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Association between serum uric acid related genetic loci and diabetic kidney disease in the Chinese type 2 diabetes patients
Association between serum uric acid related genetic loci and diabetic kidney disease in the Chinese type 2 diabetes patients Dandan Yan, Jie Wang, Feng Jiang, Rong Zhang, Xue Sun, Tao Wang, Shiyun Wang, Danfeng Peng, Zhen He, Yuqian Bao, Cheng Hu, Weiping Jia PII: DOI: Reference:
S1056-8727(16)30012-5 doi: 10.1016/j.jdiacomp.2016.02.018 JDC 6670
To appear in:
Journal of Diabetes and Its Complications
Received date: Revised date: Accepted date:
24 August 2015 4 February 2016 24 February 2016
Please cite this article as: Yan, D., Wang, J., Jiang, F., Zhang, R., Sun, X., Wang, T., Wang, S., Peng, D., He, Z., Bao, Y., Hu, C. & Jia, W., Association between serum uric acid related genetic loci and diabetic kidney disease in the Chinese type 2 diabetes patients, Journal of Diabetes and Its Complications (2016), doi: 10.1016/j.jdiacomp.2016.02.018
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ACCEPTED MANUSCRIPT Association between serum uric acid related genetic loci and diabetic kidney disease in
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the Chinese type 2 diabetes patients Dandan Yan1‡, Jie Wang1‡, Feng Jiang1, Rong Zhang1, Xue Sun1, Tao Wang1, Shiyun Wang1, Danfeng Peng1, Zhen He1, Yuqian Bao1, Cheng Hu1,2#, Weiping Jia1#
Shanghai Diabetes Institute, Shanghai Key Laboratory of Diabetes Mellitus, Shanghai Clinical
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1
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Center for Diabetes, Shanghai Jiao Tong University Affiliated Sixth People’s Hospital, Shanghai 200233 China
Shanghai Jiao Tong University Affiliated Sixth People’s Hospital, South Campus, Shanghai
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2
#
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201499, China
Corresponding authors: 1. Cheng Hu, email: [email protected]
and
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2. Weiping Jia, email: [email protected]
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Tel: +86-21-24058924; Fax: +86-21-64368031
‡ These authors contributed equally to this article. Conflict of interest: The authors declare that they have no conflict of interest.
ACCEPTED MANUSCRIPT Abstract Aim: We aimed to investigate the association between uric acid related genetic loci and DKD susceptibility in type 2 diabetes patients.
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Methods: Seventeen single nucleotide polymorphisms (SNPs) from thirteen loci related to serum uric acid were genotyped in 2,892 type 2 diabetes patients. Associations between SNPs and uric acid, SNPs and quantitative traits related to DKD or its susceptibility were evaluated.
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Results: In this study, uric acid showed a strong association with DKD (OR=1.006, p<0.0001). GCKR rs780094, SLC2A9 rs11722228, SLC2A9 rs3775948, ABCG2 rs2231142, SLC22A12
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rs505802 and NRXN2 rs506338 were positively associated with serum uric acid (p=3.79E-05, 0.0002, 2.04E-10, 2.23E-09, 0.0018 and 0.0015, respectively). SLC2A9 rs11722228 and SF1
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rs606458 were significantly associated with DKD (OR=0.864, p=0.0440; OR=1.223, p=0.0038). SLC2A9 rs3775948 and ABCG2 rs2231142 were associated with DKD marginally (OR=0.878,
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p=0.0506; OR=0.879, p=0.0698). SLC2A9 rs11722228, SLC2A9 rs3775948, ABCG2 rs2231142 and SF1 rs606458 were significantly associated with the estimated glomerular filtration rate (p=0.0005, 0.0006, 0.0003, and 0.0424, respectively). Conclusions: Our study indicated that the uric acid related alleles of SLC2A9 rs11722228,
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SLC2A9 rs3775948, ABCG2 rs2231142 might affect DKD susceptibility and possibly through
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non-uric acid pathway in the Chinese people with type 2 diabetes. Keywords: uric acid; diabetic kidney disease; SLC2A9; ABCG2; single nucleotide polymorphisms
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1. Introduction
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As a highly prevalent chronic disease around the world, type 2 diabetes mellitus (T2DM) involves several important organ systems, and diabetic kidney disease (DKD) is a highly prevalent complication of T2DM. In many countries, DKD is the leading cause of end-stage renal disease
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(Lim, 2014) and has become a serious public health problem. Previous investigations have revealed that many factors, including age, duration of diabetes, blood pressure, glucose
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metabolism, serum lipids, and serum uric acid are associated with the onset and progression of DKD (Altemtam et al., 2012; Macisaac et al., 2014). However, in clinical practice, not all patients
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show characteristics similar to those reported in previous studies. For example, epidemiological studies have revealed that strict glycaemic control can reduce the incidence of DKD (Nordwall et al., 2009). However, some patients with poorly controlled blood glucose do not develop DKD,
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whereas some patients develop DKD despite acceptable glycaemic control. Therefore, classical observational studies cannot confirm the risk factors for DKD. DKD clusters in families (Fava et al., 2000), and the risks of DKD differ among ethnic
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groups (Chandie Shaw et al., 2006), suggesting a genetic basis for the disease, so the study of DKD in genetic level could reveal the mechanism underlying this disease. Although researches
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have attempted to identify susceptible genes and genetic variants associated with DKD through candidate gene linkage studies (Placha et al., 2006) or genome-wide association studies (GWAS) (Fagerholm et al., 2012), the results of these studies are inconsistent (Tang et al., 2015)and few of the findings have been replicated (Ng et al., 2005), which is likely due to relatively small sample sizes, ethnic variations, or epigenetic changes induced by diabetes status (Villeneuve & Natarajan, 2010). Therefore, additional studies of different populations in genetic level are imperative to explain the mechanism of DKD. According to previous clinical and epidemiological studies, serum uric acid was associated with the progression of DKD, but the role of uric acid in DKD was disputable (Domrongkitchaiporn et al., 2005; Sheikhbahaei et al., 2014). Since simple observational studies could not confirm hyperuricaemia as a causative factor in the development of renal disease, and
ACCEPTED MANUSCRIPT there has been few study involving the association between uric acid related genetic variants and DKD susceptibility, the association between serum uric acid related single nucleotide polymorphisms (SNPs) and DKD susceptibility may offer more clues for the role of uric acid in
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DKD.
In our study, we chose SNPs identified to be associated with the level of serum uric acid in previous studies, and tested for the associations between uric acid related SNPs and DKD
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susceptibility or quantitative traits related to DKD in the Chinese people with T2DM.
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2. Materials and methods
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2.1. Ethics statement
Our study was approved by the institutional review board of Shanghai Jiao Tong University Affiliated Sixth People’s Hospital in accordance with the second revision of the Declaration of
2.2. Participants
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Helsinki. Each participant signed a written informed consent form.
A total of 4,049 participants diagnosed with T2DM were recruited from the Shanghai
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Diabetes Institute Inpatient Database (August 2001 to June 2007). All participants were unrelated subjects who had the same predominant genetic background and resided in Shanghai or nearby
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regions. Subjects with cancer, hepatic disease, pregnancy or other coexisting illnesses including autoimmune kidney diseases, renal artery stenosis were excluded. Subjects with T2DM were diagnosed based on the 1999 criteria of the World Health Organization (WHO). Type 1 diabetes and mitochondrial diabetes were excluded using clinical, immunological and genetic criteria. Patients with an estimated glomerular filtration rate (eGFR) < 90 mL/min per 1.73 m2 or albuminuria ≥ 30 mg/24 h or ACR (albumin to creatinine ratio) ≥30µg/mg, were diagnosed with DKD, patients with a diabetes history over 5 years but without signs of DKD were included in the study as controls. Finally, 1,157 controls with a diabetes history less than 5 years were excluded due to duration control, a total of 2,892 participants were used for further analyses. 2.3. Clinical measurements The history of every patient was recorded in detail, and the anthropometric and biochemical
ACCEPTED MANUSCRIPT traits related to diabetes were evaluated in every patient. The patients’ height (m) and weight (kg) were measured, and their body mass index (BMI) was calculated as weight/height2. Blood pressure was also measured. Serum triglyceride, total cholesterol, low-density lipoprotein
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cholesterol (LDL-c), high-density lipoprotein cholesterol (HDL-c), uric acid and creatinine levels were measured using a type 7600-020 Automated Analyser (Hitachi, Tokyo, Japan). HbA1c levels were determined by high performance liquid chromatography (HPLC) using a Bio-Rad Variant II
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haemoglobin testing system (Bio-Rad Laboratories, Hercules, CA, USA). The albuminuria level was measured with scatter turbidimetry using the BN II System (Siemens Healthcare Diagnostics
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Products GmbH, Marburg, Germany), the sample was from 24h urine collection, the measurement was repeated in three different days, and the averages of these measurements were used for further
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analysis. ACR was calculated as urine albuminuria /creatinine. The eGFR was calculated using a modification of the diet in renal disease study equation (MDRD) designed for a Chinese
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population (Ma et al., 2006).
