Skip to main content
Download PDF
  • Research article
  • Open access
  • Published:

GLUT4 gene rs5418 polymorphism is associated with increased coronary heart disease risk in a Uygur Chinese population

BMC Cardiovascular Disorders volume 22, Article number: 191 (2022) Cite this article

Abstract

Background

To explore possible associations between glucose transporter 4 (GLUT4) genetic polymorphisms in the patients with coronary heart disease (CHD) in Han and Uygur Chinese populations in Xinjiang, China.

Methods

Two GLUT4 polymorphisms (rs5418 and rs5435) were genotyped in 1262 Han (628 CHD patients and 634 healthy controls) and 896 Uyghur (397 CHD patients and 499 healthy controls) Chinese populations.

Results

In the Han Chinese population, there were no significant differences in allelic or genotypic distribution of rs5418 and rs5435 between the CHD and control groups (all P > 0.05). However, in the Uygur population, there were significant differences in genotype and allele distributions for rs5418 between CHD and the control group (all P < 0.05). Binary Logistic regression analysis showed that carriers with the rs5418 A allele had a higher risk of CHD compared to carriers of the rs5418 G allele (OR = 1.33, 95% CI: 1.069–1.649, P = 0.01), after adjustment for gender, age, drinking and smoking behavior, hypertension and diabetes. Furthermore, haploid association analysis of the two SNP loci of the GLUT4 gene showed that the AC haplotype was associated with CHD in the Uygur population (P = 0.001598; OR = 1.36, 95% CI = 1.1228–1.6406).

Conclusions

rs5418 GLUT4 gene variants are associated with CHD in the Uygur Chinese population.

Peer Review reports

Background

Coronary heart disease (CHD), also known as ischemic heart disease, is caused by myocardial ischemia and hypoxia due to changes in coronary circulation and represents the greatest mortality risk globally of all diseases. Prior reports suggest that ischemic heart disease causes 12.9 million deaths, accounting for one quarter of all global deaths annually, and is the main cause of death [1]. Many factors have been implicated in the development of coronary heart disease including smoking [2], obesity [3], sex [4], metabolic syndrome severity [5], glycerophosphocholine [6], dysglycemia and diabetes [7, 8], and sleep apnea syndrome [9, 10]. In addition to these environmental factors, several gene variants have been linked to the development and progression of CHD [11,12,13].

Glucose transporters (GLUT) are necessary for glucose uptake in cells of nearly all species [14]. Of the 14 known human GLUT genes, GLUT 1, 2, 3, and 4 have been implicated in human disease. In particular GLUT4 has been the subject of significant study as it is the main glucose transporter found in muscles and adipocytes [14]. This 12-transmembrane, 509-amino acid protein contains a large cytoplasmic loop between transmembrane helices 6 and 7 that is normally sequestered intracellularly in the basal state [15]. The cellular localization of GLUT4 is regulated by insulin through a complex mechanism, the aberration of which contributes to obesity [14]. The GLUT4 gene (also known as the solute carrier family 2, member 4 (17p13.2; OMIM 138190) encodes the GLUT4 solute carrier [16]. Many studies across multiple ethnicities have suggested a relationship between GLUT4 polymorphisms and human disease including insulin resistance [17,18,19], type 2 diabetes, [20] and obstructive sleep apnea syndrome [21]. However, few studies have investigated the association between the GLUT4 gene and CHD [22]. Thus, we aimed to evaluate a possible relationship between GLUT4 gene polymorphisms and CHD risk in Han and Uygur populations in Xinjiang, China.

Methods

Subjects

In this case–control study, patients with coronary angiography were recruited from the cardiovascular department of the First Affiliated Hospital of Xinjiang Medical University. The inclusion criteria for CHD are as follows: (1) older than 18 years of age, (2) a diagnosis of CHD according to the World Health Organization (WHO) CHD diagnostic criteria set in 1979, and (3) single vessel stenosis of > 70%, or multiple vessel stenosis of > 50%. The controls consisted of a random sample of adult’s individuals above 18 years of age who were visiting the hospital for a routine checkup and had medical records. The inclusion criteria were being devoid from any type of cardiovascular diseases and having a normal coronary artery with no evidence of plaque or peripheral vascular disease as confirmed by angiography, medical profile, or previous medical history as documented in their medical records. Individuals were excluded if they had the following conditions: cancer, acute or chronic inflammatory disease, cardiomyopathy or heart valve disease, variant angina without fixed coronary stenosis, coronary artery dilatation, X syndrome, serious liver disease (ALT > 2X normal), serious kidney disease (blood CR > 2.5 mg/dl), blood disease and pregnancy.

