamma-glutamyltransferase and associated damage among a She Chinese population. Diabet Med. 2011;28(8):924–31.

    Article  CAS  PubMed  Google Scholar 

  37. Lee DH, Jacobs Jr DR, Gross M, Steffes M. Serum gamma-glutamyltransferase was differently associated with microalbuminuria by status of hypertension or diabetes: the Coronary Artery Risk Development in Young Adults (CARDIA) Study. Clin Chem. 2005;51(7):1185–91.

    Article  CAS  PubMed  Google Scholar 

  38. Agabiti-Rosei E. From macro- to microcirculation: benefits in hypertension and diabetes. J Hypertens Suppl. 2008;26(3):S15–9.

    Article  CAS  PubMed  Google Scholar 

  39. Park S, Lakatta EG. Role of inflammation in the pathogenesis of arterial stiffness. Yonsei Med J. 2012;53(2):258–61.

    Article  PubMed Central  CAS  PubMed  Google Scholar 

  40. Bradley RD, Fitzpatrick AL, Jacobs Jr DR, Lee DH, Swords Jenny N, Herrington D. Associations between gamma-glutamyltransferase (GGT) and biomarkers of atherosclerosis: the Multi-ethnic Study of Atherosclerosis (MESA). Atherosclerosis. 2014;233(2):387–93.

    Article  PubMed Central  CAS  PubMed  Google Scholar 

  41. Paolicchi A, Emdin M, Passino C, Lorenzini E, Titta F, Marchi S, et al. Beta-lipoprotein- and LDL-associated serum gamma-glutamyltransferase in patients with coronary atherosclerosis. Atherosclerosis. 2006;186(1):80–5.

    Article  CAS  PubMed  Google Scholar 

  42. Zhao WW, Yang YH, Lu B, Feng XC, He M, Yang ZH, et al. Serum high-density lipoprotein cholesterol and progression to arterial stiffness in middle-aged and elderly Chinese. Nutr Metab Cardiovasc Dis. 2013;23(10):973–9.

    Article  CAS  PubMed  Google Scholar 

  43. Huxley RR, Barzi F, Lam TH, Czernichow S, Fang X, Welborn T, et al. Isolated low levels of high-density lipoprotein cholesterol are associated with an increased risk of coronary heart disease: an individual participant data meta-analysis of 23 studies in the Asia-Pacific region. Circulation. 2011;124(19):2056–64.

    Article  CAS  PubMed  Google Scholar 

  44. Mineo C, Shaul PW. Novel biological functions of high-density lipoprotein cholesterol. Circ Res. 2012;111(8):1079–90.

    Article  PubMed Central  CAS  PubMed  Google Scholar 

  45. Jax TW. Metabolic memory: a vascular perspective. Cardiovasc Diabetol. 2010;9:51.

    Article  PubMed Central  PubMed  Google Scholar 

  46. Orasanu G, Plutzky J. The pathologic continuum of diabetic vascular disease. J Am Coll Cardiol. 2009;53(5 Suppl):S35–42.

    Article  PubMed Central  CAS  PubMed  Google Scholar 

  47. Rosenson RS, Fioretto P, Dodson PM. Does microvascular disease predict macrovascular events in type 2 diabetes? Atherosclerosis. 2011;218(1):13–8.

    Article  CAS  PubMed  Google Scholar 

  48. Giacco F, Brownlee M. Oxidative stress and diabetic complications. Circ Res. 2010;107(9):1058–70.

    Article  PubMed Central  CAS  PubMed  Google Scholar 

  49. Wu Y, Tang L, Chen B. Oxidative stress: implications for the development of diabetic retinopathy and antioxidant therapeutic perspectives. Oxid Med Cell Longev. 2014;2014:752387.

    Article  PubMed Central  PubMed  Google Scholar 

  50. Morita K, Saruwatari J, Miyagawa H, Uchiyashiki Y, Oniki K, Sakata M, et al. Association between aldehyde dehydrogenase 2 polymorphisms and the incidence of diabetic retinopathy among Japanese subjects with type 2 diabetes mellitus. Cardiovasc Diabetol. 2013;12:132.

    Article  PubMed Central  PubMed  Google Scholar 

  51. Fernandez-Miranda C, Perez-Carreras M, Colina F, Lopez-Alonso G, Vargas C, Solis-Herruzo JA. A pilot trial of fenofibrate for the treatment of non-alcoholic fatty liver disease. Dig Liver Dis. 2008;40(3):200–5.