2.4. SNP selection, genotyping and quality control analysis Seventeen SNPs from thirteen loci, including PDZK1 (rs12129861), GCKR (rs780094), LRP2 (rs2544390), SLC2A9 (rs11722228, rs16890979, rs734553, rs3775948 and rs10489070),
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ABCG2 (rs2231142), LRRC16A (rs742132), SLC17A1 (rs1183201), SLC17A3 (rs1165205 and rs13333049), SLC22A11 (rs17300741), SLC22A12 (rs505802), NRXN2 (rs506338) and SF1
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(rs606458), which have recently been reported to be associated with the level of serum uric acid were selected (Kamatani et al., 2010; Kolz et al., 2009; Sun et al., 2014). These seventeen SNPs were genotyped using multiplex primer extension with matrix-assisted laser desorption ionization time-of-flight mass spectroscopy using the MassARRAY Compact Analyser (Sequenom, San Diego, CA, USA). All the SNPs passed quality control criteria with genotyping call rates over 90%. Individuals with over 15% of the genotypes missing were excluded. Approximately, 2,749 individuals and seventeen SNPs were retained for further analysis after the quality control.
2.5. Statistical analysis The clinical characteristics of the patients were summarized in SAS 9.2. Normality testing was performed, and skewed distributed quantitative traits were logarithmically transformed to approximate univariate normality. The median (interquartile range) and mean ± standard deviation
ACCEPTED MANUSCRIPT were determined for continuous variables. The Wilcoxon test for continuous variables and the χ2 test for categorical variables were used to determine the differences between the case group and the control group. Multivariable logistic regression analysis was used to explore the association
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between uric acid and DKD.
Associations between phenotypes and genotypes were determined in PLINK (Purcell et al., 2007) (v1.07;http://pngu.mgh.harvard.edu/~purcell/plink/) using an additive genetic model.
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Before association analysis, the Hardy-Weinberg equilibrium test was performed separately in the case group and the control group to test the quality of the data. The associations between serum
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uric acid and genotypes were analysed by linear regression adjusted for gender, age and BMI. Allele and genotype distributions between the case group and the control group were compared
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with χ2 tests or multivariate logistic regression analysis, and odds ratios (ORs) with 95% confidence intervals (CIs) were obtained. The associations between quantitative traits of DKD and
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genotypes were analysed by linear regression adjusted for gender, BMI, duration of diabetes, blood pressure, HbA1c, serum lipids and serum uric acid level. To adjust for multiple comparisons, 10,000 permutation tests were performed to assess empirical p values. Two-tailed p values <0.05 were considered to be statistically significant.
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On the basis of an estimated effect size of genetic loci for DKD (~1.2), our samples had >85% power to detect a SNP effect with a minor allele frequency (MAF) of 0.3 and >75% power to
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detect a SNP effect with a MAF of 0.2 at the level of significance of 0.05.
3. Results
All of the SNPs satisfied Hardy-Weinberg equilibrium. The clinical characteristics of the patients are shown in Table 1. Of the 1,358 patients with DKD, there were 79.5% patients with increased albuminuria, and 46.8% patients with declined eGFR. Logistic regression analysis revealed that serum uric acid was associated with DKD significantly adjusted for gender, BMI, duration of diabetes, blood pressure, HbA1c, and serum lipids (OR 1.006(1.005-1.007), p<0.0001). We firstly evaluated the associations between the SNPs and uric acid adjusted for gender, age,
ACCEPTED MANUSCRIPT BMI and eGFR as confounding factors. As shown in Table 2, GCKR rs780094, SLC2A9 rs11722228, SLC2A9 rs3775948, ABCG2 rs2231142, SLC22A12 rs505802 and NRXN2 rs506338 were positively associated with serum uric acid (p= p=3.79E-05, 0.0002, 2.04E-10, 2.23E-09,
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0.0018 and 0.0015, respectively; empirical p=0.0004, 0.002, 1.00E-04, 1.00E-04, 0.0275, and 0.0243; respectively). The association between these SNPs and uric acid remained significant after Bonferroni correction for multiple comparisons (corrected p=6.44E-04 for rs780094, 0.0034 for
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rs11722228, 3.47E-09 for rs3775948, 3.80E-08 for rs2231142, 0.0306 for rs505802 and 0.0255 for rs506338, on the basis of the association analysis between 17 SNPs and uric acid).
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Then we analysed the association between uric acid related SNPs and DKD susceptibility. As shown in Table 3, after adjusting for gender, BMI, duration of diabetes, blood pressure, HbA1c,
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serum lipids and serum uric acid level, SLC2A9 rs11722228 (OR 0.864(0.749-0.996), p=0.0440) and SF1 rs606458 (OR 1.223(1.067-1.402), p=0.0038) were associated with DKD susceptibility
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significantly. SLC2A9 rs3775948 (OR 0.878(0.770-1.000), p=0.0506) and ABCG2 rs2231142 (OR 0.879(0.764-1.010), p=0.0698) were associated with DKD susceptibility marginally. While no significant association was observed after Bonferroni correction for analysis on 17 SNPs. In addition, we further evaluated the associations between DKD related SNPs and
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quantitative traits of DKD adjusted for gender, BMI, duration of diabetes, blood pressure, HbA1c, serum lipids and serum uric acid level. As shown in Table 4, the SNPs associated with DKD
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susceptibility including SLC2A9 rs11722228, SLC2A9 rs3775948, ABCG2 rs2231142, and SF1 rs606458 were significantly associated with eGFR (β=6.026±1.733, p=0.0005; β=5.392±1.578, p=0.0006; β=6.027±1.685, p=0.0004; β=-3.334±1.642, p=0.0424), the association between three SNPs and eGFR remained significant after Bonferroni correction for multiple comparisons (corrected p=0.0085 for rs11722228, 0.0102 for rs3775948, 0.0068 for rs2231142, and 0.7208 for rs606458, on the basis of the association analysis between 17 SNPs and eGFR). In particular, the risk alleles for DKD were associated with lower eGFR levels. However, no statistically significant associations were observed between DKD related SNPs and albuminuria.
4. Discussion
ACCEPTED MANUSCRIPT In this study, we found GCKR rs780094, SLC2A9 rs11722228, SLC2A9 rs3775948, ABCG2 rs2231142, SLC22A12 rs505802, NRXN2 rs506338 were significantly associated with serum uric acid level in T2DM. We further investigated the associations between uric acid related SNPs and
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DKD susceptibility in this study. In a logistic regression analysis after adjusting for confounding factors including gender, BMI, duration of diabetes, blood pressure, HbA1c, serum lipids and serum uric acid, SLC2A9 rs11722228 and SF1 rs606458 were found to be significantly associated
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with DKD, SLC2A9 rs3775948 and ABCG2 rs2231142 were marginally associated with DKD. In a linear regression analysis, DKD related SNPs were significantly associated with eGFR, and
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individuals who carried more risk alleles tended to have lower eGFR levels. However, similar results or statistically significant associations with SNPs were not observed for albuminuria.