Clinical data collection

Baseline characteristics of all patients, such as sex, age, and smoking status were collected and are summarized in Table 1. Height and body mass were measured by a unified standard method, and patients' previous medical history such as hypertension, diabetes, abnormal blood lipid metabolism, and medications were recorded and used to diagnose related diseases in accordance with relevant Chinese or international standards [23, 24]. Habitual activities such as smoking and alcohol consumption behavior were collected from questionnaire.

Table 1 Baseline characteristic of the Han population
Full size table

Blood biochemical indexes and DNA extraction

Cubital venous blood was collected from subjects after fasting for > 12 h. Blood biochemical indexes were tested by the Medical Laboratory Center of the First Affiliated Hospital of Xinjiang Medical University using the Abbott C16000 biochemical immunity instrument (United States). The Whole Blood Genome Extraction Kit (Tiangen Biochemical Co., Ltd.) was used to extract DNA from whole blood samples. A Nanodrop ND-2000 Ultramicro Nucleic Acid Analyzer was used to detect the concentration and purity of DNA, which was then diluted to 20 ng/ul.

Genotyping

In this study, single nucleotide polymorphisms (SNPs) were typed using the TM Multiple SNP Typing Kit (Shanghai Genesky Biotechnology). The SNP allelic site was identified using a high specificity ligase reaction. Then, the ligated products of different lengths were obtained by introducing varied length non-specific sequences at the end of the ligase probe and using a ligase addition reaction. The ligated products were PCR amplified using universal primers labeled with fluorescence, as shown in Supplementary material, and the amplified products were separated by fluorescence capillary electrophoresis. Finally, the genotypes of each SNP locus were obtained by analyzing the electrophoretic patterns.

Data analyses

SPSS 20.0 software was used for statistical analysis. Student t-test is applied for the continuous variables, while Pearson chi-square test is for categorical variables. Wilcoxon Rank-Sum test was applied for body mass index (BMI). The software program SHEsis or chi-square test (http://analysis.bio-x.cn) was used to assess Hardy- Weinberg equilibrium (HWE). PLINK (http://pngu.mgh.harvard.edu/purcell/plink/) software was used to analyze the allelic and genotypic association with disease [25]. Linkage disequilibrium (LD) plot was generated from Haploview version 4.2 (Broad Institute, Cambridge, MA, United States) [26]. Haplotype association analyses were performed with PLINK. Binary logistic regression, with the adjustment of covariates was applied to calculate the odds ratios (ORs) and 95% confidence intervals (CIs). Three logistic regression models (additive, dominant, and recessive) were also used to analyze the SNPs. All tests were two-tailed, and the results were considered significant when P ≤ 0.05.

Results

Baseline characteristics

The clinical characteristics of study subjects in the Han and Uygur populations are shown in Tables 1 and 2, respectively. Significant differences between both populations were noted in regard to gender (P < 0.001), age (P < 0.001), drinking and smoking behavior (P < 0.001), hypertension (P < 0.001) and diabetes (P < 0.001). Accordingly, these variants were used as the covariates in the following binary logistic regression analysis. No statically significant difference in the BMI was found between CHD patients and controls.

Table 2 Baseline characteristic of the Uygur population
Full size table

Hardy–Weinberg analysis of the investigated SNPs

The Hardy–Weinberg analysis showed no deviations from Hardy–Weinberg Equilibrium for rs5418 and rs5435 in either the Han or Uygur populations.

