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Diabetologia

, Volume 55, Issue 10, pp 2655–2659 | Cite as

Association between KCNQ1 genetic variants and obesity in Chinese patients with type 2 diabetes

  • W. Yu
  • R. C. Ma
  • C. Hu
  • W. Y. So
  • R. Zhang
  • C. Wang
  • C. H. Tam
  • J. S. Ho
  • J. Lu
  • F. Jiang
  • S. Tang
  • M. C. Ng
  • Y. Bao
  • K. Xiang
  • W. JiaEmail author
  • J. C. N. ChanEmail author
Short Communication

Abstract

Aims/hypothesis

There is evidence of overlap between susceptibility loci for type 2 diabetes and obesity. The aim of this study is to explore the association between the established type 2 diabetes locus KCNQ1 and obesity in Han Chinese.

Methods

We recruited 6,667 and 6,606 diabetic case–control samples from Shanghai and Hong Kong, respectively. Of the samples, 7.5% and 6.3% were excluded because of genotyping failure or data missing in the association analyses of rs2237892 and rs2237895 with obesity/BMI, respectively.

Results

We found that rs2237892 was associated with lower BMI and lower incidence of overweight/obesity in diabetic patients from Hong Kong (BMI, β = −0.0060 per diabetes risk C allele for log10BMI [95% CI −0.0088, −0.0032; p = 2.83 × 10−5]; overweight/obesity, OR 0.880 for C allele [95% CI 0.807, 0.960; p = 0.004]) and in the meta-analysis of cases from the two regions (BMI, combined β = −0.0048 per C allele for log10BMI [95% CI −0.0070, −0.0026; p = 2.20 × 10−5]; overweight/obesity, combined OR 0.890 for C allele [95% CI 0.830, 0.955; p = 0.001]). rs2237895 was also related to decreased BMI (combined β = −0.0042 per diabetes risk C allele for log10BMI [95% CI −0.0062, −0.0022; p = 4.30 × 10−5]). A significant association with waist circumference was detected for rs2237892 in the pooled analyses (β = −0.0026 per C allele for log10[waist circumference] [95% CI −0.0045, −0.0007; p = 0.007]). However, neither an association with the risk of being overweight or obese nor associations with quantitive traits were detected for rs2237892 or rs2237895 in controls.

Conclusion

Our findings indicate that KCNQ1 is associated with obesity in Chinese patients with type 2 diabetes.

Keywords

Genetic variants Han Chinese KCNQ1 Obesity Type 2 diabetes 

Abbreviation

SNP

Single nucleotide polymorphism

Introduction

Obesity is a challenging public health problem because of its increasing global prevalence and strong association with adverse health consequences such as type 2 diabetes. Although many factors, including westernised diet and reduced physical activity, contribute to the epidemic of obesity, convincing evidence has shown the importance of genetic factors [1].

Obesity is associated with an elevated risk of developing type 2 diabetes, and there is evidence of overlap between susceptibility loci for type 2 diabetes and obesity. For instance, FTO and TCF7L2 have both been shown to associate with BMI and type 2 diabetes risk [2, 3]. KCNQ1 has shown the strongest association with type 2 diabetes in East Asians, possibly through its effect on beta cell function [4, 5, 6]. It has also been linked to BMI in Han Chinese control individuals [7]. However, whether it is associated with obesity is still unknown. We therefore focused on such an established locus for type 2 diabetes to assess evidence of association with obesity.

Methods

Subjects

Two sets of Han Chinese participants were recruited: (1) 6,667 Shanghai adults, comprising 3,259 type 2 diabetic patients and 3,408 controls; (2) 6,606 Hong Kong adults, including 5,962 patients with type 2 diabetes and 644 controls. All the cases were unrelated type 2 diabetic patients meeting the 1999 WHO criteria (www.who.int/entity/diabetes/currentpublications/en); all the controls from Shanghai were defined as having normal glucose tolerance after standard 75 g oral glucose tolerance tests, and those from Hong Kong with fasting plasma glucose less than 6.1 mmol/l. Detailed information has been described elsewhere [8, 9]. Normal weight, overweight and obesity were defined as BMI < 25, 25 ≤ BMI < 30 and BMI ≥30 kg/m2, respectively. The characteristics of the study participants are shown in Table 1 of the electronic supplementary material (ESM). Moreover, a subgroup of Shanghai cases (n = 1,331) participated in arginine stimulation tests. The study was approved by our institutional review boards, and all participants gave written informed consent.
Table 1

Association of rs2237892 with the risk of being overweight and obese

Group

Region

Overweight + obesity

Normal weight

OR (95% CI)

p value

C allele frequencies

Genotype counta CC/CT/TT

C allele frequencies

Genotype counta CC/CT/TT

Type 2 diabetes

Hong Kong

0.707

1,249/1,092/198

0.732

1,524/1,153/193

0.880 (0.807, 0.960)