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Uric acid has been proved to be associated with DKD in previous studies (Yan et al., 2015; Zoppini et al., 2012), but the association between uric acid related loci and DKD has not been
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elucidated clearly. In this study, we found that SNPs in SLC2A9 and ABCG2 were proved to be associated with uric acid, but the uric acid-raising alleles of these related SNPs were revealed to be protective for DKD (OR<1). This result seems conflict with some prior studies concerning the role of uric acid in DKD (Sheikhbahaei et al., 2014), which can be explained as followed. Firstly,
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DKD is an oxidative stress-based complication (Singh et al., 2011) and uric acid is an oxidant-antioxidant paradox (Glantzounis et al., 2005; Sautin & Johnson, 2008), the role of uric
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acid is still not determined to date. Besides, previous study has revealed that low as well as high serum uric acid was associated with loss of kidney function (Kanda et al., 2015). Secondly, previous studies have revealed that SNPs in SLC2A9 and ABCG2 are also associated with serum glucose level (Kleber et al., 2015; Sun et al., 2015), which is closely related to the initiation and progression of DKD (Macisaac et al., 2014), the association between these SNPs and DKD may be biased by the interaction between uric acid and serum glucose. Thirdly, DKD was a heterogeneous disease and gene expression is a complicated process，these SNPs were associated with DKD after adjusting for serum uric acid, prompting that these genes may affect DKD susceptibility independent of uric acid. Both albuminuria and eGFR are main phenotypes of DKD (Gross et al., 2005). Many factors
ACCEPTED MANUSCRIPT promote albuminuria (Thomas, 2011), but the main mechanism of albuminuria is endothelial dysfunction (Satchell & Tooke, 2008), and albuminuria can be a consequence of systemic stress (obesity, diabetes, age) without progressive organic injury, which is reversible, whether uric acid
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reflects the irreversible or reversible albuminuria still needs further studies. The decline of eGFR primarily occurs due to changes in glomerular haemodynamics and the glomerular filtration surface (Moriya et al., 2012; Osterby et al., 1988). Besides, changes of albuminuria and eGFR are
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not typically synchronized in the progression of DKD (Kramer et al., 2003; Pugliese, 2014). Therefore, the associations between SNPs and eGFR were significantly positive while similar
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results were not observed for albuminuria, prompting that the mechanism underlying albuminuria is likely genetically distinct from the mechanism underlying eGFR.
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There are some limitations in our study. First, resided in Shanghai or nearby regions, the population may have inherent bias, and we only replicated 6 of the 17 SNPs associated with serum
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uric acid level, additional studies that include more ethnic groups are needed to replicate this result. Second, although we found some uric acid related SNPs to be significantly associated with DKD susceptibility in a Chinese population, the associations did not remain significant after adjusting for multiple testing, larger sample size are needed to solve the problem. Third, we aimed to
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explain the association between uric acid and DKD in genetic level in the beginning, but according to the results, these SNPs might be associated with DKD through non-uric acid pathway,
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the mechanism through which these uric acid related genes influence DKD susceptibility is still unknown, further genetic and functional studies are imperative to clarify the role of uric acid in DKD. In conclusion, the present data revealed that the uric acid related alleles of SLC2A9 rs11722228, SLC2A9 rs3775948 and ABCG2 rs2231142 might be associated with DKD susceptibility in the Chinese people with T2DM, and the association was probably independent of uric acid.
Acknowledgments We thank the participants of the research study. We are grateful for the assistance of the nursing and medical staff at the Shanghai Clinical Centre for Diabetes. This work was funded by
ACCEPTED MANUSCRIPT grants from the National Science Foundation of China (81200582, 81322010 and 81300691), Shanghai Talent Development Grant (2012041), the Shanghai Jiao Tong Medical/Engineering Foundation (YG2014MS18) and the National Program for Support of Top-notch Young
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Professionals.
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(2007). PLINK: a tool set for whole-genome association and population-based linkage analyses. Am J Hum Genet 81, 559-575. S0002-9297(07)61352-4 [pii] 10.1086/519795 [doi] Satchell, S.C., and Tooke, J.E. (2008). What is the mechanism of microalbuminuria in diabetes: a role for the glomerular endothelium? Diabetologia 51, 714-725. 10.1007/s00125-008-0961-8 [doi] Sautin, Y.Y., and Johnson, R.J. (2008). Uric acid: the oxidant-antioxidant paradox. Nucleosides, nucleotides & nucleic acids 27, 608-619. 10.1080/15257770802138558 Sheikhbahaei, S., Fotouhi, A., Hafezi-Nejad, N., Nakhjavani, M., and Esteghamati, A. (2014). Serum uric acid, the metabolic syndrome, and the risk of chronic kidney disease in patients with type 2 diabetes. Metabolic syndrome and related disorders 12, 102-109. 10.1089/met.2013.0119 Singh, D.K., Winocour, P., and Farrington, K. (2011). Oxidative stress in early diabetic nephropathy: fueling the fire. Nat Rev Endocrinol 7, 176-184. nrendo.2010.212 [pii] 10.1038/nrendo.2010.212 [doi] Sun, X., Jiang, F., Zhang, R., Tang, S.S., Chen, M., Peng, D.F., et al. (2014). Serum uric acid levels are associated with polymorphisms in the SLC2A9, SF1, and GCKR genes in a Chinese population. Acta pharmacologica Sinica. 10.1038/aps.2014.87 Sun, X., Zhang, R., Jiang, F., Tang, S., Chen, M., Peng, D., et al. (2015). Common variants related to serum uric Acid concentrations are associated with glucose metabolism and insulin secretion in a chinese population. PloS one 10, e0116714. 10.1371/journal.pone.0116714 [doi] PONE-D-14-44686 [pii] Tang, Z.H., Zeng, F., and Zhang, X.Z. (2015). Human genetics of diabetic nephropathy. Ren Fail, 1-9. 10.3109/0886022X.2014.1000801 [doi] Thomas, M.C. (2011). Pathogenesis and progression of proteinuria. Contrib Nephrol 170, 48-56. 000324943 [pii] 10.1159/000324943 [doi] Villeneuve, L.M., and Natarajan, R. (2010). The role of epigenetics in the pathology of diabetic complications. Am J Physiol Renal Physiol 299, F14-25. ajprenal.00200.2010 [pii] 10.1152/ajprenal.00200.2010 [doi] Yan, D., Tu, Y., Jiang, F., Wang, J., Zhang, R., Sun, X., et al. (2015). Uric Acid is independently associated with diabetic kidney disease: a cross-sectional study in a Chinese population. PloS one 10, e0129797. 10.1371/journal.pone.0129797 [doi] PONE-D-15-05636 [pii] Zoppini, G., Targher, G., Chonchol, M., Ortalda, V., Abaterusso, C., Pichiri, I., et al. (2012). Serum uric acid levels and incident chronic kidney disease in patients with type 2 diabetes and preserved kidney function. Diabetes care 35, 99-104. 10.2337/dc11-1346
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Table 1 Clinical characteristics of the study population. Non-DKD
DKD
p value
Sample (n)
2,749
1,391
1,358
—
Men/Women (n)
1,330/1,419
607/751
723/668
Age (years)
63.6±11.5 24.3(22.0,26.7) 9.0(5.7,13.0)
62.5±10.3 23.9(21.6,26.2) 10.0(7.0,13.0)
64.7±12.6 24.8(22.4,27.1) 8.0(2.5,13.0)
<0.0001
BMI (kg/m2) Duration (years) SP (mmHg)
135(120,150)
130(120,145)
140(130,150)
<0.0001
SP(kPa)
18.0(16.0, 20.0)
17.3(16.0, 19.3)
18.6(17.3, 2.0)
<0.0001
DP (mmHg)
80(75,90)
85(80,90)
80(80,90)
<0.0001
DP(kPa)
10.6(10.0, 12.0)
11.3(10.6, 12.0)
10.8(10.8, 12.0)
<0.0001
HDL (mmol/L)
1.3(1.0,3.9)
1.4(1.1,4.0)
LDL (mmol/L)
2.7(1.8,3.4)
2.6(1.6,3.3)
TC (mmol/L)
4.1(1.3,5.1)
4.0(1.3,4.9)
Triglyceride (mmol/L)
1.9(1.2,2.9)
2.8(1.8,3.6)
HbA1c (%)
8.5(7.1,10.2)
HbA1c(mmol/mol) Uric acid (µmol/L) Microalbuminuria (mg/24 h)
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0.0001 <0.0001 <0.0001
0.0047
2.8(1.9,3.5)
<0.0001
4.3(1.4,5.3)
0.0006
2.0(1.3,3.0)
<0.0001
8.3(7.1,9.8)
8.8(7.1,10.7)
<0.0001
69(54, 88)
67(54, 84)
73(54, 93)
<0.0001
308(251,372)
287(239,344)
333(271,402)
<0.0001
18.2 (7.8,66.4)
8.7 (6.0,13.8)
63.2 (32.5,217.7)
<0.0001
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1.3(1.0,3.9)
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eGFR (mL/min per 1.73 m2)
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116.3 (91.4,141.1) 127 (110,148.6) 95.5 (73.3,130.5) <0.0001 The data are shown as the median (interquartile range) or mean ± standard deviation. The Wilcoxon test was used for the skewed distributed variables. The χ2 test was used to determine proportions of the categorical variables. P values < 0.05 are shown in bold. DKD: diabetic kidney disease. BMI: Body mass index. SP: Systolic blood pressure. DP: Diastolic blood pressure. HDL: High-density lipoprotein cholesterol. LDL: Low-density lipoprotein cholesterol. TC: Total cholesterol. eGFR: estimated glomerular filtration rate.
