Gene 555 (2015) 357–361
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The functional Q84R polymorphism of TRIB3 gene is associated with diabetic nephropathy in Chinese type 2 diabetic patients Weiwei Zhang a,1, Zhen Yang a,1, Xiaoyong Li a, Jie Wen b, Hongmei Zhang a, Suijun Wang c, Xuanchun Wang b, Houguang Zhou d, Wenjun Fang a, Li Qin a, Qing Su a,⁎ a
Department of Endocrinology, Xinhua Hospital, School of Medicine, Shanghai Jiaotong University, Shanghai, China Institute of Endocrinology and Diabetology at Fudan University, Huashan Hospital, Fudan University, Shanghai, China Department of Endocrinology, Clinical Geriatric Medicine, Henan Provincial People's Hospital, Zhengzhou, China d Department of Geriatrics, Huashan Hospital, Fudan University, Shanghai, China b c
a r t i c l e
i n f o
Article history: Received 9 May 2014 Received in revised form 3 October 2014 Accepted 13 November 2014 Available online 14 November 2014 Keywords: Albuminuria Oxidative stress Smoking Genotype
a b s t r a c t Increased oxidative stress and circulating free fatty acids (FFA) has been suggested to involve in the pathogenesis of diabetic nephropathy. TRIB3 can inhibit FFA and reactive oxygen species (ROS) stimulated podocyte production of MCP-1. Smoking increases the production of reactive oxygen species, which accelerates oxidative stress under hyperglycemia. To determine whether the Q84R polymorphism (rs2295490), alone or in combination with smoking, contributes to the development of diabetic nephropathy, a case–control study was performed in 812 Chinese patients with type 2 diabetes. Among patients, 214 had diabetic nephropathy with microalbuminuria (n = 156) or overt albuminuria (n = 58), and 598 did not show either of these symptoms but had diabetes for ≥10 years and were not undergoing antihypertension treatment. After adjustment for confounders, TRIB3 single-nucleotide polymorphism rs2295490 was associated with DN (OR 1.318, 95% CI 1.075, 1.653, p = 0.017); smoking was also an independent risk factor for diabetic nephropathy (1.42 [1.25–2.04], p b 0.001). In addition, we identified possible synergistic effects; i.e., the high-risk group (smokers with the AG + GG genotype) showed 2.13 times higher risk (1.51–3.96, p b 0.001) of diabetic nephropathy than the low-risk group (nonsmokers with the AA genotype) in a multiple logistic regression analysis controlled for the confounders, but no departure from additivity was found. Our results indicate that smoking and the TRIB3 G-allele is associated with an increased risk of diabetic nephropathy, which supports the hypothesis that oxidative stress contributes to the development of diabetic nephropathy. © 2014 Elsevier B.V. All rights reserved.
1. Introduction Diabetic nephropathy (DN) is a serious microvascular complication of diabetes mellitus and it is the leading cause of end-stage renal disease (ESRD) worldwide. Clustering of DN within ethnic groups and families indicates existing genetic predisposition to renal pathology (Seaquist et al., 1989). Oxidative stress resulting from overproduction of reactive oxidant species (ROS) under hyperglycemic conditions contributes to the development and progression of diabetic nephropathy (Kashihara et al., 2010). ROS can increase glomerular albumin permeability, which in
Abbreviations: FFA, free fatty acids; ROS, reactive oxygen species; DN, diabetic nephropathy; TRIB3, the mammalian tribbles homologue 3; MAPK, mitogen-activated protein kinases; HOMA-IR, homeostasis model assessment of insulin resistance; IMT, intima-media thickness. ⁎ Corresponding author at: Department of Endocrinology, Xinhua Hospital, School of Medicine, Shanghai Jiaotong University, 1665 Kongjiang Road, Shanghai, China. E-mail address: [email protected] (Q. Su). 1 Contributed equally to this work.
http://dx.doi.org/10.1016/j.gene.2014.11.031 0378-1119/© 2014 Elsevier B.V. All rights reserved.
turn leads to mesangial growth, and glomerular sclerosis (Arora and Singh, 2014). Smoking is a major environmental risk factor for diabetic nephropathy (Rossing et al., 2002). Cigarette smoke could increase the production of ROS, consequently accelerating oxidative stress under hyperglycemic conditions (Csiszar et al., 2009). The mammalian tribbles homologue 3 (TRIB3) gene, located on chromosome 20p13, has been implicated in type 2 diabetes (Liu et al., 2010; Ti et al., 2011; Beguinot, 2010). TRIB3 is an intracellular pseudokinase with a truncated kinase domain that lacks ATP-binding and catalytic residues (Du et al., 2003). TRIB3 may function as a scaffold protein to dampen potentially injurious signaling cascades, including AKT/protein kinase B and mitogen-activated protein kinases (MAPK) (Prudente et al., 2012; Formoso et al., 2011). Furthermore, a recent study has demonstrated that TRIB3 inhibits FFA and ROS stimulated podocyte production of MCP-1, suggesting that TRIB3 may be protective in diabetic kidney disease (Morse et al., 2010). The Q84R polymorphism of TRIB3, where arginine replaces glutamine at position 84, results in a gain-of-function variant (Prudente et al., 2010). Prudente et al. have first indicated that the Q84R missense polymorphism is associated with insulin resistance and related clinical
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outcomes (Prudente et al., 2005). Individuals with the R84 variant are more susceptible to abdominal obesity, hypertriglyceridemia and insulin resistance (Prudente et al., 2013; Zhang et al., 2011). Therefore, they are at risk for metabolic syndrome and predisposed to carotid atherosclerosis (Gong et al., 2009). A recent study reports an association between the Q84R polymorphism and an increased risk of type 2 diabetes mellitus in Whites (Prudente et al., 2009). However, whether the TRIB3 Q84R polymorphism increases the risk for diabetic nephropathy is still not established. Increasing evidence in both epidemiological and clinical studies suggests that combination of both genetic and environmental risk determinants contribute to the development and progression of diabetic nephropathy. It has been proposed that the genetic background is able to heavily modulate the individual response to environmental factors. In the present study, we aim to determine the influence of the TRIB3 Q84R polymorphism on the risk of diabetic nephropathy. Furthermore, we investigate whether this polymorphism and smoking show a synergistic effect on the development of nephropathy in Chinese type 2 diabetic patients. 