Implication of genetic variants near TCF7L2, SLC30A8, HHEX, CDKAL1, CDKN2A/B, IGF2BP2, and FTO in type 2 diabetes and obesity in 6,719 Asians

Article (PDF Available)inDiabetes 57(8):2226-33 · May 2008with74 Reads
DOI: 10.2337/db07-1583 · Source: PubMed
Recent genome-wide association studies have identified six novel genes for type 2 diabetes and obesity and confirmed TCF7L2 as the major type 2 diabetes gene to date in Europeans. However, the implications of these genes in Asians are unclear. We studied 13 associated single nucleotide polymorphisms from these genes in 3,041 patients with type 2 diabetes and 3,678 control subjects of Asian ancestry from Hong Kong and Korea. We confirmed the associations of TCF7L2, SLC30A8, HHEX, CDKAL1, CDKN2A/CDKN2B, IGF2BP2, and FTO with risk for type 2 diabetes, with odds ratios ranging from 1.13 to 1.35 (1.3 x 10(-12) < P(unadjusted) < 0.016). In addition, the A allele of rs8050136 at FTO was associated with increased BMI in the control subjects (P(unadjusted) = 0.008). However, we did not observe significant association of any genetic variants with surrogate measures of insulin secretion or insulin sensitivity indexes in a subset of 2,662 control subjects. Compared with subjects carrying zero, one, or two risk alleles, each additional risk allele was associated with 17% increased risk, and there was an up to 3.3-fold increased risk for type 2 diabetes in those carrying eight or more risk alleles. Despite most of the effect sizes being similar between Asians and Europeans in the meta-analyses, the ethnic differences in risk allele frequencies in most of these genes lead to variable attributable risks in these two populations. Our findings support the important but differential contribution of these genetic variants to type 2 diabetes and obesity in Asians compared with Europeans.
Implication of Genetic Variants Near TCF7L2, SLC30A8,
HHEX, CDKAL1, CDKN2A/B, IGF2BP2, and FTO in Type
2 Diabetes and Obesity in 6,719 Asians
Maggie C.Y. Ng,
Kyong Soo Park,
Bermseok Oh,
Claudia H.T. Tam,
Young Min Cho,
Hyoung Doo Shin,
Vincent K.L. Lam,
Ronald C.W. Ma,
Wing Yee So,
Yoon Shin Cho,
Hyung-Lae Kim,
Hong Kyu Lee,
Juliana C.N. Chan,
and Nam H. Cho
OBJECTIVE—Recent genome-wide association studies have
identified six novel genes for type 2 diabetes and obesity and
confirmed TCF7L2 as the major type 2 diabetes gene to date in
Europeans. However, the implications of these genes in Asians
are unclear.
ciated single nucleotide polymorphisms from these genes in
3,041 patients with type 2 diabetes and 3,678 control subjects of
Asian ancestry from Hong Kong and Korea.
RESULTS—We confirmed the associations of TCF7L2,
FTO with risk for type 2 diabetes, with odds ratios ranging from
1.13 to 1.35 (1.3 10
0.016). In addition, the
A allele of rs8050136 at FTO was associated with increased BMI
in the control subjects (P
0.008). However, we did not
observe significant association of any genetic variants with
surrogate measures of insulin secretion or insulin sensitivity
indexes in a subset of 2,662 control subjects. Compared with
subjects carrying zero, one, or two risk alleles, each additional
risk allele was associated with 17% increased risk, and there was
an up to 3.3-fold increased risk for type 2 diabetes in those
carrying eight or more risk alleles. Despite most of the effect
sizes being similar between Asians and Europeans in the meta-
analyses, the ethnic differences in risk allele frequencies in most
of these genes lead to variable attributable risks in these two
CONCLUSIONS—Our findings support the important but differ-
ential contribution of these genetic variants to type 2 diabetes
and obesity in Asians compared with Europeans. Diabetes 57:
2226–2233, 2008
ype 2 diabetes is a major health problem affect-
ing more than 170 million people worldwide. In
the next 20 years, Asia will be hit hardest, with
the diabetic populations in India and China more
than doubling (1). Type 2 diabetes is characterized by the
presence of insulin resistance and pancreatic -cell dys-
function, resulting from the interaction of genetic and
environmental factors. Until recently, few genes identified
through linkage scans or the candidate gene approach
have been confirmed to be associated with type 2 diabetes
(e.g., PPARG, KCNJ11, CAPN10, and TCF7L2). Under the
common variant– common disease hypothesis, several ge-
nome-wide association (GWA) studies on type 2 diabetes
have been conducted in large-scale case-control samples.
Six novel genes (SLC30A8, HHEX, CDKAL1, CDKN2A and
CDKN2B, IGF2BP2, and FTO) with modest effect for type 2
diabetes (odds ratio [OR] 1.14 –1.20) had been reproducibly
demonstrated in multiple populations of European ancestry.
Moreover, TCF7L2 was shown to have the largest effect for
type 2 diabetes (1.37) in the European populations to date
(2– 8). Although many of these genes may be implicated in
the insulin production/secretion pathway (TCF7L2,
(6,9 –11), FTO is associated with type 2 diabetes through its
regulation of adiposity (8,12,13). Moreover, two adjacent
regions near CDKN2A/B are associated with type 2 diabetes
and cardiovascular diseases risks, respectively (7,14 –16).
Despite the consistent associations among Europeans, the
contributions of these genetic variants in other ethnic groups
are less clear. Given the differences in environmental factors
(e.g., lifestyle), risk factor profiles (body composition and
insulin secretion/resistance patterns), and genetic back-
ground (linkage disequilibrium pattern and risk allele fre-
quencies) between Europeans and Asians, it is important to
understand the role of these genes in Asians. A recent
case-control study in 1,728 Japanese subjects revealed nom-
inal association to type 2 diabetes for variants at the
SLC30A8, HHEX, CDKAL1, CDKN2B, and FTO genes but
not IGF2BP2 (17). In the present large-scale case-control
replication study of 6,719 Asians, we aimed to test for the
association of six novel genes from GWA studies and
TCF7L2, which had the largest effect in Europeans, and their
joint effects on type 2 diabetes risk and metabolic traits.