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βvalue
SE
PZDK1
rs12129861
1
145725689
G
2.037
2.902
0.4828
Empirical p value 0.9999
GCKR
rs780094
2
27741237
A
9.599
2.325
LRP2
rs2544390
2
170204846
T
3.665
3.79E-05 0.1096
0.0004 0.8134
SLC2A9
rs11722228
4
9915741
T
SLC2A9
rs16890979
4
9922167
C
0.002 0.9993
SLC2A9
rs734553
4
9923004
A
SLC2A9
rs3775948
4
9995182
G
SLC2A9-WDR1
rs10489070
4
10276352
G
ABCG2
rs2231142
4
89052323
A
LRRC16A
rs742132
6
25607571
C
SLC17A1
rs1183201
6
25823444
A
SLC17A3
rs1165205
6
25870542
T
CDKN2A/B
rs1333049
9
77832372
SLC22A11
rs17300741
11
64331462
SLC22A12
rs505802
11
64357072
NRXN2
rs506338
11
64440920
SF1
rs606458
11
64546391
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Table 2 Associations between SNPs and uric acid in type 2 diabetes patients. Gene SNP Chr* Position (bp) Affect allele
8.673
9.955
0.0002 0.3837
6.628
9.528
0.4867
0.9999
14.66
2.297
3.601
3.298
2.04E-10 0.275
1.00E-04 0.9904
14.77
2.461
0.2166
2.618
2.23E-09 0.9341
1.00E-04 1
2.936
3.151
0.3515
0.9986
4.355
3.105
0.1609
0.9221
G
1.18
2.237
0.598
1
G
4.465
5.042
0.376
0.9993
G
8.18
2.614
0.0018
0.0275
T
8.282
2.6
A
0.739
2.397
0.0015 0.7579
0.0243 1
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2.51
AC
9.512
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2.289
P-value
*Chromosome. P values were adjusted for gender, age, BMI and eGFR. The effect allele is the allele to which the β estimate refers. P values < 0.05 are shown in bold.
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P-genotyp e
Empirical p value
1.061[0.901; 1.250]
Genotype count Case Control AA/Aa/aa AA/Aa/aa 937/402/52 875/432/50
0.4800
0.9999
1.088[0.953; 1.241]
363/635/278
329/595/327
0.2139
0.9710
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OR [95%CI]
rs12129861
1
145725689
G
0.8181
0.8040
0.1807
GCKR
rs780094
2
27741237
A
0.5333
0.5008
LRP2
rs2544390
2
170204846
T
0.4497
0.4408
0.0208 0.5068
0.968[0.850; 1.102]
278/687/417
275/641/435
0.6188
1
SLC2A9
rs11722228
4
9915741
T
0.2861
0.3043
0.1471
0.864[0.749; 0.996]
118/524/686
133/535/648
0.4853
SLC2A9
rs16890979
4
9922167
C
0.9876
0.9865
0.7170
0.778[0.432; 1.402]
1335/32/1
1293/36/0
0.0440 0.4039
SLC2A9
rs734553
4
9923004
A
0.9860
0.9860
0.9983
0.672[0.382; 1.181]
1353/37/1
1318/38/0
0.1670
0.9314
SLC2A9
rs3775948
4
9995182
G
0.5818
0.5851
0.8017
0.878[0.770; 1.000]
481/653/254
453/682/222
0.0506
0.5369
SLC2A9-WDR1
rs10489070
4
10276352
G
0.8551
0.8682
0.1613
0.883[0.732; 1.066]
1011/357/23
1027/304/27
0.1957
0.9592
ABCG2
rs2231142
4
89052323
A
0.2948
0.3056
0.3804
0.879[0.764; 1.010]
121/578/692
134/562/662
0.0698
0.6554
LRRC16A
rs742132
6
25607571
C
0.2522
0.2507
0.9033
0.981[0.847; 1.137]
100/499/787
84/510/758
0.7995
1
SLC17A1
rs1183201
6
25823444
A
0.8471
0.8487
0.8698
1.034[0.866; 1.235]
999/345/39
974/340/34
0.7079
1
SLC17A3
rs1165205
6
25870542
T
0.8421
0.8426
0.9637
1.025[0.861; 1.219]
989/353/42
963/354/36
0.7839
1
CDKN2A/B
rs1333049
9
77832372
G
0.5036
0.5140
0.4409
0.958[0.844; 1.086]
369/662/359
378/640/340
0.4981
1
SLC22A11
rs17300741
11
64331462
G
0.05252
0.05748
0.4198
0.889[0.667; 1.186]
3/140/1247
3/150/1204
0.4245
0.9999
SLC22A12
rs505802
11
64357072
G
0.7392
0.7539
0.2114
0.929[0.801; 1.076]
759/534/95
775/493/87
0.3264
0.9982
NRXN2
rs506338
11
64440920
T
0.7336
0.7478
0.2306
0.929[0.801; 1.076]
749/537/101
759/510/87
0.3259
0.9982
SF1
rs606458
11
64546391
AC
CE
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PZDK1
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Table 3 Associations between SNPs and DKD susceptibility in type 2 diabetes patients. Gene SNP Chr* Position Effect Frequency P-allele (bp) allele Case Control
0.9999
A 0.6379 0.6168 0.1061 1.223[1.067; 1.402] 551/665/169 515/644/198 0.0530 0.0038 *Chromosome. The effect allele refers to the allele which increases the serum uric acid level in this study. P-allele is the p value for the comparison of the allele frequency between the case group and control group. P-genotype is the p value for the comparison of genotype distribution between the case group and control group, which adjusted for gender, BMI, duration of diabetes, blood pressure, HbA1c, serum lipids and serum uric acid level. The OR with 95% CI is for the association between effect allele of uric acid and DKD susceptibility. Empirical p values were calculated through 10,000 permutations to compare genotype distribution between the case group and the control group. P values < 0.05 are shown in bold.
