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Predicting Type 2 Diabetes Based on Polymorphisms
From Genome-Wide Association Studies
A Population-Based Study
Mandy van Hoek,
1,2
Abbas Dehghan,
2
Jacqueline C.M. Witteman,
2
Cornelia M. van Duijn,
2,3
Andre´ G. Uitterlinden,
1,2
Ben A. Oostra,
2,3
Albert Hofman,
2
Eric J.G. Sijbrands,
1
and
A. Cecile J.W. Janssens
4
OBJECTIVE—Prediction of type 2 diabetes based on genetic
testing might improve identification of high-risk subjects. Ge-
nome-wide association (GWA) studies identified multiple new
genetic variants that associate with type 2 diabetes. The predic-
tive value of genetic testing for prediction of type 2 diabetes in
the general population is unclear.
RESEARCH DESIGN AND METHODS—We investigated 18
polymorphisms from recent GWA studies on type 2 diabetes in
the Rotterdam Study, a prospective, population-based study
among homogeneous Caucasian individuals of 55 years and older
(genotyped subjects, n⫽6,544; prevalent cases, n⫽686;
incident cases during follow-up, n⫽601; mean follow-up 10.6
years). The predictive value of these polymorphisms was exam-
ined alone and in addition to clinical characteristics using logistic
and Cox regression analyses. The discriminative accuracy of the
prediction models was assessed by the area under the receiver
operating characteristic curves (AUCs).
RESULTS—Of the 18 polymorphisms, the ADAMTS9,CDKAL1,
CDKN2A/B-rs1412829,FTO,IGF2BP2,JAZF1,SLC30A8,TCF7L2,
and WFS1 variants were associated with type 2 diabetes risk in
our population. The AUC was 0.60 (95% CI 0.57– 0.63) for
prediction based on the genetic polymorphisms; 0.66 (0.63– 0.68)
for age, sex, and BMI; and 0.68 (0.66 – 0.71) for the genetic
polymorphisms and clinical characteristics combined.
CONCLUSIONS—We showed that 9 of 18 well-established
genetic risk variants were associated with type 2 diabetes in a
population-based study. Combining genetic variants has low
predictive value for future type 2 diabetes at a population-based
level. The genetic polymorphisms only marginally improved the
prediction of type 2 diabetes beyond clinical characteristics.
Diabetes 57:3122–3128, 2008
Type 2 diabetes is a multifactorial disease caused
by a complex interplay of multiple genetic vari-
ants and many environmental factors. With the
recent genome-wide association (GWA) studies,
the number of replicated common genetic variants asso-
ciated with type 2 diabetes has rapidly increased (1–7). A
total of 18 polymorphisms have been firmly replicated
(1–7). It is unclear whether and how the currently known
genetic variants can be used in practice, because the
combined effect of these variants has not been investi-
gated in a population-based study. Particularly, because
most GWA studies were enriched for patients with a
positive family history and early onset of the disease,
association of these variants to type 2 diabetes risk in the
general population, including elderly individuals, remains
to be determined.
Because complex diseases are caused by multiple ge-
netic variants, predictive testing based on a single genetic
marker will be of limited value (8,9). Simulation studies
suggest that the predictive value could be improved by
combining multiple common low-risk variants (10 –13).
Several empirical studies on the predictive value of genetic
polymorphisms have been conducted before the recent
GWA data were available (14 –16). In a case-control study,
Weedon et al. (16) showed that combining the information
of three polymorphisms improved disease prediction, al-
beit to a limited extent. Vaxillaire et al. (15) investigated 19
polymorphisms and found that the predictive value was
low compared with clinical characteristics.
Genetic variants associated with risk of type 2 diabetes
could potentially be useful for the prediction, prevention,
and early treatment of the disease. We investigated
whether combining the currently known and well-repli-
cated genetic variants predicts type 2 diabetes in the
Rotterdam Study, a prospective population-based fol-
low-up study. We investigated whether these genetic vari-
ants improve prediction beyond clinical characteristics.
RESEARCH DESIGN AND METHODS
The design and data collection of the Rotterdam Study have been described
previously (17). In short, the Rotterdam Study is a prospective, population-
based, cohort study among 7,983 inhabitants of a Rotterdam suburb, designed
to investigate determinants of chronic diseases. Participants were aged 55
years and older. Baseline examinations took place from 1990 until 1993.
Follow-up examinations were performed in 1993–1994, 1997–1999, and 2002–
2004. Between these exams, continuous surveillance on major disease out-
comes was conducted. Information on vital status was obtained from
municipal health authorities. The medical ethics committee of the Erasmus
From the
1
Department of Internal Medicine, Erasmus University Medical
Center, Rotterdam, the Netherlands; the
2
Department of Epidemiology and
Biostatistics, Erasmus University Medical Center, Rotterdam, the Nether-
lands; the
3
Department of Clinical Genetics, Genetic Epidemiology Unit,
Erasmus University Medical Center, Rotterdam, the Netherlands; and the
4
Department of Public Health, Erasmus University Medical Center, Rotter-
dam, the Netherlands.