Allelic and genotypic association with CHD

In the Han population, neither rs5418 nor rs5435 variants showed any significant differences in allelic frequencies and genotypic distribution between the CHD and control groups (all P > 0.05). However, in the Uygur population, the allelic frequencies of A and G at the rs5418 locus were 46.22 and 53.78% in the CHD group, and 39.28 and 60.72% in the control group, respectively, representing a significant difference in allelic distribution (P = 0.003, 95% CI = 1.10–1.61). The risk of having CHD in patients with the A allele was 1.33 times greater than that of the controls. The frequencies of the T and C alleles at the rs5435 locus in the Uygur CHD group were 33.12 and 66.88%, respectively, compared to 37.37 and 62.63% in the control group, respectively, representing no significant difference in the distribution of the alleles and genotypes between the two groups at the rs5435 locus (P = 0.06, OR = 0.83, 95% CI = 0.68–1.01). We conducted further binary logistic regressions based on the additive, dominant and recessive model after controlling for the influence of gender, age, drinking and smoking behavior, hypertension and diabetes, which revealed a significant association between the rs5418 locus and the risk of CHD under all the genetic models. Compared with the G allele, the A allele increased the risk of coronary heart disease 1.33×. These data are summaries in Tables 3, 4 and 5.

Table 3 Allelic association analysis between two SNPs and CHD
Full size table
Table 4 Genotypic association analysis between two SNPs and CHD
Full size table
Table 5 Logistic regression analysis of GLUT4 polymorphisms and risk of coronary heart disease in the Uygur population
Full size table

We explored the correlations between rs5418 and other risk factors. There are no association identified between rs5418 and most of the risk factors. In Uygur population, genotype of rs5418 was significantly correlated with smoking behavior (Additional file 1: Tables S1, S2).

Haplotypes associated with CHD

Additional file 2: Figure S1 was included to show the linkage disequilibrium for the two investigated SNPs. In the Han population, haploid frequency analysis of two GLUT4 SNP loci identified three haplotypes at the two loci, yet there was no significant difference in the distribution of these haplotypes in the Han population (P > 0.05). In the Uygur population, haploid frequency analysis of two GLUT4 SNP loci identified four haplotypes. In particular, AC haplotype distribution was significantly different between the CHD and control groups in the Uygur population (P < 0.05) and the risk of developing CHD increased 1.36 for the AC haplotype carriers (Table 6).

Table 6 Association of haplotypes with CHD
Full size table

Discussion

Previous studies have shown close relationships between the incidence of CHD and LAP (liver-enriched transcriptional activator protein), (IL)-1 (interleukin 1), IL-18 (interleukin 18), ANGPTL4 (Angiopoietin-like 4), and PIN1 (Peptidylprolyl Cis/Trans Isomerase, NIMA-Interacting 1) [27,28,29,30,31] but few studies have investigated the association between GLUT4 polymorphisms and CHD. We studied GLUT4 polymorphisms and potential associations with CHD in multiple ethnic groups within a Chines population and identified a specific GLUT4 gene polymorphism that is significantly associated with the risk of developing CHD in the Uygur Chinese population.

GLUT4 is the primary glucose transporter in the human heart, which accounts for approximately 70% of all glucose transport [32]. In heart diseases like cardiac hypertrophy, heart failure, and myocardial ischemia different perturbations in expression of glucose transporters are observed, especially in GLUT1 and GLUT4, due to changes in heart glucose metabolism. GLUT4 is translocated to the cell surface in response to ischemia, insulin, and catecholamines, allowing it to increase glucose transport into cardiomyocytes. However, in the absence of stimulation, GLUT4 is trapped intracellularly [33]. Ischemia, as seen in CHD, results in increased myocardial glucose utilization derived from glycogen breakdown [34] and also leads to increased glucose transport activity [35]. The increase in glucose uptake is due to the transport of GLUT1 and GLUT4 from the intracellular ventricle to the sarcolemma. Many studies have reported GLUT4 gene polymorphisms in association with type 2 diabetes [20, 36,37,38], but there are few investigations of possible GLUT4 gene polymorphisms in CHD patients.

Of the known GLUT4 polymorphisms, rs5418 is located in the gene promoter region [39]. One prior study in a south India population found that the GLUT4 gene was detected in normal glucose tolerance and type 2 diabetes, and found differences in the ACGT haplotype of rs5418 [20]. Another study found that the rs5418 locus is associated with HBA1c levels in Japanese males [40]. The rs5418 locus has also been linked with obesity despite a similar allelic and genotypic distribution in extremely obese children and adolescents compared to normal and underweight patients, suggesting that these alleles are not involved in weight regulation [41]. The rs5435 locus was reported associated with risk of CHD in Han population in Guangdong province [22]. However, no significant association with CHD in Han and Uygur population in our study in allice and genotype distribution. The reason might be low sample size decreased the statistical power, since a significant trend was identified in logistic model under dominant model.