All subjects were recruited from Hong Kong and Korea and of Asian ancestry.
The subjects in the Hong Kong case-control study were of southern Han
Chinese ancestry residing in Hong Kong. Participants for the case cohort
consisting of 1,481 subjects with type 2 diabetes were selected from two
From the
Department of Medicine and Therapeutics, The Chinese University
of Hong Kong, Prince of Wales Hospital, Shatin, Hong Kong, China; the
Department of Internal Medicine, Seoul National University College of
Medicine, Seoul, Korea; the
Center for Genome Science, National Institute
of Health, Seoul, Korea; the
Laboratory of Genomic Diversity, Department
of Life Science, Sogang University, Seoul, Korea; the
Li Ka Shing Institute
of Health Sciences, The Chinese University of Hong Kong, Prince of Wales
Hospital, Shatin, Hong Kong, China; and the
Department of Preventive
Medicine, Ajou University School of Medicine, Suwon, Korea.
Corresponding author: Maggie C.Y. Ng,
Received 8 November 2007 and accepted 30 April 2008.
Published ahead of print at on 13 May
2008. DOI: 10.2337/db07-1583.
M.C.Y.N., K.S.P., and B.O. contributed equally to this work. H.K.L., J.C.N.C.,
and N.H.C. contributed equally to this work.
© 2008 by the American Diabetes Association. Readers may use this article as
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2226 DIABETES, VOL. 57, AUGUST 2008
sources. From the Hong Kong Diabetes Registry (18), we selected 556 patients
(age 40.4 8.3 years [mean SD], 33.2% men) with early-onset diabetes (age
at diagnosis [AAD] 40 years) and with positive family history of diabetes in
first-degree relatives. An additional 763 case subjects (age 58.2 11.7 years,
40.9% men) were randomly selected from the same registry irrespective of age
at diagnosis (AAD). From the Hong Kong Family Diabetes Study, 162
unrelated type 2 diabetic patients (age 41.8 11.6 years, 38.9% men), of whom
115 had early-onset familial diabetes, were also selected as case subjects (19).
Patients with classic type 1 diabetes with acute ketotic presentation or
continuous requirement of insulin within 1 year of diagnosis were excluded.
The inclusion of young diabetic patients with familial history may increase
genetic loading of the study population. Despite our previous findings
suggesting up to 14% presence of monogenic diabetes in the young patients
(20), 50% of these young patients were obese, mimicking the predominant
feature of type 2 diabetes. The control subjects consisted of 1,530 subjects
with normal glucose tolerance (fasting plasma glucose [FPG] 6.1 mmol/l). Of
these, 589 (age 41.4 10.5 years, 44.7% men) were recruited from the general
population participating in a community-based cardiovascular risk screening
program and from hospital staff. We recruited 941 subjects (age 15.3 1.9
years, 46.8% men) from a population-based cardiovascular risk screening
program for adolescents (21). Informed consent was obtained for each
participating subject. This study was approved by the Clinical Research Ethics
Committee of the Chinese University of Hong Kong.
The Korea Seoul National University Hospital (SNUH) case-control popu-
lation consisted of 761 unrelated patients with type 2 diabetes registered at the
Diabetes Clinic of SNUH and 632 nondiabetic control subjects. Type 2
diabetes was diagnosed using the World Health Organization (WHO) criteria
(22). Subjects positive for glutamic acid decarboxylase antibodies were
excluded. Nondiabetic control subjects were selected according to the
following criteria: 60 years old, no history of diabetes, no first-degree
relatives with diabetes, FPG 6.1 mmol/l, and A1C 5.8%. The Institutional
Review Board of the Clinical Research Institute in SNUH approved the study
protocol, and informed consent for genetic analysis was obtained from each
The Korean Health and Genome Study (KHGS) case-control population
were selected from a prospective community-based epidemiology study in the
Ansung (rural) and Ansan (urban) communities (23). In this study, eligible
subjects aged between 40 and 69 years were examined at baseline in
2001–2002 for demographic and glucose tolerance and then followed up
biannually. At baseline, 799 subjects who were on treatment for type 2
diabetes or with FPG 7 mmol/l or 2-h plasma glucose 11.1 mmol/l during
a 75-g oral glucose tolerance test (OGTT) were selected as case subjects using
the WHO criteria (22). For each case subject, approximately two sex-matched
subjects without family history of diabetes and with normal glucose level at
OGTT (FPG 7 mmol/l and 2-h plasma glucose 7.8 mmol/l) at both baseline
and follow-up visits were selected as control subjects (n 1,516). The case
and control groups were frequency matched for age. The study protocol was
approved by the Ethics Committee of the KHGS and Ajou University Medical
In all studies, general obesity was defined as BMI 25 kg/m
, which was
modified for Asian populations (24). Among the control subjects, 434 subjects
from Hong Kong and 1,516 subjects from KHGS studies underwent a 75-g
OGTT to exclude diabetes (22). Moreover, 548, 609 and 1,505 subjects from
the Hong Kong, SNUH, and KHGS studies, respectively, were measured for
both FPG and insulin to derive surrogate indexes for insulin secretion and
Clinical studies. All study subjects were examined in the morning after an
overnight fast. Anthropometric parameters and blood pressure were mea-
sured. Fasting blood samples were collected for measurement of plasma
glucose, insulin, and lipids. Using the homeostasis model assessment
(HOMA), insulin resistance index (HOMA-IR) was assessed as fasting insulin
(mU/l) FPG (mmol/l)/22.5; and -cell function (HOMA-) was assessed as
fasting insulin 20/(FPG 3.5) (25).
