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ARTICLE
Investigation of gene–diet interactions in the incretin system
and risk of type 2 diabetes: the EPIC-InterAct study
The InterAct Consortium
Abstract
Aims/hypothesis The gut incretin hormones glucagon-like
peptide-1 (GLP-1) and glucose-dependent insulinotropic pep-
tide (GIP) have a major role in the pathophysiology of type 2
diabetes. Specific genetic and dietary factors have been found
to influence the release and action of incretins. We examined
the effect of interactions between seven incretin-related genet-
ic variants in GIPR,KCNQ1,TCF7L2 and WFS1 and dietary
components (whey-containing dairy, cereal fibre, coffee and
olive oil) on the risk of type 2 diabetes in the European
Prospective Investigation into Cancer and Nutrition (EPIC)-
InterAct study.
Methods The current case-cohort study included 8086 incident
type 2 diabetes cases and a representative subcohort of 11,035
participants (median follow-up: 12.5 years). Prentice-weighted
Coxproportionalhazardregressionmodelswereusedtoinves-
tigate the associations and interactions between the dietary fac-
tors and genes in relation to the risk of type 2 diabetes.
Results An interaction (p= 0.048) between TCF7L2 variants
and coffee intake was apparent, with an inverse association
between coffee and type 2 diabetes present among carriers of
the diabetes risk allele (T) in rs12255372 (GG: HR 0.99 [95%
CI 0.97, 1.02] per cup of coffee; GT: HR 0.96 [95% CI 0.93,
0.98]); and TT: HR 0.93 [95% CI 0.88, 0.98]). In addition, an
interaction (p= 0.005) between an incretin-specific genetic risk
score and coffee was observed, again with a stronger inverse
association with coffee in carriers with more risk alleles (0–3
risk alleles: HR 0.99 [95% CI 0.94, 1.04]; 7–10 risk alleles: HR
0.95 [95% CI 0.90, 0.99]). None of these associations were
statistically significant after correction for multiple testing.
Conclusions/interpretation Our large-scale case-cohort study
provides some evidence for a possible interaction of TCF7L2
variants and an incretin-specific genetic risk score with coffee
consumption in relation to the risk of type 2 diabetes. Further
large-scale studies and/or meta-analyses are needed to confirm
these interactions in other populations.
Keywords Coffee .Dairy .Fibre .Gene–environment
interaction .GIPR .Incretins .KCNQ1 .Olive oil .TCF7L2 .
WFS1
Abbreviations
CEU Utah Residents with Northern and Western
European Ancestry
ENDB EPIC nutrient database
EPIC European Prospective Investigation into Cancer
and Nutrition
GIP Glucose-dependent insulinotropic peptide
GIPR Gastric inhibitory polypeptide receptor
GLP-1 Glucagon-like peptide-1
GWAS Genome-wide association studies
IQR Interquartile range
KCNQ1 Potassium voltage-gated channel subfamily Q
member 1
LD Linkage disequilibrium
SNP Single nucleotide polymorphism
The InterAct Consortium list of authors is shown in the electronic
supplementary material (ESM).
Electronic supplementary material The online version of this article
(doi:10.1007/s00125-016-4090-5) contains peer-reviewed but unedited
supplementary material, which is available to authorised users.
*The InterAct Consortium
heraclides.a@unic.ac.cy;
1
c/o A. Heraclides, University of Nicosia Medical School, Centre for
Primary Care and Population Health(21 Ilia Papakyriakou, Engomi(
P.O. Box 24005, 1700 Nicosia, Cyprus
heraclides.a@unic.ac.cy
DOI 10.1007/s00125-016-4090-5
Received: 10 May 2016 /Accepted: 18 July 2016 /Published online: 13 September 2016
#The Author(s) 2016. This article is published with open access at Springerlink.com
Diabetologia (2016) 59:2613–2621
Content courtesy of Springer Nature, terms of use apply. Rights reserved.
TCF7L2 Transcription factor 7-like 2
WFS1 Wolframin ER transmembrane glycoprotein
Introduction
The gut incretin hormones glucagon-like peptide-1 (GLP-1)
and glucose-dependent insulinotropic peptide (GIP) have a
major role in acute food-stimulated secretion of insulin but
also affect long-term beta cell mass and functioning [1].
Single nucleotide polymorphism (SNPs) in genes associated
with type 2 diabetes in genome-wide association studies
(GWAS) have been found to regulate the release and functioning
of incretin hormones in small-scale experimental studies [2,3].
Genetic variants in transcription factor 7-like 2 (TCF7L2)[4–8]
and Wolframin ER transmembrane glycoprotein (WFS1)[9]
have been linked to pancreatic GLP-1 sensitivity, gastric
inhibitory polypeptide receptor (GIPR) variants have been linked
to pancreatic GIP sensitivity [2], and genetic variants in potassi-
um voltage-gated channel subfamily Q member 1 (KCNQ1)
have been reported to affect GLP-1 and GIP secretion from the
gut [10–12].
