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Association of the six transmembrane protein of prostate 2 gene polymorphisms with metabolic syndrome in Han Chinese population
Diabetes & Metabolic Syndrome: Clinical Research & Reviews 7 (2013) 138–142
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Diabetes & Metabolic Syndrome: Clinical Research & Reviews journal homepage: www.elsevier.com/locate/dsx
Original article
Association of the six transmembrane protein of prostate 2 gene polymorphisms with metabolic syndrome in Han Chinese population Wenchao Zhang a,1, Mengxiong Tang a,1, Ming Zhong a, Zhihao Wang a, Yuanyuan Shang a, Huiping Gong b, Yun Zhang a, Wei Zhang a,* a Key Laboratory of Cardiovascular Remodeling and Function Research Chinese Ministry of Education and Chinese Ministry of Public Health, Qilu Hospital of Shandong University, Jinan, Shandong 250012, PR China b Department of Cardiology, Second Hospital of Shandong University, Jinan, Shandong 250033, PR China
A R T I C L E I N F O
A B S T R A C T
Keywords: Genetic association study Han Chinese Metabolic syndrome STAMP2
Aim: The six-transmembrane protein of prostate 2 (STAMP2) has been demonstrated to play a potential role in the pathogenesis of metabolic syndrome (MetS). The present study was designed to investigate the association of STAMP2 gene polymorphisms with MetS in Han Chinese population. Methods: A case-control study enrolled 350 Han Chinese subjects in two groups: 182 MetS patients and 168 control subjects. The clinical and biochemical characteristics were determined. Three single nucleotide polymorphisms (SNPs), rs1981529, rs12386756 and rs10263111 in STAMP2 gene were genotyped. The association of STAMP2 gene polymorphisms with MetS was analyzed. Results: SNPs rs1981529 and rs10263111 were found to be significantly associated with MetS phenotype in male population (P = 0.014 and 0.025). Moreover, SNP rs1981529 was found to be associated with high density lipoprotein-cholesterol in male cases and with body mass index in female cases (P = 0.014 and 0.049). SNP rs10263111 was found to be associated with both waist circumference and diastolic blood pressure in total cases (P = 0.044 and 0.033). Haplotype analysis yielded significant association of STAMP2 gene with MetS in total (global P = 0.0109) and male population (global P = 0.0004). Conclusion: Our findings revealed that STAMP2 gene polymorphisms are likely to significantly contribute to the risk of MetS in male Han Chinese population. ß 2013 Diabetes India. Published by Elsevier Ltd. All rights reserved.
1. Introduction Metabolic syndrome (MetS) is a multifactorial disorder which powerfully contributes to the development of cardiovascular disease and type 2 diabetes mellitus. Although the etiology of MetS is complicated, numerous clinical and animal studies have implicated genetic susceptibility as an important risk factor for MetS, and identified a number of genetic variants and loci in association with MetS phenotype [1–3]. Another important advance in recent metabolic disease research has been the elucidation of links between inflammatory signaling pathways and glucose/lipid metabolism [4]. Six-transmembrane protein of prostate 2 (STAMP2) was reported to coordinate inflammatory responses with metabolic function in adipocytes and was essential for maintenance of systemic metabolic homeostasis [5]. STAMP2
* Corresponding author at: 107#, Wenhua Xi Road, Jinan, Shandong 250012, P.R. China. Tel.: +86 0531 82169339; fax: +86 0531 86169356. E-mail address: [email protected] (W. Zhang). 1 These authors contributed equally to this work
knockout mice exhibited inflammation in adipose tissue, and developed a clustering of abnormalities, including insulin resistance, glucose intolerance, mild hyperglycemia and dyslipidemia [5], a phenotype similar to the human metabolic syndrome. Although the role of STAMP2 in humans seemed to be different from that in mice, Arner et al. [6] confirmed that white adipose tissue STAMP2 was associated with obesity and insulin resistance in human beings. Data above strongly suggest that STAMP2 may play an influential role in the pathogenesis of MetS. Previous studies of our research team also confirmed the importance of STAMP2 in MetS, and revealed that STAMP2 expression was down-regulated in peripheral blood mononuclear cell in MetS patients, which was correlated with carotid atherosclerosis and cardiac adaptation [7]. However, the detailed relationship of STAMP2 and MetS is still unknown. Given the important role of genetic factors in MetS, it is possible that there is genetic association of STAMP2 with MetS, and whether STAMP2 genetic variants are involved in MetS needs to be determined. Exhilaratingly, the association of STAMP2 gene polymorphisms with MetS phenotype has just been performed in European Caucasians by Miot et al. [8] and in Uygur general
1871-4021/$ – see front matter ß 2013 Diabetes India. Published by Elsevier Ltd. All rights reserved. http://dx.doi.org/10.1016/j.dsx.2013.06.011
W. Zhang et al. / Diabetes & Metabolic Syndrome: Clinical Research & Reviews 7 (2013) 138–142
population by Li et al. [9]. Wangesteen et al. [10], in particular, studied the association of STAMP2 gene polymorphisms with obesity in European Caucasians. However, there was no data about the relationship of STAMP gene polymorphisms with Han Chinese MetS prevalence. Therefore, the present study was designed to investigate the association of STAMP2 gene polymorphisms with MetS phenotype in Han Chinese population.