Gene and single nucleotide polymorphism selection. Six novel genes
identified through recent GWA studies and TCF7L2 showing reproducible
association to type 2 diabetes in Europeans were selected for replication
study (Supplementary Table 1, which is detailed in the online appendix
[available at]) (3– 8). For genes with mul-
tiple associated single nucleotide polymorphisms (SNPs), the pairwise linkage
disequilibrium D and r
were assessed using Haploview (v.3.32) (26). Only
representative SNPs with r
0.8 based on HapMap Han Chinese and
Japanese data were selected for genotyping. Two representative SNPs
(rs1333040 and rs10757278) close to CDKN2A/B that were associated with
coronary heart disease and myocardial infraction were also selected (7,14
16). Genotyping of rs13266634 at SLC30A8 failed in the KHGS samples and
was replaced by rs3802177, which is in complete linkage disequilibrium (r
1) with rs13266634. The genotyping method and quality control for the 13
studied SNPs were shown in the online appendix.
Statistical analyses. For disease association analyses, genotype frequencies
for case and control subjects in each of the three study population were
compared using logistic regression under a log additive model in PLINK
(v.0.99, ORs with 95% CIs are
presented with respect to the risk allele in the combined samples. For genes
with multiple SNPs, haplotypes with frequencies 5% were compared in
case-control samples using omnibus test implemented in PLINK. Possible
independent SNP effect was assessed by conditional omnibus analysis after
controlling for a significant SNP. An insignificant test suggests the presence of
a single- rather than multiple-association signal at the haplotype.
Meta-analysis of type 2 diabetes association for the combined samples
from the three study populations was performed by the fixed effects Cochran-
Mantel-Haenszel (CMH) test implemented in PLINK to estimate a summary
allelic OR, using study population as a strata. To correct for multiple
comparisons, 10,000 permutations of case-control labels were performed in
PLINK to assess for experiment-wise empirical P values. The effect of
additional covariates on type 2 diabetes association was tested using logistic
regression with adjustment for BMI, age, and sex in individual samples and
further adjustment for study population in combined samples.
Continuous data were expressed as means SD. BMI, insulin, and HOMA
indexes were transformed by natural logarithm to normality. Each trait was
winsorized at 4 SD from the mean to reduce the impact of outliers, which
represented 0 0.5% of the data. The values were further transformed to Z
scores with adjustment for age and sex and then combined and analyzed
under an additive model using linear regression. For quantitative trait associ-
ation analyses in the combined control samples, trait values from four groups,
including the adolescents and adults from Hong Kong, Korea SNUH, and
KHGS populations, were transformed separately before merging to account
for population differences in trait distributions. For each trait, 5,000 permu-
tations were performed to assess for experiment-wise empirical P values
using PLINK.
We tested for model fit for type 2 diabetes association tests by comparing
additive, dominant, and recessive models using logistic regression (1 degree of
freedom [df] tests) in the combined samples. Deviations from the additive
model were assessed by testing the significance of dominance effect in a
general (2 df) model that include an additive effect. To test for joint and
interaction effects of the seven genes, a representative significant SNP from
each gene was selected. Each pairwise SNP interaction was then tested in a
logistic regression model that included the main effects of all seven SNPs
under an additive model (except TCF7L2 for a dominant model due to the
small number of homozygous risk allele carriers). By assuming similar effect
size, the joint effect of the seven SNPs for type 2 diabetes risk was assessed
by calculating the OR with respect to the number of risk alleles carried under
an additive model (except TCF7L2 for a dominant model). The significance of
the trend was assessed by logistic regression for type 2 diabetes using the
categories of risk allele carried as an independent variable.
We also compared the effect size of these risk alleles between Asians and
Europeans. For type 2 diabetes association, genotype counts for SNPs in the
seven genes in type 2 diabetic case and control subjects were directly
obtained or estimated from the five European GWA studies and a Japanese
replication study (3–8,17). Meta-analyses of type 2 diabetes association for the
five European samples, four Asian samples (including three samples from the
current study), and the combined European and Asian samples were per-
formed by the CMH test. Attributable risk was calculated as (x 1)/x. The
study assumed a log additive model, x (1 f)
2f(1 f)␥⫹f
is the estimated OR and f is the risk allele frequency.
For meta-analysis for the association of FTO and BMI, the A allele of
rs9939609 and G allele of rs9930506 were used as surrogates for the risk A
allele of rs8050136 in Europeans because they are in strong linkage disequi-
librium (r
0.84 –1) in a HapMap population of Utah residents with northern
and western European ancestry (CEU population). Means and SDs were
directly obtained for rs9939609 or estimated for rs9930506 genotypes from
two European studies (nondiabetic control subjects and adult and older adult
populations from Frayling et al. [12] and Sardinia and European American
populations from Scuteri et al. [13], respectively) and for the rs8050136
genotypes from two Asian studies (17,27) and the present four samples
(adolescents and adults from Hong Kong, Korea SNUH, and KHGS control
subjects). Standardized mean difference (SMD), the difference between two
genotypic means divided by the pooled SD, and the 95% CI for the Europeans,
Asians, and combined samples were calculated with the Hedges g statistic
under the fixed effects model using MedCalc for Windows, version
(MedCalc Software, Mariakerke, Belgium).
