- Email: [email protected]
Genetic variants of human urea transporter-2 are associated with metabolic syndrome in Asian population
Clinica Chimica Acta 411 (2010) 2009–2013
Contents lists available at ScienceDirect
Clinica Chimica Acta j o u r n a l h o m e p a g e : w w w. e l s ev i e r. c o m / l o c a t e / c l i n c h i m
Genetic variants of human urea transporter-2 are associated with metabolic syndrome in Asian population Hui-Ju Tsai a,b,1, Chin-Fu Hsiao a,1, Low-Tone Ho c,d,e, Lee-Ming Chuang f,g, Chih-Tsueng He h, J. David Curb i, Thomas Quertermos j, Chao A. Hsiung a,⁎, Wayne H.-H. Sheu k,l,m,⁎ a
Division of Biostatistics and Bioinformatics, Institute of Population Health Sciences, National Health Research Institutes, Zhunan, Taiwan Department of Pediatrics, Feinberg School of Medicine, Northwestern University, Chicago, IL, USA Department of Medical Research and Education, Taipei Veterans General Hospital, Taipei, Taiwan d Section of Endocrinology and Metabolism, Department of Medicine, Taipei Veterans General Hospital, Taipei, Taiwan e Faculty of Medicine, School of Medicine, National Yang-Ming University, Taipei, Taiwan f Department of Internal Medicine, National Taiwan University Hospital, Taipei, Taiwan g Graduate Institute of Clinical Medicine, National Taiwan University, Taipei, Taiwan h Division of Endocrinology and Metabolism, Tri-Service General Hospital, National Defense Medical Center, Taipei, Taiwan i John A. Burns School of Medicine, University of Hawaii, Honolulu, HI, USA j Division of Cardiovascular Medicine, Falk Cardiovascular Research Building, Stanford University School of Medicine, Stanford, CA, USA k Division of Endocrinology and Metabolism, Department of Internal Medicine, Taichung Veterans General Hospital, Taichung, Taiwan l School of Medicine, National Yang-Ming University, Taipei, Taiwan m School of Medicine, Chung-Shan Medical University, Taichung, Taiwan b c
a r t i c l e
i n f o
Article history: Received 14 June 2010 Received in revised form 16 August 2010 Accepted 17 August 2010 Available online 23 August 2010 Keywords: Human urea transporter-2 Metabolic syndrome Asian
a b s t r a c t Background: A previous study has reported that the Ile227 and Ala357 genetic variants of human urea transporter-2 (HUT2) were associated with blood pressure in males in Asian population. In this study, we aimed to investigate five known HUT2 genetic variants with metabolic syndrome (MetS) and its related traits in the Stanford Asia-Pacific Program for Hypertension and Insulin Resistance (SAPPHIRe) study cohort. Methods: Five HUT2 single nucleotide polymorphisms (SNPs) were selected and genotyped among 1791 subjects in the SAPPHIRe study cohort. We first computed allele frequency and performed Hardy–Weinberg equilibrium (HWE) test in controls for each SNP. Next, we tested genotype associations with metabolic syndrome using multiple generalized estimating equations (GEE) models with covariate adjustment. Furthermore, multi-marker and multi-trait association tests were carried out using FBAT program. To account for multiple testing, Bonferroni correction was applied in this study. Results: Among those 5 HUT2 SNPs, SNPs 1, 2 and 3 were significantly associated with MetS in the total sample and females, separately (9 × 10−4 ≤ p ≤ 0.04), but only the association between SNP 1 and MetS in females remained statistically significant after Bonferroni correction. When testing 5 SNPs simultaneously, significant associations were found with triglycerides (TG) (p = 0.04). Likewise, significant multi-trait association (combining the data of waist circumference, TG, high density lipoprotein (HDL) cholesterol and fasting glucose together) was found with SNP 2 (p = 0.04), but both results of multi-maker and multi-trait associations did not remain significant after multiple testing correction. Conclusion: The results have provided evidence that the HUT2 gene may play a certain role in developing MetS and its related traits in Asian population. Further investigation of the HUT2 gene influencing MetS and its related traits will be warranted. © 2010 Elsevier B.V. All rights reserved.
1. Introduction
⁎ Corresponding authors. Hsiung is to be contacted at Division of Biostatistics and Bioinformatics, Institute of Population Health Sciences, National Health Research Institutes, Zhunan, Taiwan. Sheu, Division of Endocrinology and Metabolism, Department of Internal Medicine, Taichung Veterans General Hospital, Taichung, Taiwan. E-mail addresses: [email protected] (C.A. Hsiung), [email protected] (W.H.-H. Sheu). 0009-8981/$ – see front matter © 2010 Elsevier B.V. All rights reserved. doi:10.1016/j.cca.2010.08.025
Metabolic syndrome (MetS) is a cluster of metabolic abnormalities including central obesity, hypertension, hyperglycemia and dyslipidemia [1]. MetS is a growing clinical and public health problem in the United States and worldwide [2]. It has been increasing over the past few decades. Importantly, MetS has been associated with increased risk of developing cardiovascular disease (CVD), stroke, and type 2 diabetes [3]. It has been well recognized that the development of MetS
2010
H.-J. Tsai et al. / Clinica Chimica Acta 411 (2010) 2009–2013
is influenced by genetic factors, environmental factors and lifestyle changes [4]. However, the etiology of MetS remains largely unclear. Human urea transporter-2 (HUT2), the human homolog of UT-A2 in the rodent model [5], is involved in the urea permeability which might affect the kidney's regulatory ability to fluid volume. Furthermore, the regulatory mechanisms may play a critical role in disease states, for instance, diabetes and blood pressure, because the abundance of HUT2 is regulated by glucocorticoids, mineralocorticoids and vasopressin [6]. In addition, a previous study has reported that the Ile227 and Ala357 variants of the HUT2 gene were associated with blood pressure in men [7]. However, the genetic effect of HUT2 on MetS has not been examined yet. In this study, we genotyped five known HUT2 single nucleotide polymorphisms (SNPs), which were selected based on their informativeness and potential biological function, in the Stanford Asia-Pacific Program for Hypertension and Insulin Resistance (SAPPHIRe) study cohort. We then tested genotype associations of HUT2 genetic variants with MetS after adjusting covariates. To control for potential confounding due to population stratification, we used a familybased study sample with a total of 1791 Asian subjects recruited from 533 families participating in the SAPPHIRe study. Our results indicated that HUT2 genetic variants may play an important role in MetS and its related traits in Asian population. 2. Materials and methods 2.1. Subject recruitment In this study, we included a total of 1791 subjects from 533 families of Chinese and Japanese participating in the SAPPHIRe study, which is part of the Family Blood Pressure Program (FBPP). Detailed description of the study sites, subject recruitment and protocols of data collection has been described previously [8]. The study protocol was approved by the institutional review board (IRB) at all participating sites. Written informed consent was explained to and signed by each subject. 2.2. Definition of MetS Definition of MetS was based on clinical criteria from a recent joint interim statement of the International Diabetes Federation (IDF); National Heart, Lung and Blood Institute (NHLBI); American Heart Association (AHA); World Heart Federation; International Atherosclerosis Society; and International Association for the Study of Obesity [9]. In summary, a MetS case should meet any three of the following five conditions: 1) waist circumference (WC) ≥ 90 cm in males, ≥80 cm in females; 2) triglycerides (TG) ≥ 150 mg/dL in both gender; 3) high density lipoprotein (HDL) cholesterol b40 mg/dL in males, b50 mg/dL in females; 4) systolic blood pressure (SBP) ≥ 130 mmHg or diastolic blood pressure (DBP) ≥ 85 mmHg in both gender (whereas antihypertensive drug treatment in a patient with a history of hypertension is an alternate indicator); 5) fasting glucose ≥ 100 mg/dL in both gender (whereas drug treatment of elevated glucose is an alternate indicator). 2.3. SNPs selection and genotyping We genotyped five previously identified HUT2 genetic variants, which were selected based on their informativeness, potential biological function and the LD pattern across HUT2 SNPs. In short, we did not include SNPs with minor allele frequency (MAF) b 5%. If complete LD (r2 = 1) existed among two or more SNPs on the HUT2 gene, only one SNP was picked and genotyped. As a result, we selected 5 HUT2 SNPs for genotyping. The detailed information of each genotyped SNP was provided in Supplemental Table 1.