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Table 4 Associations between the SNPs and clinical features related to DKD in the study subjects. Traits Gene SNP Chr* AA Aa aa
β value
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Empirical p value Microalbumin SLC2A9 rs11722228 4 14.4(7.7,86.9) 17.0(7.8,60.9) 19.7(7.8,64.5) -34.710 25.370 0.1714 0.9235 uria SLC2A9 rs3775948 4 24.290 18.0(7.7,73.9) 18.3(7.9,61.1) 19.7(7.7,71.7) -15.120 0.5336 0.9999 (mg/24 h) ABCG2 rs2231142 4 18.2(8.1,52.4) 17.8(7.7,66.0) 18.7(7.8,70.5) -23.700 26.010 0.3624 0.9989 SF1 rs606458 11 25.270 20.3(8.0,79.4) 18.7(7.8,64.2) 14.2(7.1,54.0) 14.360 0.5699 1 MDRD SLC2A9 rs11722228 4 1.733 118.3(92.2,142.2) 117.0(91.4,142.0) 115.8(91.3,140.0) 6.026 0.0005 0.0340 (ml/min 1.73 SLC2A9 rs3775948 4 1.578 117.9(91.7,142.0) 116.9(93.6,142.8) 109.9(87.8,134.6) 5.392 0.0006 0.0365 m2) ABCG2 rs2231142 4 1.685 115.9(93.8,143.1) 117.1(90.6,142.9) 115.4(91.8,140.0) 6.027 0.0004 0.0309 SF1 rs606458 11 1.642 118.1(92.7,144.7) 116.3(92.0,140.8) 115.6(90.2,140.9) -3.334 0.4536 0.0424 *Chromosome. MDRD: Modification of diet in renal disease. AA represents the homozygous genotype for the risk allele of uric acid. Aa represents the heterozygous genotype. aa represents the homozygous for the non-risk allele of uric acid. P values were adjusted for gender, BMI, duration of diabetes, blood pressure, HbA1c, serum lipids and serum uric acid level. Empirical p values were calculated through 10,000 permutations within each trait. P values < 0.05 are shown in bold.
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1. Association between SNPs and uric acid was replicated in Chinese population.
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Highlights
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2. Uric acid related SNPs might affect DKD susceptibility independent of uric acid.
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3. Studies in genetics could offer more clues for the mechanism of DKD.
S1056-8727(16)30012-5 doi: 10.1016/j.jdiacomp.2016.02.018 JDC 6670
To appear in:
Journal of Diabetes and Its Complications
Received date: Revised date: Accepted date:
24 August 2015 4 February 2016 24 February 2016
Please cite this article as: Yan, D., Wang, J., Jiang, F., Zhang, R., Sun, X., Wang, T., Wang, S., Peng, D., He, Z., Bao, Y., Hu, C. & Jia, W., Association between serum uric acid related genetic loci and diabetic kidney disease in the Chinese type 2 diabetes patients, Journal of Diabetes and Its Complications (2016), doi: 10.1016/j.jdiacomp.2016.02.018
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ACCEPTED MANUSCRIPT Association between serum uric acid related genetic loci and diabetic kidney disease in
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the Chinese type 2 diabetes patients Dandan Yan1‡, Jie Wang1‡, Feng Jiang1, Rong Zhang1, Xue Sun1, Tao Wang1, Shiyun Wang1, Danfeng Peng1, Zhen He1, Yuqian Bao1, Cheng Hu1,2#, Weiping Jia1#
Shanghai Diabetes Institute, Shanghai Key Laboratory of Diabetes Mellitus, Shanghai Clinical
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1
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Center for Diabetes, Shanghai Jiao Tong University Affiliated Sixth People’s Hospital, Shanghai 200233 China
Shanghai Jiao Tong University Affiliated Sixth People’s Hospital, South Campus, Shanghai
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2
#
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201499, China
Corresponding authors: 1. Cheng Hu, email: [email protected]
and
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2. Weiping Jia, email: [email protected]
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Tel: +86-21-24058924; Fax: +86-21-64368031
‡ These authors contributed equally to this article. Conflict of interest: The authors declare that they have no conflict of interest.
ACCEPTED MANUSCRIPT Abstract Aim: We aimed to investigate the association between uric acid related genetic loci and DKD susceptibility in type 2 diabetes patients.
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Methods: Seventeen single nucleotide polymorphisms (SNPs) from thirteen loci related to serum uric acid were genotyped in 2,892 type 2 diabetes patients. Associations between SNPs and uric acid, SNPs and quantitative traits related to DKD or its susceptibility were evaluated.
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Results: In this study, uric acid showed a strong association with DKD (OR=1.006, p<0.0001). GCKR rs780094, SLC2A9 rs11722228, SLC2A9 rs3775948, ABCG2 rs2231142, SLC22A12
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rs505802 and NRXN2 rs506338 were positively associated with serum uric acid (p=3.79E-05, 0.0002, 2.04E-10, 2.23E-09, 0.0018 and 0.0015, respectively). SLC2A9 rs11722228 and SF1
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rs606458 were significantly associated with DKD (OR=0.864, p=0.0440; OR=1.223, p=0.0038). SLC2A9 rs3775948 and ABCG2 rs2231142 were associated with DKD marginally (OR=0.878,
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p=0.0506; OR=0.879, p=0.0698). SLC2A9 rs11722228, SLC2A9 rs3775948, ABCG2 rs2231142 and SF1 rs606458 were significantly associated with the estimated glomerular filtration rate (p=0.0005, 0.0006, 0.0003, and 0.0424, respectively). Conclusions: Our study indicated that the uric acid related alleles of SLC2A9 rs11722228,
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SLC2A9 rs3775948, ABCG2 rs2231142 might affect DKD susceptibility and possibly through
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non-uric acid pathway in the Chinese people with type 2 diabetes. Keywords: uric acid; diabetic kidney disease; SLC2A9; ABCG2; single nucleotide polymorphisms
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1. Introduction
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As a highly prevalent chronic disease around the world, type 2 diabetes mellitus (T2DM) involves several important organ systems, and diabetic kidney disease (DKD) is a highly prevalent complication of T2DM. In many countries, DKD is the leading cause of end-stage renal disease
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(Lim, 2014) and has become a serious public health problem. Previous investigations have revealed that many factors, including age, duration of diabetes, blood pressure, glucose
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metabolism, serum lipids, and serum uric acid are associated with the onset and progression of DKD (Altemtam et al., 2012; Macisaac et al., 2014). However, in clinical practice, not all patients
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show characteristics similar to those reported in previous studies. For example, epidemiological studies have revealed that strict glycaemic control can reduce the incidence of DKD (Nordwall et al., 2009). However, some patients with poorly controlled blood glucose do not develop DKD,
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whereas some patients develop DKD despite acceptable glycaemic control. Therefore, classical observational studies cannot confirm the risk factors for DKD. DKD clusters in families (Fava et al., 2000), and the risks of DKD differ among ethnic
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groups (Chandie Shaw et al., 2006), suggesting a genetic basis for the disease, so the study of DKD in genetic level could reveal the mechanism underlying this disease. Although researches
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have attempted to identify susceptible genes and genetic variants associated with DKD through candidate gene linkage studies (Placha et al., 2006) or genome-wide association studies (GWAS) (Fagerholm et al., 2012), the results of these studies are inconsistent (Tang et al., 2015)and few of the findings have been replicated (Ng et al., 2005), which is likely due to relatively small sample sizes, ethnic variations, or epigenetic changes induced by diabetes status (Villeneuve & Natarajan, 2010). Therefore, additional studies of different populations in genetic level are imperative to explain the mechanism of DKD. According to previous clinical and epidemiological studies, serum uric acid was associated with the progression of DKD, but the role of uric acid in DKD was disputable (Domrongkitchaiporn et al., 2005; Sheikhbahaei et al., 2014). Since simple observational studies could not confirm hyperuricaemia as a causative factor in the development of renal disease, and
ACCEPTED MANUSCRIPT there has been few study involving the association between uric acid related genetic variants and DKD susceptibility, the association between serum uric acid related single nucleotide polymorphisms (SNPs) and DKD susceptibility may offer more clues for the role of uric acid in
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DKD.
In our study, we chose SNPs identified to be associated with the level of serum uric acid in previous studies, and tested for the associations between uric acid related SNPs and DKD
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susceptibility or quantitative traits related to DKD in the Chinese people with T2DM.
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2. Materials and methods
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2.1. Ethics statement
Our study was approved by the institutional review board of Shanghai Jiao Tong University Affiliated Sixth People’s Hospital in accordance with the second revision of the Declaration of
2.2. Participants
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Helsinki. Each participant signed a written informed consent form.