In both disease and quantitative trait analyses, heterogeneity of ORs or
SMDs among studies or populations was assessed by Cochran’s Q statistic
(28) using MedCalc (online appendix). In case of significant heterogeneity (Q
DIABETES, VOL. 57, AUGUST 2008 2227
statistic P 0.1), the effect size calculated from the random effects model
(DerSimonian and Laird for disease analyses) using MedCalc was also
All statistical tests were performed by PLINK or SAS v.9.1 (SAS Institute,
Cary, NC) unless specified otherwise. Because the studied genes are well
replicated and posterior power calculations (online appendix) demonstrated
that the present sample size had sufficient power to detect the observed effect
sizes at -level of 0.05 but insufficient power at a corrected -level of 0.0038
for some cases of modest effects (e.g., FTO) or rare at-risk allele frequency
(e.g., TCF7L2), a nominal P value 0.05 was considered significant in this
We genotyped 13 representative SNPs from 7 genes impli-
cated in type 2 diabetes in recent GWA studies in 3,041
type 2 diabetic case subjects and 3,678 nondiabetic control
subjects from a Chinese population in Hong Kong and two
Korean populations. The clinical characteristics of the
subjects are summarized in Table 1. Table 2 showed the
meta-analyses of type 2 diabetes association under a log
additive model. There was no heterogeneity of ORs
among the three study populations except for CDKN2A/B
(rs10811661) (Q statistic P 0.03), with a random effect
OR of 1.32 (1.15–1.52). Apart from two SNPs at CDKN2A/B
(rs564398 and rs1333040), all other 11 SNPs were signifi-
cantly associated with type 2 diabetes, with ORs ranging
from 1.09 to 1.35 (1.3 10
P 0.016) in the
combined samples (Table 2). Eight of the 11 SNPs re-
mained significant after adjustment for multiple compari-
son by permutation (1.0 10
0.012) (Table
2) despite nonsignificance of CDKN2A/B (rs10757278),
TCF7L2 (rs7903146), and FTO (rs8050136). Because mul-
tiple SNPs with little or moderate linkage disequilibrium at
0.56), CDKN2A/B (r
0.002–0.31), and
0.25– 0.55) were studied (Supplementary Table
2), we examined haplotype associations but did not reveal
more significant association than single marker analyses
(Supplementary Table 3). Further haplotype analyses by
conditioning rs7756992 on CDKAL1 haplotypes and
rs7923837 on HHEX haplotypes revealed no significant
residual associations (P 0.05; data not shown), suggest-
ing that these two SNPs are sufficient to explain the
respective multiple associations at CDKAL1 and HHEX.
Although residual association was observed after condi-
tioning rs10811661 on CDKN2A/B haplotypes (P 0.023),
the much stronger single marker association of rs10811661
compared with rs10757278 (P 1.3 10
vs. 0.015;
Table 2) suggests the former is the key associated SNP.
Taken together, seven key SNPs from these genes were
significant without correction for multiple comparisons. In
this regard, TCF7L2 (rs7903146) showed the strongest effect
on type 2 diabetes risk (OR 1.35), followed by CDKN2A/B
(rs10811661), CDKAL1 (rs7756992), HHEX (rs7923837),
IGF2BP2 (rs4402960), SLC30A8 (rs13266634), and FTO
(rs8050136). These seven SNPs were further examined in
the subsequent analyses.
The association for type 2 diabetes was also tested by
adjustment for BMI, age, sex, and/or study population in
both individual and combined samples. Most SNPs
showed similar effect sizes with or without adjustment for
covariates in both individual (data not shown) and com-
bined samples. However, the association for type 2 diabe-
tes was lost for FTO (rs8050136) after covariate
adjustment (OR 1.13, P 0.016 vs. 1.09, P 0.13 with or
without adjustment in the combined samples) (Table 2;
Supplementary Table 4).
We further examined the association of the seven SNPs
with quantitative traits in the combined control samples.
The risk A allele of FTO was significantly associated with
increased BMI (P 0.008) (Table 3) and obesity defined as
BMI 25 kg/m
(OR [95% CI] 1.18 [1.01–1.39]). In addition,
the risk alleles at SLC30A8 and TCF7L2 were associated
with increased FPG (P 0.023) and decreased insulin at
120 min during the OGTT (P 0.038), respectively (Table
3). However, only FTO (rs8050136) showed trend of associ-
ation after multiple comparison correction (P
0.057). None of the SNPs showed significant associations
with insulin secretion (HOMA-) or insulin sensitivity
When we tested for the best fit model, all seven SNPs did
not show significant dominance effects (Supplementary
Table 5); thus, the joint and interaction effects analyses
were performed using an additive/multiplicative model
(except the dominant model for TCF7L2). None of the
pairwise SNP interactions was significant (data not
shown). However, there was a significant increase in risk
for type 2 diabetes with increasing number of risk alleles
(P 0.001) in gene-dosage analysis. Compared with 9% of
subjects carrying zero, one, or two risk alleles, each
additional risk allele was associated with 17% increased
Clinical characterization of study populations
Hong Kong Korea SNUH Korea KHGS
Type 2
diabetes Control subjects
Type 2
diabetes Control subjects
Type 2
diabetes Control subjects
n 1,481 1,530 761 632 799 1,516
Men/women 598/883 703/827 354/407 287/345 428/371 805/711
Age (year) 49.7 13.7 25.3 14.4 59.2 9.9 64.7 3.6 56.1 8.6 55.8 8.7
AAD (year) 43.6 13.7 50.0 10.1 52.8 9.2
BMI (kg/m
25.1 4.2 21.0 3.7 24.5 2.9 23.6 3.1 25.5 3.3 24.2 3.2
Fasting glucose (mmol/l) 4.8 0.4 4.9 0.5 4.6 0.4
Glucose at 120 min (mmol/l) 5.6 1.2 5.8 1.2
Fasting insulin (pmol/l) 39.0 (37.6–40.4) 41.7 (40.1–43.3) 39.3 (38.2–40.5)
Insulin at 120 min (pmol/l) 236.7 (228.1–245.5) 100.4 (95.8–105.1)
HOMA-IR 1.4 (1.3–1.4) 1.5 (1.5–1.6) 1.3 (1.3–1.4)
HOMA- 103.1 (99.3–107.0) 102.9 (98.5–107.5) 125.6 (121.4–130)
Obesity (%) 46.8 13.3 38.2 33.1 54.3 39.0
Metabolic syndrome (%) 57.9 2.4 68.6 23.7 67.1 21.6
Data are means SD, geometric mean (95% CI), or percent.
2228 DIABETES, VOL. 57, AUGUST 2008
risk and up to 3.3-fold increased risk for type 2 diabetes in
those 4% subjects carrying eight or more risk alleles
(Supplementary Fig. 1).