2. Material and methods 2.1. Participants We selected type 2 diabetic subjects (n = 812) who were southern Han Chinese ancestry and were residing in the metropolitan area of Shanghai. All studied individuals were regularly attended the Department of Endocrinology and Metabolism at Shanghai Jiaotong University Affiliated Xinhua Hospital between January 2012 and October 2013. Type 2 diabetes was diagnosed according to the 2003 American Diabetes Association diagnostic criteria for diabetes, and subjects were divided into no diabetic nephropathy and diabetic nephropathy groups according to their 24-h albumin excretion rates (AERs). The no diabetic nephropathy group (n = 598) consisted of patients who had type 2 diabetes for at least 10 years and did not show albuminuria (AER b 30 mg/24 h). After ruling out urinary tract infection, hematuria, nephritis, and other conditions, the diabetic nephropathy group (n = 214) was further subdivided into a microalbuminuria group (n = 170, 300 mg/24 h N AER ≥ 30 mg/24 h), and an overt albuminuria group (n = 44, AER ≥ 300 mg/24 h), with AER determined in at least two consecutive overnight samples. The abbreviated Modification of Diet in Renal Disease formula recalibrated for Chinese was used to estimate glomerular filtration rate expressed in milliliters per minute per 1.73 m2: estimated glomerular filtration rate (eGFR) = 186 × [serum creatinine × 0.011]−1.154 × [age]− 0.203 × [0.742 if female] × 1.233, where serum creatinine is expressed as micromoles per liter and 1.233 is the adjusting coefficient for Chinese (Liu et al., 2014). All of the patients underwent a standardized clinical and laboratory evaluation. The latest known A1C and blood pressure values were obtained from hospital records or from measurements at the inclusion visit. A1C was measured with high-pressure liquid chromatography. The homeostasis model assessment of insulin resistance (HOMA-IR) was calculated according to the equation described by Matthews et al. (1985). Information on smoking habits was obtained using questionnaires. Patients who had smoked at least 1 cigarette per day for at least 1 year at the time of study recruitment were stratified into the smoking group. Written informed consent was obtained from all the participants. The study was approved by the Institutional Review Board of Xinhua Hospital affiliated with Shanghai Jiao-Tong University School of Medicine. 2.2. DNA extraction and genotyping DNA was isolated from peripheral blood leukocytes by using the conventional phenol/chloroform method. The A/G variant (rs2295490) in the TRIB3 gene was genotyped in the present study.
The genotyping was performed by using TaqMan allelic discrimination assays (Applied Biosystems, Foster City, CA, USA). Ninety six samples were run in duplicates with a 100% concordance rate. The allele frequencies of SNP rs2295490 were comparable with the HapMap CHB database and in Hardy–Weinberg equilibrium (P N 0.05). 2.3. Statistical analysis The clinical and laboratory values were shown as means ± SD or as median with inter-quartile range. Comparisons of the clinical and laboratory parameters between the diabetic nephropathy group and control groups were performed with one-way ANOVA and χ2 test. Non-normally distributed data were logarithmically transformed before analysis and are presented as median with inter-quartile range. Hardy–Weinberg equilibrium was assessed using the χ2 test. One-way ANOVA was used to assess effects of the genotype on different variables. Analysis of covariance was performed adjusting for age, sex, and BMI. Independent contributions of the A/G variant and smoking to the risk of diabetic nephropathy were performed by multivariate logistic regression analysis to estimate the odds ratios (ORs) and 95% confidence intervals (CI) for diabetic nephropathy. Potential confounding variables including gender, age, duration of diabetes, hypertension, A1C, triglyceride, total cholesterol, blood pressure and HOMA-IR were controlled in the regression models. Data management and statistical analysis were performed with the SPSS Statistical Package (version 13.0; SPSS Inc., Chicago, IL). P values of b 0.05 was considered statistically significant. Power calculation was performed by Quanto software version 1.2.4 (http://hydra.usc.edu/gxe). Combined effects of genotype and smoking on the risk of diabetic nephropathy were analyzed by multivariate logistic regression analysis with stratification on the basis of genotype (A/A, A/G or G/G) and smoking status (smokers or nonsmokers). A low-risk genotype and no history of smoking were considered as reference groups. We used the following formulas to assess the interaction as departure from additivity: Synergy index (S) = [ORA + B+ − 1]/[ORA + B− + ORA − B+ − 2], relative excess risk due to interaction (RERI) = ORA + B + − ORA + B− − ORA − B+ + 1 and attributable proportion due to interaction (AP) = [ORA + B + − ORA + B − − ORA − B + + 1]/[ORA + B +]. If S = 1, then there is no sign of interaction between the factors; if S N 1, then there is a positive interaction. 3. Results The clinical characteristics of participating subjects are shown in Table 1. When compared patients with diabetic nephropathy subjects with patients without diabetic nephropathy, the diabetic nephropathy subjects showed significant differences in several clinical and laboratory characteristics in this study (Table 1). With the exception of the A/A genotype patients who had a lower AER level than the A/G and G/G patients (P b 0.001), there were no differences between the G allele gene carriers and noncarriers with regard to clinical and laboratory characteristics. The genotypic distribution of the Q84R polymorphism in each group was in Hardy–Weinberg equilibrium (P N 0.05). However, because there were no significant differences between the genotype distributions in the microalbuminuria and overt albuminuria groups, we combined these samples into a diabetic nephropathy group. The G allele frequency in the no diabetic nephropathy group were clearly lower than those in the diabetic nephropathy group (21.4% vs. 27.6% for the G allele, P = 0.009) (Table 2). In unadjusted analysis, the Q84R genotype was significantly associated with diabetic nephropathy (OR = 1.478, 95% CI 1.079–2.025, p = 0.019) (Table 2). In addition, the crude risk of having diabetic nephropathy was significantly increased by ever smoking (OR = 1.42, 95% CI 1.25–2.04, p b 0.001).