Despite the direct involvement of food ingestion in the
incretin response, the role of diet in the regulation of the
incretin system is largely unknown. Some dietary factors have
been found to stimulate the release and action of incretin
hormones in animal studies and small-scale experimental
studies in humans. Whey protein (present in all dairy products
except cheese) has been found to increase postprandial GIP
and GLP-1 concentrations compared with isoenergetic
non-whey containing meals [13–15] or dairy casein protein
[16,17]. Furthermore, whey-containing dairy products have
been found to have a disproportionally high insulin index
(typical blood insulin response to various foods) compared
with their corresponding glycaemic index [18]. Similarly, in-
take of olive oil has been found to increase the production of
both GIP and GLP-1 in healthy individuals [19] and GLP-1 in
individuals with type 2 diabetes [20] compared with an
isoenergetic intake of butter. A postprandial increase in the
level of intact GLP-1 has also been found after consumption
of purified oleic acid in animal studies [21]. Dietary fibre,
especially of cereal origin, has also been found to stimulate
GIP and GLP-1 release in healthy individuals [22,23].
Finally, coffee, via mechanisms involving chlorogenic acid
(the major polyphenol in coffee), has been found to increase
production of GLP-1 compared with isoenergetic beverages [24].
All of the above dietary factors have also been linked to a
reduction in the risk of type 2 diabetes in epidemiological
studies [25]. In the European Prospective Investigation into
Cancer and Nutrition (EPIC) - InterAct study, an inverse
association between dairy products and type 2 diabetes was
found [26], while dietary fibre and olive oil did not show an
association [27,28].
We hypothesised that the association between specific
foods potentially influencing incretin release (whey-
containing dairy, cereal fibre, olive oil and coffee) and type
2 diabetes risk is modified by incretin-related genetic variants.
We investigated this hypothesis in the large population-based
EPIC-InterAct study conducted in eight European countries.
Methods
Study population The design and methods of the InterAct
study, nested within the EPIC cohorts, are described in detail
elsewhere [29]. All participants provided written informed
consent, and the study was approved by the local ethics
committee in the participating countries and the internal
review board of the International Agency for Research on
Cancer. Briefly, the study population included participants
from 26 centres in the eight of the ten countries participating
in EPIC who had available blood samples and information on
diabetes (France, Italy, Spain, the UK, the Netherlands,
Germany, Denmark and Sweden). All ascertained and verified
incident type 2 diabetes cases between 1991 and 2007 (3.99
million person-years at risk, n= 12,403) were included in the
case group. A centre-stratified, representative subcohort of
16,835 individuals was selected as the control group.
Prevalent diabetes cases (n= 548) and individuals with
uncertain diabetes status (n= 133) were excluded from the
subcohort, leaving 16,154 individuals for analysis. Due to
the random selection of the control group, 778 incident type
2 diabetes cases were part of the subcohort, leaving a total
study sample of 27,779 individuals.
For the current analysis, we excluded participants with
abnormal estimated energy intake (top 1% and bottom 1%
of the distribution of the ratio of reported energy intake over
estimated energy requirements, assessed by the basal
metabolic rate) (n=619) and those with missing information
on dietary intake (n= 117). Participants with no data on any of
the SNPs of interest (including all Danish participants)
(n= 7617) were also excluded. For our interaction analysis,
the models were adjusted for demographic and lifestyle
factors, thus participants with no information on these key
covariates were also excluded. Therefore, 18,638 individuals
were included in the analysis, comprising of 8086 cases and
11,035 subcohort participants (including 483 cases in the
subcohort). For some of these participants, information on
specific SNPs was missing, thus the sample size for the
analysis of each SNP differs slightly.
Case ascertainment Ascertaining incident type 2 diabetes
involved a review of the existing EPIC datasets at each centre
using multiple sources of evidence including self-report,
linkage to disease and drug registers, hospital admissions
and mortality data. Information from any follow-up visit or
2614 Diabetologia (2016) 59:2613–2621
Content courtesy of Springer Nature, terms of use apply. Rights reserved.
external evidence with a date later than the baseline visit
was used. To increase the specificity of the definition for
these cases, we sought further evidence including individ-
ual medical records review in some centres. Follow-up
was censored at the date of diagnosis, 31 December
2007 or the date of death, whichever occurred first. In
total, 12,403 verified incident type 2 diabetes cases were
identified.
Choice of dietary factors and dietary assessment After a
detailed literature review, we identified four dietary
factors (whey-containing dairy, olive oil, coffee and
cereal fibre) for which there was evidence for increasing
postprandial incretin levels [13–15].Self-orinterviewer-
administered country-specific validated dietary question-
naires and/or diet records (Sweden only) were used to
assess the usual food intake of participants. Nutrient
intake was estimated using the standardised EPIC nutrient
database (ENDB) [30]. Whey-containing dairy was
calculated by subtracting the intake of cheese from the
total intake of dairy products including milk, yoghurt,
milk-based puddings and cream desserts. Olive oil
consumption was reported on its own or as an ingredient
in recipes, depending on the country. In the Swedish study
centre Umeå, olive oil was not assessed in the question-
naire,andintheUK,itwasonlyreportedasaningredient
in recipes. Therefore, these centres had to be excluded
from the olive oil interaction analysis. Coffee intake included
both caffeinated and decaffeinated coffee. Cereal fibre was
derived from the ENDB by adding the fibre content of cereal-
based products, including bread, rice, wheat-based pasta, crisp
bread, rusks and breakfast cereals [31].