We examined for ethnic differences of SNP association
with type 2 diabetes and BMI using the current data and
published studies (3– 8,12,13,17,27). Although TCF7L2
demonstrated the strongest effect on type 2 diabetes in
both Europeans (OR 1.44) and Asians (1.44), other genes
had modest effect in Europeans (1.11–1.23) and Asians
(1.12–1.27) (Table 4; Supplementary Fig. 2). Moreover,
CDKAL1 (rs7756992) showed stronger effect sizes in
Asians than in Europeans (1.26 vs. 1.14) (Table 4).
In the meta-analysis of FTO (rs8050136) and BMI in
Europeans, AC and AA genotypes were associated with an
increase of 0.09 (0.07– 0.11) and 0.19 (0.17– 0.21) SMD of
BMI, respectively, when compared with CC genotype
(Supplementary Fig. 3). The respective effect was weaker
in Asians, corresponding to an increase of 0.05 (0.006 to
0.10) and 0.10 (0.07 to 0.26) SMD of BMI, respectively,
for AC and AA genotypes. The difference reached signifi-
cance when comparing ACAA with CC genotypes (SMD
0.05 [0.0001–0.11]). Although the SMDs of BMI between
ACAA and CC groups were similar in study groups
within both Europeans and Asians, the effect of rs8050136
on BMI was significantly stronger in Europeans than in
Asians (Q statistic P 0.02).
Our study provides important insights for the impact of the
new type 2 diabetes genes identified through GWA studies.
To our knowledge, this is the largest replication study in
Asians up to now. We confirm the type 2 diabetes associ-
ation of seven representative risk alleles for these seven
genes found in Europeans (3– 8), suggesting many of the
variants associated with type 2 diabetes in Europeans are
also associated in Asians. These genetic effects seem to be
additive. Despite differences in effect size of each gene, a
crude estimate suggests up to 3.3-fold increased type 2
diabetes risk in subjects carrying eight or more risk alleles
compared with those carrying two or fewer risk alleles
(Supplementary Fig. 1). Two adjacent regions near
CDKN2A/B have been reported to be associated with type
2 diabetes and cardiovascular diseases. Our data confirm
the association of type 2 diabetes for rs10811661, found in
the European type 2 diabetes studies (3,4,8), but not
rs564398, found only in the Wellcome Trust Case Control
Consortium Study (8). In addition, we found that the
cardiovascular disease risk loci (rs1333040 and
rs10757278) (14 –16) were not associated with type 2
Our findings are further supported by a recent Japanese
study on 864 case subjects and 864 control subjects that
demonstrated nominal association to type 2 diabetes for
variants at the SLC30A8, HHEX, CDKAL1, CDKN2B, and
FTO genes with similar ORs (1.19 –1.46) compared with
our data (17). The lack of association at IGF2BP2 in their
study was partly due to the smaller sample size. Meta-
analyses of the Japanese and our data confirmed the
significant associations to type 2 diabetes in all seven
genes (Supplementary Fig. 2). It is of note that different
ascertainment criteria were used in the present three
populations. These differences in phenotypes and environ-
mental exposure and the use of the same statistics for both
matched and unmatched samples may bias the estimation
of the actual effect size in the general population. For
Associations of seven genes with type 2 diabetes in Chinese and Korean populations
Gene SNP Chr
Hong Kong Korea SNUH Korea KHGS Combined (95% CI)†
risk AF
risk AF
(95% CI)† P value
risk AF
risk AF
(95% CI)† P value
risk AF
risk AF
(95% CI)† P value
(95% CI)† P value
Total n 3,011 1,393 2,315 6,719
Case/control subjects 1,481/1,530 761/632 799/1,516 3,041/3,678
IGF2BP2 rs4402960 3 186994381 T/G 0.255 0.245 1.05 (0.93–1.18) 0.413 0.331 0.296 1.18 (1.00–1.39) 0.049 0.318 0.273 1.24 (1.09–1.42) 0.001 1.14 (1.06–1.23) 8.1 10
CDKAL1 rs7754840 6 20769229 C/G 0.420 0.358 1.29 (1.17–1.43) 1.0 10
0.519 0.464 1.24 (1.07–1.44) 0.005 0.529 0.468 1.27 (1.13–1.44) 1.0 10
1.28 (1.19–1.37) 4.6 10
1.0 10
CDKAL1 rs7756992 6 20787688 G/A 0.517 0.462 1.24 (1.12–1.37) 2.9 10
0.586 0.526 1.26 (1.09–1.47) 0.002 0.601 0.530 1.33 (1.17–1.50) 8.2 10
1.28 (1.19–1.37) 3.9 10
1.0 10
SLC30A8§ rs13266634 8 118253964 C/T 0.572 0.532 1.17 (1.06–1.3) 0.002 0.627 0.585 1.18 (1.02–1.38) 0.029 0.590 0.582 1.03 (0.91–1.17) 0.636 1.13 (1.05–1.21) 6.5 10
CDKN2A/B rs564398 9 22019547 C/T 0.108 0.102 1.07 (0.91–1.27) 0.407 0.156 0.128 1.24 (1.01–1.54) 0.044 0.127 0.135 0.93 (0.77–1.11) 0.421 1.06 (0.95–1.18) 0.284 0.978
CDKN2A/B rs1333040 9 22073404 T/C 0.692 0.675 1.08 (0.97–1.21) 0.154 0.664 0.684 0.91 (0.77–1.07) 0.250 0.690 0.680 1.05 (0.92–1.20) 0.477 1.03 (0.96–1.11) 0.402 0.998
