Open Access

Association of BUD13 polymorphisms with metabolic syndrome in Chinese population: a case-control study

  • Lili Zhang1,
  • Yueyue You1,
  • Yanhua Wu2,
  • Yangyu Zhang1,
  • Mohan Wang1,
  • Yan Song1,
  • Xinyu Liu1 and
  • Changgui Kou1Email author
Lipids in Health and Disease201716:127

DOI: 10.1186/s12944-017-0520-8

Received: 5 May 2017

Accepted: 16 June 2017

Published: 28 June 2017

Abstract

Background

BUD13 homolog (BUD13), one of submits of the retention and splicing complex, was identified in yeast as a splicing factor that affected nuclear pre-mRNA retention. While more and more studies demonstrated that BUD13 played a potential role in the pathogenesis of metabolic syndrome (MetS). This objective was to reassess whether novel locus of BUD13 were linked to MetS and individual complements in the northeast of China.

Methods

A total of 3850 individuals were recruited in this case-control study, including 1813 MetS cases and 2037 healthy controls. The diagnostic criteria was according to the International Diabetes Federation (IDF). Metabolic complements such as waist circumference (WC), triglyceride, high-density lipoprotein cholesterol (HDL-C), systolic and diastolic blood pressure (SBP and DBP), and fasting glucose were measured. We explored the association between two novel single nucleotide polymorphism (SNPs) of BUD13 (rs7118999 and rs10488698) and MetS and its complements.

Results

Using binary logistic regression analysis we found that there were no significant associations between SNPs and MetS in different heritance models (all P > 0.05). However, novel locus of BUD13 were linked to individual complements in MetS cases. Rs7118999 conferred to risk of WC (P = 0.016) and the carrier of TT might have higher susceptibility to MetS. While rs10488698 was associated with HDL-C (P = 0.001) and the carrier of TT was significantly associated with higher level of HDL-C.

Conclusions

We concluded that novel mutations in BUD13 did not confer risk for MetS in our study population, but these mutations changed the level of metabolic complements.

Keywords

Metabolic syndrome BUD13 Single nucleotide polymorphism

Introduction

Metabolic syndrome (MetS) is a cluster of metabolic abnormalities, including raised triglyceride levels, low high-density lipoprotein cholesterol levels, raised blood pressure, and raised glucose levels [1]. Available evidences show that MetS is strongly increasing the risk of developing cardiovascular diseases, type 2 diabetes and all caused mortality [24]. Due to escalating prevalence rates and its risk for the development of several chronic diseases, MetS has become the most important health challenge at the global scale [3].

Previous studies indicated that the pathogenesis of MetS might be caused by genetics background, environmental factors and gene-environment interaction [2, 5, 6]. Furthermore, Henneman et al. [7] found that the heritability of MetS based on family study was 10.6%, indicating that gene played essential in the development of MetS. Knowledge of exact genetic factors underlying MetS development might help to explain the etiology of MetS. BUD13 was submits of the retention and splicing complex in yeast [8], while Lin et al. [5] demonstrated that its variant significantly influenced on human development of MetS. Furthermore, Meta-analysis indicated multiple genes linking to MetS, mostly of genes involving in lipids levels, and the heritability of individual components of MetS were range from 21.9–42.9% [7, 9]. Increased studies noted that BUD13 involved in lipid metabolism [5, 10, 11], suggesting BUD13 might played an essential role in the pathogenesis of MetS and its traits through changing lipid levels.

To the best of our knowledge, we selected novel SNPs (rs7118999 and rs10488698) of the BUD13 to evaluate their association with MetS and MetS complements in a sample of the Jilin province, using a case-control study design.