W. Zhang et al. / Gene 555 (2015) 357–361 Table 1 Clinical and laboratory characteristics of type 2 diabetic patients with and without diabetic nephropathy. DN+
DN−
p
n Age (years) Age at diagnosis of diabetes (years) Diabetes duration (years) Hypertension (%) A1C (%) SBP (mm Hg) DBP (mm Hg) FPG (mmol/l) Fasting plasma insulin (mU/l) HOMA-IR TG (mmol/l) TC (mmol/l) LDL-c (mmol/l) HDL-c (mmol/l) eGFR Smoking (%)
214 62.6 ± 8.4 52.0 ± 9.2 10.7 ± 7.6 63.6 7.0 ± 1.3 147.6 ± 19.5 95.1 ± 11.8 7.5 ± 2.1 24.3 ± 32.6 4.5 (2.9–8.3) 1.7 (1.1–2.5) 5.1 ± 1.0 2.9 ± 0.8 1.3 ± 0.3 105 (85–127) 35 (16.4)
598 62.7 ± 10.9 52.9 ± 10.6 9.8 ± 7.1 57.2 6.8 ± 1.1 142.9 ± 23.2 86.1 ± 11.7 7.4 ± 2.6 22.5 ± 32.2 3.9 (2.3–8.3) 1.4 (1.0–2.2) 5.4 ± 1.0 3.1 ± 0.8 1.4 ± 0.3 128 (108–146) 48 (8.0)
– 0.971 0.421 0.308 0.437 0.091 0.062 0.126 0.725 0.635 0.438 0.358 0.010 0.099 0.146 b0.001 0.001
Hypoglycemic treatments Insulin (%) OHA (%) Insulin + OHA (%)
96 (44.9) 59 (27.6) 59 (27.6)
317 (53.0) 160 (26.8) 121 (20.2)
0.052
A1c, glycated hemoglobin; SBP, systolic blood pressure; DBP, diastolic blood pressure; TG, Triglycerides; TC, total cholesterol; HDL-c, high-density lipoprotein-cholesterol; LDL-c, low-density lipoprotein-cholesterol; FPG, fasting plasma glucose; HOMA-IR, Homeostasis model assessment-insulin resistance; eGFR, estimated glomerular filtration rate; OHA, oral hypoglycemic agent. Data are means ± SD, medians (interquartile range), or n (%). Values of HOMA-IR, and fasting plasma insulin were calculated for no diabetic nephropathy (n = 59) and diabetic nephropathy (n = 160) patients, who were not receiving insulin therapy. P values were obtained by an unpaired Student t test or χ2 analysis, as appropriate.
To evaluate the independent contributions of the polymorphism and smoking to the risk of diabetic nephropathy, multivariate logistic regression analyses of type 2 diabetic patients with and without diabetic nephropathy were performed with the possible confounders. We obtained the following values for the individual confounders: gender (OR = 0.90, 95% CI 0.51–1.1.59, p = 0.633); diabetes duration (OR = 1.06, 95% CI 1.01–1.12, p = 0.019); hypertension (OR = 2.28, 95% CI 1.74–3.22, P b 0.001); A1C (OR = 1.13, 95% CI 0.97–1.43, p = 0.122); triglyceride (OR = 1.51, 95% CI 1.25–2.13, p = 0.003) and smoking (OR = 1.54, 95% CI 1.14–2.33, p = 0.023); the Q84R variant significantly increased the risk of diabetic nephropathy (OR = 1.47, 95% CI 1.18–2.34, p = 0.005). To explore the possible interaction between the variant and smoking, we used multivariate logistic regression with stratification according to smoking status and genotype. The high-risk group (ever smoking plus G/G + A/G genotype) had 2.13 times increased risk of diabetic nephropathy (95% CI 1.51–3.96) compared with the low-risk group (Table 3). The S was 1.97, PERI was 0.54 and AP was 25.37%, thus indicating a possible positive synergistic interaction between smoking and genotype.
Table 2 Genotype and allele frequencies of the TRIB3 Q84R polymorphism. rs2295490 Genotype
RAF (G-allele), % χ2 p value OR (95% CI)a p valuea
AA AG GG
DN+
DN−
52.3 (112) 40.2 (86) 7.5 (16) 27.6
61.9 (370) 33.4 (200) 4.7 (28) 21.4
0.009 1.318 (1.075–1.653) 0.017
RAF, risk allele frequency. a Logistic regression adjusted for sex, age at diabetes onset, duration of diabetes and HbA1c.
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Table 3 Combined analysis of the effects of rs2295490 genotype and smoking. GG + AG genotype
Ever smoked
− − + +
− + − +
P value
OR (95% CI)
b0.001 0.006 b0.001
1.00 (Ref.) 1.40 (1.23–2.02) 1.15 (1.085–1.41) 2.13 (1.51–3.96)
No diabetic nephropathy control subjects versus diabetic nephropathy case subjects in a multivariate logistic regression analysis with patients stratified according to smoking status and genotype, adjusted for age at diabetes onset, diabetes duration, A1C, and sex. Patients that never smoked, without the rs2295490 AA genotype, were considered as referent (Ref.) group. The combined effect was 0.60, which does not indicate departure from additivity.
