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Diabetologia

, Volume 55, Issue 4, pp 981–995 | Cite as

Association of genetic variation in FTO with risk of obesity and type 2 diabetes with data from 96,551 East and South Asians

  • H. Li
  • T. O. Kilpeläinen
  • C. Liu
  • J. Zhu
  • Y. Liu
  • C. Hu
  • Z. Yang
  • W. Zhang
  • W. Bao
  • S. Cha
  • Y. Wu
  • T. Yang
  • A. Sekine
  • B. Y. Choi
  • C. S. Yajnik
  • D. Zhou
  • F. Takeuchi
  • K. Yamamoto
  • J. C. Chan
  • K. R. Mani
  • L. F. Been
  • M. Imamura
  • E. Nakashima
  • N. Lee
  • T. Fujisawa
  • S. Karasawa
  • W. Wen
  • C. V. Joglekar
  • W. Lu
  • Y. Chang
  • Y. Xiang
  • Y. Gao
  • S. Liu
  • Y. Song
  • S. H. Kwak
  • H. D. Shin
  • K. S. Park
  • C. H. D. Fall
  • J. Y. Kim
  • P. C. Sham
  • K. S. L. Lam
  • W. Zheng
  • X. Shu
  • H. Deng
  • H. Ikegami
  • G. V. Krishnaveni
  • D. K. Sanghera
  • L. Chuang
  • L. Liu
  • R. Hu
  • Y. Kim
  • M. Daimon
  • K. Hotta
  • W. Jia
  • J. S. Kooner
  • J. C. Chambers
  • G. R. Chandak
  • R. C. Ma
  • S. Maeda
  • R. Dorajoo
  • M. Yokota
  • R. Takayanagi
  • N. Kato
  • X. LinEmail author
  • R. J. F. LoosEmail author
Open Access
Article

Abstract

Aims/hypothesis

FTO harbours the strongest known obesity-susceptibility locus in Europeans. While there is growing evidence for a role for FTO in obesity risk in Asians, its association with type 2 diabetes, independently of BMI, remains inconsistent. To test whether there is an association of the FTO locus with obesity and type 2 diabetes, we conducted a meta-analysis of 32 populations including 96,551 East and South Asians.

Methods

All studies published on the association between FTO-rs9939609 (or proxy [r 2 > 0.98]) and BMI, obesity or type 2 diabetes in East or South Asians were invited. Each study group analysed their data according to a standardised analysis plan. Association with type 2 diabetes was also adjusted for BMI. Random-effects meta-analyses were performed to pool all effect sizes.

Results

The FTO-rs9939609 minor allele increased risk of obesity by 1.25-fold/allele (p = 9.0 × 10−19), overweight by 1.13-fold/allele (p = 1.0 × 10−11) and type 2 diabetes by 1.15-fold/allele (p = 5.5 × 10−8). The association with type 2 diabetes was attenuated after adjustment for BMI (OR 1.10-fold/allele, p = 6.6 × 10−5). The FTO-rs9939609 minor allele increased BMI by 0.26 kg/m2 per allele (p = 2.8 × 10−17), WHR by 0.003/allele (p = 1.2 × 10−6), and body fat percentage by 0.31%/allele (p = 0.0005). Associations were similar using dominant models. While the minor allele is less common in East Asians (12–20%) than South Asians (30–33%), the effect of FTO variation on obesity-related traits and type 2 diabetes was similar in the two populations.

Conclusions/interpretation

FTO is associated with increased risk of obesity and type 2 diabetes, with effect sizes similar in East and South Asians and similar to those observed in Europeans. Furthermore, FTO is also associated with type 2 diabetes independently of BMI.

Keywords

Asians FTO Meta-analysis Obesity Type 2 diabetes 

Abbreviations

GWAS

Genome-wide association study

MAF

Minor allele frequency

PAR

Population-attributable risk

SNP

Single-nucleotide polymorphism

Introduction

Large-scale genome-wide association studies (GWAS) in mainly white Europeans have identified at least 50 genetic loci to be robustly associated with obesity-related traits [1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12]. A cluster of common variants in the first intron of the fat mass and obesity-associated gene (FTO) was the first obesity-susceptibility locus to be identified by two independent GWAS in 2007 [1, 2] and has since been consistently replicated by many others and for a variety of obesity-related traits [7, 9, 13, 14, 15]. Of all currently identified obesity-susceptibility loci, the FTO locus has the most pronounced effect on BMI and obesity risk, at least in individuals of European descent. Each minor allele of any commonly investigated variant in FTO increases BMI by 0.30–0.40 kg/m2 (equivalent to 870–1,150 g for a person 1.7 m tall) and risk of obesity by ∼20% [7, 15]. The minor allele of the FTO variant is common (minor allele frequency (MAF)=∼42%) in white Europeans, such that 66% of Europeans carry at least one risk allele and 18% carry two risk alleles. Because of the high prevalence of the risk allele and its relatively strong effect on BMI, the FTO locus explains most (0.34%), yet little, of the variation in BMI in Europeans [7].

FTO has also been examined as an obesity-susceptibility locus in populations of non-white European origin. While the initial replication efforts in East Asian populations were inconsistent [16, 17], a growing number of studies have provided evidence that genetic variation in FTO influences BMI and obesity risk also in Chinese, Japanese, Korean and Filipino populations [18, 19, 20, 21, 22, 23, 24, 25, 26, 27]. A GWAS for BMI in 7,861 Koreans identified variation in FTO (rs9939609) as the most significantly associated locus, nearly reaching genome-wide significance (p = 1.5 × 10−7) [28]. Furthermore, literature-based meta-analyses in Asians reported that the minor allele for the rs9939609 FTO single-nucleotide polymorphism (SNP) significantly (p = 9 × 10−9) increased the risk of obesity, but no other obesity-related traits were examined [18, 29, 30]. Fewer studies in South Asians have been reported, two of which confirmed the association between the FTO locus and obesity susceptibility [31, 32], whereas one did not [33]. The prevalence of the risk allele in East Asians (∼20%) and South Asians (∼30%) is substantially lower than in Europeans, and the reported effect sizes in both East and South Asians vary widely for BMI (OR 0.13–0.83 kg/m2 per minor allele) and obesity risk (OR 1.02–1.48 per minor allele) [16, 18, 20, 21, 22, 23, 24, 25, 27, 34, 35, 36, 37, 38, 39].

FTO was first identified as a type 2 diabetes-susceptibility gene, but, as further adjustment for BMI abolished the association with type 2 diabetes [1], it was suggested that FTO is primarily an obesity-susceptibility locus. However, the BMI-independent role of FTO in type 2 diabetes remains a matter of debate, particularly in Asians but also in white Europeans. While several studies have reported that the association between the FTO locus and risk of type 2 diabetes remained significant after adjustment for BMI [15, 18, 33, 35, 40, 41], others could not confirm this [21, 30, 32, 37, 42].

To firmly establish the association between the FTO locus and obesity susceptibility in East and South Asians and to assess its effect size and potential heterogeneity across Asian populations, we performed a systematic meta-analysis of data from 32 populations, including 96,551 men and women, using standardised study-specific association analyses. Furthermore, we examined whether the FTO locus is associated with type 2 diabetes independently of its association with BMI.

Methods

Literature search and study identification

We designed a meta-analysis based on de novo analyses of data according to a standardised plan to achieve the greatest consistency possible across studies. We identified all published studies (before September 2010) that had examined the association of genetic variation in FTO with risk of obesity and type 2 diabetes and with obesity-related continuous traits in East and South Asian adults (age ≥18 years) by a PubMed literature search using the key words ‘FTO’, ‘fat mass and obesity associated gene’ and ‘genome-wide association study’. References from the identified papers were subsequently screened to identify additional studies and to ensure that the list of eligible studies was complete. The literature search was carried out by two investigators independently, who cross-checked their search results for completeness.

