Abstract
This study aims to investigate the relationship between single nucleotide polymorphisms (SNPs) of sex hormone binding globulin (SHBG) and type 2 diabetes mellitus (T2DM) in an Uighur population. One hundred and fourteen T2DM male patients (with a history of diabetes or diagnosed as diabetic by the oral glucose tolerance test) and 173 healthy males from the Uighur ethnic group were included in the study to test the following SNPs of SHBG: rs727428, rs1799941, rs6259, rs6257, rs858521, rs858518, rs3760213, and rs11078701. The body mass index (BMI), blood pressure, and waist circumference, and lipid, glucose, HbA1c, insulin, HOMA-IR, testosterone, and SHBG levels of enrolled individuals were measured. We used the t-test or rank sum test and Chi-square test to analyze the difference and compare numeration data, respectively, between the case and control groups. Comparisons among multiple groups were carried out using analysis of variance, and the correlation between variables was determined by nonparametric Spearman rank correlation analysis; multivariate logistic regression analysis was used to assess the risk of abnormal glucose in the two groups. There was a significant difference (P < 0.05) in BMI, blood pressure, and waist circumference, and lipid, glucose, HbA1c, insulin, and HOMA-IR levels between the case and control groups. The risk factors for diabetes included testosterone (P = 0.042) and SHBG (P = 0.001). The distribution of rs858521 (P = 0.001), rs1799941 (2.3%, P = 0.032), rs6259 (2.5%, P = 0.040), and rs727428 (3.4%, P = 0.016) was significantly different between the case and control groups (P < 0.05). In the control group, there was linkage disequilibrium (LD) between rs727428 and rs6259, while in the case group LD was found among rs858518, rs3760213, rs1799941, and rs6257. The frequency of rs858518-rs3760213-rs1799941-rs6257 haplotype TCGC was significantly different between the two groups (P = 0.033). Both testosterone and SHBGwere found to be risk factors of diabetes in the Uighur population, and SNPs of SHBG may contribute to T2DM.
Keywords: Diabetes, single Nucleotide Polymorphism (SNPs), sex hormone binding globulin (SHBG), Uighur ethnic group
Introduction
The increasing incidence of type 2 diabetes (T2DM) is a major problem [1,2]. T2DM is a metabolic syndrome characterized by hyperglycemia. In T2DM, insufficient insulin secretion and/or insulin resistance (IR) affect the sugar, fat, protein, water, and electrolyte balance in patients [3]. T2DM is not caused by a single pathophysiologic mechanism but is a result of multiple factors. Although genetic variations and environmental factors are vital for the development and progression of T2DM [4], the mechanism of their involvement in T2DM has not yet been fully elucidated due to the complexity of T2DM etiology and pathogenesis.
Sex hormone binding globulin (SHBG) is an important protein involved in cellular signaling pathways [5] and is mainly produced by the liver. Certain metabolic diseases affect serum SHBG levels [6]. A role of SHBG as a risk factor for T2DM has been observed in many clinical trials and large prospective cohorts of men and women across ethnic populations [7]. It has been demonstrated that circulating SHBG is strongly associated with systemic metabolism and is predictive of insulin resistance and diabetes [8]. A meta-analysis reported that SHBG and estradiol are independent risk factors for the development of T2DM in women [9]. Some studies report that diabetes risk is potently determined by obesity and glycemia rather than the sex hormones and that SHBG and its polymorphisms (rs6259, rs6257 and rs1799941) do not predict any risks in men or women [10].
In this study, we comparatively studied single nucleotide polymorphisms (SNPs) of the SHBG gene between subjects with T2DM and those without T2DM, to explore the correlation between the polymorphisms of the SHBG gene and the risk of T2DM in the Uygur population of Xinjiang, China.
Materials and methods
Subjects
Our methodology was designed as a case-control study on the outpatients visiting the Department of Endocrinology at the First Affiliated Hospital of Xinjiang Medical University. The individuals receiving a physical examination at the same department between Sep 2016 and Dec 2016 were also included in the study to record the general data of the patients and the results of their physical and laboratory examination. Informed consent was obtained from all the subjects, and the research plan was approved by the ethics committee of the First Affiliated Hospital of Xinjiang Medical University.
