Skip to content

Advertisement

  • Research article
  • Open Access
  • Open Peer Review

A variant in KCNQ1 gene predicts metabolic syndrome among northern urban Han Chinese women

Contributed equally
BMC Medical Genetics201819:153

https://doi.org/10.1186/s12881-018-0652-3

  • Received: 19 September 2017
  • Accepted: 23 July 2018
  • Published:
Open Peer Review reports

Abstract

Background

Previous studies have reported that the potassium voltage-gated channel subfamily Q member 1 (KCNQ1) gene is associated with diabetes in both European and Asian population. This study aims to find a predictable single nucleotide polymorphism (SNP) to predict the risk of metabolic syndrome (MetS) through investigating the association of SNP in KCNQ1 gene with MetS in Han Chinese women of northern urban area.

Methods

Six SNPs were selected and genotyped in 1381 unrelated women aged 21 and above, who have had physical check-up in Shandong Provincial Qianfoshan Hospital. Cox proportional model was conducted to access the association between SNPs and MetS.

Results

Sixty one women developed MetS between 2010 and 2015 during the 3055 person-year of follow-up. The cumulative incidence density was 19.964/1000 person-year. The SNP rs163182 was associated with MetS both in the additive genetic model (RR = 1.658, 95% CI: 1.144–2.402) and in the recessive genetic model (RR = 2.461, 95% CI: 1.347–4.496). It remained significant after adjustment. This relationship was also observed in MetS components (BMI and SBP).

Conclusion

A novel association between rs163182 and MetS was found in this study, which can predict the occurrence of MetS among northern urban Han Chinese women. More investigations are needed to be done to assess the possible pathway in which KCNQ1 gene affects MetS.

Keywords

  • Metabolic syndrome
  • KCNQ1
  • Single nucleotide polymorphism (SNP)
  • Cohort study

Background

Metabolic Syndrome (MetS) is a complex disorder that is characterized by obesity, hyperglycemia, dyslipidemia, hypertension and insulin resistance [1]. It can increase the risk of type 2 diabetes and cardiovascular disease (CVD) [2]. In recent years, the prevalence of MetS is high (26.8% in females) in urban areas of China, and sex heterogeneity has been found in the relationships between risk factors and MetS [3]. Much research of Genome-Wide Association Studies (GWAS) and candidate gene studies have been conducted in Indian [4], Finnish [5], Chinese [69], Taiwanese and Caucasian youth [10]. However, these mechanisms were not consistent among the previous studies, and most of the studies were based on cross-sectional studies. Thus, this study aimed to investigate whether the selected SNPs would be associated with MetS in Chinese women based on a cohort study.

KCNQ1 is a gene that provides instructions for making potassium channels. The gene is expressed in a wild variety of tissues such as cardiac muscle, inner ear, kidney, lung, stomach, and intestine. Cardiac long QT syndrome and congenital deafness are associated with KCNQ1 gene. However, KCNQ1 is also expressed in pancreas, and it could influence the insulin secretion [11]. SNPs in KCNQ1 are significantly associated with lower HOMA-B values [12]. Most studies indicated that the KCNQ1 gene was a diabetes susceptibility gene in different ancestors [13, 14]. Rs231359, rs2237895, rs2237897, rs2237892, and rs231361 polymorphisms were confirmed among Chinese [12, 15, 16]. Type 2 diabetes was the major consequence of MetS [17], and MetS might be an important risk factor for type 2 diabetes [18]. Meanwhile, insulin resistance is a central feature of MetS and the function of the polymorphisms in KCNQ1 gene for MetS has not been investigated. Previous research had confirmed that both genetic and environmental factors contribute to the pathogenesis of MetS [1, 19]. Some studies only involved few factors such as smoking or drinking.

In this study, we established a cohort study with 1381 females based on the routine health check-up systems in the urban Han Chinese. We aimed to investigate the association among the selected SNPs in the KCNQ1 gene with MetS in Chinese women after adjusting for potential confounding variables, as well as to provide a genetic basis to establish a prediction model of MetS for the personalized health management for women. Meanwhile, we also explored the relationship between SNP and MetS components.

Methods

Study subjects

This was a cohort study based on the Center for Health Management of Shandong Provincial Qianfoshan Hospital. The health examination database contained persons in Jinan, representing the middle to upper class population of Shandong Province [20, 21]. The participants had two or more records from 2010 to 2015. We selected by a simple random sampling from those who was free of MetS or cardiovascular disease on the first physical examination, and collected their blood samples from April to September 2016. A standardized questionnaire was used to investigate the environmental and dietary risk factors (Additional file 1). Their fasting blood samples were collected and stored in an ultra-low temperature freezer. A total of 1381 women (aged 21 to 81) were included. All individuals were Han Chinese and they were genetically unrelated to each other.

The study was approved by the Ethics Committee of School of Public Health, Shandong University (the number: 20120315). Written informed consent was obtained from all of the participants before the study.