Our literature search identified 38 publications, one of which was excluded because it was a subsample of another identified study. We invited the corresponding authors of the remaining 37 publications to join our meta-analysis, of which 26 agreed to participate and eventually 22 submitted raw data or summary statistics. We also included a Korean population with previously unpublished data (Y. M. Kim, J. Shin, C.B. Lee, M.K. Kim, Y. Tabara, T. Miki and B.Y. Choi), which was presented by a contributing author.

Taken together, our meta-analysis included data for 31 populations from 22 publications and one unpublished study, with 96,551 individuals altogether. The study identification and selection process is illustrated in Fig. 1.
Fig. 1

Study identification and inclusion in the meta-analyses

All studies were conducted according to the Declaration of Helsinki. Informed consent was obtained from all participants, and the studies were approved by the ethics committees of the participating institutions.

Genotyping

The rs9939609 FTO SNP was examined in 18 studies, whereas proxy SNPs were used in 14 studies. More specifically, the rs8050135 SNP was genotyped in 11 studies of East Asians and one of South Asians, and the rs3751812 and rs17817449 SNP were each genotyped once in studies of East Asians (electronic supplementary material [ESM] Table 1). The linkage disequilibrium between rs9939609 and the three proxies (rs8050135, 3751812, rs17817449) is perfect (r 2 = 1) in populations of East Asian origin, based on CHB+JPT data from the HapMap (Rel 24/Phase II). The linkage disequilibrium between rs9939609 and rs8050135 in Indian Asians is very high (r 2 > 0.98), based on a subsample (n = 305) of the participating Lolipop study.

The genotyping success rate and concordance rate were >95%, and genotype distributions were in Hardy–Weinberg equilibrium (p > 0.01) in all participating studies (ESM Table 1).

Statistical analysis

As case–control definitions and statistical analyses used in the published papers were inconsistent, we asked analysts of each of the participating cohorts to re-analyse their data according to a standardised analysis plan. Summary statistics of each study were subsequently meta-analysed.

Obesity-susceptibility traits and type 2 diabetes

Overweight was defined as a BMI ≥24 kg/m2, and obesity as a BMI ≥28 kg/m2 according to the definition proposed by the Working Group on Obesity in China [43]. Anthropometric data, including weight, height, waist circumference, hip circumference and body fat percentage, were collected in each study as described previously (ESM Table 1), BMI was calculated as weight (kg) divided by height squared (m2), and WHR as waist circumference (cm) divided by hip circumference (cm). Raw data were used for analyses.

Type 2 diabetes was defined as meeting one or more of the following criteria: (1) fasting glucose ≥7.0 mmol/l; (2) 2-h glucose ≥11.1 mmol/l; (3) previous diagnosis of type 2 diabetes; (4) HbA1c ≥6.5% (48 mmol/mol); (5) self-reported type 2 diabetes (ESM Table 1).

Study-specific de novo data analyses

Association analyses within each study were performed for the total population and for men and women separately using additive and dominant genetic models. The associations of FTO-rs9939609 (or proxy) with risk of obesity and type 2 diabetes were assessed with multiple logistic regression models. Generalised linear models were used to assess the associations of FTO-rs9939609 (or proxy) with obesity-related continuous traits. In studies with a case–control design, analyses for continuous traits were conducted in control samples only. All analyses were adjusted for age and sex (sex-stratified analyses were only adjusted for age). The association with type 2 diabetes was also analysed with adjustment for BMI. Adjustments were performed by including the covariates (age, sex and/or BMI) as a linear term in the association model.

Summary statistics from the study-specific association analyses were reported in a standardised Excel form by the analysts of each study and collected centrally for meta-analyses.

Meta-analyses

Data extraction from the forms and meta-analyses was performed independently by two investigators and cross-checked for consistency. All ambiguities were clarified with the respective analysts before the final meta-analyses.

ORs and beta coefficients from the individual studies were pooled using DerSimonian and Laird random-effects meta-analyses [44]. Meta-analyses were performed of all studies combined. Because of differences in genetic background as well as in susceptibility to obesity and type 2 diabetes, meta-analyses were also stratified by East Asian and South Asian origin of the populations. Furthermore, East Asians were further stratified according to their country of origin.

Between-study heterogeneity was tested by Cochrane’s Q test and quantified by the I 2 index. I 2 values of <25%, 25–75% and >75% were defined as low, moderate and high heterogeneity, respectively [45]. To examine the sources of heterogeneity in our meta-analyses, we performed random-effects meta-regressions, where the between-study variance was estimated with the restricted maximum likelihood approach. Meta-regressions included the following study-specific variables as covariates: year of publication, country of origin, sample size, study design, mean age and mean BMI.

A funnel plot, along with Begg’s and Egger’s tests, was used to test for the presence of publication bias.

Statistical analyses were performed with the Stata 9.0 software (StataCorp LP, College Station, TX, USA). Meta-analyses and meta-regressions were implemented by the metan and metareg commands of Stata, respectively. p < 0.05 was considered to be significant, except for Cochrane’s Q test for heterogeneity and Begg’s and Egger’s tests for publication bias, where a level of p < 0.10 was used.

The variation in obesity-related continuous traits explained by the FTO variant was evaluated using the equation 2f(1−f)a 2, where f is the frequency of the variant and a is its additive standardised effect [5]. Population-attributable risk (PAR) was calculated as \( {\text{PAR}} = (X - {1})/X \). Assuming a multiplicative model, \( X = {\left( {{1} - f} \right)^{{2}}} + {2}f\left( {{1} - f} \right)\gamma + {f^{{2}}}{\gamma^2} \), where γ is the estimated OR, and f is the frequency of risk allele [46].

Results

Characteristics of populations included in the meta-analyses

Analyses were conducted in Chinese Hans (China Mainland: n = 10; Singapore: n = 2), Japanese (n = 7), Indians (n = 7), Koreans (n = 4), Singapore Malays (n = 1) and Filipinos (n = 1; Table 1). Fifteen of the populations were case–control designed for obesity (n = 3) or type 2 diabetes (n = 8) or both (n = 4), whereas 17 populations were population-based. The mean age and BMI of the populations ranged from 27.9 to 66.8 years and from 20.5 to 27.1 kg/m2, respectively. The prevalence in population-based studies ranged from 3.1% to 37.9% for obesity and from 2.9% to 41.9% for type 2 diabetes.
Table 1

Descriptive information of studies included in the meta-analyses, sorted by ethnicity, study design and publication year

Paper

Study

Publication year

Ethnicity

Country

Study design

Sample size

Mean age (years)

Mean BMI (kg/m2)

FTO SNP

MAF

Obese

OW

NW

T2DM

NFG

QT analyses

Li et al. [16]

NHAPC

2008

East Asian

China

Population based

472

1,215

1,503

423

1,893

3,190

58.62

24.43

rs9939609

0.11

Sha et al. [55]

GSBC

2009

East Asian

China

Population based

78

326

1,223

n.a.

n.a.

1,627

34.49

22.21

rs9939609

0.12

Hu et al. [56]

SHDS

2009

East Asian

China

Case–controlb

n.a.

n.a.

n.a.

1,759

1,791

1,791

57.33

23.57

rs8050136

0.12

Li et al. [35]

WDS

2010

East Asian

China

Case–controla, b

243

976

1,368

877

1,405

1,405

44.23

21.45

rs9939609

0.12

Cheung et al. [24]

CRISPS

2010

East Asian

China

Case–controla

419

n.a.

691

n.a.

n.a.

691

44.98

21.19

rs8050136

0.12

Liu et al. [18]

n.a.

2010

East Asian

China

Case–controla, b

277

794

893

1,767

1,961

1,961

58.09

24.52

rs9939609

0.12

Ng et al. [21]

CUHK

2010

East Asian

China

Case–controlb, c

1,147

2,293

2,432

5,872

583

583

41.31

22.87

rs3751812

0.12

Shu et al. [42]

SGWAS

2010

East Asian

China

Case–controlb

n.a.

n.a.

n.a.

1,043

2,170

2,170

49.24

23.30

rs9939609

0.12

Wen et al. [57]

FLSGS

2010

East Asian

China

Case–controlb

n.a.

n.a.

n.a.