4. Discussion In the present study, the frequencies of the G allele of TRIB3 gene in type 2 diabetic patients with diabetic nephropathy (27.6%) were higher than the corresponding frequencies in patients without diabetic nephropathy (21.4%), and the Q84R polymorphism showed significant risk associations with diabetic nephropathy (OR 1.318, 95% CI 1.075, 1.653, p = 0.017), when adjustments were made for other risk factors. Smoking was another independent risk factor for diabetic nephropathy (1.42 [1.25–2.04], p b 0.001). Moreover, we also detected a possible synergistic effect of these two factors on diabetic nephropathy; the high-risk group (smokers with the AG + GG genotype) showed 2.07 times higher risk (1.49–3.92, p b 0.001) of diabetic nephropathy than the low-risk group (nonsmokers with the AA genotype). This is the first report on the effects of the higher prevalence of the G allele and the interaction between this allele and smoking in the development of diabetic nephropathy in type 2 diabetic patients. TRIB3 R84 variant is a gain of function mutation which in vivo plays a pathogenic role in modulating the risk of several metabolic and cardiovascular abnormalities (Gong et al., 2009). Individuals carrying the TRIB3 R84 variant are at risk for developing type 2 diabetes (Prudente et al., 2009). And the variant is associated with increased carotid intima-media thickness (IMT) both in Caucasians and in Asians (Formoso et al., 2011; Prudente et al., 2005), therefore, implying the deleterious role exerted by the variant on the susceptibility to diabetic macrovascular complication. However, no previous studies have considered the genotype influence on diabetic microvascular disease, such as diabetic nephropathy. DN is a multifactorial progressive disease where the pathogenesis of the disease is extremely complex involving many different cells, molecules, and factors (Koh et al., 2014; Lenoir et al., 2014; Dellamea et al., 2014). Hyperglycemia has generally been considered as the key initiator of kidney damage associated with DN by activation and dysregulation of several metabolic pathways (Fragiadaki et al., 2014). Chronic extracellular hyperglycemia in diabetes stimulates reactive oxygen species (ROS) production and increases oxidative stress (Fiorentino et al., 2013; Singh et al., 2011). It has been established that excessive circulating FFAs also stimulates ROS formation through PKC activation (Inoguchi et al., 2000). Excess generation of ROS target cellular proteins, nucleic acids, or membrane lipids and damage their cellular structure and function (Maiese et al., 2009). ROS has also been shown to increase production of proinflammatory cytokines via activation of NF-κB, inducing chronic inflammation in kidney (Forbes et al., 2008; Ndisang et al., 2014). A recent study has reported that ROS and FFA augment TRIB3 expression in kidney (Morse et al., 2010). High levels of FFA could induce TRIB3 expression, thereby reducing the activity of ACC and ultimately promoting the oxidation of FFA. In addition, TRIB3 specifically inhibits MCP-1 expression in podocytes, most likely by modulating the signaling cascade of AKT-MAPK-NF-κB. TRIB3 conceivably can serve as a protective agent against the inflammatory renal injury induced largely by ROS in diabetic nephropathy (Morse et al., 2010). In our present study,
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the Q84R polymorphism showed significant risk associations with diabetic nephropathy. The substitution of arginine for glutamine at position 84 maybe attenuate the ability of TRIB3 to impede oxidative stress induced inflammation in DN, which requires further in vitro and in vivo studies. Smoking is generally accepted as a risk factor for diabetic nephropathy (Chuahirun et al., 2003; Phisitkul et al., 2008). In the present study, we also found that smoking was an independent risk factor for the onset and progression of diabetic nephropathy after adjustment for possible confounders, which is consistent with previous findings. Smoking could increase the production of ROS, consequently accelerating oxidative stress under overnutrition conditions. During smoking, nicotine increased the generation of superoxide by activating NADPH oxidase and PKC, which ultimately caused kidney injury (Chakkarwar, 2012). The gene TRIB3 84R carriers exhibited decreased resistance to oxidative stress, and smoking increased the production of ROS. Thus, the aggravation of oxidative stress under overnutrition conditions may reflect the possible synergistic effects of the combination of the AG + GG genotype and constant smoking on the development of DN. The present study was performed using a relatively larger sample size, it is statistically well powered. However, there are still some limitations which must be clearly acknowledged. Firstly, we lack measurements of ROS and antioxidant status from the study subjects, so we cannot analyze this as a possible link between genotype and disease. Another limitation of our study was that chronic complications will develop with longer follow-up. In most patients with type 2 diabetes, the highest incidence of microvascular disease occurs after 10 years. Control subjects in our study were without diabetic complications for a mean of 9.7 years after the onset of type 2 diabetes. Finally, given that long-term glycemia and duration of diabetes are major determinants of progression to nephropathy, our groups with and without diabetic nephropathy had similar mean A1C. However, possible periods of poor glycemic control in individual patients during the observation period could influence our results. In conclusion, we identified the G-allele of the TRIB3 gene as an independent risk factors for diabetic nephropathy. To the best of our knowledge, this is the first study reporting a positive association of G allele with development of diabetic kidney disease. Additionally, a combination of both TRIB3 AG + GG genotype and smoking showed synergistic effect on the development of diabetic nephropathy, supporting the idea that interplay between genetic and non-genetic risk determinants contributes to the development and progression of DN. Therefore, investigation of gene environment interactions in this way may improve targeting of therapy to diabetic patients who are more susceptible to kidney disease. Conflict of interests The authors declare that there is no conflict of interests that could be perceived as prejudicing the impartiality of the study. Acknowledgments This work was supported by the Shanghai Science and Technology Commission (10411956600), Natural Science Foundation of Shanghai (11ZR1405300), National Natural Science Foundation of China (81300667, 81000332, 81170322, 81370953, 81370935), Shanghai Health System Outstanding Young Talents Training Program (XYQ2013098), and Chinese Society of Endocrinology (201201). References Arora, M.K., Singh, U.K.1, 2014. Oxidative stress: meeting multiple targets in pathogenesis of diabetic nephropathy. Curr. Drug Targets 15, 531–538. Beguinot, F., 2010. Tribbles homologue 3 (TRIB3) and the insulin-resistance genes in type 2 diabetes. Diabetologia 53, 1831–1834.