2. Subjects, materials and methods 2.1. Study population A total of 350 unrelated Han Chinese subjects were recruited from Qilu Hospital of Shandong University: 182 case subjects with MetS, and 168 control subjects without abnormality. MetS was defined according to the International Diabetes Federation worldwide definition in 2005 [11], that is waist circumference at least 90 cm for men and 80 cm for women plus two of the following factors: (1) raised triglycerides (1.70 mmol/L or specific treatment for this lipid abnormality); (2) reduced high density lipoprotein-cholesterol (1.03 mmol/L for men and 1.29 mmol/L for women or specific treatment for this lipid abnormality); (3) elevated blood pressure (systolic blood pressure130 mmHg or diastolic blood pressure 80 mmHg or treatment of previously diagnosed hypertension; or (4) elevated fasting blood glucose at least 5.6 mmol/L or previously diagnosed type 2 diabetes. The study was conducted according to the declaration of Helsinki. All subjects signed a written informed consent before enrollment, and this study was approved by the institutional ethics committees of Qilu Hospital of Shandong University. 2.2. Clinical measurements The clinical and biochemical characteristics of the subjects were determined. Weight, height, waist and hip circumferences, and systolic and diastolic blood pressures were measured by trained personnel. Body mass index and waist-to-hip ratio were calculated from these data. Peripheral blood samples were collected in the morning after subjects had fasted for 12–14 h. Laboratory measurements including triglycerides, total cholesterol, high density lipoprotein-cholesterol, low density lipoprotein-cholesterol, fasting blood glucose, insulin, and uric acid were tested.
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Insulin resistance was assessed by the homeostasis model assessment equation [12]. 2.3. SNP selection and genotyping Three single nucleotide polymorphisms (SNPs), rs1981529, rs12386756 and rs10263111 with minor allele frequency of >5% were selected based on Pubmed data and previous studies. SNP rs1981529 is a missense variant (Gly-Asp) located in exon 2. rs12386756 and rs10263111 are SNPs located in introns. A polymerase chain reaction-restriction fragment length polymorphism method was used for genotyping. Genomic DNA was extracted from peripheral blood leukocytes using a modified phenol/chloroform method and stored at 80 8C until analyzed. PCR primers were designed for each SNP. Primer sequences were 50 -GAGGTTGTTGCTGATGTCTAC-30 and 50 -CCCAGAAGACCACCCTACT-30 for rs1981529, 50 -CACCTGGCCCCATATCAC-30 and 50 CAAACACGGCGGCTCACG-30 for rs12386756, 50 -CAAAGTATGGTTTCTCCTGAGTGTA-30 and 50 - GGACCCTGTTCCTC TGAC30 for rs10263111. In particular, a C to T replacement was introduced to create a digestion site for enzyme BstZ17I in the forward primer of rs10263111. PCR was carried out in a total volume of 25 mL (TaKaRa Taq HS 0.1 mL, 10 PCR Buffer 2.5 mL, dNTP mixture 1 mL, Forward Primer 0.5 mL, Reward Primer 0.5 mL, Genomic DNA 0.5 mL, ddH2O 19.9 mL). The thermal cycle was as follows: 95 8C for 5 min, 35 cycles of 95 8C for 30 s, 55 8C for 30 s, and 72 8C for 1 min, followed by a final extension step at 72 8C for 6 min. PCR products (3 mL) were visualized in an ethidium bromide (EtBr)-stained 3% agarose gel in 1 TAE buffer. Randomly selected PCR products were also subjected to gene sequence analysis to validate the PCR results. PCR products were then digested with restriction enzymes (New England Biolabs, Beijing, China) according to the instructions of the manufacturer. Digestion products were visualized by electrophoresis through an EtBr stained 4% agarose gel in 1 TAE buffer. 2.4. Statistical analysis The Kolmogorov–Smirnov test was used to test for normal distribution. Comparisons of the clinical and biochemical characteristics between case and control groups were analyzed using t test or nonparametric test whether distribution was normal or skewed. Hardy–Weinberg equilibrium was tested by x2 test with
Table 1 Clinical and biochemical characteristics of the study population. Total
Age (years) BMI (kg/m2) WC (cm) WHR SBP (mmHg) DBP (mmHg) TG (mmol/L) TC (mmol/L) HDL-C (mmol/L) LDL-C (mmol/L) UA (umol/L) FBG (mmol/L) Insulin (uU/mL) HOMA-IR Smoking (Yes/No)
Male
Female
Control (n = 168)
Case (n = 182)
P value
Control (n = 66)
Case (n = 81)
P value
Control (n = 102)
Case (n = 101)
P value
51.42 9.29 24.09 84.00 0.86 0.06 116.00 76.00 1.00 4.58 0.81 1.53 0.35 2.87 0.71 257.65 78.22 4.89 9.42 1.99 63/105
53.31 8.64 28.39 97.00 0.93 0.06 150.00 90.00 2.01 5.32 1.10 1.23 0.35 3.55 0.93 326.80 90.19 5.80 17.91 4.89 66/116
0.052 <0.001 <0.001 <0.001 <0.001 <0.001 <0.001 <0.001 <0.001 <0.001 <0.001 <0.001 <0.001 <0.001 0.811
51.23 9.72 24.77 88.00 0.89 0.04 116.85 9.37 77.89 6.35 1.08 0.40 4.54 0.76 1.46 0.36 2.88 0.68 306.95 74.17 5.04 10.24 4.66 1.86 63/3
49.74 9.76 28.81 100.00 0.96 0.05 149.66 23.11 97.59 13.45 2.42 1.15 5.13 1.12 1.11 0.23 3.35 0.93 376.25 92.31 5.67 22.07 13.11 5.30 65/16
0.366 <0.001 <0.001 <0.001 <0.001 <0.001 <0.001 <0.001 <0.001 0.001 <0.001 <0.001 <0.001 <0.001 0.006
51.54 9.05 23.87 2.89 81.22 8.08 0.83 0.05 116.00 75.00 0.98 4.61 0.83 1.57 0.34 2.86 0.73 227.68 64.43 4.735 9.88 2.03 0/102
56.12 6.42 28.40 3.45 94.44 8.56 0.90 0.06 150.00 90.00 1.91 5.47 1.07 1.33 0.38 3.70 0.90 289.59 68.33 5.80 16.80 4.55 1/100
<0.001 <0.001 <0.001 <0.001 <0.001 <0.001 <0.001 <0.001 <0.001 <0.001 <0.001 <0.001 <0.001 <0.001 0.498
BMI, body mass index; WC, waist circumference; WHR, waist-to-hip ratio; SBP, systolic blood pressure; DBP, diastolic blood pressure; TG, triglyceride; TC, total cholesterol; HDL-C, high density lipoprotein-cholesterol; LDL-C, low density lipoprotein-cholesterol; UA, uric acid; FBG, fasting blood glucose; HOMA-IR, homeostasis model assessment for insulin resistance. Data are mean SD for normal variables or are medians for skewed variables; P values were analyzed using t-test or nonparametric test.