1,160

1,127

1,127

59.09

24.13

rs8050136

0.12

Chang et al. [23]

NTUH

2008

East Asian

Taiwan

Case–controla, b

737

677

719

881

1,254

1,254

61.19

21.60

rs9939609

0.14

Cha et al. [25]

Kirin

2008

East Asian

Korea

Population based

252

304

361

n.a.

n.a.

917

27.91

26.39

rs17817449

0.14

Cha et al. [58]

KCMS

2009

East Asian

Korea

Population based

61

261

688

n.a.

n.a.

1,010

43.14

22.77

rs8050136

0.12

Kim et al. (unpublished data)

YangPyeung Cardiovascular Cohort Study

 

East Asian

Korea

Population based

339

995

1,092

194

2,061

2,426

57.60

24.49

rs9939609

0.12

Ng et al. [34]

Korea SNUH

2008

East Asian

Korea

Case–controlb

n.a.

n.a.

n.a.

758

629

629

64.70

23.52

rs8050136

0.12

Takeuchi et al. [59]

CAGE-Amagasaki

2009

East Asian

Japan

Population based

388

1,562

3,719

n.a.

n.a.

5,660

48.86

22.99

rs9939609

0.19

Takeuchi et al. [59]

CAGE-Fukuoka

2009

East Asian

Japan

Population based

721

3,763

8,076

n.a.

n.a.

12,560

62.59

23.05

rs9939609

0.19

Takeuchi et al. [59]

CAGE-BMI

2009

East Asian

Japan

Population based

168

607

1,006

n.a.

n.a.

1,781

66.82

23.69

rs9939609

0.20

Karasawa et al. [19]

Takahata

2010

East Asian

Japan

Population based

220

886

1,533

215

2,306

2,639

63.04

23.48

rs9939609

0.20

Hotta et al. [20]

GWASJPN obesity

2008

East Asian

Japan

Case–controla

1,559

n.a.

1,541

n.a.

n.a.

1,541

47.52

21.21

rs9939609

0.18

Omori et al. [37]

RIKEN T2D

2008

East Asian

Japan

Case–controlb

n.a.

n.a.

n.a.

4,584

2,262

2,262

44.84

22.86

rs8050136

0.20

Takeuchi et al. [59]

CAGE-T2DM

2009

East Asian

Japan

Case–controlb

n.a.

n.a.

n.a.

6,781

7,307

n.a.

64.35

23.47

rs9939609

0.19

Marvelle et al. [27]

CLHNS

2008

East Asian

Philippines

Population based

321

560

836

155

1,463

1,717

48.51

24.31

rs9939609

0.18

Tan et al. [22]

SP2

2008

East Asian

Singapore (Chinese)

Population based

195

624

1,609

145

2,248

2,430

48.11

22.88

rs8050136

0.12

Tan et al. [22]

SiMES

2008

East Asian

Singapore (Malays)

Population based

848

826

846

787

1,248

2,520

59.04

26.38

rs8050136

0.30

Tan et al. [22]

SDCS

2008

East Asian

Singapore (Chinese)

Case–controlc

426

809

757

n.a.

n.a.

n.a.

64.27

25.34

rs8050136

0.14

Chambers et al. [6]

LOLIPOP (IA317)

2008

South Asian

India

Population based

727

858

536

434

1,651

2,247

48.22

26.83

rs8050136

0.33

Chambers et al. [6]

LOLIPOP (IA610)

2008

South Asian

India

Population based

2,479

2,647

1,423

1,780

4,715

7,060

55.38

27.14

rs8050136

0.32

Tan et al. [22]

SINDI

2008

South Asian

India

Population based

760

910

858

974

1,348

2,528

58.01

26.20

rs8050136

0.33

Yajnik et al. [33]

Parthenon

2009

South Asian

India

Population based

136

320

511

n.a.

n.a.

967

32.44

23.76

rs9939609

0.33

Yajnik et al. [33]

PMNS

2009

South Asian

India

Population based

59

271

1,546

50

1,681

1,876

32.71

20.83

rs9939609

0.31

Sanghera et al. [40]

Sikh Diabetes Study

2008

South Asian

India

Case–controlb

n.a.

n.a.

n.a.

1,138

765

765

50.85

26.25

rs9939609

0.31

Yajnik et al. [33]

WELLGEN

2009

South Asian

India

Case–controlb

n.a.

n.a.

n.a.

1,967

1,681

1,681

32.39

20.50

rs9939609

0.31

Individuals from CAGE-T2DM study were selected from other three CAGE population-based studies

aObese case–control study

bT2DM case–control study

cObese case–control study conducted in T2DM cases

n.a., data not available or not used in meta-analysis; NFG, normal fasting glucose; NW, normal weight; OW, overweight; QT, quantitative trait; T2DM, type 2 diabetes

The MAF of FTO-rs9939609 (or proxy) is 12–14% in Chinese Hans and Koreans, 18–20% in Japanese and Filipinos, and 30–33% in Singapore Malays and Indians (Table 1).

Associations with obesity and overweight

A total of 24 populations (n obese = 13,032; n overweight = 22,474; n normalweight = 35,767) were available for meta-analyses of the association between the FTO variant and risk of obesity and overweight.

Each additional FTO-rs9939609 minor (A) allele increased the odds of obesity by 1.25 (p = 9.0 × 10−19) compared with normal weight individuals (Fig. 2), and by 1.17 (p = 7.4 × 10−11) compared with non-obese individuals (ESM Fig. 1). Each additional minor allele increased the odds of overweight by 1.13 (p = 1.0 × 10−11; ESM Fig. 2). The odds of obesity and overweight were the same in both East Asian and South Asian populations (p = 0.18 and 0.84, respectively; ESM Table 2). Associations were similar in men and women (ESM Table 3). The heterogeneity across all studies was low (13% ≤ I 2 ≤ 19%).
Fig. 2

Association of FTO-rs9939609 (or proxy) with obesity. Study-specific association analyses assumed an additive genetic model, comparing obese with normal-weight individuals, adjusted for age and sex. Effect sizes were combined using random-effects meta-analyses (DerSimonian–Laird method)

When a dominant genetic model was used, the odds were only slightly higher than for the additive genetic model (ESM Table 4).

Association with type 2 diabetes

In our meta-analysis of 22 populations (n cases = 33,744, n controls = 43,549), each additional FTO-rs9939609 minor allele increased the odds of type 2 diabetes by 1.15 (p = 5.5 × 10−8) when adjusted for age and sex (Fig. 3). Further adjustment for BMI attenuated, but did not abolish, the association with type 2 diabetes (OR 1.10, p = 6.6 × 10−5) (Fig. 4). Results were similar in East Asians and South Asians (ESM Table 2), in men and women (ESM Table 3), and when a dominant model was used (ESM Table 4).
Fig. 3

Association of FTO-rs9939609 (or proxy) with type 2 diabetes. Study-specific association analyses assumed an additive genetic model adjusted for age and sex. Effect sizes were combined using random-effects meta-analyses (DerSimonian–Laird method)

Fig. 4

Association of FTO-rs9939609 (or proxy) with type 2 diabetes adjusted for BMI. Study-specific association analyses assumed an additive genetic model adjusted for age, sex, and BMI. Effect sizes were combined using random-effects meta-analyses (DerSimonian–Laird method)

The association results across all studies showed moderate heterogeneity (44% ≤ I 2 ≤ 48%; Figs 3 and 4). Meta-regression analyses revealed that the difference in study design contributed to some of the heterogeneity. Subsequent subgroup analyses showed that the association with type 2 diabetes was more pronounced in studies with a case–control design (OR [95% CI] = 1.19 [1.14, 1.23], p = 3.7 × 10−19, I 2 = 0.0%) than in cohort studies (OR [95% CI] = 1.09 [0.99, 1.20], p = 0.07, I 2 = 54.4%), which showed moderate heterogeneity (ESM Table 5).