Corresponding author: Dr. Eric J.G. Sijbrands, e.sijbrands@erasmusmc.nl.
Received 27 March 2008 and accepted 1 August 2008.
Published ahead of print at http://diabetes.diabetesjournals.org on 11 August
2008. DOI: 10.2337/db08-0425.
© 2008 by the American Diabetes Association. Readers may use this article as
long as the work is properly cited, the use is educational and not for profit,
and the work is not altered. See http://creativecommons.org/licenses/by
-nc-nd/3.0/ for details.
The costs of publication of this article were defrayed in part by the payment of page
charges. This article must therefore be hereby marked “advertisement” in accordance
with 18 U.S.C. Section 1734 solely to indicate this fact.
ORIGINAL ARTICLE
3122 DIABETES, VOL. 57, NOVEMBER 2008
Medical Center approved the study protocol, and all participants gave their
written informed consent.
Data collection. At baseline, prevalent cases of diabetes were diagnosed by
a nonfasting or postload glucose level (after oral glucose tolerance testing)
ⱖ11.1 mmol/l and/or treatment with antidiabetic medication (oral medication
or insulin) and the diagnosis of diabetes as registered by a general practitio-
ner. During follow-up, diabetes was diagnosed at fasting plasma glucose levels
ⱖ7.0 mmol/l and/or a nonfasting plasma glucose levels ⱖ11.0 mmol/l and/or
treatment with antidiabetic medication (oral medication or insulin) (18,19)
and the diagnosis of diabetes as registered by a general practitioner. Patients
registered in general practitioners’ records as type 1 diabetic were excluded
from the present analyses (n⫽15).
The CDKAL1 rs7754840, CDKN2AB rs10811661, FTO rs8050136, HHEX
rs1111875, IGF2BP2 rs4402960, KCNJ11 rs5219, PPARG rs1801282, SLC30A8
rs13266634, and TCF7L2 rs7903146 polymorphisms were genotyped by means
of TaqMan allelic discrimination assays. DNA material was available for 6,544
of the 7,983 participants for the TaqMan analyses. The assays were designed
and optimalized by Applied Biosystems (Foster City, CA; http://store.
appliedbiosystems.com). Genotypes were determined in 2-ng genomic DNA.
Reactions were performed on the TaqMan Prism 7900HT platform. The
analyses were performed as described previously (20). Assays were run on 90
blood bank samples to test for adequate cluster separation. A total of 325
samples were genotyped in duplo. Success rates for TaqMan genotyping
ranged from 93.2 to 96.7%, with the exception of 86.1% for IGF2BP2 and 87.4%
for HHEX. TaqMan duplicate error rates for the HHEX and IGF2BP2 polymor-
phisms were 1.2 and 0.6%
The ADAMTS9 rs4411878 (proxy for rs4607103, r
2
⫽0.95), CDC123-
CAMK1D rs11257622 (proxy for rs12779790, r
2
⫽0.83), CDKN2A/B rs1412829
(proxy for rs564398, r
2
⫽0.97), JAZF1 rs1635852 (proxy for rs864745, r
2
⫽
0.97), NOTCH2 rs1493694 (proxy for rs10923931, r
2
⫽1.0), TCF2 rs4430796,
THADA rs7578597, TSPAN8-LGR5 rs1353362 (proxy for rs7961581, r
2
⫽0.96),
and WFS1 rs10012946 (proxy for rs10010131, r
2
⫽1.0) genotypes were derived
from the genotype data of the version 3 Illumina Infinium II HumanHap550
SNP chip array. From a total of 6,449 subjects, there was sufficient DNA
material for the array. Samples with a call rate ⬍97.5% (n⫽209), excess
autosomal heterozygosity ⬎0.336 (approximate false discovery rate [FDR]
⬍0.1% [n⫽21]), or mismatch between called and phenotypic sex (n⫽36) or
with outliers identified by the identity-by-state (IBS) clustering analysis with
⬎3 SDs from population mean (n⫽102) or IBS probabilities ⬎97% (n⫽129)
were excluded from the analysis; in total, 5,974 samples remained for
analyses.
The availability of Illumina 550K array data enabled us to compare
genotype calls between TaqMan and Illumina data for the FTO, HHEX,
IGF2BP2, SLC30A8, TCF7L2, and CDKAL1 polymorphisms as well. Concor-
dance rates ranged between 98.6 and 99.7%. To increase success rates, we
merged the data and deleted pairs that were not concordant. The success rates
for the polymorphisms increased to 98.4 –99.4%.
