http://informahealthcare.com/erc ISSN: 0743-5800 (print), 1532-4206 (electronic) Endocr Res, 2015; 40(2): 79–82 ! 2015 Informa Healthcare USA, Inc. DOI: 10.3109/07435800.2014.952015
Impact of variants of the EXT2 gene on Type 2 diabetes and its related traits in the Chinese han population Qian Ren1, Jianzhong Xiao2, Xueyao Han1*, Wenying Yang2, and Linong Ji1* 1
Department of Endocrinology and Metabolism, Peking University People’s Hospital, Peking University Diabetes Center, Beijing, P.R. China and Department of Endocrinology and Metabolism, China–Japan Friendship Hospital, Beijing, P.R. China
2
Abstract
Keywords
Purpose/Aim of the study: Exostosin 2 (EXT2) is involved in early pancreatic development and the regulation of insulin synthesis. In this study, we aim to evaluate the contribution of EXT2 to the genetic pathogenesis of type 2 diabetes and its related traits in the Chinese population. Materials and methods: A case–control study in a Chinese Han population was conducted that included 4766 patients with type 2 diabetes and 4596 control subjects from 14 different regions of China. Three single nucleotide polymorphism (SNP), rs3740878, rs11037909 and rs1113132, in the EXT2 gene were genotyped using the Illumina GoldenGate Genotyping assay. Results: After adjusting for sex, age and body mass index, logistic regression analysis revealed that the EXT2 gene had no association with type 2 diabetes using an additive genetic model [rs3740878 (Odds Ratio (OR) ¼ 0.996, 95% confidence interval (CI) 0.928–1.069, p ¼ 0.910), rs11037909 (OR ¼ 1.003, 95%CI 0.933–1.078, p ¼ 0.931), and rs1113132 (OR ¼ 0.993, 95% CI 0.925–1.065, p ¼ 0.842)]. None of these SNPs were associated with beta cell function as determined using the baseline disposition index, early phase insulin secretion and Oral Glucose Tolerance Test (OGTT) total disposition index. Conclusions: Our study suggests that the EXT2 gene might not have a major role in the development of type 2 diabetes in the Chinese population.
Beta-cell function, diabetes, EXT2
Introduction According to the results of the China National Diabetes and Metabolic Disorders Study, the prevalence of total diabetes and prediabetes in China is 9.7% and 15.5%, respectively (1). Over 90% of the Chinese diabetes patients have type 2 diabetes. Type 2 diabetes is a complex disease; multiple genes are involved in its onset and development. Large case-control studies using whole genome arrays of single nucleotide polymorphisms (genome-wide association studies) have identified multiple common risk alleles for type 2 diabetes in addition to confirming associated loci identified by previous candidate gene studies. To date, more than 70 genetic loci have been reported to be associated with type 2 diabetes. However, these initial studies were only performed in adults of European descent; thus, studies in other ethnic groups are necessary to fully understand the effects of these loci on disease susceptibility.
*Linong Ji and Xueyao Han contributed equally to this work. Correspondence: Linong Ji, Department of Endocrinology and Metabolism, Peking University People’s Hospital, No 11, Xizhimen South Street, Beijing 100044, P.R. China. Tel: 8610 88325578. Fax: 8610 88324371. E-mail: [email protected].
History Received 18 December 2013 Revised 20 May 2014 Accepted 19 July 2014 Published online 9 September 2014
Exostosin 2 (EXT2) modulates the hedgehog signaling pathway, which is a pathway involved in early pancreatic development and the regulation of insulin synthesis (2,3). In 2007, the EXT2 gene was first identified to be associated with type 2 diabetes in a two-stage, genome-wide association French case-control cohort study (OR ¼ 1.26, p ¼ 1.2 104) (4). The statistical significance of the top three loci in this study involves three SNPs (rs1113132, rs11037909 and rs3740878) located in the introns of the EXT-2 gene, at the telomeric end of chromosome 11q. However, discrepancies were also discovered in subsequent replicated studies in different populations (5–13). Therefore, it is worth reevaluating the effect of EXT2 on type 2 diabetes as well as its related traits. The relationship between the EXT2 gene and beta cell function has not been completely confirmed because different methods were used for evaluating beta cell function in previous studies (5,14). In this large sample study, we measured the plasma glucose and insulin levels in triplicate during OGTT to calculate the beta cell function. The disposition index (DI) is the product of insulin secretion and insulin sensitivity and is believed to be the gold standard for measuring beta-cell function (15–17). Thus, we evaluated the association between the EXT2 gene and beta cell function by using the baseline dispositionindex (DIb), early phase insulin secretion (IGI) and OGTT total disposition index (ISSI-2).
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Q. Ren et al.
Endocr Res, 2015; 40(2): 79–82
Materials and methods
DNA extraction and genotyping
Subjects
DNA was extracted from peripheral blood leukocytes by the salting-out procedure. The SNPs rs3740878, rs11037909 and rs1113132 were genotyped in 4766 patients with diabetes and 4596 controls without diabetes. The genotypes were detected by an Illumina GoldenGate assay using the Illumina protocol (Illumina Inc, San Diego, CA).