Associations with obesity-related continuous traits

The meta-analyses of the association of FTO-rs9939609 with BMI, waist circumference, hip circumference, WHR and body fat percentage included 30 (n = 71,022), 22 (n = 51,543), 20 (n = 48,508), 20 (n = 48,508) and nine (n = 19,580) populations, respectively.

Each additional FTO-rs9939609 minor allele was associated with a 0.26 kg/m2 higher BMI (p = 2.8 × 10−17; equivalent to ∼750 g/allele for a person 1.7 m tall) (Fig. 5), 0.51 cm larger waist circumference (p = 3.0 × 10−9) (ESM Fig. 3), 0.36 cm larger hip circumference (p = 0.0003) (ESM Fig. 4), 0.003 greater WHR (p = 1.2 × 10−6; ESM Fig. 5), and 0.31% higher body fat percentage (p = 0.0005) (ESM Fig. 6). All associations were very similar between East and South Asians (ESM Table 2), between men and women (ESM Table 3), or when a dominant genetic model was used (ESM Table 4).
Fig. 5

Association of FTO-rs9939609 (or proxy) with BMI. Study-specific association analyses assumed an additive genetic model adjusted for age and sex. Effect sizes were combined using random-effect meta-analyses (DerSimonian–Laird method)

We observed moderate heterogeneity across studies in the associations with BMI and hip circumference (BMI: I 2 = 33%; hip circumference: I 2 = 51%; Fig. 5; ESM Fig. 4). Meta-regression suggested that, for BMI, the heterogeneity was mainly due to difference in mean age and mean BMI among different populations. For hip circumference, the heterogeneity was mainly attributed to difference in mean BMI, i.e. the effect of the FTO minor allele tended to be larger in populations with a mean BMI ≥24 kg/m2, compared with those with a mean BMI <24 kg/m2.

FTO-rs9939609 explained 0.16% and 0.20% of the inter-individual variation in BMI in East and South Asian populations, respectively. The proportion of variation in other obesity-related continuous traits explained by FTO-rs9939609 was <0.10% (ESM Table 2).

Publication bias

The funnel plots for the associations with obesity, type 2 diabetes, waist circumference, WHR and body fat percentage were symmetrical and the results for Begg’s and Egger’s tests were non-significant (p ≥ 0.10), indicating that our results were not affected by publication bias (ESM Fig. 7). However, there was some evidence of publication bias and/or genetic heterogeneity for BMI (Begg’s test, p = 0.08; Egger’s test, p = 0.07) and hip circumference (Begg’s test, p = 0.03; Egger’s test, p = 0.08; ESM Fig. 7).

Discussion

This meta-analysis, combining data of 96,551 Asians from 32 populations, further confirms that genetic variation in FTO is associated with increased risk of obesity in East and South Asians. Despite differences in genetic background and obesity susceptibility between East and South Asians, the effect of FTO on obesity and related traits was generally similar to, or only somewhat smaller than, those reported for white Europeans. We furthermore confirm that variation in FTO is associated with increased risk of type 2 diabetes, an association that, unlike in white Europeans, is not abolished after adjustment for BMI in both East and South Asians.

Large-scale studies in individuals of white European descent have reported that each additional FTO minor allele increases the odds of obesity by 1.20–1.32-fold [1, 5, 7, 47]. The association with obesity observed in Asians in the present study was remarkably similar, with each additional minor allele increasing obesity risk by 1.25-fold (95% CI 1.19, 1.31), consistent with the association observed for obesity in previous literature-based meta-analyses of case–control studies in East and South Asians [18, 29, 30].

The association of the FTO variant with overweight was the same in East and South Asians (OR 1.13 per minor allele) and very similar to the effects (ORs ranging from 1.13 to 1.18) that have been reported in large-scale studies of white Europeans [1, 7, 47]. While the effect sizes observed for the influence of FTO on obesity and overweight in Asians are very similar to those of Europeans, it should be noted that the definitions of obesity and overweight are different, as BMI cut-offs are somewhat lower in Asians than in Europeans, consistent with the association of BMI with metabolic disease [48].

The FTO minor allele increases BMI by 0.26 kg/m2 (equivalent to ∼750 g/allele for a person 1.7 m tall) in Asians, with very similar results for East and South Asians. This observation suggests that the effect of FTO on BMI in Asians is substantially smaller than the effect observed in a meta-analysis of more than 125,000 white Europeans (0.39 kg/m2 per minor allele, or 1,130 g per minor allele) [7]. This difference may be due to the fact that BMI in Asians does not represent exactly the same adiposity phenotype as in Europeans. However, given that other large-scale studies in white Europeans have reported effects for FTO on BMI that range between 0.26 and 0.39 kg/m2, the comparison between Asians and Europeans should be made with caution [1, 3, 5, 15, 47]. The FTO variant also showed convincing association with measures of fat distribution such as waist and hip circumference and WHR in Asians. Despite the often described difference in abdominal obesity between East and South Asians, the effect sizes were very similar in the two groups. Consistent with the observations for BMI, the effect sizes tended to be somewhat smaller than those reported for white Europeans. For example, each additional FTO minor allele increased waist circumference by 0.51 cm in Asians, whereas large-scale studies in Europeans have reported an increase of 0.73–1.00 cm [1, 9, 47].

As the MAF of the FTO variant is substantially lower in Asians (East Asians, ∼17%; South Asians, ∼32%) than in white Europeans (∼45%), and as the effect of this allele on obesity-related traits is similar or somewhat lower in Asians than in white Europeans, the overall contribution of genetic variation in FTO to obesity susceptibility will be lower in Asians, in particular East Asians. For example, the FTO variant explained less of the inter-individual variation in BMI in Asians (East Asians, 0.16%; South Asians, 0.20%) than in white Europeans (0.34%) [7]. Furthermore, the low risk allele frequency led to a lower PAR for the risk of obesity (East Asians, 8.3%; South Asians, 10.6%) and overweight (East Asians, 4.1%; South Asians, 7.8%) in Asians than in white Europeans (obesity, 20.4%; overweight, 12.7%) [1].

The FTO locus was first identified in a GWAS for type 2 diabetes in white Europeans, i.e. each minor allele increased the odds of diabetes by 1.15-fold [1]. However, after adjustment for BMI, the association between the FTO variant and type 2 diabetes was completely abolished (OR 1.03), suggesting that FTO is primarily an obesity-susceptibility locus [1]. In our meta-analysis, we observed a similar effect of FTO on risk of type 2 diabetes, with each minor allele increasing the odds by 1.15-fold. Interestingly, adjustment for BMI did not abolish the association, but only slightly attenuated it to a 1.10-fold increased risk of type 2 diabetes for each additional minor allele. These observations were similar in East and South Asians, suggesting that the FTO locus influences the risk of type 2 diabetes, at least in part, independently of its effect on BMI. The reason for the discrepancy between the original observations in Europeans and our observations in Asians are not known, but may be due to the fact that FTO seems to have a smaller effect on BMI in Asians than in Europeans. It may also be due to the fact that BMI, as suggested above, represents a different adiposity phenotype in Asians than in Europeans because of differences in body composition. Although BMI is a marker for general adiposity, it does not distinguish between fat mass and fat-free mass and does not reflect regional fat distribution. Observational studies have suggested that, for a given amount of total body fat, East and South Asians have more abdominal fat and less muscle mass than white Europeans [49, 50]. However, while it has been generally believed that in white Europeans the association with type 2 diabetes is fully mediated by the effect of FTO on BMI, not all studies confirm this observation. A recent large-scale study in 41,504 Scandinavians found that the FTO minor allele indeed increased type 2 diabetes risk (OR 1.13), but this association remained present (OR 1.09) after adjustment for BMI, consistent with the observations in the present study. The biological pathways that underlie the independent association between FTO variation with obesity and type 2 diabetes remain unclear. However, results of gene expression studies have shown that FTO expression in human islets cells is not associated with BMI [51], whereas FTO mRNA and protein levels in muscle are increased in individuals with type 2 diabetes compared with non-diabetic obese individuals or healthy lean controls [52]. Furthermore, FTO overproduction in myotubes suggested a role for FTO in oxidative metabolism, lipogenesis and oxidative stress in muscle, a cluster of metabolic defects characteristic of type 2 diabetes [52].