For polymerase chain reaction (PCR) conditions, genotyping was carried out using the 5′ nuclease detection assay or TaqMan to detect the HUT2 genetic variants [10,11]. The sequences of the primers and probes used in the TaqMan assay, and PCR conditions were described previously [7]. After PCR, fluorescence was read by an ABI 7700 machine. 2.4. Statistical analysis 2.4.1. Allele frequencies and Hardy–Weinberg equilibrium Allele frequencies of five HUT2 SNPs were computed using genotype data of controls (all control subjects, males and females, separately). We performed χ2 tests to examine whether there was a difference between males and females. Next, we examined Hardy– Weinberg equilibrium (HWE) test in controls using χ2 tests. In this study, all the genotyped SNPs were under HWE (Supplemental Table 1). 2.4.2. Pair-wise linkage disequilibrium (LD) estimation For 5 genotyped HUT2 SNPs, we computed pair-wise linkage disequilibrium (LD) in controls using r2 measure, which was calculated using an iterative expectation–maximization (EM) algorithm implemented in the Haploview software [12]. 2.4.3. Association tests of demographic and genotype data The primary outcome was MetS, and the secondary outcomes were continuous variables related with MetS, including WC, TG, HDL cholesterol and fasting glucose. Student's t-test and χ2 test were performed to compare the distribution's difference of continuous variables and categorical variables between males and females, respectively. Next, to account for the dependency among family members, we applied multiple generalized estimating equations (GEE) models to estimate genetic effect of each SNP on MetS. Specifically, in this study, the GEE models treated each family as a unit and were performed with the adjustment of age, ethnicity, education, smoking status and alcohol intake under additive, dominant and recessive models, individually. All the GEE analyses were performed using SAS version 9.1.3 (SAS institute, Cary, NC). 2.4.4. Multi-marker and multi-trait association tests Furthermore, to examine 5 SNPs simultaneously, we applied a linear combination test implemented in Family-Based Association Tests (FBAT) program [13]. This analytical approach was to form a linear combination of the individual Z statistics computed for each marker [14]. We investigated multi-marker association with MetS related continuous traits (WC, SBP, DBP, TG, HDL cholesterol and fasting glucose) using this linear combination test. In addition, since those MetS related traits shared a certain degree of correlation, we tested those MetS related traits simultaneously using a multi-trait test implemented in the FBAT program. Similarly, the statistics computes a linear combination test for multiple traits, that is, combines each individual test statistics across multiple traits for a single SNP. 2.4.5. Multiple testing correction False positive results have been a critical concern when a number of association tests are performed. To address this issue, we applied Bonferroni correction, which is known as the most stringent approach, to correct for multiple testing. 3. Results 3.1. Clinical features and genotyping characteristics Clinical characteristics of 1791 subjects (803 males and 988 females, individually) were summarized in Table 1a. In general, males had significant higher values of body mass index (BMI), WC, TG, total
H.-J. Tsai et al. / Clinica Chimica Acta 411 (2010) 2009–2013
2011
Table 1a Clinical features of study participants. Mean ± std
All
Males
Females
pa
Number (Chinese/Japanese) Age (years) BMI (kg/m2) Waist circumference (cm) Total cholesterol (mg/dL) LDL-cholesterol (mg/dL) HDL cholesterol (mg/dL) Triglyceride Fasting glucose (mg/dL) Fasting insulin (uU/ml) HOMA insulin resistance
1791 (1309/482) 51.0 ± 8.9 25.7 ± 3.8 86.2 ± 11.1 193.0 ± 38.6 120.5 ± 36.1 45.1 ± 12.8 142.0 ± 90.5 95.0 ± 19.5 7.8 ± 5.4 1.9 ± 1.6
803 (609/194) 50.6 ± 8.8 26.2 ± 3.5 90.7 ± 9.1 189.2 ± 36.3 119.5 ± 34.9 39.9 ± 10.2 155.3 ± 96.0 96.1 ± 19.0 8.0 ± 5.7 2.0 ± 1.5
988 (700/288) 51.2 ± 9.0 25.3 ± 4.0 82.5 ± 11.2 196.1 ± 40.2 121.2 ± 37.1 49.3 ± 13.2 131.2 ± 84.3 94.1 ± 19.9 7.6 ± 5.2 1.9 ± 1.6
0.22 b10−4 b10−4 2 × 10−3 0.86 b10−4 b10−4 0.04 0.09 0.11
a
Student t-tests were performed to compare the difference in distribution of continuous variables between males and females, respectively.
cholesterol and fasting glucose (Table 1a) and had a higher percentage of MetS and hypertension than females (Table 1b). On the other hand, females had a significant higher value of HDL cholesterol than males (Table 1a). Five SNPs of the HUT2 gene were genotyped in 1791 subjects. The minor allele frequency (MAF) of each SNP was provided in Supplemental Table 1. Using p b 0.05 as the statistical significance cutoff, none of the 5 HUT2 SNPs was out of HWE. Additionally, pairwise LD patterns in controls were calculated and presented in Supplemental Fig. 1.
Table 1b Number of subjects with MetS and meeting each criterion of MetS. N (%)
Males
Females
pa
MetS High blood pressure High waist circumference Hypertriglyceridemia Low HDL cholesterol High fasting glucose Hypertension
419 (52.2%) 463 (57.7%) 414(51.56%) 334 (41.6%) 445 (55.4%) 239 (29.8%) 542 (67.5%)
442 (44.7%) 469 (47.5%) 547(55.36%) 285 (28.9%) 551 (55.8%) 220 (22.3%) 587 (59.4%)
2 × 10−3 b 10−4 0.108 b 10−4 0.88 3 × 10−4 4 × 10−4
3.2. Single marker association
a χ2 tests were performed to compare the difference in categorical variables between males and females, respectively.