Patients with type 2 diabetes and control subjects were selected from the participants of The China National Diabetes and Metabolic Disorders Study (1). All participants were of Han Chinese ancestry and from the mainland of China, representing 14 provinces and districts in China. Diabetes mellitus was diagnosed in accordance with 1999 World Health Organization criteria (18). All patients were unrelated (age 55.8 ± 11.0 (X ± SD); 2037 men and 2729 women). Control subjects were defined in accordance with the 1999 World Health Organization criteria (i.e. fasting plasma glucose 56.1 mmol/l and 2-h plasma glucose 57.8 mmol/l). Control subjects that did not have diabetes also met the following criteria: [1] no family history of type 2 diabetes; [2] 40 years of age; [3] 51.7 mmol/l of serum triglyceride, or 51.0 mmol/l of high density lipoprotein; [4] 528 kg/m2 of BMI; and [5] no history of cancer, thyroid disease or rheumatic disease. All control subjects were unrelated (age 50.7 ± 8.5; 1487 men and 3109 women). The clinical characteristics of all study subjects are described in Table 1. All participants provided written informed consent, and the Ethics Committee of China–Japan Friendship Hospital approved the study protocol. Anthropometric and biochemical measurements All participants were examined in the morning after an overnight fast of 10–12 h. Demographic characteristics, personal and family medical history, lifestyle risk factors, and anthropometric measurements (height, weight) were collected using standard protocols (1). Participants with no history of diabetes were given a standard 75-g glucose solution. However, for safety reasons, participants with a selfreported history of diabetes were given a steamed bun that contained approximately 80 g of complex carbohydrates. Blood samples were drawn at 0, 30, and 120 min after the glucose or carbohydrate load to measure glucose and insulin concentrations. Plasma glucose was measured using the hexokinase enzymatic method in each participating center of the China National Diabetes and Metabolic Disorders Study. Fasting serum insulin levels were measured centrally using a radioimmunoassay (Beckman insulin kit, Prague, Czech Republic). Table 1. The clinical characteristics of study subjects.
Clinical characteristics Sex (Male/Female) Age(years) BMI (kg/m2) Fasting plasma glucose (mmol/L) OGTT 2 h plasma glucose (mmol/L) Fasting serum insulin (mU/L) OGTT 2 h serum insulin (mU/L)
Diabetes (n ¼ 4766)
Control (n ¼ 4596)
2037/2729 55.8 ± 11.0 25.8 ± 3.3 8.1 ± 2.7
1487/3109 50.7 ± 8.5 23.0 ± 2.4 5.0 ± 0.52
14.3 ± 5.1
5.7 ± 1.1
8.8 (6.0–12.6) 32.7 (18.8–60.8)
6.4 (5.0–8.5) 22.2 (14.0–34.5)
Data are shown as mean ± standard deviation or median (interquartile range). All variables are significantly different between control and T2D at p50.001.
Statistical analysis Data are given as the mean ± SD for variables with normal distribution and otherwise as medians (interquartile range). Variables not normally distributed were logarithmically transformed before statistical analysis. 2 tests were used to determine whether individual polymorphisms were congruent with the Hardy–Weinberg equilibrium. The differences of allele and genotype frequencies between the diabetes and the control individuals were analyzed using Pearson’s 2 test. Logistic regression analysis was used to calculate ORs, 95% CIs and corresponding p values, after adjusting for sex, age and BMI as covariates. Multiple linear regression analysis was used to analyze the genotype–phenotype relationship under an additive genetic model. All statistical tests were performed using the SPSS program version 13.0 for Windows (SPSS, Chicago, IL). A p value 50.05 was considered statistically significant (two-tailed). The baseline beta cell function was defined as the baseline disposition index (DIb) (19). Early phase insulin secretion was calculated as an insulinogenic index (IGI) (20). The OGTT total disposition index was defined as the insulin secretionsensitivity index-2 (ISSI-2) (20,21). The equations for DIb, IGI, and ISSI-2 were as follows: DIb ¼ HOMA-B ð1=HOMA-IRÞ ¼ 20 FI=ðFPG 3:5Þ=ðFPG FI=22:5Þ IGI ¼ ðI30 FIÞ=ðPG30 FPGÞ ISSI2 ¼ AUCIns 0-120 =AUCGlu 0-120 MSI ðMSI ¼ 10 000=ðFPG FI MPG MIÞ0:5 Þ (FI: fasting insulin, FPG: fasting plasma glucose, I30: OGTT 30 min insulin, PG30: OGTT 30 min plasma glucose, AUCIns0-120: total area under the insulin curve, AUCGlu0-120: total area under the glucose curve, MSI: Matsuda index. MPG: mean plasma glucose concentration during OGTT, MI: mean plasma insulin concentration during OGTT. For all the equations mentioned above, insulin was measured in mU/ml and glucose was measured in mmol/l, except in MSI, glucose was measured in mg/dl.) The power calculation was performed by Quanto software version 1.2.3 (University of Southern California, Los Angeles, CA), using a type 2 diabetes prevalence of 9.7% in China (1) and an additive genetic model for the EXT2 gene (7). The minor allele frequencies (MAFs) of rs3740878, rs11037909 and rs1113132 were obtained from this study.
