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Diabetes
Effect of Common Genetic Variants Associated with
Type 2 Diabetes and Glycemic Traits on α- and β-cell
Function and Insulin Action in Man
Anna Jonsson1,2, Claes Ladenvall1, Tarunveer Singh Ahluwalia1,2, Jasmina Kravic1, Ulrika
Krus1, Jalal Taneera1, Bo Isomaa3,4, Tiinamaija Tuomi3,5, Erik Renström1, Leif Groop1,6 and
Valeriya Lyssenko1,7
From the 1Department of Clinical Sciences, Diabetes and Endocrinology, Lund University,
Malmö, Sweden, Lund University Diabetes Centre, Malmö, Sweden, 2Novo Nordisk
Foundation Centre for Basic Metabolic Research, Section of Metabolic Genetics, Faculty of
Health and Medical and Sciences, University of Copenhagen, Copenhagen, Denmark,
3
Folkhälsan Research Centre, Helsinki, Finland, 4Department of Social Services and Health
Care, Jakobstad, Finland, 5Department of Medicine, Helsinki University Central Hospital,
Helsinki, Finland, 6Finnish Institute of Molecular Medicine (FIMM), Helsinki University,
Helsinki, Finland, 7Steno Diabetes Center A/S, Gentofte, Denmark
Running title: Genetic variants, insulin and glucagon secretion
Word count: Abstract 197, Main text 1,997
Items on display: 4 Figures, 8 Supplementary Tables, 5 Supplementary Figures
Abbreviations
SNP - single nucleotide polymorphism, OGTT - oral glucose tolerance test, FPG – fasting
plasma glucose, ISI - insulin sensitivity index, HOMA-IR - insulin resistance index, CIR corrected insulin response, DI - disposition index.
Address for correspondence
Anna Jonsson
Novo Nordisk Foundation Centre for Basic Metabolic Research
Section of Metabolic Genetics
Faculty of Health and Medical and Sciences
University of Copenhagen
DIKU Building
Universitetsparken 1, 1st floor
DK-2100 Copenhagen Ø
Denmark
Phone: 46-735-364541
E-Mail: [email protected]
1
Diabetes Publish Ahead of Print, published online April 4, 2013
Diabetes
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Abstract
Although meta-analyses of genome-wide association studies have identified more than 60
single nucleotide polymorphisms (SNPs) associated with type 2 diabetes and/or glycemic
traits, there is little information whether these variants also affect α-cell function. The aim of
the present study was to evaluate the effects of glycemia-associated genetic loci on islet
function in vivo and in vitro. We studied 43 SNPs in 4,654 normoglycemic participants from
the Finnish population-based PPP-Botnia study. Islet function was assessed, in vivo, by
measuring insulin and glucagon concentrations during OGTT, and, in vitro, by measuring
glucose stimulated insulin and glucagon secretion from human pancreatic islets. Carriers of
risk variants in BCL11A, HHEX, ZBED3, HNF1A, IGF1 and NOTCH2 showed elevated,
while those in CRY2, IGF2BP2, TSPAN8 and KCNJ11 decreased fasting and/or 2hr glucagon
concentrations in vivo. Variants in BCL11A, TSPAN8, and NOTCH2 affected glucagon
secretion both in vivo and in vitro. The MTNR1B variant was a clear outlier in the relationship
analysis between insulin secretion and action, as well as between insulin, glucose and
glucagon. Many of the genetic variants shown to be associated with type 2 diabetes or
glycemic traits also exert pleiotropic in vivo and in vitro effects on islet function.
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Diabetes
In the last few years genome wide association studies have substantially increased the
knowledge of genetic variants predisposing to type 2 diabetes. Although the majority of these
SNPs seem to influence insulin secretion, few if any studies have assessed their simultaneous
effects on β- and α-cell function in vivo and in vitro. The aim of the present study was to
provide a comprehensive evaluation of the effects of genetic loci associated with type 2
diabetes [1] and/or glucose and insulin levels [2] on islet function, in vivo, in a large well
characterized population-based study from the western coast of Finland, the PPP-Botnia
study, and, in vitro, in human pancreatic islets. Islet function was assessed by measuring
insulin and glucagon concentrations during an oral glucose tolerance test (OGTT).
Research Design and Methods
Study population
The PPP-Botnia Study (Prevalence, Prediction and Prevention of diabetes) is a populationbased study from the Botnia region of Western Finland. 4,654 non-diabetic subjects (fasting
plasma glucose (FPG) <7.0 mmol/l and 2hr plasma glucose <11.1 mmol/l) were included in
the current study [3] (Supplementary Table 1).
Measurements
Blood samples were drawn at 0, 30 and 120 minutes of the OGTT. Insulin was measured
using a fluoroimmunometric assay (AutoDelfia, PerkinElmer, Waltham, MA, USA), and
serum glucagon using radioimmunoassay (Millipore, St. Charles, MO, USA). Insulin
sensitivity
index
(ISI)
was
calculated
as
10,000/√(fasting
P-glucose×fasting
P-
insulin×meanOGTTglucose×meanOGTTinsulin) [4]. Insulin secretion was assessed as corrected
insulin response during OGTT (CIR=100×insulin at 30 min/[glucose at 30 min×(glucose 30
3
Diabetes
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min-3.89)]) [5] or as disposition index, i.e. insulin secretion adjusted for insulin sensitivity
(DI=CIR×ISI).
Genotyping
Genotyping was performed either by matrix-assisted laser desorption-ionization time-of-flight
mass spectrometry on the MassARRAY platform (Sequenom, San Diego, CA, USA) or by an
allelic discrimination method with a TaqMan assay on the ABI 7900 platform (Applied
Biosystems, Foster City, CA, USA). We obtained an average genotyping success rate of
98.1%, and the average concordance rate, based on 10,578 (6.5%) duplicate comparisons was
99.9%. Hardy-Weinberg equilibrium was fulfilled for all studied variants (P>0.05).
Glucose-stimulated insulin and glucagon secretion in human pancreatic islets
Glucose-stimulated insulin and glucagon secretion were performed in high (16.7 mmol/l) and
low (1.0 mmol/l) glucose concentration in the medium as described previously [6] and were
available from 56 non-diabetic human pancreatic islet donors. Given the limited number of
donors for genetic analyses, the relationships between absolute genotypic means of insulin
and glucagon concentrations and the 95% CI were plotted separately for different genotype
carriers rather than contrasting mean differences in phenotypes between genotypes.
Statistical Analysis
Variables are presented as mean ± standard deviation (SD), and if not normally distributed as
median [interquartile range (IQR)]. Genotype-phenotype correlations were studied using
linear regression analyses adjusted for age, gender and BMI. Non-normally distributed
variables were logarithmically (natural) transformed for analyses. All statistical analyses were
4
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Diabetes
performed with IBM SPSS Statistics version 19.0 (IBM, Armonk, NY) and PLINK version
1.07 [7, 8].
Results
Effect of SNPs on metabolic variables
Insulin secretion We observed the strongest effect on reduced insulin secretion for the
MTNR1B rs10830963 (CIR P=4.3×10-22; DI P=3.4×10-32). Additionally, we confirmed that
genetic variants in CDKAL1 (CIR P=1.6×10-7; DI P=2.5×10-11), HHEX (CIR P=1.4×10-4; DI
P=0.0021), GIPR (CIR P=3.1×10-4; DI P=0.0061), GCK (CIR P=0.0010; DI P=9.2×10-4),
KCNQ1 (CIR P=0.0032; DI P=0.0054), CDKN2A/2B (CIR P=0.016; DI P=0.0072), FADS1
(CIR P=6.6×10-4), IGF2BP2 (DI P=0.0013), JAZF1 (DI P=0.015), KCNJ11 (CIR P=0.019),
PPARG (DI P=0.023), ZFAND6 (CIR P=0.026), CHCHD2P9 (DI P=0.039), ARAP1 (CIR
P=0.039) and BCL11A (CIR P=0.048) were associated with reduced insulin secretion
(Supplementary Table 2).
