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Journal of Clinical Endocrinology & Metabolism , doi:10.1210/jc.2008-0161
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The Journal of Clinical Endocrinology & Metabolism Vol. 93, No. 10 4107-4112
Copyright © 2008 by The Endocrine Society

Zinc Transporter-8 Gene (SLC30A8) Is Associated with Type 2 Diabetes in Chinese

Jie Xiang1, Xiao-Ying Li1, Min Xu1, Jie Hong, Yun Huang, Jiao-Rong Tan, Xi Lu, Meng Dai, Bing Yu and Guang Ning

Shanghai Clinical Center for Endocrine and Metabolic Diseases (J.X., X.-Y.L., M.X., J.H., Y.H., J.-R.T., X.L., M.D., G.N.), Shanghai Institute of Endocrinology and Metabolism and Shanghai Key Laboratory for Endocrine Tumors, Rui-Jin Hospital, Shanghai Jiao-Tong University School of Medicine, Shanghai 200025, China; and Department of Molecular and Clinical Genetics (B.Y.), Royal Prince Alfred Hospital and Central Clinical School, University of Sydney, Sydney 2050, Australia

Address all correspondence and requests for reprints to: Guang Ning, M.D., Ph.D., Department of Endocrinology and Metabolism, Rui-Jin Hospital, Shanghai Jiao-Tong University School of Medicine, Rui-Jin 2nd Road, Shanghai 200025, China. E-mail: guangning{at}medmail.com.cn.


   Abstract
Top
 Abstract
Introduction
 Subjects and Methods
 Results
 Discussion
 References
 
Context: Several genome-wide association studies identified a strong association of SLC30A8 with type 2 diabetes in individuals of European ancestry. The effect of the association of rs13266634 with type 2 diabetes or related glycemic traits has not been fully extended to non-European populations, and a comprehensive examination of common variants in the gene has not yet been carried out in Han Chinese.

Objective: The objective of the study was to investigate the association of SLC30A8 with type 2 diabetes in Chinese.

Design: A comprehensive gene-based association study was performed using 14 tagging single-nucleotide polymorphism (SNPs) of SLC30A8 in Han Chinese subjects with normal glucose tolerance (NGT; n = 721), impaired glucose regulation (IGR; n = 375), and type 2 diabetes (n = 521).

Results: A significant association for SNP rs13266634 was observed between patients with type 2 diabetes and NGT controls (P = 0.016). The association was also observed between combined type 2 diabetes/IGR and NGT subjects (P = 0.002). The adjusted odds ratios for homozygote CC vs. TT at this locus were 1.71 for type 2 diabetes (95% confidence interval 1.19–2.45, P = 0.002) and 1.77 for type 2 diabetes and IGR (95% confidence interval 1.29–2.42, P = 0.0001). We further studied the genotype-phenotype correlation in 70 Han Chinese using iv glucose tolerance test and found an association between SNP rs13266634 and acute insulin response to glucose and disposition index (adjusted P = 0.012 and 0.004, respectively).

Conclusions: Our results provide evidence that SLC30A8 is a susceptible locus for type 2 diabetes in Chinese population, and its variant can influence insulin secretion.


   Introduction
Top
 Abstract
 Introduction
Subjects and Methods
 Results
 Discussion
 References
 
Zinc transporter 8, a member of the zinc transporter family, is coded by solute carrier family 30 member 8 gene (SLC30A8) on chromosome 8q24.11. The encoded protein has 369 amino acid residues, with six transmembrane domains and a histidine-rich loop between transmembrane domains IV and V, like other family members (1). It has been reported that SLC30A8 is expressed predominantly in pancreatic β-cells and transports zinc from cytoplasm into insulin secretary vesicles (2), in which insulin is stored as a solid hexamer bound with two Zn2+ ions before secretion (3, 4, 5). Zinc plays an important role in all processes of insulin trafficking, i.e. synthesis, storage, and secretion (2). The variations in SLC30A8 may affect zinc accumulation in insulin granules and hence influence insulin stability and insulin trafficking. Glucose stimulated insulin secretion is enhanced in INS-1 cells transfected with SLC30A8 in a high-glucose challenge (6).