Covariate assessment Questionnaires were used to collect
information on lifestyle factors and socioeconomic status at
baseline [32]. For the current analysis we used a four category
physical activity index reflecting occupational and recreational
physical activity [33]. Educational attainment was categorised
as: none; primary school; technical school; secondary school;
and further education including university degree. Smoking
status was categorised as: never; former; and current smoker.
Alcohol consumption was categorised as: 0 g/day; >0–6g/day;
>6–12 g/day; >12–24 g/day; and >24 g/day. Total energy in-
take was assessed as kcal/day (converted to MJ/day), and in-
take of specific foods and nutrients of interest as g/day.
Anthropometric measures including weight, height and waist
circumference were collected at baseline by standardised proce-
dures and adjusted for clothing [34]. Information on prevalent
diseases was obtained at baseline including stroke, myocardial
infarction, hypertension and hyperlipidaemia.
DNA extraction, genotyping and SNP selection DNA was
extracted from up to 1 ml of buffy coat for each individual
from a citrated blood sample. A detailed account on the DNA
extraction and genotyping procedures has been published
previously [35]. Briefly, a total of 10,027 participants (4644
cases) were randomly selected across all centres (except
Denmark) for genome-wide genotyping using the Illumina
660 W-Quad BeadChip (Illumina, San Diego, CA, USA). In
addition, 9794 EPIC-InterAct participants with available
DNA and not selected for genome-wide measurement were
genotyped using the Illumina Cardio-Metabochip (Illumina,
San Diego, CA, USA) [35].
Seven SNPs (GIPR: rs10423928; TCF7L2: rs7903146 and
rs12255372; WFS1: rs10010131; KCNQ1: rs151290,
rs2237892 and rs163184) associated with type 2 diabetes
in GWAS [36] were selected based on evidence from
small-scale experimental studies that had revealed major
roles in the regulation of the release and functioning of
incretin hormones [2,3,7,8]. As TCF7L2 has several
point mutations implicated in type 2 diabetes, we chose
to use the ones for which there is evidence for involvement
in the incretin system, based on small-scale experimental
studies in humans. More specifically, two independent
studies have collectively shown that TCF7L2 rs7903146,
rs7901695 and rs12255372 influence the second phase of
GLP-1-induced insulin secretion [7,8]. Since TCF7L2
rs7903146 and TCF7L2 rs7901695 are in strong linkage
disequilibrium (LD) (R
2
=0.98, D’= 0.88) it is likely that
the two SNPs capture the same genetic information, thus of
the two, we only included rs7903146 in our analysis. The
KCNQ1 SNPs included in our analysis were in low LD
(R
2
<0.25).
Four of the aforementioned seven SNPs (GIPR
rs10423928, TCF7L2 rs7903146, WFS1 rs10010131 and
KCNQ1 rs163184) were available from direct genotyping
within the entire EPIC-InterAct study on Sequenom or
Taqman platforms. Information on two of the remaining
SNPs (TCF7L2 rs12255372 and KCNQ1 rs2237892) was
available from genotyping with the GWAS Illumina
660 W-Quad Chip and Illumina Cardio-Metabochip.
The SNP KCNQ1 rs151290 was not available on any
of the genotyping arrays and KCNQ1 rs163171 was
chosen as a proxy SNP (r
2
= 0.94, D’= 1.0 in the CEU
[Utah Residents with Northern and Western European
Ancestry] of 1000 Genomes phase 3 dataset assessed via
www.ensembl.org, accessed 21 January 2015). No signif-
icant deviation from Hardy–Weinberg equilibrium was
observed (p>0.01).
An unweightedgenetic risk score was constructed based on
all unlinked genetic variants (TCF7L2 rs7903146, KCNQ1
rs163184, KCNQ1 rs2237892, GIPR rs10423928 and WFS1
rs10010131) that were significantly related to type 2 diabetes
within the study population. Minor allele frequencies for all
SNPs can be found in the electronic supplementary material
(ESM) Table 1.
Diabetologia (2016) 59:2613–2621 2615
Content courtesy of Springer Nature, terms of use apply. Rights reserved.
Statistical analyses Prentice-weighted Cox regression was
used with age as the underlying time-scale, incident type 2
diabetes as the outcome and each SNP (GIPR: rs10423928;
TCF7L2: rs7903146 and rs12255372; WFS1: rs10010131;
KCNQ1: rs151290, rs2237892 and rs163184) as well as the
genetic risk score, in turn, as exposure variables, stratified by
centre and age at baseline (rounded to the nearest integer) and
adjusted for sex. For each SNP, per genotype and additive
genetic effects were modelled using the minor allele as the
effect allele. Gene–diet interactions for each SNP (as well as
genetic risk score) and intake of each dietary factor
(whey-containing dairy, cereal fibre, coffee and olive oil) were
modelled assuming an additive genetic effect by inclusion of a
multiplicative interaction term in the model. This model was
further adjusted for physical activity, education, BMI,
smoking status, total energy intake, intake of fruit and
vegetables, meat, soft drinks and alcohol, and mutual
adjustment of the four dietary factors of interest. All dietary
factors, except coffee, were energy-adjusted by the residual
method [37]. Dietary factors were scaled to represent one
serving. Calculation of one serving for each food item was
based on previous publications from the EPIC-InterAct study
and main EPIC studies: dairy 150 g/day; cereal fibre 10 g/day;
olive oil 10 g/day; and coffee 125 g/day [26,38,39].