Insulin action The variants in MTNR1B (P=6.3×10-4), CHCHD2P9 (P=0.011), FADS1
(P=0.013), PPARG (P=0.041) and CDKAL1 (P=0.042) were associated with reduced insulin
sensitivity (Supplementary Table 3).
Glucagon secretion To explore whether these genetic variants would also influence α-cell
function, we analyzed glucagon concentrations during OGTT. The variants in BCL11A
(P=0.0069), HHEX (P=0.0096), ZBED3 (P=0.033) and HNF1A (P=0.047) were associated
with elevated, while variants in CRY2 (P=0.0021), IGF2BP2 (P=0.0036) and TSPAN8
(P=0.034) were associated with reduced fasting glucagon levels. Furthermore, variants in
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IGF1 (P=0.0048), ZBED3 (P=0.0094) and NOTCH2 (P=0.016) were associated with
increased and the variant in KCNJ11 (P=0.027) with decreased postprandial glucagon levels
(Supplementary Table 4).
Effect of SNPs on relationships between metabolic variables
Insulin secretion vs. sensitivity (CIR vs. ISI) We plotted the effects of the tested genetic
variants on CIR and ISI (Figure 1). Most SNPs were related to insulin secretion and
sensitivity in a linear fashion, with SNPs in the MTNR1B, CDKAL1, GCK, CDKN2A/2B and
PPARG loci in the lower left quadrant, reflecting both impaired insulin secretion and action,
with MTNR1B being the most extreme. In contrast variants in GIPR, FADS1, CAMK1D and
TCF7L2 showed relatively high insulin sensitivity despite low insulin secretion. A genotypecentric analysis (Supplementary Figure 1) also showed that homozygous carriers of MTNR1B,
CDKAL1 and GCK risk genotypes had CIR values below the values expected from ISI values
whereas TT carriers of TCF7L2 showed rather high insulin sensitivity.
Glucose vs. insulin levels The variant in MTNR1B showed both high fasting glucose and
insulin levels (Figure 2, Supplementary Table 5 and 6). At the same glucose level,
CHCHD2P9 showed the highest and FADS1 the lowest fasting insulin concentration, while, at
the same fasting insulin concentration, FTO showed the lowest and GCK the highest fasting
glucose levels. Homozygous carriers of the TCF7L2 risk variant had the lowest, whereas
homozygous carriers of HMGA2 showed the highest insulin levels (Supplementary Figure 2).
These differences were partially also seen at 2hr of the OGTT.
Glucose vs. glucagon levels The highest fasting glucose level related to glucagon level was
seen for MTNR1B (Figure 3, Supplementary Figure 3), which was also reflected by high
fasting and 2hr insulin/glucose ratios (Supplementary Table 7, Supplementary Figures 4 and
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Diabetes
5). In line with previous reports, the TCF7L2 variant showed low glucagon levels [9] and
homozygous T-allele carriers showed high fasting and 2hr glucose levels, despite low
insulin/glucagon ratio.
Glucagon vs. insulin levels in human pancreatic islets The risk allele carriers of variants in
CAMK1D, TCF7L2, GIPR, TSPAN8 and KLF14 showed lower, while the risk allele carriers
of CDKAL1, BCL11A, CRY2, FADS1, NOTCH2, HMGA2 and GCK showed higher glucagon
levels than expected from the 95% CI of the relationship between mean values of insulin and
glucagon concentrations measured at normal glucose levels. At high glucose, carriers of the
risk alleles at DGKB, GIPR, JAZF1, CDKN2A/2B, HMGA2 and NOTCH2 displayed high
glucagon levels, while those of TCF7L2, CAMK1D, KLF14, FTO and ZBED3 showed lower
glucagon levels (Figure 4).
Discussion
In the present study, we demonstrate that the effects of some of the genetic variants associated
with type 2 diabetes and/or glycemic traits seem to involve the crosstalk between hormone
secretion from the β- and α-cells. The novel findings in the present study were that we
observed associations of variants in 10 loci with glucagon secretion, of which 6 loci (HHEX,
IGF2BP2, CDKAL1, FADS1, GCK and CDKN2A/2B) were also associated with reduced
insulin secretion.
Effects of variants on the relationship between CIR and ISI were similar to those observed by
Grarup et al. [10], but we also showed that MTNR1B and CDKAL1 were clear outliers with
strong effects on both impaired insulin secretion and sensitivity. These analyses used betavalues which reflect fractions of the standard deviations. We therefore extended the analyses
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by analyzing the relationship between the absolute values of CIR and ISI in different genotype
carriers, which allowed us to obtain a quantitative effect of SNPs. These analyses differ from
those presented by Grarup et al. as we now related the effect of the homozygous risk
genotypes to the mean values of a reference population instead of expressing them as allelic
changes in fraction of standard deviations. It can, of course, be questioned whether
homozygosity for risk alleles would best describe their effect on this relationship, but it has
the advantage that it allows us to show the absolute effects of the variants.
Importantly, we show that many of the genetic variants seem to affect the relationship
between insulin/glucagon secretion and blood glucose levels both at fasting and 2hr during
OGTT. This would imply that in the risk carriers, reduced early insulin secretion in
conjunction with impaired suppression of glucagon secretion and/or action would lead to
excessive delivery of glucose into the circulation. An interesting finding was that, again,
MTNR1B was different from the rest of the variants and showed elevated glucose despite high
insulin/glucagon ratio. There is no obvious explanation to these findings apart from that the
ratio between insulin and glucagon may be more important than the individual insulin and
glucagon levels for the glucose-raising effect of MTNR1B. Also, MTNR1B is mostly
expressed in β-cells, while MTNR1A predominating in α-cells. Until now, no variant in the
MTNR1A gene has been associated with type 2 diabetes or elevated glucose levels.
In keeping with a previous study [11], the rs7903146 variant in TCF7L2 was associated with
lower glucagon values both in vivo and in vitro. In contrast to some previous studies [12, 13]
we did not observe a significant reduction in fasting nor glucose-stimulated insulin secretion
for this variant in the current study. The PPP-study is a population-based study including
people aged 18-75 years, most of whom had normal glucose tolerance. In the previous studies
the effect on insulin secretion was clearly stronger in hyperglycemic individuals or in those
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Diabetes
who subsequently developed diabetes. The same was seen in the present study; if we restrict
the analysis to hyperglycemic individuals (FPG >5.5 mmol/l), we see a clear effect of the
TCF7L2 SNP on reduced insulin secretion (N=1,440, beta -0.109 (0.035), P=0.002). This
effect might be too small to fully explain the glucose-increasing effect of the SNP, nor is it
easy to see an effect of reduced glucagon levels. One possibility is that the TCF7L2 variant
has effects on hepatic glucose production as previously shown [13-15] which cannot be
explained by effects of glucagon on the liver.
From the figures relating the mean values of fasting and 2hr insulin/glucagon with fasting and
2hr glucose concentrations (Supplementary Figure 6), it was obvious that homozygous
carriers of HMGA2 differed most from the others. Thus, while carriers of two HMGA2 risk
alleles showed increased insulin secretion and decreased insulin sensitivity, possibly
protecting them from type 2 diabetes, they demonstrated low insulin/ glucagon, which in turn
led to elevated glucose. In vitro, homozygous risk allele carriers showed reduced suppressive
effect of high glucose on glucagon secretion. HMGA2 encodes for a non-histone protein with
structural DNA-binding domains and may act as a transcriptional regulating factor. The
current data suggest that it may involve a complex interplay between islet function and insulin
action.