Measurements

The questionnaire, anthropometric and fasting laboratory assay were conducted in each participant. Although there was a general questionnaire in the original cohort [22], the details of lifestyle factors in women were restricted. A detailed questionnaire was applied to these women, including basic demographic information (age, education and occupation), physiological condition (menarche, dysmenorrhea, menopause and reproductive system disease), marriage and pregnancy situation (marriage age, the number of births and abortions), lifestyles (taste preference, sleep, housework, smoking, secondhand smoking and drinking) and family medical history (diabetes, hypertension, obesity, hyperlipidemia and CVDs). Food Frequency Questionnaire (FFQ) was also conducted by asking dietary habits within one month. Some details of the questionnaire are shown in Additional file 2: Table S1.

The anthropometric measurements included weight, height and blood pressure. Weight and height were measured by standardized procedures. Body mass index (BMI) was calculated in accordance with weight/height2 (kg/m2). Blood pressure was measured on the right arm by an automated sphygmomanometer after 5-min rest. The biomarkers related to MetS, including systolic blood pressure (SBP), diastolic blood pressure (DBP), fasting plasma glucose (FPG), total cholesterol (TC), triglyceride (TG), low-density lipoprotein-cholesterol (LDL-C) and high-density lipoprotein cholesterol (HDL-C), were measured by the standard clinical and laboratory protocol in the Center for Health Management of Shandong Provincial Qianfoshan Hospital, which was described previously [23, 24].

Diagnosis of MetS

Considering the practical conditions of physical examination management in Shandong Province and the physiological characteristics of our study subjects, the diagnostic criterion adopted in this study is the one that is recommended by Diabetes Society of the Chinese Medical Association (CDS) [25]. MetS was defined as three or more of the following disorders: (1) overweight or obesity (BMI ≥ 25.0 kg/M2); (2) hypertension (SBP ≥ 140 mmHg, DBP ≥ 90 mmHg or diagnosed before); (3) hyperglycemia (FPG ≥ 6.1 mmol/L or 2 h post-meal glucose ≥7.8 mmol/L, or diagnosed before); and (4) dyslipidemia (TG ≥ 1.7 mmol/L, or HDL < 1.0 mmol/L in female).

SNP selection, genotyping and quality control analysis

We selected six SNPs (rs2237892, rs231361, rs2237895, rs231359, rs2237897 and rs163182) from the KCNQ1 gene, which were reported to be associated with diabetes [15, 18, 26] . The minor allele frequency (MAF) of the above six SNPs was greater than 0.05 in the Chinese Han population from the NCBI dbSNPs database (http://0-www.ncbi.nlm.nih.gov.brum.beds.ac.uk/). DNA was extracted from venous blood samples, which were collected in the morning after 8 h of fasting, and then stored in an ultra-low temperature freezer. Genomic DNA extraction and genotyping were accomplished by the company, BioMiao Biological Technology (Beijing) Co., Ltd.

Statistical analyses

Taking missing values of clinical covariates into account, multiple imputations were performed using Amelia II by R3.3.2. All imputation variables had less than 10% missing observations before imputation.

The Hardy-Weinberg Equilibrium (HWE) of six SNPs was performed utilizing the Chi-squared test by R3.3.2. The linkage disequilibrium (LD) was performed by Haploview4.2. Markers of SNP were not included in the analysis, in the conditions that the call rate was less than 95%, and the P value from a test of HWE was less than 0.05.

Continuous variables were described with mean (standard deviation) and categorical variables were summarized as percentages. Differences in the baseline between MetS and non-MetS during the follow-up were compared using Student’s t-test for continuous variables, and Chi-squared test for categorical variables. The Cox proportional hazards model was utilized to discover the association of SNPs in the additive model, dominant model and recessive model after adjusting for potential environmental confounding. The differences between SNPs and metabolic syndrome components were analyzed by the covariance analysis. Aiming to investigate whether SNPs could contribute to the efficiency of the prediction model, we compared the area under receiver operator characteristic curve (AUC) between with and without SNP. A two-tailed P value of less than 0.05 was regarded as statistically significant. These statistical analyses were performed using SAS version 9.4.

Results

Basic characteristics

This cohort (n = 1381) had a mean age of 39.5 at baseline. During the 3055 person-year of follow-up, 61 women developed MetS between 2010 and 2015. The cumulative incidence density was 19.964/1000 person-year. The baseline characteristics of metabolic syndrome components for participants are shown in Table 1. Cases had significantly higher BMI, SBP, DBP, FPG, TG, TC and LDL-C, but lower HDL-C than controls.
Table 1

Baseline clinical characteristics of the women according to the MetS status

 

MetS (n = 61)

Non-MetS (n = 1320)

T test

P

Age (year)

53.984 ± 12.027

38.812 ± 11.621

−9.950

< 0.001

BMI (kg/m2)

26.588 ± 3.136

22.079 ± 2.923

−11.740

< 0.001

SBP (mmHg)

134.180 ± 15.898

118.698 ± 14.650

−5.690

< 0.001

DBP (mmHg)

79.672 ± 9.916

72.471 ± 9.644

−8.040

< 0.001

FPG (mmol/L)

5.483 ± 0.811

4.997 ± 0.501

−4.640

< 0.001

TG (mmol/L)

1.525 ± 0.852

0.927 ± 0.498

−5.440

< 0.001

TC (mmol/L)