Statistical analyses. Associations of individual polymorphisms were inves-
tigated using Cox proportional hazards models for the prediction of incident
type 2 diabetes and logistic regression analyses for the prediction of prevalent
and incident type 2 diabetes together. Analyses were performed crude and
adjusted for age, sex, and BMI. We also applied Cox proportional hazards
models and logistic regression analyses to investigate the combined predictive
value of 1) the 18 polymorphisms (all polymorphisms included as separate
independent categorical variables); 2) the risk allele score based on the 18
polymorphisms (assuming all effect sizes of equal weight); 3) age, sex, and
BMI; and 4) age, sex, and BMI and all polymorphisms on type 2 diabetes risk.
The risk allele score was calculated by summing up the number of risk alleles
for each participant with complete genotype information, with risk alleles
being the alleles associated with increased risk of type 2 diabetes (1–5). The
risk allele score assumes that all genetic variants have the same effect, i.e.,
minor differences in effects size are ignored. The association between the risk
allele score and the predicted probabilities was quantified by the Spearman
correlation coefficient.
The discriminative accuracy was evaluated by the area under the receiver
operating characteristic (ROC) curves (AUCs). The AUC can range from 0.5
(total lack of discrimination) to 1.0 (perfect discrimination). AUCs were
calculated for the predicted risks of the logistic regression model, the risk
allele score, and the linear predictor values of the Cox proportional hazards
models. AUCs were compared with Analyze-it version 2.11 (www.analyze-
it.com), which uses the method of Hanley and McNeil for ROC curve analyses
(21,22).
The analyses were repeated for subgroups for age (cutoff 70 years of age)
and BMI (cutoff 26 kg/m
2
). All analyses were performed with SPSS software
version 12.0.1.
Simulation analyses. A simulation analysis was performed to quantify the
expected AUC for prediction of incident type 2 diabetes based on the odds
ratios (ORs) of the investigated polymorphisms in literature (OR 1.09 for
ADAMTS9, 1.11 for CDC123-CAMK1D, 1.12 for CDKAL1, 1.12 for CDKN2A/B
rs1412829, 1.20 for CDKN2A/B rs10811661, 1.17 for FTO, 1.13 for HHEX,
1.14 for IGF2BP2, 1.10 for JAZF1, 1.14 for KCNJ11, 1.13 for NOTCH2, 1.14 for
PPARG, 1.12 for SLC30A8, 1.10 for TCF2, 1.37 for TCF7L2, 1.15 for THADA,
1.09 for TSPAN8, and 1.11 for WFS1) (1,2,4,6,7,23). The method of simulation
has been described in detail previously (10). In brief, we simulated genetic
profiles and type 2 diabetes status for 100,000 individuals, of whom 10.3% were
supposed to have incident type 2 diabetes, as observed in our population.
Genetic profiles were constructed from the polymorphisms based on
observed allele frequencies. Under the assumption that each polymor-
phism has two alleles and that allele proportions were in Hardy-Weinberg
equilibrium, genotype frequencies for the single polymorphisms were
calculated. Assuming that the polymorphisms segregate independently, for
each individual, a genotype was randomly assigned. Disease risks associ-
ated with the genetic profiles were modeled using Bayes’ theorem. The
likelihood ratio of the genetic profile was calculated by multiplying the
likelihood ratios of the single genotypes. The OR of the heterozygous
genotypes compared with the homozygous nonrisk genotypes were derived
from the three large GWA studies (1,2,4). Finally, disease status was
modeled by a procedure that compares disease risk of each subject to a
randomly drawn value between 0 and 1 from a uniform distribution. This
procedure ensures that for each genomic profile, the percentage of people
who will develop the disease equals the disease risk associated with that
profile, when the subgroup of individuals with that profile is sufficiently
large. The simulation was repeated 10 times to obtain a robust estimate of
the AUC. The AUC was obtained as the c-statistic by the function somers2,
which is available in the Hmisc library of R software (version 2.5.1;
www.R-project.org, accessed December 2007).
RESULTS
Baseline characteristics. A total of 6,544 participants
were successfully genotyped for at least one polymor-
phism. Complete genotype information on all polymor-
phisms was present in 5,297 subjects (of whom 490 were
incident cases and 545 were prevalent cases). Age (P⫽
0.11), sex (P⫽0.22), BMI (P⫽0.30), and presence of type
2 diabetes (P⫽0.20) were not significantly different
between successfully genotyped individuals or individuals
with one or more missing genotypes. General characteris-
tics of the population are shown in Table 1. Individuals
with type 2 diabetes had higher BMI, higher waist circum-
ference, more often hypertension, and lower HDL choles-
terol compared with individuals without type 2 diabetes.
All polymorphisms were in Hardy Weinberg equilibrium in
the total population and in individuals without type 2
diabetes (highest
2
3.58, 2 d.f., P⫽0.06 for PPARG
rs1801282).