CDKN2A/B rs10757278 9 22114477 G/A 0.524 0.495 1.12 (1.01–1.24) 0.030 0.449 0.453 0.98 (0.85–1.14) 0.834 0.477 0.449 1.12 (0.99–1.27) 0.069 1.09 (1.02–1.17) 0.015 0.167
CDKN2A/B rs10811661 9 22124094 T/C 0.614 0.568 1.21 (1.09–1.34) 3.5 10
0.619 0.512 1.55 (1.33–1.81) 2.0 10
0.601 0.546 1.25 (1.10–1.41) 4.4 10
1.29 (1.20–1.38) 1.3 10
1.0 10
HHEX rs1111875 10 94452862 C/T 0.305 0.288 1.09 (0.97–1.21) 0.144 0.347 0.305 1.21 (1.03–1.43) 0.019 0.352 0.309 1.23 (1.08–1.41) 0.002 1.16 (1.07–1.24) 1.6 10
HHEX rs5015480 10 94455539 C/T 0.185 0.172 1.09 (0.95–1.25) 0.228 0.219 0.186 1.22 (1.02–1.47) 0.034 0.227 0.183 1.32 (1.13–1.54) 3.7 10
1.20 (1.09–1.31) 9.0 10
HHEX rs7923837 10 94471897 G/A 0.205 0.176 1.20 (1.06–1.37) 0.005 0.262 0.210 1.33 (1.12–1.59) 0.002 0.252 0.224 1.17 (1.01–1.35) 0.033 1.22 (1.12–1.33) 3.3 10
3.0 10
TCF7L2 rs7903146 10 114748339 T/C 0.030 0.023 1.30 (0.95–1.76) 0.099 0.037 0.025 1.53 (0.98–2.39) 0.063 0.031 0.024 1.29 (0.90–1.87) 0.169 1.35 (1.10–1.67) 0.005 0.058
FTO rs8050136 16 52373776 A/C 0.156 0.136 1.18 (1.02–1.37) 0.028 0.138 0.122 1.15 (0.92–1.44) 0.228 0.124 0.118 1.06 (0.88–1.28) 0.535 1.13 (1.02–1.25) 0.016 0.177
AF, allele frequency; Chr, chromosome. *All alleles were indexed to the forward strand of NCBI Build 36. Risk alleles were defined according to association results from combined
samples. †ORs (95% CI) were reported with respect to the risk allele using a log additive model in logistic regression. ‡Fixed effects Cochran-Mantel-Haenszel test was shown for
meta-analysis in the combined samples. §Rs13266634 assay was failed in the Korea KHGS samples and was replaced by rs3802177.
DIABETES, VOL. 57, AUGUST 2008 2229
example, the Hong Kong population consisted of young-
onset diabetic patients who may be contaminated by
monogenic diabetes, whereas some adolescent control
subjects may develop diabetes in the future. Removal of
these young case and control subjects (Supplementary
Table 6) resulted in similar effect sizes in both the Hong
Kong and combined samples compared with Table 2.
In this study, we also confirmed the association of FTO
with obesity, which indirectly modulates type 2 diabetes
risk as found in Europeans (8,12,13). Interestingly, both
the Japanese study (17) and a Chinese study (n 3,210)
(27) failed to demonstrate association of FTO (rs8050136)
with obesity or BMI. The discrepancy might be due to
population-specific bias and/or insufficient power. Our
meta-analysis demonstrated significant association of FTO
(ACAA vs. CC) with BMI in Asians, although their risk
allele frequency and effect size were lower compared with
We were unable to demonstrate association of any
genes with insulin secretion capacity in nondiabetic
subjects as assessed by HOMA- index, in contrast with
the significant findings at CDKAL1 (rs7756992) and
CDKN2A/B (rs10811661) in Japanese subjects (17).
HOMA- index is a less sensitive surrogate for -cell
function compared with insulinogenic index derived from
OGTT or hyperglycemic clamp. This will compromise the
study power, which could be further reduced by the
relatively low minor allele frequency in Asians for some of
the genes, such as TCF7L2.
Europeans and Asians are different in their environ-
mental risk profiles, body composition, and genetic
backgrounds. In particular, Asians are at risk for type 2
diabetes at a lower level of obesity, partly due to their
increased predisposition to visceral adiposity (29) and
reduced pancreatic -cell function (30). In the meta-
analyses, TCF7L2 rs7903146 showed the strongest effect
(OR 1.44) in both Europeans and Asians. Moreover, the
effect sizes of most risk alleles are similar in the two
populations except for CDKAL1 rs7756992 (Table 4;
Supplementary Fig. 2). In addition to the consistent
Associations of seven genes with metabolic traits in the combined Chinese and Korean control samples
Trait n (95% CI)†
value (95% CI)†
IGF2BP2: rs4402960 (T/G)* CDKAL1: rs7756992 (G/A)*
BMI (kg/m
3,667 0.022 (0.074 to 0.029) 0.394 0.969 0.014 (0.060 to 0.031) 0.532 0.996
Fasting glucose (mmol/l) 3,678 0.008 (0.044 to 0.060) 0.763 1.000 0.030 (0.015 to 0.075) 0.196 0.775
Glucose at 120 min (mmol/l) 1,950 0.015 (0.086 to 0.056) 0.671 1.000 0.042 (0.105 to 0.021) 0.190 0.776
Fasting insulin (pmol/l) 2,662 0.049 (0.011 to 0.109) 0.111 0.563 0.014 (0.068 to 0.040) 0.609 0.998
Insulin at 120 min (pmol/l) 1,947 0.006 (0.065 to 0.077) 0.868 1.000 0.011 (0.073 to 0.052) 0.739 1.000
HOMA-IR 2,662 0.052 (0.008 to 0.112) 0.090 0.479 0.011 (0.064 to 0.043) 0.698 1.000
HOMA- 2,662 0.026 (0.035 to 0.086) 0.406 0.976 0.014 (0.068 to 0.039) 0.600 0.998
SLC30A8: rs13266634 (C/T)* CDKN2A/B: rs10811661 (T/C)*
BMI (kg/m
0.010 (0.036 to 0.057) 0.662 1.000 0.016 (0.062 to 0.029) 0.483 0.992