Results There was no significant difference in the genotype success rate between the patient and control groups. Hardy–Weinberg equilibrium (H–W equilibrium) was determined for both the
EXT2 Gene, diabetes and beta cell
DOI: 10.3109/07435800.2014.952015
control and patient groups. The H–W equilibrium maintains that genetic variation of the population remains constant in absence of distribution disturbing factors and the equation can be used to calculate the genetic variation of a population at equilibrium. The genotype frequencies of rs3740878, rs11037909 and rs1113132 were in accordance with H–W equilibrium in the type 2 diabetes group as well as in the control group (p40.05). Table 2 summarizes the results of the case-control study. The distribution of alleles and genotypes of all of the SNPs were not significantly different between the patient and control groups. After adjusting for sex, age and body mass index (BMI), logistic regression analysis using an additive genetic model revealed that the risk alleles rs3740878 (allele A), rs11037909 (allele T) and rs1113132 (allele C) had no association with type 2 diabetes. In subgroup populations in which type 2 diabetes was diagnosed in patients less than 45 years of age with a BMI less than 24 kg/m2, there was also no association observed between the three SNPs and type 2 diabetes (rs3740878, OR ¼ 1.001, 95% CI 0.853–1.174, p ¼ 0.990; rs11037909, OR ¼ 1.014, 95% CI 0.863–1.192, p ¼ 0.865; rs1113132, OR ¼ 1.011, 95% CI 0.862–1.187, p ¼ 0.890). According to the prevalence of type 2 diabetes (9.7%) in China (1), and using an additive genetic model for an SNP with an MAF of at least 40% as calculated in our study, our study has 90% power in detecting an effect size of 1.1 at a p value of 0.05, and 99% power if the effect size is more than 1.2. For the analysis of genotype–phenotype relationships, only control participants were examined because treatment for diabetes may have distorted this relationship. We examined the potential influence of the three SNPs (rs3740878, rs11037909 and rs1113132) on fasting and postprandial plasma glucose, fasting and postprandial serum insulin, obesity index (as measured by BMI), baseline beta cell function (DIb), early phase insulin secretion (as measured by IGI) and OGTT total beta cell function (as measured by ISSI2). As shown in Electronic Supplementary Material, we determined that none of the three SNPs were associated with pancreatic beta cell function.
Discussion The current case-control study showed that all three EXT2 gene SNPs (rs3740878, rs11037909 and rs1113132) were not associated with pancreatic beta cell function as estimated by DIb, IGI and ISSI-2. In addition, we were not able to replicate the association between the EXT2 gene and type 2 diabetes in the Chinese population. The EXT2 gene was first identified to be associated with type 2 diabetes in a two-stage, genome-wide association study
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in a French case-control cohort study (OR ¼ 1.26, p ¼ 1.2 104) (4). However, subsequent studies in the French (7), African American (8), German (9), Pima Indian (10), and Mexican (11) populations could not replicate these previous results. The genetic background of Asians is different from those of other ethnic groups, including those of Europe, America, Africa and others. For example, according to the dbSNP database (Available from: http://www.ncbi.nlm.nih. gov/SNP/, accessed February 2013), the minor allele frequency of the rs3740878 SNP of EXT2 is between 0.076–0.13 in African Americans, 0.272–0.302 in Europeans, and more than 0.4 in Asians, especially the Chinese population. Thus, it was worth re-evaluating the effect of the EXT2 gene on the Chinese population. There were only two previous studies evaluating the relationship between the EXT2 gene and type 2 diabetes in the Chinese population. However, their sample size was too small to determine whether the risk allele really increased the risk of type 2 diabetes in Chinese subjects. Using the prevalence value of type 2 diabetes (9.7%) in China (1) and using an additive genetic model for a SNP with a MAF of at least 40% and with an effect size at least 1.15 (the smallest OR in the French GWAS study cohort) (4), the power of one study was 24% (424 patients) (12) and only 12% (286 patients) in a separate study (13). However, in the current study, we increased the power to at least 90% using a much larger sample size (4766 patients with diabetes and 4596 control subjects). In the previous study in China, the participants were only from Beijing and Shanghai. Another previous study that used over 350 000 genome-wide autosomal SNPs in over 6000 Han Chinese samples from ten provinces of China showed an obvious north-south genetic differentiation in the Chinese population (22). Thus, the population structure posed a challenge to genetic studies. One approach to counteract this problem may be raising the power by increasing the sample size. In addition, all of the participants in our study were selected from separate geographical areas of China, including 14 provinces and districts; therefore, the possibility of false positive results due to sampling bias was lowered. In this large sample size case control study, we determined that there is no association between EXT2 variants and type 2 diabetes in the Chinese population. Thus, variants of the EXT2 gene are unlikely to influence the rates of type 2 diabetes in the Chinese population. We also evaluated the genotype-phenotype association of the EXT2 gene in the control group because the EXT2 gene encodes a glycosyltransferase involved in early pancreatic development and the regulation of insulin synthesis by modulating the hedgehog signaling pathway (2,3). The EXT2 rs3740878 risk T allele has been nominally associated with insulin secretion in two previous studies (5,14). In one
Table 2. Association of EXT2 gene with type 2 diabetes in Chinese population. SNP rs3740878 rs11037909 rs1113132
Position (bp)
Genotype
Risk allele
Major allele
Case MAFa
Control MAF
OR (95%CI)
p Value
44 214 378 44 212 190 44 209 979
A/G T/C C/G
A T C
A T C
0.406 0.407 0.404
0.403 0.406 0.400
0.996 (0.928–1.069) 1.003 (0.933–1.078) 0.993 (0.925–1.065)
0.910 0.931 0.842
a: MAF: minor allele frequency.
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study, HOMA beta was used to calculate beta cell function. However, the use of only HOMA-beta to evaluate beta-cell function may lead to incorrect conclusion (23). In another study, IGI was used; however, IGI only reflects insulin secretion during the early phase. In this study, we measured the glucose and insulin levels in triplicate for the OGTT test to calculate the baseline beta cell function as well as for the early phase and the total OGTT process. To further examine the relationship between the EXT2 gene and beta cell function, we determined the baseline disposition index (DIb), early phase insulin secretion (IGI) and OGTT total disposition index (ISSI-2). In our study, we determined that none of the three SNPs were associated with pancreatic beta cell function. We also further analyzed the relationship between EXT2 and type 2 diabetes in a subgroup population in which the diagnosed type 2 diabetic patients were less than 45 years of age with a BMI less than 24 kg/m2. However, no association between the three SNPs and type 2 diabetes in this particular subgroup could be confirmed.