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No association was observed for the SNPs in total and female groups. But SNPs rs1981529 and rs10263111 were found to be significantly associated with MetS phenotype in males (OR 4.143 [95% CI 1.329–12.918], P = 0.014 for rs1981529; 2.138 [1.098– 4.164], P = 0.025 for rs10263111).
2df. Using a co-dominant model, binary logistic regression analysis of risk factors was performed adjusting for sex, age and smoking, with ORs and 95% CIs shown. Genotype distribution for the SNPs in case and control subjects was compared using x2 test with 2df. The association between genotypes and continuous variables was tested by analysis of one-way ANOVA or nonparametric test. Linkage disequilibrium and haplotype frequencies were estimated by the SHEsis online software (http://analysis.bio-x.cn) [13]. A P value < 0.05 was considered significant (two-sided). Data analyses were performed by SPSS v. 17.0.
3.3. Association of STAMP2 gene polymorphisms with MetS components The association of genotypes with quantitative traits of MetS components was further analyzed in total, male and female MetS cases respectively. SNP rs1981529 was found to be associated with HDL-C in male cases and with BMI in female cases (P = 0.014 and 0.049) (Table 3(A)). SNP rs10263111 was found to be associated with both waist circumference and DBP in total cases (P = 0.044 and 0.033) (Table 3(B)). As is shown in Table 3, the median values of HDL-C level increased gradually among C allele carriers of rs1981529 in male cases and the mean values of BMI declined gradually in female cases. The values of waist circumference and DBP increased gradually among G allele carriers of rs10263111 in total cases. None of the MetS components revealed significant association with rs12386756 (P > 0.05 for all).
3. Results 3.1. Characteristics of the subjects The clinical and biochemical characteristics of the study subjects, 182 Han Chinese MetS cases and 168 Han Chinese controls, are shown in Table 1. Except age and smoking, all variables had significant differences between case and control subjects in total, male and female groups respectively (P < 0.001 for all). The three SNPs, rs1981529, rs12386756 and rs10263111 were successfully genotyped in all subjects. No departure from Hardy–Weinberg equilibrium was observed for the three investigated SNPs.
3.4. Haplotype analysis of STAMP2 gene polymorphisms The SNPs rs1981529, rs10263111 and rs12386756 showed slight linkage disequilibrium with each other (r2 = 0.403, 0.567 and 0.333). As is shown in Table 4, a total of five haplotypes were observed in our sample. The haplotype distribution was quite different between cases and controls in total (global P = 0.0109) and male population (global P = 0.0004). In total population, the frequencies of haplotypes (T-C-G) and (T-G-A) were significantly lower in MetS cases than that in controls (P = 0.0265 and 0.0107). In male population, the frequencies of haplotypes (C-G-G) and (TG-G) were significant higher in MetS cases than that in controls
3.2. Association of STAMP2 gene polymorphisms with MetS The genotype distribution of the three SNPs between case and control subjects was compared using x2 test. As is shown in Table 2, the genotype distribution of rs1981529 was different between cases and controls in male population (P = 0.021), and that of rs10263111 was different in both male (P = 0.047) and female populations (P = 0.048). We then explored the association of each SNP and MetS phenotype using binary logistic regression analysis of risk factors with adjustment for sex, age and smoking. Table 2 Genotype distribution between case and control subjects. SNPs
rs1981529
rs12386756
rs10263111
Genotype
TT TC CC AA AG GG CC CG GG
Total
Male
Female
Control
Case
P value
Control
Case
P value
Control
Case
P value
141 25 2 98 63 7 108 55 5
150 30 1 108 67 7 115 63 4
0.670
62 4 0 44 20 2 47 19 0
64 16 1 45 31 5 45 32 4
0.021
79 21 2 54 43 5 61 36 5
86 15 0 63 36 2 70 31 0
0.185
(83.9) (14.9) (1.2) (58.3) (37.5) (4.2) (64.3) (32.7) (3.0)
(82.4) (17.0) (0.5) (59.3) (36.8) (3.8) (63.2) (34.6) (2.2)
0.976
0.846
(93.9) (6.1) (0) (66.7) (30.3) (3.0) (71.2) (28.8) (0)
(79.0) (19.8) (1.2) (55.6) (38.3) (6.2) (55.6) (39.5) (4.9)
0.392
0.047
(77.5) (20.6) (2.0) (52.9) (42.2) (4.9) (29.8) (35.3) (4.9)
(85.1) (14.9) (0) (62.4) (35.6) (2.0) (69.3) (30.7) (0)
0.280
0.048
Data are n (%); P values were calculated by x2 test.
Table 3 Association of MetS components with SNPs. A. Association of MetS components with rs1981529. rs1981529 HDL-C (mmol/L) in male cases BMI (kg/m2) in female cases
TT 1.01(0.88–1.21) 28.59 3.61
TC 1.17(1.02–1.58) 27.25 1.94
CC 1.22 –
P value 0.014 0.049
B. Association of MetS components with rs10263111. rs10263111 WC (cm) in total cases DBP (mmHg) in total cases
CC 97.36 9.18 90.00(84.00–100.00)
CG 98.82 10.52 90.00(90.00–105.00)
GG 109.75 20.81 110.00(96.25–127.50)
BMI, body mass index; WC, waist circumference; DBP, diastolic blood pressure; HDL-C, high density lipoprotein-cholesterol. Data are mean SD for normal variables or are median (25–75%) for skewed variables; P values were analyzed using one-way ANOVA or nonparametric test.