Fasting glucose (mmol/l) 0.055 (0.008–0.102) 0.023 0.139 0.034 (0.012 to 0.08) 0.152 0.673
Glucose at 120 min (mmol/l) 0.046 (0.019 to 0.111) 0.162 0.718 0.017 (0.046 to 0.08) 0.603 0.998
Fasting insulin (pmol/l) 0.030 (0.085 to 0.026) 0.291 0.909 0.002 (0.055 to 0.052) 0.947 1.000
Insulin at 120 min (pmol/l) 0.054 (0.010 to 0.119) 0.100 0.521 0.023 (0.086 to 0.040) 0.476 0.990
HOMA-IR 0.022 (0.077 to 0.034) 0.443 0.983 0.00002 (0.05352 to 0.05356) 0.999 1.000
HOMA-␤⫺0.052 (0.107 to 0.004) 0.067 0.389 0.011 (0.065 to 0.043) 0.695 1.000
HHEX: rs7923837 (G/A)* TCF7L2: rs7903146 (T/C)*
BMI (kg/m
0.033 (0.090 to 0.024) 0.259 0.878 0.015 (0.133 to 0.162) 0.846 1.000
Fasting glucose (mmol/l) 0.025 (0.032 to 0.082) 0.393 0.968 0.051 (0.096 to 0.199) 0.494 0.991
Glucose at 120 min (mmol/l) 0.013 (0.064 to 0.089) 0.748 1.000 0.090 (0.29 to 0.110) 0.377 0.967
Fasting insulin (pmol/l) 0.005 (0.061 to 0.071) 0.881 1.000 0.052 (0.123 to 0.226) 0.560 0.996
Insulin at 120 min (pmol/l) 0.053 (0.130 to 0.023) 0.174 0.744 0.211 (0.411 to 0.012) 0.038 0.230
HOMA-IR 0.003 (0.064 to 0.069) 0.932 1.000 0.056 (0.119 to 0.230) 0.533 0.996
HOMA- 0.017 (0.049 to 0.084) 0.606 0.998 0.032 (0.143 to 0.206) 0.722 1.000
FTO: rs8050136 (A/C)*
BMI (kg/m
0.094 (0.024–0.164) 0.008 0.057
Fasting glucose (mmol/l) 0.004 (0.074 to 0.066) 0.910 1.000
Glucose at 120 min (mmol/l) 0.024 (0.072 to 0.120) 0.621 0.999
Fasting insulin (pmol/l) 0.011 (0.093 to 0.071) 0.786 1.000
Insulin at 120 min (pmol/l) 0.034 (0.063 to 0.131) 0.491 0.992
HOMA-IR 0.007 (0.089 to 0.075) 0.871 1.000
HOMA-␤⫺0.011 (0.093 to 0.071) 0.798 1.000
*Risk alleles/non-risk alleles are indicated in the parentheses as defined according to Table 2. †Analyses were performed by combining Z
scores of age- and sex-adjusted metabolic traits in the control subjects of four populations separately and then analyzed for association by
linear regression. values, 95% CI, and asymptotic P values (t statistic) are shown. value represents the difference of Z score in the trait
value associated with each copy of the risk allele.
2230 DIABETES, VOL. 57, AUGUST 2008
association of PPARG Pro12Ala (ORs for Ala allele 1.14
and 1.76, respectively) and KCNJ11 Glu23Lys (OR for
Lys allele 1.14 and 1.23, respectively) polymorphisms to
type 2 diabetes in both Europeans (3,4,8) and Asians
(31,32), many of these genes are believed to play
important roles in insulin secretion (3,6,10,33). This is in
keeping with the prevailing view that abnormalities in
-cell function play a critical role in defining the risk
and development of type 2 diabetes in different popula-
tions (34). On the other hand, ethnic differences in risk
allele frequencies for genes, such as CDKAL1,
CDKN2A/B, HHEX, TCF7L2, and FTO, may lead to
differences in attributable risks (e.g., 7.9 vs. 21.6% for
CDKAL1, 22.5 vs. 9.2% for HHEX, and 20.2 vs. 2.2% for
TCF7L2, in Europeans vs. Asians, respectively) and thus
alter their impacts on different populations (Table 4).
Our previous work and that of others suggest the
presence of additional risk loci at TCF7L2 for type 2
diabetes in Chinese compared with Europeans (35,36).
Given the differences in linkage disequilibrium pattern
and risk allele frequencies, it will be valuable to further
examine these genes thoroughly to search for popula-
tion-specific and/or shared culprit disease loci and the
associated phenotypes in different ethnic groups.
The Hong Kong study was supported by the Research
Grant Council Central Allocation Scheme (CUHK 1/04C)
and the Chinese University of Hong Kong Direct Grant
(2006.1.041). The Korea SNUH study was supported by a
grant from the Korea Health 21 R&D Project, Ministry of
Health & Welfare, Republic of Korea (00-PJ3-PG6-GN07-
001). The KHGS study work was supported by the intra-
mural grant and the extramural grant of the National
Institute of Health, Korea (2001: 2003-347-6111-221, 2004:
347-6111-213, and 2005: 347-24002-440-215).
We thank all study subjects participating in these three
studies. For the Hong Kong study, we thank Cherry Chiu
and Dr. Ying Wang for recruitment of study subjects and
Lunan Chow, Kevin Yu, and Patty Tse for computing and
laboratory support. We thank the Centre for Clinical Trials
and the Information Technology Services Centre for com-
puting resources support. We thank all nursing and med-
ical staff at the Prince of Wales Hospital Diabetes and
Endocrine Centre for their dedication and professional-
ism. For the Korea SNUH study, we thank In Suk Ha and
Hyun Jung Lim for recruitment of study subjects and Mi
Ok Kang for laboratory support. We thank all the staff at
the Genome Research Center for Diabetes and Endocrine
Disease at SNUH for their dedication and professionalism.