We performed association tests of the five HUT2 genetic variants with MetS using GEE models in the total study sample, males and
Table 2 Genotype association of each SNP in the HUT2 gene with MetS. SNP
Genetic modela (Nb)
Gender
1
GG (492) GC or CC (838) GG (218) GC or CC (373) GG (274) GC or CC (465) CC (572) CT or TT (744) CC (254) CT or TT (332) CC (318) CT or TT (412) CC (481) CT or TT (837) CC (213) CT or TT (373) CC (268) CT or TT (464) GG (862) GA or AA (783) GG (398) GA or AA (352) GG (464) GA or AA (431) GG (615) GA or AA (1024) GG (278) GA or AA (469) GG (337) GA or AA (555)
M+F
2
3
5
7
a
M F M+F M F M+F M F M+F M F M+F M F
Adjusted modelc
Unadjusted OR (95% CI)
p
OR (95% CI)
p
Ref 0.8 (0.6, Ref 1.0 (0.7, Ref 0.7 (0.5, Ref 0.9 (0.7, Ref 0.9 (0.7, Ref 0.8 (0.6, Ref 0.8 (0.6, Ref 1.0 (0.7, Ref 0.7 (0.5, Ref 0.9 (0.8, Ref 0.8 (0.6, Ref 1.0 (0.8, Ref 1.0 (0.8, Ref 1.0 (0.7, Ref 1.1 (0.8,
– 0.03 – 0.87 – 6 × 10−3 d – 0.22 – 0.72 – 0.24 – 0.07 – 0.80 – 0.04 – 0.53 – 0.13 – 0.80 – 0.80 – 0.97 – 0.54
Ref 0.7 (0.6, 0.9) Ref 1.0 (0.7, 1.4) Ref 0.6 (0.4, 0.8) Ref 0.8 (0.6, 1.0) Ref 0.9 (0.6, 1.3) Ref 0.7 (0.5, 1.0) Ref 0.8 (0.6, 1.0) Ref 0.9 (0.7, 1.4) Ref 0.7 (0.5, 0.9) Ref 1.0 (0.8, 1.3) Ref 0.9 (0.7, 1.2) Ref 1.2 (0.9,1.6) Ref 1.0 (0.8, 1.3) Ref 1.0 (0.7,1.3) Ref 1.1 (0.8,1.5)
– 0.01 – 0.78 – 9 × 10−4 e – 0.04 – 0.65 – 0.04 – 0.04 – 0.75 – 9 × 10−3 – 0.70 – 0.45 – 0.24 – 0.78 – 0.84 – 0.44
1.0) 1.4) 0.9) 1.1) 1.3) 1.1) 1.0) 1.4) 1.0) 1.1) 1.1) 1.3) 1.3) 1.4) 1.4)
Under a recessive model (using allele C or G as the reference allele for each SNP). Number of subjects. Adjusted by age, gender, ethnicity, education, smoking status and alcohol intake for the total sample. Adjusted by age, ethnicity, education, smoking status and alcohol intake for males and females, respectively. d p b 0.05 was presented in bold. e Underlined p value indicates statistical significance after Bonferroni correction (pBonferroni correction = 3.33 × 10−3 (0.05/15)). b c
2012
H.-J. Tsai et al. / Clinica Chimica Acta 411 (2010) 2009–2013
Table 3 Multi-marker association of all 5 HUT2 SNPs with MetS related traits. Multi-marker association Trait
Z_LC
p
WC Systolic BP Diastolic BP TG HDL Fasting glucose
1.69 1.40 1.28 1.76 -0.54 1.51
0.05 0.08 0.10 0.04a 0.70 0.07
a
P b 0.05 was presented in bold.
females, separately, with adjustment of age, ethnicity, education, smoking status and alcohol intake (Table 2). Specifically, there were significant associations of SNP 1, SNP 2 and SNP 3 with MetS in the total sample and females, separately (9 × 10−4 ≤ p ≤ 0.04) (Table 2). Interestingly, the association between SNP 1 and MetS remained statistically significant after Bonferroni correction. In addition, we also examined associations of the five HUT2 genetic variants with each component of MetS such as high blood pressure, high waist circumference, hypertriglyceridemia, low HDL cholesterol and high fasting glucose in the total study sample, males and females, respectively, with and without the adjustment of covariates. The association results were presented in Supplemental Table 2a–e. 3.3. Multi-marker and multi-trait associations Next, we performed multi-marker associations with MetS related continuous traits (WC, SBP, DBP, TG, HDL cholesterol and fasting glucose, separately). We carried out a linear combination test implemented in the FBAT program to simultaneously test the association between 5 HUT2 SNPs and each of MetS related continuous traits. The results in Table 3 showed that significant associations were found with TG (p = 0.04), but it did not remain significant after multiple testing correction. We also carried out multi-trait associations (combining WC, TG, HDL cholesterol and fasting glucose together) with each SNP of the HUT2 gene. The results in Table 4 indicated that a significant association was observed with SNP 2 (p = 0.04). Likewise, this association did not remain significant after multiple testing correction. Additionally, even though some of the study subjects took antihypertensive medication, we included SBP and DBP, separately, as part of a multi-trait set and performed independent multi-trait associations. Only borderline significance was observed in SNP 2 (Supplemental Table 3a and b). 4. Discussion In this study, we examined the association of five genetic variants in the HUT2 gene with MetS and its related traits in Asian population. We found significant associations of SNPs 1, 2 and 3 with MetS in the total sample and females, separately. Using SNP 1 as an example, subjects carrying the CC genotype had a lower risk to develop MetS than those carrying the CG or GG genotype. When testing 5 SNPs Table 4 Multi-traita association of MetS related traits with 5 HUT2 SNPs. Multi-trait association Marker
Z_LC
p
SNP SNP SNP SNP SNP
1.50 1.73 1.18 0.60 0.96
0.07 0.04b 0.12 0.28 0.17
a b
1 2 3 5 7
Traits used in analysis were as follow: WC, TG, HDL cholesterol and fasting glucose. P b 0.05 was presented in bold.