A total of 4,049 participants diagnosed with T2DM were recruited from the Shanghai
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Diabetes Institute Inpatient Database (August 2001 to June 2007). All participants were unrelated subjects who had the same predominant genetic background and resided in Shanghai or nearby
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regions. Subjects with cancer, hepatic disease, pregnancy or other coexisting illnesses including autoimmune kidney diseases, renal artery stenosis were excluded. Subjects with T2DM were diagnosed based on the 1999 criteria of the World Health Organization (WHO). Type 1 diabetes and mitochondrial diabetes were excluded using clinical, immunological and genetic criteria. Patients with an estimated glomerular filtration rate (eGFR) < 90 mL/min per 1.73 m2 or albuminuria ≥ 30 mg/24 h or ACR (albumin to creatinine ratio) ≥30µg/mg, were diagnosed with DKD, patients with a diabetes history over 5 years but without signs of DKD were included in the study as controls. Finally, 1,157 controls with a diabetes history less than 5 years were excluded due to duration control, a total of 2,892 participants were used for further analyses. 2.3. Clinical measurements The history of every patient was recorded in detail, and the anthropometric and biochemical
ACCEPTED MANUSCRIPT traits related to diabetes were evaluated in every patient. The patients’ height (m) and weight (kg) were measured, and their body mass index (BMI) was calculated as weight/height2. Blood pressure was also measured. Serum triglyceride, total cholesterol, low-density lipoprotein
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cholesterol (LDL-c), high-density lipoprotein cholesterol (HDL-c), uric acid and creatinine levels were measured using a type 7600-020 Automated Analyser (Hitachi, Tokyo, Japan). HbA1c levels were determined by high performance liquid chromatography (HPLC) using a Bio-Rad Variant II
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haemoglobin testing system (Bio-Rad Laboratories, Hercules, CA, USA). The albuminuria level was measured with scatter turbidimetry using the BN II System (Siemens Healthcare Diagnostics
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Products GmbH, Marburg, Germany), the sample was from 24h urine collection, the measurement was repeated in three different days, and the averages of these measurements were used for further
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analysis. ACR was calculated as urine albuminuria /creatinine. The eGFR was calculated using a modification of the diet in renal disease study equation (MDRD) designed for a Chinese
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population (Ma et al., 2006).
2.4. SNP selection, genotyping and quality control analysis Seventeen SNPs from thirteen loci, including PDZK1 (rs12129861), GCKR (rs780094), LRP2 (rs2544390), SLC2A9 (rs11722228, rs16890979, rs734553, rs3775948 and rs10489070),
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ABCG2 (rs2231142), LRRC16A (rs742132), SLC17A1 (rs1183201), SLC17A3 (rs1165205 and rs13333049), SLC22A11 (rs17300741), SLC22A12 (rs505802), NRXN2 (rs506338) and SF1
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(rs606458), which have recently been reported to be associated with the level of serum uric acid were selected (Kamatani et al., 2010; Kolz et al., 2009; Sun et al., 2014). These seventeen SNPs were genotyped using multiplex primer extension with matrix-assisted laser desorption ionization time-of-flight mass spectroscopy using the MassARRAY Compact Analyser (Sequenom, San Diego, CA, USA). All the SNPs passed quality control criteria with genotyping call rates over 90%. Individuals with over 15% of the genotypes missing were excluded. Approximately, 2,749 individuals and seventeen SNPs were retained for further analysis after the quality control.
2.5. Statistical analysis The clinical characteristics of the patients were summarized in SAS 9.2. Normality testing was performed, and skewed distributed quantitative traits were logarithmically transformed to approximate univariate normality. The median (interquartile range) and mean ± standard deviation
ACCEPTED MANUSCRIPT were determined for continuous variables. The Wilcoxon test for continuous variables and the χ2 test for categorical variables were used to determine the differences between the case group and the control group. Multivariable logistic regression analysis was used to explore the association
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between uric acid and DKD.
Associations between phenotypes and genotypes were determined in PLINK (Purcell et al., 2007) (v1.07;http://pngu.mgh.harvard.edu/~purcell/plink/) using an additive genetic model.
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Before association analysis, the Hardy-Weinberg equilibrium test was performed separately in the case group and the control group to test the quality of the data. The associations between serum
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uric acid and genotypes were analysed by linear regression adjusted for gender, age and BMI. Allele and genotype distributions between the case group and the control group were compared
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with χ2 tests or multivariate logistic regression analysis, and odds ratios (ORs) with 95% confidence intervals (CIs) were obtained. The associations between quantitative traits of DKD and
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genotypes were analysed by linear regression adjusted for gender, BMI, duration of diabetes, blood pressure, HbA1c, serum lipids and serum uric acid level. To adjust for multiple comparisons, 10,000 permutation tests were performed to assess empirical p values. Two-tailed p values <0.05 were considered to be statistically significant.
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On the basis of an estimated effect size of genetic loci for DKD (~1.2), our samples had >85% power to detect a SNP effect with a minor allele frequency (MAF) of 0.3 and >75% power to
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detect a SNP effect with a MAF of 0.2 at the level of significance of 0.05.
3. Results
All of the SNPs satisfied Hardy-Weinberg equilibrium. The clinical characteristics of the patients are shown in Table 1. Of the 1,358 patients with DKD, there were 79.5% patients with increased albuminuria, and 46.8% patients with declined eGFR. Logistic regression analysis revealed that serum uric acid was associated with DKD significantly adjusted for gender, BMI, duration of diabetes, blood pressure, HbA1c, and serum lipids (OR 1.006(1.005-1.007), p<0.0001). We firstly evaluated the associations between the SNPs and uric acid adjusted for gender, age,
ACCEPTED MANUSCRIPT BMI and eGFR as confounding factors. As shown in Table 2, GCKR rs780094, SLC2A9 rs11722228, SLC2A9 rs3775948, ABCG2 rs2231142, SLC22A12 rs505802 and NRXN2 rs506338 were positively associated with serum uric acid (p= p=3.79E-05, 0.0002, 2.04E-10, 2.23E-09,
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0.0018 and 0.0015, respectively; empirical p=0.0004, 0.002, 1.00E-04, 1.00E-04, 0.0275, and 0.0243; respectively). The association between these SNPs and uric acid remained significant after Bonferroni correction for multiple comparisons (corrected p=6.44E-04 for rs780094, 0.0034 for
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rs11722228, 3.47E-09 for rs3775948, 3.80E-08 for rs2231142, 0.0306 for rs505802 and 0.0255 for rs506338, on the basis of the association analysis between 17 SNPs and uric acid).
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Then we analysed the association between uric acid related SNPs and DKD susceptibility. As shown in Table 3, after adjusting for gender, BMI, duration of diabetes, blood pressure, HbA1c,
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serum lipids and serum uric acid level, SLC2A9 rs11722228 (OR 0.864(0.749-0.996), p=0.0440) and SF1 rs606458 (OR 1.223(1.067-1.402), p=0.0038) were associated with DKD susceptibility
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significantly. SLC2A9 rs3775948 (OR 0.878(0.770-1.000), p=0.0506) and ABCG2 rs2231142 (OR 0.879(0.764-1.010), p=0.0698) were associated with DKD susceptibility marginally. While no significant association was observed after Bonferroni correction for analysis on 17 SNPs. In addition, we further evaluated the associations between DKD related SNPs and
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quantitative traits of DKD adjusted for gender, BMI, duration of diabetes, blood pressure, HbA1c, serum lipids and serum uric acid level. As shown in Table 4, the SNPs associated with DKD
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susceptibility including SLC2A9 rs11722228, SLC2A9 rs3775948, ABCG2 rs2231142, and SF1 rs606458 were significantly associated with eGFR (β=6.026±1.733, p=0.0005; β=5.392±1.578, p=0.0006; β=6.027±1.685, p=0.0004; β=-3.334±1.642, p=0.0424), the association between three SNPs and eGFR remained significant after Bonferroni correction for multiple comparisons (corrected p=0.0085 for rs11722228, 0.0102 for rs3775948, 0.0068 for rs2231142, and 0.7208 for rs606458, on the basis of the association analysis between 17 SNPs and eGFR). In particular, the risk alleles for DKD were associated with lower eGFR levels. However, no statistically significant associations were observed between DKD related SNPs and albuminuria.