For the KHGS study, we thank Dr. Younjhin Ahn for
epidemiological study design and Seung Hun Cha, Hye Ree
Han, and Min Hyung Ryu for laboratory support. We thank
all staff at the Center for Clinical Epidemiology, Ajou
University Medical Center for their dedication for the
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Gene Chr SNP
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P for
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†Meta-analysis from this study and Horikoshi et al. (17). DerSimonian and Laird random effects OR (95% CI) for SNPs with heterogeneous
between-group effects was 1.29 (1.16 –1.43) for rs10811661. ‡Meta-analysis by combining European and Asian data.
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    • "We hypothesize that obesity may modify the association between IGF2BP2 and T2DM—also called the interaction of IGF2BP2 and obesity with T2DM. Since some variants are known to affect the risk of T2DM through obesity [11,12], this work aimed to evaluate the interaction effect of IGF2BP2 and obesity on T2DM susceptibility. "
    [Show abstract] [Hide abstract] ABSTRACT: Background The objective of this study was to systematically evaluate the contribution of the insulin-like growth factor 2 mRNA-binding protein 2 (IGF2BP2) to type 2 diabetes mellitus (T2DM) and its interaction with obesity to T2DM susceptibility. Methods To clarify whether IGF2BP2 is an independent risk factor for T2DM in Chinese population, we conducted a study with a total of 2,301 Chinese Han subjects, including 1,166 T2DM patients and 1,135 controls, for the genotype of a most common and widely studied polymorphism—rs4402960 of IGF2BP2. Genotyping was performed by iPLEX technology. Gene and environment interaction analysis was performed by using multiple logistic regression models. Results The repeatedly confirmed association between IGF2BP2 (rs4402960) and T2DM had not been replicated in this cohort (P = 0.182). Interestingly, we found that obese subjects (body mass index (BMI) ≥ 28.0 kg/m2) bearing the minor A allele had an increased risk to develop T2DM (P = 0.008 for allele analysis and P < 0.001 for genotype analysis). Conclusions The present study provided data suggesting that the wild C allele of IGF2BP2 (rs4402960) had a protective effect against T2DM in obese subjects of Chinese Han population.
    Full-text · Article · Jul 2014
    • "Moreover, among those diagnosed with type 2 diabetes, more than 93% were also overweight. These findings suggest that obesity and diabetes may share some same genetic background, consistent with previous studies[39], [40]. "
    [Show abstract] [Hide abstract] ABSTRACT: Cigarette smoke is a strong risk factor for obesity and cardiovascular disease. The effect of genetic variants involved in nicotine metabolism on obesity or body composition has not been well studied. Though many genetic variants have previously been associated with adiposity or body fat distribution, a single variant usually confers a minimal individual risk. The goal of this study is to evaluate the joint association of multiple variants involved in cigarette smoke or nicotine dependence with obesity-related phenotypes in American Indians. To achieve this goal, we genotyped 61 tagSNPs in seven genes encoding nicotine acetylcholine receptors (nAChRs) in 3,665 American Indians participating in the Strong Heart Family Study. Single SNP association with obesity-related traits was tested using family-based association, adjusting for traditional risk factors including smoking. Joint association of all SNPs in the seven nAChRs genes were examined by gene-family analysis based on weighted truncated product method (TPM). Multiple testing was controlled by false discovery rate (FDR). Results demonstrate that multiple SNPs showed weak individual association with one or more measures of obesity, but none survived correction for multiple testing. However, gene-family analysis revealed significant associations with waist circumference (p = 0.0001) and waist-to-hip ratio (p = 0.0001), but not body mass index (p = 0.20) and percent body fat (p = 0.29), indicating that genetic variants are jointly associated with abdominal, but not general, obesity among American Indians. The observed combined genetic effect is independent of cigarette smoking per se. In conclusion, multiple variants in the nAChR gene family are jointly associated with abdominal obesity in American Indians, independent of general obesity and cigarette smoking per se.
    Full-text · Article · Jul 2014
    • "In energy homeostasis, multiple hormones with metabolic and haemodynamic effects are involved in the brain-gut-pancreas-liver axis [35]. Habitual consumption of high GI diet will initiate a sequence of metabolic and neurohormonal events that can stimulate hunger, promote fat deposition, increase insulin secretion, thereby putting the pancreatic beta cells under chronic stress resulting in early onset of type 2 diabetes [35,38], especially in the presence of other risk factors such as genetic variants [39]. In rat models, high GI food have been shown to increase post-prandial rise in plasma glucose and insulin levels, increase plasma triglyceride concentrations, decrease adiponectin levels, increase body fat and decrease lean body mass [38]. "
    [Show abstract] [Hide abstract] ABSTRACT: The role of a low glycemic index (GI) diet in the management of adolescent obesity remains controversial. In this study, we aim to evaluate the impact of low GI diet versus a conventional Chinese diet on the body mass index (BMI) and other obesity indices of obese adolescents. Obese adolescents aged 15-18 years were identified from population-recruited, territory-wide surveys. Obesity was defined as BMI >=95th percentile of Hong Kong local age- and sex-specific references. Eligible subjects were randomized to either an intervention with low GI diet (consisting of 45-50% carbohydrate, 30-35% fat and 15-20% protein) or conventional Chinese diet as control (consisting of 55-60% carbohydrate, 25-30% fat and 10-15% protein). We used random intercept mixed effects model to compare the differential changes across the time points from baseline to month 6 between the 2 groups. 104 obese adolescents were recruited (52 in low GI group and 52 in control group; 43.3% boys). Mean age was 16.7 +/- 1.0 years and 16.8 +/-1.0 years in low GI and control group respectively. 58.7% subjects completed the study at 6 months (65.4% in low GI group and 51.9% in control group). After adjustment for age and sex, subjects in the low GI group had a significantly greater reduction in obesity indices including BMI, body weight and waist circumference (WC) compared to subjects in the control group (all p <0.05). After further adjustment for physical activity levels, WC was found to be significantly lower in the low GI group compared to the conventional group (p = 0.018). Low GI diet in the context of a comprehensive lifestyle modification program may be an alternative to conventional diet in the management of obese adolescents.Trial registration number: Ref. No: NCT 01278563.
    Full-text · Article · Feb 2014
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