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The ENPP1 K121Q polymorphism is associated with type 2 diabetes and related metabolic phenotypes in a Taiwanese population
Molecular and Cellular Endocrinology 433 (2016) 20e25
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The ENPP1 K121Q polymorphism is associated with type 2 diabetes and related metabolic phenotypes in a Taiwanese population Tun-Jen Hsiao a, Eugene Lin b, c, d, * a
College of Public Health and Nutrition, Taipei Medical University, Taipei, Taiwan, ROC Institute of Clinical Medical Science, China Medical University, Taichung, Taiwan, ROC c Vita Genomics, Inc., Taipei, Taiwan, ROC d TickleFish Systems Corporation, Seattle, USA b
a r t i c l e i n f o
a b s t r a c t
Article history: Received 14 March 2016 Received in revised form 25 May 2016 Accepted 25 May 2016 Available online 26 May 2016
Increased risk of developing type 2 diabetes (T2D) has been associated with a single nucleotide polymorphism (SNP), rs1044498 (K121Q), in the ectonucleotide pyrophosphatase/phosphodiesterase 1 (ENPP1) gene, but this association is unclear among Asians. In this replication study, we reassessed whether the ENPP1 rs1044498 SNP is associated with T2D, obesity, and T2D/obesity-related metabolic traits in a Taiwanese population. A total of 1513 Taiwanese subjects were assessed in this study. The ENPP1 rs1044498 SNP was genotyped by the Taqman assay. T2D/Obesity-related quantitative traits, such as waist circumference and fasting glucose, were measured. Our data showed a significant association of the ENPP1 rs1044498 SNP with T2D (P < 0.001) among the subjects. Moreover, the ENPP1 rs1044498 SNP was significantly associated with T2D/obesity-related metabolic traits, such as waist circumference (P ¼ 0.002) and fasting glucose (P < 0.001), among the subjects. However, we found no association of ENPP1 rs1044498 with obesity (BMI S 27 kg/m2). Our study indicates that the ENPP1 rs1044498 SNP is associated with T2D, waist circumference, and fasting glucose in Taiwanese subjects. © 2016 Elsevier Ireland Ltd. All rights reserved.
Keywords: Body mass index ENPP1 Metabolic phenotypes Obesity Single nucleotide polymorphisms Type 2 diabetes
1. Introduction The ectonucleotide pyrophosphatase/phosphodiesterase 1 (ENPP1) gene, located at chromosomal region 6q22eq23, encodes a membrane glycoprotein that inhibits insulin receptor tyrosine kinase activity when it is over-expressed (Maddux and Goldfine, 2000). Some speculate that the protein encoded by the ENPP1 gene may play a key role in insulin resistance and hyperglycemia, which are common characteristics of type 2 diabetes (T2D) (Pizzuti et al., 1999). Consequently, the ENPP1 gene may be a susceptibility gene for T2D, obesity, and insulin resistance (Goldfine et al., 2008). A single nucleotide polymorphism (SNP), rs1044498, in exon 4 of the ENPP1 gene causes an amino acid change from lysine to glutamine at codon 121 (K121Q), resulting in a functional protein, which has been the focus of attention in the T2D field. An in vitro
Abbreviations: ANOVA, analysis of variance; BMI, body mass index; CIs, confidence intervals; ORs, Odds ratios; ENPP1, ectonucleotide pyrophosphatase/phosphodiesterase 1; SNP, Single nucleotide polymorphism; T2D, type 2 diabetes. * Corresponding author. Vita Genomics, Inc., 7 Fl., No. 6, Sec. 1, Jung-Shing Road, Wugu Shiang, Taipei, Taiwan. E-mail address: [email protected] (E. Lin). http://dx.doi.org/10.1016/j.mce.2016.05.020 0303-7207/© 2016 Elsevier Ireland Ltd. All rights reserved.
study showed that the C allele of the ENPP1 rs1044498 (K121Q) SNP has a stronger interaction with the insulin receptor and more potent inhibitory effects on insulin signaling compared to the A allele (Costanzo et al., 2001). Due to escalating prevalence rates, T2D and obesity have become major public health problems in Taiwan and at the global scale (Wild et al., 2004; Kelly et al., 2008). Several studies suggest that the C allele of the ENPP1 rs1044498 SNP confers an increased risk of acquiring T2D in various populations (Abate et al., 2005; Meyre et al., 2005; McAteer et al., 2008). However, T2D susceptibility associated with this ENPP1 variant differs depending on ethnicity, especially in studies of Asian populations (Table 1). The ENPP1 rs1044498 SNP has been reported to increase the risk of acquiring T2D in South Asians residing in the USA (Abate et al., 2005), India (Abate et al., 2005), and Taiwan (Wang et al., 2012). In contrast, this association has not been replicated in Japanese (Keshavarz et al., 2006), Taiwanese (Chen et al., 2006), Korean (Seo et al., 2008), Indian (Bhatti et al., 2010), and Chinese (Zhao et al., 2011; Shi et al., 2011) populations. Several meta-analysis studies have also suggested that the ENPP1 rs1044498 SNP is associated with an increased risk of developing T2D in European and Asian populations (McAteer et al., 2008; Li, 2012; Jing et al., 2012; Tang
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Table 1 The association studies of the rs1044498 SNP in the ENPP1 gene with T2D among different ethnic groups in Asian populations. Study
Population
No. of participants
Results
Abate et al., 2005 Abate et al., 2005 Wang et al., 2012 Keshavarz et al., 2006 Chen et al., 2006 Seo et al., 2008 Bhatti et al., 2010 Zhao et al., 2011 Shi et al., 2011
South Asians in USA Indian Taiwanese Japanese Taiwanese Korean Indian Chinese Chinese
1083 679 604 1787 2706 1945 654 3893 1524
Significant association with T2D Significant association with T2D Significant association with T2D No association with T2D No association with T2D No association with T2D No association with T2D No association with T2D No association with T2D
ENPP1 ¼ ectonucleotide pyrophosphatase/phosphodiesterase 1, SNP ¼ single nucleotide polymorphism, T2D ¼ type 2 diabetes.