5.149 ± 0.803

4.634 ± 0.846

−4.660

< 0.001

LDL-C (mmol/L)

3.131 ± 0.657

2.633 ± 0.670

−5.680

< 0.001

HDL-C(mmol/L)

1.430 ± 0.292

1.600 ± 0.294

4.430

< 0.001

BMI indicted body mass index, SBP systolic blood pressure, DBP diastolic blood pressure, FPG fasting plasma glucose TG triglycerides, TC total cholesterol, LDL-C low-density lipoprotein-cholesterol, HDL-C, high-density lipoprotein-cholesterol

In this study, women rarely smoked or drank. Besides, secondhand smoking showed no statistical significance with MetS. Additional file 2: Table S1 shows the frequency and percentages of environmental variables of the study. Significant differences in marital status, education, sleep, housework time, trip mode, menopausal status, pregnancy information (the number of births and abortions), reproductive system disease, taste preference, intake of fruit and meat between cases and controls were found by simple Cox proportional model (Additional file 2: Table S2).

SNPs in KCNQ1 gene and MetS

Rs231361 was not in genotype quality or Hardy-Weinberg equilibrium (P < 0.05) (Additional file 2: Table S3). Hence, it was excluded. The genotype and allele distribution of the five SNPs are shown in Table 2. Only rs163182 showed a difference between MetS and non-MetS both in genotypes (P = 0.016) and in allele distribution (P = 0.010). We carried out LD mapping in Additional file 3: Figure S1. Rs231359 was isolated compared with the other SNPs (r2 = 0). The r2 of each pairwise LD among rs2237892, rs163182, rs2237895 and rs2237897 was modest (0.10 < r2 < 0.69).
Table 2

Distribution of genotype and allele of SNPs in the KCNQ1 gene

SNP

Genotype

number of Genotype

P(G)a

Major/minor allele

number of allele

P (A)b

0/1/2

MetS

Non-MetS

MetS

Non-MetS

rs163182

GG/CG/CC

18/26/14

550/593/156

0.016

G/C

62/54

1693/905

0.010

rs231359

AA/CA/CC

40/18/2

814/445/50

0.782

A/C

98/22

2073/545

0.511

rs2237895

AA/CA/CC

23/32/6

609/570/130

0.361

A/C

78/44

1788/830

0.312

rs2237897

CC/CT/TT

23/29/9

561/592/159

0.685

C/T

75/47

1714/910

0.384

rs2237892

CC/CT/TT

25/32/4

619/564/130

0.308

C/T

82/40

1802/824

0.743

aThe P value of Chi-square test for the genotype between MetS and non-MetS

bP value of Chi-square test for the allele of SNPs

Table 3 shows the association analyses of the five SNPs with MetS by Cox proportional model. Single SNP analysis revealed that the rs163182 was associated with MetS in the additive genetic model (relative risk (RR) =1.658, 95% CI: 1.144–2.402) and the recessive genetic model (RR = 2.461, 95% CI: 1.347–4.496) (Table 3). Other SNPs suggested no association with MetS. After adjusting for age, the results showed that with the increase of the number of C alleles in rs163182 genotype, RR was 1.531 (95%CI: 1.064–2.204), and RR of CG + CC genotype vs GG was 2.086 (95%CI: 1.14–3.816). After adjusting for possible confounding factors (age, marital status, education, sleep, housework, trip mode, menopausal status, the number of births, the number of abortions, reproductive system disease, intake of fruit and fresh meat), the SNP remained significantly associated with MetS in the additive genetic model (RR = 1.776, 95% CI: 1.166–2.704) and the recessive genetic model (RR = 2.976, 95% CI: 1.488–5.951) (Table 3). Other four SNPs in the KCNQ1 gene had no statistical significance in this study (P > 0.05).
Table 3

The association analyses of the five SNPs with MetS with Cox proportional model

SNP

RR (95% CI)

P

Adjusted Ra (95% CI)

Adjusted P

Adjusted RRb (95% CI)

Adjusted P

rs163182

 GG/CG/CCc

1.658 (1.144–2.402)

0.008

1.531 (1.064–2.204)

0.022

1.776 (1.166–2.704)

0.007

 CG + CCd

1.603 (0.919–2.797)

0.096

1.538 (0.881–2.685)

0.130

1.624 (0.878–3.003)

0.122

 CCe

2.461 (1.347–4.496)

0.003

2.086 (1.14–3.816)

0.017

2.976 (1.488–5.951)

0.002

rs231359

 AA/CA/CCc

0.881 (0.553–1.406)

0.596

0.951 (0.596–1.516)

0.832

0.914 (0.551–1.515)

0.727

 CA + CCd

0.862 (0.504–1.474)

0.586

0.94 (0.549–1.611)

0.823

0.959 (0.538–1.71)

0.887

 CCe

0.874 (0.213–3.578)

0.851

0.959 (0.234–3.932)

0.954

0.503 (0.068–3.739)

0.502

rs2237895

 AA/CA/CCc

1.216 (0.838–1.763)

0.304

1.138 (0.777–1.665)

0.507

1.273 (0.836–1.939)