simultaneously, we found significant associations of TG. Similarly, when testing 5 traits simultaneously, we found a significant association of SNP 2. The results provided important evidence in examining the associations of HUT2 gene polymorphisms with MetS and its related traits in Asian population. The association between HUT2 variation and the risk of MetS is biologically plausible. The HUT2 gene encodes the kidney-specific human urea transporter. Previous animal studies documented that vasopressin has affected the increasing expression of HUT2 mRNA and protein [15,16]. The effects of vasopressin in kidney include increasing urine concentration and decreasing urine excretion [17]. Additionally, vasopressin also elevates peripheral vascular resistance and leads to increasing arterial blood pressure [18]. It has been reported in rat models that a low-protein intake has an impact on upregulation of HUT2 mRNA and protein [19,20]. Consequently, upregulation of HUT2 could increase urea recycling and reabsorption, and reduce urine loss [21]. In other words, it is likely that the effect of vasopressin on increasing arterial blood pressure may be through increasing the expression level of HUT2 protein. Although SNPs 1, 2 and 3 of the HUT2 gene do not cause any change in amino acid sequence, it is still likely that the nucleotide substitution at those SNPs might affect the efficiency of HUT2 mRNA processing and/or stability. A supporting example is that the C957T gene polymorphism, a synonymous SNP in the DRD2 gene, has empirically measurable biological consequences [22]. Alternatively, these three HUT2 SNPs may be in high or complete LD with some important functional SNPs in the HUT2 gene, which are MetS causal SNPs and were not genotyped in this study. This study has several strengths. First, there has been a considerable disagreement over diagnostic criteria of the MetS across different ethnicities. Therefore, to ensure not to misclassify the MetS in this study, we applied the unified definition of the metabolic syndrome based on a recent joint interim statement of several major organizations, including the International Diabetes Federation (IDF); National Heart, Lung and Blood Institute (NHLBI); American Heart Association (AHA); World Heart Federation; International Atherosclerosis Society; and International Association for the Study of Obesity [9]. Second, we identified the significant association between SNP 1 in the HUT2 gene and MetS in the total sample and females, separately. Interestingly, the association remained statistically significant after accounting for Bonferroni correction. Since false-positive association has been a critical concern in candidate gene association studies, applying the Bonferroni correction, which has been recognized as the most stringent approach, would ensure that the observed association was less likely to be a false positive result. Third, to account for potential confounding due to population stratification, we recruited study participants using a family-based study design, which is robust against population stratification [23]. Based upon our data, several limitations should be considered. First, due to sample size constraints, we did not examine association in Chinese and Japanese populations, separately. On the other hand, since this study is a family-based study design, potential confounding due to population stratification should be under control. Future investigation of these two groups, separately, is warranted. Another limitation is that even though SNP 1 locates in exon 1 of HUT2 gene, no functional study has been reported previously. It is likely that SNP 1 may be in high or complete LD with some important functional SNPs in HUT2 gene, which are MetS causal SNPs. As such, other SNPs in HUT2 gene may be also of interest for further investigation. Last, we genotyped 5 HUT2 SNPs in this study. As mentioned earlier, those genotyped SNPs may be in high or complete LD with some important functional SNPs in HUT2 gene, which are MetS causal SNPs. For further investigation, we plan to focus on and genotype two types of SNPs in HUT2 gene: (1) SNPs found to be significantly associated with MetS related traits or functional SNPs substantiated by the literature; and (2) tagSNPs based on search of available databases, for example,
H.-J. Tsai et al. / Clinica Chimica Acta 411 (2010) 2009–2013
SeattleSNP (http://pga.mbt.washington.edu/), NIEHS SNPs Program (http://egp.gs.washington.edu/), the Innate Immunity Programs for Genomic Applications (IIPGA) (http://innateimmunity.net/), and the international HapMap Project (http://www.hapmap.org/). Taken together, our study provided strong evidence that SNP 1 in the HUT2 gene was associated with MetS in Asian population in females. Further investigation on biological functions of the HUT2 gene in relation to the development of MetS and its related traits may provide new insights for MetS preventive strategies. Acknowledgements We would like to thank all study subjects and their families for their support and participating in this study. We thank Dr. WenChang Wang for his discussion of the manuscript, and Ms. Kuan-Yi Hung and Yue-Ming Chen for their assistance with coordinating and computing. We also thank all group members in the SAPPHIRe project for their help. This work was supported by a grant (PH-099-pp-03) from the National Health Research Institutes (Taiwan) and a grant (U01 HL54527-0151) from the National Heart Lung and Blood Institute (USA). Appendix A. Supplementary data Supplementary data to this article can be found online at doi:10.1016/j.cca.2010.08.025. References [1] Third Report of the National Cholesterol Education Program (NCEP) Expert Panel on Detection, Evaluation, and Treatment of High Blood Cholesterol in Adults (Adult Treatment Panel III) final report. Circulation 2002;106:3143–421. [2] Martinez MA, Puig JG, Mora M, Aragon R, O'Dogherty P, Anton JL, et al. Metabolic syndrome: prevalence, associated factors, and C-reactive protein: the MADRIC (MADrid RIesgo Cardiovascular) Study. Metabolism 2008;57:1232–40. [3] Andreassi MG. Metabolic syndrome, diabetes and atherosclerosis: influence of gene–environment interaction. Mutat Res 2009;667:35–43. [4] Miyamoto Y, Morisaki H, Yamanaka I, Kokubo Y, Masuzaki H, Okayama A, et al. Association study of 11beta-hydroxysteroid dehydrogenase type 1 gene polymorphisms and metabolic syndrome in urban Japanese cohort. Diabetes Res Clin Pract 2009;85:132–8.
2013
[5] Olives B, Martial S, Mattei MG, Matassi G, Rousselet G, Ripoche P, et al. Molecular characterization of a new urea transporter in the human kidney. FEBS Lett 1996;386:156–60. [6] Sands JM. Renal urea transporters. Curr Opin Nephrol Hypertens 2004;13:525–32. [7] Ranade K, Wu KD, Hwu CM, Ting CT, Pei D, Pesich R, et al. Genetic variation in the human urea transporter-2 is associated with variation in blood pressure. Hum Mol Genet 2001;10:2157–64. [8] Ranade K, Wu KD, Risch N, Olivier M, Pei D, Hsiao CF, et al. Genetic variation in aldosterone synthase predicts plasma glucose levels. Proc Natl Acad Sci USA 2001;98:13219–24. [9] Alberti KG, Eckel RH, Grundy SM, Zimmet PZ, Cleeman JI, Donato KA, et al. Harmonizing the metabolic syndrome: a joint interim statement of the International Diabetes Federation Task Force on Epidemiology and Prevention; National Heart, Lung, and Blood Institute; American Heart Association; World Heart Federation; International Atherosclerosis Society; and International Association for the Study of Obesity. Circulation 2009;120:1640–5. [10] Schutte BC, Ranade K, Pruessner J, Dracopoli N. Optimized conditions for cloning PCR products into an XcmI T-vector. Biotechniques 1997;22:40–2 44. [11] Livak KJ, Flood SJ, Marmaro J, Giusti W, Deetz K. Oligonucleotides with fluorescent dyes at opposite ends provide a quenched probe system useful for detecting PCR product and nucleic acid hybridization. PCR Methods Appl 1995;4:357–62. [12] Barrett JCFB, Maller J, Daly MJ. Haploview: analysis and visualization of LD and haplotype maps. Bioinformatics 2005;21:263–5. [13] Lunetta KL, Faraone SV, Biederman J, Laird NM. Family-based tests of association and linkage that use unaffected sibs, covariates, and interactions. Am J Hum Genet 2000;66:605–14. [14] Xu X, Rakovski C, Laird N. An efficient family-based association test using multiple markers. Genet Epidemiol 2006;30:620–6. [15] Wade JB, Lee AJ, Liu J, Ecelbarger CA, Mitchell C, Bradford AD, et al. UT-A2: a 55kDa urea transporter in thin descending limb whose abundance is regulated by vasopressin. Am J Physiol Renal Physiol 2000;278:F52–62. [16] Brooks HL, Ageloff S, Kwon TH, Brandt W, Terris JM, Seth A, et al. cDNA array identification of genes regulated in rat renal medulla in response to vasopressin infusion. Am J Physiol Renal Physiol 2003;284:F218–28. [17] Preiser JC. Year in review 2008: critical care—metabolism. Crit Care 2009;13:228. [18] Gueugniaud PY, David JS, Chanzy E, Hubert H, Dubien PY, Mauriaucourt P, et al. Vasopressin and epinephrine vs. epinephrine alone in cardiopulmonary resuscitation. N Engl J Med 2008;359:21–30. [19] Ashkar ZM, Martial S, Isozaki T, Price SR, Sands JM. Urea transport in initial IMCD of rats fed a low-protein diet: functional properties and mRNA abundance. Am J Physiol 1995;268:F1218–23. [20] Kim D, Klein JD, Racine S, Murrell BP, Sands JM. Urea may regulate urea transporter protein abundance during osmotic diuresis. Am J Physiol Renal Physiol 2005;288: F188–97. [21] Bagnasco SM. Role and regulation of urea transporters. Pflugers Arch 2005;450: 217–26. [22] Duan J, Wainwright MS, Comeron JM, Saitou N, Sanders AR, Gelernter J, et al. Synonymous mutations in the human dopamine receptor D2 (DRD2) affect mRNA stability and synthesis of the receptor. Hum Mol Genet 2003;12:205–16. [23] Rabinowitz D, Laird N. A unified approach to adjusting association tests for population admixture with arbitrary pedigree structure and arbitrary missing marker information. Hum Hered 2000;50:211–23.