Several studies independently revealed a strong association of single-nucleotide polymorphism (SNP) rs13266634, a nonsynonymous Arg325Trp (C>T) variant in SLC30A8 with type 2 diabetes. The C allele of this variant is the risk allele for type 2 diabetes (7, 8, 9, 10) and is associated with decreased pancreatic β-cell function (11, 12, 13). Although this particular SNP rs13266634 has also been replicated in Hong Kong Han Chinese (11), the in-depth gene-based association study in Chinese population has not been reported between SLC30A8 and type 2 diabetes. In this study, we presented the comprehensive association results in 1617 unrelated subjects from a Han Chinese population in Shanghai using 14 tagging SNPs.


   Subjects and Methods
Top
 Abstract
 Introduction
 Subjects and Methods
Results
 Discussion
 References
 
Subjects

The studied subjects were recruited from a two-step blood glucose survey in a single urban community of Shanghai in 2004. First, all permanent residents aged 40–80 yr were invited for the survey. A total of 9219 subjects participated in this investigation including a questionnaire about lifestyle and use of medications and anthropometrical measurements and fasting capillary glucose tests. In the second step, after exclusion of subjects with self-reported diabetes mellitus, 1835 subjects were randomly selected and invited for further investigation, including a 75-g oral glucose tolerance test (OGTT) and blood and urine sampling. The clinical characteristics were not significantly different between the selected 1835 and the other 7384 subjects, including age, gender, systolic blood pressure, diastolic blood pressure, and fasting capillary glucose.

The 1835 subjects were allocated into normal glucose tolerance [NGT, n = 721, average age 59.7 yr; 270 males, 451 females; body mass index (BMI); 24.09 kg/m2], impaired glucose regulation (IGR, n = 375, average age 63.7 yr; 140 males, 235 females; BMI 25.68 kg/m2); and type 2 diabetic patients (n = 521, average age 62.6 yr; 210 males, 311 females; BMI 26.27 kg/m2). IGR was defined as impaired fasting glucose (fasting plasma glucose level ≥6.1 mmol/liter and <7.0 mmol/liter) and/or impaired glucose tolerance (2 h OGTT plasma glucose level ≥7.8 mmol/liter and <11.1 mmol/liter). Type 2 diabetes was diagnosed according to the 1999 World Health Organization criteria (fasting plasma glucose level ≥7.0 mmol/liter or 2 h OGTT plasma glucose level ≥11.1 mmol/liter). A fasting plasma glucose level less than 6.1 mmol/liter and a 2-h OGTT plasma glucose level less than 7.8 mmol/liter was defined as normoglycemia.

Body height and weight and waist and hip circumferences were measured by the same physician. Blood pressure was measured at the right arm with an automated electronic device (model 1 Plus; Omron; Omron, Japan). The fasting and 2-h OGTT plasma glucose, serum triglycerides, total cholesterol, high-density lipoprotein, and low-density lipoprotein were determined. Fasting plasma insulin was measured by RIA (Sangon Co., Shanghai, China). Using the homeostasis model assessment of insulin resistance index (HOMA-IR) was assessed as fasting plasma insulin (in milliunits per milliliter) fasting plasma glucose (in millimoles per liter)/22.5, and homeostasis model assessment of β-cell function (HOMA-β) was assessed as fasting plasma insulin (in milliunits per milliliter) · 20/fasting plasma glucose – 3.5) (in millimoles per liter)/22.5 (14). The study protocol was approved by the Institutional Review Board of the Rui-Jin Hospital, Shanghai. Informed consent was obtained from each participant.

Genotyping

Genomic DNA was extracted from peripheral blood leukocytes with standard phenol/chloroform-based method (15). Genotyping was performed using the Sequenom iPLEX assay (Sequenom, Cambridge, MA). Locus-specific PCR primers and allele-specific detection primers were designed using the MassARRAY Assay Design 3.0 software (Sequenom). Allele detection was performed by primer extension of multiplex products with detection using matrix-assisted laser desorption/ionization time-of-flight mass spectrometry. The mass spectrograms were analyzed by the MassARRAY TYPER software (Sequenom). The detailed list of SNPs and their genotyping assays can be found in supplemental Table 1, published as supplemental data on The Endocrine Society’s Journals Online Web site at http://jcem.endojournals.org.