0.260

 CA + CCd

1.428 (0.851–2.396)

0.178

1.266 (0.753–2.130)

0.374

1.475 (0.836–2.604)

0.180

 CCe

0.999 (0.430–2.321)

0.998

0.986 (0.424–2.293)

0.974

1.082 (0.417–2.805)

0.871

rs2237897

 CC/CT/TTc

1.188 (0.828–1.705)

0.349

1.187 (0.835–1.687)

0.341

1.296 (0.876–1.915)

0.194

 CT + TTd

1.267 (0.755–2.126)

0.371

1.341 (0.798–2.253)

0.267

1.383 (0.788–2.43)

0.259

 TT e

1.235 (0.608–2.506)

0.559

1.123 (0.553–2.280)

0.747

1.46 (0.687–3.101)

0.325

rs2237892

 CC/CT/TTc

1.062 (0.728–1.550)

0.754

1.074 (0.739–1.563)

0.707

1.201 (0.792–1.82)

0.389

 CT + TTd

1.277 (0.766–2.127)

0.348

1.319 (0.791–2.198)

0.289

1.435 (0.821–2.506)

0.205

 TTe

0.636 (0.231–1.754)

0.382

0.619 (0.225–1.708)

0.355

0.849 (0.295–2.439)

0.761

aAdjusted for age

bAdjusted for age, marital status, education, sleep, housework, trip mode, menopausal status, the number of births, the number of abortions, reproductive system disease, intake of fruit and fresh meat

cAdditive model

dDominant model

eRecessive model

Aiming to test and verify the effect of rs136182 on MetS prediction, we compared the performance on two prediction models of MetS using age, menopausal status, the number of births and the intake of fruit, with and without rs163182. The AUC for the prediction model with and without rs163182 was 0.743 (95% CI, 0.719–1.766) and 0.756 (95% CI, 0.732–0.779) respectively. To a certain extent, the efficiency was improved.

SNP rs163182 and metabolic syndrome components

The association between rs163182 and metabolic components was also explored (Table 4). The difference between SBP and the genotypes (GG, CG and CC) was analyzed under the covariance analysis after adjusting for age (P = 0.043). The differences of other biomarkers DBP, FPG, TG and HDL-C were not detected between each three genotypes. The differences between rs163182 genotype and BMI were conducted by non-parameters Wilcoxon symbols test, which suggested rs163182 was associated with BMI (P = 0.032).
Table 4

Comparison of metabolic syndrome components between different groups of rs163182 genotypes

 

rs163182

P a

P b

GG (n = 568)

CG (n = 619)

CC (n = 170)

SBP

119.083 ± 14.853

118.877 ± 14.580

122.671 ± 16.859

0.043

0.138

DBP

72.541 ± 9.690

72.673 ± 9.654

74.073 ± 10.500

0.378

0.414

FPG

5.008 ± 0.531

5.003 ± 0.494

5.090 ± 0.602

0.323

0.508

TG

0.927 ± 0.495

0.951 ± 0.526

0.999 ± 0.493

0.433

0.854

HDL-C

1.600 ± 0.297

1.596 ± 0.299

1.564 ± 0.281

0.341

0.753

BMI

22.135 ± 2.871

22.237 ± 3.104

22.899 ± 3.502

0.032c

0.020

BMI indicted body mass index, SBP systolic blood pressure, DBP diastolic blood pressure, FPG fasting plasma glucose, TG triglycerides, TC total cholesterol, LDL-C low-density lipoprotein-cholesterol, HDL-C high-density lipoprotein-cholesterol

aThe P value of covariance analysis after adjusting for age

bThe P value of the homogeneity test

cThe P value of non-parameters Wilcoxon symbols test for BMI

Discussion

In this study, we discovered a novel association between KCNQ1-rs163182 and MetS, which suggests that rs163182 is an independent predictor for MetS. Meanwhile, rs163182 was also associated with MetS components (BMI and SBP) in Han Chinese women of northern urban area. Rs163182 may play a role in the biological metabolism.

In the current study, the etiology of MetS refers to environmental confounding factors, genetic susceptibility, as well as their interactions [27, 28]. Identifying genes had the following functions: dramatically improve understanding of the mechanisms of MetS [29], identify people at high risk, and prevent the development of diabetes and CVD. The relationships among SNPs, environmental factors and MetS had received considerable attention. In our study, we reviewed a large amount of literature about SNPs for MetS and its components (obesity, hyperglycemia, dyslipidemia, hypertension and insulin resistance). According to the preliminary study, rs163182, which carried C-allele in the KCNQ1 gene, was more likely to develop MetS. In addition, it remained statistically significant after adjusting for potential risk factors. That indicates rs163182 in the KCNQ1 gene is a novel independent predictor for MetS.