Contents lists available at ScienceDirect
Clinica Chimica Acta j o u r n a l h o m e p a g e : w w w. e l s ev i e r. c o m / l o c a t e / c l i n c h i m
Genetic variants of human urea transporter-2 are associated with metabolic syndrome in Asian population Hui-Ju Tsai a,b,1, Chin-Fu Hsiao a,1, Low-Tone Ho c,d,e, Lee-Ming Chuang f,g, Chih-Tsueng He h, J. David Curb i, Thomas Quertermos j, Chao A. Hsiung a,⁎, Wayne H.-H. Sheu k,l,m,⁎ a
Division of Biostatistics and Bioinformatics, Institute of Population Health Sciences, National Health Research Institutes, Zhunan, Taiwan Department of Pediatrics, Feinberg School of Medicine, Northwestern University, Chicago, IL, USA Department of Medical Research and Education, Taipei Veterans General Hospital, Taipei, Taiwan d Section of Endocrinology and Metabolism, Department of Medicine, Taipei Veterans General Hospital, Taipei, Taiwan e Faculty of Medicine, School of Medicine, National Yang-Ming University, Taipei, Taiwan f Department of Internal Medicine, National Taiwan University Hospital, Taipei, Taiwan g Graduate Institute of Clinical Medicine, National Taiwan University, Taipei, Taiwan h Division of Endocrinology and Metabolism, Tri-Service General Hospital, National Defense Medical Center, Taipei, Taiwan i John A. Burns School of Medicine, University of Hawaii, Honolulu, HI, USA j Division of Cardiovascular Medicine, Falk Cardiovascular Research Building, Stanford University School of Medicine, Stanford, CA, USA k Division of Endocrinology and Metabolism, Department of Internal Medicine, Taichung Veterans General Hospital, Taichung, Taiwan l School of Medicine, National Yang-Ming University, Taipei, Taiwan m School of Medicine, Chung-Shan Medical University, Taichung, Taiwan b c
a r t i c l e
i n f o
Article history: Received 14 June 2010 Received in revised form 16 August 2010 Accepted 17 August 2010 Available online 23 August 2010 Keywords: Human urea transporter-2 Metabolic syndrome Asian
a b s t r a c t Background: A previous study has reported that the Ile227 and Ala357 genetic variants of human urea transporter-2 (HUT2) were associated with blood pressure in males in Asian population. In this study, we aimed to investigate five known HUT2 genetic variants with metabolic syndrome (MetS) and its related traits in the Stanford Asia-Pacific Program for Hypertension and Insulin Resistance (SAPPHIRe) study cohort. Methods: Five HUT2 single nucleotide polymorphisms (SNPs) were selected and genotyped among 1791 subjects in the SAPPHIRe study cohort. We first computed allele frequency and performed Hardy–Weinberg equilibrium (HWE) test in controls for each SNP. Next, we tested genotype associations with metabolic syndrome using multiple generalized estimating equations (GEE) models with covariate adjustment. Furthermore, multi-marker and multi-trait association tests were carried out using FBAT program. To account for multiple testing, Bonferroni correction was applied in this study. Results: Among those 5 HUT2 SNPs, SNPs 1, 2 and 3 were significantly associated with MetS in the total sample and females, separately (9 × 10−4 ≤ p ≤ 0.04), but only the association between SNP 1 and MetS in females remained statistically significant after Bonferroni correction. When testing 5 SNPs simultaneously, significant associations were found with triglycerides (TG) (p = 0.04). Likewise, significant multi-trait association (combining the data of waist circumference, TG, high density lipoprotein (HDL) cholesterol and fasting glucose together) was found with SNP 2 (p = 0.04), but both results of multi-maker and multi-trait associations did not remain significant after multiple testing correction. Conclusion: The results have provided evidence that the HUT2 gene may play a certain role in developing MetS and its related traits in Asian population. Further investigation of the HUT2 gene influencing MetS and its related traits will be warranted. © 2010 Elsevier B.V. All rights reserved.
1. Introduction
⁎ Corresponding authors. Hsiung is to be contacted at Division of Biostatistics and Bioinformatics, Institute of Population Health Sciences, National Health Research Institutes, Zhunan, Taiwan. Sheu, Division of Endocrinology and Metabolism, Department of Internal Medicine, Taichung Veterans General Hospital, Taichung, Taiwan. E-mail addresses: [email protected] (C.A. Hsiung), [email protected] (W.H.-H. Sheu). 0009-8981/$ – see front matter © 2010 Elsevier B.V. All rights reserved. doi:10.1016/j.cca.2010.08.025
Metabolic syndrome (MetS) is a cluster of metabolic abnormalities including central obesity, hypertension, hyperglycemia and dyslipidemia [1]. MetS is a growing clinical and public health problem in the United States and worldwide [2]. It has been increasing over the past few decades. Importantly, MetS has been associated with increased risk of developing cardiovascular disease (CVD), stroke, and type 2 diabetes [3]. It has been well recognized that the development of MetS
2010
H.-J. Tsai et al. / Clinica Chimica Acta 411 (2010) 2009–2013
is influenced by genetic factors, environmental factors and lifestyle changes [4]. However, the etiology of MetS remains largely unclear. Human urea transporter-2 (HUT2), the human homolog of UT-A2 in the rodent model [5], is involved in the urea permeability which might affect the kidney's regulatory ability to fluid volume. Furthermore, the regulatory mechanisms may play a critical role in disease states, for instance, diabetes and blood pressure, because the abundance of HUT2 is regulated by glucocorticoids, mineralocorticoids and vasopressin [6]. In addition, a previous study has reported that the Ile227 and Ala357 variants of the HUT2 gene were associated with blood pressure in men [7]. However, the genetic effect of HUT2 on MetS has not been examined yet. In this study, we genotyped five known HUT2 single nucleotide polymorphisms (SNPs), which were selected based on their informativeness and potential biological function, in the Stanford Asia-Pacific Program for Hypertension and Insulin Resistance (SAPPHIRe) study cohort. We then tested genotype associations of HUT2 genetic variants with MetS after adjusting covariates. To control for potential confounding due to population stratification, we used a familybased study sample with a total of 1791 Asian subjects recruited from 533 families participating in the SAPPHIRe study. Our results indicated that HUT2 genetic variants may play an important role in MetS and its related traits in Asian population. 2. Materials and methods 2.1. Subject recruitment In this study, we included a total of 1791 subjects from 533 families of Chinese and Japanese participating in the SAPPHIRe study, which is part of the Family Blood Pressure Program (FBPP). Detailed description of the study sites, subject recruitment and protocols of data collection has been described previously [8]. The study protocol was approved by the institutional review board (IRB) at all participating sites. Written informed consent was explained to and signed by each subject. 2.2. Definition of MetS Definition of MetS was based on clinical criteria from a recent joint interim statement of the International Diabetes Federation (IDF); National Heart, Lung and Blood Institute (NHLBI); American Heart Association (AHA); World Heart Federation; International Atherosclerosis Society; and International Association for the Study of Obesity [9]. In summary, a MetS case should meet any three of the following five conditions: 1) waist circumference (WC) ≥ 90 cm in males, ≥80 cm in females; 2) triglycerides (TG) ≥ 150 mg/dL in both gender; 3) high density lipoprotein (HDL) cholesterol b40 mg/dL in males, b50 mg/dL in females; 4) systolic blood pressure (SBP) ≥ 130 mmHg or diastolic blood pressure (DBP) ≥ 85 mmHg in both gender (whereas antihypertensive drug treatment in a patient with a history of hypertension is an alternate indicator); 5) fasting glucose ≥ 100 mg/dL in both gender (whereas drug treatment of elevated glucose is an alternate indicator). 2.3. SNPs selection and genotyping We genotyped five previously identified HUT2 genetic variants, which were selected based on their informativeness, potential biological function and the LD pattern across HUT2 SNPs. In short, we did not include SNPs with minor allele frequency (MAF) b 5%. If complete LD (r2 = 1) existed among two or more SNPs on the HUT2 gene, only one SNP was picked and genotyped. As a result, we selected 5 HUT2 SNPs for genotyping. The detailed information of each genotyped SNP was provided in Supplemental Table 1.