Despite the fact that our meta-analyses included Asians with different genetic backgrounds, the overall heterogeneity of the association effects was generally only low to moderate. Interestingly, we found that the associations were generally very similar in East and South Asians, although these populations are known to have genetically different origins [53]. Furthermore, we found no differences between men and women, consistent with the observations in white Europeans [7]. We found some evidence that age may contribute to the heterogeneity of the association between FTO and BMI. Life course effects have been reported in white Europeans [54], and longitudinal analyses will be needed to establish this in Asian populations. Longitudinal studies are also more appropriate than cross-sectional studies for disentangling the intricate interplay between FTO, obesity and type 2 diabetes throughout life [15].

It should be noted that the association between FTO variation and obesity risk in Asians had been established in three earlier meta-analyses [18, 29, 30]. These meta-analyses were substantially smaller than the present ones and focused solely on case–control analyses of obesity and type 2 diabetes, while no continuous traits were studied. The meta-analysis by Liu et al [18] included individuals of East and South Asian origin, which were analysed together without comparison of effect sizes between the two populations. This study also examined the association with type 2 diabetes, but did not explore the association after adjustment for BMI [18]. Furthermore, the three previous meta-analyses were all literature-based and thus more prone to publication bias, whereas our meta-analysis was designed on the basis of a de novo analysis of data according to a standardised plan in all studies identified as having available data and agreement to participate. No evidence of publication bias was observed except for the associations with BMI and hip circumference. The analytical consistency across studies helped minimise between-study heterogeneity. Although our results are representative of individuals of Southeast Asian, East Asian and South Asian descent, the association of FTO with risk of obesity and type 2 diabetes in other Asian populations remains to be examined.

In summary, we have firmly established that genetic variation in the first intron of FTO is associated with increased risk of obesity and type 2 diabetes in Asians, with effect sizes similar to those in Europeans. Furthermore, we confirm that the association of FTO with risk of type 2 diabetes is partly independent of BMI.

Notes

Acknowledgements

A full list of acknowledgements can be found in the ESM. This work was supported by the following: Hong Kong Government Research Grants Council Central Allocation Scheme (CUHK 1/04C), Research Grants Council Earmarked Research Grant (CUHK4724/07M) and the Innovation and Technology Fund of the Government of the Hong Kong SAR (ITS/487/09FP); National Institutes of Health (DK078150, TW05596, HL085144, TW008288) and pilot funds (RR20649, ES10126, and DK56350); National Institutes of Health (R01 AR050496, R21 AG027110, R01 AG026564, R01 AR057049-01A1, R21 AA015973) and Specialized Center of Research (P50 AR055081) and National Science Foundation of China (81000363, 31000554) and the PhD Programs Foundation of Ministry of Education of China (20100201120058) and the Fundamental Research Funds for the Central Universities, Shanghai Leading Academic Discipline Project (S30501); National Research Foundation of Korea (NRF, no. 20100020617); the Japanese Society for the Promotion of Science (17209021) and of Priority Area ‘Applied Genomics’ (1601223, 17019028, 18018020, 20018026); the Korea Health 21 R&D Project, Korean Ministry of Health & Welfare (00-PJ3-PG6-GN07-001); the National Institute for Health Research Comprehensive Biomedical Research Centre at Imperial College Healthcare NHS Trust, the British Heart Foundation (SP/04/002), the Medical Research Council (G0700931), the Wellcome Trust (084723/Z/08/Z) and the National Institute for Health Research (RP-PG-0407-10371); the Knowledge Innovation Program of Shanghai Institutes for Biological Sciences, Chinese Academy of Sciences (2009KIP401), the Chief Scientist Program of Shanghai Institutes for Biological Sciences, Chinese Academy of Sciences (SIBS2008006), the Knowledge Innovation Program Project of the Chinese Academy of Sciences (KSCX1-YW-02), the National Natural Science Foundation of China (30930081), and National High Technology Research and Development Program (863 Program) (2009AA022704), National 973 Program (2011CB504002); the National Science Council (NSC 95-3112-B002-002, NSC 96-2752-B002-008-PAE) of Taiwan; major program of Shanghai Municipality for Basic Research (08dj1400601), National Natural Science Foundation of China (30800617); National Institute of Health (NIH/FIC KO1 TW006087, NIH/NIDDK R01 DK082766); the Program for the National Natural Science Foundation of China (NSFC-30872116) and New Century Excellent Talents in the University of China (NCET-04-0707); Council of Scientific and Industrial Research, Government of India (NWP0032); the National Heart, Lung, and Blood Institute, National Institutes of Health, US Department of Health and Human Services (N01WH22110, 24152, 32100-2, 32105-6, 32108-9, 32111-13, 32115, 32118-32119, 32122, 42107-26, 42129-32, 44221) and the National Institute of Diabetes and Digestive and Kidney Diseases R01 (DK062290) from the National Institutes of Health.

Contribution statement

RJFL, HL and XL contributed to the conception and design of the study. CL and TOK performed the literature search, designed the analysis plan, performed the meta-analyses and researched the data. HL, TOK, CL and RJFL wrote the manuscript. HL, JZ, YL, CH, ZY, WZ, WB, SC, YW, TY, AS, BYC, CSY, DZ, FT, KY, JCC, KRM, LFB, MI, EN, NL, TF, SK, WW, CVJ, WL, YC, YX, YG, SL, YS, SHK, HDS, KSP, CHDF, JYK, PCS, KSLL, WZ, XS, HD, HI, GVK, DKS, LC, LL, RH, YK, MD, KH, WJ, JSK, JCC, GRC, RCM, SM, RD, MY, RT, NK, XL and RJFL collected study-specific data, analysed the study-specific data according to the standardised analysis plan, and reviewed and edited the manuscript. All authors have approved the final version of the manuscript to be published.

Duality of interest

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

Open Access

This article is distributed under the terms of the Creative Commons Attribution Noncommercial License which permits any noncommercial use, distribution, and reproduction in any medium, provided the original author(s) and source are credited.

Supplementary material

125_2011_2370_MOESM1_ESM.pdf (70 kb)
ESM Acknowledgements (PDF 69 kb)
125_2011_2370_MOESM2_ESM.pdf (114 kb)
ESM Figure 1 (PDF 113 kb)
125_2011_2370_MOESM3_ESM.pdf (113 kb)
ESM Figure 2 (PDF 113 kb)
125_2011_2370_MOESM4_ESM.pdf (110 kb)
ESM Figure 3 (PDF 109 kb)
125_2011_2370_MOESM5_ESM.pdf (112 kb)
ESM Figure 4 (PDF 111 kb)
125_2011_2370_MOESM6_ESM.pdf (103 kb)
ESM Figure 5 (PDF 103 kb)
125_2011_2370_MOESM7_ESM.pdf (94 kb)
ESM Figure 6 (PDF 94 kb)
125_2011_2370_MOESM8_ESM.pdf (50 kb)
ESM Figure 7 (PDF 49 kb)
125_2011_2370_MOESM9_ESM.pdf (103 kb)
ESM Table 1 Information on definition of type 2 diabetes and genotyping quality control (PDF 103 kb)
125_2011_2370_MOESM10_ESM.pdf (34 kb)
ESM Table 2 Associations of FTO rs9939609 (or proxy SNP) with obesity, overweight, type 2 diabetes and obesity-related continuous traits in East Asians and South Asians separately, assuming an additive genetic model (PDF 33 kb)
125_2011_2370_MOESM11_ESM.pdf (15 kb)
ESM Table 3 Associations of FTO rs9939609 (or proxy SNP) with obesity, overweight, type 2 diabetes and obesity-related continuous traits in men and women separately, assuming an additive genetic model (PDF 15 kb)
125_2011_2370_MOESM12_ESM.pdf (30 kb)
ESM Table 4 Associations of FTO rs9939609 (proxy SNPs) with obesity, overweight, type 2 diabetes and obesity-related continuous traits, assuming a dominant genetic model (PDF 29 kb)
125_2011_2370_MOESM13_ESM.pdf (77 kb)
ESM Table 5 Subgroup analyses for the associations of FTO rs9939609 (proxy SNPs) with type 2 diabetes, BMI and hip circumference (PDF 76 kb)