SLC30A8 genomic sequence of 41.62 kb, along with an additional 5 kb of it up- and downstream, was retrieved from the HapMap database (data release 22/phase II, April 2007, on National Center for Biotechnology Information B36 assembly; http://www.hapmap.org/) (16). Eighteen tagging SNPs were selected using the standard pairwise method in Tagger (17), which is implemented in Haploview (version 3.32; http://www.broad.mit.edu/mpg/haploview/) (18) based on the minor allele frequencies of Han Chinese in HapMap database with a threshold of 1%, r2 of 0.8, and limit of detection of 3.0. This method also allows the user to force in the marker of interest; the SNP previously verified associated with type 2 diabetes in this region (rs13266634) was included in the 18 tagging SNPs (7). These tagging SNPs were able to capture all 59 (100%) of the common SNPs at r2 >0.8 (Fig. 1Go and supplemental Table 2). This meant that any marker that was not eventually chosen as a tagging marker was already strongly correlated with at least one of the tagging markers with r2 >0.8. Two SNPs (rs12542770 and rs16889471) failed to meet the criteria from the Sequenom platform. Another two SNPs, rs7002176 and rs7015338, were also excluded due to a low call rate (57%) and incompatibility with Hardy-Weinberg expectations (P < 0.001), respectively. Finally, the remaining 14 tagging SNPs were used in the analyses, which had an average call rate of 98.6% and captured 91.5% of the variants (54 of 59) in the region. The average concordance rate for the 14 tagging SNPs was 99.6% based on 243 blind comparisons.


Figure 1
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  		FIG. 1. LD plot across the SLC30A8 locus. The horizontal black line depicts the 51.62kb region of chromosome (chr) 16 analyzed in our study. The 59 SNPs captured by the 18 tagging SNPs are listed below the black line. The 18 tagging SNPs are indicated by black arrows. An LD plot is depicted based on the measure D' at the bottom of the figure: each diamond represents the pairwise magnitude of LD, with red color indicating strong linkage disequilibrium (D' >0.8) and statistically significant (logarithm of odds score >2.0). Figure prepared with LocusView, Broad Institute, Cambridge, MA, unpublished software by T. Petryshen, A. Kirby, and M. Ainscow (26 ).

 
We also genotyped SNP rs13266634 in an independent set of subjects (n = 70) with normoglycemia (n = 48) and IGR (n = 22), in whom iv glucose tolerance test (IVGTT) was performed as described previously (19). The IVGTT results made it possible to further explore the association of SNP rs13266634 with pancreatic β-cell function. Acute insulin response to glucose (AIRg) and insulin sensitivity index (Si) were calculated with the Bergman MINIMID computer program (20). Disposition index (DI) was calculated as the product of Si and AIRg. Genotypes of SNP rs13266634 in the 70 subjects were obtained by direct sequencing on an ABI 377 genetic analyzer (Applied Biosystems, Foster City, CA).

Statistical analyses

Deviation from Hardy-Weinberg equilibrium for genotypes at individual locus was assessed using the {chi}2 test. For association analysis of each SNP with disease states (type 2 diabetes vs. NGT and type 2 diabetes/IGR vs. NGT), we used simple {chi}2 test implemented in Haploview software (18). The strength of the association between the presence of the disease and the constitutionally determined alleles was estimated by the odds ratio (OR) with 95% confidence interval (CI). Allelic ORs were obtained from Pearson’s goodness-of-fit {chi}2 test. Logistic regression was performed for the two associated SNPs with type 2 diabetes status under an additive genetic model with or without adjustment for age, gender, and waist circumference. We performed tests for association with quantitative traits in the combined NGT subjects, IGR subjects and type 2 diabetic patients. Tests for association between genotypes and quantitative traits in the combined large samples were performed using linear regression model under an additive model with or without adjustment as suggested by the others’ studies (11, 12). The association tests for traits in the small set of the samples were also performed under an additive model. Indices of insulin secretion were adjusted for age, gender, waist circumference, and indices of insulin sensitivity, and other associated traits were adjusted for age, gender, and waist circumference. The statistical analyses were performed using SAS version 8.1 (SAS Institute, Cary, NC). Measurements with a skewed distribution were normalized by logarithmic transformation.


   Results
Top
 Abstract
 Introduction
 Subjects and Methods
 Results
Discussion
 References
 
Clinical and biochemical characteristics

The clinical and biochemical characteristics of all subjects in the association study are summarized in Table 1Go, whereas those of the subset individuals who received IVGTT are detailed in supplemental Table 3.