For polymerase chain reaction (PCR) conditions, genotyping was carried out using the 5′ nuclease detection assay or TaqMan to detect the HUT2 genetic variants [10,11]. The sequences of the primers and probes used in the TaqMan assay, and PCR conditions were described previously [7]. After PCR, fluorescence was read by an ABI 7700 machine. 2.4. Statistical analysis 2.4.1. Allele frequencies and Hardy–Weinberg equilibrium Allele frequencies of five HUT2 SNPs were computed using genotype data of controls (all control subjects, males and females, separately). We performed χ2 tests to examine whether there was a difference between males and females. Next, we examined Hardy– Weinberg equilibrium (HWE) test in controls using χ2 tests. In this study, all the genotyped SNPs were under HWE (Supplemental Table 1). 2.4.2. Pair-wise linkage disequilibrium (LD) estimation For 5 genotyped HUT2 SNPs, we computed pair-wise linkage disequilibrium (LD) in controls using r2 measure, which was calculated using an iterative expectation–maximization (EM) algorithm implemented in the Haploview software [12]. 2.4.3. Association tests of demographic and genotype data The primary outcome was MetS, and the secondary outcomes were continuous variables related with MetS, including WC, TG, HDL cholesterol and fasting glucose. Student's t-test and χ2 test were performed to compare the distribution's difference of continuous variables and categorical variables between males and females, respectively. Next, to account for the dependency among family members, we applied multiple generalized estimating equations (GEE) models to estimate genetic effect of each SNP on MetS. Specifically, in this study, the GEE models treated each family as a unit and were performed with the adjustment of age, ethnicity, education, smoking status and alcohol intake under additive, dominant and recessive models, individually. All the GEE analyses were performed using SAS version 9.1.3 (SAS institute, Cary, NC). 2.4.4. Multi-marker and multi-trait association tests Furthermore, to examine 5 SNPs simultaneously, we applied a linear combination test implemented in Family-Based Association Tests (FBAT) program [13]. This analytical approach was to form a linear combination of the individual Z statistics computed for each marker [14]. We investigated multi-marker association with MetS related continuous traits (WC, SBP, DBP, TG, HDL cholesterol and fasting glucose) using this linear combination test. In addition, since those MetS related traits shared a certain degree of correlation, we tested those MetS related traits simultaneously using a multi-trait test implemented in the FBAT program. Similarly, the statistics computes a linear combination test for multiple traits, that is, combines each individual test statistics across multiple traits for a single SNP. 2.4.5. Multiple testing correction False positive results have been a critical concern when a number of association tests are performed. To address this issue, we applied Bonferroni correction, which is known as the most stringent approach, to correct for multiple testing. 3. Results 3.1. Clinical features and genotyping characteristics Clinical characteristics of 1791 subjects (803 males and 988 females, individually) were summarized in Table 1a. In general, males had significant higher values of body mass index (BMI), WC, TG, total
H.-J. Tsai et al. / Clinica Chimica Acta 411 (2010) 2009–2013
2011
Table 1a Clinical features of study participants. Mean ± std
All
Males
Females
pa
Number (Chinese/Japanese) Age (years) BMI (kg/m2) Waist circumference (cm) Total cholesterol (mg/dL) LDL-cholesterol (mg/dL) HDL cholesterol (mg/dL) Triglyceride Fasting glucose (mg/dL) Fasting insulin (uU/ml) HOMA insulin resistance
1791 (1309/482) 51.0 ± 8.9 25.7 ± 3.8 86.2 ± 11.1 193.0 ± 38.6 120.5 ± 36.1 45.1 ± 12.8 142.0 ± 90.5 95.0 ± 19.5 7.8 ± 5.4 1.9 ± 1.6
803 (609/194) 50.6 ± 8.8 26.2 ± 3.5 90.7 ± 9.1 189.2 ± 36.3 119.5 ± 34.9 39.9 ± 10.2 155.3 ± 96.0 96.1 ± 19.0 8.0 ± 5.7 2.0 ± 1.5
988 (700/288) 51.2 ± 9.0 25.3 ± 4.0 82.5 ± 11.2 196.1 ± 40.2 121.2 ± 37.1 49.3 ± 13.2 131.2 ± 84.3 94.1 ± 19.9 7.6 ± 5.2 1.9 ± 1.6
0.22 b10−4 b10−4 2 × 10−3 0.86 b10−4 b10−4 0.04 0.09 0.11
a
Student t-tests were performed to compare the difference in distribution of continuous variables between males and females, respectively.
cholesterol and fasting glucose (Table 1a) and had a higher percentage of MetS and hypertension than females (Table 1b). On the other hand, females had a significant higher value of HDL cholesterol than males (Table 1a). Five SNPs of the HUT2 gene were genotyped in 1791 subjects. The minor allele frequency (MAF) of each SNP was provided in Supplemental Table 1. Using p b 0.05 as the statistical significance cutoff, none of the 5 HUT2 SNPs was out of HWE. Additionally, pairwise LD patterns in controls were calculated and presented in Supplemental Fig. 1.
Table 1b Number of subjects with MetS and meeting each criterion of MetS. N (%)
Males
Females
pa
MetS High blood pressure High waist circumference Hypertriglyceridemia Low HDL cholesterol High fasting glucose Hypertension
419 (52.2%) 463 (57.7%) 414(51.56%) 334 (41.6%) 445 (55.4%) 239 (29.8%) 542 (67.5%)
442 (44.7%) 469 (47.5%) 547(55.36%) 285 (28.9%) 551 (55.8%) 220 (22.3%) 587 (59.4%)
2 × 10−3 b 10−4 0.108 b 10−4 0.88 3 × 10−4 4 × 10−4
3.2. Single marker association
a χ2 tests were performed to compare the difference in categorical variables between males and females, respectively.