The present study also provides some novel insight for the potential mechanism by which the
BCL11A variant could increase the risk of type 2 diabetes. BCL11A rs243021 was originally
identified as a risk variant for type 2 diabetes in the DIAGRAM+ meta-analysis [1]. The
protein encoded by BCL11A is a zinc-finger protein with highest expression in the fetal brain
and the adult central nervous system. While its biological function is not fully elucidated, it
might be involved in the signal transduction from cell to cell or in the neuronal regulation of
hormonal secretion. Association of variants in BCL11A with reduced insulin secretion has
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previously been reported [16]. Here we report novel associations of a BCL11A variant with
increased glucagon levels in vivo. Additionally, in vitro, homozygous risk allele carriers of the
BCL11A variant had glucagon levels above the expected 95% CI of the relationship between
insulin and glucagon secretion in human pancreatic islets. Together, results from the present
paper and previous reports suggest that this variant might predispose to increased risk of type
2 diabetes via reduced insulin and increased glucagon secretion.
In conclusion, we here provide a comprehensive description of how common genetic variants
associated with type 2 diabetes and/or glycemic traits influence insulin and glucagon secretion
in vivo and in vitro in human pancreatic islets. The data clearly emphasize the need to
consider effects on α-cell function when studying mechanisms by which these variants
increase risk of type 2 diabetes.
Acknowledgements
Studies at LUDC, Malmö were supported by grants from the Swedish Research Council,
including a Linné grant (No.31475113580) and a Strategic Research Grant EXODIAB (Dnr
2009-1039), the Heart and Lung Foundation, the Swedish Diabetes Research Society, the
European Community's Seventh Framework Programme grant ENGAGE (Health-F4-2007201413) and CEED3 (223211), the Diabetes Programme at the Lund University, the Påhlsson
Foundation, the Crafoord Foundation, the Knut and Alice Wallenberg Foundation, Novo
Nordisk Foundation and the European Foundation for the Study of Diabetes (EFSD). The
PPP-Botnia study were supported by grants from the Sigrid Juselius Foundation, Folkhälsan
Research Foundation, the Finnish Diabetes Research Society, the Signe and Ane Gyllenberg
Foundation, Swedish Cultural Foundation in Finland, Ollqvist Foundation, Ministry of
Education of Finland, Foundation for Life and Health in Finland, Jakobstad Hospital, Medical
Society of Finland, Närpes Research Foundation and the Vasa and Närpes Health centers.
Human islets were provided by the Nordic network for clinical islets transplantation by the
courtesy of Dr. Olle Korsgren, Uppsala, Sweden. We thank the patients for their participation
and the Botnia Study Group for clinically studying the patients. No potential conflicts of
interest relevant to this article were reported.
Anna Jonsson participated in genotyping and study design, researched and interpreted the data
and wrote the manuscript. Claes Ladenvall, Tarunveer Singh Ahluwalia and Jasmina Kravic
researched the data. Ulrika Krus, Jalal Taneera and Erik Renström were responsible for
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Diabetes
mRNA analyses and the insulin and glucagon secretion measurements from the human
pancreatic islets. Bo Isomaa and Tiinamaija Tuomi collected and phenotyped the PPP-Botnia
study population, Leif Groop and Valeriya Lyssenko designed and supervised the study,
interpreted the data and contributed to discussion. All co-authors reviewed and approved the
final version of the manuscript. Prof. Leif Groop is the guarantor of this work and, as such,
had full access to the data in the study and takes responsibility for the integrity of the data and
the accuracy of the data analysis.
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Diabetes
0.05
BCL11A
NOTCH2
HNF1A
DUSP9(m)
FTO
ADAMTS9
KCNQ1*
HMGA2
PROX1
KLF14
IGF1
GLIS3
TSPAN8
THADA
ZBED3 SLC2A2
CAMK1D
CHCHD2P9
PRC1
ADCY5 DUSP9(f)
MADD
TP53INP1 WFS1
TCF7L2
PPARG
SLC30A8
ADRA2A
CRY2
JAZF1
DGKB
ARAP1
IGF2BP2
KCNJ11 ZFAND6
KCNQ1†
VPS13C
CIR
0.00
-0.05
CDKN2A/2B
FADS1
HHEX
GIPR
GCK
CDKAL1
-0.10
-0.15
MTNR1B
0.04
0.02
0.00
-0.02
-0.04
-0.06
ISI
Figure 1. Graphic representation of effects of genetic variants on relationship between insulin secretion and insulin
sensitivity in non-diabetic participants in the PPP-Botnia study.
Effect sizes are betas from linear regression adjusted for age, gender and BMI. Whereas most genetic variants either increased
insulin secretion or insulin sensitivity, those in MTNR1B, CDKAL1 and GCK were located to the lower left quadrant,
reflecting both impaired insulin secretion and insulin sensitivity, with MTNR1B being the most extreme. In contrast, GIPR
and FADS1 showed relatively high insulin sensitivity despite low insulin secretion. KCNQ1† represents rs2237895, while
KCNQ1* represents rs231362. Black circles P<0.05; White circles P>0.05.
A
B
0.15
0.15
ADCY5
MTNR1B
GCK
0.10
0.10
CDKAL1
CDKN2A/2B
GIPR
2hr glucose
Fasting glucose
CHCHD2P9
VPS13C
KCNQ1†
GCK
MADD
0.05
THADA
CDKN2A/2B
PPARG
KCNQ1†
JAZF1
HNF1A IGF2BP2
GLIS3
HMGA2
KCNQ1*
WFS1 TP53INP1
HHEX
KCNJ11 ARAP1 BCL11A
ZFAND6
IGF1
TSPAN8
ZBED3
VPS13C SLC2A2
SLC30A8
DUSP9(f)
ADRA2A
ADAMTS9
CAMK1D DUSP9(m)
PRC1
NOTCH2
ADCY5
GIPR
KLF14
PROX1
DGKB
FADS1
0.00
CDKAL1
CRY2
CHCHD2P9
TCF7L2
FTO
MTNR1B
ZBED3
MADD
TP53INP1
KCNQ1*
KLF14
TCF7L2
HMGA2
TSPAN8
JAZF1
SLC2A2
IGF2BP2
SLC30A8
CAMK1D
CRY2
PPARG
HNF1A
ADRA2A
KCNJ11
DGKB
DUSP9(f)
BCL11A
ADAMTS9
ARAP1
IGF1
PROX1
HHEX
NOTCH2
0.05
0.00
ZFAND6 WFS1
THADA
-0.05
DUSP9(m)
GLIS3
PRC1
FADS1
FTO
-0.05
-0.04
-0.02
0.00
0.02
Fasting insulin
0.04
0.06
-0.10
-0.05
0.00
0.05
0.10
2hr insulin
Figure 2. Graphic representation of effects of genetic variants on relationship between glucose and insulin levels during
OGTT in non-diabetic participants of the PPP-Botnia study.
Effect sizes are betas from linear regression adjusted for age, gender and BMI. KCNQ1† represents rs2237895, while
KCNQ1* represents rs231362. Black circles P<0.05; White circles P>0.05. Panel A shows the relationship between fasting
insulin and fasting glucose. MTNR1B showed the highest effect on fasting glucose despite elevated insulin levels.