et al., 2014). While the ENPP1 rs1044498 SNP has been associated with obesity in Austrian and French subjects (Meyre et al., 2005), a case-control study in Chinese subjects revealed that there was no association (Zhao et al., 2011). Addressing the challenge of reproducibility is crucial in genetic association studies, especially for T2D and obesity research (Li and Meyre, 2013). The association of ENPP1 rs1044498 and disease susceptibility for T2D and obesity is unclear in Asian populations. Therefore, we conducted a replication study to evaluate the association between the ENPP1 rs1044498 polymorphism and susceptibility to T2D, as well as obesity, in a case-control association study of Taiwanese adults. We also estimated the potential effects of these associations on T2D/obesity-related metabolic traits. 2. Materials and methods 2.1. Study population This study incorporated subjects from two previous studies. The first study by Hsiao et al. (2013) used a cohort consisting of 960 participants who underwent general health examinations at the Taipei Medical University Hospital in Taipei, Taiwan in 2008 (Hsiao et al., 2013). All subjects were unrelated adults and glucose-tolerant healthy controls, where glucose tolerance was defined as a fasting plasma glucose level of less than 126 mg/dl. Approval was obtained from the Internal Review Boards of the Taipei Medical University Hospital before conducting the study. Each subject signed the approved informed consent form. The second study by Wu et al. (2009) used a cohort consisting of 553 Taiwanese patients with T2D who were recruited from the TriService General Hospital in Taipei, Taiwan in 2002. All of the recruited patients fulfilled the following criteria: (i) the subjects’ age was between 30 and 75 years old (this criterion may be due to the typical study design when the previous study was conducted [Wu et al., 2009]), (ii) the subjects had been diagnosed with T2D for more than 5 years (this criterion was incorporated to assess the long-term effects of T2D in the previous study [Wu et al., 2009]), (iii) the subjects had a fasting plasma glucose level of greater than 126 mg/dl, and (iv) the subjects had a HbA1C level that was greater than 6%. Approval was obtained from the Internal Review Board of the Tri-Service General Hospital before conducting this study, and each subject signed the approved informed consent form. Using criteria defined by the Department of Health in Taiwan, the term “obesity” in this study is defined as body mass index (BMI) S 27 kg/m2 (Fu et al., 2008). BMI was calculated as weight in kilograms divided by squared height in meters (kg/m2). Height was measured using a standard steel strip stadiometer, and weight was determined using a digital electronic scale. Height without shoes and body weight in light clothing were measured to the nearest 0.1 cm and 0.1 kg, respectively. Waist circumference was measured at the midway point between the lower rib margin and the superior
iliac crest in a horizontal plane with flexible anthropometric tape. Furthermore, fasting plasma glucose was analyzed in all subjects (Hsiao et al., 2013). Blood samples were drawn with minimal trauma from an antecubital vein in the morning after an overnight fast. Biochemical markers, such as fasting glucose, were assessed using a biochemical autoanalyzer (Beckman Coulter, CA, USA). 2.2. Genotyping DNA was isolated from blood samples using a QIAamp DNA blood kit following the manufacturer’s instructions (Qiagen, Valencia, CA, USA). Two-hundred microliters of blood was dissolved in 200 ml of distilled water for DNA extraction (Wu et al., 2009). Before the PCR reaction, an aliquot of extracted DNA was diluted to a concentration of 10 mg/ml. The quality of the isolated genomic DNA was evaluated using agarose gel electrophoresis, and the quantity was determined by spectrophotometry. SNP genotyping was carried out using the Taqman SNP genotyping assay (ABI: Applied Biosystems Inc., Foster City, CA, USA). The primers and probes of SNPs were from an ABI assay on demand kit. Reactions were performed according to the manufacturer’s protocol. Probe fluorescence signal detection was conducted using the ABI Prism 7900 Real-Time PCR System. 2.3. Statistical analysis Categorical data were assessed using the chi-square test. Continuous demographic variables were compared between T2D and control subjects using Student’s t-test or analysis of variance (ANOVA). Fisher’s exact test was used to examine differences in allele frequencies between T2D and control subjects as well as those between obese and non-obese subjects. In addition, we assessed the association of the investigated SNP with obesityrelated metabolic traits by using a general linear model that used age, gender, BMI, and T2D status as covariates (Hsiao et al., 2009). To estimate the association of the investigated SNP with T2D, we conducted a logistic regression analysis to evaluate the odds ratios (ORs) and their 95% confidence intervals (CIs), adjusting for covariates, including age, gender, and BMI. Furthermore, we estimated the association of the investigated SNP with obesity by logistic regression analysis, adjusting for age, gender, and T2D status. The genotype frequencies were assessed for Hardy-Weinberg equilibrium using a c2 goodness-of-fit test. Multiple testing was adjusted by the Bonferroni correction. The criterion for significance was set at P < 0.05 for all tests. Data are presented as the mean ± standard deviation. The power to detect significant associations was evaluated by QUANTO software (http://biostats.usc.edu/Quanto.html). In the case-control association study for obesity, we combined samples from different study populations. We performed Cochran’s Q test to assess heterogeneity between study populations.
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3. Results When T2D and control subjects were pooled (n ¼ 1513), there were significant differences in BMI (P < 0.001), waist circumference (P < 0.001), and fasting glucose (P < 0.001) between T2D and control subjects (Table 2). As shown in Table 2, the distributions of age and gender were well matched in unrelated T2D and control subjects (P ¼ 0.16 and P ¼ 0.051, respectively). Next, we assessed whether the ENPP1 rs1044498 SNP is correlated with T2D/obesity-related metabolic traits in the sample population. Table 3 describes the demographic and clinical characteristics of the study population separated by genotypes among (a) all, (b) T2D, and (c) control subjects. There was a significant difference (P ¼ 0.002) after Bonferroni correction (P < 0.05/ 5 ¼ 0.01) in waist circumference between the AA homozygotes and combined CC and AC genotypes among the entire sample population, as shown in Table 3(a). Additionally, among the entire sample population, there was a significant difference (P < 0.001) in fasting glucose after the Bonferroni correction between the CC homozygotes and AA homozygotes, between the CC homozygotes and combined AA and AC genotypes, as well as between the AA homozygotes and combined CC and AC genotypes (Table 3(a)). However, there was no evidence of a significant association between ENPP1 rs1044498 and quantitative traits among the T2D or control subjects when the sample population was further stratified into T2D and control groups (Table 3(b) and Table 3(c)). Table 4 shows the genotype and allele distributions of the ENPP1 rs1044498 SNP in the case and control groups for the entire sample population with (a) T2D vs. control subjects and (b) obese vs. nonobese subjects. Among the T2D and control subjects, a significant increased risk of T2D was found for the ENPP1 rs1044498 SNP in the allelic test (P < 0.001). However, an increased risk of obesity was not detected in the allelic test (P ¼ 0.584) among obese and nonobese subjects. In addition, the genotype frequency distribution for the ENPP1 rs1044498 SNP was in accordance with the HardyeWeinberg equilibrium among the control subjects (P ¼ 0.324). The genotype frequency distribution was also in HardyeWeinberg equilibrium among the obese subjects (P ¼ 0.768). Moreover, the OR analysis showed risk genotypes of variants of ENPP1 rs1044498 before and after adjusting for covariates, indicating an increased T2D risk among the subjects (Table 4). As demonstrated in Table 4, there was an indication of an increased T2D risk among the T2D and control subjects before adjustment of covariates for genetic models, including the co-dominant model (CC vs. AA; OR ¼ 7.29; 95% CI ¼ 3.44e15.42; P < 0.001), recessive model (CC vs. AA þ AC; OR ¼ 6.49; 95% CI ¼ 3.07e13.70; P < 0.001), and dominant model (CC þ AC vs. AA; OR ¼ 1.84; 95% CI ¼ 1.46e2.33; P < 0.001). The analysis also showed that there was an indication of an increased T2D risk (P < 0.001) among the T2D and control subjects after adjustment of covariates, including age, gender, and BMI. However, there was no indication of an increased risk of obesity among the obese and non-obese subjects before and
Table 2 Demographic and clinical characteristics of study subjects. Characteristic
T2D
Control
P valuea
No. of subjects Age (years) Gender (Male %) BMI (kg/m2) Waist circumference (cm) Fasting glucose (mg/dl)
553 62.2 ± 8.9 52.8% 25.6 ± 3.8 89.7 ± 10.2 162.3 ± 55.2
960 61.7 ± 47.6% 24.3 ± 85.4 ± 95.9 ±
0.160 0.051 <0.001 <0.001 <0.001
4.6 3.0 9.0 8.6
Data are presented as mean ± standard deviation. BMI ¼ body mass index, T2D ¼ type 2 diabetes. a P values were obtained by comparing the T2D subjects with control subjects.