P value 0.044 0.033
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Table 4 Haplotype frequencies of STAMP2 gene polymorphisms in case and control subjects. Haplotype
C-G-G T-C-A T-C-G T-G-A T-G-G
Total(P = 0.0109)
Male(P = 0.0004)
Female(P = 0.2765)
Control
Case
P value
OR [95% CI]
Control
Case
P value
OR [95% CI]
Control
Case
P value
OR [95% CI]
0.086 0.728 0.078 0.042 0.065
0.091 0.766 0.039 0.011 0.093
0.8397 0.2529 0.0265 0.0107 0.1685
1.055 1.220 0.478 0.262 1.480
0.030 0.747 0.109 0.071 0.043
0.111 0.721 0.032 0.026 0.110
0.0088 0.6181 0.0083 0.0645 0.0336
4.000 0.876 0.268 0.343 2.780
0.123 0.714 0.060 0.026 0.077
0.074 0.802 0.045 0.000 0.079
0.0851 0.1032 0.4342 – 0.9892
0.557 1.474 0.704 – 1.005
[0.626–1.780] [0.867–1.717] [0.246–0.929] [0.087–0.789] [0.845–2.592]
[1.319–12.127] [0.520–1.475] [0.095–0.755] [0.105–1.118] [1.047–7.383]
[0.284–1.092] [0.923–2.352] [0.291–1.702] [0.486–2.077]
The order of SNPs is rs1981529-rs10263111-rs12386756. Data are %. Frequencies and P values were calculated by SHEsis online software. Those with frequency < 0.03 in both control and case groups were ignored in analysis.
(P = 0.0088 and 0.0336), and the frequency of haplotype (T-C-G) in MetS cases was lower than that in controls (P = 0.0083). 4. Discussion In this article, we explored the association of STAMP2 gene polymorphisms with MetS in Han Chinese population, and revealed STAMP2 gene polymorphisms as risk factors for MetS in male Han Chinese. Furthermore, we also identified particular SNPs in STAMP2 gene that were associated with MetS related components. STAMP2, also known as TNF-induced adipose-related protein (TIARP) or six-transmembrane epithelial antigen of prostate 4 (STEAP4), belongs to a family termed STAMP or STEAP family [14– 16]. STAMP2 has been proved to be essential for maintenance of systemic metabolic homeostasis in animal models [5] and be associated with obesity and insulin resistance in human [6]. Moreover, previous findings of our research team also support the innegligible role of STAMP2 in MetS [7]. In order to further explore the association of STAMP2 with MetS, we investigated three STAMP2 gene polymorphisms (rs1981529, rs12386756 and rs10263111) in this study to determine whether STAMP2 contributes to MetS development at the genetic level. In this study, we found that SNPs rs1981529 and rs10263111 were significantly associated with MetS phenotype in male Han Chinese population. Logistic regression showed that the minor alleles of these two SNPs increased the risk of getting MetS for male Han Chinese. Why these polymorphisms were associated with MetS only in males is unclear. And interactions with sex have also been found in the studies of Miot et al. [8] and Li et al. [9]. In vitro studies have shown that the expression of STAMP2 was upregulated by androgens in human prostate cancer [15]. The different findings between males and females in our research might be related to the regulation by androgens. Moreover, interactions with environmental factors, in particular nutritional, have been shown. Men and women may eat different amount of nutriments, especially lipids. However, these are only speculations and need to be further determined. Miot et al. [8] have recently reported that in European Caucasians no significant association was found between STAMP2 SNPs, including rs12386756 and rs10263111 genotyped in our samples with the prevalence or incidence of MetS. In the research of Li et al. [9], they found that SNP rs1981529 was significantly associated with MetS in Uygur population. What’s different from ours is that they found the minor allele of rs1981529 was a protective factor for MetS in females. Therefore, some points from the present study are in agreement with those of the previous studies while others are not. The diverse may be related to the difference in genetic background between ethnic populations. Han Chinese people may have a different genetic background from Uygur population and European Caucasians [9]. What’s more, the difference of living environment and nutrition conditions may also lead to the different results.
Further analysis in our study revealed that the two SNPs, rs1981529 and rs10263111, were also associated with MetS components. SNP rs1981529 was found to be associated with HDLC in male cases and with BMI in female cases, and SNP rs10263111 was associated with both waist circumference and DBP in total cases. As is known, low HDL-C level is one of the MetS components and high HDL-C level is a known protective factor for MetS. Theoretically, HDL-C level should gradually decline among the risk allele carriers. However, in our study, HDL-C level gradually increased among C (risk factor) allele carriers of rs1981529 (TT < TC < CC) in male cases. Similar phenomenon was also found in the study of Li et al. [9]. They found that minor allele of SNP rs1981529 was a protective factor for MetS in female Uygur population, but HDL-C levels gradually declined among the protective allele carriers of rs1981529. The reason for this ambivalent phenomenon is unknown. Probably, it can be explained as the body’s fight against MetS. However, this needs to be further investigated. Haplotype analysis showed that four haplotypes, (rs1981529rs10263111-rs12386756, T-C-G), (T-G-A), (C-G-G) and (T-G-G) were associated with MetS phenotype. However, these hapoltypes only accounted for a small part (<30% taken together) of the total observed haplotypes, suggesting that they might not play an important role in MetS. In conclusion, our study revealed that STAMP2 gene polymorphisms were significantly associated with MetS phenotype and its risk components, and proved that STAMP2 gene polymorphisms were risk factors for MetS in male Han Chinese population.
Conflict of interest None declared. Acknowledgements This work was supported by the research grants from Key Technologies R&D Program of Shandong Province (2006GG2202020 and 2010G0020262), the Natural Science Foundation of Shandong Province (Y2005C11, Y2008C47, ZR2009CM022, ZR2009CM025 and BS2009YY026), the National Natural Science Foundation of China (30871038, 30971215, 81070192, 81070141 and 81100605) and the National Basic Research Program of China (973 Program, Grant No.: 2009CB521904).