Conclusions In conclusion, our study suggests that variants of the EXT2 gene might not have a major role in the development of type 2 diabetes in the Chinese population.
Funding This study was supported by the China Medical Association Research Fund, the National Basic Research Program of China (973 program, 2011CB504001), and the National High Technology Research and Development Program of China (863 Program, No. 2012AA02A509).
Acknowledgements We appreciate all of the patients and control subjects for their agreement to participate in our study. We also thank all of the members of The China National Diabetes and Metabolic Disorders Study Group.
Declaration of interest The authors report no conflicts of interest. The authors alone are responsible for the content and writing of the paper.
References 1. Yang W, Lu J, Weng J, et al. Prevalence of diabetes among men and women in China. N Engl J Med 2010;362:1090–101. 2. Apelqvist A, Ahlgren U, Edlund H. Sonic hedgehog directs specialised mesoderm differentiation in the intestine and pancreas. Curr Biol 1997;7:801–4. 3. Thomas MK, Rastalsky N, Lee JH, Habener JF. Hedgehog signaling regulation of insulin production by pancreatic betacells. Diabetes 2000;49:2039–47.
4. Sladek R, Rocheleau G, Rung J, et al. A genome-wide association study identifies novel risk loci for type 2 diabetes. Nature 2007;445: 881–5. 5. Horikoshi M, Hara K, Ito C, et al. Variations in the HHEX gene are associated with increased risk of type 2 diabetes in the Japanese population. Diabetologia 2007;50:2461–6. 6. Omori S, Tanaka Y, Takahashi A, et al. Association of CDKAL1, IGF2BP2, CDKN2A/B, HHEX, SLC30A8, and KCNJ11 with susceptibility to type 2 diabetes in a Japanese population. Diabetes 2008;57:791–5. 7. Cauchi S, Proenca C, Choquet H, et al. Analysis of novel risk loci for type 2 diabetes in a general French population: the D.E.S.I.R. study. J Mol Med (Berl) 2008;86:341–8. 8. Lewis JP, Palmer ND, Hicks PJ, et al. Association analysis in African Americans of European-derived type 2 diabetes single nucleotide polymorphisms from whole-genome association studies. Diabetes 2008;57:2220–5. 9. Herder C, Rathmann W, Strassburger K, et al. Variants of the PPARG, IGF2BP2, CDKAL1, HHEX, and TCF7L2 genes confer risk of type 2 diabetes independently of BMI in the German KORA studies. Horm Metab Res 2008;40:722–6. 10. Rong R, Hanson RL, Ortiz D, et al. Association analysis of variation in/near FTO, CDKAL1, SLC30A8, HHEX, EXT2, IGF2BP2, LOC387761, and CDKN2B with type 2 diabetes and related quantitative traits in Pima Indians. Diabetes 2009;58: 478–88. 11. Gutierrez-Vidal R, Rodriguez-Trejo A, Canizales-Quinteros S, et al. LOC387761 polymorphism is associated with type 2 diabetes in the Mexican population. Genet Test Mol Biomarkers 2011;15:79–83. 12. Wu Y, Li H, Loos RJ, et al. Common variants in CDKAL1, CDKN2A/B, IGF2BP2, SLC30A8, and HHEX/IDE genes are associated with type 2 diabetes and impaired fasting glucose in a Chinese Han population. Diabetes 2008;57:2834–42. 13. Ma C, Sheng H, Luo J. Association studies of polymorphisms in EXT2(exostoses 2) gene with type 2 diabetes in southern Chinese population. J Jiangsu Univ (Medicine Edition) 2010;20:256–62. 14. Moore AF, Jablonski KA, McAteer JB, et al. Extension of type 2 diabetes genome-wide association scan results in the diabetes prevention program. Diabetes 2008;57:2503–10. 15. DeFronzo RA. Lilly lecture 1987. The triumvirate: beta-cell, muscle, liver. A collusion responsible for NIDDM. Diabetes 1988; 37:667–87. 16. Bi Y, Zeng L, Zhu D, et al. Association of beta-cell function and insulin sensitivity with fasting and 2-h plasma glucose in a large Chinese population. Diabetes Obes Metab 2012;14:174–80. 17. DeFronzo RA, Banerji MA, Bray GA, et al. Determinants of glucose tolerance in impaired glucose tolerance at baseline in the Actos Now for Prevention of Diabetes (ACT NOW) study. Diabetologia 2010;53:435–45. 18. Alberti KG, Zimmet PZ. Definition, diagnosis and classification of diabetes mellitus and its complications. Part 1: diagnosis and classification of diabetes mellitus provisional report of a WHO consultation. Diabet Med 1998;15:539–53. 19. Bi Y, Zhu D, Jing Y, et al. Decreased beta cell function and insulin sensitivity contributed to increasing fasting glucose in Chinese. Acta Diabetol 2012;49:S51–8. 20. Retnakaran R, Qi Y, Goran MI, Hamilton JK. Evaluation of proposed oral disposition index measures in relation to the actual disposition index. Diabet Med 2009;26:1198–203. 21. Matsuda M, DeFronzo RA. Insulin sensitivity indices obtained from oral glucose tolerance testing:comparison with the euglycemic insulin clamp. Diabetes Care 1999;22:1462–70. 22. Chen J, Zheng H, Bei JX, et al. Genetic structure of the Han Chinese population revealed by genome-wide SNP variation. Am J Hum Genet 2009;85:775–85. 23. Sung KC, Reaven GM, Kim SH. Utility of Homeostasis Model Assessment of beta-Cell Function in Predicting Diabetes in 12 924 Healthy Koreans. Diabetes Care 2010;33:200–2.