CHCHD2P9 showed increased, while FADS1 showed decreased fasting insulin. Panel B shows the relationship between 2hr
insulin and 2hr glucose. GCK, CDKAL1, ZBED3 and MTNR1B all showed high postprandial glucose levels despite elevated
insulin levels.
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A
B
0.15
0.15
ADCY5
MTNR1B
GCK
0.10
CDKN2A/2B
CDKAL1
0.10
CHCHD2P9
GIPR MTNR1B
KCNQ1†
ZBED3
2hr glucose
Fasting glucose
VPS13C
GCK
MADD
0.05
IGF2BP2
0.00
-0.05
-0.03
THADA
CDKAL1
CHCHD2P9 CDKN2A/2B
DGKB
PPARG
FADS1 JAZF1
CRY2
KCNQ1†
HNF1A
GLIS3
HMGA2
TP53INP1
HHEX
KCNQ1*
ARAP1
TCF7L2
WFS1
TSPAN8
ZFAND6
BCL11A
ZBED3
SLC30A8 ADRA2A
IGF1
KCNJ11
SLC2A2
DUSP9(f)
VPS13C
CAMK1D
ADCY5
PRC1 ADAMTS9
DUSP9(m)
NOTCH2
GIPR
PROX1
KLF14
FTO
-0.02
-0.01
0.00
Fasting glucagon
0.01
0.02
TCF7L2
SLC2A2
0.00
FTO
TP53INP1
0.05
KCNJ11
KCNQ1*
MADD
KLF14
TSPAN8
IGF2BP2
CRY2
JAZF1
CAMK1D
PRC1
-0.10
-0.04
IGF1
ARAP1
PROX1
HHEX
DUSP9(m)
0.03
HNF1A
BCL11A
ADAMTS9
-0.05
HMGA2
SLC30A8
PPARG
ADRA2A
DUSP9(f)
DGKB
NOTCH2
WFS1
GLIS3
ZFAND6
THADA
FADS1
-0.02
0.00
0.04
0.02
2hr glucagon
Figure 3. Graphic representation of effects of genetic variants on relationship between glucose and glucagon levels during
OGTT in non-diabetic participants of the PPP-Botnia study.
Effect sizes are betas from linear regression adjusted for age, gender and BMI. KCNQ1† represents rs2237895, while
KCNQ1* represents rs231362. Black circles P<0.05; White circles P>0.05. Panel A shows the relationship between fasting
glucagon and fasting glucose. The highest fasting glucose level related to glucagon level was seen for MTNR1B. IGF2BP2
and CRY2 showed reduced glucagon secretion, while BCL11A and HHEX were associated with increased fasting glucagon.
Panel B shows the relationship between 2hr glucagon and 2hr glucose. ZBED3, IGF1 and NOTCH2 showed elevated, while
KCNJ11 showed decreased postprandial glucagon secretion.
14
Page 15 of 34
Diabetes
A
Homozygous non-risk
Homozygous risk
Heterozygous
25
25
25
JAZF1
HHEX
HNF1A
MADD
CDKN2A/2B
20
VPS13C
GIPR
CAMK1D
20
FTO
CHCHD2P9
HHEX
15
ADRA2A
10
SLC2A2
SLC30A8
5
20
CDKAL1
DGKB
TP53INP1
15
THADA
10
NOTCH2
CRY2
HMGA2
BCL11A
JAZF1
FADS1
FTO
15
CRY2
10
IGF2BP2
KLF14
TSPAN8
GIPR
TCF7L2
CDKN2A/2B
THADA
HMGA2
CAMK1D
PPARG
0
0.0
CDKN2A/2B
NOTCH2
5
5
ZFAND6
MTNR1B
GCK
MADD
PROX1
Glucagon (pg/islet/h)
Glucagon (pg/islet/h)
TP53INP1
WFS1
Glucagon (pg/islet/h)
ARAP1
0.1
0.2
0.3
0.4
0
0.0
0.5
0.1
0.2
Insulin (ng/islet/h)
0.3
0.4
0
0.0
0.5
0.1
0.2
0.3
0.4
0.5
Insulin (ng/islet/h)
Insulin (ng/islet/h)
B
Homozygous non-risk
Homozygous risk
Heterozygous
25
25
25
ARAP1
HNF1A
MADD
HHEX
CAMK1D
FTO
DGKB
CDKN2A/2B
PROX1
20
MADD
Glucagon (pg/islet/h)
Glucagon (pg/islet/h)
KCNQ1*
HHEX
GIPR
15
KCNJ11
DGKB
10
KCNQ1†
MTNR1B
ADAMTS9
THADA
DGKB
15
TP53INP1
10
ARAP1
ADRA2A
PPARG
ADRA2A
5
SLC2A2
15
0.5
1.0
Insulin (ng/islet/h)
1.5
0
0.0
0.5
MTNR1B
GCK
TP53INP1
PROX1
WFS1
10
BCL11A
IGF2BP2
KCNJ11
ZBED3
5
KLF14
1.0
TCF7L2
CAMK1D
CDKN2A/2B
PPARG
JAZF1
NOTCH2
HMGA2
CHCHD2P9
NOTCH2
THADA
0
0.0
CDKN2A/2B
GCK
HMGA2
5
GIPR
20
GIPR
CHCHD2P9
Glucagon (pg/islet/h)
JAZF1
TP53INP1
20
FTO
1.5
0
0.0
Insulin (ng/islet/h)
0.5
1.0
1.5
Insulin (ng/islet/h)
Figure 4. Relationship between glucagon and insulin secretion in human pancreatic islets at low and high glucose level.
Panel A shows secretion at low glucose (1 mmol/l). The risk allele carriers of variants in CAMK1D, TCF7L2, GIPR, TSPAN8 and KLF14 showed lower glucagon secretion, while the risk allele
carriers of CDKAL1, BCL11A, CRY2, FADS1, NOTCH2, HMGA2 and GCK showed glucagon levels above the expected 95% CI of the relationship between insulin and glucagon secretion.
Panel B shows secretion at high glucose (16.7 mmol/l). The risk allele carriers of DGKB, GIPR, JAZF1, CDKN2A/2B, HMGA2 and NOTCH2 remained high glucagon secretion, while those of
FTO, CAMK1D, TCF7L2, KLF14 and ZBED3 had low glucagon.
15
Diabetes
72x62mm (600 x 600 DPI)
Page 16 of 34
Page 17 of 34
Diabetes
1593x701mm (96 x 96 DPI)
Diabetes
1593x701mm (96 x 96 DPI)
Page 18 of 34
Page 19 of 34
Diabetes
1599x913mm (96 x 96 DPI)
Diabetes
Page 20 of 34
Supplementary Tables and Figures
Tables
Supplementary Table 1. Characteristics of the PPP-Botnia study participants.
Supplementary Table 2. Effect of genetic variants on insulin secretion in non-diabetic participants of
the PPP-Botnia study.
Supplementary Table 3. Effect of genetic variants on BMI and insulin sensitivity in non-diabetic
participants of the PPP-Botnia study.
Supplementary Table 4. Effect of genetic variants on glucagon levels during OGTT in non-diabetic
participants of the PPP-Botnia study.
Supplementary Table 5. Effect of genetic variants on glucose levels during OGTT in non-diabetic
participants of the PPP-Botnia study.
Supplementary Table 6. Effect of genetic variants on insulin levels during OGTT in non-diabetic
participants of the PPP-Botnia study.
Supplementary Table 7. Effect of genetic variants on insulin to glucagon ratios during OGTT in nondiabetic participants of the PPP-Botnia study.
Supplementary Table 8. Overview of investigated genetic variants.