4. Discussion
ACCEPTED MANUSCRIPT In this study, we found GCKR rs780094, SLC2A9 rs11722228, SLC2A9 rs3775948, ABCG2 rs2231142, SLC22A12 rs505802, NRXN2 rs506338 were significantly associated with serum uric acid level in T2DM. We further investigated the associations between uric acid related SNPs and
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DKD susceptibility in this study. In a logistic regression analysis after adjusting for confounding factors including gender, BMI, duration of diabetes, blood pressure, HbA1c, serum lipids and serum uric acid, SLC2A9 rs11722228 and SF1 rs606458 were found to be significantly associated
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with DKD, SLC2A9 rs3775948 and ABCG2 rs2231142 were marginally associated with DKD. In a linear regression analysis, DKD related SNPs were significantly associated with eGFR, and
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individuals who carried more risk alleles tended to have lower eGFR levels. However, similar results or statistically significant associations with SNPs were not observed for albuminuria.
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Uric acid has been proved to be associated with DKD in previous studies (Yan et al., 2015; Zoppini et al., 2012), but the association between uric acid related loci and DKD has not been
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elucidated clearly. In this study, we found that SNPs in SLC2A9 and ABCG2 were proved to be associated with uric acid, but the uric acid-raising alleles of these related SNPs were revealed to be protective for DKD (OR<1). This result seems conflict with some prior studies concerning the role of uric acid in DKD (Sheikhbahaei et al., 2014), which can be explained as followed. Firstly,
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DKD is an oxidative stress-based complication (Singh et al., 2011) and uric acid is an oxidant-antioxidant paradox (Glantzounis et al., 2005; Sautin & Johnson, 2008), the role of uric
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acid is still not determined to date. Besides, previous study has revealed that low as well as high serum uric acid was associated with loss of kidney function (Kanda et al., 2015). Secondly, previous studies have revealed that SNPs in SLC2A9 and ABCG2 are also associated with serum glucose level (Kleber et al., 2015; Sun et al., 2015), which is closely related to the initiation and progression of DKD (Macisaac et al., 2014), the association between these SNPs and DKD may be biased by the interaction between uric acid and serum glucose. Thirdly, DKD was a heterogeneous disease and gene expression is a complicated process，these SNPs were associated with DKD after adjusting for serum uric acid, prompting that these genes may affect DKD susceptibility independent of uric acid. Both albuminuria and eGFR are main phenotypes of DKD (Gross et al., 2005). Many factors
ACCEPTED MANUSCRIPT promote albuminuria (Thomas, 2011), but the main mechanism of albuminuria is endothelial dysfunction (Satchell & Tooke, 2008), and albuminuria can be a consequence of systemic stress (obesity, diabetes, age) without progressive organic injury, which is reversible, whether uric acid
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reflects the irreversible or reversible albuminuria still needs further studies. The decline of eGFR primarily occurs due to changes in glomerular haemodynamics and the glomerular filtration surface (Moriya et al., 2012; Osterby et al., 1988). Besides, changes of albuminuria and eGFR are
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not typically synchronized in the progression of DKD (Kramer et al., 2003; Pugliese, 2014). Therefore, the associations between SNPs and eGFR were significantly positive while similar
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results were not observed for albuminuria, prompting that the mechanism underlying albuminuria is likely genetically distinct from the mechanism underlying eGFR.
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There are some limitations in our study. First, resided in Shanghai or nearby regions, the population may have inherent bias, and we only replicated 6 of the 17 SNPs associated with serum
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uric acid level, additional studies that include more ethnic groups are needed to replicate this result. Second, although we found some uric acid related SNPs to be significantly associated with DKD susceptibility in a Chinese population, the associations did not remain significant after adjusting for multiple testing, larger sample size are needed to solve the problem. Third, we aimed to
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explain the association between uric acid and DKD in genetic level in the beginning, but according to the results, these SNPs might be associated with DKD through non-uric acid pathway,
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the mechanism through which these uric acid related genes influence DKD susceptibility is still unknown, further genetic and functional studies are imperative to clarify the role of uric acid in DKD. In conclusion, the present data revealed that the uric acid related alleles of SLC2A9 rs11722228, SLC2A9 rs3775948 and ABCG2 rs2231142 might be associated with DKD susceptibility in the Chinese people with T2DM, and the association was probably independent of uric acid.
Acknowledgments We thank the participants of the research study. We are grateful for the assistance of the nursing and medical staff at the Shanghai Clinical Centre for Diabetes. This work was funded by
ACCEPTED MANUSCRIPT grants from the National Science Foundation of China (81200582, 81322010 and 81300691), Shanghai Talent Development Grant (2012041), the Shanghai Jiao Tong Medical/Engineering Foundation (YG2014MS18) and the National Program for Support of Top-notch Young
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Professionals.
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Table 1 Clinical characteristics of the study population. Non-DKD
DKD
p value
Sample (n)
2,749
1,391
1,358
—
Men/Women (n)
1,330/1,419
607/751
723/668
Age (years)
63.6±11.5 24.3(22.0,26.7) 9.0(5.7,13.0)
62.5±10.3 23.9(21.6,26.2) 10.0(7.0,13.0)
64.7±12.6 24.8(22.4,27.1) 8.0(2.5,13.0)
<0.0001
BMI (kg/m2) Duration (years) SP (mmHg)
135(120,150)
130(120,145)
140(130,150)
<0.0001
SP(kPa)
18.0(16.0, 20.0)
17.3(16.0, 19.3)
18.6(17.3, 2.0)
<0.0001
DP (mmHg)
80(75,90)
85(80,90)
80(80,90)
<0.0001
DP(kPa)
10.6(10.0, 12.0)
11.3(10.6, 12.0)
10.8(10.8, 12.0)
<0.0001
HDL (mmol/L)
1.3(1.0,3.9)
1.4(1.1,4.0)
LDL (mmol/L)
2.7(1.8,3.4)
2.6(1.6,3.3)
TC (mmol/L)
4.1(1.3,5.1)
4.0(1.3,4.9)
Triglyceride (mmol/L)
1.9(1.2,2.9)
2.8(1.8,3.6)
HbA1c (%)
8.5(7.1,10.2)
HbA1c(mmol/mol) Uric acid (µmol/L) Microalbuminuria (mg/24 h)
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MA NU SC
0.0001 <0.0001 <0.0001
0.0047
2.8(1.9,3.5)
<0.0001
4.3(1.4,5.3)
0.0006
2.0(1.3,3.0)
<0.0001
8.3(7.1,9.8)
8.8(7.1,10.7)
<0.0001
69(54, 88)
67(54, 84)
73(54, 93)
<0.0001
308(251,372)
287(239,344)
333(271,402)
<0.0001
18.2 (7.8,66.4)
8.7 (6.0,13.8)
63.2 (32.5,217.7)
<0.0001
CE
PT
1.3(1.0,3.9)
AC
eGFR (mL/min per 1.73 m2)
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Total
116.3 (91.4,141.1) 127 (110,148.6) 95.5 (73.3,130.5) <0.0001 The data are shown as the median (interquartile range) or mean ± standard deviation. The Wilcoxon test was used for the skewed distributed variables. The χ2 test was used to determine proportions of the categorical variables. P values < 0.05 are shown in bold. DKD: diabetic kidney disease. BMI: Body mass index. SP: Systolic blood pressure. DP: Diastolic blood pressure. HDL: High-density lipoprotein cholesterol. LDL: Low-density lipoprotein cholesterol. TC: Total cholesterol. eGFR: estimated glomerular filtration rate.