after adjustment for covariates, including age, gender, and T2D status (Table 4). Finally, statistical power analysis revealed that the present study had a 99.9% power to detect associations of ENPP1 rs1044498 with T2D among the T2D and control subjects. Statistical power analysis revealed that the present study had a 56.7% power to detect associations of ENPP1 rs1044498 with obesity among the obese and non-obese subjects. Furthermore, there was no heterogeneity in the OR analysis for obesity among different study populations by Cochran’s Q test (P ¼ 0.358). 4. Discussion Our replication study is the first study to date to examine whether the main effects of the ENPP1 rs1044498 SNP are significantly associated with the risk of T2D and obesity among Taiwanese individuals. We also investigated the association between ENPP1 rs1044498 and T2D/obesity-related quantitative traits to examine whether this SNP confers a risk of T2D/obesity according to its effect on any of these intermediate traits. In this study, we found that ENPP1 rs1044498 was linked with T2D and T2D/obesity-related metabolic traits such as waist circumference and fasting glucose. However, there was no indication of an association between the C allele of ENPP1 rs1044498 and obesity. Still, it is of interest that the ORs found in this study were higher those that were previously reported for T2D and control subjects. Perhaps the low BMI and relatively narrow BMI range of the population decreased the power to observe such an interaction between the ENPP1 rs1044498 and obesity while increasing the ORs. Here, we report for the first time that the ENPP1 rs1044498 SNP may play an important role in the modulation of T2D, waist circumference, and fasting glucose in a Taiwanese population. The ENPP1 rs1044498 polymorphism has been widely implicated to affect the T2D risk (Abate et al., 2005; Meyre et al., 2005; McAteer et al., 2008), although genetic evidence of its effect on T2D has been inconsistent. In this study, we observed that there was a significant association of ENPP1 rs1044498 with T2D before and after covariate adjustment in OR analysis. Our results are in agreement with those of several other studies (Abate et al., 2005; Meyre et al., 2005; McAteer et al., 2008; Wang et al., 2012; Li, 2012; Jing et al., 2012; Tang et al., 2014). Abate et al. (2005) suggested that the ENPP1 rs1044498 polymorphism was involved in T2D both in South Asians in the USA, in Indian subjects, and in Caucasians in the USA. Furthermore, Meyre et al. (2005) reported an association between ENPP1 rs1044498 and T2D in Austrian and French subjects. Wang et al. (2012) also demonstrated that the ENPP1 rs1044498 polymorphism was likely to influence T2D in a Taiwanese population. McAteer et al. (2008) performed a meta-analysis on data from 30 studies including 42,042 subjects in white populations and detected a significant association between ENPP1 rs1044498 and T2D. The following meta-analysis studies by Li (2012) (n ¼ 11,855) and Jing et al. (2012) (n ¼ 4644) indicated that the C allele of ENPP1 rs1044498 may contribute to the susceptibility for T2D in a Chinese population. Moreover, a recent meta-analysis study (n ¼ 56,961) by Tang et al. (2014) found that the C allele of the ENPP1 rs1044498 SNP may be involved with T2D susceptibility in Caucasians and Asians. The underlying mechanisms of the ENPP1 rs1044498 SNP with respect to the pathogenesis of T2D are unknown (Abate et al., 2006). A study in transfected cells showed that the C allele of ENPP1 rs1044498 is a stronger inhibitor of insulin receptor function and insulin action than the more frequent A allele because it can more strongly interact with the insulin receptor present in the cell membrane (Costanzo et al., 2001). Likewise, an investigation using a transgenic mouse model suggested that overexpression of the
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Table 3 Demographic and clinical characteristics of study subjects by genotypes for (a) all subjects; (b) T2D subjects; and (c) control subjects. Characteristic (a) All subjects No. of subjects Age (years) d Gender (Male %) e BMI (kg/m2) f Waist circumference (cm) Fasting glucose (mg/dl) g (b) T2D subjects No. of subjects Age (years) h Gender (Male %) i BMI (kg/m2) j Waist circumference (cm) Fasting glucose (mg/dl) k (c) Control subjects No. of subjects Age (years) h Gender (Male %) i BMI (kg/m2) j Waist circumference (cm) Fasting glucose (mg/dl) k
g
k
k
CC
AC
AA
P valuea
P valueb
P valuec
41 63.5 ± 8.7 53.7% 25.1 ± 3.5 88.2 ± 9.7 145.7 ± 52.1
350 62.3 ± 6.9 46.3% 24.9 ± 3.5 87.7 ± 9.8 124.2 ± 47.7
1122 61.7 ± 6.2 50.4% 24.8 ± 3.4 86.7 ± 9.6 117.9 ± 45.8
0.060 0.674 0.480 0.103 <0.001
0.090 0.587 0.526 0.158 <0.001
0.044 0.258 0.385 0.002 <0.001
32 64.0 ± 9.7 50.0% 25.1 ± 3.1 88.0 ± 8.4 161.2 ± 48.6
153 63.3 ± 8.9 51.0% 25.6 ± 3.9 89.9 ± 10.3 160.8 ± 52.4
368 61.5 ± 8.8 53.8% 25.7 ± 3.9 89.7 ± 10.3 163.0 ± 56.9
0.114 0.672 0.410 0.141 0.856
0.210 0.740 0.426 0.114 0.903
0.016 0.500 0.642 0.735 0.662
9 61.8 ± 66.7% 25.2 ± 89.2 ± 90.6 ±
197 61.5 ± 42.6% 24.4 ± 86.0 ± 95.8 ±
754 61.7 ± 48.7% 24.3 ± 85.2 ± 95.9 ±
0.967 0.279 0.374 0.040 0.058
0.945 0.246 0.381 0.046 0.054
0.595 0.200 0.711 0.035 0.586
3.2 4.9 13.8 5.7
4.7 3.1 9.2 8.2
4.5 3.0 8.8 8.8
Data are presented as mean ± standard deviation. BMI ¼ body mass index, T2D ¼ type 2 diabetes. The Bonferroni correction sets the significance cut-off at 0.01 (0.05/5). a P values were obtained by comparing the subjects of the CC genotype with those of the AA genotype. b P values were obtained by comparing the subjects of the combined AA þ AC genotypes with those of the CC genotype. c P values were obtained by comparing the subjects of the combined CC þ AC genotypes with those of the AA genotype. d Adjusted for gender, BMI, and T2D status. e Adjusted for age, BMI, and T2D status. f Adjusted for age, gender, and T2D status. g Adjusted for age, gender, BMI, and T2D status. h Adjusted for gender and BMI. i Adjusted for age and BMI. j Adjusted for age and gender. k Adjusted for age, gender, and BMI.
Table 4 Distributions of alleles and genotypes as well as odds ratio analysis with odds ratios before and after adjustment for covariates in (a) the T2D and control subjects; and (b) the obese and non-obese subjects. Case allele (A/C) and genotype (AA/AC/CC) Control allele (A/C) and genotype (AA/AC/CC) Comparison (a) T2D and control subjects 889/217 368/153/32
1705/215 754/197/9
(b) obese (BMIS27) and non-obese (BMI < 27) subjects 558/98 2036/334 238/82/8 884/268/33
OR (95% CI)
P Value Adjusted OR (95% CI)a P valuea
CC vs. AAb 7.29 (3.44e15.42) <0.001 7.43 (3.47e15.88) CC vs. AA þ ACc 6.49 (3.07e13.70) <0.001 6.47 (3.04e13.77) CC þ AC vs. AAb 1.84 (1.46e2.33) <0.001 1.84 (1.45e2.34) CC vs. AAb 0.90 (0.41e1.98) CC vs. AA þ ACc 0.87 (0.40e1.91) CC þ AC vs. AAb 1.11 (0.84e1.46)
0.794 0.59 (0.26e1.32) 0.733 0.61 (0.27e1.34) 0.456 0.99 (0.75e1.32)
<0.001 <0.001 <0.001 0.201 0.218 0.944
BMI ¼ body mass index, CI ¼ confidence interval, OR ¼ odds ratio, T2D ¼ type 2 diabetes. a Analysis among T2D and control subjects was obtained after adjustment for covariates including age, gender, and BMI. Analysis among obese and non-obese subjects was obtained after adjustment for covariates including age, gender, and T2D status. b AA as the reference genotype. c AA þ AC as the reference genotype.