We performed association tests of the five HUT2 genetic variants with MetS using GEE models in the total study sample, males and
Table 2 Genotype association of each SNP in the HUT2 gene with MetS. SNP
Genetic modela (Nb)
Gender
1
GG (492) GC or CC (838) GG (218) GC or CC (373) GG (274) GC or CC (465) CC (572) CT or TT (744) CC (254) CT or TT (332) CC (318) CT or TT (412) CC (481) CT or TT (837) CC (213) CT or TT (373) CC (268) CT or TT (464) GG (862) GA or AA (783) GG (398) GA or AA (352) GG (464) GA or AA (431) GG (615) GA or AA (1024) GG (278) GA or AA (469) GG (337) GA or AA (555)
M+F
2
3
5
7
a
M F M+F M F M+F M F M+F M F M+F M F
Adjusted modelc
Unadjusted OR (95% CI)
p
OR (95% CI)
p
Ref 0.8 (0.6, Ref 1.0 (0.7, Ref 0.7 (0.5, Ref 0.9 (0.7, Ref 0.9 (0.7, Ref 0.8 (0.6, Ref 0.8 (0.6, Ref 1.0 (0.7, Ref 0.7 (0.5, Ref 0.9 (0.8, Ref 0.8 (0.6, Ref 1.0 (0.8, Ref 1.0 (0.8, Ref 1.0 (0.7, Ref 1.1 (0.8,
– 0.03 – 0.87 – 6 × 10−3 d – 0.22 – 0.72 – 0.24 – 0.07 – 0.80 – 0.04 – 0.53 – 0.13 – 0.80 – 0.80 – 0.97 – 0.54
Ref 0.7 (0.6, 0.9) Ref 1.0 (0.7, 1.4) Ref 0.6 (0.4, 0.8) Ref 0.8 (0.6, 1.0) Ref 0.9 (0.6, 1.3) Ref 0.7 (0.5, 1.0) Ref 0.8 (0.6, 1.0) Ref 0.9 (0.7, 1.4) Ref 0.7 (0.5, 0.9) Ref 1.0 (0.8, 1.3) Ref 0.9 (0.7, 1.2) Ref 1.2 (0.9,1.6) Ref 1.0 (0.8, 1.3) Ref 1.0 (0.7,1.3) Ref 1.1 (0.8,1.5)
– 0.01 – 0.78 – 9 × 10−4 e – 0.04 – 0.65 – 0.04 – 0.04 – 0.75 – 9 × 10−3 – 0.70 – 0.45 – 0.24 – 0.78 – 0.84 – 0.44
1.0) 1.4) 0.9) 1.1) 1.3) 1.1) 1.0) 1.4) 1.0) 1.1) 1.1) 1.3) 1.3) 1.4) 1.4)
Under a recessive model (using allele C or G as the reference allele for each SNP). Number of subjects. Adjusted by age, gender, ethnicity, education, smoking status and alcohol intake for the total sample. Adjusted by age, ethnicity, education, smoking status and alcohol intake for males and females, respectively. d p b 0.05 was presented in bold. e Underlined p value indicates statistical significance after Bonferroni correction (pBonferroni correction = 3.33 × 10−3 (0.05/15)). b c
2012
H.-J. Tsai et al. / Clinica Chimica Acta 411 (2010) 2009–2013
Table 3 Multi-marker association of all 5 HUT2 SNPs with MetS related traits. Multi-marker association Trait
Z_LC
p
WC Systolic BP Diastolic BP TG HDL Fasting glucose
1.69 1.40 1.28 1.76 -0.54 1.51
0.05 0.08 0.10 0.04a 0.70 0.07
a
P b 0.05 was presented in bold.
females, separately, with adjustment of age, ethnicity, education, smoking status and alcohol intake (Table 2). Specifically, there were significant associations of SNP 1, SNP 2 and SNP 3 with MetS in the total sample and females, separately (9 × 10−4 ≤ p ≤ 0.04) (Table 2). Interestingly, the association between SNP 1 and MetS remained statistically significant after Bonferroni correction. In addition, we also examined associations of the five HUT2 genetic variants with each component of MetS such as high blood pressure, high waist circumference, hypertriglyceridemia, low HDL cholesterol and high fasting glucose in the total study sample, males and females, respectively, with and without the adjustment of covariates. The association results were presented in Supplemental Table 2a–e. 3.3. Multi-marker and multi-trait associations Next, we performed multi-marker associations with MetS related continuous traits (WC, SBP, DBP, TG, HDL cholesterol and fasting glucose, separately). We carried out a linear combination test implemented in the FBAT program to simultaneously test the association between 5 HUT2 SNPs and each of MetS related continuous traits. The results in Table 3 showed that significant associations were found with TG (p = 0.04), but it did not remain significant after multiple testing correction. We also carried out multi-trait associations (combining WC, TG, HDL cholesterol and fasting glucose together) with each SNP of the HUT2 gene. The results in Table 4 indicated that a significant association was observed with SNP 2 (p = 0.04). Likewise, this association did not remain significant after multiple testing correction. Additionally, even though some of the study subjects took antihypertensive medication, we included SBP and DBP, separately, as part of a multi-trait set and performed independent multi-trait associations. Only borderline significance was observed in SNP 2 (Supplemental Table 3a and b). 4. Discussion In this study, we examined the association of five genetic variants in the HUT2 gene with MetS and its related traits in Asian population. We found significant associations of SNPs 1, 2 and 3 with MetS in the total sample and females, separately. Using SNP 1 as an example, subjects carrying the CC genotype had a lower risk to develop MetS than those carrying the CG or GG genotype. When testing 5 SNPs Table 4 Multi-traita association of MetS related traits with 5 HUT2 SNPs. Multi-trait association Marker
Z_LC
p
SNP SNP SNP SNP SNP
1.50 1.73 1.18 0.60 0.96
0.07 0.04b 0.12 0.28 0.17
a b
1 2 3 5 7
Traits used in analysis were as follow: WC, TG, HDL cholesterol and fasting glucose. P b 0.05 was presented in bold.
simultaneously, we found significant associations of TG. Similarly, when testing 5 traits simultaneously, we found a significant association of SNP 2. The results provided important evidence in examining the associations of HUT2 gene polymorphisms with MetS and its related traits in Asian population. The association between HUT2 variation and the risk of MetS is biologically plausible. The HUT2 gene encodes the kidney-specific human urea transporter. Previous animal studies documented that vasopressin has affected the increasing expression of HUT2 mRNA and protein [15,16]. The effects of vasopressin in kidney include increasing urine concentration and decreasing urine excretion [17]. Additionally, vasopressin also elevates peripheral vascular resistance and leads to increasing arterial blood pressure [18]. It has been reported in rat models that a low-protein intake has an impact on upregulation of HUT2 mRNA and protein [19,20]. Consequently, upregulation of HUT2 could increase urea recycling and reabsorption, and reduce urine loss [21]. In other words, it is likely that the effect of vasopressin on increasing arterial blood pressure may be through increasing the expression level of HUT2 protein. Although SNPs 1, 2 and 3 of the HUT2 gene do not cause any change in amino acid sequence, it is still likely that the nucleotide substitution at those SNPs might affect the efficiency of HUT2 mRNA processing and/or stability. A supporting example is that the C957T gene polymorphism, a synonymous SNP in the DRD2 gene, has empirically measurable biological consequences [22]. Alternatively, these three HUT2 SNPs may be in high or complete LD with some important functional SNPs in the HUT2 gene, which are MetS causal SNPs and were not genotyped in this study. This study has several strengths. First, there has been a considerable disagreement over diagnostic criteria of the MetS across different ethnicities. Therefore, to ensure not to misclassify the MetS in this study, we applied the unified definition of the metabolic syndrome based on a recent joint interim statement of several major organizations, including the International Diabetes Federation (IDF); National Heart, Lung and Blood Institute (NHLBI); American Heart Association (AHA); World Heart Federation; International Atherosclerosis Society; and International Association for the Study of Obesity [9]. Second, we identified the significant association between SNP 1 in the HUT2 gene and MetS in the total sample and females, separately. Interestingly, the