Figures
Supplementary Figure 1. Relationship between insulin secretion (CIR) and insulin sensitivity (ISI).
Supplementary Figure 2. Relationship between glucose and insulin levels during OGTT in 4,654 nondiabetic participants of the PPP-Botnia study.
Supplementary Figure 3. Relationship between glucose and glucagon levels during OGTT in 4,654
non-diabetic participants of the PPP-Botnia study.
Supplementary Figure 4. Graphic representation of effects of genetic variants on relationship between
glucose and insulin to glucagon ratios during OGTT in non-diabetic participants of the PPP-Botnia
study.
Supplementary Figure 5. Relationship between glucose levels and insulin to glucagon ratios during
OGTT in 4,654 non-diabetic participants of the PPP-Botnia study.
Page 21 of 34
Diabetes
Supplementary Table 1. Characteristics of the PPPBotnia study participants.
Number (m/f)
4,654 (2,159/2,495)
Age (years)
47.9 ± 15.2
BMI (kg/m2)
26.2 ± 4.3
Insulin sensitivity index
147 [112]
Corrected insulin response
150 [154]
Disposition index
21,450 [23,450]
Data are mean ± SD or median [IQR].
Diabetes
Page 22 of 34
Supplementary Table 2. Effect of genetic variants on insulin secretion in non-diabetic
participants of the PPP-Botnia study.
CIR
DI
Gene
SNP
Beta
SE
P-value
Beta
SE
P-value
ADAMTS9
rs4607103
0.010
0.020
0.61
-0.004 0.021
0.86
ADCY5
rs11708067
-0.020 0.022
0.36
-0.013 0.023
0.57
ADRA2A
rs10885122
-0.037 0.025
0.14
-0.016 0.026
0.55
ARAP1
rs1552224
-0.042 0.020
0.039
-0.035 0.021
0.10
BCL11A
rs243021
0.033
0.016
0.048
0.020
0.017
0.24
CAMK1D
rs12779790
-0.010 0.020
0.61
0.017
0.021
0.42
CDKAL1
rs7754840
-0.094 0.018
1.6×10-7
-0.126 0.019 2.5×10-11
CDKN2A/2B
rs10811661
-0.056 0.023
0.016
-0.066 0.024
0.0072
CHCHD2P9
rs13292136
-0.010 0.024
0.68
-0.051 0.025
0.039
CRY2
rs11605924
-0.031 0.017
0.061
-0.025 0.017
0.14
DGKB
rs2191349
-0.030 0.016
0.064
-0.032 0.017
0.056
DUSP9 (female) rs5945326
-0.013 0.026
0.62
-0.007 0.028
0.80
DUSP9 (male)
rs5945326
0.030
0.026
0.12
0.043
0.020
0.026
-0.030 0.017
0.078
FADS1
rs174550
-0.055 0.016
6.6×10-4
FTO
rs9939609
0.012
0.017
0.47
0.010
0.018
0.58
GCK
rs4607517
-0.082 0.025
0.0010
-0.087 0.026
9.2×10-4
-4
GIPR
rs10423928
-0.066 0.018
3.1×10
-0.052 0.019
0.0061
GLIS3
rs7034200
0.001
0.016
0.94
-0.005 0.017
0.77
HHEX
rs1111875
-0.061 0.016
1.4×10-4
-0.052 0.017
0.0021
HMGA2
rs1531343
-0.002 0.033
0.96
-0.033 0.035
0.34
HNF1A
rs7957197
0.022
0.019
0.23
0.019
0.019
0.33
IGF1
rs35767
0.004
0.021
0.85
0.001
0.021
0.96
-0.033 0.018
0.062
IGF2BP2
rs4402960
-0.060 0.019
0.0013
JAZF1
rs864745
-0.028 0.016
0.084
-0.041 0.017
0.015
KCNJ11
rs5219
-0.038 0.016
0.019
-0.030 0.017
0.079
KCNQ1
rs2237895
-0.049 0.017
0.0032
-0.048 0.017
0.0054
KCNQ1
rs231362
0.008
0.016
0.63
-0.008 0.017
0.62
KLF14
rs972283
0.000
0.017
0.98
-0.008 0.017
0.64
MADD
rs7944584
-0.019 0.021
0.36
-0.030 0.022
0.17
MTNR1B
rs10830963
-0.168 0.017 4.3×10-22 -0.215 0.018 3.4×10-32
NOTCH2
rs10923931
0.029
0.024
0.23
0.023
0.025
0.36
PPARG
rs1801282
-0.025 0.023
0.27
-0.055 0.024
0.023
PRC1
rs8042680
-0.011 0.018
0.54
-0.014 0.018
0.44
PROX1
rs340874
-0.003 0.016
0.85
0.002
0.017
0.93
SLC2A2
rs11920090
0.000
0.024
0.995
-0.012 0.025
0.64
SLC30A8
rs13266634
-0.031 0.016
0.059
-0.021 0.017
0.22
TCF7L2
rs7903146
-0.030 0.021
0.16
-0.013 0.022
0.57
THADA
rs7578597
-0.009 0.034
0.80
-0.001 0.036
0.97
TP53INP1
rs896854
-0.028 0.016
0.079
-0.023 0.017
0.18
TSPAN8
rs7961581
-0.002 0.019
0.93
-0.007 0.020
0.74
VPS13C
rs17271305
0.012
0.017
0.48
-0.003 0.017
0.85
WFS1
rs10010131
-0.020 0.016
0.21
-0.018 0.017
0.28
ZBED3
rs4457053
-0.002 0.019
0.93
-0.016 0.020
0.43
ZFAND6
rs11634397
-0.039 0.017
0.026
-0.018 0.018
0.33
Beta and SE from linear regression analysis adjusted for age, gender and BMI denotes the effect size by each risk allele (additive model)
on phenotype. Analyses of DUSP9 rs5945326 are stratified by gender and adjusted for age and BMI.
Page 23 of 34
Diabetes
Supplementary Table 3. Effect of genetic variants
diabetic participants of the PPP-Botnia study.