ENPP1 C allele in both muscle and liver tissues may be a cause of insulin resistance and hyperglycemia of T2D in vivo (Maddux et al., 2006). Another study demonstrated that suppression of ENPP1 expression improves insulin sensitivity both in transfected cells and in an animal model of T2D (Zhou et al., 2009). In a transgenic mouse model, Pan et al. (2011) also found that ENPP1 overexpression in adipose tissue induces insulin resistance in the presence of excessive fat intake. In contrast to the abovementioned reports and ours (Abate et al., 2005; Meyre et al., 2005; McAteer et al., 2008; Wang et al., 2012; Li, 2012; Jing et al., 2012; Tang et al., 2014), there was no association between ENPP1 rs1044498 and T2D in individuals of some Asian ethnicities, including Japanese (Keshavarz et al., 2006), Taiwanese (Chen et al., 2006), Korean (Seo et al., 2008), and Chinese (Zhao
et al., 2011; Shi et al., 2011) populations. Potential factors underlying the discordant results found between these studies and ours include the sample size, study design, phenotype definitions, limited covariate adjustment, population stratification, and ethnicities assessed (Hsiao et al., 2013, 2014; Hsiao and Lin, 2014; Hsiao and Lin, 2015). Importantly, the C allele frequency of ENPP1 rs1044498 varies considerably between different ethnic populations, ranging from 16.7% in European Americans, 10.5% in Japanese subjects, 86.9% in African Americans, 4.2% in Chinese subjects, 9.4% in Koreans, and 14.3% in the present Taiwanese population assessed in our study (Keshavarz et al., 2006; Seo et al., 2008). Further, we speculate that ENPP1 rs1044498 interacts with other genetic or environmental factors that are specific to ethnicity and thus has a different phenotypic expression in different
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populations (Grarup et al., 2006). In this regard over-fitting of genotype-phenotype relations may influence the prevalence of T2D due to artificially constructed haplotypes of ENPP1 rs1044498 with other SNPs in the ENPP1 gene across different ethnic groups (McAteer et al., 2008). It is worth mentioning that the assumption of population homogeneity in case-control association studies can lead to both false negative and false positive errors because different populations may often have different patterns of linkage disequilibrium (LD) (Conrad et al., 2006). When there are discordant LD patterns between populations, tag SNPs, which consist of highly correlated common variants across a genomic region, are unique in diverse populations, particularly those of African-ancestry and nonAfrican-ancestry (Carlson et al., 2004). Thus, strategies based on fine-mapping or tag SNPs would serve the goal of more narrowly defining the genetic loci of interest and potentially identifying causal variants when the genetic effect of ENPP1 on T2D and related traits are evaluated. By genotyping 39 tag SNPs in the ENPP1 gene in USA Caucasians, Stolerman et al. (2008) indicated that ENPP1 rs1044498 had the strongest association with T2D-related traits that likely arise from this SNP and the effects of other SNPs in the region were not independent from ENPP1 rs1044498 because of their LD with ENPP1 rs1044498. In addition, another study by Bochenski et al. (2006) genotyped 5 tag SNPs, including rs1044498 in the ENPP1 gene, and found no association of these SNPs with T2D or obesity in Polish Caucasians. Our analyses demonstrated that the ENPP1 rs1044498 SNP predicts waist circumference and fasting glucose in Taiwanese lezsubjects. In line with our results, a previous study by Gonza nchez et al. (2008) reported a positive association of the C Sa allele of the ENPP1 rs1044498 SNP with waist circumference in a Spanish population. Furthermore, another study by Stolerman et al. (2008) showed that the C allele of ENPP1 rs1044498 was associated with fasting plasma glucose, but not with waist circumference, in USA Caucasian subjects. Additionally, Prakash et al. (2013) suggested an association of ENPP1 rs1044498 with fasting glucose in an Indian population. However, some studies reported no evidence of an association between ENPP1 rs1044498 and fasting glucose in Taiwanese (Chen et al., 2006), Iranian (Saberi et al., 2011), and Chinese (Zhao et al., 2011) populations. Another investigation also failed to show evidence of an impact of ENPP1 rs1044498 on waist circumference and fasting glucose in Danish subjects (Grarup et al., 2006). Furthermore, our analyses showed that there was no association between ENPP1 rs1044498 and BMI in Taiwanese subjects. In accordance with our analysis, several studies indicated that there was no evidence of an association between ENPP1 rs1044498 and BMI in Danish (Grarup et al., 2006), Taiwanese (Chen et al., 2006), Iranian (Saberi et al., 2011), and Chinese (Zhao et al., 2011) populations. On the other hand, some reports suggested that ENPP1 rs1044498 was linked with BMI in Italian (Prudente et al., 2007), Caucasian-American (Matsuoka et al., 2006), African-American (Matsuoka et al., 2006), and Spanish (Gonz alez-S anchez et al., 2008) subjects. Possible explanations for the discrepancies between these studies include the different phenotypes assessed, insufficient sample sizes, different study designs, different ethnicities assessed, population stratification, and a lack of adjustment for confounding effects (Hsiao et al., 2013, 2014; Hsiao and Lin, 2014; Hsiao and Lin, 2015). Further, our analyses did not significantly implicate ENPP1 rs1044498 in the risk of obesity (BMI S 27). In accordance with our study, several studies indicated that ENPP1 rs1044498 was not reproducibly associated with obesity in USA Caucasian (with BMI between the 90th and 97th percentiles; Lyon et al., 2006), USA Caucasian (with BMI S 30; Stolerman et al., 2008), Polish (with BMI between the 90th and 97th percentiles; Lyon et al., 2006), U. K.
(with BMI S 30; Weedon et al., 2006), Belgian (with BMI S 30; Peeters et al., 2009), Chinese (with BMI S 28; Zhao et al., 2011), and Indian (with BMI S 30; Prakash et al., 2013) populations. On the other hand, an association of the ENPP1 rs1044498 SNP was found with obesity (BMI S 30) in Austrian and French subjects (Meyre et al., 2005) with class II obesity (BMI S 35) in a French population (Meyre et al., 2007) and with obesity (BMI S 35) in a Moroccan population (El Achhab et al., 2009). In addition, Wang et al. (2011) conducted a meta-analysis of 24,324 subjects and found that ENPP1 rs1044498rs1044498 was associated with obesity in European adult populations. Finally, it should be noted that T2D symptoms may include weight loss around the time of diagnosis and that the use of antidiabetic medications, such as insulin, may induce significant weight gain for T2D patients (Mitri and Hamdy, 2009). Therefore, cautious conclusions may be drawn when assessing the association of ENPP1 rs1044498 and obesity. This study has both strengths and limitations. The main weakness of this study is an insufficient sample size of obese cases in the context of polygenic studies (Li and Meyre, 2013; Lin et al., 2009; Liou et al., 2012; Lane et al., 2012). In future work, prospective clinical trials with very large sample sizes are necessary to facilitate a thorough evaluation of the association of this SNP with T2D, obesity, and T2D/obesity-related metabolic traits (Lin et al., 2009; Liou et al., 2012; Lane et al., 2012). Another potential limitation of our study involved the recruitment criteria used in the two included study cohorts. Different characteristics of the two reference hospitals may have resulted in genetic heterogeneity, which has been excluded by estimating Cochran’s Q test for no heterogeneity. Due to a lack of heterogeneity, we were able to pool data from the two study cohorts and thereby considerably increase the statistical power of the analysis (De Cosmo et al., 2009). Furthermore, the hospital-setting for recruitment used in our study could be prone to selection bias, known as Berkson’s bias (Roberts et al., 1978), so that the observed prevalence and distribution of T2D may not be extrapolated to the entire population. Additionally, hip circumference data were unavailable, so we were unable to calculate waist-hip ratios to give more information regarding the association between the explored SNP and phenotypes of T2D and obesity. On the other hand, an important strength of our study was the use of T2D/obesity-related metabolic data, which provided a unique opportunity to examine the associations between the ENPP1 rs1044498 polymorphism and metabolic traits. In conclusion, we carried out an extensive analysis of the association of the ENPP1 rs1044498 SNP with T2D, obesity, and T2D/ obesity-related metabolic traits in Taiwanese subjects. Our findings demonstrate that the ENPP1 rs1044498 SNP is a determinant of T2D and metabolic traits, such as waist circumference and fasting glucose. However, these results should be interpreted with caution due to a small sample size. Replication studies with larger sample sizes will likely shed further light on the association of the ENPP1 rs1044498 polymorphism with at least some of the evaluated traits. Independent replication studies using large sample sizes may provide further insights into the role of the ENPP1 rs1044498 polymorphism found in this study. Funding The authors extend their sincere thanks to Vita Genomics, Inc. and SBIR grants from the Department of Economic Affairs (S099000280249-154) in Taiwan for funding this research. Conflict of interest All of the authors declare that they have no conflict of interest.