association remained statistically significant after accounting for Bonferroni correction. Since false-positive association has been a critical concern in candidate gene association studies, applying the Bonferroni correction, which has been recognized as the most stringent approach, would ensure that the observed association was less likely to be a false positive result. Third, to account for potential confounding due to population stratification, we recruited study participants using a family-based study design, which is robust against population stratification [23]. Based upon our data, several limitations should be considered. First, due to sample size constraints, we did not examine association in Chinese and Japanese populations, separately. On the other hand, since this study is a family-based study design, potential confounding due to population stratification should be under control. Future investigation of these two groups, separately, is warranted. Another limitation is that even though SNP 1 locates in exon 1 of HUT2 gene, no functional study has been reported previously. It is likely that SNP 1 may be in high or complete LD with some important functional SNPs in HUT2 gene, which are MetS causal SNPs. As such, other SNPs in HUT2 gene may be also of interest for further investigation. Last, we genotyped 5 HUT2 SNPs in this study. As mentioned earlier, those genotyped SNPs may be in high or complete LD with some important functional SNPs in HUT2 gene, which are MetS causal SNPs. For further investigation, we plan to focus on and genotype two types of SNPs in HUT2 gene: (1) SNPs found to be significantly associated with MetS related traits or functional SNPs substantiated by the literature; and (2) tagSNPs based on search of available databases, for example,
H.-J. Tsai et al. / Clinica Chimica Acta 411 (2010) 2009–2013
SeattleSNP (http://pga.mbt.washington.edu/), NIEHS SNPs Program (http://egp.gs.washington.edu/), the Innate Immunity Programs for Genomic Applications (IIPGA) (http://innateimmunity.net/), and the international HapMap Project (http://www.hapmap.org/). Taken together, our study provided strong evidence that SNP 1 in the HUT2 gene was associated with MetS in Asian population in females. Further investigation on biological functions of the HUT2 gene in relation to the development of MetS and its related traits may provide new insights for MetS preventive strategies. Acknowledgements We would like to thank all study subjects and their families for their support and participating in this study. We thank Dr. WenChang Wang for his discussion of the manuscript, and Ms. Kuan-Yi Hung and Yue-Ming Chen for their assistance with coordinating and computing. We also thank all group members in the SAPPHIRe project for their help. This work was supported by a grant (PH-099-pp-03) from the National Health Research Institutes (Taiwan) and a grant (U01 HL54527-0151) from the National Heart Lung and Blood Institute (USA). Appendix A. Supplementary data Supplementary data to this article can be found online at doi:10.1016/j.cca.2010.08.025. References [1] Third Report of the National Cholesterol Education Program (NCEP) Expert Panel on Detection, Evaluation, and Treatment of High Blood Cholesterol in Adults (Adult Treatment Panel III) final report. Circulation 2002;106:3143–421. [2] Martinez MA, Puig JG, Mora M, Aragon R, O'Dogherty P, Anton JL, et al. Metabolic syndrome: prevalence, associated factors, and C-reactive protein: the MADRIC (MADrid RIesgo Cardiovascular) Study. Metabolism 2008;57:1232–40. [3] Andreassi MG. Metabolic syndrome, diabetes and atherosclerosis: influence of gene–environment interaction. Mutat Res 2009;667:35–43. [4] Miyamoto Y, Morisaki H, Yamanaka I, Kokubo Y, Masuzaki H, Okayama A, et al. Association study of 11beta-hydroxysteroid dehydrogenase type 1 gene polymorphisms and metabolic syndrome in urban Japanese cohort. Diabetes Res Clin Pract 2009;85:132–8.
2013
[5] Olives B, Martial S, Mattei MG, Matassi G, Rousselet G, Ripoche P, et al. Molecular characterization of a new urea transporter in the human kidney. FEBS Lett 1996;386:156–60. [6] Sands JM. Renal urea transporters. Curr Opin Nephrol Hypertens 2004;13:525–32. [7] Ranade K, Wu KD, Hwu CM, Ting CT, Pei D, Pesich R, et al. Genetic variation in the human urea transporter-2 is associated with variation in blood pressure. Hum Mol Genet 2001;10:2157–64. [8] Ranade K, Wu KD, Risch N, Olivier M, Pei D, Hsiao CF, et al. Genetic variation in aldosterone synthase predicts plasma glucose levels. Proc Natl Acad Sci USA 2001;98:13219–24. [9] Alberti KG, Eckel RH, Grundy SM, Zimmet PZ, Cleeman JI, Donato KA, et al. Harmonizing the metabolic syndrome: a joint interim statement of the International Diabetes Federation Task Force on Epidemiology and Prevention; National Heart, Lung, and Blood Institute; American Heart Association; World Heart Federation; International Atherosclerosis Society; and International Association for the Study of Obesity. Circulation 2009;120:1640–5. [10] Schutte BC, Ranade K, Pruessner J, Dracopoli N. Optimized conditions for cloning PCR products into an XcmI T-vector. Biotechniques 1997;22:40–2 44. [11] Livak KJ, Flood SJ, Marmaro J, Giusti W, Deetz K. Oligonucleotides with fluorescent dyes at opposite ends provide a quenched probe system useful for detecting PCR product and nucleic acid hybridization. PCR Methods Appl 1995;4:357–62. [12] Barrett JCFB, Maller J, Daly MJ. Haploview: analysis and visualization of LD and haplotype maps. Bioinformatics 2005;21:263–5. [13] Lunetta KL, Faraone SV, Biederman J, Laird NM. Family-based tests of association and linkage that use unaffected sibs, covariates, and interactions. Am J Hum Genet 2000;66:605–14. [14] Xu X, Rakovski C, Laird N. An efficient family-based association test using multiple markers. Genet Epidemiol 2006;30:620–6. [15] Wade JB, Lee AJ, Liu J, Ecelbarger CA, Mitchell C, Bradford AD, et al. UT-A2: a 55kDa urea transporter in thin descending limb whose abundance is regulated by vasopressin. Am J Physiol Renal Physiol 2000;278:F52–62. [16] Brooks HL, Ageloff S, Kwon TH, Brandt W, Terris JM, Seth A, et al. cDNA array identification of genes regulated in rat renal medulla in response to vasopressin infusion. Am J Physiol Renal Physiol 2003;284:F218–28. [17] Preiser JC. Year in review 2008: critical care—metabolism. Crit Care 2009;13:228. [18] Gueugniaud PY, David JS, Chanzy E, Hubert H, Dubien PY, Mauriaucourt P, et al. Vasopressin and epinephrine vs. epinephrine alone in cardiopulmonary resuscitation. N Engl J Med 2008;359:21–30. [19] Ashkar ZM, Martial S, Isozaki T, Price SR, Sands JM. Urea transport in initial IMCD of rats fed a low-protein diet: functional properties and mRNA abundance. Am J Physiol 1995;268:F1218–23. [20] Kim D, Klein JD, Racine S, Murrell BP, Sands JM. Urea may regulate urea transporter protein abundance during osmotic diuresis. Am J Physiol Renal Physiol 2005;288: F188–97. [21] Bagnasco SM. Role and regulation of urea transporters. Pflugers Arch 2005;450: 217–26. [22] Duan J, Wainwright MS, Comeron JM, Saitou N, Sanders AR, Gelernter J, et al. Synonymous mutations in the human dopamine receptor D2 (DRD2) affect mRNA stability and synthesis of the receptor. Hum Mol Genet 2003;12:205–16. [23] Rabinowitz D, Laird N. A unified approach to adjusting association tests for population admixture with arbitrary pedigree structure and arbitrary missing marker information. Hum Hered 2000;50:211–23.