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The functional Q84R polymorphism of TRIB3 gene is associated with diabetic nephropathy in Chinese type 2 diabetic patients Weiwei Zhang a,1, Zhen Yang a,1, Xiaoyong Li a, Jie Wen b, Hongmei Zhang a, Suijun Wang c, Xuanchun Wang b, Houguang Zhou d, Wenjun Fang a, Li Qin a, Qing Su a,⁎ a
Department of Endocrinology, Xinhua Hospital, School of Medicine, Shanghai Jiaotong University, Shanghai, China Institute of Endocrinology and Diabetology at Fudan University, Huashan Hospital, Fudan University, Shanghai, China Department of Endocrinology, Clinical Geriatric Medicine, Henan Provincial People's Hospital, Zhengzhou, China d Department of Geriatrics, Huashan Hospital, Fudan University, Shanghai, China b c
a r t i c l e
i n f o
Article history: Received 9 May 2014 Received in revised form 3 October 2014 Accepted 13 November 2014 Available online 14 November 2014 Keywords: Albuminuria Oxidative stress Smoking Genotype
a b s t r a c t Increased oxidative stress and circulating free fatty acids (FFA) has been suggested to involve in the pathogenesis of diabetic nephropathy. TRIB3 can inhibit FFA and reactive oxygen species (ROS) stimulated podocyte production of MCP-1. Smoking increases the production of reactive oxygen species, which accelerates oxidative stress under hyperglycemia. To determine whether the Q84R polymorphism (rs2295490), alone or in combination with smoking, contributes to the development of diabetic nephropathy, a case–control study was performed in 812 Chinese patients with type 2 diabetes. Among patients, 214 had diabetic nephropathy with microalbuminuria (n = 156) or overt albuminuria (n = 58), and 598 did not show either of these symptoms but had diabetes for ≥10 years and were not undergoing antihypertension treatment. After adjustment for confounders, TRIB3 single-nucleotide polymorphism rs2295490 was associated with DN (OR 1.318, 95% CI 1.075, 1.653, p = 0.017); smoking was also an independent risk factor for diabetic nephropathy (1.42 [1.25–2.04], p b 0.001). In addition, we identified possible synergistic effects; i.e., the high-risk group (smokers with the AG + GG genotype) showed 2.13 times higher risk (1.51–3.96, p b 0.001) of diabetic nephropathy than the low-risk group (nonsmokers with the AA genotype) in a multiple logistic regression analysis controlled for the confounders, but no departure from additivity was found. Our results indicate that smoking and the TRIB3 G-allele is associated with an increased risk of diabetic nephropathy, which supports the hypothesis that oxidative stress contributes to the development of diabetic nephropathy. © 2014 Elsevier B.V. All rights reserved.
1. Introduction Diabetic nephropathy (DN) is a serious microvascular complication of diabetes mellitus and it is the leading cause of end-stage renal disease (ESRD) worldwide. Clustering of DN within ethnic groups and families indicates existing genetic predisposition to renal pathology (Seaquist et al., 1989). Oxidative stress resulting from overproduction of reactive oxidant species (ROS) under hyperglycemic conditions contributes to the development and progression of diabetic nephropathy (Kashihara et al., 2010). ROS can increase glomerular albumin permeability, which in
Abbreviations: FFA, free fatty acids; ROS, reactive oxygen species; DN, diabetic nephropathy; TRIB3, the mammalian tribbles homologue 3; MAPK, mitogen-activated protein kinases; HOMA-IR, homeostasis model assessment of insulin resistance; IMT, intima-media thickness. ⁎ Corresponding author at: Department of Endocrinology, Xinhua Hospital, School of Medicine, Shanghai Jiaotong University, 1665 Kongjiang Road, Shanghai, China. E-mail address: [email protected] (Q. Su). 1 Contributed equally to this work.
http://dx.doi.org/10.1016/j.gene.2014.11.031 0378-1119/© 2014 Elsevier B.V. All rights reserved.
turn leads to mesangial growth, and glomerular sclerosis (Arora and Singh, 2014). Smoking is a major environmental risk factor for diabetic nephropathy (Rossing et al., 2002). Cigarette smoke could increase the production of ROS, consequently accelerating oxidative stress under hyperglycemic conditions (Csiszar et al., 2009). The mammalian tribbles homologue 3 (TRIB3) gene, located on chromosome 20p13, has been implicated in type 2 diabetes (Liu et al., 2010; Ti et al., 2011; Beguinot, 2010). TRIB3 is an intracellular pseudokinase with a truncated kinase domain that lacks ATP-binding and catalytic residues (Du et al., 2003). TRIB3 may function as a scaffold protein to dampen potentially injurious signaling cascades, including AKT/protein kinase B and mitogen-activated protein kinases (MAPK) (Prudente et al., 2012; Formoso et al., 2011). Furthermore, a recent study has demonstrated that TRIB3 inhibits FFA and ROS stimulated podocyte production of MCP-1, suggesting that TRIB3 may be protective in diabetic kidney disease (Morse et al., 2010). The Q84R polymorphism of TRIB3, where arginine replaces glutamine at position 84, results in a gain-of-function variant (Prudente et al., 2010). Prudente et al. have first indicated that the Q84R missense polymorphism is associated with insulin resistance and related clinical
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outcomes (Prudente et al., 2005). Individuals with the R84 variant are more susceptible to abdominal obesity, hypertriglyceridemia and insulin resistance (Prudente et al., 2013; Zhang et al., 2011). Therefore, they are at risk for metabolic syndrome and predisposed to carotid atherosclerosis (Gong et al., 2009). A recent study reports an association between the Q84R polymorphism and an increased risk of type 2 diabetes mellitus in Whites (Prudente et al., 2009). However, whether the TRIB3 Q84R polymorphism increases the risk for diabetic nephropathy is still not established. Increasing evidence in both epidemiological and clinical studies suggests that combination of both genetic and environmental risk determinants contribute to the development and progression of diabetic nephropathy. It has been proposed that the genetic background is able to heavily modulate the individual response to environmental factors. In the present study, we aim to determine the influence of the TRIB3 Q84R polymorphism on the risk of diabetic nephropathy. Furthermore, we investigate whether this polymorphism and smoking show a synergistic effect on the development of nephropathy in Chinese type 2 diabetic patients. 