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Acknowledgements The authors would like to thank Dr. Hua-Mei Chang of Vita Genomics, Dr. Yuchi Hwang of Vita Genomics, Dr. Dee Pei of the Cardinal Tien Hospital, Dr. Yi-Jen Hung of the Tri-Service General Hospital, and Dr. Shi-Wen Kuo of the Buddhist Xindian Tzu Chi General Hospital for collaborating in this research. References Abate, N., Chandalia, M., Satija, P., et al., 2005. ENPP1/PC-1 K121Q polymorphism and genetic susceptibility to type 2 diabetes. Diabetes 54, 1207e1213. Abate, N., Chandalia, M., Di Paola, R., Foster, D.W., Grundy, S.M., Trischitta, V., 2006. Mechanisms of disease: ectonucleotide pyrophosphatase phosphodiesterase 1 as a ’gatekeeper’ of insulin receptors. Nat. Clin. Pract. Endocrinol. Metab. 2, 694e701. Bhatti, J.S., Bhatti, G.K., Mastana, S.S., Ralhan, S., Joshi, A., Tewari, R., 2010. ENPP1/ PC-1 K121Q polymorphism and genetic susceptibility to type 2 diabetes in North Indians. Mol. Cell Biochem. 345, 249e257. 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The ENPP1 K121Q polymorphism is not associated with type 2 diabetes in northern Chinese. Acta Diabetol. 48, 303e310. Stolerman, E.S., Manning, A.K., McAteer, J.B., et al., 2008. Haplotype structure of the ENPP1 Gene and Nominal Association of the K121Q missense single nucleotide polymorphism with glycemic traits in the Framingham Heart Study. Diabetes 57, 1971e1977. Tang, S.T., Shen, X.R., Tang, H.Q., et al., 2014. Association of the ENPP1 K121Q polymorphism with susceptibility to type 2 diabetes in different populations: evidence based on 40 studies. Endocr. J. 61, 1093e1103. Wang, C.H., Ke, W.S., Lin, E., 2012. Evaluation of the ENPP1 and PLIN single nucleotide polymorphisms with type 2 diabetes in a Taiwanese population: evidence for replication and gene-gene interaction. J. Investig. Med. 60, 1169e1173. Wang, R., Zhou, D., Xi, B., et al., 2011. 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Molecular and Cellular Endocrinology journal homepage: www.elsevier.com/locate/mce
The ENPP1 K121Q polymorphism is associated with type 2 diabetes and related metabolic phenotypes in a Taiwanese population Tun-Jen Hsiao a, Eugene Lin b, c, d, * a
College of Public Health and Nutrition, Taipei Medical University, Taipei, Taiwan, ROC Institute of Clinical Medical Science, China Medical University, Taichung, Taiwan, ROC c Vita Genomics, Inc., Taipei, Taiwan, ROC d TickleFish Systems Corporation, Seattle, USA b
a r t i c l e i n f o
a b s t r a c t
Article history: Received 14 March 2016 Received in revised form 25 May 2016 Accepted 25 May 2016 Available online 26 May 2016
Increased risk of developing type 2 diabetes (T2D) has been associated with a single nucleotide polymorphism (SNP), rs1044498 (K121Q), in the ectonucleotide pyrophosphatase/phosphodiesterase 1 (ENPP1) gene, but this association is unclear among Asians. In this replication study, we reassessed whether the ENPP1 rs1044498 SNP is associated with T2D, obesity, and T2D/obesity-related metabolic traits in a Taiwanese population. A total of 1513 Taiwanese subjects were assessed in this study. The ENPP1 rs1044498 SNP was genotyped by the Taqman assay. T2D/Obesity-related quantitative traits, such as waist circumference and fasting glucose, were measured. Our data showed a significant association of the ENPP1 rs1044498 SNP with T2D (P < 0.001) among the subjects. Moreover, the ENPP1 rs1044498 SNP was significantly associated with T2D/obesity-related metabolic traits, such as waist circumference (P ¼ 0.002) and fasting glucose (P < 0.001), among the subjects. However, we found no association of ENPP1 rs1044498 with obesity (BMI S 27 kg/m2). Our study indicates that the ENPP1 rs1044498 SNP is associated with T2D, waist circumference, and fasting glucose in Taiwanese subjects. © 2016 Elsevier Ireland Ltd. All rights reserved.
Keywords: Body mass index ENPP1 Metabolic phenotypes Obesity Single nucleotide polymorphisms Type 2 diabetes
1. Introduction The ectonucleotide pyrophosphatase/phosphodiesterase 1 (ENPP1) gene, located at chromosomal region 6q22eq23, encodes a membrane glycoprotein that inhibits insulin receptor tyrosine kinase activity when it is over-expressed (Maddux and Goldfine, 2000). Some speculate that the protein encoded by the ENPP1 gene may play a key role in insulin resistance and hyperglycemia, which are common characteristics of type 2 diabetes (T2D) (Pizzuti et al., 1999). Consequently, the ENPP1 gene may be a susceptibility gene for T2D, obesity, and insulin resistance (Goldfine et al., 2008). A single nucleotide polymorphism (SNP), rs1044498, in exon 4 of the ENPP1 gene causes an amino acid change from lysine to glutamine at codon 121 (K121Q), resulting in a functional protein, which has been the focus of attention in the T2D field. An in vitro
Abbreviations: ANOVA, analysis of variance; BMI, body mass index; CIs, confidence intervals; ORs, Odds ratios; ENPP1, ectonucleotide pyrophosphatase/phosphodiesterase 1; SNP, Single nucleotide polymorphism; T2D, type 2 diabetes. * Corresponding author. Vita Genomics, Inc., 7 Fl., No. 6, Sec. 1, Jung-Shing Road, Wugu Shiang, Taipei, Taiwan. E-mail address: [email protected] (E. Lin). http://dx.doi.org/10.1016/j.mce.2016.05.020 0303-7207/© 2016 Elsevier Ireland Ltd. All rights reserved.
study showed that the C allele of the ENPP1 rs1044498 (K121Q) SNP has a stronger interaction with the insulin receptor and more potent inhibitory effects on insulin signaling compared to the A allele (Costanzo et al., 2001). Due to escalating prevalence rates, T2D and obesity have become major public health problems in Taiwan and at the global scale (Wild et al., 2004; Kelly et al., 2008). Several studies suggest that the C allele of the ENPP1 rs1044498 SNP confers an increased risk of acquiring T2D in various populations (Abate et al., 2005; Meyre et al., 2005; McAteer et al., 2008). However, T2D susceptibility associated with this ENPP1 variant differs depending on ethnicity, especially in studies of Asian populations (Table 1). The ENPP1 rs1044498 SNP has been reported to increase the risk of acquiring T2D in South Asians residing in the USA (Abate et al., 2005), India (Abate et al., 2005), and Taiwan (Wang et al., 2012). In contrast, this association has not been replicated in Japanese (Keshavarz et al., 2006), Taiwanese (Chen et al., 2006), Korean (Seo et al., 2008), Indian (Bhatti et al., 2010), and Chinese (Zhao et al., 2011; Shi et al., 2011) populations. Several meta-analysis studies have also suggested that the ENPP1 rs1044498 SNP is associated with an increased risk of developing T2D in European and Asian populations (McAteer et al., 2008; Li, 2012; Jing et al., 2012; Tang
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Table 1 The association studies of the rs1044498 SNP in the ENPP1 gene with T2D among different ethnic groups in Asian populations. Study
Population
No. of participants
Results
Abate et al., 2005 Abate et al., 2005 Wang et al., 2012 Keshavarz et al., 2006 Chen et al., 2006 Seo et al., 2008 Bhatti et al., 2010 Zhao et al., 2011 Shi et al., 2011
South Asians in USA Indian Taiwanese Japanese Taiwanese Korean Indian Chinese Chinese
1083 679 604 1787 2706 1945 654 3893 1524
Significant association with T2D Significant association with T2D Significant association with T2D No association with T2D No association with T2D No association with T2D No association with T2D No association with T2D No association with T2D
ENPP1 ¼ ectonucleotide pyrophosphatase/phosphodiesterase 1, SNP ¼ single nucleotide polymorphism, T2D ¼ type 2 diabetes.