Gene
SNP
BMI
Beta
SE
ADAMTS9
rs4607103
-0.239 0.106
ADCY5
rs11708067
-0.009 0.119
ADRA2A
rs10885122
0.150
0.133
ARAP1
rs1552224
-0.195 0.110
BCL11A
rs243021
-0.007 0.088
CAMK1D
rs12779790
-0.064 0.106
0.032
0.098
CDKAL1
rs7754840
CDKN2A/2B
rs10811661
-0.125 0.126
0.205
0.126
CHCHD2P9
rs13292136
CRY2
rs11605924
0.088
0.088
DGKB
rs2191349
-0.135 0.086
DUSP9 (female) rs5945326
0.169
0.150
DUSP9 (male)
rs5945326
-0.057 0.095
0.009
0.088
FADS1
rs174550
FTO
rs9939609
0.394
0.090
GCK
rs4607517
-0.033 0.134
GIPR
rs10423928
-0.222 0.097
GLIS3
rs7034200
-0.010 0.088
HHEX
rs1111875
-0.082 0.087
HMGA2
rs1531343
-0.139 0.175
HNF1A
rs7957197
0.122
0.100
IGF1
rs35767
-0.035 0.110
IGF2BP2
rs4402960
-0.118 0.096
JAZF1
rs864745
0.096
0.088
KCNJ11
rs5219
0.108
0.088
KCNQ1
rs2237895
-0.016 0.089
KCNQ1
rs231362
0.017
0.087
KLF14
rs972283
0.093
0.089
MADD
rs7944584
0.285
0.110
0.046
0.094
MTNR1B
rs10830963
NOTCH2
rs10923931
-0.234 0.129
-0.125 0.123
PPARG
rs1801282
PRC1
rs8042680
0.138
0.094
PROX1
rs340874
-0.084 0.088
SLC2A2
rs11920090
-0.305 0.128
SLC30A8
rs13266634
-0.081 0.088
TCF7L2
rs7903146
-0.031 0.114
THADA
rs7578597
-0.091 0.185
TP53INP1
rs896854
0.015
0.086
TSPAN8
rs7961581
-0.208 0.103
VPS13C
rs17271305
-0.025 0.089
WFS1
rs10010131
-0.007 0.087
ZBED3
rs4457053
-0.084 0.103
ZFAND6
rs11634397
-0.067 0.094
on BMI and insulin sensitivity in non-
P-value
0.025
0.94
0.26
0.078
0.93
0.55
0.74
0.32
0.11
0.32
0.12
0.26
0.55
0.92
1.3×10-5
0.81
0.023
0.91
0.34
0.43
0.23
0.75
0.22
0.28
0.22
0.86
0.85
0.30
0.0097
0.62
0.070
0.31
0.14
0.34
0.017
0.36
0.79
0.62
0.86
0.043
0.78
0.94
0.42
0.47
ISI
Beta
-0.012
0.008
0.010
0.002
-0.011
0.020
-0.025
-0.004
-0.041
0.003
-0.004
0.015
0.009
0.027
0.002
-0.003
0.023
-0.008
0.004
-0.027
-0.007
-0.002
-0.022
-0.012
0.012
0.003
-0.008
-0.009
-0.012
-0.040
-0.009
-0.032
0.001
0.007
-0.010
0.019
0.014
0.009
-0.003
-0.013
-0.014
0.006
-0.011
0.018
SE
0.013
0.015
0.017
0.014
0.011
0.013
0.012
0.016
0.016
0.011
0.011
0.017
0.013
0.011
0.011
0.017
0.012
0.011
0.011
0.022
0.013
0.014
0.012
0.011
0.011
0.011
0.011
0.011
0.014
0.012
0.016
0.015
0.012
0.011
0.016
0.011
0.014
0.023
0.011
0.013
0.011
0.011
0.013
0.012
P-value
0.36
0.58
0.57
0.86
0.32
0.13
0.042
0.79
0.011
0.80
0.70
0.39
0.52
0.013
0.85
0.84
0.065
0.45
0.69
0.23
0.59
0.87
0.066
0.26
0.30
0.82
0.47
0.43
0.37
6.3×10-4
0.57
0.041
0.97
0.51
0.53
0.091
0.34
0.69
0.81
0.33
0.23
0.58
0.39
0.12
Beta and SE from linear regression analysis adjusted for age, gender and BMI (for ISI) denotes the effect size by each risk allele (additive
model) on phenotype. Analyses of DUSP9 rs5945326 are stratified by gender and adjusted for age and BMI (for ISI).
Diabetes
Page 24 of 34
Supplementary Table 4. Effect of genetic variants on glucagon levels during OGTT in
non-diabetic participants of the PPP-Botnia study.
Fasting glucagon
2hr glucagon
Gene
SNP
Beta
SE
P-value Beta
SE
PADAMTS9
rs4607103
-0.001 0.009
0.95
-0.012 0.010
0.20
ADCY5
rs11708067
-0.018 0.010
0.086
-0.017 0.011
0.11
ADRA2A
rs10885122
0.001
0.012
0.92
-0.008 0.012
0.50
ARAP1
rs1552224
0.007
0.010
0.49
0.002
0.010
0.83
0.011
0.008
0.15
BCL11A
rs243021
0.021
0.008
0.0069
CAMK1D
rs12779790
0.014
0.009
0.14
0.003
0.010
0.77
CDKAL1
rs7754840
0.011
0.009
0.22
0.011
0.009
0.23
CDKN2A/2B
rs10811661
0.005
0.011
0.67
-0.004 0.012
0.71
CHCHD2P9
rs13292136
0.003
0.011
0.75
-0.021 0.011 0.062
-0.007 0.008
0.39
CRY2
rs11605924
-0.024 0.008
0.0021
DGKB
rs2191349
-0.001 0.008
0.90
0.000
0.008
0.98
DUSP9 (female) rs5945326
0.000
0.012
0.995
-0.003 0.013
0.85
DUSP9 (male)
rs5945326
0.003
0.009
0.73
0.002
0.009
0.81
FADS1
rs174550
-0.010 0.008
0.18
0.000
0.008
0.99
FTO
rs9939609
-0.003 0.008
0.67
-0.003 0.008
0.72
GCK
rs4607517
-0.001 0.012
0.93
-0.003 0.012
0.78
GIPR
rs10423928
-0.013 0.009
0.14
-0.008 0.009
0.37
GLIS3
rs7034200
0.002
0.008
0.79
0.005
0.008
0.52
-0.008 0.008
0.33
HHEX
rs1111875
0.020
0.008
0.0096
HMGA2
rs1531343
-0.012 0.015
0.42
0.007
0.016
0.64
0.009
0.009
0.32
HNF1A
rs7957197
0.017
0.009
0.047
0.004
0.010
0.70
IGF1
rs35767
0.028
0.010 0.0048
-0.011 0.009
0.21
IGF2BP2
rs4402960
-0.024 0.008
0.0036
JAZF1
rs864745
-0.005 0.008
0.48
0.006
0.008
0.44
-0.008 0.008
0.30
KCNJ11
rs5219
-0.018 0.008 0.027
KCNQ1
rs2237895
0.002
0.008
0.83
0.005
0.008
0.53
KCNQ1
rs231362
0.002
0.008
0.75
0.010
0.008
0.19
KLF14
rs972283
-0.011 0.008
0.16
-0.012 0.008
0.13
MADD
rs7944584
-0.005 0.010
0.62
-0.008 0.010
0.42
MTNR1B
rs10830963
-0.004 0.008
0.60
0.001
0.008
0.87
0.013
0.011
0.26
NOTCH2
rs10923931
0.028
0.012 0.016
PPARG
rs1801282
0.010
0.011
0.39
-0.001 0.011
0.94
PRC1
rs8042680
-0.001 0.008
0.89
-0.001 0.009
0.88
PROX1
rs340874
0.003
0.008
0.71
-0.002 0.008
0.84
SLC2A2
rs11920090
-0.020 0.012
0.088
-0.015 0.012
0.21
SLC30A8
rs13266634
-0.005 0.008
0.55
0.007
0.008
0.39
TCF7L2
rs7903146
-0.009 0.010
0.39
-0.014 0.010
0.18
THADA
rs7578597
0.010
0.016
0.56
0.009
0.017
0.61
TP53INP1
rs896854
0.000
0.008
0.99
-0.011 0.008
0.16
-0.012 0.009
0.18
TSPAN8
rs7961581
-0.019 0.009
0.034
VPS13C
rs17271305
0.002
0.008
0.77
-0.008 0.008
0.32
WFS1
rs10010131
-0.003 0.008
0.71
0.005
0.008
0.55
ZBED3
rs4457053
0.019
0.009
0.033
0.024
0.009 0.0094
ZFAND6
rs11634397
-0.008 0.008
0.33
0.002
0.008
0.84
Beta and SE from linear regression analysis adjusted for age, gender and BMI denotes the effect size by each risk allele (additive model)
on phenotype. Analyses of DUSP9 rs5945326 are stratified by gender and adjusted for age and BMI.
Page 25 of 34
Diabetes
Supplementary Table 5. Effect of genetic variants
diabetic participants of the PPP-Botnia study.