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Original article
Association of the six transmembrane protein of prostate 2 gene polymorphisms with metabolic syndrome in Han Chinese population Wenchao Zhang a,1, Mengxiong Tang a,1, Ming Zhong a, Zhihao Wang a, Yuanyuan Shang a, Huiping Gong b, Yun Zhang a, Wei Zhang a,* a Key Laboratory of Cardiovascular Remodeling and Function Research Chinese Ministry of Education and Chinese Ministry of Public Health, Qilu Hospital of Shandong University, Jinan, Shandong 250012, PR China b Department of Cardiology, Second Hospital of Shandong University, Jinan, Shandong 250033, PR China
A R T I C L E I N F O
A B S T R A C T
Keywords: Genetic association study Han Chinese Metabolic syndrome STAMP2
Aim: The six-transmembrane protein of prostate 2 (STAMP2) has been demonstrated to play a potential role in the pathogenesis of metabolic syndrome (MetS). The present study was designed to investigate the association of STAMP2 gene polymorphisms with MetS in Han Chinese population. Methods: A case-control study enrolled 350 Han Chinese subjects in two groups: 182 MetS patients and 168 control subjects. The clinical and biochemical characteristics were determined. Three single nucleotide polymorphisms (SNPs), rs1981529, rs12386756 and rs10263111 in STAMP2 gene were genotyped. The association of STAMP2 gene polymorphisms with MetS was analyzed. Results: SNPs rs1981529 and rs10263111 were found to be significantly associated with MetS phenotype in male population (P = 0.014 and 0.025). Moreover, SNP rs1981529 was found to be associated with high density lipoprotein-cholesterol in male cases and with body mass index in female cases (P = 0.014 and 0.049). SNP rs10263111 was found to be associated with both waist circumference and diastolic blood pressure in total cases (P = 0.044 and 0.033). Haplotype analysis yielded significant association of STAMP2 gene with MetS in total (global P = 0.0109) and male population (global P = 0.0004). Conclusion: Our findings revealed that STAMP2 gene polymorphisms are likely to significantly contribute to the risk of MetS in male Han Chinese population. ß 2013 Diabetes India. Published by Elsevier Ltd. All rights reserved.
1. Introduction Metabolic syndrome (MetS) is a multifactorial disorder which powerfully contributes to the development of cardiovascular disease and type 2 diabetes mellitus. Although the etiology of MetS is complicated, numerous clinical and animal studies have implicated genetic susceptibility as an important risk factor for MetS, and identified a number of genetic variants and loci in association with MetS phenotype [1–3]. Another important advance in recent metabolic disease research has been the elucidation of links between inflammatory signaling pathways and glucose/lipid metabolism [4]. Six-transmembrane protein of prostate 2 (STAMP2) was reported to coordinate inflammatory responses with metabolic function in adipocytes and was essential for maintenance of systemic metabolic homeostasis [5]. STAMP2
* Corresponding author at: 107#, Wenhua Xi Road, Jinan, Shandong 250012, P.R. China. Tel.: +86 0531 82169339; fax: +86 0531 86169356. E-mail address: [email protected] (W. Zhang). 1 These authors contributed equally to this work
knockout mice exhibited inflammation in adipose tissue, and developed a clustering of abnormalities, including insulin resistance, glucose intolerance, mild hyperglycemia and dyslipidemia [5], a phenotype similar to the human metabolic syndrome. Although the role of STAMP2 in humans seemed to be different from that in mice, Arner et al. [6] confirmed that white adipose tissue STAMP2 was associated with obesity and insulin resistance in human beings. Data above strongly suggest that STAMP2 may play an influential role in the pathogenesis of MetS. Previous studies of our research team also confirmed the importance of STAMP2 in MetS, and revealed that STAMP2 expression was down-regulated in peripheral blood mononuclear cell in MetS patients, which was correlated with carotid atherosclerosis and cardiac adaptation [7]. However, the detailed relationship of STAMP2 and MetS is still unknown. Given the important role of genetic factors in MetS, it is possible that there is genetic association of STAMP2 with MetS, and whether STAMP2 genetic variants are involved in MetS needs to be determined. Exhilaratingly, the association of STAMP2 gene polymorphisms with MetS phenotype has just been performed in European Caucasians by Miot et al. [8] and in Uygur general
1871-4021/$ – see front matter ß 2013 Diabetes India. Published by Elsevier Ltd. All rights reserved. http://dx.doi.org/10.1016/j.dsx.2013.06.011
W. Zhang et al. / Diabetes & Metabolic Syndrome: Clinical Research & Reviews 7 (2013) 138–142
population by Li et al. [9]. Wangesteen et al. [10], in particular, studied the association of STAMP2 gene polymorphisms with obesity in European Caucasians. However, there was no data about the relationship of STAMP gene polymorphisms with Han Chinese MetS prevalence. Therefore, the present study was designed to investigate the association of STAMP2 gene polymorphisms with MetS phenotype in Han Chinese population.
2. Subjects, materials and methods 2.1. Study population A total of 350 unrelated Han Chinese subjects were recruited from Qilu Hospital of Shandong University: 182 case subjects with MetS, and 168 control subjects without abnormality. MetS was defined according to the International Diabetes Federation worldwide definition in 2005 [11], that is waist circumference at least 90 cm for men and 80 cm for women plus two of the following factors: (1) raised triglycerides (1.70 mmol/L or specific treatment for this lipid abnormality); (2) reduced high density lipoprotein-cholesterol (1.03 mmol/L for men and 1.29 mmol/L for women or specific treatment for this lipid abnormality); (3) elevated blood pressure (systolic blood pressure130 mmHg or diastolic blood pressure 80 mmHg or treatment of previously diagnosed hypertension; or (4) elevated fasting blood glucose at least 5.6 mmol/L or previously diagnosed type 2 diabetes. The study was conducted according to the declaration of Helsinki. All subjects signed a written informed consent before enrollment, and this study was approved by the institutional ethics committees of Qilu Hospital of Shandong University. 2.2. Clinical measurements The clinical and biochemical characteristics of the subjects were determined. Weight, height, waist and hip circumferences, and systolic and diastolic blood pressures were measured by trained personnel. Body mass index and waist-to-hip ratio were calculated from these data. Peripheral blood samples were collected in the morning after subjects had fasted for 12–14 h. Laboratory measurements including triglycerides, total cholesterol, high density lipoprotein-cholesterol, low density lipoprotein-cholesterol, fasting blood glucose, insulin, and uric acid were tested.