Supplementary material available online.
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Impact of variants of the EXT2 gene on Type 2 diabetes and its related traits in the Chinese han population Qian Ren1, Jianzhong Xiao2, Xueyao Han1*, Wenying Yang2, and Linong Ji1* 1
Department of Endocrinology and Metabolism, Peking University People’s Hospital, Peking University Diabetes Center, Beijing, P.R. China and Department of Endocrinology and Metabolism, China–Japan Friendship Hospital, Beijing, P.R. China
2
Abstract
Keywords
Purpose/Aim of the study: Exostosin 2 (EXT2) is involved in early pancreatic development and the regulation of insulin synthesis. In this study, we aim to evaluate the contribution of EXT2 to the genetic pathogenesis of type 2 diabetes and its related traits in the Chinese population. Materials and methods: A case–control study in a Chinese Han population was conducted that included 4766 patients with type 2 diabetes and 4596 control subjects from 14 different regions of China. Three single nucleotide polymorphism (SNP), rs3740878, rs11037909 and rs1113132, in the EXT2 gene were genotyped using the Illumina GoldenGate Genotyping assay. Results: After adjusting for sex, age and body mass index, logistic regression analysis revealed that the EXT2 gene had no association with type 2 diabetes using an additive genetic model [rs3740878 (Odds Ratio (OR) ¼ 0.996, 95% confidence interval (CI) 0.928–1.069, p ¼ 0.910), rs11037909 (OR ¼ 1.003, 95%CI 0.933–1.078, p ¼ 0.931), and rs1113132 (OR ¼ 0.993, 95% CI 0.925–1.065, p ¼ 0.842)]. None of these SNPs were associated with beta cell function as determined using the baseline disposition index, early phase insulin secretion and Oral Glucose Tolerance Test (OGTT) total disposition index. Conclusions: Our study suggests that the EXT2 gene might not have a major role in the development of type 2 diabetes in the Chinese population.
Beta-cell function, diabetes, EXT2
Introduction According to the results of the China National Diabetes and Metabolic Disorders Study, the prevalence of total diabetes and prediabetes in China is 9.7% and 15.5%, respectively (1). Over 90% of the Chinese diabetes patients have type 2 diabetes. Type 2 diabetes is a complex disease; multiple genes are involved in its onset and development. Large case-control studies using whole genome arrays of single nucleotide polymorphisms (genome-wide association studies) have identified multiple common risk alleles for type 2 diabetes in addition to confirming associated loci identified by previous candidate gene studies. To date, more than 70 genetic loci have been reported to be associated with type 2 diabetes. However, these initial studies were only performed in adults of European descent; thus, studies in other ethnic groups are necessary to fully understand the effects of these loci on disease susceptibility.
*Linong Ji and Xueyao Han contributed equally to this work. Correspondence: Linong Ji, Department of Endocrinology and Metabolism, Peking University People’s Hospital, No 11, Xizhimen South Street, Beijing 100044, P.R. China. Tel: 8610 88325578. Fax: 8610 88324371. E-mail: [email protected].
History Received 18 December 2013 Revised 20 May 2014 Accepted 19 July 2014 Published online 9 September 2014
Exostosin 2 (EXT2) modulates the hedgehog signaling pathway, which is a pathway involved in early pancreatic development and the regulation of insulin synthesis (2,3). In 2007, the EXT2 gene was first identified to be associated with type 2 diabetes in a two-stage, genome-wide association French case-control cohort study (OR ¼ 1.26, p ¼ 1.2 104) (4). The statistical significance of the top three loci in this study involves three SNPs (rs1113132, rs11037909 and rs3740878) located in the introns of the EXT-2 gene, at the telomeric end of chromosome 11q. However, discrepancies were also discovered in subsequent replicated studies in different populations (5–13). Therefore, it is worth reevaluating the effect of EXT2 on type 2 diabetes as well as its related traits. The relationship between the EXT2 gene and beta cell function has not been completely confirmed because different methods were used for evaluating beta cell function in previous studies (5,14). In this large sample study, we measured the plasma glucose and insulin levels in triplicate during OGTT to calculate the beta cell function. The disposition index (DI) is the product of insulin secretion and insulin sensitivity and is believed to be the gold standard for measuring beta-cell function (15–17). Thus, we evaluated the association between the EXT2 gene and beta cell function by using the baseline dispositionindex (DIb), early phase insulin secretion (IGI) and OGTT total disposition index (ISSI-2).
80
Q. Ren et al.
Endocr Res, 2015; 40(2): 79–82
Materials and methods
DNA extraction and genotyping
Subjects
DNA was extracted from peripheral blood leukocytes by the salting-out procedure. The SNPs rs3740878, rs11037909 and rs1113132 were genotyped in 4766 patients with diabetes and 4596 controls without diabetes. The genotypes were detected by an Illumina GoldenGate assay using the Illumina protocol (Illumina Inc, San Diego, CA).