Gene
SNP
Fasting glucose
Beta
SE
ADAMTS9
rs4607103
-0.005 0.014
-0.010 0.016
ADCY5
rs11708067
ADRA2A
rs10885122
0.003
0.018
ARAP1
rs1552224
0.017
0.015
BCL11A
rs243021
0.011
0.012
CAMK1D
rs12779790
-0.002 0.014
CDKAL1
rs7754840
0.045
0.013
0.039
0.017
CDKN2A/2B
rs10811661
CHCHD2P9
rs13292136
0.039
0.017
CRY2
rs11605924
0.031
0.012
DGKB
rs2191349
0.034
0.011
DUSP9 (female) rs5945326
0.000
0.018
DUSP9 (male)
rs5945326
-0.005 0.014
FADS1
rs174550
0.027
0.012
FTO
rs9939609
-0.027 0.012
GCK
rs4607517
0.061
0.018
-0.013 0.013
GIPR
rs10423928
GLIS3
rs7034200
0.025
0.012
HHEX
rs1111875
0.016
0.012
HMGA2
rs1531343
0.020
0.023
HNF1A
rs7957197
0.025
0.013
IGF1
rs35767
0.007
0.015
IGF2BP2
rs4402960
0.025
0.013
JAZF1
rs864745
0.028
0.012
KCNJ11
rs5219
0.006
0.012
KCNQ1
rs2237895
0.030
0.012
KCNQ1
rs231362
0.019
0.012
KLF14
rs972283
-0.017 0.012
MADD
rs7944584
0.056
0.015
MTNR1B
rs10830963
0.134
0.012
NOTCH2
rs10923931
-0.013 0.017
PPARG
rs1801282
0.043
0.016
PRC1
rs8042680
-0.005 0.013
PROX1
rs340874
-0.020 0.012
SLC2A2
rs11920090
0.008
0.017
SLC30A8
rs13266634
0.003
0.012
TCF7L2
rs7903146
0.021
0.015
THADA
rs7578597
0.047
0.025
TP53INP1
rs896854
0.023
0.012
TSPAN8
rs7961581
0.008
0.014
0.000
0.012
VPS13C
rs17271305
WFS1
rs10010131
0.019
0.012
ZBED3
rs4457053
0.004
0.014
ZFAND6
rs11634397
0.010
0.012
on glucose levels during OGTT in non-
P-value
0.73
0.54
0.89
0.24
0.37
0.91
4.5×10-4
0.018
0.023
0.0083
0.0033
1.00
0.70
0.020
0.025
7.1×10-4
0.31
0.034
0.16
0.40
0.060
0.61
0.051
0.015
0.61
0.012
0.097
0.15
1.4×10-4
5.6×10-27
0.47
0.0094
0.66
0.095
0.64
0.78
0.17
0.055
0.049
0.56
0.997
0.096
0.76
0.41
2hr glucose
Beta
SE
-0.011 0.038
0.139
0.042
0.015
0.047
-0.010 0.039
-0.001 0.031
0.021
0.038
0.090
0.035
0.090
0.045
0.084
0.045
0.020
0.032
0.001
0.031
-0.001 0.048
-0.036 0.039
-0.051 0.031
0.060
0.032
0.124
0.048
0.080
0.035
-0.038 0.031
-0.019 0.031
0.026
0.063
0.014
0.036
-0.015 0.039
0.020
0.034
0.035
0.031
0.012
0.031
0.073
0.032
0.045
0.031
0.035
0.032
0.049
0.039
0.071
0.033
-0.019 0.046
0.017
0.044
-0.046 0.034
-0.015 0.031
0.025
0.046
0.020
0.032
0.034
0.041
-0.047 0.066
0.048
0.031
0.033
0.037
0.064
0.032
-0.031 0.031
0.070
0.036
-0.041 0.033
P-value
0.77
9.6×10-4
0.75
0.80
0.97
0.59
0.0097
0.045
0.063
0.53
0.98
0.98
0.36
0.100
0.064
0.0098
0.021
0.22
0.54
0.68
0.69
0.70
0.56
0.27
0.71
0.023
0.15
0.26
0.21
0.034
0.69
0.70
0.18
0.63
0.59
0.54
0.41
0.48
0.12
0.37
0.043
0.32
0.056
0.22
Beta and SE from linear regression analysis adjusted for age, gender and BMI denotes the effect size by each risk allele (additive model)
on phenotype. Analyses of DUSP9 rs5945326 are stratified by gender and adjusted for age and BMI.
Diabetes
Page 26 of 34
Supplementary Table 6. Effect of genetic variants on insulin levels during OGTT in nondiabetic participants of the PPP-Botnia study.
Gene
SNP
Fasting insulin
2hr insulin
Beta
SE
P-value
Beta
SE
P-value
ADAMTS9
rs4607103
0.011
0.013
0.40
-0.004 0.020
0.83
ADCY5
rs11708067
-0.003 0.015
0.84
-0.010 0.023
0.65
ADRA2A
rs10885122
-0.016 0.017
0.36
-0.017 0.026
0.50
ARAP1
rs1552224
0.001
0.014
0.95
-0.013 0.021
0.53
BCL11A
rs243021
0.006
0.011
0.58
0.024
0.017
0.16
CAMK1D
rs12779790
-0.024 0.013
0.069
-0.015 0.020
0.47
0.022
0.012
0.075
CDKAL1
rs7754840
0.051
0.019
0.0068
CDKN2A/2B
rs10811661
0.002
0.015
0.91
0.021
0.024
0.38
0.047
0.024
0.054
CHCHD2P9
rs13292136
0.045
0.016
0.0054
CRY2
rs11605924
-0.006 0.011
0.61
0.005
0.017
0.78
DGKB
rs2191349
-0.011 0.011
0.33
0.002
0.016
0.88
DUSP9 (female) rs5945326
-0.005 0.017
0.77
-0.023 0.024
0.34
DUSP9 (male)
rs5945326
-0.009 0.013
0.51
-0.014 0.022
0.52
FADS1
rs174550
-0.027 0.011
0.017
-0.041 0.017
0.015
-0.007 0.012
0.52
FTO
rs9939609
0.036
0.017
0.037
GCK
rs4607517
-0.006 0.017
0.75
0.036
0.026
0.17
GIPR
rs10423928
-0.009 0.012
0.48
-0.007 0.019
0.70
GLIS3
rs7034200
-0.005 0.011
0.65
0.008
0.017
0.62
HHEX
rs1111875
0.017
0.011
0.12
-0.020 0.017
0.22
HMGA2
rs1531343
0.018
0.023
0.43
0.037
0.034
0.28
HNF1A
rs7957197
0.007
0.013
0.58
0.000
0.019
0.98
IGF1
rs35767
-0.011 0.014
0.44
-0.004 0.021
0.87
IGF2BP2
rs4402960
0.015
0.012
0.23
0.025
0.018
0.17
JAZF1
rs864745
0.002
0.011
0.88
0.003
0.017
0.87
KCNJ11
rs5219
-0.008 0.011
0.46
-0.013 0.017
0.45
KCNQ1
rs2237895
-0.004 0.011
0.73
-0.006 0.017
0.73
KCNQ1
rs231362
0.003
0.011
0.78
-0.003 0.017
0.88
KLF14
rs972283
0.006
0.011
0.57
-0.005 0.017
0.77
MADD
rs7944584
-0.003 0.014
0.84
-0.008 0.021
0.70
0.019
0.012
0.12
MTNR1B
rs10830963
0.066
0.018 2.3×10-4
NOTCH2
rs10923931
0.008
0.016
0.63
-0.003 0.025
0.90
PPARG
rs1801282
0.025
0.016
0.11
0.025
0.024
0.29
PRC1
rs8042680
0.006
0.012
0.61
-0.016 0.018
0.39
PROX1
rs340874
-0.002 0.011
0.83
-0.004 0.017
0.81
SLC2A2
rs11920090
0.009
0.016
0.60
-0.012 0.025
0.64
SLC30A8
rs13266634
-0.019 0.011
0.091
0.004
0.017
0.80
TCF7L2
rs7903146
-0.014 0.015
0.36
-0.008 0.022
0.71
THADA
rs7578597
-0.005 0.024
0.82
-0.047 0.036
0.18
TP53INP1
rs896854
-0.002 0.011
0.89
0.012
0.017
0.48
TSPAN8
rs7961581
0.019
0.013
0.14
0.009
0.020
0.64
VPS13C
rs17271305
0.003
0.011
0.80
0.014
0.017
0.42
WFS1
rs10010131
-0.008 0.011
0.46
-0.020 0.017
0.23
-0.005 0.013
0.70
ZBED3
rs4457053
0.054
0.020
0.0060
ZFAND6
rs11634397
-0.021 0.012
0.077
-0.034 0.018
0.057
Beta and SE from linear regression analysis adjusted for age, gender and BMI denotes the effect size by each risk allele (additive model)
on phenotype. Analyses of DUSP9 rs5945326 are stratified by gender and adjusted for age and BMI.