    Article  CAS  PubMed  Google Scholar 

Download references

Acknowledgement

The authors wish to thank all of the study participants. This study was supported by KAKENHI (No. 23510348) and by a grant from Smoking Research Foundation.

Author information

Authors and Affiliations

  1. Division of Preventive Cardiology, Department of Cardiovascular Medicine, Kumamoto University Hospital, Kumamoto, Japan

    Hideaki Jinnouchi, Seiji Hokimoto & Hisao Ogawa

  2. Jinnouchi Clinic, Diabetes Care Center, Kumamoto, Japan

    Hideaki Jinnouchi & Akira Yoshida

  3. Division of Pharmacology and Therapeutics, Graduate School of Pharmaceutical Sciences, Kumamoto University, 5-1, Oe-honmachi, 862-0973, Kumamoto, Japan

    Kazunori Morita, Takahiro Tanaka, Ayami Kajiwara, Yuki Kawata, Kentaro Oniki, Junji Saruwatari & Kazuko Nakagawa

  4. Center for Clinical Pharmaceutical Sciences, Kumamoto University, Kumamoto, Japan

    Kazuko Nakagawa

  5. Japanese Red Cross Kumamoto Health Care Center, Kumamoto, Japan

    Koji Otake & Yasuhiro Ogata

  6. Department of Cardiovascular Medicine, Faculty of Life Sciences, Graduate School of Medical Sciences, Kumamoto University, Kumamoto, Japan

    Seiji Hokimoto & Hisao Ogawa

Authors
  1. Hideaki Jinnouchi
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Kazunori Morita
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Takahiro Tanaka
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Ayami Kajiwara
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Yuki Kawata
    View author publications

    You can also search for this author in PubMed Google Scholar

  6. Kentaro Oniki
    View author publications

    You can also search for this author in PubMed Google Scholar

  7. Junji Saruwatari
    View author publications

    You can also search for this author in PubMed Google Scholar

  8. Kazuko Nakagawa
    View author publications

    You can also search for this author in PubMed Google Scholar

  9. Koji Otake
    View author publications

    You can also search for this author in PubMed Google Scholar

  10. Yasuhiro Ogata
    View author publications

    You can also search for this author in PubMed Google Scholar

  11. Akira Yoshida
    View author publications

    You can also search for this author in PubMed Google Scholar

  12. Seiji Hokimoto
    View author publications

    You can also search for this author in PubMed Google Scholar

  13. Hisao Ogawa
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Kazuko Nakagawa.

Additional information

Competing interests

The authors declare that they have no competing interests.

Authors’ contributions

All authors have made substantial contributions to conception and design, or acquisition of data, or analysis and interpretation of data; have been involved in drafting the manuscript or revising it critically for important intellectual content; and have given final approval of version to be published. All authors read and approved the final manuscript.

Additional files

Additional file 1: Table S1.

Factors associated with serum GGT level stratified by the GGT1 genotype in the T2DM subjects in the univariate regression model. Table S2. Interactive effects between the GGT1 genotype and dyslipidemia on the baPWV levels in the general subjects.

Additional file 2: Figure S1.

The ROC curves of HDL-C for high baPWV (≥1750 cm/sec) in the GGT1 G allele carriers (a) and non-carriers (b) in the general subjects. The HDL-C level was associated significantly with a high baPWV only in G allele carriers (AUC 0.63, 95%CI 0.52 - 0.74, P = 0.02).AUC, area under the curve; CI, confidence interval; ROC, receiver operating characteristic; HDL-C, high-density lipoprotein cholesterol; baPWV, brachial-ankle pulse wave velocity.

Rights and permissions

Open Access  This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made.

The images or other third party material in this article are included in the article’s Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder.

To view a copy of this licence, visit https://creativecommons.org/licenses/by/4.0/.

The Creative Commons Public Domain Dedication waiver (https://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated in a credit line to the data.

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Jinnouchi, H., Morita, K., Tanaka, T. et al. Interactive effects of a common γ-glutamyltransferase 1 variant and low high-density lipoprotein-cholesterol on diabetic macro- and micro-angiopathy. Cardiovasc Diabetol 14, 49 (2015). https://doi.org/10.1186/s12933-015-0212-5

Download citation

  • Received:

  • Accepted:

  • Published:

  • DOI: https://doi.org/10.1186/s12933-015-0212-5

Keywords

Cardiovascular Diabetology

ISSN: 1475-2840