The country-specific Cox regression coefficients in the
genetic main effects analysis and gene–diet interaction
analyses were estimated and combined using random-effects
meta-analysis. Between-study heterogeneity was estimated using I
2
.
In the interaction analysis, we established the number of
independent genetic variables from a correlation matrix
(Pearson’s r) of all genetic variables tested in the interaction
analysis (seven SNPs), and a genetic risk score (range 0–10)
by spectral decomposition [40].Basedonthenumberof
independent genetic variables (n= 7) and dietary factors
(n=4) tested, pvalues below the multiple testing corrected
significance threshold of 0.0018 (=0.05/28) were considered
to be statistically significant.
All statistical analyses were performed using the SAS
Enterprise Guide 6.1 (SAS Institute, Cary, NC, USA) and
SAS 9.4 (SAS Institute). Meta-analyses were performed using
R (version 3.1.2, www.r-project.org) and the R function
‘metagen’available from the R package ‘meta’(version
4.3-0) in PROC IML.
Results
Participants in the subcohort (n= 11,035) were followed up
for a median (interquartile range, IQR) of 12.5 (2.4) years,
and 64% were women. The population was on average
middle-aged (median [IQR] age 51.5 [14.0]) and had a median
(IQR) BMI of 25.5 (5.4) kg/m
2
(Table 1).Themedian(IQR)
intake of the dietary factors of interest was 7.2 (5.2) g/day for
cereal fibre, 248 (280) g/day for whey-containing dairy
products, 1.7 (19.9) g/day for olive oil and 233 (417) g/day
for coffee (Table 1). Study population characteristics stratified
by country can be found in ESM Table 2.
All selected SNPs, with the exception of KCNQ1 rs163171
(the only proxy SNP used in our analysis), were associated
Tabl e 1 Baseline characteristics of the EPIC-InterAct subcohort par-
ticipants with data available for at least one SNP of interest
Characteristic Participants
with missing
data (%)
Subcohort
participants
(n= 11,035)
Age (years) 0 51.5 (45.0–59.0)
Sex (female) 0 7065 (64.0)
BMI (kg/m
2
) 0 25.5 (23.0–28.4)
Waist (cm) 8.9 85.0 (76.0–94.5)
Highest school level 0
None 943 (8.6)
Primary 3613 (32.7)
Technical 2418 (21.9)
Secondary 1745 (15.8)
Further education 2316 (21.0)
Smoking status 0
Never 5356 (48.5)
Former 2941 (26.7)
Current 2738 (24.8)
Cambridge Index of Physical Activity 0
Inactive 2754 (25.0)
Moderately inactive 3800 (34.4)
Moderately active 2500 (22.7)
Active 1981 (18.0)
Energy intake (MJ/day) 0 8.5 (6.9–10.5)
Cereal fibre (g/day) 0 7.2 (5.0–10.2)
Fruit and vegetables (g/day) 0 310 (189–467)
Whey-containing dairy products
(g/day)
0 248 (129–409)
Meat (g/day) 0 70.0 (43.9–103.4)
Olive oil (g/day) 17.0 1.7 (0.0–19.9)
Coffee (g/day) 0 233 (83–500)
Soft drinks (g/day) 0 2.6 (0.0–64.3)
Alcohol intake (g/day) 0
0 1928 (17.5)
>0–6 3976 (36.0)
>6–12 1649 (14.9)
>12–24 1709 (15.5)
>24 1773 (16.1)
Hypertension 0.6 2116 (19.2)
Hyperlipidaemia 24.8 1617 (14.7)
Stroke 10.4 78 (0.7)
Myocardial infarction 1.8 134 (1.2)
Data are median (25th–75th percentile) or number (%)
2616 Diabetologia (2016) 59:2613–2621
Content courtesy of Springer Nature, terms of use apply. Rights reserved.
with incident type 2 diabetes (ESM Table 3). We found no
evidence of an interaction with any of the dietary factors for
WFS1 rs10010131, or KCNQ1 rs163171, rs163184 and
rs2237892. For GIPR rs10423928, we observed a marginally
non-significant interaction with olive oil (p=0.050)(Table 2).
We identified a nominally significant interaction between a
TCF7L2 variant and coffee consumption (rs12255372:
p
Interaction
=0.048, Table 2). Overall, coffee intake was
inversely related to the risk of type 2 diabetes (ESM
Tab le 4). Stratification by rs12255372 genotype indicated a
lack of an association between coffee intake and type 2
diabetes in non-carriers of the risk allele (rs12255372-GG:
HR 0.99 [95% CI 0.97, 1.02] per cup of coffee; Table 2), while
with each additional type 2 diabetes risk allele, the inverse
association between coffee intake and risk of type 2 diabetes
became stronger (rs12255372-GT: HR 0.96 [95% CI 0.93,
0.98] per cup; rs12255372-TT: HR 0.93 [95% CI 0.88, 0.98]
per cup; Table 2). TCF7L2 rs12255372 is in moderate LD to
TCF7L2 rs7903146 (see Methods section) for which we
observed a similar trend for interaction with coffee (Table 2).
There was no evidence for heterogeneity between countries
(I
2
=8.8%).