Inclusion criteria and exclusion criteria
The inclusion criteria were as follows: 1) male patients over 40 years old; 2) patients of pure ethnic Uygur origin; 3) resident of Xinjiang for more than 5 years; 4) meeting the diagnostic criteria for T2DM. The healthy controls had similar criteri but had no history of diabetes and did not show an abnormal result on the oral glucose tolerance test (OGTT).
The exclusion criteria included: 1) patients with serious complications of the heart, lung, kidney, liver, brain or any other organ; 2) patients with thyroid disorders; 3) patients with psychological and mental diseases; 4) patients with a history of tumors related to the gonads or receiving exogenous hormone (including glucocorticoid, sex hormone, thyroid hormone, growth hormone) treatment at the time of screening; 5) patients with a history of pituitary disease (including pituitary tumor, increased pituitary hormone secretion or hypopituitarism); 6) patients with type 1 diabetes and other special types of diabetes; 7) those who did not provide their consent to draw blood.
Questionnaire survey
All subjects received a standardized face to face questionnaire survey, which was carried out in the native language of the subjects by the trained staff of our group. The survey included general information, such as age, address, education, marital status, job nature, and family income; lifestyle such as smoking history and drinking history; childbearing history; personal and family history of diabetes; history of diabetic complications or hypertension; history of metabolic syndrome such as dyslipidemia, hyperuricemia, and the medication they are currently using.
Anthroposomatological information
The anthroposomatological information including height, weight, blood pressure and waist circumference of all the subjects was collected by the trained staff of our group.
Sample collection and determination of the T2DM-related biochemical index
Fasting blood was collected from the anterior elbow vein of each subject after 8-10 h of fasting for determining the biochemical index: serum lipid levels, testosterone, SHBG, insulin levels, glycosylated hemoglobin, and plasma glucose levels. Fasting blood glucose (FBG) was evaluated by OGTT for all subjects except those who had already been diagnosed with diabetes. In OGTT, the blood glucose was tested at 2 h after consumption of glucose water (75 g anhydrous glucose powder or 82.5 g glucose monohydrate dissolved in 200-300 mL warm water). The post-prandial glucose (PBG) was tested in patients 2 h after consumption of food.
T2DM diagnostic criteria
The diagnoses of T2DM and abnormal blood sugar were based on the diagnostic criteria fordiabetes recommended by WHO in 1999.
SNP selection
SNPs in the SHBG gene were selected from the NCBI dbSNP database (http://www.ncbi.nlm.nih.gov/projects/SNP) and the International HapMap database (http://hapmap.ncbi.nlm.nih.gov/) according to the following criteria: 1) at least 5% minor allele frequency (plus) in the Chinese population, and 2) an r2 threshold of less than 0.8 as the lower limit for linkage disequilibrium (LD). The tag SNPs were chosen to constitute a set of minimal height information markers to minimize redundant data.
Finally, we searched the published literature for SNPs that were already reported to affect the levels of circulating SHBG. Eight SNPs, including rs727428, rs1799941, rs6259, rs6257, rs858521, rs858518, rs3760213, and rs11078701 were selected for further analysis.
Statistical analysis
SPSS 21.0 statistical software was used for data processing. The data were represented as mean ± standard deviation (SD) if the data were normally distributed, and the data that were not normally distributed were expressed as median and interquantile range. The difference between the two groups was analyzed using t-test or rank sum test. The enumeration data were compared using Chi-square test. Comparisons among multiple groups were carried out using analysis of variance. The correlation between the variables was analyzed by non-parametric Spearman’s rank correlation analysis, and multivariate logistic regression analysis was used to analyze the risk of T2DM. The significance level was set as α = 0.05, and the difference was considered significant when P < 0.05.
The original data collected on the ABI3730XL sequencer were analyzed with GeneMapper 4.1 software (Applied Biosystems Co., Ltd., USA). The genotype data of SHBG gene were obtained from the HapMap database, and the r2 values were compared using post hoc multiple comparisons. Haploview 4.2 software was used for LD and haplotype analysis. LD Block was estimated using the solid spine of the LD algorithm. Thus, the LD map and haplotype data were obtained.