et al., 2014). While the ENPP1 rs1044498 SNP has been associated with obesity in Austrian and French subjects (Meyre et al., 2005), a case-control study in Chinese subjects revealed that there was no association (Zhao et al., 2011). Addressing the challenge of reproducibility is crucial in genetic association studies, especially for T2D and obesity research (Li and Meyre, 2013). The association of ENPP1 rs1044498 and disease susceptibility for T2D and obesity is unclear in Asian populations. Therefore, we conducted a replication study to evaluate the association between the ENPP1 rs1044498 polymorphism and susceptibility to T2D, as well as obesity, in a case-control association study of Taiwanese adults. We also estimated the potential effects of these associations on T2D/obesity-related metabolic traits. 2. Materials and methods 2.1. Study population This study incorporated subjects from two previous studies. The first study by Hsiao et al. (2013) used a cohort consisting of 960 participants who underwent general health examinations at the Taipei Medical University Hospital in Taipei, Taiwan in 2008 (Hsiao et al., 2013). All subjects were unrelated adults and glucose-tolerant healthy controls, where glucose tolerance was defined as a fasting plasma glucose level of less than 126 mg/dl. Approval was obtained from the Internal Review Boards of the Taipei Medical University Hospital before conducting the study. Each subject signed the approved informed consent form. The second study by Wu et al. (2009) used a cohort consisting of 553 Taiwanese patients with T2D who were recruited from the TriService General Hospital in Taipei, Taiwan in 2002. All of the recruited patients fulfilled the following criteria: (i) the subjects’ age was between 30 and 75 years old (this criterion may be due to the typical study design when the previous study was conducted [Wu et al., 2009]), (ii) the subjects had been diagnosed with T2D for more than 5 years (this criterion was incorporated to assess the long-term effects of T2D in the previous study [Wu et al., 2009]), (iii) the subjects had a fasting plasma glucose level of greater than 126 mg/dl, and (iv) the subjects had a HbA1C level that was greater than 6%. Approval was obtained from the Internal Review Board of the Tri-Service General Hospital before conducting this study, and each subject signed the approved informed consent form. Using criteria defined by the Department of Health in Taiwan, the term “obesity” in this study is defined as body mass index (BMI) S 27 kg/m2 (Fu et al., 2008). BMI was calculated as weight in kilograms divided by squared height in meters (kg/m2). Height was measured using a standard steel strip stadiometer, and weight was determined using a digital electronic scale. Height without shoes and body weight in light clothing were measured to the nearest 0.1 cm and 0.1 kg, respectively. Waist circumference was measured at the midway point between the lower rib margin and the superior
iliac crest in a horizontal plane with flexible anthropometric tape. Furthermore, fasting plasma glucose was analyzed in all subjects (Hsiao et al., 2013). Blood samples were drawn with minimal trauma from an antecubital vein in the morning after an overnight fast. Biochemical markers, such as fasting glucose, were assessed using a biochemical autoanalyzer (Beckman Coulter, CA, USA). 2.2. Genotyping DNA was isolated from blood samples using a QIAamp DNA blood kit following the manufacturer’s instructions (Qiagen, Valencia, CA, USA). Two-hundred microliters of blood was dissolved in 200 ml of distilled water for DNA extraction (Wu et al., 2009). Before the PCR reaction, an aliquot of extracted DNA was diluted to a concentration of 10 mg/ml. The quality of the isolated genomic DNA was evaluated using agarose gel electrophoresis, and the quantity was determined by spectrophotometry. SNP genotyping was carried out using the Taqman SNP genotyping assay (ABI: Applied Biosystems Inc., Foster City, CA, USA). The primers and probes of SNPs were from an ABI assay on demand kit. Reactions were performed according to the manufacturer’s protocol. Probe fluorescence signal detection was conducted using the ABI Prism 7900 Real-Time PCR System. 2.3. Statistical analysis Categorical data were assessed using the chi-square test. Continuous demographic variables were compared between T2D and control subjects using Student’s t-test or analysis of variance (ANOVA). Fisher’s exact test was used to examine differences in allele frequencies between T2D and control subjects as well as those between obese and non-obese subjects. In addition, we assessed the association of the investigated SNP with obesityrelated metabolic traits by using a general linear model that used age, gender, BMI, and T2D status as covariates (Hsiao et al., 2009). To estimate the association of the investigated SNP with T2D, we conducted a logistic regression analysis to evaluate the odds ratios (ORs) and their 95% confidence intervals (CIs), adjusting for covariates, including age, gender, and BMI. Furthermore, we estimated the association of the investigated SNP with obesity by logistic regression analysis, adjusting for age, gender, and T2D status. The genotype frequencies were assessed for Hardy-Weinberg equilibrium using a c2 goodness-of-fit test. Multiple testing was adjusted by the Bonferroni correction. The criterion for significance was set at P < 0.05 for all tests. Data are presented as the mean ± standard deviation. The power to detect significant associations was evaluated by QUANTO software (http://biostats.usc.edu/Quanto.html). In the case-control association study for obesity, we combined samples from different study populations. We performed Cochran’s Q test to assess heterogeneity between study populations.
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3. Results When T2D and control subjects were pooled (n ¼ 1513), there were significant differences in BMI (P < 0.001), waist circumference (P < 0.001), and fasting glucose (P < 0.001) between T2D and control subjects (Table 2). As shown in Table 2, the distributions of age and gender were well matched in unrelated T2D and control subjects (P ¼ 0.16 and P ¼ 0.051, respectively). Next, we assessed whether the ENPP1 rs1044498 SNP is correlated with T2D/obesity-related metabolic traits in the sample population. Table 3 describes the demographic and clinical characteristics of the study population separated by genotypes among (a) all, (b) T2D, and (c) control subjects. There was a significant difference (P ¼ 0.002) after Bonferroni correction (P < 0.05/ 5 ¼ 0.01) in waist circumference between the AA homozygotes and combined CC and AC genotypes among the entire sample population, as shown in Table 3(a). Additionally, among the entire sample population, there was a significant difference (P < 0.001) in fasting glucose after the Bonferroni correction between the CC homozygotes and AA homozygotes, between the CC homozygotes and combined AA and AC genotypes, as well as between the AA homozygotes and combined CC and AC genotypes (Table 3(a)). However, there was no evidence of a significant association between ENPP1 rs1044498 and quantitative traits among the T2D or control subjects when the sample population was further stratified into T2D and control groups (Table 3(b) and Table 3(c)). Table 4 shows the genotype and allele distributions of the ENPP1 rs1044498 SNP in the case and control groups for the entire sample population with (a) T2D vs. control subjects and (b) obese vs. nonobese subjects. Among the T2D and control subjects, a significant increased risk of T2D was found for the ENPP1 rs1044498 SNP in the allelic test (P < 0.001). However, an increased risk of obesity was not detected in the allelic test (P ¼ 0.584) among obese and nonobese subjects. In addition, the genotype frequency distribution for the ENPP1 rs1044498 SNP was in accordance with the HardyeWeinberg equilibrium among the control subjects (P ¼ 0.324). The genotype frequency distribution was also in HardyeWeinberg equilibrium among the obese subjects (P ¼ 0.768). Moreover, the OR analysis showed risk genotypes of variants of ENPP1 rs1044498 before and after adjusting for covariates, indicating an increased T2D risk among the subjects (Table 4). As demonstrated in Table 4, there was an indication of an increased T2D risk among the T2D and control subjects before adjustment of covariates for genetic models, including the co-dominant model (CC vs. AA; OR ¼ 7.29; 95% CI ¼ 3.44e15.42; P < 0.001), recessive model (CC vs. AA þ AC; OR ¼ 6.49; 95% CI ¼ 3.07e13.70; P < 0.001), and dominant model (CC þ AC vs. AA; OR ¼ 1.84; 95% CI ¼ 1.46e2.33; P < 0.001). The analysis also showed that there was an indication of an increased T2D risk (P < 0.001) among the T2D and control subjects after adjustment of covariates, including age, gender, and BMI. However, there was no indication of an increased risk of obesity among the obese and non-obese subjects before and
Table 2 Demographic and clinical characteristics of study subjects. Characteristic
T2D
Control
P valuea
No. of subjects Age (years) Gender (Male %) BMI (kg/m2) Waist circumference (cm) Fasting glucose (mg/dl)
553 62.2 ± 8.9 52.8% 25.6 ± 3.8 89.7 ± 10.2 162.3 ± 55.2
960 61.7 ± 47.6% 24.3 ± 85.4 ± 95.9 ±
0.160 0.051 <0.001 <0.001 <0.001
4.6 3.0 9.0 8.6
Data are presented as mean ± standard deviation. BMI ¼ body mass index, T2D ¼ type 2 diabetes. a P values were obtained by comparing the T2D subjects with control subjects.