In this study, we found that the genotypic and allelic frequencies of rs5418 locus were not significantly different in either the CHD or the control group of a Han Chinese population. However, in the Uygur population, we found that the distribution frequencies of the three genotypes and alleles in the two groups were significantly different. Differences in findings between these populations are likely related to the races studied. As a multi-ethnic region, Xinjiang has 45 ethnic groups, in addition to the Han and Uygur populations. In addition to the different genetic backgrounds of these groups, there are a variety of cultural and lifestyle differences that may also play a contributing role. Our findings that the AA genotype and the A allele at the rs5418 site GLUT4 gene are associated with susceptibility to CHD in the Uygur Chinese population, it may be beneficial provide for future clinical diagnosis and treatment. This study was limited to only patients from two ethnic groups in Xinjiang, China. Future studies should include more ethnic groups from this area or study populations in other regions of China or worldwide. Further functional validation of increased susceptibility to CHD development is also needed to confirm a definitive role of rs5418 site mutations in the GLUT4 gene and elucidate the effects of these mutations on CHD pathogenesis or etiology.

Conclusions

GLUT4 gene polymorphisms at the rs5418 site were associated with CHD susceptibility in the Uygur, but not Han, Chinese population of Xinjiang, China.

Availability of data and materials

The datasets of genotyping data in the current study are available as the PLINK binary format in the Github repository [https://github.com/2022yufei/genotyping].

Abbreviations

CHD:

Coronary heart disease

GLUT4 :

Glucose transporter 4 gene

SNPs:

Single nucleotide polymorphisms

PCR:

Polymerase chain reaction

HWE:

Hardy–Weinberg equilibrium

OR:

Odds ratio

References

  1. Lozano R, Naghavi M, Foreman K, Lim S, Shibuya K, Aboyans V, et al. Global and regional mortality from 235 causes of death for 20 age groups in 1990 and 2010: a systematic analysis for the Global Burden of Disease Study 2010. Lancet. 2012;380(9859):2095–128.

    Article  Google Scholar 

  2. Khoramdad M, Vahedian-Azimi A, Karimi L, Rahimi-Bashar F, Amini H, Sahebkar AJ. Association between passive smoking and cardiovascular disease: a systematic review and meta-analysis. IUBMB Life. 2020;72(4):677–86.

    Article  CAS  Google Scholar 

  3. Chirinos D, Llabre M, Goldberg R, Gellman M, Mendez A, Cai J, et al. Defining abdominal obesity as a risk factor for coronary heart disease in the U.S.: results from the Hispanic Community Health Study/Study of Latinos (HCHS/SOL). Diabetes Care. 2020;43(8):1774–80.

    Article  Google Scholar 

  4. Bancks M, Akhabue E, Rana J, Reis JP, Schreiner PJ, Yano Y, et al. Sex differences in cardiovascular risk factors before and after the development of type 2 diabetes and risk for incident cardiovascular disease. Diabetes Res Clin Pract. 2020;166:108334.

    Article  Google Scholar 

  5. DeBoer M, Gurka M, Golden S, Musani SK, Sims M, Vishnu A, et al. Independent associations between metabolic syndrome severity and future coronary heart disease by sex and race. J Am Coll Cardiol. 2017;69(9):1204–5.

    Article  Google Scholar 

  6. Syme C, Czajkowski S, Shin J, Abrahamowicz M, Leonard G, Perron M, et al. Glycerophosphocholine metabolites and cardiovascular disease risk factors in adolescents: a cohort study. Circulation. 2016;134(21):1629–36.

    Article  CAS  Google Scholar 

  7. Ross S, Gerstein H, Eikelboom J, Anand S, Yusuf S, Paré G. Mendelian randomization analysis supports the causal role of dysglycaemia and diabetes in the risk of coronary artery disease. Eur Heart J. 2015;36(23):1454–62.

    Article  Google Scholar 

  8. Laakso M, Kuusisto J. Insulin resistance and hyperglycaemia in cardiovascular disease development. Nat Rev Endocrinol. 2014;10(5):293–302.