Materials and methods

Study population

This study incorporated subjects from Jilin province in the northeast of China, in order to evaluate whether novel locus of BUD13 was linked to MetS and individual complements. The study of community-based consisted of 3850 participants, including 1813 MetS and 2037 non-MetS. MetS was diagnosed according to IDF criteria [12], Which required that subjects with three or more of the following conditions were diagnosed as MetS a) Central obesity with a waist circumference ≥ 80 cm in females and ≥85 cm in males for Chinese subjects b) Triglycerides ≥ 1.7 mmol/L or using drug treatment to elevate triglycerides c) HDL-C < 1.00 mmol/L in males and <1.30 mmol/L for females, or using drug treatment to reduce HDL-C d) SBP ≥ 130 mmHg and DBP ≥ 85 mmHg, or using antihypertensive drug treatment in a patient with a history of hypertension and e) fasting plasma glucose ≥5.6 mmol/L or using anti-diabetic drug therapy.

The study was approved by the ethics committee of the School of Public Health, Jilin University. All subjects signed the approved informed consent.

Genotyping

The two SNPs (rs7118999 and rs10488698) of BUD13 were selected using the haploview 4.2 software (http://hapmap.ncbi.nlm.nih.gov/), and the minor allele frequency of the above two SNPs was greater than 0.05 in Chinese population.

DNA was isolated from peripheral blood samples using a commercial DNA extraction kit (Hangzhou, China). SNP genotyping was determined by MALDI-TOF-MS (Sequenom, San, Diego, CA, USA) using the Mass ARRAY system, and completed genotyping reactions were divided into a 384-well spectro CHIP. The detection rate of rs7118999 was 93.1% (1687/1813) in MetS cases, and the detection rate of rs10488698 was 99.8% (1810/1813) in MetS cases.

All statistical analyses were conducted using the SPSS program (version 21.0), and the online SNP Stats (http://bioinfo.iconcologia.net/SNPStats) program. Intergroup comparisons of means using the Student’s t-test. We conducted the chi-square test to compare the difference from two categorical data. For each SNP, Hardy-Weinberg disequilibrium was tested by χ2 test with 1 degree of freedom. Binary logistic regression analysis was used to evaluate the association of the chosen SNP with MetS by adjusted age, gender, smoking and drinking. There are three inheritance models in this study, including codominant model (TT vs CT vs CC), dominant model (CT + TT vs CC) and recessive model (TT vs CT + CC). Furthermore, we estimated the association of the investigated SNP with individual components of MetS using general linear model (GLM) by adjusted age, gender, smoking and drinking. P-value ≤0.05 was considered statistically significant.

Results

Characteristics of the subjects

The characteristics of the study population, 1813 MetS cases and 2037 non-MetS subjects, were shown in Table 1. The prevalence of MetS in our cross-sectional survey was 47.1%. The distribution of age and gender were well matched. Moreover, MetS subjects showed significantly increased risk levels for all of the MetS component variables (Waist circumference, triglyceride, systolic blood pressure and diastolic blood pressure, high density lipoprotein, fasting glucose) and the habit of smoking and drinking (all P < 0.001).
Table 1

Characteristics of study subjects

Characteristic

Case

Control

t/χ2

P-value

No. of subjects, n

1813

2037

  

Age (years)

49.5 ± 9.7

49.5 ± 9.4

−0.062

0.950

Gender

    

 Male, n (%)

903(49.8)

1024(50.3)

0.082

0.774

 Female, n (%)

910(50.2)

1013(49.7)

  

Smoking

    

 Never

1077

1203

21.902

<0.001

 Former

199

144

  

 Current

537

690

  

Drinking

    

 No

1172

1440

16.803

<0.001

 Yes

641

597

  

MetS components

    

 WC(cm)

91.4 ± 8.3

75.0 ± 7.0

−66.045

<0.001

 Triglyceride (mg/mL)

3.2 ± 2.6

1.1 ± 0.5

−35.939

<0.001

 HDL-C (mg/dL)

1.2 ± 0.3

1.6 ± 0.4

42.628

<0.001

 SBP (mm Hg)

144.6 ± 19.1

120.2 ± 15.9

−43.146

<0.001

 DBP (mm Hg)

87.8 ± 11.0

74.6 ± 9.5

−39.980

<0.001

 Fasting glucose (mg/dL)