after adjustment for covariates, including age, gender, and T2D status (Table 4). Finally, statistical power analysis revealed that the present study had a 99.9% power to detect associations of ENPP1 rs1044498 with T2D among the T2D and control subjects. Statistical power analysis revealed that the present study had a 56.7% power to detect associations of ENPP1 rs1044498 with obesity among the obese and non-obese subjects. Furthermore, there was no heterogeneity in the OR analysis for obesity among different study populations by Cochran’s Q test (P ¼ 0.358). 4. Discussion Our replication study is the first study to date to examine whether the main effects of the ENPP1 rs1044498 SNP are significantly associated with the risk of T2D and obesity among Taiwanese individuals. We also investigated the association between ENPP1 rs1044498 and T2D/obesity-related quantitative traits to examine whether this SNP confers a risk of T2D/obesity according to its effect on any of these intermediate traits. In this study, we found that ENPP1 rs1044498 was linked with T2D and T2D/obesity-related metabolic traits such as waist circumference and fasting glucose. However, there was no indication of an association between the C allele of ENPP1 rs1044498 and obesity. Still, it is of interest that the ORs found in this study were higher those that were previously reported for T2D and control subjects. Perhaps the low BMI and relatively narrow BMI range of the population decreased the power to observe such an interaction between the ENPP1 rs1044498 and obesity while increasing the ORs. Here, we report for the first time that the ENPP1 rs1044498 SNP may play an important role in the modulation of T2D, waist circumference, and fasting glucose in a Taiwanese population. The ENPP1 rs1044498 polymorphism has been widely implicated to affect the T2D risk (Abate et al., 2005; Meyre et al., 2005; McAteer et al., 2008), although genetic evidence of its effect on T2D has been inconsistent. In this study, we observed that there was a significant association of ENPP1 rs1044498 with T2D before and after covariate adjustment in OR analysis. Our results are in agreement with those of several other studies (Abate et al., 2005; Meyre et al., 2005; McAteer et al., 2008; Wang et al., 2012; Li, 2012; Jing et al., 2012; Tang et al., 2014). Abate et al. (2005) suggested that the ENPP1 rs1044498 polymorphism was involved in T2D both in South Asians in the USA, in Indian subjects, and in Caucasians in the USA. Furthermore, Meyre et al. (2005) reported an association between ENPP1 rs1044498 and T2D in Austrian and French subjects. Wang et al. (2012) also demonstrated that the ENPP1 rs1044498 polymorphism was likely to influence T2D in a Taiwanese population. McAteer et al. (2008) performed a meta-analysis on data from 30 studies including 42,042 subjects in white populations and detected a significant association between ENPP1 rs1044498 and T2D. The following meta-analysis studies by Li (2012) (n ¼ 11,855) and Jing et al. (2012) (n ¼ 4644) indicated that the C allele of ENPP1 rs1044498 may contribute to the susceptibility for T2D in a Chinese population. Moreover, a recent meta-analysis study (n ¼ 56,961) by Tang et al. (2014) found that the C allele of the ENPP1 rs1044498 SNP may be involved with T2D susceptibility in Caucasians and Asians. The underlying mechanisms of the ENPP1 rs1044498 SNP with respect to the pathogenesis of T2D are unknown (Abate et al., 2006). A study in transfected cells showed that the C allele of ENPP1 rs1044498 is a stronger inhibitor of insulin receptor function and insulin action than the more frequent A allele because it can more strongly interact with the insulin receptor present in the cell membrane (Costanzo et al., 2001). Likewise, an investigation using a transgenic mouse model suggested that overexpression of the
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23
Table 3 Demographic and clinical characteristics of study subjects by genotypes for (a) all subjects; (b) T2D subjects; and (c) control subjects. Characteristic (a) All subjects No. of subjects Age (years) d Gender (Male %) e BMI (kg/m2) f Waist circumference (cm) Fasting glucose (mg/dl) g (b) T2D subjects No. of subjects Age (years) h Gender (Male %) i BMI (kg/m2) j Waist circumference (cm) Fasting glucose (mg/dl) k (c) Control subjects No. of subjects Age (years) h Gender (Male %) i BMI (kg/m2) j Waist circumference (cm) Fasting glucose (mg/dl) k
g
k
k
CC
AC
AA
P valuea
P valueb
P valuec
41 63.5 ± 8.7 53.7% 25.1 ± 3.5 88.2 ± 9.7 145.7 ± 52.1
350 62.3 ± 6.9 46.3% 24.9 ± 3.5 87.7 ± 9.8 124.2 ± 47.7
1122 61.7 ± 6.2 50.4% 24.8 ± 3.4 86.7 ± 9.6 117.9 ± 45.8
0.060 0.674 0.480 0.103 <0.001
0.090 0.587 0.526 0.158 <0.001
0.044 0.258 0.385 0.002 <0.001
32 64.0 ± 9.7 50.0% 25.1 ± 3.1 88.0 ± 8.4 161.2 ± 48.6
153 63.3 ± 8.9 51.0% 25.6 ± 3.9 89.9 ± 10.3 160.8 ± 52.4
368 61.5 ± 8.8 53.8% 25.7 ± 3.9 89.7 ± 10.3 163.0 ± 56.9
0.114 0.672 0.410 0.141 0.856
0.210 0.740 0.426 0.114 0.903
0.016 0.500 0.642 0.735 0.662
9 61.8 ± 66.7% 25.2 ± 89.2 ± 90.6 ±
197 61.5 ± 42.6% 24.4 ± 86.0 ± 95.8 ±
754 61.7 ± 48.7% 24.3 ± 85.2 ± 95.9 ±
0.967 0.279 0.374 0.040 0.058
0.945 0.246 0.381 0.046 0.054
0.595 0.200 0.711 0.035 0.586
3.2 4.9 13.8 5.7
4.7 3.1 9.2 8.2
4.5 3.0 8.8 8.8
Data are presented as mean ± standard deviation. BMI ¼ body mass index, T2D ¼ type 2 diabetes. The Bonferroni correction sets the significance cut-off at 0.01 (0.05/5). a P values were obtained by comparing the subjects of the CC genotype with those of the AA genotype. b P values were obtained by comparing the subjects of the combined AA þ AC genotypes with those of the CC genotype. c P values were obtained by comparing the subjects of the combined CC þ AC genotypes with those of the AA genotype. d Adjusted for gender, BMI, and T2D status. e Adjusted for age, BMI, and T2D status. f Adjusted for age, gender, and T2D status. g Adjusted for age, gender, BMI, and T2D status. h Adjusted for gender and BMI. i Adjusted for age and BMI. j Adjusted for age and gender. k Adjusted for age, gender, and BMI.
Table 4 Distributions of alleles and genotypes as well as odds ratio analysis with odds ratios before and after adjustment for covariates in (a) the T2D and control subjects; and (b) the obese and non-obese subjects. Case allele (A/C) and genotype (AA/AC/CC) Control allele (A/C) and genotype (AA/AC/CC) Comparison (a) T2D and control subjects 889/217 368/153/32
1705/215 754/197/9
(b) obese (BMIS27) and non-obese (BMI < 27) subjects 558/98 2036/334 238/82/8 884/268/33
OR (95% CI)
P Value Adjusted OR (95% CI)a P valuea
CC vs. AAb 7.29 (3.44e15.42) <0.001 7.43 (3.47e15.88) CC vs. AA þ ACc 6.49 (3.07e13.70) <0.001 6.47 (3.04e13.77) CC þ AC vs. AAb 1.84 (1.46e2.33) <0.001 1.84 (1.45e2.34) CC vs. AAb 0.90 (0.41e1.98) CC vs. AA þ ACc 0.87 (0.40e1.91) CC þ AC vs. AAb 1.11 (0.84e1.46)
0.794 0.59 (0.26e1.32) 0.733 0.61 (0.27e1.34) 0.456 0.99 (0.75e1.32)
<0.001 <0.001 <0.001 0.201 0.218 0.944
BMI ¼ body mass index, CI ¼ confidence interval, OR ¼ odds ratio, T2D ¼ type 2 diabetes. a Analysis among T2D and control subjects was obtained after adjustment for covariates including age, gender, and BMI. Analysis among obese and non-obese subjects was obtained after adjustment for covariates including age, gender, and T2D status. b AA as the reference genotype. c AA þ AC as the reference genotype.