We also observed some effect modification in the
association between cereal fibre and type 2 diabetes by
TCF7L2 variants, but the interaction terms did not reach
statistical significance. As observed for coffee, the interaction
of cereal fibre with TCF7L2 SNPs was slightly stronger for
rs12255372 (p
Interaction
= 0.08, Table 2) than for rs7903146
(p
Interaction
=0.30, Table 2).
A genetic risk score based on the diabetes risk alleles
TCF7L2 rs7903146, KCNQ1 rs163184, KCNQ1 rs2237892,
GIPR rs10423928 and WFS1 rs10010131 also showed a
nominally significant interaction with coffee intake
(p= 0.005, Table 3). The inverse association between coffee
intake and risk of type 2 diabetes was stronger in carriers of six
or more incretin-specific risk alleles (Table 3). After exclusion
of TCF7L2 rs7903146 from the risk score the interaction ef-
fect remained significant, indicating that the effect was not
onlydrivenbytheTCF7L2 SNP that showed a trend for
interaction with coffee in the single SNP analysis. None of
the abovementioned interactions can be considered significant
after correction for multiple testing.
Sensitivity analyses of the statistically significant
interactions were carried out excluding participants with
prevalent stroke, myocardial infarction and cancer as well as
cases occurring within the first two years of follow-up to
examine the possibility of confounding and reverse causation
by these factors. No notable changes in effect estimates and
pvalues were observed. Since olive oil consumption varied
considerably between countries (ESM Table 2), we performed
further sensitivity analysis specifically for all interactions
involving olive oil. First, we excluded centres with extremely
low consumption of olive oil (all centres where the median
intake was 0), which included all of the centres in France, the
Netherlands and Sweden. In this analysis, estimates and
pvalues did not change notably. Second, we assessed
interactions between each SNP and a binary variable
indicating consumption vs non-consumption of olive oil. No
interaction effects between olive oil consumption and any of
the SNPs of interest were identified (data not shown).
Discussion
Summary of findings Our primary aims were to investigate
the presence of multiplicative interactions between the intake
of foods as well as beverages and specific SNPs relevant to the
incretin system, and to identify how these interactions affect
incident type 2 diabetes in a European case-cohort study. We
identified a possible interaction between TCF7L2 and coffee,
with carriers of the type 2 diabetes risk-conferring T allele
benefiting more from coffee consumption than non-carriers.
As far as the other genes are concerned, no compelling
evidence of interaction was observed. The genetic risk score
also showed evidence for an interaction with coffee. None of
the aforementioned interactions passed the pvalue threshold
for statistical significance after correction for multiple testing,
thus all nominally significant findings should be interpreted
with caution, as we cannot exclude the possibility of a Type I
error. On the other hand, since very large sample sizes are
required to detect gene–environment interactions of small
magnitude, Type II errors are also possible.
Gene–diet interactions involving TCF7L2 In line with the
current findings, previous studies have also identified
interactions between TCF7L2 variants and dietary components
[41–43], but none of these studies investigated an interaction
with coffee. In our study, we found a nominally significant
interaction between SNP rs12255372 in TCF7L2 and coffee
consumption, where the inverse association of coffee intake
and type 2 diabetes was only present among participants
carrying the risk-conferring T allele and was stronger in
homozygous compared with heterozygous carriers.
The evidence from previous studies regarding interactions
between TCF7L2 variants and dietary components has
focused mainly on wholegrain cereal and dietary fibre.
Previous studies [41–43] reported that TCF7L2 rs7903146
interacted with whole grains and cereal fibre intake, with the
protective effect of dietary fibre appearing only in individuals
homozygous for the C allele. In our study, we did find a
similar trend of a protective association between cereal fibre
and type 2 diabetes only among TCF7L2 rs7903146 CC
carriers and rs12255372 GG carriers, but these interactions
did not reach statistical significance (p= 0.30 and p= 0.08,
respectively). Similarly, a previous study also failed to show
an interaction between TCF7L2 rs12255372 and dietary fibre
Diabetologia (2016) 59:2613–2621 2617
Content courtesy of Springer Nature, terms of use apply. Rights reserved.