    Article  CAS  Google Scholar 

  9. Lee C, Sethi R, Li R, Ho HH, Hein T, Jim MH, et al. Obstructive sleep apnea and cardiovascular events after percutaneous coronary intervention. Circulation. 2016;133(21):2008–17.

    Article  Google Scholar 

  10. Gami A, Olson E, Shen W, Wright RS, Ballman KV, Hodge DO, et al. Obstructive sleep apnea and the risk of sudden cardiac death: a longitudinal study of 10,701 adults. J Am Coll Cardiol. 2013;62(7):610–6.

    Article  Google Scholar 

  11. Kessler T, Schunkert H. coronary artery disease genetics enlightened by genome-wide association studies. JACC Basic Transl Sci. 2021;6(7):610–23.

    Article  Google Scholar 

  12. Nikpay M, Goel A, Won HH, Hall LM, Willenborg C, Kanoni S, et al. A comprehensive 1000 Genomes–based genome-wide association meta-analysis of coronary artery disease. Nat Genet. 2015;47(10):1121–30.

    Article  CAS  Google Scholar 

  13. Matsunaga H, Ito K, Akiyama M, Takahashi A, Koyama S, Nomura S, et al. Transethnic meta-analysis of genome-wide association studies identifies three new loci and characterizes population-specific differences for coronary artery disease. Circul Genomic Precis Med. 2020;13(3):e002670.

    Article  CAS  Google Scholar 

  14. Sun L, Zeng X, Yan C, Sun X, Gong X, Rao Y, et al. Crystal structure of a bacterial homologue of glucose transporters GLUT1-4. Nature. 2012;490(7420):361–6.

    Article  CAS  Google Scholar 

  15. Mishra R, Wei C, Hresko R, Bajpai R, Heitmeier M, Matulis SM, et al. In silico modeling-based identification of glucose transporter 4 (GLUT4)-selective inhibitors for cancer therapy. J Biol Chem. 2015;290(23):14441–53.

    Article  CAS  Google Scholar 

  16. Al-Lahham Y, Mendes A, Souza E, Alberton D, Rego F, Valdameri G, et al. Interleukin-18 (rs187238) and glucose transporter 4 (rs5435) polymorphisms in Euro-Brazilians with type 1 diabetes. Genet Mol Res. 2017;16(3).

  17. Zisman A, Peroni O, Abel E, Michael MD, Mauvais-Jarvis F, Lowell BB, et al. Targeted disruption of the glucose transporter 4 selectively in muscle causes insulin resistance and glucose intolerance. Nat Med. 2000;6(8):924–8.

    Article  CAS  Google Scholar 

  18. Abel E, Peroni O, Kim J, Kim YB, Boss O, Hadro E, et al. Adipose-selective targeting of the GLUT4 gene impairs insulin action in muscle and liver. Nature. 2001;409(6821):729–33.

    Article  CAS  Google Scholar 

  19. Jiang L, Yao L, Yang Y, Ke D, Batey R, Wang J, et al. Jiangzhi Capsule improves fructose-induced insulin resistance in rats: Association with repair of the impaired sarcolemmal glucose transporter-4 recycling. J Ethnopharmacol. 2016;194:288–98.

    Article  Google Scholar 

  20. Bodhini D, Radha V, Ghosh S, Majumder PP, Rao MR, Mohan V. GLUT4 gene polymorphisms and their association with type 2 diabetes in south Indians. Diabetes Technol Ther. 2011;13(9):913–20.

    Article  CAS  Google Scholar 

  21. Yin T, Li N, Ai L, Yao X, Hong J, Zhou L, et al. Association of glucose transporter 4 gene polymorphism with hypoxia caused by obstructive sleep apnea syndrome and with related inflammatory factors. Zhong guo Yi Xue Ke Xue Yuan Xue Bao. 2014;36(4):400–9.

    CAS  Google Scholar 

  22. Xiang Z, Lu J, Yeda C, Meihua L, Jiheng Q, et al. Association of GLUT4 gene polymorphisms with risk of coronary heart disease in Han population in Guangdong province. Chin J Public Health. 2016;32(5):636.

    Google Scholar 

  23. Zhong M, Tan H, Gong H, Wang S, Zhang Y, Zhang W. Increased serum visfatin in patients with metabolic syndrome and carotid atherosclerosis. Clin Endocrinol (Oxf). 2008;69(6):878–84.