ACCEPTED MANUSCRIPT
βvalue
SE
PZDK1
rs12129861
1
145725689
G
2.037
2.902
0.4828
Empirical p value 0.9999
GCKR
rs780094
2
27741237
A
9.599
2.325
LRP2
rs2544390
2
170204846
T
3.665
3.79E-05 0.1096
0.0004 0.8134
SLC2A9
rs11722228
4
9915741
T
SLC2A9
rs16890979
4
9922167
C
0.002 0.9993
SLC2A9
rs734553
4
9923004
A
SLC2A9
rs3775948
4
9995182
G
SLC2A9-WDR1
rs10489070
4
10276352
G
ABCG2
rs2231142
4
89052323
A
LRRC16A
rs742132
6
25607571
C
SLC17A1
rs1183201
6
25823444
A
SLC17A3
rs1165205
6
25870542
T
CDKN2A/B
rs1333049
9
77832372
SLC22A11
rs17300741
11
64331462
SLC22A12
rs505802
11
64357072
NRXN2
rs506338
11
64440920
SF1
rs606458
11
64546391
RI
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Table 2 Associations between SNPs and uric acid in type 2 diabetes patients. Gene SNP Chr* Position (bp) Affect allele
8.673
9.955
0.0002 0.3837
6.628
9.528
0.4867
0.9999
14.66
2.297
3.601
3.298
2.04E-10 0.275
1.00E-04 0.9904
14.77
2.461
0.2166
2.618
2.23E-09 0.9341
1.00E-04 1
2.936
3.151
0.3515
0.9986
4.355
3.105
0.1609
0.9221
G
1.18
2.237
0.598
1
G
4.465
5.042
0.376
0.9993
G
8.18
2.614
0.0018
0.0275
T
8.282
2.6
A
0.739
2.397
0.0015 0.7579
0.0243 1
PT
ED
2.51
AC
9.512
CE
MA NU SC
2.289
P-value
*Chromosome. P values were adjusted for gender, age, BMI and eGFR. The effect allele is the allele to which the β estimate refers. P values < 0.05 are shown in bold.
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P-genotyp e
Empirical p value
1.061[0.901; 1.250]
Genotype count Case Control AA/Aa/aa AA/Aa/aa 937/402/52 875/432/50
0.4800
0.9999
1.088[0.953; 1.241]
363/635/278
329/595/327
0.2139
0.9710
RI
PT
OR [95%CI]
rs12129861
1
145725689
G
0.8181
0.8040
0.1807
GCKR
rs780094
2
27741237
A
0.5333
0.5008
LRP2
rs2544390
2
170204846
T
0.4497
0.4408
0.0208 0.5068
0.968[0.850; 1.102]
278/687/417
275/641/435
0.6188
1
SLC2A9
rs11722228
4
9915741
T
0.2861
0.3043
0.1471
0.864[0.749; 0.996]
118/524/686
133/535/648
0.4853
SLC2A9
rs16890979
4
9922167
C
0.9876
0.9865
0.7170
0.778[0.432; 1.402]
1335/32/1
1293/36/0
0.0440 0.4039
SLC2A9
rs734553
4
9923004
A
0.9860
0.9860
0.9983
0.672[0.382; 1.181]
1353/37/1
1318/38/0
0.1670
0.9314
SLC2A9
rs3775948
4
9995182
G
0.5818
0.5851
0.8017
0.878[0.770; 1.000]
481/653/254
453/682/222
0.0506
0.5369
SLC2A9-WDR1
rs10489070
4
10276352
G
0.8551
0.8682
0.1613
0.883[0.732; 1.066]
1011/357/23
1027/304/27
0.1957
0.9592
ABCG2
rs2231142
4
89052323
A
0.2948
0.3056
0.3804
0.879[0.764; 1.010]
121/578/692
134/562/662
0.0698
0.6554
LRRC16A
rs742132
6
25607571
C
0.2522
0.2507
0.9033
0.981[0.847; 1.137]
100/499/787
84/510/758
0.7995
1
SLC17A1
rs1183201
6
25823444
A
0.8471
0.8487
0.8698
1.034[0.866; 1.235]
999/345/39
974/340/34
0.7079
1
SLC17A3
rs1165205
6
25870542
T
0.8421
0.8426
0.9637
1.025[0.861; 1.219]
989/353/42
963/354/36
0.7839
1
CDKN2A/B
rs1333049
9
77832372
G
0.5036
0.5140
0.4409
0.958[0.844; 1.086]
369/662/359
378/640/340
0.4981
1
SLC22A11
rs17300741
11
64331462
G
0.05252
0.05748
0.4198
0.889[0.667; 1.186]
3/140/1247
3/150/1204
0.4245
0.9999
SLC22A12
rs505802
11
64357072
G
0.7392
0.7539
0.2114
0.929[0.801; 1.076]
759/534/95
775/493/87
0.3264
0.9982
NRXN2
rs506338
11
64440920
T
0.7336
0.7478
0.2306
0.929[0.801; 1.076]
749/537/101
759/510/87
0.3259
0.9982
SF1
rs606458
11
64546391
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Table 3 Associations between SNPs and DKD susceptibility in type 2 diabetes patients. Gene SNP Chr* Position Effect Frequency P-allele (bp) allele Case Control
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A 0.6379 0.6168 0.1061 1.223[1.067; 1.402] 551/665/169 515/644/198 0.0530 0.0038 *Chromosome. The effect allele refers to the allele which increases the serum uric acid level in this study. P-allele is the p value for the comparison of the allele frequency between the case group and control group. P-genotype is the p value for the comparison of genotype distribution between the case group and control group, which adjusted for gender, BMI, duration of diabetes, blood pressure, HbA1c, serum lipids and serum uric acid level. The OR with 95% CI is for the association between effect allele of uric acid and DKD susceptibility. Empirical p values were calculated through 10,000 permutations to compare genotype distribution between the case group and the control group. P values < 0.05 are shown in bold.
ACCEPTED MANUSCRIPT
Table 4 Associations between the SNPs and clinical features related to DKD in the study subjects. Traits Gene SNP Chr* AA Aa aa
β value
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Empirical p value Microalbumin SLC2A9 rs11722228 4 14.4(7.7,86.9) 17.0(7.8,60.9) 19.7(7.8,64.5) -34.710 25.370 0.1714 0.9235 uria SLC2A9 rs3775948 4 24.290 18.0(7.7,73.9) 18.3(7.9,61.1) 19.7(7.7,71.7) -15.120 0.5336 0.9999 (mg/24 h) ABCG2 rs2231142 4 18.2(8.1,52.4) 17.8(7.7,66.0) 18.7(7.8,70.5) -23.700 26.010 0.3624 0.9989 SF1 rs606458 11 25.270 20.3(8.0,79.4) 18.7(7.8,64.2) 14.2(7.1,54.0) 14.360 0.5699 1 MDRD SLC2A9 rs11722228 4 1.733 118.3(92.2,142.2) 117.0(91.4,142.0) 115.8(91.3,140.0) 6.026 0.0005 0.0340 (ml/min 1.73 SLC2A9 rs3775948 4 1.578 117.9(91.7,142.0) 116.9(93.6,142.8) 109.9(87.8,134.6) 5.392 0.0006 0.0365 m2) ABCG2 rs2231142 4 1.685 115.9(93.8,143.1) 117.1(90.6,142.9) 115.4(91.8,140.0) 6.027 0.0004 0.0309 SF1 rs606458 11 1.642 118.1(92.7,144.7) 116.3(92.0,140.8) 115.6(90.2,140.9) -3.334 0.4536 0.0424 *Chromosome. MDRD: Modification of diet in renal disease. AA represents the homozygous genotype for the risk allele of uric acid. Aa represents the heterozygous genotype. aa represents the homozygous for the non-risk allele of uric acid. P values were adjusted for gender, BMI, duration of diabetes, blood pressure, HbA1c, serum lipids and serum uric acid level. Empirical p values were calculated through 10,000 permutations within each trait. P values < 0.05 are shown in bold.
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ACCEPTED MANUSCRIPT
1. Association between SNPs and uric acid was replicated in Chinese population.
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Highlights
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2. Uric acid related SNPs might affect DKD susceptibility independent of uric acid.
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3. Studies in genetics could offer more clues for the mechanism of DKD.