References

  1. 1.
    Frayling TM, Timpson NJ, Weedon MN et al (2007) A common variant in the FTO gene is associated with body mass index and predisposes to childhood and adult obesity. Science 316:889–894PubMedCrossRefGoogle Scholar
  2. 2.
    Scuteri A, Sanna S, Chen WM et al (2007) Genome-wide association scan shows genetic variants in the FTO gene are associated with obesity-related traits. PLoS Genet 3:e115PubMedCrossRefGoogle Scholar
  3. 3.
    Loos RJ, Lindgren CM, Li S et al (2008) Common variants near MC4R are associated with fat mass, weight and risk of obesity. Nat Genet 40:768–775PubMedCrossRefGoogle Scholar
  4. 4.
    Willer CJ, Speliotes EK, Loos RJ et al (2009) Six new loci associated with body mass index highlight a neuronal influence on body weight regulation. Nat Genet 41:25–34PubMedCrossRefGoogle Scholar
  5. 5.
    Thorleifsson G, Walters GB, Gudbjartsson DF et al (2009) Genome-wide association yields new sequence variants at seven loci that associate with measures of obesity. Nat Genet 41:18–24PubMedCrossRefGoogle Scholar
  6. 6.
    Chambers JC, Elliott P, Zabaneh D et al (2008) Common genetic variation near MC4R is associated with waist circumference and insulin resistance. Nat Genet 40:716–718PubMedCrossRefGoogle Scholar
  7. 7.
    Speliotes EK, Willer CJ, Berndt SI et al (2010) Association analyses of 249,796 individuals reveal 18 new loci associated with body mass index. Nat Genet 42:937–948PubMedCrossRefGoogle Scholar
  8. 8.
    Lindgren CM, Heid IM, Randall JC et al (2009) Genome-wide association scan meta-analysis identifies three loci influencing adiposity and fat distribution. PLoS Genet 5:e1000508PubMedCrossRefGoogle Scholar
  9. 9.
    Heard-Costa NL, Zillikens MC, Monda KL et al (2009) NRXN3 is a novel locus for waist circumference: a genome-wide association study from the CHARGE Consortium. PLoS Genet 5:e1000539PubMedCrossRefGoogle Scholar
  10. 10.
    Heid IM, Jackson AU, Randall JC et al (2010) Meta-analysis identifies 13 new loci associated with waist–hip ratio and reveals sexual dimorphism in the genetic basis of fat distribution. Nat Genet 42:949–960PubMedCrossRefGoogle Scholar
  11. 11.
    Meyre D, Delplanque J, Chevre JC et al (2009) Genome-wide association study for early-onset and morbid adult obesity identifies three new risk loci in European populations. Nat Genet 41:157–159PubMedCrossRefGoogle Scholar
  12. 12.
    Scherag A, Dina C, Hinney A et al (2010) Two new Loci for body-weight regulation identified in a joint analysis of genome-wide association studies for early-onset extreme obesity in French and German study groups. PLoS Genet 6:e1000916PubMedCrossRefGoogle Scholar
  13. 13.
    Kring SI, Holst C, Zimmermann E et al (2008) FTO gene associated fatness in relation to body fat distribution and metabolic traits throughout a broad range of fatness. PLoS One 3:e2958PubMedCrossRefGoogle Scholar
  14. 14.
    Hotta K, Nakamura M, Nakamura T et al (2010) Polymorphisms in NRXN3, TFAP2B, MSRA, LYPLAL1, FTO and MC4R and their effect on visceral fat area in the Japanese population. J Hum Genet 55:738–742PubMedCrossRefGoogle Scholar
  15. 15.
    Hertel JK, Johansson S, Sonestedt E et al (2011) FTO, Type 2 diabetes, and weight gain throughout adult life: a meta-analysis of 41,504 subjects from the Scandinavian HUNT, MDC, and MPP studies. Diabetes 60:1637–1644PubMedCrossRefGoogle Scholar
  16. 16.
    Li H, Wu Y, Loos RJ et al (2008) Variants in the fat mass- and obesity-associated (FTO) gene are not associated with obesity in a Chinese Han population. Diabetes 57:264–268PubMedCrossRefGoogle Scholar
  17. 17.
    Horikoshi M, Hara K, Ito C et al (2007) Variations in the HHEX gene are associated with increased risk of type 2 diabetes in the Japanese population. Diabetologia 50:2461–2466PubMedCrossRefGoogle Scholar
  18. 18.
    Liu Y, Liu Z, Song Y et al (2010) Meta-analysis added power to identify variants in FTO associated with type 2 diabetes and obesity in the Asian population. Obesity (Silver Spring) 18:1619–1624CrossRefGoogle Scholar
  19. 19.
    Karasawa S, Daimon M, Sasaki S et al (2010) Association of the common fat mass and obesity associated (FTO) gene polymorphism with obesity in a Japanese population. Endocr J 57:293–301PubMedCrossRefGoogle Scholar
  20. 20.
    Hotta K, Nakata Y, Matsuo T et al (2008) Variations in the FTO gene are associated with severe obesity in the Japanese. J Hum Genet 53:546–553PubMedCrossRefGoogle Scholar
  21. 21.
    Ng MC, Tam CH, So WY et al (2010) Implication of genetic variants near NEGR1, SEC16B, TMEM18, ETV5/DGKG, GNPDA2, LIN7C/BDNF, MTCH2, BCDIN3D/FAIM2, SH2B1, FTO, MC4R, and KCTD15 with obesity and type 2 diabetes in 7705 Chinese. J Clin Endocrinol Metab 95:2418–2425PubMedCrossRefGoogle Scholar
  22. 22.
    Tan JT, Dorajoo R, Seielstad M et al (2008) FTO variants are associated with obesity in the Chinese and Malay populations in Singapore. Diabetes 57:2851–2857PubMedCrossRefGoogle Scholar
  23. 23.
    Chang YC, Liu PH, Lee WJ et al (2008) Common variation in the fat mass and obesity-associated (FTO) gene confers risk of obesity and modulates BMI in the Chinese population. Diabetes 57:2245–2252PubMedCrossRefGoogle Scholar
  24. 24.
    Cheung CY, Tso AW, Cheung BM et al (2010) Obesity susceptibility genetic variants identified from recent genome-wide association studies: implications in a Chinese population. J Clin Endocrinol Metab 95:1395–1403PubMedCrossRefGoogle Scholar
  25. 25.
    Cha SW, Choi SM, Kim KS et al (2008) Replication of genetic effects of FTO polymorphisms on BMI in a Korean population. Obesity (Silver Spring) 16:2187–2189CrossRefGoogle Scholar
  26. 26.
    Lee HJ, Kim IK, Kang JH et al (2010) Effects of common FTO gene variants associated with BMI on dietary intake and physical activity in Koreans. Clin Chim Acta 411:1716–1722PubMedCrossRefGoogle Scholar
  27. 27.
    Marvelle AF, Lange LA, Qin L, Adair LS, Mohlke KL (2008) Association of FTO with obesity-related traits in the Cebu Longitudinal Health and Nutrition Survey (CLHNS) Cohort. Diabetes 57:1987–1991PubMedCrossRefGoogle Scholar
  28. 28.
    Cho YS, Go MJ, Kim YJ et al (2009) A large-scale genome-wide association study of Asian populations uncovers genetic factors influencing eight quantitative traits. Nat Genet 41:527–534PubMedCrossRefGoogle Scholar
  29. 29.
    Peng S, Zhu Y, Xu F, Ren X, Li X, Lai M (2011) FTO gene polymorphisms and obesity risk: a meta-analysis. BMC Med 9:71PubMedCrossRefGoogle Scholar
  30. 30.