2. Material and methods 2.1. Participants We selected type 2 diabetic subjects (n = 812) who were southern Han Chinese ancestry and were residing in the metropolitan area of Shanghai. All studied individuals were regularly attended the Department of Endocrinology and Metabolism at Shanghai Jiaotong University Affiliated Xinhua Hospital between January 2012 and October 2013. Type 2 diabetes was diagnosed according to the 2003 American Diabetes Association diagnostic criteria for diabetes, and subjects were divided into no diabetic nephropathy and diabetic nephropathy groups according to their 24-h albumin excretion rates (AERs). The no diabetic nephropathy group (n = 598) consisted of patients who had type 2 diabetes for at least 10 years and did not show albuminuria (AER b 30 mg/24 h). After ruling out urinary tract infection, hematuria, nephritis, and other conditions, the diabetic nephropathy group (n = 214) was further subdivided into a microalbuminuria group (n = 170, 300 mg/24 h N AER ≥ 30 mg/24 h), and an overt albuminuria group (n = 44, AER ≥ 300 mg/24 h), with AER determined in at least two consecutive overnight samples. The abbreviated Modification of Diet in Renal Disease formula recalibrated for Chinese was used to estimate glomerular filtration rate expressed in milliliters per minute per 1.73 m2: estimated glomerular filtration rate (eGFR) = 186 × [serum creatinine × 0.011]−1.154 × [age]− 0.203 × [0.742 if female] × 1.233, where serum creatinine is expressed as micromoles per liter and 1.233 is the adjusting coefficient for Chinese (Liu et al., 2014). All of the patients underwent a standardized clinical and laboratory evaluation. The latest known A1C and blood pressure values were obtained from hospital records or from measurements at the inclusion visit. A1C was measured with high-pressure liquid chromatography. The homeostasis model assessment of insulin resistance (HOMA-IR) was calculated according to the equation described by Matthews et al. (1985). Information on smoking habits was obtained using questionnaires. Patients who had smoked at least 1 cigarette per day for at least 1 year at the time of study recruitment were stratified into the smoking group. Written informed consent was obtained from all the participants. The study was approved by the Institutional Review Board of Xinhua Hospital affiliated with Shanghai Jiao-Tong University School of Medicine. 2.2. DNA extraction and genotyping DNA was isolated from peripheral blood leukocytes by using the conventional phenol/chloroform method. The A/G variant (rs2295490) in the TRIB3 gene was genotyped in the present study.
The genotyping was performed by using TaqMan allelic discrimination assays (Applied Biosystems, Foster City, CA, USA). Ninety six samples were run in duplicates with a 100% concordance rate. The allele frequencies of SNP rs2295490 were comparable with the HapMap CHB database and in Hardy–Weinberg equilibrium (P N 0.05). 2.3. Statistical analysis The clinical and laboratory values were shown as means ± SD or as median with inter-quartile range. Comparisons of the clinical and laboratory parameters between the diabetic nephropathy group and control groups were performed with one-way ANOVA and χ2 test. Non-normally distributed data were logarithmically transformed before analysis and are presented as median with inter-quartile range. Hardy–Weinberg equilibrium was assessed using the χ2 test. One-way ANOVA was used to assess effects of the genotype on different variables. Analysis of covariance was performed adjusting for age, sex, and BMI. Independent contributions of the A/G variant and smoking to the risk of diabetic nephropathy were performed by multivariate logistic regression analysis to estimate the odds ratios (ORs) and 95% confidence intervals (CI) for diabetic nephropathy. Potential confounding variables including gender, age, duration of diabetes, hypertension, A1C, triglyceride, total cholesterol, blood pressure and HOMA-IR were controlled in the regression models. Data management and statistical analysis were performed with the SPSS Statistical Package (version 13.0; SPSS Inc., Chicago, IL). P values of b 0.05 was considered statistically significant. Power calculation was performed by Quanto software version 1.2.4 (http://hydra.usc.edu/gxe). Combined effects of genotype and smoking on the risk of diabetic nephropathy were analyzed by multivariate logistic regression analysis with stratification on the basis of genotype (A/A, A/G or G/G) and smoking status (smokers or nonsmokers). A low-risk genotype and no history of smoking were considered as reference groups. We used the following formulas to assess the interaction as departure from additivity: Synergy index (S) = [ORA + B+ − 1]/[ORA + B− + ORA − B+ − 2], relative excess risk due to interaction (RERI) = ORA + B + − ORA + B− − ORA − B+ + 1 and attributable proportion due to interaction (AP) = [ORA + B + − ORA + B − − ORA − B + + 1]/[ORA + B +]. If S = 1, then there is no sign of interaction between the factors; if S N 1, then there is a positive interaction. 3. Results The clinical characteristics of participating subjects are shown in Table 1. When compared patients with diabetic nephropathy subjects with patients without diabetic nephropathy, the diabetic nephropathy subjects showed significant differences in several clinical and laboratory characteristics in this study (Table 1). With the exception of the A/A genotype patients who had a lower AER level than the A/G and G/G patients (P b 0.001), there were no differences between the G allele gene carriers and noncarriers with regard to clinical and laboratory characteristics. The genotypic distribution of the Q84R polymorphism in each group was in Hardy–Weinberg equilibrium (P N 0.05). However, because there were no significant differences between the genotype distributions in the microalbuminuria and overt albuminuria groups, we combined these samples into a diabetic nephropathy group. The G allele frequency in the no diabetic nephropathy group were clearly lower than those in the diabetic nephropathy group (21.4% vs. 27.6% for the G allele, P = 0.009) (Table 2). In unadjusted analysis, the Q84R genotype was significantly associated with diabetic nephropathy (OR = 1.478, 95% CI 1.079–2.025, p = 0.019) (Table 2). In addition, the crude risk of having diabetic nephropathy was significantly increased by ever smoking (OR = 1.42, 95% CI 1.25–2.04, p b 0.001).