6.6 ± 2.4

4.8 ± 1.0

−30.243

<0.001

Associations with MetS risk and quantitative metabolic traits

The distributions of rs7118999 and rs10488698 conformed to Hardy-Weinberg equilibrium among the subjects (P = 0.42, 0.73, respectively). The comparisons of genotype distributions of the polymorphisms in the BUD13 between subjects with and without MetS using different model of inheritance were shown in Table 2. We then explored the association of each SNP and MetS using binary logistic regression analysis of risk factors with adjustment for age, gender, smoking and drinking. In the case and control groups, no significant associations between SNPs and MetS were observed in different heritance models.
Table 2

BUD13 association with MetS

SNP

Inheritance model

genotype

case

control

Adjusted OR(95%CI)

P-value

rs7118999

Codominant

CC

725

825

1.00

 
  

CT

771

840

1.03(0.90–1.19)

0.656

  

TT

191

254

0.84(0.68–1.04)

0.101

 

Dominant

CC

725

825

1.00

 
  

CT/TT

962

1094

0.99(0.86–1.13)

0.845

 

Recessive

CC/CT

1496

1665

1.00

 
  

TT

191

254

0.82(0.67–1.01)

0.058

rs10488698

Codominant

CC

1543

1733

1.00

 
  

CT

254

288

0.98(0.82–1.18)

0.823

  

TT

13

11

1.33(0.59–3.00)

0.491

 

Dominant

CC

1543

1733

1.00

 
  

CT/TT

267

299

0.99(0.83–1.19)

0.931

 

Recessive

CC/CT

1797

2021

1.00

 
  

TT

13

11

1.33(0.59–3.00)

0.486

OR was adjusted for age, gender, smoking and drinking

As shown in Table 3, we also explored to association between novel SNPs and metabolism complements in with MetS subjects. Rs7118999 associated with WC in MetS cases (P = 0.016) and the carrier of TT was significantly associated with higher WC. However, rs10488698 was associated with HDL-C in MetS cases (P = 0.001) and the carrier of TT was significantly associated with higher level of HDL-C. However, our results did not exhibit association between the two SNPs with the rest of MetS components (P>0.05).
Table 3

Association between BUD13 with complements

characteristics

CC

CT

TT

t/χ2

P-value

rs7118999

725

771

191

  

 WC(cm)

91.8 ± 7.9

90.9 ± 8.8

92.3 ± 8.1

4.139

0.016

 Triglyceride (mg/mL)

3.2 ± 2.6

3.3 ± 2.6

3.3 ± 3.0

0.337

0.714

 HDL-C (mg/dL)

1.16 ± 0.28

1.16 ± 0.30

1.16 ± 0.30

0.114

0.892

 SBP (mm Hg)

145.4 ± 19.6

143.6 ± 18.8

144.8 ± 18.8

1.820

0.162

 DBP (mm Hg)

88.1 ± 10.9

87.7 ± 11.0

87.4 ± 10.8

0.826

0.438

 Fasting glucose (mg/dL)

6.6 ± 2.3

6.6 ± 2.4

6.8 ± 3.3

0.693

0.500

rs10488698

1543

254

13

  

 WC(cm)

91.4 ± 8.0

91.5 ± 8.3

90.3 ± 5.1

0.132

0.876

 Triglyceride (mg/mL)

3.3 ± 2.6

3.2 ± 2.7

2.6 ± 2.5

0.687

0.503

 HDL-C (mg/dL)

1.21 ± 0.33

1.22 ± 0.33

1.27 ± 0.30

6.721

0.001

 SBP (mm Hg)

144.5 ± 19.1

144.9 ± 19.2

147.2 ± 19.2

0.018

0.982

 DBP (mm Hg)

87.7 ± 10.9

88.3 ± 11.2

83.7 ± 12.1

1.221

0.295

 Fasting glucose (mg/dL)

6.6 ± 2.4

6.7 ± 2.7

7.2 ± 3.2

0.432

0.649

P-value was adjusted for age, gender, smoking and drinking

Discussion

This study incorporated subjects from community-based cross-sectional study with a sample of Jilin province in the northeast of China. According to the IDF diagnostic criteria [12], the prevalence of MetS was 47.1% in Jilin province in 2012. This prevalence was higher than the prevalence reported in China in 2010 (33.9%) [13]. Here, we demonstrated that novel mutations in BUD13 did not confer risk for MetS among Jilin population, but these mutations changed the level of metabolic complements.