Patients with type 2 diabetes and control subjects were selected from the participants of The China National Diabetes and Metabolic Disorders Study (1). All participants were of Han Chinese ancestry and from the mainland of China, representing 14 provinces and districts in China. Diabetes mellitus was diagnosed in accordance with 1999 World Health Organization criteria (18). All patients were unrelated (age 55.8 ± 11.0 (X ± SD); 2037 men and 2729 women). Control subjects were defined in accordance with the 1999 World Health Organization criteria (i.e. fasting plasma glucose 56.1 mmol/l and 2-h plasma glucose 57.8 mmol/l). Control subjects that did not have diabetes also met the following criteria: [1] no family history of type 2 diabetes; [2] 40 years of age; [3] 51.7 mmol/l of serum triglyceride, or 51.0 mmol/l of high density lipoprotein; [4] 528 kg/m2 of BMI; and [5] no history of cancer, thyroid disease or rheumatic disease. All control subjects were unrelated (age 50.7 ± 8.5; 1487 men and 3109 women). The clinical characteristics of all study subjects are described in Table 1. All participants provided written informed consent, and the Ethics Committee of China–Japan Friendship Hospital approved the study protocol. Anthropometric and biochemical measurements All participants were examined in the morning after an overnight fast of 10–12 h. Demographic characteristics, personal and family medical history, lifestyle risk factors, and anthropometric measurements (height, weight) were collected using standard protocols (1). Participants with no history of diabetes were given a standard 75-g glucose solution. However, for safety reasons, participants with a selfreported history of diabetes were given a steamed bun that contained approximately 80 g of complex carbohydrates. Blood samples were drawn at 0, 30, and 120 min after the glucose or carbohydrate load to measure glucose and insulin concentrations. Plasma glucose was measured using the hexokinase enzymatic method in each participating center of the China National Diabetes and Metabolic Disorders Study. Fasting serum insulin levels were measured centrally using a radioimmunoassay (Beckman insulin kit, Prague, Czech Republic). Table 1. The clinical characteristics of study subjects.
Clinical characteristics Sex (Male/Female) Age(years) BMI (kg/m2) Fasting plasma glucose (mmol/L) OGTT 2 h plasma glucose (mmol/L) Fasting serum insulin (mU/L) OGTT 2 h serum insulin (mU/L)
Diabetes (n ¼ 4766)
Control (n ¼ 4596)
2037/2729 55.8 ± 11.0 25.8 ± 3.3 8.1 ± 2.7
1487/3109 50.7 ± 8.5 23.0 ± 2.4 5.0 ± 0.52
14.3 ± 5.1
5.7 ± 1.1
8.8 (6.0–12.6) 32.7 (18.8–60.8)
6.4 (5.0–8.5) 22.2 (14.0–34.5)
Data are shown as mean ± standard deviation or median (interquartile range). All variables are significantly different between control and T2D at p50.001.
Statistical analysis Data are given as the mean ± SD for variables with normal distribution and otherwise as medians (interquartile range). Variables not normally distributed were logarithmically transformed before statistical analysis. 2 tests were used to determine whether individual polymorphisms were congruent with the Hardy–Weinberg equilibrium. The differences of allele and genotype frequencies between the diabetes and the control individuals were analyzed using Pearson’s 2 test. Logistic regression analysis was used to calculate ORs, 95% CIs and corresponding p values, after adjusting for sex, age and BMI as covariates. Multiple linear regression analysis was used to analyze the genotype–phenotype relationship under an additive genetic model. All statistical tests were performed using the SPSS program version 13.0 for Windows (SPSS, Chicago, IL). A p value 50.05 was considered statistically significant (two-tailed). The baseline beta cell function was defined as the baseline disposition index (DIb) (19). Early phase insulin secretion was calculated as an insulinogenic index (IGI) (20). The OGTT total disposition index was defined as the insulin secretionsensitivity index-2 (ISSI-2) (20,21). The equations for DIb, IGI, and ISSI-2 were as follows: DIb ¼ HOMA-B ð1=HOMA-IRÞ ¼ 20 FI=ðFPG 3:5Þ=ðFPG FI=22:5Þ IGI ¼ ðI30 FIÞ=ðPG30 FPGÞ ISSI2 ¼ AUCIns 0-120 =AUCGlu 0-120 MSI ðMSI ¼ 10 000=ðFPG FI MPG MIÞ0:5 Þ (FI: fasting insulin, FPG: fasting plasma glucose, I30: OGTT 30 min insulin, PG30: OGTT 30 min plasma glucose, AUCIns0-120: total area under the insulin curve, AUCGlu0-120: total area under the glucose curve, MSI: Matsuda index. MPG: mean plasma glucose concentration during OGTT, MI: mean plasma insulin concentration during OGTT. For all the equations mentioned above, insulin was measured in mU/ml and glucose was measured in mmol/l, except in MSI, glucose was measured in mg/dl.) The power calculation was performed by Quanto software version 1.2.3 (University of Southern California, Los Angeles, CA), using a type 2 diabetes prevalence of 9.7% in China (1) and an additive genetic model for the EXT2 gene (7). The minor allele frequencies (MAFs) of rs3740878, rs11037909 and rs1113132 were obtained from this study.