W. Zhang et al. / Gene 555 (2015) 357–361 Table 1 Clinical and laboratory characteristics of type 2 diabetic patients with and without diabetic nephropathy. DN+
DN−
p
n Age (years) Age at diagnosis of diabetes (years) Diabetes duration (years) Hypertension (%) A1C (%) SBP (mm Hg) DBP (mm Hg) FPG (mmol/l) Fasting plasma insulin (mU/l) HOMA-IR TG (mmol/l) TC (mmol/l) LDL-c (mmol/l) HDL-c (mmol/l) eGFR Smoking (%)
214 62.6 ± 8.4 52.0 ± 9.2 10.7 ± 7.6 63.6 7.0 ± 1.3 147.6 ± 19.5 95.1 ± 11.8 7.5 ± 2.1 24.3 ± 32.6 4.5 (2.9–8.3) 1.7 (1.1–2.5) 5.1 ± 1.0 2.9 ± 0.8 1.3 ± 0.3 105 (85–127) 35 (16.4)
598 62.7 ± 10.9 52.9 ± 10.6 9.8 ± 7.1 57.2 6.8 ± 1.1 142.9 ± 23.2 86.1 ± 11.7 7.4 ± 2.6 22.5 ± 32.2 3.9 (2.3–8.3) 1.4 (1.0–2.2) 5.4 ± 1.0 3.1 ± 0.8 1.4 ± 0.3 128 (108–146) 48 (8.0)
– 0.971 0.421 0.308 0.437 0.091 0.062 0.126 0.725 0.635 0.438 0.358 0.010 0.099 0.146 b0.001 0.001
Hypoglycemic treatments Insulin (%) OHA (%) Insulin + OHA (%)
96 (44.9) 59 (27.6) 59 (27.6)
317 (53.0) 160 (26.8) 121 (20.2)
0.052
A1c, glycated hemoglobin; SBP, systolic blood pressure; DBP, diastolic blood pressure; TG, Triglycerides; TC, total cholesterol; HDL-c, high-density lipoprotein-cholesterol; LDL-c, low-density lipoprotein-cholesterol; FPG, fasting plasma glucose; HOMA-IR, Homeostasis model assessment-insulin resistance; eGFR, estimated glomerular filtration rate; OHA, oral hypoglycemic agent. Data are means ± SD, medians (interquartile range), or n (%). Values of HOMA-IR, and fasting plasma insulin were calculated for no diabetic nephropathy (n = 59) and diabetic nephropathy (n = 160) patients, who were not receiving insulin therapy. P values were obtained by an unpaired Student t test or χ2 analysis, as appropriate.
To evaluate the independent contributions of the polymorphism and smoking to the risk of diabetic nephropathy, multivariate logistic regression analyses of type 2 diabetic patients with and without diabetic nephropathy were performed with the possible confounders. We obtained the following values for the individual confounders: gender (OR = 0.90, 95% CI 0.51–1.1.59, p = 0.633); diabetes duration (OR = 1.06, 95% CI 1.01–1.12, p = 0.019); hypertension (OR = 2.28, 95% CI 1.74–3.22, P b 0.001); A1C (OR = 1.13, 95% CI 0.97–1.43, p = 0.122); triglyceride (OR = 1.51, 95% CI 1.25–2.13, p = 0.003) and smoking (OR = 1.54, 95% CI 1.14–2.33, p = 0.023); the Q84R variant significantly increased the risk of diabetic nephropathy (OR = 1.47, 95% CI 1.18–2.34, p = 0.005). To explore the possible interaction between the variant and smoking, we used multivariate logistic regression with stratification according to smoking status and genotype. The high-risk group (ever smoking plus G/G + A/G genotype) had 2.13 times increased risk of diabetic nephropathy (95% CI 1.51–3.96) compared with the low-risk group (Table 3). The S was 1.97, PERI was 0.54 and AP was 25.37%, thus indicating a possible positive synergistic interaction between smoking and genotype.
Table 2 Genotype and allele frequencies of the TRIB3 Q84R polymorphism. rs2295490 Genotype
RAF (G-allele), % χ2 p value OR (95% CI)a p valuea
AA AG GG
DN+
DN−
52.3 (112) 40.2 (86) 7.5 (16) 27.6
61.9 (370) 33.4 (200) 4.7 (28) 21.4
0.009 1.318 (1.075–1.653) 0.017
RAF, risk allele frequency. a Logistic regression adjusted for sex, age at diabetes onset, duration of diabetes and HbA1c.
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Table 3 Combined analysis of the effects of rs2295490 genotype and smoking. GG + AG genotype
Ever smoked
− − + +
− + − +
P value
OR (95% CI)
b0.001 0.006 b0.001
1.00 (Ref.) 1.40 (1.23–2.02) 1.15 (1.085–1.41) 2.13 (1.51–3.96)
No diabetic nephropathy control subjects versus diabetic nephropathy case subjects in a multivariate logistic regression analysis with patients stratified according to smoking status and genotype, adjusted for age at diabetes onset, diabetes duration, A1C, and sex. Patients that never smoked, without the rs2295490 AA genotype, were considered as referent (Ref.) group. The combined effect was 0.60, which does not indicate departure from additivity.