on type 2 diabetes risk [44]. Furthermore, another large study
of 46,000 individuals failed to find an interaction between
TCF7L2 rs4506565 (a SNP in high LD with rs7903146) and
intake of wholegrain cereal in relation to blood glucose and
insulin levels [45]. Although these studies show a trend
towards an interaction, even larger sample sizes or improved
assessment methods may be needed to achieve the appropriate
statistical power for detecting this specific interaction. We
have to acknowledge, however, that overall no association
between cereal fibre and risk for type 2 diabetes was detected
Tabl e 2 Multivariable-adjusted HR (95% CI) for risk of type 2 diabetes per one serving per day increment of each dietary factor, stratified byincretin-
related SNPs, in the EPIC-InterAct study (n= 18,638)
SNP HR (95% CI) p
Interaction
TCF7L2 rs12255372 (n
cases
/n
total
) GG (3,216/8,171) GT (3,520/7,661) TT
a
(906/1,807)
Whey-containing dairy (150 g/day) 1.04 (1.00, 1.08) 0.98 (0.94, 1.02) 1.06 (0.97, 1.15) 0.67
Cereal fibre (10 g/day) 0.94 (0.81, 1.09) 1.06 (0.92, 1.23) 1.35 (0.97, 1.88) 0.08
Coffee (125 g/day) 0.99 (0.97, 1.02) 0.96 (0.93, 0.98) 0.93 (0.88, 0.98) 0.048
Olive oil (10 g/day) (n
cases
/n
total
)
b
0.97 (0.91, 1.04) (2,546/6,459) 0.98 (0.92, 1.04) (2,919/6,475) 0.97 (0.88, 1.08) (781/1,585) 0.67
TCF7L2 rs7903146 (n
cases
/n
total
) CC (3,197/8,196) CT (3,622/7,896) TT
a
(977/1,879)
Whey-containing dairy (150 g/day) 1.04 (1.00, 1.08) 0.99 (0.95, 1.03) 1.02 (0.94, 1.11) 0.89
Cereal fibre (10 g/day) 0.94 (0.81, 1.09) 1.06 (0.92, 1.24) 1.13 (0.83, 1.54) 0.30
Coffee (125 g/day) 0.99 (0.97, 1.01) 0.95 (0.93, 0.98) 0.95 (0.90, 1.00) 0.08
Olive oil (10 g/day) (n
cases
/n
total
)
b
0.98 (0.92, 1.04) (2,539/6,500) 1.00 (0.94, 1.06) (3,002/6,659) 0.90 (0.81, 0.98) (835/1,630) 0.90
KCNQ1 rs163171 (n
cases
/n
total
) CC (4,647/10,652) CT (2,605/6,110) TT
a
(391/883)
Whey-containing dairy (150 g/day) 1.03 (0.99, 1.06) 1.01 (0.96, 1.05) 1.09 (0.94, 1.25) 0.77
Cereal fibre (10 g/day) 1.04 (0.92, 1.19) 0.93 (0.79, 1.11) 0.64 (0.36, 1.16) 0.18
Coffee (125 g/day) 0.98 (0.96, 1.00) 0.95 (0.92, 0.98) 1.03 (0.95, 1.11) 0.75
Olive oil (10 g/day) (n
cases
/n
total
)
b
0.99 (0.94, 1.04) (3,862/8,841) 0.95 (0.89, 1.02) (2,083/4,985) 0.97 (0.78, 1.22) (302/699) 0.56
KCNQ1 rs163184 (n
cases
/n
total
) TT (2,007/4,850) GT (3,971/9,135) GG
a
(1,977/4,367)
Whey-containing dairy (150 g/day) 1.01 (0.96, 1.07) 1.00 (0.97, 1.04) 1.02 (0.97, 1.07) 0.59
Cereal fibre (10 g/day) 0.97 (0.80, 1.17) 0.89 (0.77, 1.03) 1.17 (0.98, 1.40) 0.69
Coffee (125 g/day) 0.97 (0.94, 1.00) 0.97 (0.95, 0.99) 0.97 (0.93, 1.00) 0.22
Olive oil (10 g/day) (n
cases
/n
total
)
b
1.00 (0.93, 1.08) (1,668/4,025) 0.96 (0.91, 1.01) (3,240/7,551) 0.94 (0.87, 1.01) (1,591/3,529) 0.97
KCNQ1 rs2237892 (n
cases
/n
total
)CC
a
(6,148/13,869) CT (701/1,647) TT (20/61)
Whey-containing dairy (150 g/day) 1.01 (0.98, 1.04) 1.09 (1.00, 1.19) –
c
0.56
Cereal fibre (10 g/day) 0.99 (0.88, 1.11) 0.97 (0.69, 1.37) –
c
0.81
Coffee (125 g/day) 0.98 (0.96, 0.99) 0.97 (0.92, 1.02) –
c
0.27
Olive oil (10 g/day) (n
cases
/n
total
)
b
0.95 (0.91, 0.99) (5,100/11,579) 1.17 (0.98, 1.39) (529/1,250) –
c
(13/38) 0.07
GIPR rs10423928 (n
cases
/n
total
) TT (4,925/11,548) AT (2,684/6,014) AA
a
(345/784)
Whey-containing dairy (150 g/day) 1.04 (1.00,1.07) 0.99 (0.95, 1.04) 1.04 (0.90, 1.22) 0.51
Cereal fibre (10 g/day) 0.98 (0.87, 1.12) 1.18 (1.00, 1.40) 0.66 (0.43, 1.01) 0.55
Coffee (125 g/day) 0.97 (0.95, 0.99) 0.97 (0.94, 0.99) 0.93 (0.85, 1.02) 0.92
Olive oil (10 g/day) (n
cases
/n
total
)
b
0.93 (0.88, 0.98) (3,989/9,439) 1.04 (0.97, 1.11) (2,226/5,003) 1.38 (1.02, 1.85) (283/657) 0.050
WFS1 rs10010131 (n
cases
/n
total
)GG
a
(2,857/6,392) AG (3,424/8,095) AA (1,185/2,773)
Whey-containing dairy (150 g/day) 1.00 (0.96, 1.04) 1.04 (1.00, 1.08) 0.99 (0.92, 1.07) 0.95
Cereal fibre (10 g/day) 1.02 (0.86, 1.20) 0.99 (0.85, 1.14) 0.81 (0.63, 1.04) 0.72
Coffee (125 g/day) 0.97 (0.95, 1.00) 0.98 (0.96, 1.00) 0.94 (0.91, 0.98) 0.49
Olive oil (10 g/day) (n
cases
/n
total
)
b
0.98 (0.92, 1.04) (2,378/5,346) 0.97 (0.91, 1.03) (2,783/6,616) 0.92 (0.84, 1.01) (935/2,215) 0.27
Cox regression models were stratified by centre and age (years) and adjusted for sex, study centre, educational attainment, physical activity, smoking
status, BMI, alcohol consumption, soft drink consumption, fruit and vegetable intake, meat intake, total energy intake, and mutual adjustment of thefour
dietary factors of interest (whey-containing-dairy, cereal fibre, olive oil and coffee)
a
Type 2 diabetes risk allele
b
Participants in the UK centres (n
cases
=685/n
total
= 1594) and the Swedish centre Umeå (n
cases
=785/n
total
= 1693) were excluded from this specific
analysis due to inaccurate assessment of olive oil consumption
c
Numbers are too low to estimate HR for rare allele homozygotes
2618 Diabetologia (2016) 59:2613–2621
Content courtesy of Springer Nature, terms of use apply. Rights reserved.