It has been confirmed that the KCNQ1 gene was associated with diabetes in population of both Asian and European descent [13, 14, 30]. Meanwhile, there were few studies about rs163182 [15, 31]. In a genome-wide association study for type 2 diabetes in Han Chinese [15], it validated the association between rs163182 and diabetes (OR = 1.28), which was conducted in southern China. However, there was a heterogeneity compared with our study. In our study, the KCNQ1 rs2237892, rs231361, rs2237895, rs231359, rs2237897, rs163182 and many environmental factors were surveyed. Although we did not find positive results of FPG, we discovered a novel association between rs163182 and MetS. In the study by Chen et al. [32], the KCNQ1 gene was associated with lipid parameters, TG, HDL-C, and apo A1 in a middle-aged Chinese Han population. A possibility has been mentioned that KCNQ1 may be a molecule affecting insulin sensitivity [33, 34]. The gene of KCNQ1 not only plays an important role in blood glucose metabolism but also regulates other metabolic substances. The result that rs163182-C would increase the risk of MetS’ occurrence was understood. On the other hand, referring to the causal inference [35], MetS was a risk factor for diabetes and the KCNQ1 gene was associated with diabetes. Thus, the KCNQ1 gene may influence the occurrence of MetS.

We also investigated the role of environmental variables to MetS. Widow or divorce, more housework, peri-menopause or menopause, multiple pregnancies (including birth and abortion), and reproductive system disease would increase the risk of MetS (RR > 1.0), while highly-educated people, normal diet (means the taste preference is reasonable), and more fruit or meat would reduce the onset of MetS. That suggested the lifestyle or the information of fertility may influence the MetS, which was consistent with previous studies [36, 37]. We also explored the interactions between rs163182 and these factors, of which the results were negative. That indicates the interactions between rs163182 and environmental factors to MetS are a minor effect.

Aiming to evaluate the prediction effect of rs163182, we calculated AUC with and without rs163182. The result reveals the KCNQ1 gene may provide a new method for modeling a risk prediction for MetS, which can use rs163182 to achieve the personalized health management in Han Chinese women of northern urban area.

The association between rs163182 and MetS components was also researched. The statistical significance was found among BMI and SBP in rs163182 genotype. The study of Sinha et al. [38] used KCNQ1 to exhibit both differential methylation and differential gene expression by comparing adipocytes between obese and never-obese women. The KCNQ1 plays roles in cardiac tissue. Mutations in this gene may impair the function of heart. However, the specific mechanisms need to be further validated.

Considering the survival data in this study, Cox proportional model was applied. To our knowledge, few investigations were conducted by cohort study using Cox proportional model [39] adjusting for other potential variables.

The study also has several limitations. Firstly, the participants comprised only women who came to the Center for Health Management of Shandong Provincial Qianfoshan Hospital, and might not represent the general population. In some way, it could restrict female to avoid gender confounding. Secondly, the time of follow-up in this study was not long enough, that result in the limited number of cases. So this study will be followed up continuously in the future. Thirdly, because of the large population in the Center for Health Management, the subjects were selected simply and randomly based on the database that may be a limitation in some way. In the follow-up study, a more sophisticated strategy could be employed to verify the result, such as a nest case-control study.

Conclusions

It was the first time to discover that rs163182 in KCNQ1 gene would raise the risk of MetS and elevate the level of BMI and SBP. It partly explains the mechanism of MetS and may provide a new comprehension of molecular mechanism. Of course, further research is needed to evaluate the genetic marker in different populations.

Notes

Abbreviations

AUC: 

Area under receiver operator characteristic curve

BMI: 

Body mass index

CI: 

Confidence interval

CVD: 

Cardiovascular disease

DBP: 

Diastolic blood pressure

FPG: 

Fasting plasma glucose

GWAS: 

Genome Wide Association Study

HDL-C: 

High-density lipoprotein-cholesterol

HWE: 

Hardy-Weinberg Equilibrium

KCNQ1: 

potassium voltage-gated channel subfamily Q member 1

LDL-C: 

low-density lipoprotein-cholesterol

MAF: 

Minor allele frequency

MetS: 

Metabolic syndrome

RR: 

Relative risk

SBP: 

Systolic blood pressure

SNP: 

Single nucleotide polymorphisms

TC: 

Total cholesterol

TG: 

Triglycerides

Declarations

Acknowledgments

The authors wish to acknowledge their colleagues for invaluable work and the participants of the Center for Health Management of Shandong Provincial Qianfoshan Hospital.

Ethical approval and consent to participate

The study was approved by the ethics committee of School of Public Health, Shandong University (the number: 20120315) and written consent forms were obtained from all individual participants.

Funding

The study was funded by National Natural Science Foundation of China (No.81273082), Taishan industrial expert program (No.tscy20150403) and Project of Priority Research from Department of Science and Technology of Shandong Province (Grant No. 2016GSF201075). The funding agencies were not involved in study design, analysis and interpretation.

Availability of data and materials

The clinical raw datasets supporting our findings will be available upon request from the corresponding author due to some patient confidentiality.

Authors’ contributions

YL, CW, YC, FX and CZ participated in study conception and design; TY and JG made substaintial contributions to design; YL, CW, YC, TY, JG, XC and LD participated in the collection and cleaning of data; YL, ZY, WZ, FT, QX and XC performed the statistical analyses; YL and CW wrote the manuscript. YC and LD revised the manuscript critically for important intellectual content; FX and CZ contributed to the interpretation of the results and revised the manuscript critically. All authors approved the final version of the manuscript.