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  		TABLE 1. Clinical and biochemical characteristics of NGT, IGR, and type 2 diabetic subjects

 
SNPs and linkage disequilibrium (LD) structures of SLC30A8 gene

The 59 common SNPs and the 18 tagging SNPs in the region are shown in Fig. 1Go. The genomic position, nucleic acid composition, and minor allele frequencies of the 14 genotyped SNPs are shown in Table 2Go. The standardized pairwise LD coefficients D' and r2 are shown in supplemental Table 4.


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  		TABLE 2. SLC30A8 sequence variants and association with type 2 diabetes and IGR

 
Case-control association

The allelic distributions of the 14 tagging SNPs for SLC30A8 were not significantly different between the IGR and type 2 diabetic subjects in this study (data not shown). However, the same allelic distributions were interesting with some significant differences between the cases (type 2 diabetes or combined type 2 diabetes and IGR) and controls (NGT) (Table 2Go). The variant rs13266634 was significantly associated with type 2 diabetes or combined type 2 diabetes and IGR (P = 0.016 and 0.002, respectively). The C allele of rs13266634 was significantly associated with the increased risk for type 2 diabetes and combined type 2 diabetes and IGR. The ORs for homozygous genotypes CC vs. TT of this locus were 1.52 for type 2 diabetes (95% CI 1.08–2.14, P = 0.01) and 1.57 for combined type 2 diabetes and IGR (95% CI 1.17–2.11, P = 0.0009), and adjustment for age, gender, and waist circumference did not change the result (adjusted OR 1.71 and 1.77, P = 0.002 and 0.0001, respectively). The ORs for the heterozygotes were not significant for type 2 diabetes and IGR before or after adjustment (P > 0.05).

An additional SNP rs2466293, which is located in the 3'-untranslated region, showed a significance in the association analysis (P = 0.012) between the combined type 2 diabetes/IGR and NGT controls (P = 0.012) but not between the type 2 diabetes and NGT (P = 0.063, Table 2Go). Homozygous carriers of the risk allele G had ORs of 1.46 (95% CI 1.02–2.09, P = 0.048) and 1.50 (95% CI 1.09–2.07, P = 0.03) relative to noncarriers of G allele for type 2 diabetes and combined type 2 diabetes and IGR, respectively. The adjusted ORs for type 2 diabetes and combined type 2 diabetes and IGR were 1.41 (95% CI 0.96–2.06, P = 0.14) and 1.49 (95% CI 1.06–2.08, P = 0.07), respectively.

Metabolic traits association

The genotype-phenotype correlation was performed between the two significant SNPs (rs13266634 and rs2466293) and body weight, plasma glucose, plasma insulin, HOMA-IR, and HOMA-β in all subjects with NGT, IGR, and type 2 diabetes. We did not find any association between these metabolic traits and the two SNPs before or after adjustment for age, gender, waist circumference, and diabetes status (supplemental Table 5).

Table 3Go summarizes the association between rs13266634 and IVGTT-derived metabolic traits in the subjects (n = 70) with NGT and IGR. The risk allele C was correlated with lower AIRg after adjustment for age, gender, waist circumference, and Si (P = 0.012 by linear regression). The correlation between the genotype and Si was not observed by linear regression before or after adjustment for age, gender, and waist circumference (P > 0.05). Moreover, the C allele was correlated with lower DI by linear regression before and after adjustment for age, gender, and waist circumference (P = 0.014 and 0.004, respectively).


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  		TABLE 3. Si, AIRg, and DI association with rs13266634 of SLC30A8

 

   Discussion
Top
 Abstract
 Introduction
 Subjects and Methods
 Results
 Discussion
References
 
In general, our results are consistent with previous findings by Steinthorsdottir et al. (11) and in the several genome-wide association studies of European ancestry populations (7, 8, 9, 10). The minor allele frequency (T allele, 0.47) of rs13266634 in Shanghai Han Chinese was comparable with that in Hongkong Chinese (0.48) (11). Because the allelic distributions of the 14 tagging SNPs were not significantly different between IGR and type 2 diabetic subjects, we believed that these two groups had similar genetic variances around SLC30A8 region and could be pooled together with the common characteristics of impaired glucose regulation. The pooled group could also boost the statistical power.