0.004

Shanghai

0.719

639/504/97

0.740

969/658/127

0.909 (0.807, 1.023)

0.115

Meta-analysis

0.890 (0.830, 0.955)

0.001

Controls

Hong Kong

0.624

48/65/16

0.641

187/248/50

0.980 (0.718, 1.338)

0.900

Shanghai

0.657

420/434/115

0.655

971/1,056/260

1.016 (0.907, 1.139)

0.778

Meta-analysis

1.012 (0.909, 1.126)

0.832

The ORs with 95% CIs shown are for the type 2 diabetes risk C allele

aThe numbers shown in the Genotype count columns represent the numbers of remaining samples after excluding samples of genotype failure or missing data

p values were adjusted for age, sex and duration of diabetes in patients with type 2 diabetes, and for age and sex in controls

Anthropometric measurements

Anthropometric variables including height (m), weight (kg), waist circumference (cm) and hip circumference (cm) were measured, and BMI and waist/hip ratio were calculated.

Single nucleotide polymorphism selection and genotyping

We selected two single nucleotide polymorphisms (SNPs) from KCNQ1, rs2237892 and rs2237895, which were associated with type 2 diabetes in our previous study [6]. Genotyping of the Shanghai samples was conducted using primer extension of multiplex products with detection by matrix-assisted laser desorption/ionisation time-of-flight mass spectroscopy (MassARRAY Compact Analyzer; Sequenom, San Diego, CA, USA). Genotyping of the Hong Kong samples was performed at deCODE Genetics (Reykjavik, Iceland) using the Centaurus (Nanogen, San Diego, CA, USA) platform. After genotyping quality control for rs2237892 and rs2237895, 7.2% and 6.0%, respectively, of the samples were excluded.

Statistical analyses

Allele frequencies were determined by gene counting and each SNP was examined by the Hardy–Weinberg equilibrium test (a two-tailed p value of <0.01 was considered statistically significant). Pairwise linkage disequilibrium was determined using Haploview (version 4.2; www.broad.mit.edu/mpg/haploview/). Logistic regression was used to examine for associations between SNPs and the risk of being overweight and obese. Linear regression was applied to test for the effects of SNPs on quantitive traits. All analyses were adjusted for covariates such as age, sex, duration of diabetes and geographical region. BMI, waist circumference and waist/hip ratio were log10 transformed before linear regression because of their skewed distribution. An additive genetic model was employed in the analyses. All these analyses were conducted at the two-tailed 5% significance level and performed using SAS software (version 8.0; SAS Institute, Cary, NC, USA). Meta-analysis statistics were generated using Comprehensive Meta-Analysis (version 2; Biostat, Englewood, NJ, USA).

Results

rs2237892 and rs2237895 both conformed to Hardy–Weinberg equilibrium and were in modest linkage disequilibrium (r 2  = 0.192 and 0.182 in Hong Kong and Shanghai samples, respectively).

We first analysed the effects of KCNQ1 variants on the risk of being overweight and obese. In patients with type 2 diabetes, we found that rs2237892 was associated with a decreased risk of being overweight and obese in Hong Kong samples (OR 0.880 for diabetes risk C allele; 95% CI 0.807, 0.960; p = 0.004). Meta-analysis of cases from the two regions yielded a statistically significant result for rs2237892 (combined OR 0.890 for C allele; 95% CI 0.830, 0.955; p = 0.001) (Table 1). Similar findings were detected for rs2237895 (ESM Table 2). In controls, we did not observe any association of these SNPs with the risk of being overweight and obese.

We then investigated the effects of SNPs on BMI. Significant evidence of association with BMI was shown for rs2237892 in the type 2 diabetic patients from Hong Kong (β = −0.0060 per C allele for log10BMI; 95% CI −0.0088, −0.0032; p = 2.83 × 10−5) and also in the meta-analysis (β = −0.0048 per C allele for log10BMI; 95% CI −0.0070, −0.0026; p = 2.20 × 10−5) (Table 2); Similar results were observed for rs2237895 (Table 2). In controls, we did not detect any association of these SNPs with BMI.
Table 2

The effects of KCNQ1 genetic variants on BMI

Group

Region

rs2237892

rs2237895

TT

CT

CC

β (95% CI)a

p value

AA

AC

CC

β (95% CI)a

p value

Type 2 diabetes

Hong Kong

25.25 (0.21)

24.93 (0.08)

24.60 (0.07)

−0.0060 (−0.0088, −0.0032)

2.83 × 10−5

24.91 (0.09)

24.76 (0.08)

24.60 (0.13)

−0.0029 (−0.0054, −0.0003)

0.028

Shanghai

24.42 (0.24)

24.17 (0.10)

23.99 (0.09)