Results
Comparison of clinical data
A total of 287 subjects were enrolled in the study and were divided into case group (type 2 diabetes group) and control group according to the diagnostic criteria for diabetes. BMI, blood pressure, serum lipid levels and plasma glucose levels in the case group were all significantly (P < 0.05) higher than those determined in the control group (Table 1). The case group had significantly (P < 0.05) higher levels of SHBG and testosterone as compared to their levels in the control group. Fasting insulin levels were significantly (P < 0.05) lower in the case group compared to their levels in the control group.
Table 1.
Values of metabolic measures (BMI, blood pressure, total cholesterol and waist circumference) in the groups
| BMI | blood pressure (mmHg) (high pressure/low pressure) | Total cholesterol (mmol/L) | waist circumference (cm) | P value | |
|---|---|---|---|---|---|
| Healthy group | 24.77±9.5 | 130.0±42/76.9±25.0 | 4.85±2.2 | 90.8±10 | 0.034 |
| T2DM group | 27.58±9.9 | 142.5±47.1/82.8±36 | 5.65±2.5 | 98.88±30 |
Multivariate non-conditional logistic regression analysis of T2DM indicators
Logistic regression analysis showed that BMI, blood pressure, serum lipid levels and waist circumference were risk factors for T2DM. Additionally, SHBG (P = 0.001, 95% CI = 0.863-0.965) and testosterone (P = 0.042, 95% CI = 0.977-1.058) were also risk factors for T2DM.
Hardy-Weinberg equilibrium test of genotype distribution
The allele distribution of the SNP loci (rs11078701, rs858521, rs858518, rs3760213, rs1799941, rs6257, rs6259, and rs727428) of the SHBG gene was tested by the goodness of fit Chi-square test for Hardy-Weinberg (H-W) genetic equilibrium. The results showed that all SNP loci rs11078701 (HWP = 0.068/0.160), rs858521 (HWP = 0.000/0.167), rs858518 (HWP = 0.143/0.197), rs3760213 (HWP = 0.062/0.110), and rs1799941 (HWP = 0.887/1.000) were in compliance with the Hardy-Weinberg equilibrium. Thus, the samples were a good representative of the population.
Comparisons of the distribution of SNP loci
The distributions of alleles among the case and control groups were compared. The results showed that there was a significant difference in the allele distribution of rs858521 (P = 0.003), rs1799941 (P = 0.032), rs6259 (P = 0.040), and rs727428 (P = 0.016) between the two groups. The distribution of rs1799941 A/A and rs6257 T/T was not found in the case group, while rs1799941 G/A and A/A and rs6257C/T and T/T were not found in the control group.
Distribution and odds ratio of alleles of SNP loci in case and control groups
Analysis of the odds ratio of the distribution of different alleles of 8 SNP loci showed that the differences in distribution of different alleles of rs858521 (χ2 = 10.131, P = 0.001), rs6259 (χ2 = 4.851, P = 0.028) and rs727428 (χ2 = 6.378, P = 0.012) were significant between the case and control groups, while the allele distribution of the remaining 5 SNP loci was not observed in the case group or control group. There was no significant difference in the allele distribution of rs11078701, rs858518 and rs3760213 between the case and control groups.
Analysis of linkage disequilibrium of 8 SNPs in SHBG gene
Linkage disequilibrium (LD) analysis was performed on the SNP loci in case and control groups that matched with the Hardy-Weinberg equilibrium. The results showed that there was a LD among the SNP loci rs727428 and rs6259 in the control group (D’ = 1). In the case group, there was a LD among the loci rs858518-rs3760213-rs1799941-rs6257 (D’ = 1) (Figures 1 and 2; Tables 2 and 3).
Figure 1.
LD map of SNPs in SHBG in the control group.
Figure 2.
LD map of SNPs in SHBG in the case group.
Table 2.
SHBG gene SNP site chain relationship |D’| value in control group
| Loci | rs858521 | rs858518 | rs3760213 | rs6259 | rs727428 |
|---|---|---|---|---|---|
| rs11078701 | 0.050 | 0.183 | 0.337 | 0.052 | 0.098 |
| rs858521 | - | 0.093 | 0.182 | 0.037 | 0.155 |
| rs858518 | - | - | 0.209 | 0.117 | 0.004 |
| rs3760213 | - | - | - | 0.057 | 0.030 |
| rs6259 | - | - | - | - | 1.000 |
Table 3.