Individual effects of clinical characteristics and poly-
morphisms on type 2 diabetes risk. Table 2 shows the
effect of each polymorphism on type 2 diabetes risk
(prevalent and incident type 2 diabetes). The minor alleles
of the CDKAL1,FTO,IGF2BP2, and TCF7L2 variants
were associated with increased risk of type 2 diabetes. The
ADAMTS9, CDKN2A/B rs1412829, JAZF,SLC30A8, and
WFS1 minor alleles decreased type 2 diabetes risk. Cox
regression analyses restricted to incident type 2 diabetes
results gave similar results. Adjustment for age, sex, and
BMI did not materially change the results, except for the
FTO polymorphism, for which the effect on type 2 diabetes
risk disappeared after adjustment for BMI.
In a Cox regression analysis, age (hazard ratio [HR] 1.02
[95% CI 1.01–1.03]), sex (0.67 [0.57– 0.79]), and BMI (1.14
[1.12–1.16]) affected prospective type 2 diabetes risk.
Risk allele score and risk of type 2 diabetes. Figure 1
shows the ORs associated with increasing risk allele
scores compared with the reference group (0 –12 risk
alleles) in a logistic regression model. Individuals carrying
21 risk alleles or more (14.4% of the population) had
M. VAN HOEK AND ASSOCIATES
DIABETES, VOL. 57, NOVEMBER 2008 3123
significantly higher type 2 diabetes risk (7.2% of the
population carried 21 alleles, OR 1.90 [1.07–3.40]; 4.4% had
22 alleles, 2.11 [1.15–3.86]; 2.0% had 23 alleles, 2.11 [1.07–
4.18]; and 1.8% had 24 –32 alleles, 2.10 [1.04 – 4.22]) com-
pared with the reference group of 0 –12 alleles (n⫽109;
2.0% of the population). In a Cox regression analysis on
incident cases of diabetes, this figure was similar (data not
shown). The per-allele HR was 1.04 (95% CI 1.02–1.07)
(P⫽0.001).
Risk allele score. Figure 2 shows the predicted type 2
diabetes risks from the logistic regression model that
included all 18 genetic polymorphisms in relation to the
risk allele score. The Spearman correlation coefficient was
0.60, indicating a wide range of predicted risks for each
value of the risk allele scores. When analyzing only inci-
dent type 2 diabetes, this figure was similar (Spearmen rho
0.59; figure not shown).
Analyses of discriminative accuracy. Figure 3 shows
the ROC curves for the prediction of incident type 2
diabetes based on the genetic polymorphisms, clinical
characteristics, and both. The AUC was 0.60 (95% CI
0.57– 0.63) for prediction based on the genetic polymor-
phisms; 0.66 (0.63– 0.68) for age, sex, and BMI; and 0.68
(0.66 – 0.71) for the genetic polymorphisms and clinical
characteristics combined. The difference between the
AUCs for clinical characteristics with and without the
genetic polymorphisms was significant (P⬍0.0001).
The AUC of the risk allele score was 0.56 (0.53– 0.59).
When including only the significantly associated genetic
variants of the current study (ADAMTS9,CDKAL1,
CDKN1412829,FTO,IGFBP2,JAZF1,TCF7L2,SLC30A8,
and WFS1), the AUC was 0.58 (0.56 – 0.61). Combining
the KCNJ11,PPARG, and TCF7L2 variants resulted in
an AUC of 0.53 (0.50 – 0.55). Based on the simulation
study, the expected AUC for all genetic polymorphisms
using the effect sizes described in literature was 0.57.
When combining incident and prevalent type 2 diabetes
cases, the AUC of all polymorphisms was 0.60 (0.58 –
0.62).
In subgroup analyses, the AUC of the all polymorphisms
was 0.62 (95% CI 0.58 – 0.67) in the low BMI group and 0.59
(0.56 – 0.62) in the high BMI subgroup. The AUC was 0.61
(0.59 – 0.65) in the low age-group and 0.63 (0.58 – 0.67) in
the high age-groups.
DISCUSSION
We investigated the predictive value of 18 type 2 diabetes
risk polymorphisms from the recent GWA studies for the
prediction of type 2 diabetes in a large, prospective,
population-based study of elderly individuals. Our study
shows that combining information of these 18 well-repli-
cated variants has relatively low discriminative accuracy
for the prediction of type 2 diabetes in a general popula-
tion (AUC 0.60). The 18 genetic variants identified to date
did not substantially improve the discriminative accuracy
of disease prediction based on clinical characteristics.
In line with the results of the GWA studies (1–5,24),
ADAMTS9,CDKAL1,CDKN2A/B,FTO,IGFBP2,JAZF1,
SLC30A8,TCF7L2, and WFS1 were associated type 2
diabetes risk in our population. Some of these effects were
slightly stronger than the effects described previously. In
contrast to most previous studies, the KCNJ11 polymor-
phism was not associated. The ORs of the other polymor-
phisms were similar to previously published results but
not statistically significant, which may be explained by the
smaller number of type 2 diabetes cases and therefore
smaller power to reach significance in our prospective
study.