Results There was no significant difference in the genotype success rate between the patient and control groups. Hardy–Weinberg equilibrium (H–W equilibrium) was determined for both the
EXT2 Gene, diabetes and beta cell
DOI: 10.3109/07435800.2014.952015
control and patient groups. The H–W equilibrium maintains that genetic variation of the population remains constant in absence of distribution disturbing factors and the equation can be used to calculate the genetic variation of a population at equilibrium. The genotype frequencies of rs3740878, rs11037909 and rs1113132 were in accordance with H–W equilibrium in the type 2 diabetes group as well as in the control group (p40.05). Table 2 summarizes the results of the case-control study. The distribution of alleles and genotypes of all of the SNPs were not significantly different between the patient and control groups. After adjusting for sex, age and body mass index (BMI), logistic regression analysis using an additive genetic model revealed that the risk alleles rs3740878 (allele A), rs11037909 (allele T) and rs1113132 (allele C) had no association with type 2 diabetes. In subgroup populations in which type 2 diabetes was diagnosed in patients less than 45 years of age with a BMI less than 24 kg/m2, there was also no association observed between the three SNPs and type 2 diabetes (rs3740878, OR ¼ 1.001, 95% CI 0.853–1.174, p ¼ 0.990; rs11037909, OR ¼ 1.014, 95% CI 0.863–1.192, p ¼ 0.865; rs1113132, OR ¼ 1.011, 95% CI 0.862–1.187, p ¼ 0.890). According to the prevalence of type 2 diabetes (9.7%) in China (1), and using an additive genetic model for an SNP with an MAF of at least 40% as calculated in our study, our study has 90% power in detecting an effect size of 1.1 at a p value of 0.05, and 99% power if the effect size is more than 1.2. For the analysis of genotype–phenotype relationships, only control participants were examined because treatment for diabetes may have distorted this relationship. We examined the potential influence of the three SNPs (rs3740878, rs11037909 and rs1113132) on fasting and postprandial plasma glucose, fasting and postprandial serum insulin, obesity index (as measured by BMI), baseline beta cell function (DIb), early phase insulin secretion (as measured by IGI) and OGTT total beta cell function (as measured by ISSI2). As shown in Electronic Supplementary Material, we determined that none of the three SNPs were associated with pancreatic beta cell function.
Discussion The current case-control study showed that all three EXT2 gene SNPs (rs3740878, rs11037909 and rs1113132) were not associated with pancreatic beta cell function as estimated by DIb, IGI and ISSI-2. In addition, we were not able to replicate the association between the EXT2 gene and type 2 diabetes in the Chinese population. The EXT2 gene was first identified to be associated with type 2 diabetes in a two-stage, genome-wide association study
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in a French case-control cohort study (OR ¼ 1.26, p ¼ 1.2 104) (4). However, subsequent studies in the French (7), African American (8), German (9), Pima Indian (10), and Mexican (11) populations could not replicate these previous results. The genetic background of Asians is different from those of other ethnic groups, including those of Europe, America, Africa and others. For example, according to the dbSNP database (Available from: http://www.ncbi.nlm.nih. gov/SNP/, accessed February 2013), the minor allele frequency of the rs3740878 SNP of EXT2 is between 0.076–0.13 in African Americans, 0.272–0.302 in Europeans, and more than 0.4 in Asians, especially the Chinese population. Thus, it was worth re-evaluating the effect of the EXT2 gene on the Chinese population. There were only two previous studies evaluating the relationship between the EXT2 gene and type 2 diabetes in the Chinese population. However, their sample size was too small to determine whether the risk allele really increased the risk of type 2 diabetes in Chinese subjects. Using the prevalence value of type 2 diabetes (9.7%) in China (1) and using an additive genetic model for a SNP with a MAF of at least 40% and with an effect size at least 1.15 (the smallest OR in the French GWAS study cohort) (4), the power of one study was 24% (424 patients) (12) and only 12% (286 patients) in a separate study (13). However, in the current study, we increased the power to at least 90% using a much larger sample size (4766 patients with diabetes and 4596 control subjects). In the previous study in China, the participants were only from Beijing and Shanghai. Another previous study that used over 350 000 genome-wide autosomal SNPs in over 6000 Han Chinese samples from ten provinces of China showed an obvious north-south genetic differentiation in the Chinese population (22). Thus, the population structure posed a challenge to genetic studies. One approach to counteract this problem may be raising the power by increasing the sample size. In addition, all of the participants in our study were selected from separate geographical areas of China, including 14 provinces and districts; therefore, the possibility of false positive results due to sampling bias was lowered. In this large sample size case control study, we determined that there is no association between EXT2 variants and type 2 diabetes in the Chinese population. Thus, variants of the EXT2 gene are unlikely to influence the rates of type 2 diabetes in the Chinese population. We also evaluated the genotype-phenotype association of the EXT2 gene in the control group because the EXT2 gene encodes a glycosyltransferase involved in early pancreatic development and the regulation of insulin synthesis by modulating the hedgehog signaling pathway (2,3). The EXT2 rs3740878 risk T allele has been nominally associated with insulin secretion in two previous studies (5,14). In one
Table 2. Association of EXT2 gene with type 2 diabetes in Chinese population. SNP rs3740878 rs11037909 rs1113132
Position (bp)
Genotype
Risk allele
Major allele
Case MAFa
Control MAF
OR (95%CI)
p Value
44 214 378 44 212 190 44 209 979
A/G T/C C/G
A T C
A T C
0.406 0.407 0.404
0.403 0.406 0.400
0.996 (0.928–1.069) 1.003 (0.933–1.078) 0.993 (0.925–1.065)
0.910 0.931 0.842
a: MAF: minor allele frequency.
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Endocr Res, 2015; 40(2): 79–82
study, HOMA beta was used to calculate beta cell function. However, the use of only HOMA-beta to evaluate beta-cell function may lead to incorrect conclusion (23). In another study, IGI was used; however, IGI only reflects insulin secretion during the early phase. In this study, we measured the glucose and insulin levels in triplicate for the OGTT test to calculate the baseline beta cell function as well as for the early phase and the total OGTT process. To further examine the relationship between the EXT2 gene and beta cell function, we determined the baseline disposition index (DIb), early phase insulin secretion (IGI) and OGTT total disposition index (ISSI-2). In our study, we determined that none of the three SNPs were associated with pancreatic beta cell function. We also further analyzed the relationship between EXT2 and type 2 diabetes in a subgroup population in which the diagnosed type 2 diabetic patients were less than 45 years of age with a BMI less than 24 kg/m2. However, no association between the three SNPs and type 2 diabetes in this particular subgroup could be confirmed.