−0.0028 (−0.0065, 0.0008)

0.123

24.38 (0.10)

24.09 (0.09)

23.55 (0.17)

−0.0065 (−0.0097, −0.0032)

1.21 × 10−4

Meta−analysis

−0.0048 (−0.0070, −0.0026)

2.20 × 10−5

−0.0042 (−0.0062, −0.0022)

4.30 × 10−5

Controls

Hong Kong

22.39 (0.40)

22.58 (0.18)

22.12 (0.24)

−0.0018 (−0.0091, 0.0054)

0.620

22.62 (0.22)

22.36 (0.18)

21.78 (0.41)

−0.0055 (−0.0125, 0.0014)

0.116

Shanghai

23.27 (0.17)

23.18 (0.08)

23.31 (0.09)

0.0013 (−0.0018, 0.0043)

0.410

23.32 (0.08)

23.24 (0.09)

23.16 (0.19)

−0.0016 (−0.0047, 0.0014)

0.301

Meta−analysis

0.0008 (−0.0020, 0.0036)

0.568

−0.0023 (−0.0051, 0.0005)

0.112

Data are shown as geometric means (SE)

Analyses in type 2 diabetic patients were adjusted for age, sex and duration of diabetes; analyses in controls were adjusted for age and sex

aβs and 95% CIs were calculated for log10BMI and refer to type 2 diabetes risk alleles (C allele of rs2237892 and C allele of rs2237895)

We attempted to examine whether the effect of KCNQ1 on BMI was mediated by beta cell function using the subgroup of patients who had participated in arginine stimulation tests with beta cell function evaluated by acute insulin response to arginine. As for the effect of rs2237895 on log10BMI, a similar effect size was observed after additionally adjusting for beta cell function (β = −0.0026 after further adjusting for beta cell function vs β = −0.0032 before adjustment for beta cell function). A similar phenomenon was found for rs2237892 (data not shown).

We also pooled the two samples to analyse the effects of SNPs on waist circumference and waist/hip ratio. We found that rs2237892 was significantly associated with waist circumference only in cases (β = −0.0026 per C allele for log10[waist circumference]; 95% CI −0.0045, −0.0007; p = 0.007) (ESM Table 3). However, the significance was not retained after further adjusting for BMI (p = 0.648).

Moreover, we performed logistic regression analyses in the pooled samples, with type 2 diabetes as the outcome, and the genetic variants, BMI and an interaction between them as predictors, and simultaneously adjusting for age, sex and geographical region. Results showed an interaction of rs2237892 with BMI on type 2 diabetes risk (p = 0.012). We then compared the effect of rs2237892 on diabetes risk in individuals of normal weight to that in overweight/obese participants. A greater effect on diabetes risk was detected in participants with lower BMI (OR 1.612 [95% CI 1.472, 1.765] for C allele in individuals of normal weight; OR 1.361 [95% CI 1.206, 1.536] for C allele in individuals who were overweight/obese).

Discussion

The current study suggested an association of KCNQ1 with obesity in type 2 diabetic patients. Nevertheless, similar effect sizes were found even when we additionally adjusted for beta cell function in the association analyses of SNPs with log10BMI. It may suggest that the association between KCNQ1 and BMI was independent of beta cell function. A possibility has been raised that Kcnq1 may be a novel element affecting insulin sensitivity, because a striking increase in insulin sensitivity was found in Kcnq1 knockout mice [10]. In view of the close relationship between insulin resistance and obesity, it is tempting to speculate that the association between KCNQ1 and obesity might be mediated by insulin sensitivity. Moreover, we found an association between rs2237892 and waist circumference, but the significance was not retained after further adjusting for BMI, which implied that KCNQ1 was most likely involved in overall adiposity but not central adiposity.

It is worth noting that there was a disparity between patients with type 2 diabetes and controls with respect to the effect of KCNQ1 on obesity in the study, which is distinct from a previous study which showed an association of KCNQ1 with BMI in control individuals in the same direction as that observed in our diabetic patients [7].

Combined with the findings in our previous study [6], we noted that the risk alleles for type 2 diabetes at KCNQ1 (C alleles of rs2237892 and rs2237895) were associated with a reduced risk of being overweight and obese as well as a decreased BMI in diabetic individuals. Regarding the interaction between rs2237892 and BMI on the risk of type 2 diabetes, and the finding that the effect of rs2237892 on diabetes risk was greater for individuals with lower BMI than for those with higher BMI, we hypothesised that KCNQ1 might participate in the pathogenesis of type 2 diabetes via non-BMI-mediated pathways.