We found a significant association of risk allele C of SNP rs13266634 with type 2 diabetes in Chinese. The association between the combined type 2 diabetes/IGR and NGT controls was more significant than the association between the type 2 diabetes and NGT controls (21, 22), which was probably due to the limited sample size. These results emphasize again that the association study is sensitive to sample size, especially in detection of modest effect of a multifactorial trait such as type 2 diabetes. Some negative association results seen in European ancestry population could suffer from the similar problem with an insufficient power in the case-control studies (23). Negative association results could also be found in a different ethnic population (24), suggesting different ethnic groups may not share identical genetic risk factors.

In addition, we identified a novel marginal association of SNP rs2466293 located in 3'-untranslated region of SLC30A8 with combined type 2 diabetes and IGR. Neither the association for type 2 diabetes alone nor for the combined type 2 diabetes and IGR remained significant after the adjustment for age, gender, and waist circumference in the logistic regression analysis. This variant was associated with the verified SNP rs13266634 in a very strong LD (D' = 0.99 and r2 = 0.47). These results suggest that SNP rs13266634 may be the real causative allele associated with decreased β-cell function. SNP rs13266634 of SLC30A8, located at the COOH-terminal region of the protein structure, results in a nonsynonymous mutation (Arg325Trp, CGG>TGG). This DNA variant might be gain of function and affect the posttranslational modification mechanism in the COOH terminus of ZnT-8 (25).

The other nonsynonymous SNP rs16889462 (Arg325Gln) in SLC30A8 was not genotyped because this variant is perfectly in LD (D' = 1.00 and r2 = 1.00) with rs3802178 according to the Han Chinese HapMap (16) (Table 2Go). We failed to identify any association of SNP rs3802178 with type 2 diabetes, and the most likely explanation was that there were no true associations at this SNP rs3802178 and the nonsynonymous SNP rs16889462.

We further evaluated the pancreatic β-cell function associated with SNP rs13266634 and rs2466293 in all subjects including NGT, IGR, and naïve type 2 diabetic patients. None of the two SNPs were associated with insulin secretion indices derived from OGTT. However, we adopted the AIRg derived from IVGTTs to evaluate the first-phase insulin secretion stimulated by glucose in another independent group of subjects with NGT and IGR. The subjects with homozygous risk allele C had a decreased insulin secretion by AIRg, and SNP rs13266634 was associated with a decrease in glucose DI. These results support the correlation of SNP rs13266634 with pancreatic β-cell function. We cannot explain the discrepancy between the lack of association of SNP rs13266634 with insulin secretion parameter derived from OGTT and the association with insulin secretion parameter derived from IVGTT. It could be that HOMA-β is not a sensitive parameter to assess pancreatic β-cell function. The association of SNP rs13266634 with insulin sensitivity was not identified either using OGTT or IVGTT, which was also consistent with the other studies (12, 13).

In conclusion, SNP rs13266634 of SLC30A8 gene is associated with type 2 diabetes, and combined type 2 diabetes and IGR in Chinese Hans. Moreover, SNP rs13266634 is correlated with glucose-stimulated insulin secretion.


   Acknowledgments
 
We thank Dr. Hao-yan Chen for support in statistical analysis. The present study would not have been possible without the participation of the volunteer subjects.


   Footnotes
 
This work was supported by Grant 2006 CB 503904 from 973 Project, Grants 30570880 and 30725037 from the National Nature Science Foundation of China, Grants 04DZ05907 and 06JC14053 from the Shanghai Commission for Science and Technology, and Grants E03007 and Y0204 from the E-Institute of Shanghai Universities, Shanghai Education Commission.

Disclosure Information: All authors have nothing to declare.

First Published Online July 15, 2008

1 J.Y., J.-Y.L., and M.X. contributed equally to this work. Back

Abbreviations: AIRg, Acute insulin response to glucose; BMI, body mass index; CI, confidence interval; DI, disposition index; HOMA-β, homeostasis model assessment of β-cell function; HOMA-IR, homeostasis model assessment of insulin resistance index; IGR, impaired glucose regulation; IVGTT, iv glucose tolerance test; LD, linkage disequilibrium; NGT, normal glucose tolerance; OGTT, oral glucose tolerance test; OR, odds ratio; Si, insulin sensitivity index; SNP, single-nucleotide polymorphism.

Received January 22, 2008.

Accepted July 3, 2008.


   References
Top
 Abstract
 Introduction
 Subjects and Methods
 Results
 Discussion
 References
 

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Endocrinology Endocrine Reviews J. Clin. End. & Metab.
Molecular Endocrinology Recent Prog. Horm. Res. All Endocrine Journals