In this literature, we for the first time explored association between novel SNPs in BUD13 and MetS. It has been noted that genetic variants are linking to the development of MetS in different populations. Previous literatures showed that SNPs of rs10790162 [10, 14, 15], rs11216129 [5] and rs623908 [5] contributed to the susceptibility for MetS in Chinese [1, 2], India [3] and Taiwanese population [5]. In this study, the distribution of genotype frequencies of rs7118999 and rs10488698 was no difference between subjects with and without MetS in different model of inheritance (P > 0.05).

Furthermore, novel locus of BUD13 were linked to individual complements in MetS cases. The carrier of TT in rs7118999 conferred to risk of MetS by increasing the level of WC. While rs10488698 might a protected factor by increasing the level of HDL-C. Similarly, many studies also investigated that BUD13 variants associated with triglyceride [5, 10, 14, 16, 17], LDL [14], total cholesterol [17] and HDL-C [5], but not discovered rs12286037 and rs28927680 with HDL in Finish [18]. Therefore, the correlation of BUD13 variants with serum lipid levels was not yet clear. Firstly, factors like age, gender, ethnicity, lifestyle and genetic background influenced the association between SNPs with serum lipid levels [19]. Secondly, inter-genetic variant might play an important role in the level of serum lipid [20]. The gene regions of APOA1/C3/A4/A5/BUD13 and BUD13/ZNF were significantly influencing the association with lipid metabolism [10, 16, 2123].

There are certain limitations to our study. Firstly, our studies subjects were coming from the cross-sectional study, which might limit its ability to detect association between BUD13 and MetS, largely because of bias [24]. Secondly, the etiology of MetS might be caused by multiple factors, such as genetics background, nutritional status and environmental factors. Our study only discussed associations between gene and MetS in the northeast of China, so we could not evaluate the same association in other population. Lastly, the detection rate of SNPs might influence the distribution of MetS and its complements. Therefore, these peculiar characteristics might be contributing factors to the findings of our study.

Conclusion

We indicated that novel mutations in BUD13 did not confer risk for MetS in our study population, but these mutations changed the level of metabolic complements. The carrier of TT in rs7118999 conferred to risk of MetS by increasing the level of WC. While the carrier of TT in rs10488698 might be protective factor for MetS, who had high level of HDL-C. Considering the complex environment and genetic disease complex mechanism, independent replication studies are needed to provide further insights into the role of the BUD13.

Abbreviations

DBP: 

diastolic blood pressure

HDL-C: 

high-density lipoprotein cholesterol

MetS: 

metabolic syndrome

SBP: 

systolic blood pressure

SNP: 

single nucleotide polymorphism

WC: 

waist circumference

Declarations

Acknowledgements

Not applicable.

Funding

This work was supported by a competitive grant from the Scientific Research Foundation of the Health Bureau of Jilin Province, China (2011Z116).

Availability of data and materials

Not applicable.

Authors’ contributions

LZ, YY, YW, YZ, and CK designed and performed the study. LZ, YW, YZ, and YY analyzed the data. LZ drafted the manuscript. YY, MW, YS and XL participated in revising draft of the manuscript. All authors approved the final version of the manuscript.

Competing interests

The authors declare that they have no competing interests.

Consent for publication

Not applicable.

Ethics approval and consent to participate

The study was approved by the ethics committee of the School of Public Health, Jilin University. All subject signed the approved informed consent.

Open AccessThis article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated.

Authors’ Affiliations

(1)
Department of Epidemiology and Biostatistics, School of Public Health, Jilin University
(2)
Division of Clinical Epidemiology, First Hospital of Jilin University

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Copyright

© The Author(s). 2017

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