    Article  CAS  Google Scholar 

  24. Johansson L, Johansson L, Ridderstrale M. The visfatin (PBEF1) G-948T gene polymorphism is associated with increased high-density lipoprotein cholesterol in obese subjects. Metabolism. 2008;57(11):1558–62.

    Article  CAS  Google Scholar 

  25. Purcell S, Neale B, Todd-Brown K, Thomas L, Ferreira MA, Bender D, et al. PLINK: a tool set for whole-genome association and population-based linkage analyses. Am J Hum Genet. 2007;81(3):559–75.

    Article  CAS  Google Scholar 

  26. Belfer I, Buzas B, Evans C, Hipp H, Phillips G, Taubman J, et al. Haplotype structure of the beta adrenergic receptor genes in US Caucasians and African Americans. Eur J Hum Genet. 2005;13(3):341–51.

    Article  CAS  Google Scholar 

  27. Wei W, Li X, Feng Q, Kubo M, Kullo I, Peissig P, et al. LPA variants are associated with residual cardiovascular risk in patients receiving statins. Circulation. 2018;138(17):1839–49.

    Article  CAS  Google Scholar 

  28. Tsimikas S, Duff G, Berger P, Rogus J, Huttner K, Clopton P, et al. Pro-inflammatory interleukin-1 genotypes potentiate the risk of coronary artery disease and cardiovascular events mediated by oxidized phospholipids and lipoprotein(a). J Am Coll Cardiol. 2014;63(17):1724–34.

    Article  CAS  Google Scholar 

  29. Tiret L, Godefroy T, Lubos E, Nicaud V, Tregouet D, Barbaux S, et al. Genetic analysis of the interleukin-18 system highlights the role of the interleukin-18 gene in cardiovascular disease. Circulation. 2005;112(5):643–50.

    Article  CAS  Google Scholar 

  30. Stitziel N, Stirrups K, Masca N, Erdmann J, Ferrario PG, König IR, et al. Coding variation in ANGPTL4, LPL, and SVEP1 and the risk of coronary disease. N Engl J Med. 2016;374(12):1134–44.

    Article  CAS  Google Scholar 

  31. Wang J, Du W, Bai J, Cheng S, Zhang YH. Association cdtojoNS: The association of rs2233679 in the PIN1 gene promoter with the risk of Coronary Artery Disease in Chinese female individuals. J Stroke Cerebrovasc Dis. 2020;29(8):104935.

    Article  Google Scholar 

  32. Mueckler M, Thorens B. The SLC2 (GLUT) family of membrane transporters. Mol Aspects Med. 2013;34:121–38.

    Article  CAS  Google Scholar 

  33. Szablewski L. Glucose transporters in healthy heart and in cardiac disease. Int J Cardiol. 2017;230:70–5.

    Article  Google Scholar 

  34. Depré C, Rider M, Hue L. Mechanisms of control of heart glycolysis. Eur J Biochem. 1998;258(2):277–90.

    Article  Google Scholar 

  35. Young L, Coven D, Russell RR. Cellular and molecular regulation of cardiac glucose transport. J Nucl Cardiol. 2000;7(3):267–76.

    Article  CAS  Google Scholar 

  36. Al-Lahham Y, Mendes A, Souza E, Alberton D, Rego F, Valdameri G,et al. Interleukin-18 (rs187238) and glucose transporter 4 (rs5435) polymorphisms in Euro-Brazilians with type 1 diabetes. Genet Mol Res. 2017;16(3).

  37. Pontiroli A, Capra F, Veglia F, Ferrari M, Xiang K, Bell G, et al. Genetic contribution of polymorphism of the GLUT1 and GLUT4 genes to the susceptibility to type 2 (non-insulin-dependent) diabetes mellitus in different populations. Acta Diabetol. 1996;33(3):193–7.

    Article  CAS  Google Scholar 

  38. Alcolado J, Baroni M. Restriction fragment length polymorphisms at the GLUT4 and GLUT1 gene loci in type 2 diabetes. Diabet Med J Br Diabet Assoc. 1992;9(1):58–60.

    Article  CAS  Google Scholar 

  39. Bjørbaek C, Echwald S, Hubricht P, Vestergaard H, Hansen T, Zierath J, et al. Genetic variants in promoters and coding regions of the muscle glycogen synthase and the insulin-responsive GLUT4 genes in NIDDM. Diabetes. 1994;43(8):976–83.