    Xi B, Mi J (2009) FTO polymorphisms are associated with obesity but not with diabetes in East Asian populations: a meta-analysis. Biomed Environ Sci 22:449–457PubMedCrossRefGoogle Scholar
  31. 31.
    Dorajoo R, Blakemore AI, Sim X et al (2011) Replication of 13 obesity loci among Singaporean Chinese. Malay and Asian–Indian populations. Int J Obes (Lond). doi: 10.1038/ijo.2011.86
  32. 32.
    Ramya K, Radha V, Ghosh S, Majumder PP, Mohan V (2010) Genetic variations in the FTO gene are associated with type 2 diabetes and obesity in south Indians (CURES-79). Diabetes Technol Ther 13:33–42CrossRefGoogle Scholar
  33. 33.
    Yajnik CS, Janipalli CS, Bhaskar S et al (2009) FTO gene variants are strongly associated with type 2 diabetes in South Asian Indians. Diabetologia 52:247–252PubMedCrossRefGoogle Scholar
  34. 34.
    Ng MC, Park KS, Oh B et al (2008) Implication of genetic variants near TCF7L2, SLC30A8, HHEX, CDKAL1, CDKN2A/B, IGF2BP2, and FTO in type 2 diabetes and obesity in 6,719 Asians. Diabetes 57:2226–2233PubMedCrossRefGoogle Scholar
  35. 35.
    Li X, Song F, Jiang H et al (2010) A genetic variation in the fat mass- and obesity-associated gene is associated with obesity and newly diagnosed type 2 diabetes in a Chinese population. Diabetes Metab Res Rev 26:128–132PubMedCrossRefGoogle Scholar
  36. 36.
    Shi J, Long J, Gao YT et al (2010) Evaluation of genetic susceptibility loci for obesity in Chinese women. Am J Epidemiol 172:244–254PubMedCrossRefGoogle Scholar
  37. 37.
    Omori S, Tanaka Y, Takahashi A et al (2008) Association of CDKAL1, IGF2BP2, CDKN2A/B, HHEX, SLC30A8, and KCNJ11 with susceptibility to type 2 diabetes in a Japanese population. Diabetes 57:791–795PubMedCrossRefGoogle Scholar
  38. 38.
    Tabara Y, Osawa H, Guo H et al (2009) Prognostic significance of FTO genotype in the development of obesity in Japanese: the J-SHIPP study. Int J Obes (Lond) 33:1243–1248CrossRefGoogle Scholar
  39. 39.
    Han X, Luo Y, Ren Q et al (2010) Implication of genetic variants near SLC30A8, HHEX, CDKAL1, CDKN2A/B, IGF2BP2, FTO, TCF2, KCNQ1, and WFS1 in type 2 diabetes in a Chinese population. BMC Med Genet 11:81PubMedCrossRefGoogle Scholar
  40. 40.
    Sanghera DK, Ortega L, Han S et al (2008) Impact of nine common type 2 diabetes risk polymorphisms in Asian Indian Sikhs: PPARG2 (Pro12Ala), IGF2BP2, TCF7L2 and FTO variants confer a significant risk. BMC Med Genet 9:59PubMedCrossRefGoogle Scholar
  41. 41.
    Takeuchi F, Yamamoto K, Katsuya T et al (2011) Association of genetic variants for susceptibility to obesity with type 2 diabetes in Japanese individuals. Diabetologia 54:1350–1359PubMedCrossRefGoogle Scholar
  42. 42.
    Shu XO, Long J, Cai Q et al (2010) Identification of new genetic risk variants for type 2 diabetes. PLoS Genet 6:e1001127PubMedCrossRefGoogle Scholar
  43. 43.
    Zhou BF, Cooperative Meta-Analysis Group of the Working Group on Obesity in China (2002) Predictive values of body mass index and waist circumference for risk factors of certain related diseases in Chinese adults: study on optimal cut-off points of body mass index and waist circumference in Chinese adults. Biomed Environ Sci 15:83–96PubMedGoogle Scholar
  44. 44.
    DerSimonian R, Laird N (1986) Meta-analysis in clinical trials. Control Clin Trials 7:177–188PubMedCrossRefGoogle Scholar
  45. 45.
    Higgins JP, Thompson SG (2002) Quantifying heterogeneity in a meta-analysis. Stat Med 21:1539–1558PubMedCrossRefGoogle Scholar
  46. 46.
    Ng MC, Tam CH, Lam VK, So WY, Ma RC, Chan JC (2007) Replication and identification of novel variants at TCF7L2 associated with type 2 diabetes in Hong Kong Chinese. J Clin Endocrinol Metab 92:3733–3737PubMedCrossRefGoogle Scholar
  47. 47.
    Li S, Zhao JH, Luan J et al (2009) Cumulative effects and predictive value of common obesity–susceptibility variants identified by genome-wide association studies. Am J Clin Nutr 91:184–190PubMedCrossRefGoogle Scholar
  48. 48.
    Consultation WHOE (2004) Appropriate body-mass index for Asian populations and its implications for policy and intervention strategies. Lancet 363:157–163CrossRefGoogle Scholar
  49. 49.
    Lear SA, Humphries KH, Kohli S, Chockalingam A, Frohlich JJ, Birmingham CL (2007) Visceral adipose tissue accumulation differs according to ethnic background: results of the Multicultural Community Health Assessment Trial (M-CHAT). Am J Clin Nutr 86:353–359PubMedGoogle Scholar
  50. 50.
    Chan JC, Malik V, Jia W et al (2009) Diabetes in Asia: epidemiology, risk factors, and pathophysiology. JAMA 301:2129–2140PubMedCrossRefGoogle Scholar
  51. 51.
    Kirkpatrick CL, Marchetti P, Purrello F et al (2010) Type 2 diabetes susceptibility gene expression in normal or diabetic sorted human alpha and beta cells: correlations with age or BMI of islet donors. PLoS One 5:e11053PubMedCrossRefGoogle Scholar
  52. 52.
    Bravard A, Lefai E, Meugnier E et al (2011) FTO is increased in muscle during type 2 diabetes, and its overexpression in myotubes alters insulin signaling, enhances lipogenesis and ROS production, and induces mitochondrial dysfunction. Diabetes 60:258–268PubMedCrossRefGoogle Scholar
  53. 53.
    Consortium HP-AS, Abdulla MA, Ahmed I et al (2009) Mapping human genetic diversity in Asia. Science 326:1541–1545PubMedCrossRefGoogle Scholar
  54. 54.
    Hardy R, Wills AK, Wong A et al (2009) Life course variations in the associations between FTO and MC4R gene variants and body size. Hum Mol Genet 19:545–552PubMedCrossRefGoogle Scholar
  55. 55.
    Sha BY, Yang TL, Zhao LJ et al (2009) Genome-wide association study suggested copy number variation may be associated with body mass index in the Chinese population. J Hum Genet 54:199–202PubMedCrossRefGoogle Scholar
  56. 56.
    Hu C, Zhang R, Wang C et al (2009) PPARG, KCNJ11, CDKAL1, CDKN2A-CDKN2B, IDE-KIF11-HHEX, IGF2BP2 and SLC30A8 are associated with type 2 diabetes in a Chinese population. PLoS One 4:e7643PubMedCrossRefGoogle Scholar
  57. 57.
    Wen J, Ronn T, Olsson A et al (2010) Investigation of type 2 diabetes risk alleles support CDKN2A/B, CDKAL1, and TCF7L2 as susceptibility genes in a Han Chinese cohort. PLoS One 5:e9153PubMedCrossRefGoogle Scholar
  58. 58.
    Cha S, Koo I, Park BL et al (2009) Genetic effects of FTO and MC4R polymorphisms on body mass in constitutional types. Evid Based Complement Alternat Med. doi: 10.1093/ecam/nep162
  59. 59.
    Takeuchi F, Serizawa M, Yamamoto K et al (2009) Confirmation of multiple risk loci and genetic impacts by a genome-wide association study of type 2 diabetes in the Japanese population. Diabetes 58:1690–1699PubMedCrossRefGoogle Scholar