in the EPIC-InterAct study, while such an association was
observed in many other prospective cohort studies resulting
in a clearly significant protective effect in a meta-analysis
[28].
Potential mechanisms Our analyses show some evidence for
a possible TCF7L2–coffee interaction, as well as an overall
interaction of an incretin-specific genetic risk score with
coffee on type 2 diabetes risk. The genetic risk score included
SNPs preselected based on their involvement in the incretin
system, therefore it may represent an overall genetic
predisposition to defects in postprandial insulin secretion,
which is somewhat attenuated by coffee consumption.
TCF7L2 is a transcription factor for proteins involved in the
proper functioning of the Wnt signalling pathway, essential
for insulin secretion and beta cell proliferation. Being in
positive feedback with GLP-1, TCF7L2 is believed to
enhance pancreatic incretin sensitivity [13], as well as increase
the production of GIP [14]. KCNQ1 plays an important role in
the transport of incretins in enterocytes by exocytosis and thus
incretin secretion from the gut [3,10]. GIPR is the receptor for
GIP and thus is directly involved in pancreatic incretin
sensitivity [2,3], while WFS1 codes for Wolframin, which is
responsible for the proper folding of proinsulin, and is thus
linked to pancreatic incretin sensitivity [3,9].
A study by Faerch et al [46] suggests that a major effect of
TCF7L2 variants on type 2 diabetes is mediated through lower
secretion of GIP. In addition, the type 2 diabetes risk allele (T)
in TCF7L2 rs7903146 and rs12255372 has been linked to
impaired insulin secretion and incretin action [7,8], and more
specifically, a reduction in the second phase of GLP-1-induced
insulin secretion [8]. Given this, it would be rational to
speculate that foods and beverages which tend to stimulate
the secretion of incretins, such as coffee, will have the tendency
to compensate, partly, for any genetic defects in the incretin
system and thus confer higher protection from type 2 diabetes
in individuals carrying the predisposing polymorphisms.
Consistent with our results, a randomised crossover trial among
individuals with type 2 diabetes that investigated the
postprandial effects of three different isoenergetic diets on
glucose levels concluded that a dietary pattern characterised
by high coffee consumption resulted in a more pronounced
insulin response with similar postprandial glucose levels; an
effect attributed to increased GIP hormone release [47]. This
effect of coffee is possibly brought about via delayed intestinal
glucose absorption [24], but pathways involving direct effects
on beta cells cannot be excluded; thus, further experimental
studies that take incretin levels into account are required to
elucidate the exact mechanisms.
Study strengths and limitations Study power is a major issue
in gene–environment interaction studies. Many existing
gene–environment interaction studies of type 2 diabetes are
underpowered, particularly if the interactions are of small
magnitude [48]. With a sample of approximately 20,000 our
study is adequately powered for detecting gene–environment
interactions of moderate magnitude. With 8291 cases of type 2
diabetes, our gene–environment interaction analysis is the
largest ever reported from a single study. Still, we did not find
an interaction that passed the multiple testing significance
threshold, suggesting that either no interactions exist in the
study population or that even larger sample sizes are required
to detect them.
The fact that our study includes participants from eight
different European countries significantly increases the external
validity of our findings, at least in individuals of European origin.