A risk allele score, obtained by counting the number of
risk alleles, can be used as a simple proxy of the combined
effect of multiple polymorphisms. Risk allele scores ignore
the effect sizes of the individual genetic variants, but a
previous simulation study has shown that this has limited
impact on the discriminative accuracy (11). In contrast, we
found a wide range of predicted risks for each value of the
risk allele score, suggesting that differences in the variants
carried result in substantial differences in actual disease
risks. The risk allele score associated with modest in-
creases in disease risk and the AUC for prediction was
0.56. When taking effect size differences between polymor-
phisms into account, the AUC was 0.60, showing that the
risk allele score predicted less accurately. Other empirical
studies have not investigated the differences in diagnostic
accuracy between simple risk allele scores and predicted
risks obtained from regression models (14 –16,25).
The discriminative accuracy of predictive genetic test-
ing in complex diseases depends on the number of genes
involved, the risk allele frequencies, and the size of the
associated risks (10). The maximum discriminative accu-
TABLE 1
General characteristics of genotyped participants at study baseline by type 2 diabetes status
All
participants
Subjects without
type 2 diabetes
Incident case subjects
with type 2 diabetes
Prevalent cases with
type 2 diabetes
n6,544 5,221 601 686
Age (years) 69.5 ⫾0.11 69.0 ⫾0.13 68.2 ⫾0.32* 73.6 ⫾0.35†
Men (%) 40.7 40.4 44.3 39.8
BMI (kg/m
2
)26.3 ⫾0.05 26.0 ⫾0.05 28.0 ⫾0.15† 26.8 ⫾0.15†
Waist circumference (cm) 90.5 ⫾0.14 89.6 ⫾0.16 94.7 ⫾0.45† 93.8 ⫾0.46†
Systolic blood pressure (mmHg) 139.3 ⫾0.3 137.9 ⫾0.31 143.5 ⫾0.85† 146.8 ⫾0.93†
Diastolic blood pressure (mmHg) 73.7 ⫾0.1 73.6 ⫾0.2 75.5 ⫾0.5† 73.1 ⫾0.5
Hypertension (%) 33.4 30.5 46.9† 52.9†
Total cholesterol (mmol/l) 6.6 ⫾0.02 6.6 ⫾0.02 6.6 ⫾0.05 6.5 ⫾0.05
HDL cholesterol (mmol/l) 1.34 ⫾0.005 1.37 ⫾0.005 1.25 ⫾0.01† 1.25 ⫾0.01†
Current smoking (%) 22.1 22.5 25.5 22.1
Former smoking (%) 40.7 42.0 42.8 39.0
Continuous variables are expressed as means ⫾SE. Type 2 diabetes status was missing for 36 individuals. *P⬍0.05, †P⬍0.001 for
comparison with subjects without type 2 diabetes. Comparisons between groups were performed using ANOVA for continuous variables and
2
test for categorical variables.
GENETIC PREDICTION OF TYPE 2 DIABETES
3124 DIABETES, VOL. 57, NOVEMBER 2008
racy is determined by the heritability of the disease (10).
Based on previously published effect sizes for the 18
polymorphisms, we predicted that the AUC would be 0.57;
and based on our empirical data, we found that it was 0.60.
This difference is explained by the fact that some polymor-
phisms had a slightly larger effect in our population than
TABLE 2
Individual effects of nine polymorphisms on type 2 diabetes risk with and without adjustment for covariates
Gene variant
Risk
allele Genotype (n)
Percent case
subjects Percent
control
subjects
All case subjects Incident type 2 diabetes
Prevalent Incident
OR
(95% CI)
Crude HR
(95% CI)
Adjusted HR
(95% CI)*
ADAMTS9 C CC (3,450) 61.9 61.1 57.4 1.0 1.0 1.0
rs4411878† CT (2,159) 32.5 34.7 37.1 0.84 (0.74–0.97) 0.88 (0.73–1.05) 0.87 (0.73–1.04)