ENPP1 C allele in both muscle and liver tissues may be a cause of insulin resistance and hyperglycemia of T2D in vivo (Maddux et al., 2006). Another study demonstrated that suppression of ENPP1 expression improves insulin sensitivity both in transfected cells and in an animal model of T2D (Zhou et al., 2009). In a transgenic mouse model, Pan et al. (2011) also found that ENPP1 overexpression in adipose tissue induces insulin resistance in the presence of excessive fat intake. In contrast to the abovementioned reports and ours (Abate et al., 2005; Meyre et al., 2005; McAteer et al., 2008; Wang et al., 2012; Li, 2012; Jing et al., 2012; Tang et al., 2014), there was no association between ENPP1 rs1044498 and T2D in individuals of some Asian ethnicities, including Japanese (Keshavarz et al., 2006), Taiwanese (Chen et al., 2006), Korean (Seo et al., 2008), and Chinese (Zhao
et al., 2011; Shi et al., 2011) populations. Potential factors underlying the discordant results found between these studies and ours include the sample size, study design, phenotype definitions, limited covariate adjustment, population stratification, and ethnicities assessed (Hsiao et al., 2013, 2014; Hsiao and Lin, 2014; Hsiao and Lin, 2015). Importantly, the C allele frequency of ENPP1 rs1044498 varies considerably between different ethnic populations, ranging from 16.7% in European Americans, 10.5% in Japanese subjects, 86.9% in African Americans, 4.2% in Chinese subjects, 9.4% in Koreans, and 14.3% in the present Taiwanese population assessed in our study (Keshavarz et al., 2006; Seo et al., 2008). Further, we speculate that ENPP1 rs1044498 interacts with other genetic or environmental factors that are specific to ethnicity and thus has a different phenotypic expression in different
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populations (Grarup et al., 2006). In this regard over-fitting of genotype-phenotype relations may influence the prevalence of T2D due to artificially constructed haplotypes of ENPP1 rs1044498 with other SNPs in the ENPP1 gene across different ethnic groups (McAteer et al., 2008). It is worth mentioning that the assumption of population homogeneity in case-control association studies can lead to both false negative and false positive errors because different populations may often have different patterns of linkage disequilibrium (LD) (Conrad et al., 2006). When there are discordant LD patterns between populations, tag SNPs, which consist of highly correlated common variants across a genomic region, are unique in diverse populations, particularly those of African-ancestry and nonAfrican-ancestry (Carlson et al., 2004). Thus, strategies based on fine-mapping or tag SNPs would serve the goal of more narrowly defining the genetic loci of interest and potentially identifying causal variants when the genetic effect of ENPP1 on T2D and related traits are evaluated. By genotyping 39 tag SNPs in the ENPP1 gene in USA Caucasians, Stolerman et al. (2008) indicated that ENPP1 rs1044498 had the strongest association with T2D-related traits that likely arise from this SNP and the effects of other SNPs in the region were not independent from ENPP1 rs1044498 because of their LD with ENPP1 rs1044498. In addition, another study by Bochenski et al. (2006) genotyped 5 tag SNPs, including rs1044498 in the ENPP1 gene, and found no association of these SNPs with T2D or obesity in Polish Caucasians. Our analyses demonstrated that the ENPP1 rs1044498 SNP predicts waist circumference and fasting glucose in Taiwanese lezsubjects. In line with our results, a previous study by Gonza nchez et al. (2008) reported a positive association of the C Sa allele of the ENPP1 rs1044498 SNP with waist circumference in a Spanish population. Furthermore, another study by Stolerman et al. (2008) showed that the C allele of ENPP1 rs1044498 was associated with fasting plasma glucose, but not with waist circumference, in USA Caucasian subjects. Additionally, Prakash et al. (2013) suggested an association of ENPP1 rs1044498 with fasting glucose in an Indian population. However, some studies reported no evidence of an association between ENPP1 rs1044498 and fasting glucose in Taiwanese (Chen et al., 2006), Iranian (Saberi et al., 2011), and Chinese (Zhao et al., 2011) populations. Another investigation also failed to show evidence of an impact of ENPP1 rs1044498 on waist circumference and fasting glucose in Danish subjects (Grarup et al., 2006). Furthermore, our analyses showed that there was no association between ENPP1 rs1044498 and BMI in Taiwanese subjects. In accordance with our analysis, several studies indicated that there was no evidence of an association between ENPP1 rs1044498 and BMI in Danish (Grarup et al., 2006), Taiwanese (Chen et al., 2006), Iranian (Saberi et al., 2011), and Chinese (Zhao et al., 2011) populations. On the other hand, some reports suggested that ENPP1 rs1044498 was linked with BMI in Italian (Prudente et al., 2007), Caucasian-American (Matsuoka et al., 2006), African-American (Matsuoka et al., 2006), and Spanish (Gonz alez-S anchez et al., 2008) subjects. Possible explanations for the discrepancies between these studies include the different phenotypes assessed, insufficient sample sizes, different study designs, different ethnicities assessed, population stratification, and a lack of adjustment for confounding effects (Hsiao et al., 2013, 2014; Hsiao and Lin, 2014; Hsiao and Lin, 2015). Further, our analyses did not significantly implicate ENPP1 rs1044498 in the risk of obesity (BMI S 27). In accordance with our study, several studies indicated that ENPP1 rs1044498 was not reproducibly associated with obesity in USA Caucasian (with BMI between the 90th and 97th percentiles; Lyon et al., 2006), USA Caucasian (with BMI S 30; Stolerman et al., 2008), Polish (with BMI between the 90th and 97th percentiles; Lyon et al., 2006), U. K.
(with BMI S 30; Weedon et al., 2006), Belgian (with BMI S 30; Peeters et al., 2009), Chinese (with BMI S 28; Zhao et al., 2011), and Indian (with BMI S 30; Prakash et al., 2013) populations. On the other hand, an association of the ENPP1 rs1044498 SNP was found with obesity (BMI S 30) in Austrian and French subjects (Meyre et al., 2005) with class II obesity (BMI S 35) in a French population (Meyre et al., 2007) and with obesity (BMI S 35) in a Moroccan population (El Achhab et al., 2009). In addition, Wang et al. (2011) conducted a meta-analysis of 24,324 subjects and found that ENPP1 rs1044498rs1044498 was associated with obesity in European adult populations. Finally, it should be noted that T2D symptoms may include weight loss around the time of diagnosis and that the use of antidiabetic medications, such as insulin, may induce significant weight gain for T2D patients (Mitri and Hamdy, 2009). Therefore, cautious conclusions may be drawn when assessing the association of ENPP1 rs1044498 and obesity. This study has both strengths and limitations. The main weakness of this study is an insufficient sample size of obese cases in the context of polygenic studies (Li and Meyre, 2013; Lin et al., 2009; Liou et al., 2012; Lane et al., 2012). In future work, prospective clinical trials with very large sample sizes are necessary to facilitate a thorough evaluation of the association of this SNP with T2D, obesity, and T2D/obesity-related metabolic traits (Lin et al., 2009; Liou et al., 2012; Lane et al., 2012). Another potential limitation of our study involved the recruitment criteria used in the two included study cohorts. Different characteristics of the two reference hospitals may have resulted in genetic heterogeneity, which has been excluded by estimating Cochran’s Q test for no heterogeneity. Due to a lack of heterogeneity, we were able to pool data from the two study cohorts and thereby considerably increase the statistical power of the analysis (De Cosmo et al., 2009). Furthermore, the hospital-setting for recruitment used in our study could be prone to selection bias, known as Berkson’s bias (Roberts et al., 1978), so that the observed prevalence and distribution of T2D may not be extrapolated to the entire population. Additionally, hip circumference data were unavailable, so we were unable to calculate waist-hip ratios to give more information regarding the association between the explored SNP and phenotypes of T2D and obesity. On the other hand, an important strength of our study was the use of T2D/obesity-related metabolic data, which provided a unique opportunity to examine the associations between the ENPP1 rs1044498 polymorphism and metabolic traits. In conclusion, we carried out an extensive analysis of the association of the ENPP1 rs1044498 SNP with T2D, obesity, and T2D/ obesity-related metabolic traits in Taiwanese subjects. Our findings demonstrate that the ENPP1 rs1044498 SNP is a determinant of T2D and metabolic traits, such as waist circumference and fasting glucose. However, these results should be interpreted with caution due to a small sample size. Replication studies with larger sample sizes will likely shed further light on the association of the ENPP1 rs1044498 polymorphism with at least some of the evaluated traits. Independent replication studies using large sample sizes may provide further insights into the role of the ENPP1 rs1044498 polymorphism found in this study. Funding The authors extend their sincere thanks to Vita Genomics, Inc. and SBIR grants from the Department of Economic Affairs (S099000280249-154) in Taiwan for funding this research. Conflict of interest All of the authors declare that they have no conflict of interest.
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