    Article  Google Scholar 

  40. Xi C, Miyaki K, Ikeda S, Song Y, Sinbo T, Muramatsu M. Association of GLUT4 gene variants with HbA1c level in Japanese men. Endocr J. 2012;59(8):677–84.

    Article  CAS  Google Scholar 

  41. Friedel S, Antwerpen B, Hoch A, Vogel C, Grassl W, Geller F, et al. Glucose transporter 4 gene: association studies pertaining to alleles of two polymorphisms in extremely obese children and adolescents and in normal and underweight controls. Ann N Y Acad Sci. 2002;967:554–7.

    Article  CAS  Google Scholar 

Download references

Acknowledgements

In this study, the authors thank all the organizations, individuals, and participants who supported this project.

Funding

This study was supported in part by grants from the National Natural Science Foundation of China (#8170020548 and #8177021253).

Author information

Authors and Affiliations

  1. Xinjiang Key Laboratory of Cardiovascular Disease, Clinical Medical Research Institute, The First Affiliated Hospital of Xinjiang Medical University, Urumqi, 830054, People’s Republic of China

    Fei Yu, Fen Liu, Qian Zhao, Jun-Yi Luo, Jin-Yu Zhang & Yi-Ning Yang

  2. Department of Cardiology, First Affiliated Hospital of Xinjiang Medical University, Urumqi, 830054, People’s Republic of China

    Fei Yu, Fen Liu, Xiao-Mei Li, Qian Zhao, Jun-Yi Luo & Yi-Ning Yang

  3. Rehabilitation Medicine Department, First Affiliated Hospital of Xinjiang Medical University, Urumqi, 830054, People’s Republic of China

    Jin-Yu Zhang

Authors
  1. Fei Yu
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Fen Liu
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Xiao-Mei Li
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Qian Zhao
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Jun-Yi Luo
    View author publications

    You can also search for this author in PubMed Google Scholar

  6. Jin-Yu Zhang
    View author publications

    You can also search for this author in PubMed Google Scholar

  7. Yi-Ning Yang
    View author publications

    You can also search for this author in PubMed Google Scholar

Contributions

YNY and FL conceived and designed the experiment. JYZ, QZ, and JYL collected blood samples and extracted DNA. Study quality control and modification were performed by XML, YNY and FL. Data analyzing and paper writing were done by FY. In the final version of the manuscript, all authors approved.

Corresponding author

Correspondence to Yi-Ning Yang.

Ethics declarations

Ethics approval and consent to participate

This study was approved by the ethics committee of the First Affiliated Hospital of Xinjiang Medical University (Amend number: 20100116-01). All patients who participated in this study signed informed consent.

Consent for publication

The authors affirm that human research participants provided informed consent for publication.

Competing interests

The authors declare that they have no competing interests.

Additional information

Publisher's Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Supplementary Information

Additional file 1: Table S1.

Baseline characteristic of the Han population according to the genotype of rs5418. Table S2. Baseline characteristic of the Uygur population according to the genotype of rs5418.

Additional file 2: Figure S1.

Haploview analysis for D' and r2 pairwise measures of LD between rs5418 and rs5435. D' values and confidence levels (LOD) are represented as black for D' = 1, LOD > 2; shades of pink for high D', LOD < 2; white for D' < 1, LOD < 2. r2 values are represented as black for r2 = 1, white for r2 = 0, with intermediate values for 0 < r2 < 1 indicated by shades of grey. The numbers within the squares represent the D' or r2 scores for pairwise LD.

Rights and permissions

Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visithttp://creativecommons.org/licenses/by/4.0/. 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 in a credit line to the data.

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Yu, F., Liu, F., Li, XM. et al. GLUT4 gene rs5418 polymorphism is associated with increased coronary heart disease risk in a Uygur Chinese population. BMC Cardiovasc Disord 22, 191 (2022). https://doi.org/10.1186/s12872-022-02630-9

Download citation

  • Received:

  • Accepted:

  • Published:

  • DOI: https://doi.org/10.1186/s12872-022-02630-9

Keywords

BMC Cardiovascular Disorders

ISSN: 1471-2261

Contact us