4. Discussion In the present study, the frequencies of the G allele of TRIB3 gene in type 2 diabetic patients with diabetic nephropathy (27.6%) were higher than the corresponding frequencies in patients without diabetic nephropathy (21.4%), and the Q84R polymorphism showed significant risk associations with diabetic nephropathy (OR 1.318, 95% CI 1.075, 1.653, p = 0.017), when adjustments were made for other risk factors. Smoking was another independent risk factor for diabetic nephropathy (1.42 [1.25–2.04], p b 0.001). Moreover, we also detected a possible synergistic effect of these two factors on diabetic nephropathy; the high-risk group (smokers with the AG + GG genotype) showed 2.07 times higher risk (1.49–3.92, p b 0.001) of diabetic nephropathy than the low-risk group (nonsmokers with the AA genotype). This is the first report on the effects of the higher prevalence of the G allele and the interaction between this allele and smoking in the development of diabetic nephropathy in type 2 diabetic patients. TRIB3 R84 variant is a gain of function mutation which in vivo plays a pathogenic role in modulating the risk of several metabolic and cardiovascular abnormalities (Gong et al., 2009). Individuals carrying the TRIB3 R84 variant are at risk for developing type 2 diabetes (Prudente et al., 2009). And the variant is associated with increased carotid intima-media thickness (IMT) both in Caucasians and in Asians (Formoso et al., 2011; Prudente et al., 2005), therefore, implying the deleterious role exerted by the variant on the susceptibility to diabetic macrovascular complication. However, no previous studies have considered the genotype influence on diabetic microvascular disease, such as diabetic nephropathy. DN is a multifactorial progressive disease where the pathogenesis of the disease is extremely complex involving many different cells, molecules, and factors (Koh et al., 2014; Lenoir et al., 2014; Dellamea et al., 2014). Hyperglycemia has generally been considered as the key initiator of kidney damage associated with DN by activation and dysregulation of several metabolic pathways (Fragiadaki et al., 2014). Chronic extracellular hyperglycemia in diabetes stimulates reactive oxygen species (ROS) production and increases oxidative stress (Fiorentino et al., 2013; Singh et al., 2011). It has been established that excessive circulating FFAs also stimulates ROS formation through PKC activation (Inoguchi et al., 2000). Excess generation of ROS target cellular proteins, nucleic acids, or membrane lipids and damage their cellular structure and function (Maiese et al., 2009). ROS has also been shown to increase production of proinflammatory cytokines via activation of NF-κB, inducing chronic inflammation in kidney (Forbes et al., 2008; Ndisang et al., 2014). A recent study has reported that ROS and FFA augment TRIB3 expression in kidney (Morse et al., 2010). High levels of FFA could induce TRIB3 expression, thereby reducing the activity of ACC and ultimately promoting the oxidation of FFA. In addition, TRIB3 specifically inhibits MCP-1 expression in podocytes, most likely by modulating the signaling cascade of AKT-MAPK-NF-κB. TRIB3 conceivably can serve as a protective agent against the inflammatory renal injury induced largely by ROS in diabetic nephropathy (Morse et al., 2010). In our present study,
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the Q84R polymorphism showed significant risk associations with diabetic nephropathy. The substitution of arginine for glutamine at position 84 maybe attenuate the ability of TRIB3 to impede oxidative stress induced inflammation in DN, which requires further in vitro and in vivo studies. Smoking is generally accepted as a risk factor for diabetic nephropathy (Chuahirun et al., 2003; Phisitkul et al., 2008). In the present study, we also found that smoking was an independent risk factor for the onset and progression of diabetic nephropathy after adjustment for possible confounders, which is consistent with previous findings. Smoking could increase the production of ROS, consequently accelerating oxidative stress under overnutrition conditions. During smoking, nicotine increased the generation of superoxide by activating NADPH oxidase and PKC, which ultimately caused kidney injury (Chakkarwar, 2012). The gene TRIB3 84R carriers exhibited decreased resistance to oxidative stress, and smoking increased the production of ROS. Thus, the aggravation of oxidative stress under overnutrition conditions may reflect the possible synergistic effects of the combination of the AG + GG genotype and constant smoking on the development of DN. The present study was performed using a relatively larger sample size, it is statistically well powered. However, there are still some limitations which must be clearly acknowledged. Firstly, we lack measurements of ROS and antioxidant status from the study subjects, so we cannot analyze this as a possible link between genotype and disease. Another limitation of our study was that chronic complications will develop with longer follow-up. In most patients with type 2 diabetes, the highest incidence of microvascular disease occurs after 10 years. Control subjects in our study were without diabetic complications for a mean of 9.7 years after the onset of type 2 diabetes. Finally, given that long-term glycemia and duration of diabetes are major determinants of progression to nephropathy, our groups with and without diabetic nephropathy had similar mean A1C. However, possible periods of poor glycemic control in individual patients during the observation period could influence our results. In conclusion, we identified the G-allele of the TRIB3 gene as an independent risk factors for diabetic nephropathy. To the best of our knowledge, this is the first study reporting a positive association of G allele with development of diabetic kidney disease. Additionally, a combination of both TRIB3 AG + GG genotype and smoking showed synergistic effect on the development of diabetic nephropathy, supporting the idea that interplay between genetic and non-genetic risk determinants contributes to the development and progression of DN. Therefore, investigation of gene environment interactions in this way may improve targeting of therapy to diabetic patients who are more susceptible to kidney disease. Conflict of interests The authors declare that there is no conflict of interests that could be perceived as prejudicing the impartiality of the study. Acknowledgments This work was supported by the Shanghai Science and Technology Commission (10411956600), Natural Science Foundation of Shanghai (11ZR1405300), National Natural Science Foundation of China (81300667, 81000332, 81170322, 81370953, 81370935), Shanghai Health System Outstanding Young Talents Training Program (XYQ2013098), and Chinese Society of Endocrinology (201201). References Arora, M.K., Singh, U.K.1, 2014. Oxidative stress: meeting multiple targets in pathogenesis of diabetic nephropathy. Curr. Drug Targets 15, 531–538. Beguinot, F., 2010. Tribbles homologue 3 (TRIB3) and the insulin-resistance genes in type 2 diabetes. Diabetologia 53, 1831–1834.
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