TT (321) 5.6 4.2 5.5 0.84 (0.62–1.13) 0.68 (0.45–1.04) 0.68 (0.45–1.04)
CDC123/ C TT (3,952) 64.4 67.4 67.9 1.0 1.0 1.0
CAMK1D TC (1,758) 31.5 28.8 29.6 1.04 (0.90–1.20) 0.98 (0.82–1.19) 0.98 (0.81–1.18)
rs11257622‡ CC (213) 4.2 3.8 3.5 1.18 (0.84–1.64) 1.07 (0.69–1.67) 1.05 (0.68–1.63)
CDKAL1 C GG (3,097) 47.1 45.4 48.7 1.0 1.0 1.0
rs7754840 GC (2,692) 41.1 43.0 41.9 1.05 (0.93–1.20) 1.09 (0.92–1.29) 1.10 (0.93–1.31)
CC (633) 11.8 11.6 9.4 1.31 (1.07–1.61) 1.31 (1.001–1.72) 1.38 (1.06–1.80)
CDKN2A/B A AA (1,915) 31.3 35.2 32.1 1.0 1.0 1.0
rs1412829§ AG (2,910) 47.7 50.4 49.2 0.97 (0.84–1.12) 0.94 (0.78–1.13) 0.89 (0.74–1.08)
GG (1,098) 21.0 14.5 18.7 0.93 (0.77–1.12) 0.72 (0.56–0.94) 0.70 (0.54–0.92)
CDKN2A/B T TT (4,131) 72.0 63.1 66.0 1.0 1.0 1.0
rs10811661 TC (1,865) 24.7 34.5 30.1 0.95 (0.83–1.09) 1.17 (0.99–1.39) 1.13 (0.95–1.34)
CC (232) 3.2 2.4 3.9 0.70 (0.49–1.02) 0.64 (0.37–1.08) 0.68 (0.40–1.17)
FTO A CC (2,526) 38.3 38.4 39.8 1.0 1.0 1.0
rs8050136 CA (2,944) 44.0 46.3 46.3 1.01 (0.88–1.16) 1.03 (0.86–1.23) 1.00 (0.84–1.20)
AA (927) 17.7 15.3 14.0 1.23 (1.02–1.47) 1.12 (0.88–1.43) 1.06 (0.83–1.36)
HHEX C CC (2,235) 36.1 36.3 34.8 1.0 1.0 1.0
rs1111875 CT (3,097) 50.1 46.1 48.4 0.96 (0.84–1.10) 0.91 (0.76–1.09) 0.93 (0.78–1.12)
TT (1,052) 13.7 17.6 16.8 0.89 (0.74–1.07) 1.01 (0.80–1.27) 1.01 (0.79–1.28)
IGF2BP2 T GG (3,101) 46.9 47.7 49.4 1.0 1.0 1.0
rs4402960 GT (2,650) 41.8 41.5 42.0 1.04 (0.91–1.18) 1.01 (0.85–1.19) 1.01 (0.85–1.20)
TT (575) 11.3 10.8 8.6 1.35 (1.09–1.66) 1.24 (0.95–1.63) 1.23 (0.94–1.62)
JAZF1 T TT (1,646) 31.0 29.8 27.1 1.0 1.0 1.0
rs1635852¶ TC (2,927) 46.5 48.6 49.8 0.85 (0.73–0.98) 0.88 (0.73–1.07) 0.85 (0.70–1.04)
CC (1,357) 22.5 21.6 23.1 0.85 (0.71–1.02) 0.86 (0.68–1.09) 0.84 (0.66–1.06)
KCNJ11 G AA (2,394) 37.7 39.9 39.2 1.0 1.0 1.0
rs5219 AG (2,925) 48.3 46.6 47.8 1.00 (0.88–1.15) 0.97 (0.81–1.16) 0.97 (0.81–1.16)
GG (807) 14.0 13.5 13.0 1.07 (0.88–1.30) 1.02 (0.79–1.32) 1.02 (0.79–1.32)
NOTCH2 T CC (4,670) 77.8 79.0 78.8 1.0 1.0 1.0
rs1493692储CT (1,168) 20.7 19.7 19.6 1.04 (0.89–1.22) 1.03 (0.84–1.27) 1.07 (0.86–1.32)
TT (92) 1.4 1.3 1.6 0.86 (0.50–1.49) 0.75 (0.35–1.57) 0.74 (0.35–1.56)
PPARG C CC (4,888) 79.7 76.9 77.1 1.0 1.0 1.0
rs1801282 CG (1,322) 19.1 21.7 21.1 0.95 (0.81–1.10) 1.03 (0.84–1.25) 0.99 (0.81–1.21)
GG (108) 1.2 1.4 1.8 0.70 (0.41–1.20) 0.76 (0.38–1.54) 0.66 (0.33–1.34)
SLC30A8 C CC (3,176) 49.8 54.1 48.8 1.0 1.0 1.0
rs13266634 CT (2,709) 43.7 38.2 42.4 0.91 (0.80–1.04) 0.82 (0.69–0.97) 0.84 (0.70–0.99)
TT (546) 6.6 7.7 8.8 0.75 (0.59–0.96) 0.80 (0.59–1.09) 0.84 (0.62–1.14)
TCF2 G AA (1,560) 25.9 24.1 26.8 1.0 1.0 1.0
rs4430796 AG (2,924) 48.1 50.7 49.6 1.06 (0.91–1.24) 1.11 (0.90–1.36) 1.14 (0.92–1.40)
GG (1,417) 26.1 25.2 23.6 1.16 (0.97–1.39) 1.16 (0.91–1.47) 1.16 (0.91–1.48)
TCF7L2 T CC (3,292) 43.7 47.5 52.6 1.0 1.0 1.0
rs7903146 CT (2,587) 42.4 42.3 39.7 1.23 (1.08–1.41) 1.19 (1.01–1.41) 1.20 (1.01–1.42)
TT (554) 13.9 10.2 7.7 1.82 (1.48–2.24) 1.48 (1.12–1.95) 1.62 (1.22–2.14)
THADA T TT (4,608) 79.9 78.8 77.3 1.0 1.0 1.0
Rs7578597 TC (1,227) 19.0 19.0 21.1 0.88 (0.75–1.03) 0.91 (0.73–1.12) 0.90 (0.72–1.12)
CC (94) 1.1 2.2 1.6 1.01 (0.60–1.67) 1.32 (0.74–2.34) 1.51 (0.85–2.67)