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Insulin resistance was assessed by the homeostasis model assessment equation [12]. 2.3. SNP selection and genotyping Three single nucleotide polymorphisms (SNPs), rs1981529, rs12386756 and rs10263111 with minor allele frequency of >5% were selected based on Pubmed data and previous studies. SNP rs1981529 is a missense variant (Gly-Asp) located in exon 2. rs12386756 and rs10263111 are SNPs located in introns. A polymerase chain reaction-restriction fragment length polymorphism method was used for genotyping. Genomic DNA was extracted from peripheral blood leukocytes using a modified phenol/chloroform method and stored at 80 8C until analyzed. PCR primers were designed for each SNP. Primer sequences were 50 -GAGGTTGTTGCTGATGTCTAC-30 and 50 -CCCAGAAGACCACCCTACT-30 for rs1981529, 50 -CACCTGGCCCCATATCAC-30 and 50 CAAACACGGCGGCTCACG-30 for rs12386756, 50 -CAAAGTATGGTTTCTCCTGAGTGTA-30 and 50 - GGACCCTGTTCCTC TGAC30 for rs10263111. In particular, a C to T replacement was introduced to create a digestion site for enzyme BstZ17I in the forward primer of rs10263111. PCR was carried out in a total volume of 25 mL (TaKaRa Taq HS 0.1 mL, 10 PCR Buffer 2.5 mL, dNTP mixture 1 mL, Forward Primer 0.5 mL, Reward Primer 0.5 mL, Genomic DNA 0.5 mL, ddH2O 19.9 mL). The thermal cycle was as follows: 95 8C for 5 min, 35 cycles of 95 8C for 30 s, 55 8C for 30 s, and 72 8C for 1 min, followed by a final extension step at 72 8C for 6 min. PCR products (3 mL) were visualized in an ethidium bromide (EtBr)-stained 3% agarose gel in 1 TAE buffer. Randomly selected PCR products were also subjected to gene sequence analysis to validate the PCR results. PCR products were then digested with restriction enzymes (New England Biolabs, Beijing, China) according to the instructions of the manufacturer. Digestion products were visualized by electrophoresis through an EtBr stained 4% agarose gel in 1 TAE buffer. 2.4. Statistical analysis The Kolmogorov–Smirnov test was used to test for normal distribution. Comparisons of the clinical and biochemical characteristics between case and control groups were analyzed using t test or nonparametric test whether distribution was normal or skewed. Hardy–Weinberg equilibrium was tested by x2 test with
Table 1 Clinical and biochemical characteristics of the study population. Total
Age (years) BMI (kg/m2) WC (cm) WHR SBP (mmHg) DBP (mmHg) TG (mmol/L) TC (mmol/L) HDL-C (mmol/L) LDL-C (mmol/L) UA (umol/L) FBG (mmol/L) Insulin (uU/mL) HOMA-IR Smoking (Yes/No)
Male
Female
Control (n = 168)
Case (n = 182)
P value
Control (n = 66)
Case (n = 81)
P value
Control (n = 102)
Case (n = 101)
P value
51.42 9.29 24.09 84.00 0.86 0.06 116.00 76.00 1.00 4.58 0.81 1.53 0.35 2.87 0.71 257.65 78.22 4.89 9.42 1.99 63/105
53.31 8.64 28.39 97.00 0.93 0.06 150.00 90.00 2.01 5.32 1.10 1.23 0.35 3.55 0.93 326.80 90.19 5.80 17.91 4.89 66/116
0.052 <0.001 <0.001 <0.001 <0.001 <0.001 <0.001 <0.001 <0.001 <0.001 <0.001 <0.001 <0.001 <0.001 0.811
51.23 9.72 24.77 88.00 0.89 0.04 116.85 9.37 77.89 6.35 1.08 0.40 4.54 0.76 1.46 0.36 2.88 0.68 306.95 74.17 5.04 10.24 4.66 1.86 63/3
49.74 9.76 28.81 100.00 0.96 0.05 149.66 23.11 97.59 13.45 2.42 1.15 5.13 1.12 1.11 0.23 3.35 0.93 376.25 92.31 5.67 22.07 13.11 5.30 65/16
0.366 <0.001 <0.001 <0.001 <0.001 <0.001 <0.001 <0.001 <0.001 0.001 <0.001 <0.001 <0.001 <0.001 0.006
51.54 9.05 23.87 2.89 81.22 8.08 0.83 0.05 116.00 75.00 0.98 4.61 0.83 1.57 0.34 2.86 0.73 227.68 64.43 4.735 9.88 2.03 0/102
56.12 6.42 28.40 3.45 94.44 8.56 0.90 0.06 150.00 90.00 1.91 5.47 1.07 1.33 0.38 3.70 0.90 289.59 68.33 5.80 16.80 4.55 1/100
<0.001 <0.001 <0.001 <0.001 <0.001 <0.001 <0.001 <0.001 <0.001 <0.001 <0.001 <0.001 <0.001 <0.001 0.498
BMI, body mass index; WC, waist circumference; WHR, waist-to-hip ratio; SBP, systolic blood pressure; DBP, diastolic blood pressure; TG, triglyceride; TC, total cholesterol; HDL-C, high density lipoprotein-cholesterol; LDL-C, low density lipoprotein-cholesterol; UA, uric acid; FBG, fasting blood glucose; HOMA-IR, homeostasis model assessment for insulin resistance. Data are mean SD for normal variables or are medians for skewed variables; P values were analyzed using t-test or nonparametric test.