Copyright information

© The Author(s) 2011

Authors and Affiliations

  • H. Li
    • 1
  • T. O. Kilpeläinen
    • 2
  • C. Liu
    • 1
  • J. Zhu
    • 1
  • Y. Liu
    • 3
  • C. Hu
    • 4
  • Z. Yang
    • 5
  • W. Zhang
    • 6
  • W. Bao
    • 7
  • S. Cha
    • 8
  • Y. Wu
    • 9
  • T. Yang
    • 10
  • A. Sekine
    • 11
  • B. Y. Choi
    • 12
  • C. S. Yajnik
    • 13
  • D. Zhou
    • 1
  • F. Takeuchi
    • 14
  • K. Yamamoto
    • 15
  • J. C. Chan
    • 16
  • K. R. Mani
    • 17
  • L. F. Been
    • 18
  • M. Imamura
    • 19
  • E. Nakashima
    • 20
  • N. Lee
    • 21
  • T. Fujisawa
    • 22
  • S. Karasawa
    • 23
  • W. Wen
    • 24
  • C. V. Joglekar
    • 13
  • W. Lu
    • 25
  • Y. Chang
    • 26
  • Y. Xiang
    • 27
  • Y. Gao
    • 27
  • S. Liu
    • 28
  • Y. Song
    • 29
  • S. H. Kwak
    • 30
  • H. D. Shin
    • 31
  • K. S. Park
    • 30
  • C. H. D. Fall
    • 32
  • J. Y. Kim
    • 8
  • P. C. Sham
    • 33
  • K. S. L. Lam
    • 33
  • W. Zheng
    • 24
  • X. Shu
    • 24
  • H. Deng
    • 34
    • 35
    • 36
  • H. Ikegami
    • 37
  • G. V. Krishnaveni
    • 38
  • D. K. Sanghera
    • 18
  • L. Chuang
    • 39
  • L. Liu
    • 7
  • R. Hu
    • 5
  • Y. Kim
    • 40
  • M. Daimon
    • 23
  • K. Hotta
    • 11
  • W. Jia
    • 4
  • J. S. Kooner
    • 41
  • J. C. Chambers
    • 6
  • G. R. Chandak
    • 17
  • R. C. Ma
    • 16
  • S. Maeda
    • 19
  • R. Dorajoo
    • 42
    • 43
  • M. Yokota
    • 44
  • R. Takayanagi
    • 45
  • N. Kato
    • 14
  • X. Lin
    • 1
    Email author
  • R. J. F. Loos
    • 2
    Email author
  1. 1.Institute for Nutritional Sciences, Shanghai Institutes for Biological SciencesChinese Academy of SciencesShanghaiPeople’s Republic of China
  2. 2.MRC Epidemiology Unit, Institute of Metabolic Science Box 285Addenbrooke’s HospitalCambridgeUK
  3. 3.Institutes of Biomedical SciencesFudan UniversityShanghaiPeople’s Republic of China
  4. 4.Shanghai Diabetes Institute, Department of Endocrinology and Metabolism, Shanghai Clinical Center of DiabetesShanghai Jiao Tong University Affiliated Sixth People’s HospitalShanghaiPeople’s Republic of China
  5. 5.Department of Endocrinology and Metabolism, Huashan Hospital, Institute of Endocrinology and Diabetology at Fudan University, Shanghai Medical SchoolFudan UniversityShanghaiPeople’s Republic of China
  6. 6.Department Epidemiology and Biostatistics, School of Public HealthImperial College LondonLondonUK
  7. 7.Department of Nutrition and Food Hygiene and MOE Key Lab of Environment and Health, School of Public Health, Tongji Medical CollegeHuazhong University of Science and TechnologyWuhanPeople’s Republic of China
  8. 8.Division of Constitutional Medicine ResearchKorea Institute of Oriental MedicineDaejeonSouth Korea
  9. 9.Department of GeneticsUniversity of North CarolinaChapel HillUSA
  10. 10.Key Laboratory of Biomedical Information Engineering of Ministry of Education, and Institute of Molecular Genetics, School of Life Science and TechnologyXi’an Jiaotong UniversityXi’anPeople’s Republic of China
  11. 11.EBM Research CenterKyoto University Graduate School of MedicineKyotoJapan
  12. 12.Department of Preventive MedicineHanYang University College of MedicineSeoulSouth Korea
  13. 13.Diabetology Research CentreKEM Hospital and Research CentrePuneIndia
  14. 14.National Center for Global Health and MedicineTokyoJapan
  15. 15.Division of Genome Analysis, Medical Institute of BioregulationKyushu UniversityFukuokaJapan
  16. 16.Department of Medicine and TherapeuticsChinese University of Hong Kong, Prince of Wales HospitalShatinPeople’s Republic of China
  17. 17.Centre for Cellular and Molecular Biology (CCMB-CSIR)HyderabadIndia
  18. 18.University of Oklahoma Health Sciences CenterOklahoma CityUSA
  19. 19.Laboratory for Endocrinology and MetabolismRIKEN Center for Genomic MedicineYokohamaJapan
  20. 20.Department of Diabetes and EndocrinologyChubu Rosai HospitalNagoyaJapan
  21. 21.USC Office of Population Studies FoundationUniversity of San CarlosCebuPhilippines
  22. 22.Department of Geriatric Medicine and NephrologyOsaka University Graduate School of MedicineSuitaJapan
  23. 23.Third Department of Internal Medicine, and Global Center of Excellence Program Study GroupYamagata University School of MedicineYamagataJapan
  24. 24.Division of Epidemiology, Department of MedicineVanderbilt School of MedicineNashvilleUSA
  25. 25.Shanghai Institute of Preventive MedicineShanghaiPeople’s Republic of China
  26. 26.National Taiwan University Hospital Bei-Hu branchTaipeiTaiwan
  27. 27.Department of EpidemiologyShanghai Cancer InstituteShanghaiPeople’s Republic of China
  28. 28.Center for Metabolic Disease Prevention, School of Public Health and David Geffen School of MedicineUCLALos AngelesUSA
  29. 29.Division of Preventive Medicine, Brigham & Women’s HospitalHarvard Medical SchoolBostonUSA
  30. 30.Department of Internal MedicineSeoul National University College of MedicineSeoulSouth Korea
  31. 31.Department of Life ScienceSogang UniversitySeoulSouth Korea
  32. 32.MRC Lifecourse Epidemiology UnitUniversity of Southampton, Southampton General HospitalSouthamptonUK
  33. 33.Li Ka Shing Faculty of MedicineUniversity of Hong KongHong KongPeople’s Republic of China
  34. 34.School of MedicineUniversity of MissouriKansas CityUSA
  35. 35.Center of Systematic Biomedical ResearchUniversity of Shanghai for Science and TechnologyShanghaiPeople’s Republic of China
  36. 36.Institute of Bioscience and Biotechnology, School of ScienceBeijing Jiaotong UniversityBeijingPeople’s Republic of China
  37. 37.Department of Endocrinology, Metabolism and DiabetesKinki University School of MedicineOsakaJapan
  38. 38.Epidemiology Research UnitHoldsworth Memorial HospitalMysoreIndia
  39. 39.Department of Internal MedicineNational Taiwan University HospitalTaipeiTaiwan
  40. 40.Department of Preventive MedicineDong-A University College of MedicineBusanSouth Korea
  41. 41.National Heart & Lung Institute, Hammersmith Hospital, Hammersmith Campus, Faculty of MedicineImperial College LondonLondonUK
  42. 42.Genome Institute of SingaporeAgency for Science, Technology and ResearchSingaporeRepublic of Singapore
  43. 43.Department of Genomics of Common Disease, School of Public Health, Hammersmith HospitalImperial College LondonLondonUK
  44. 44.Department of Genome Science, School of DentistryAichi-Gakuin UniversityNagoyaJapan
  45. 45.Department of Medicine and Bioregulatory Science, Graduate School of Medical SciencesKyushu UniversityFukuokaJapan

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