Consent for publication

Not applicable.

Competing interests

The authors declare that they have no competing interests.

Publisher’s Note

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

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)
Division of Biostatistics, School of Public Health, Shandong University, 44 Wenhua Xilu, Jinan, 250010, Shandong, China
(2)
Shandong Provincial Qianfoshan Hospital, Shandong University, 16766 Jingshi Rd, Jinan, 250014, China
(3)
Jinan Kingmed Center for Clinical Laboratory Co, Ltd., 554 Zhengfeng Rd, Jinan, 250010, Shandong, China
(4)
Linyi Centre for Adverse Drug Reaction Monitoring, Linyi, 276000, Shandong, China
(5)
Department of Imaging and Nuclear Medicine, Taishan Medical University, 619 Changcheng Rd, Tai’an, 271016, Shandong, China

References

  1. Lusis AJ, Attie AD, Reue K. Metabolic syndrome: from epidemiology to systems biology. Nat Rev Genet. 2008;9(11):819–30. https://doi.org/10.1038/nrg2468.View ArticlePubMedPubMed CentralGoogle Scholar
  2. Kaur J. A comprehensive review on metabolic syndrome. Cardiol Res Pract. 2014; https://doi.org/10.1155/2014/943162.
  3. Song Q-B, Zhao Y, Liu Y-Q, Zhang J, Xin S-J, Dong G-H. Sex difference in the prevalence of metabolic syndrome and cardiovascular-related risk factors in urban adults from 33 communities of China: the CHPSNE study. Diabetes Vasc Dis Res. 2015;12(3):189–98. https://doi.org/10.1177/1479164114562410.View ArticleGoogle Scholar
  4. Zabaneh D, Balding DJ. A genome-wide association study of the metabolic syndrome in Indian Asian men. PLoS One. 2010;5(8):1–6. https://doi.org/10.1371/journal.pone.0011961.View ArticleGoogle Scholar
  5. Kristiansson K, Perola M, Tikkanen E, Kettunen J, Surakka I, Havulinna A, Stancáková A, Barnes C, Widen E, Kajantie E, et al. Genome-wide screen for metabolic syndrome susceptibility loci reveals strong lipid gene contribution but no evidence for common genetic basis for clustering of metabolic syndrome traits. Circ Cardiovasc Genet. 2012;5:242–9. https://doi.org/10.1161/CIRCGENETICS.111.961482.View ArticlePubMedPubMed CentralGoogle Scholar
  6. Wang T, Huang Y, Xiao XH, Wang DM, Diao CM, Zhang F, Xu LL, Zhang YB, Li WH, Zhang LL, et al. The association between common genetic variation in the FTO gene and metabolic syndrome in Han Chinese. Chin Med J. 2010;123:1852–8. https://doi.org/10.3760/cma.j.issn.0366-6999.2010.14.005.PubMedView ArticleGoogle Scholar
  7. Yang J, Liu J, Jing L, Li W, Li X, He Y, Ye L. Genetic association study with metabolic syndrome and metabolic-related traits in a cross-sectional sample and a 10-year longitudinal sample of chinese elderly population. PLoS One. 2014;9:e100548.View ArticlePubMedPubMed CentralGoogle Scholar
  8. Yan YX, Dong J, Zhang J, Liu F, Wang W, Zhang L, He Y. Polymorphisms in NR3C1 gene associated with risk of metabolic syndrome in a Chinese population. Endocrine. 2014;47:740–8.View ArticlePubMedGoogle Scholar
  9. Ong KL, Jiang CQ, Liu B, Jin Y, Tso A, Tam S, Wong K, Tomlinson B, Cheung B, Lin J. Association of a genetic variant in the apolipoprotein A5 gene with the metabolic syndrome in Chinese. Clin Endocrinol. 2011;74:206–13. https://doi.org/10.1111/j.1365-2265.2010.03899.x.View ArticleGoogle Scholar
  10. Liu PH, Chang YC, Der Jiang Y, Chen W, Chang T, Kuo S, Lee K, Hsiao P, Chiu K, Chuang L. Genetic variants of TCF7L2 are associated with insulin resistance and related metabolic phenotypes in Taiwanese adolescents and Caucasian young adults. J Clin Endocrinol Metab. 2009;94:3575–82. https://doi.org/10.1210/jc.2009-0609.View ArticlePubMedGoogle Scholar
  11. Yamagata K, Senokuchi T, Lu M, Takemoto M, Fazlul Karim M, Go C, Sato Y, Hatta M, Yoshizawa T, Araki E, et al. Voltage-gated K+channel KCNQ1 regulates insulin secretion in MIN6 β-cell line. Biochem Biophys Res Commun. 2011;407:620–5. https://doi.org/10.1016/j.bbrc.2011.03.083.View ArticlePubMedGoogle Scholar