SHBG gene SNP site chain relationship |D’| value in case group
| Loci | rs858518 | rs3760213 | rs1799941 | rs6257 | rs6259 | rs727428 |
|---|---|---|---|---|---|---|
| rs11078701 | 0.141 | 0.249 | 0.490 | 1.000 | 0.117 | 0.009 |
| rs858518 | - | 0.313 | 1.000 | 1.000 | 0.081 | 0.017 |
| rs3760213 | - | - | 0.126 | 1.000 | 0.030 | 0.244 |
| rs1799941 | - | - | - | 1.000 | 1.000 | 1.000 |
| rs6257 | - | - | - | - | 1.000 | 1.000 |
| rs6259 | - | - | - | - | - | 0.853 |
Construction of haplotype of SHBG gene and its distribution frequency analysis
Haplotypes were constructed for the SNP loci obtained from the LD analysis, and their distribution frequencies were analyzed. The results showed that there was a significant difference in the distribution frequency of haplotype TCGC (rs858518-rs3760213-rs1799941-rs6257) in the SHBG gene between the case and control groups in the Uygur population (OR = 0.697, 95% CI = 0.867-1.743, P = 0.033). The differences in the distribution frequencies of rs6259-rs727428 haplotype GA (OR = 0.468, 95% CI = 0.320-0.683, P < 0.001) and GG (OR = 1.478, 95% CI = 1.053-2.074, P = 0.037) were also statistically significant between the two groups.
Discussion
The specific nucleotide polymorphism loci in the SHBG gene associated with the pathogenesis of T2DM may act through a mechanism that affects the synthesis and functions of these proteins. Our results showed that the difference in the distribution of different genotypes of rs858521 loci in the Uygur middle-aged and elderly men was significant between the case and control groups. Allele C was associated with a higher risk for T2DM compared to the risk associated with allele G. The difference in the distribution of rs1799941 and rs6259 genotypes was also significant (P < 0.05) between the case and control groups. However, allele G was associated with a higher risk for T2DM compared to the risk associated with allele A. The difference in the distribution frequency of s858518-rs3760213-rs1799941-rs6257 haplotype TCGC was significant (P < 0.05) between the case and control groups. The difference in the frequency of rs6259-rs727428 haplotype GA and GG was statistically significant between the case and control groups. Our results provide important clues to explore the relationship between SNPs of SHBG and glucose metabolism at a genetic level.
SHBG and liver enzyme levels are both risk factors of T2DM. SHBG affects the glucose and lipid levels and this association depends on the liver fat content, liver enzymes or sex hormone concentrations [11]. By analysis of the data from the women’s health study and doctors’ health study II, SHBG was confirmed to play an important role in the pathogenesis of T2DM at both genetic and phenotypic levels [12]. Studies have shown that rs1799941 SNP is located at the 5’ terminal of the non-translated region (G > A) 8 nucleotides upstream of the translation initiation site in the promoter sequence [13]. Investigation on the relationship between the rs179994 genotype and the serum SHBG level suggested this position plays a crucial role in determining the SHBG splicing variants in circulating SHBG levels. However, the association between rs1799941 SNP and diabetes is unclear. A meta-analysis suggested that there is a differential effect of the rs1799941 polymorphism-related risk of T2DM among the genders [14]. Studies on Caucasian women and Middle Eastern women suggested that gene polymorphisms of rs6257, rs6259 (D356N), rs727428 and rs1799941 loci are associated with reduced SHBG levels and the occurrence of PCOS [15-19]. rs13894, rs858521 and rs9898876 are also considered to be associated with achange of SHBG level in the pathogenesis of T2DM and insulin resistance [20-22]. These specific SHBG polymorphisms are not associated with BMI, suggesting that SNPs affect the risk of T2DM independently of lipid factors.
In summary, SHBG was previously believed to be associated with the transportation of sex steroids. The elucidation of the internal structure of SHBG and the discovery of secondary messenger systems activated by unrelated SHBG indicate that SHBG is involved in additional functions [23-25]. Epidemiological studies have revealed an association between low SHBG levels and the increased risk of T2DM. Recent genetic research suggests that this association may be causal.
Acknowledgements
This work was supported by the State Key Laboratory of Pathogenesis, Prevention, Treatment of High Incidence Diseases in Central Asia Fund, China (SKL-HIDCA-2017-Y10).
Disclosure of conflict of interest
None.
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