Tabl e 3 Multivariable-adjusted HR (95% CI) stratified by genetic risk score and pvalue for the interaction of each dietary factor with incident type 2
diabetes in the EPIC-InterAct study (n= 18,638)
Genetic risk score
a
p
Interaction
0–34567–10
n
cases
/n
total
642/1710 1188/2890 1684/3780 1509/3331 1147/2326
Whey-containing dairy (150 g/day) 0.96 (0.86, 1.06) 1.09 (1.03, 1.16) 0.98 (0.93, 1.03) 1.00 (0.95, 1.06) 1.03 (0.96, 1.10) 0.90
Cereal fibre (10 g/day) 0.96 (0.70, 1.30) 0.84 (0.65, 1.09) 0.85 (0.68, 1.05) 0.96 (0.77, 1.21) 1.07 (0.84, 1.36) 0.79
Coffee (125 g/day) 0.99 (0.94, 1.04) 0.97 (0.93, 1.02) 0.97 (0.94, 1.00) 0.96 (0.92, 0.99) 0.95 (0.90, 0.99) 0.005
n
cases
/n
total
489/1304 945/2338 1383/3131 1249/2776 972/1984
Olive oil
b
(10 g/day) 0.88 (0.75, 1.03) 0.98 (0.88, 1.08) 0.97 (0.88, 1.06) 0.96 (0.88, 1.05) 0.98 (0.89, 1.08) 0.27
Cox regression models were stratified by centre and age (years) and adjusted for sex, centre, educational attainment, physical activity, smoking status,
BMI, alcoholconsumption, soft drink consumption, fruit and vegetable intake, meat intake, total energy intake and mutual adjustment of the four dietary
factors of interest (whey-containing dairy, cereal fibre, olive oil and coffee)
a
The genetic risk score was constructed using the risk alleles for TCF7L2 rs7903146, KCNQ1 rs163184, KCNQ1 rs2237892, GIPR rs10423928 and
WFS1 rs10010131
b
Participants in the UK centres (n
cases
=685/n
total
= 1594) and the Swedish centre Umeå (n
cases
=785/n
total
= 1693) were excluded from specific analysis
due to inaccurate assessment of olive oil consumption
Diabetologia (2016) 59:2613–2621 2619
Content courtesy of Springer Nature, terms of use apply. Rights reserved.
Despite accurate assessment of both exposure (dietary fac-
tors) and outcome (diabetes) and the temporal relationship
between the two, the inherent issue of dietary misreporting,
which affects all observational studies, cannot be completely
excluded.
The selection of dietary factors in our analysis was based
on a review of the existing literature with the aim of identifying
factors that have been shown to stimulate incretin release over
and above macronutrient intake. However, we have to
acknowledge that there are certainly more dietary factors and
SNPs that influence the incretin system that are yet to be
discovered. Hence, our analysis cannot be viewed as a
comprehensive report on all possible interactions between
SNPs and dietary factors in the incretin system.
Conclusion
Our population-based case–cohort study provides evidence for
the possible interaction of a TCF7L2 variant and an incretin-
specific genetic risk score with coffee consumption affecting
the risk of type 2 diabetes. Further adequately powered studies
or meta-analyses of smaller studies are required to replicate
and confirm these interactions.
Acknowledgements We thank all EPIC participants and staff for their
contribution to the study. We thank N. Kerrison (MRC Epidemiology
Unit, Cambridge, UK) for managing the data for the InterAct Project.
Funding Funding for the InterAct project was provided by the EU FP6
programme (grant no. LSHM_CT_2006_037197). In addition, InterAct
investigators acknowledge funding from the following agencies: YTvdS:
Dutch research council (NWO-ZonMW; grant no. 40-00812-98-10040),
verification of diabetes cases in EPIC-NL was additionally funded by NL
Agency grant IGE05012 and an Incentive Grant from the Board of the
UMC Utrecht; EA: Health Research Fund (FIS) of the Spanish Ministry
of Health, Navarre Regional Government and CIBER Epidemiología y
Salud Pública (CIBERESP); GB: Spanish Ministry of Health (ISCIII
RETICC RD 06/0020/0091) and the Catalan Institute of Oncology
(ICO-IDIBELL), Barcelona, Spain; PWF: Swedish Research Council,
Novo Nordisk, Swedish Diabetes Association, Swedish Heart-Lung
Foundation; RK: German Cancer Aid, German Ministry of Research
(BMBF); TJK: Cancer Research UK; KTK: Medical Research Council
UK, Cancer Research UK; CN: Health Research Fund (FIS) of the
Spanish Ministry of Health; Murcia Regional Government (no. 6236);
PMN: Swedish Research Council; KO: Danish Cancer Society; OR: The
Västerboten County Council; YTvdS, IS, AMWS and DLvdA: Dutch
Ministry of Public Health, Welfare and Sports (VWS), Netherlands
Cancer Registry (NKR), LK Research Funds, Dutch Prevention Funds,
Dutch ZON (Zorg Onderzoek Nederland), World Cancer Research Fund
(WCRF), Statistics Netherlands; AT: Danish Cancer Society; RT: AIRE-
ONLUS Ragusa, AVIS-Ragusa, Sicilian Regional Government; ER:
Imperial College Biomedical Research Centre.
Duality of interest The authors declare that there is no duality of inter-
est associated with this manuscript.
Contribution statement AH conceptualised the project and wrote up
the aims, specific objectives and initial analysis plan. AH was responsible
for writing up the introduction and discussion sections of the manuscript.
KM conducted the statistical analysis, made amendments to the analysis
plan and prepared the study results. KM was responsible for writing up
the methods and resultssections of the manuscript. Both AH and KM had
access to all data for this study and take responsibility for the manuscript
contents. All authors qualify for authorship. They have all contributed to
the conception and design of the study, interpretation of the data, critical
revision of the article for important intellectual content and final approval
of the version to be published.
Open Access This article is distributed under the terms of the Creative
Commons Attribution 4.0 International License (http://
creativecommons.org/licenses/by/4.0/), which permits unrestricted use,
distribution, and reproduction in any medium, provided you give appro-
priate credit to the original author(s) and the source, provide a link to the
Creative Commons license, and indicate if changes were made.
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