  12. Qi Q, Li H, Loos RJF, Liu C, Wu Y, Hu F, Wu H, Lu L, Yu Z, Lin X. Common variants in KCNQ1 are associated with type 2 diabetes and impaired fasting glucose in a Chinese Han population. Hum Mol Genet. 2009;18:3508–15. https://doi.org/10.1093/hmg/ddp294.View ArticlePubMedGoogle Scholar
  13. Yasuda K, Miyake K, Horikawa Y, Hara K, Osawa H, Furuta H, Hirota Y, Mori H, Jonsson A, Sato Y, et al. Variants in KCNQ1 are associated with susceptibility to type 2 diabetes mellitus. Nat Genet. 2008;40:1092–7. https://doi.org/10.1038/ng.207.View ArticlePubMedGoogle Scholar
  14. Unoki H, Takahashi A, Kawaguchi T, Hara K, Horikoshi M, Andersen G, Ng D, Holmkvist J, Borch-Johnsen K, Jørgensen T, et al. SNPs in KCNQ1 are associated with susceptibility to type 2 diabetes in east Asian and European populations. Nat Genet. 2008;40:1098–102.View ArticlePubMedGoogle Scholar
  15. Li X, Wei D, He H, Zhang J, Wang C, Ma M, Wang B, Yu T, Pan L, Xue F, et al. Association of the adiponectin gene (ADIPOQ) +45 T > G polymorphism with the metabolic syndrome among Han Chinese in Sichuan province of China. Asia Pac J Clin Nutr. 2012;21:296–301.PubMedGoogle Scholar
  16. Tsai F, Yang C, Chen C, Chuang L, Lu C, Chang C, Wang T, Chen R, Shiu C, Liu Y, et al. A genome-wide association study identifies susceptibility variants for type 2 diabetes in Han Chinese. PLoS Genet. 2010;6:e1000847. https://doi.org/10.1371/journal.pgen.1000847.View ArticlePubMedPubMed CentralGoogle Scholar
  17. Ford ES. Risks for all-cause mortality, cardiovascular disease, and diabetes associated with the metabolic syndrome: a summary of the evidence. Diabetes Care. 2005;28:1769–78. https://doi.org/10.2337/diacare.28.7.1769.View ArticlePubMedGoogle Scholar
  18. Shin JA, Lee JH, Lim SY, Ha HS, Kwon HS, Park YM, Lee WC, Kang MI, Yim HW, Yoon KH, Son HY. Metabolic syndrome as a predictor of type 2 diabetes, and its clinical interpretations and usefulness. J Diabetes Investig. 2013;4:334–43. https://doi.org/10.1111/jdi.12075.View ArticlePubMedPubMed CentralGoogle Scholar
  19. Roche HM, Phillips C, Gibney MJ. The metabolic syndrome: the crossroads of diet and genetics. Proc Nutr Soc. 2005;64:371–7. https://doi.org/10.1079/PNS2005445.View ArticlePubMedGoogle Scholar
  20. Zhang W, Chen Q, Yuan Z, Liu J, Du Z, Tang F, Jia H, Xue F, Zhang C. A routine biomarker-based risk prediction model for metabolic syndrome in urban Han Chinese population. BMC Public Health. 2015;15:64. https://doi.org/10.1186/s12889-015-1424-z.View ArticlePubMedPubMed CentralGoogle Scholar
  21. Chen Y, Wang C, Liu Y, Yuan Z, Zhang W, Li X, Yang Y, Sun X, Xue F, Zhang C. Incident hypertension and its prediction model in a prospective northern urban Han Chinese cohort study. J Hum Hypertens. 2016:1–7. https://doi.org/10.1038/jhh.2016.23.
  22. Ding L, Zhang C, Zhang G, Zhang T, Zhao M, Ji X, Yuan Z, Liu R, Tang F, Xue F. A new insight into the role of plasma fibrinogen in the development of metabolic syndrome from a prospective cohort study in urban Han Chinese population. Diabetol Metab Syndr. 2015;7:110. https://doi.org/10.1186/s13098-015-0103-7.View ArticlePubMedPubMed CentralGoogle Scholar
  23. Wu S, Lin H, Zhang C, Zhang Q, Zhang D, Zhang Y, Meng W, Zhu Z, Tang F, Xue F, Liu Y. Association between erythrocyte parameters and metabolic syndrome in urban Han Chinese: a longitudinal cohort study. BMC Public Health. 2013;13:989. https://doi.org/10.1186/1471-2458-13-989.View ArticlePubMedPubMed CentralGoogle Scholar
  24. Zhu Z, Liu Y, Zhang C, Yuan Z, Zhang Q, Tang F, Lin H, Zhang Y, Liu L, Xue F. Identification of cardiovascular risk components in urban Chinese with metabolic syndrome and application to coronary heart disease prediction: a longitudinal study. PLoS One. 2013;8:1–10. https://doi.org/10.1371/journal.pone.0084204.View ArticleGoogle Scholar
  25. Lu YH, Lu JM, Wang SY, Li CL, Liu LS, Zheng RP, Tian H, Wang XL, Yang LJ, Zhang YQ, Pan CY. Comparison of the diagnostic criteria of metabolic syndrome by international diabetes federation and that by Chinese Medical Association diabetes branch. National Medical Journal of China. 2006;86:386–9.PubMedGoogle Scholar