TSPAN8/
LGR5 C TT (3,018) 50.4 48.3 51.4 1.0 1.0 1.0
Rs1353362** TC (2,409) 40.3 41.8 40.6 1.05 (0.92–1.20) 1.10 (0.92–1.31) 1.08 (0.90–1.29)
CC (493) 9.3 9.9 8.0 1.25 (0.99–1.57) 1.29 (0.96–1.73) 1.21 (0.90–1.62)
WFS1 C CC (2,179) 40.7 40.4 35.8 1.0 1.0 1.0
Rs1412829†† CT (2,801) 44.6 45.7 47.8 0.83 (0.73–0.96) 0.86 (0.72–1.04) 0.87 (0.73–1.05)
TT (949) 14.7 13.9 16.4 0.77 (0.63–0.93) 0.77 (0.59–1.00) 0.76 (0.58–0.99)
*HR adjusted for age, sex, and BMI; †proxy for rs4607103, r
2
⫽0.95; ‡proxy for rs12779790, r
2
⫽0.83; §proxy for rs564398, r
2
⫽0.97; ¶proxy
for rs864745, r
2
⫽0.97; 储proxy for rs10923931, r
2
⫽1.0; **proxy for rs7961581, r
2
⫽0.96; ††proxy for rs10010131, r
2
⫽1.0.
M. VAN HOEK AND ASSOCIATES
DIABETES, VOL. 57, NOVEMBER 2008 3125
described in the literature (1– 4,6,7,16,23). Nonetheless,
the discriminative accuracy of all known replicated type 2
diabetes susceptibility variants to date was rather low.
However, our analysis was based on 18 common variants
with relatively small effects. The heritability of type 2
diabetes is estimated to range from 30 to 70% depending
on the population investigated (26), and many common
variants with small effects or fewer rare variants with
stronger effects are still to be discovered. These may
further improve the discriminative accuracy of predictive
genetic testing for type 2 diabetes.
Several previous studies have investigated the predic-
tive value of multiple genetic variants in type 2 diabetes,
either alone or in addition to clinical characteristics. An
overview of the studies performed so far in Caucasian
populations and the number of genes investigated is
provided in Table 3. Weedon et al. (16) investigated three
variants that were also in our study and reported an AUC
of 0.58, whereas we found an AUC of 0.53 for the same
polymorphisms. The population of Weedon et al. consisted
in large part of patients who had early onset of type 2
diabetes or a positive family history of type 2 diabetes,
whereas our population included elderly subjects from the
general population. The percentage of variance of the
disease explained by genetic factors is expected to be
higher in populations with a positive family history for the
OR (95% CI)
Number of risk alleles
(% per category)
(2.0%) (2.6%) (4.9%) (8.3%) (11.5%) (14.4%) (15.9%) (13.9%) (10.9%) (7.2%) (4.4%) (2.0%) (1.8%)
0.0
1.0
2.0
3.0
4.0
5.0
0-12 13 14 15 16 17 18 19 20 21 22 23 24-36
FIG. 1. Odds ratios for type 2 diabetes according to the number of risk alleles carried.
Predicted risk (%)
Number of risk alleles
0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 3
6
0
10
20
30
40
50
FIG. 2. Correlation of predicted type 2 diabetes risks with the risk allele score. Predicted risks of type 2 diabetes were obtained from the logistic
regression model with 18 genetic polymorphisms as independent categorical variables.
GENETIC PREDICTION OF TYPE 2 DIABETES
3126 DIABETES, VOL. 57, NOVEMBER 2008
disease, and this may lead to a higher diagnostic accuracy
for genetic variants. Recently, Cauchi et al. (25) investi-
gated 15 genetic variants that were associated in GWA
analyses in a French case-control study. The AUC of the
genetic variants together with age, sex, and BMI was 0.86.