Conclusions In conclusion, our study suggests that variants of the EXT2 gene might not have a major role in the development of type 2 diabetes in the Chinese population.
Funding This study was supported by the China Medical Association Research Fund, the National Basic Research Program of China (973 program, 2011CB504001), and the National High Technology Research and Development Program of China (863 Program, No. 2012AA02A509).
Acknowledgements We appreciate all of the patients and control subjects for their agreement to participate in our study. We also thank all of the members of The China National Diabetes and Metabolic Disorders Study Group.
Declaration of interest The authors report no conflicts of interest. The authors alone are responsible for the content and writing of the paper.
References 1. Yang W, Lu J, Weng J, et al. Prevalence of diabetes among men and women in China. N Engl J Med 2010;362:1090–101. 2. Apelqvist A, Ahlgren U, Edlund H. Sonic hedgehog directs specialised mesoderm differentiation in the intestine and pancreas. Curr Biol 1997;7:801–4. 3. Thomas MK, Rastalsky N, Lee JH, Habener JF. Hedgehog signaling regulation of insulin production by pancreatic betacells. Diabetes 2000;49:2039–47.
4. Sladek R, Rocheleau G, Rung J, et al. A genome-wide association study identifies novel risk loci for type 2 diabetes. Nature 2007;445: 881–5. 5. Horikoshi M, Hara K, Ito C, et al. Variations in the HHEX gene are associated with increased risk of type 2 diabetes in the Japanese population. Diabetologia 2007;50:2461–6. 6. Omori S, Tanaka Y, Takahashi A, et al. Association of CDKAL1, IGF2BP2, CDKN2A/B, HHEX, SLC30A8, and KCNJ11 with susceptibility to type 2 diabetes in a Japanese population. Diabetes 2008;57:791–5. 7. Cauchi S, Proenca C, Choquet H, et al. Analysis of novel risk loci for type 2 diabetes in a general French population: the D.E.S.I.R. study. J Mol Med (Berl) 2008;86:341–8. 8. Lewis JP, Palmer ND, Hicks PJ, et al. Association analysis in African Americans of European-derived type 2 diabetes single nucleotide polymorphisms from whole-genome association studies. Diabetes 2008;57:2220–5. 9. Herder C, Rathmann W, Strassburger K, et al. Variants of the PPARG, IGF2BP2, CDKAL1, HHEX, and TCF7L2 genes confer risk of type 2 diabetes independently of BMI in the German KORA studies. Horm Metab Res 2008;40:722–6. 10. Rong R, Hanson RL, Ortiz D, et al. Association analysis of variation in/near FTO, CDKAL1, SLC30A8, HHEX, EXT2, IGF2BP2, LOC387761, and CDKN2B with type 2 diabetes and related quantitative traits in Pima Indians. Diabetes 2009;58: 478–88. 11. Gutierrez-Vidal R, Rodriguez-Trejo A, Canizales-Quinteros S, et al. LOC387761 polymorphism is associated with type 2 diabetes in the Mexican population. Genet Test Mol Biomarkers 2011;15:79–83. 12. Wu Y, Li H, Loos RJ, et al. Common variants in CDKAL1, CDKN2A/B, IGF2BP2, SLC30A8, and HHEX/IDE genes are associated with type 2 diabetes and impaired fasting glucose in a Chinese Han population. Diabetes 2008;57:2834–42. 13. Ma C, Sheng H, Luo J. Association studies of polymorphisms in EXT2(exostoses 2) gene with type 2 diabetes in southern Chinese population. J Jiangsu Univ (Medicine Edition) 2010;20:256–62. 14. Moore AF, Jablonski KA, McAteer JB, et al. Extension of type 2 diabetes genome-wide association scan results in the diabetes prevention program. Diabetes 2008;57:2503–10. 15. DeFronzo RA. Lilly lecture 1987. The triumvirate: beta-cell, muscle, liver. A collusion responsible for NIDDM. Diabetes 1988; 37:667–87. 16. Bi Y, Zeng L, Zhu D, et al. Association of beta-cell function and insulin sensitivity with fasting and 2-h plasma glucose in a large Chinese population. Diabetes Obes Metab 2012;14:174–80. 17. DeFronzo RA, Banerji MA, Bray GA, et al. Determinants of glucose tolerance in impaired glucose tolerance at baseline in the Actos Now for Prevention of Diabetes (ACT NOW) study. Diabetologia 2010;53:435–45. 18. Alberti KG, Zimmet PZ. Definition, diagnosis and classification of diabetes mellitus and its complications. Part 1: diagnosis and classification of diabetes mellitus provisional report of a WHO consultation. Diabet Med 1998;15:539–53. 19. Bi Y, Zhu D, Jing Y, et al. Decreased beta cell function and insulin sensitivity contributed to increasing fasting glucose in Chinese. Acta Diabetol 2012;49:S51–8. 20. Retnakaran R, Qi Y, Goran MI, Hamilton JK. Evaluation of proposed oral disposition index measures in relation to the actual disposition index. Diabet Med 2009;26:1198–203. 21. Matsuda M, DeFronzo RA. Insulin sensitivity indices obtained from oral glucose tolerance testing:comparison with the euglycemic insulin clamp. Diabetes Care 1999;22:1462–70. 22. Chen J, Zheng H, Bei JX, et al. Genetic structure of the Han Chinese population revealed by genome-wide SNP variation. Am J Hum Genet 2009;85:775–85. 23. Sung KC, Reaven GM, Kim SH. Utility of Homeostasis Model Assessment of beta-Cell Function in Predicting Diabetes in 12 924 Healthy Koreans. Diabetes Care 2010;33:200–2.
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