  26. Wen W, Zheng W, Okada Y, Takeuchi F, Tabara Y, Hwang J, Dorajoo R, Li H, Tsai F, Yang X, et al. Meta-analysis of genome-wide association studies in east Asian-ancestry populations identifies four new loci for body mass index. Hum Mol Genet. 2014;23:5492–504. https://doi.org/10.1093/hmg/ddu248.View ArticlePubMedPubMed CentralGoogle Scholar
  27. Wu Y, Yu Y, Zhao T, Fu Y, Qi Y, Yang G, Yao W, Su Y, Ma Y, Shi J, Jiang J, Kou C. Interactions of environmental factors and APOA1-APOC3-APOA4-APOA5 gene cluster gene polymorphisms with metabolic syndrome. PLoS One. 2016;11(1):e0147946. https://doi.org/10.1371/journal.pone.0147946.View ArticlePubMedPubMed CentralGoogle Scholar
  28. Shen J, Arnett DK, Peacock JM, Parnell LD, Kraja A, Hixson JE, Tsai MY, Lai CQ, Kabagambe EK, Straka RJ, Ordovas JM. Interleukin1beta genetic polymorphisms interact with polyunsaturated fatty acids to modulate risk of the metabolic syndrome. J Nutr. 2007;137:1846.View ArticlePubMedGoogle Scholar
  29. Pollex RL, Hegele RA. Genetic determinants of the metabolic syndrome. Nat Clin Pr Cardiovasc Med. 2006;3:482–9. https://doi.org/10.1038/ncpcardio0638 View ArticleGoogle Scholar
  30. Gao K, Wang J, Li L, Zhai Y, Ren Y, You H, Wang B, Wu X, Li J, Liu Z, Li X, Huang Y, Luo XP, Hu D, Ohno K, Wang C. Polymorphisms in four genes (KCNQ1 rs151290, KLF14 rs972283, GCKR rs780094 and MTNR1B rs10830963) and their correlation with type 2 diabetes mellitus in han Chinese in Henan province, China. Int J Environ Res Public Health. 2016;13:1–13. https://doi.org/10.3390/ijerph13030260.View ArticleGoogle Scholar
  31. Bai H, Liu H, Suyalatu S, Guo X, Chu S, Chen Y, Lan T, Borjigin B, Orlov Y, Posukh O, Yang X, Guilan G, Osipova L, Wu Q, Narisu N. Association analysis of genetic variants with type 2 diabetes in a Mongolian population in China. J Diabetes Res. 2015; https://doi.org/10.1155/2015/613236.
  32. Chen Z, Yin Q, Ma G, Qian Q. KCNQ1 gene polymorphisms are associated with lipid parameters in a Chinese Han population. Cardiovasc Diabetol. 2010;9:35. https://doi.org/10.1186/1475-2840-9-35.View ArticlePubMedPubMed CentralGoogle Scholar
  33. Boini KM, Graf D, Hennige AM, Koka S, Kempe D, Wang K, Ackermann T, Föller M, Vallon V, Pfeifer K, et al. Enhanced insulin sensitivity of gene-targeted mice lacking functional KCNQ1. Am J Physiol Regul Integr Comp Physiol. 2009;296:R1695–701. https://doi.org/10.1152/ajpregu.90839.2008.View ArticlePubMedPubMed CentralGoogle Scholar
  34. Müssig K, Staiger H, Machicao F, Kirchhoff K, Guthoff M, Schäfer S, Kantartzis K, Silbernagel G, Stefan N, Holst J, et al. Association of type 2 diabetes candidate polymorphisms in KCNQ1 with incretin and insulin secretion. Diabetes. 2009;58:1715–20. https://doi.org/10.2337/db08-1589.View ArticlePubMedPubMed CentralGoogle Scholar
  35. Pearl J. Causal inference in statistics: an overview. Stat Surv. 2009;3:96–146. https://doi.org/10.1214/09-SS057.View ArticleGoogle Scholar
  36. Yoo S, Nicklas T, Baranowski T, Zakeri I, Yang S, Srinivasan S, Berenson G. Comparison of dietary intakes associated with metabolic syndrome risk factors in young adults : the Bogalusa heart study. Am J Clin Nutr. 2004;80:841–8.View ArticlePubMedGoogle Scholar
  37. Zuo H, Shi Z, Hu X, Wu M, Guo Z, Hussain A. Prevalence of metabolic syndrome and factors associated with its components in Chinese adults. Metabolism. 2009;58:1102–8. https://doi.org/10.1016/j.metabol.2009.04.008.View ArticlePubMedGoogle Scholar
  38. Arner P, Sinha I, Thorell A, Rydén M, Dahlman-Wright K, Dahlman I. The epigenetic signature of subcutaneous fat cells is linked to altered expression of genes implicated in lipid metabolism in obese women. Clin Epigenetics. 2015;7:1–13. https://doi.org/10.1186/s13148-015-0126-9.View ArticleGoogle Scholar
  39. Therneau T, Grambsch P. Modeling survival data: extending the cox model. Technometrics. 2002;44:85–6. https://doi.org/10.1198/tech.2002.s656.View ArticleGoogle Scholar

Copyright

© The Author(s). 2018

Advertisement