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No association was observed for the SNPs in total and female groups. But SNPs rs1981529 and rs10263111 were found to be significantly associated with MetS phenotype in males (OR 4.143 [95% CI 1.329–12.918], P = 0.014 for rs1981529; 2.138 [1.098– 4.164], P = 0.025 for rs10263111).
2df. Using a co-dominant model, binary logistic regression analysis of risk factors was performed adjusting for sex, age and smoking, with ORs and 95% CIs shown. Genotype distribution for the SNPs in case and control subjects was compared using x2 test with 2df. The association between genotypes and continuous variables was tested by analysis of one-way ANOVA or nonparametric test. Linkage disequilibrium and haplotype frequencies were estimated by the SHEsis online software (http://analysis.bio-x.cn) [13]. A P value < 0.05 was considered significant (two-sided). Data analyses were performed by SPSS v. 17.0.
3.3. Association of STAMP2 gene polymorphisms with MetS components The association of genotypes with quantitative traits of MetS components was further analyzed in total, male and female MetS cases respectively. SNP rs1981529 was found to be associated with HDL-C in male cases and with BMI in female cases (P = 0.014 and 0.049) (Table 3(A)). SNP rs10263111 was found to be associated with both waist circumference and DBP in total cases (P = 0.044 and 0.033) (Table 3(B)). As is shown in Table 3, the median values of HDL-C level increased gradually among C allele carriers of rs1981529 in male cases and the mean values of BMI declined gradually in female cases. The values of waist circumference and DBP increased gradually among G allele carriers of rs10263111 in total cases. None of the MetS components revealed significant association with rs12386756 (P > 0.05 for all).
3. Results 3.1. Characteristics of the subjects The clinical and biochemical characteristics of the study subjects, 182 Han Chinese MetS cases and 168 Han Chinese controls, are shown in Table 1. Except age and smoking, all variables had significant differences between case and control subjects in total, male and female groups respectively (P < 0.001 for all). The three SNPs, rs1981529, rs12386756 and rs10263111 were successfully genotyped in all subjects. No departure from Hardy–Weinberg equilibrium was observed for the three investigated SNPs.
3.4. Haplotype analysis of STAMP2 gene polymorphisms The SNPs rs1981529, rs10263111 and rs12386756 showed slight linkage disequilibrium with each other (r2 = 0.403, 0.567 and 0.333). As is shown in Table 4, a total of five haplotypes were observed in our sample. The haplotype distribution was quite different between cases and controls in total (global P = 0.0109) and male population (global P = 0.0004). In total population, the frequencies of haplotypes (T-C-G) and (T-G-A) were significantly lower in MetS cases than that in controls (P = 0.0265 and 0.0107). In male population, the frequencies of haplotypes (C-G-G) and (TG-G) were significant higher in MetS cases than that in controls
3.2. Association of STAMP2 gene polymorphisms with MetS The genotype distribution of the three SNPs between case and control subjects was compared using x2 test. As is shown in Table 2, the genotype distribution of rs1981529 was different between cases and controls in male population (P = 0.021), and that of rs10263111 was different in both male (P = 0.047) and female populations (P = 0.048). We then explored the association of each SNP and MetS phenotype using binary logistic regression analysis of risk factors with adjustment for sex, age and smoking. Table 2 Genotype distribution between case and control subjects. SNPs
rs1981529
rs12386756
rs10263111
Genotype
TT TC CC AA AG GG CC CG GG
Total
Male
Female
Control
Case
P value
Control
Case
P value
Control
Case
P value
141 25 2 98 63 7 108 55 5
150 30 1 108 67 7 115 63 4
0.670
62 4 0 44 20 2 47 19 0
64 16 1 45 31 5 45 32 4
0.021
79 21 2 54 43 5 61 36 5
86 15 0 63 36 2 70 31 0
0.185
(83.9) (14.9) (1.2) (58.3) (37.5) (4.2) (64.3) (32.7) (3.0)
(82.4) (17.0) (0.5) (59.3) (36.8) (3.8) (63.2) (34.6) (2.2)
0.976
0.846
(93.9) (6.1) (0) (66.7) (30.3) (3.0) (71.2) (28.8) (0)
(79.0) (19.8) (1.2) (55.6) (38.3) (6.2) (55.6) (39.5) (4.9)
0.392
0.047
(77.5) (20.6) (2.0) (52.9) (42.2) (4.9) (29.8) (35.3) (4.9)
(85.1) (14.9) (0) (62.4) (35.6) (2.0) (69.3) (30.7) (0)
0.280
0.048
Data are n (%); P values were calculated by x2 test.
Table 3 Association of MetS components with SNPs. A. Association of MetS components with rs1981529. rs1981529 HDL-C (mmol/L) in male cases BMI (kg/m2) in female cases
TT 1.01(0.88–1.21) 28.59 3.61
TC 1.17(1.02–1.58) 27.25 1.94
CC 1.22 –
P value 0.014 0.049
B. Association of MetS components with rs10263111. rs10263111 WC (cm) in total cases DBP (mmHg) in total cases
CC 97.36 9.18 90.00(84.00–100.00)
CG 98.82 10.52 90.00(90.00–105.00)
GG 109.75 20.81 110.00(96.25–127.50)
BMI, body mass index; WC, waist circumference; DBP, diastolic blood pressure; HDL-C, high density lipoprotein-cholesterol. Data are mean SD for normal variables or are median (25–75%) for skewed variables; P values were analyzed using one-way ANOVA or nonparametric test.