Unfortunately, the AUC was not calculated for genes and
clinical characteristics separately to assess the additive
value of genetic information. Some of the included vari-
ants were specifically identified in this case-control study
and had relatively large effects on type 2 diabetes risk
compared with effects found in meta-analyzed GWA stud-
ies (1,2,4,6). It is therefore difficult to generalize these
findings to other populations, and we expect that our
population-based prospective study yielded more realistic
estimates. Two other studies investigated the improve-
ment of the discriminative accuracy by adding genetic test
results to clinical characteristics (14,15). Even though we
included the 18 firmly replicated polymorphisms to date
and they predominantly tested other genetic variants, our
findings were similar, demonstrating no substantial added
value of genetic information beyond clinical characteris-
tics (14,15,27).
We can only speculate on the reasons why these genetic
variants have little added value beyond clinical character-
istics. First, the effects of the genetic variants on type 2
diabetes risk could be exerted through clinical character-
istics such as BMI, which implies that including both genes
and intermediate factors in the regression model will
reduce the effect of the gene. However, adjustment for
clinical characteristics did not substantially change the
effect of the genetic variants on type 2 diabetes risk (Table
2). Second, the effects of age, sex, and BMI on type 2
diabetes risk in our population may outweigh the contri-
bution of the genetic variants. Such an effect was illus-
trated in an earlier study, which showed that a genetic
predisposition became apparent in subjects with less other
risk factors (28). In our elderly population, one may expect
that nongenetic risk factors are more prevalent compared
with younger populations. However, the AUC for the
genetic variants was higher than expected from the simu-
lation analyses. This makes an underestimation of the
contribution of the genetic variants in our population
unlikely.
Obvious strengths of our study are the large size of
the study population, the population-based design, and the
long period of follow-up. Despite these advantages, the
number of incident type 2 diabetes cases was still rela-
tively low to demonstrate statistically significant effects of
low-risk susceptibility genes. Furthermore, we had insuf-
ficient statistical power to formally investigate gene-gene
and gene-environment interactions. In age and BMI sub-
group analyses, the estimates for prediction based on the
genetic variants were similar and showed overlapping CIs.
However, because of smaller numbers of cases in the
subgroups, these results should be interpreted with cau-
tion. Cauchi et al. (25) reported gene-gene interactions of
recently discovered loci but did not report on the effects of
these interactions on the AUC. Earlier studies found no
evidence for strong gene-gene interactions (15,16). Taking
into account these interactions may further improve the
discriminative accuracy.
In conclusion, we showed that 9 of 18 currently well-
established genetic risk variants were associated with type
2 diabetes in a population-based study. The currently
known and replicated genetic variants found in GWA
studies contributed modestly to the prediction of type 2
diabetes in a population-based setting and marginally
improved the risk prediction when added to clinical char-
acteristics. Future research should aim at identifying and
replicating new genetic susceptibility variants and gene-
gene and gene-environment interactions to approach
levels of discriminative accuracy that enable the identifi-
cation of at-risk groups.
ACKNOWLEDGMENTS
The Rotterdam Study is supported by the Erasmus Medical
Center and Erasmus University Rotterdam; the Nether-
lands Organization for Scientific Research; the Nether-
lands Organization for Health Research and Development
(ZonMw); the Research Institute for Diseases in the El-
derly; the Ministry of Education, Culture and Science; the
Ministry of Health, Welfare and Sports; the European
Commission; and the Municipality of Rotterdam. This
study was further supported by the Centre for Medical
Systems Biology in the framework of the Netherlands
Genomics Initiative. The genome-wide association data-
base of the Rotterdam Study was made possible through
funding from the Dutch Research Organisation (nr.175.
010.2005.011).
0.0
0.2
0.4
0.6
0.8
1.0
0.0 0.2 0.4 0.6 0.8 1.0
1-Specificity
Genetic Polymorphisms
Clinical Characteristics (age, sex, BMI)
Both
Sensitivity
FIG. 3. ROC curves for prediction of incident type 2 diabetes based on
18 genetic polymorphisms, clinical characteristics (age, sex, and BMI),
and both.
TABLE 3
Overview of diagnostic accuracies obtained from earlier empiri-
cal studies on genetic risk variants and type 2 diabetes
Study
Genetic
variants
(n)
AUC
genetic
variants
AUC clinical
characteristics
AUC
combined
Weedon et al.
(16) 3 0.58 NR NR
Lyssenko et al.
(14,27) 2 NR 0.68* 0.68
Vaxillaire et al.
(15) 3 0.56 0.82† 0.84
Cauchi et al.
(25) 15 NR NR† 0.86
NR, not reported. *Clinical characteristics: fasting plasma glucose
and BMI. †Clinical characteristics: age, sex, and BMI.
M. VAN HOEK AND ASSOCIATES
DIABETES, VOL. 57, NOVEMBER 2008 3127
We thank Dr. Fernando Rivadeneira for making the
Illumina 550K data from the Rotterdam Study available.
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