Despite the positive association detected in the study, the possibility of a spurious association still cannot be excluded. Obesity as well as C alleles of rs2237892 and rs2237895 are associated with increased risk of type 2 diabetes, so type 2 diabetic patients carrying the ‘diabetes-protective’ allele T of rs2237892 or allele A of rs2237895 must have developed type 2 diabetes through an alternative mechanism, such as higher BMI, than that of the ‘diabetes risk’ allele C carriers of rs2237892 or rs2237895. This may set up a false-positive association between KCNQ1 non-risk alleles and an elevated BMI. Therefore, we cannot exclude the possibility that the association between KCNQ1 and BMI discovered in our study is the result of ascertainment bias.

In summary, we found that KCNQ1 was associated with obesity in patients with type 2 diabetes. Further genetic and functional investigations are needed to replicate the association and reveal the underlying mechanisms.

Notes

Acknowledgements

We thank all the participants of this study. For the Shanghai study, we thank H. Lu, X. Ma and X. Hou (all from the Shanghai Diabetes Institute) for the recruitment of study participants, and X. Li and J. Xu (both from the Shanghai Diabetes Institute) for the provision of DNA. For the Hong Kong study, we would like to thank C. Chiu (Department of Medicine and Therapeutics, the Chinese University of Hong Kong) for the recruitment of study participants, and L. Chow, K. Yu, A. Ng and P. Tse (all from the Department of Medicine and Therapeutics, the Chinese University of Hong Kong) for computing and laboratory support. We thank deCODE Genetics for help with genotyping of the Hong Kong samples, and the Chinese University of Hong Kong Centre for Clinical Trials and Information Technology Services Centre for support of computing resources. We gratefully acknowledge all nursing and medical staff at the Shanghai Clinical Center for Diabetes and the Prince of Wales Hospital Diabetes and Endocrine Centre for their dedication and professionalism.

Funding

This work was supported by National 973 Program (2011CB504001), National Natural Science Foundation of China (30800617 and 81170735), National Key Technology R&D Program of China (2009BAI80B02), programmes from Shanghai Municipality for Basic Research (08dj1400601 and 11JC1409600), Excellent Young Medical Expert of Shanghai (XYQ2011041), Chen-Guang Project (09CG07), Shanghai Rising-Star Program (09QA1404400), the Innovation Fund for PhD Students (BXJ201233) from Shanghai Jiao Tong University School of Medicine, the Hong Kong Foundation for Research and Development in Diabetes established under the auspices of The Chinese University of Hong Kong, the Liao Wun Yuk Diabetes Fund, the Innovation and Technology Fund (ITS/487/09FP) and a Chinese University Direct Grant (2041576).

Duality of interest

The authors declare that there is no duality of interest associated with this manuscript.

Contribution statement

WY and RM participated in genotyping, performed statistical analysis and drafted the manuscript. CH was responsible for the conception and design of the study and revised the manuscript. RZ prepared the DNA samples and participated in genotyping. CW participated in genotyping. CT and JH contributed to statistical analysis. MN, JL, FJ and ST participated in acquisition of data and clinical studies. WS and YB participated in the clinical study and contributed to the discussion. KX contributed to the discussion. WJ and JC supervised the study and revised the manuscript. All authors contributed to the interpretation of data, critical review and approval of the final version of the manuscript to be published.

Supplementary material

125_2012_2636_MOESM1_ESM.pdf (56 kb)
ESM Table 1 Clinical characteristics of the study participants (PDF 56 kb)
125_2012_2636_MOESM2_ESM.pdf (62 kb)
ESM Table 2 Association of rs2237895 with the risk of being overweight and obese (PDF 62 kb)
125_2012_2636_MOESM3_ESM.pdf (58 kb)
ESM Table 3 The effects of KCNQ1 on waist circumference and waist-hip ratio (PDF 58 kb)

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Copyright information

© Springer-Verlag 2012

Authors and Affiliations

  • W. Yu
    • 1
  • R. C. Ma
    • 2
  • C. Hu
    • 1
  • W. Y. So
    • 2
  • R. Zhang
    • 1
  • C. Wang
    • 1
  • C. H. Tam
    • 2
  • J. S. Ho
    • 2
  • J. Lu
    • 1
  • F. Jiang
    • 1
  • S. Tang
    • 1
  • M. C. Ng
    • 2
    • 3
  • Y. Bao
    • 1
  • K. Xiang
    • 1
  • W. Jia
    • 1
    Email author
  • J. C. N. Chan
    • 2
    Email author
  1. 1.Department of Endocrinology and Metabolism, Shanghai Diabetes Institute, Shanghai Key Laboratory of Diabetes Mellitus, Shanghai Clinical Center for Diabetes, Shanghai Jiao Tong University Affiliated Sixth People’s HospitalShanghaiPeople’s Republic of China
  2. 2.Department of Medicine and Therapeutics, The Chinese University of Hong Kong, Prince of Wales HospitalShatinPeople’s Republic of China
  3. 3.Wake Forest University School of MedicineWinston-SalemUSA

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