P value 0.044 0.033
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Table 4 Haplotype frequencies of STAMP2 gene polymorphisms in case and control subjects. Haplotype
C-G-G T-C-A T-C-G T-G-A T-G-G
Total(P = 0.0109)
Male(P = 0.0004)
Female(P = 0.2765)
Control
Case
P value
OR [95% CI]
Control
Case
P value
OR [95% CI]
Control
Case
P value
OR [95% CI]
0.086 0.728 0.078 0.042 0.065
0.091 0.766 0.039 0.011 0.093
0.8397 0.2529 0.0265 0.0107 0.1685
1.055 1.220 0.478 0.262 1.480
0.030 0.747 0.109 0.071 0.043
0.111 0.721 0.032 0.026 0.110
0.0088 0.6181 0.0083 0.0645 0.0336
4.000 0.876 0.268 0.343 2.780
0.123 0.714 0.060 0.026 0.077
0.074 0.802 0.045 0.000 0.079
0.0851 0.1032 0.4342 – 0.9892
0.557 1.474 0.704 – 1.005
[0.626–1.780] [0.867–1.717] [0.246–0.929] [0.087–0.789] [0.845–2.592]
[1.319–12.127] [0.520–1.475] [0.095–0.755] [0.105–1.118] [1.047–7.383]
[0.284–1.092] [0.923–2.352] [0.291–1.702] [0.486–2.077]
The order of SNPs is rs1981529-rs10263111-rs12386756. Data are %. Frequencies and P values were calculated by SHEsis online software. Those with frequency < 0.03 in both control and case groups were ignored in analysis.
(P = 0.0088 and 0.0336), and the frequency of haplotype (T-C-G) in MetS cases was lower than that in controls (P = 0.0083). 4. Discussion In this article, we explored the association of STAMP2 gene polymorphisms with MetS in Han Chinese population, and revealed STAMP2 gene polymorphisms as risk factors for MetS in male Han Chinese. Furthermore, we also identified particular SNPs in STAMP2 gene that were associated with MetS related components. STAMP2, also known as TNF-induced adipose-related protein (TIARP) or six-transmembrane epithelial antigen of prostate 4 (STEAP4), belongs to a family termed STAMP or STEAP family [14– 16]. STAMP2 has been proved to be essential for maintenance of systemic metabolic homeostasis in animal models [5] and be associated with obesity and insulin resistance in human [6]. Moreover, previous findings of our research team also support the innegligible role of STAMP2 in MetS [7]. In order to further explore the association of STAMP2 with MetS, we investigated three STAMP2 gene polymorphisms (rs1981529, rs12386756 and rs10263111) in this study to determine whether STAMP2 contributes to MetS development at the genetic level. In this study, we found that SNPs rs1981529 and rs10263111 were significantly associated with MetS phenotype in male Han Chinese population. Logistic regression showed that the minor alleles of these two SNPs increased the risk of getting MetS for male Han Chinese. Why these polymorphisms were associated with MetS only in males is unclear. And interactions with sex have also been found in the studies of Miot et al. [8] and Li et al. [9]. In vitro studies have shown that the expression of STAMP2 was upregulated by androgens in human prostate cancer [15]. The different findings between males and females in our research might be related to the regulation by androgens. Moreover, interactions with environmental factors, in particular nutritional, have been shown. Men and women may eat different amount of nutriments, especially lipids. However, these are only speculations and need to be further determined. Miot et al. [8] have recently reported that in European Caucasians no significant association was found between STAMP2 SNPs, including rs12386756 and rs10263111 genotyped in our samples with the prevalence or incidence of MetS. In the research of Li et al. [9], they found that SNP rs1981529 was significantly associated with MetS in Uygur population. What’s different from ours is that they found the minor allele of rs1981529 was a protective factor for MetS in females. Therefore, some points from the present study are in agreement with those of the previous studies while others are not. The diverse may be related to the difference in genetic background between ethnic populations. Han Chinese people may have a different genetic background from Uygur population and European Caucasians [9]. What’s more, the difference of living environment and nutrition conditions may also lead to the different results.
Further analysis in our study revealed that the two SNPs, rs1981529 and rs10263111, were also associated with MetS components. SNP rs1981529 was found to be associated with HDLC in male cases and with BMI in female cases, and SNP rs10263111 was associated with both waist circumference and DBP in total cases. As is known, low HDL-C level is one of the MetS components and high HDL-C level is a known protective factor for MetS. Theoretically, HDL-C level should gradually decline among the risk allele carriers. However, in our study, HDL-C level gradually increased among C (risk factor) allele carriers of rs1981529 (TT < TC < CC) in male cases. Similar phenomenon was also found in the study of Li et al. [9]. They found that minor allele of SNP rs1981529 was a protective factor for MetS in female Uygur population, but HDL-C levels gradually declined among the protective allele carriers of rs1981529. The reason for this ambivalent phenomenon is unknown. Probably, it can be explained as the body’s fight against MetS. However, this needs to be further investigated. Haplotype analysis showed that four haplotypes, (rs1981529rs10263111-rs12386756, T-C-G), (T-G-A), (C-G-G) and (T-G-G) were associated with MetS phenotype. However, these hapoltypes only accounted for a small part (<30% taken together) of the total observed haplotypes, suggesting that they might not play an important role in MetS. In conclusion, our study revealed that STAMP2 gene polymorphisms were significantly associated with MetS phenotype and its risk components, and proved that STAMP2 gene polymorphisms were risk factors for MetS in male Han Chinese population.
Conflict of interest None declared. Acknowledgements This work was supported by the research grants from Key Technologies R&D Program of Shandong Province (2006GG2202020 and 2010G0020262), the Natural Science Foundation of Shandong Province (Y2005C11, Y2008C47, ZR2009CM022, ZR2009CM025 and BS2009YY026), the National Natural Science Foundation of China (30871038, 30971215, 81070192, 81070141 and 81100605) and the National Basic Research Program of China (973 Program, Grant No.: 2009CB521904).
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