Page 27 of 34
Diabetes
Supplementary Table 7. Effect of genetic variants on insulin to glucagon ratios during OGTT
in non-diabetic participants of the PPP-Botnia study.
Gene
ADAMTS9
ADCY5
ADRA2A
ARAP1
BCL11A
CAMK1D
CDKAL1
CDKN2A/2B
CHCHD2P9
CRY2
DGKB
DUSP9 (female)
DUSP9 (male)
FADS1
FTO
GCK
GIPR
GLIS3
HHEX
HMGA2
HNF1A
IGF1
IGF2BP2
JAZF1
KCNJ11
KCNQ1
KCNQ1
KLF14
MADD
MTNR1B
NOTCH2
PPARG
PRC1
PROX1
SLC2A2
SLC30A8
TCF7L2
THADA
TP53INP1
TSPAN8
VPS13C
WFS1
ZBED3
ZFAND6
SNP
rs4607103
rs11708067
rs10885122
rs1552224
rs243021
rs12779790
rs7754840
rs10811661
rs13292136
rs11605924
rs2191349
rs5945326
rs5945326
rs174550
rs9939609
rs4607517
rs10423928
rs7034200
rs1111875
rs1531343
rs7957197
rs35767
rs4402960
rs864745
rs5219
rs2237895
rs231362
rs972283
rs7944584
rs10830963
rs10923931
rs1801282
rs8042680
rs340874
rs11920090
rs13266634
rs7903146
rs7578597
rs896854
rs7961581
rs17271305
rs10010131
rs4457053
rs11634397
Fasting Insulin/glucagon
Beta
SE
P-value
0.018
0.018
0.33
0.007
0.020
0.74
-0.018 0.023
0.44
-0.005 0.019
0.80
-0.029 0.015
0.053
-0.034 0.018
0.060
0.015
0.017
0.36
0.006
0.021
0.78
0.018
0.022
0.41
0.017
0.015
0.26
-0.002 0.015
0.89
-0.012 0.024
0.61
-0.015 0.018
0.40
-0.013 0.015
0.39
-0.005 0.015
0.73
-0.002 0.023
0.93
-0.001 0.017
0.97
-0.007 0.015
0.65
0.003
0.015
0.85
0.024
0.029
0.42
-0.001 0.017
0.97
-0.012 0.019
0.53
0.044
0.016
0.0072
0.007
0.015
0.62
0.008
0.015
0.58
0.005
0.015
0.77
0.009
0.015
0.55
0.031
0.015
0.042
0.015
0.019
0.42
0.032
0.016
0.049
-0.007 0.022
0.73
0.020
0.022
0.36
0.006
0.016
0.69
-0.001 0.015
0.96
0.025
0.022
0.26
-0.007 0.015
0.63
-0.005 0.019
0.81
-0.032 0.032
0.32
0.002
0.015
0.88
0.043
0.018
0.014
-0.004 0.015
0.80
-0.017 0.015
0.26
-0.025 0.017
0.15
-0.016 0.016
0.33
2hr Insulin/glucagon
Beta
SE
P-value
0.015
0.027
0.57
0.002
0.030
0.95
-0.028 0.035
0.43
-0.022 0.028
0.44
0.013
0.023
0.56
-0.004 0.027
0.88
0.055
0.025
0.029
0.017
0.033
0.60
0.023
0.033
0.48
0.019
0.023
0.41
-0.004 0.022
0.84
0.019
0.033
0.58
-0.005 0.028
0.85
-0.048 0.022
0.032
0.031
0.023
0.18
0.053
0.035
0.13
-0.006 0.025
0.82
0.015
0.023
0.51
0.001
0.022
0.97
0.033
0.044
0.45
-0.023 0.026
0.38
-0.026 0.028
0.34
0.024
0.025
0.33
0.004
0.023
0.85
0.008
0.022
0.73
-0.020 0.023
0.39
0.017
0.022
0.44
0.030
0.023
0.19
0.008
0.028
0.77
0.102
0.024 2.6×10-5
-0.027 0.033
0.41
0.046
0.032
0.15
-0.017 0.025
0.49
0.009
0.023
0.71
-0.027 0.034
0.43
0.020
0.023
0.37
0.012
0.029
0.67
-0.111 0.048
0.020
0.012
0.023
0.59
0.030
0.027
0.26
0.008
0.023
0.73
-0.037 0.022
0.10
0.038
0.026
0.15
-0.041 0.024
0.089
Beta and SE from linear regression analysis adjusted for age, gender and BMI denotes the effect size by each risk allele (additive model)
on phenotype. Analyses of DUSP9 rs5945326 are stratified by gender and adjusted for age and BMI.
Diabetes
Page 28 of 34
Supplementary Table 8. Overview of investigated genetic variants.
Gene
Chr
SNP
RA
RAF
ADAMTS9
ADCY5
ADRA2A
ARAP1
BCL11A
CAMK1D
CDKAL1
3
3
10
11
2
10
6
rs4607103
rs11708067
rs10885122
rs1552224
rs243021
rs12779790
rs7754840
C
A
G
A
A
G
C
0.77
0.84
0.88
0.79
0.45
0.21
0.31
Identified by
association to
Type 2 diabetes
Fasting glucose
Fasting glucose
Type 2 diabetes
Type 2 diabetes
Type 2 diabetes
Type 2 diabetes
CDKN2A/2B
9
rs10811661
T
0.84
Type 2 diabetes
CHCHD2P9
CRY2
DGKB
DUSP9
FADS1
FTO
GCK
GIPR
GLIS3
HHEX
HMGA2
HNF1A
IGF1
IGF2BP2
9
11
7
X
11
16
7
19
9
10
12
12
12
3
rs13292136
rs11605924
rs2191349
rs5945326
rs174550
rs9939609
rs4607517
rs10423928
rs7034200
rs1111875
rs1531343
rs7957197
rs35767
rs4402960
C
A
T
G
T
A
A
A
A
G
C
T
G
A
0.86
0.52
0.50
0.25
0.59
0.38
0.12
0.27
0.50
0.53
0.07
0.26
0.79
0.30
Type 2 diabetes
Fasting glucose
Fasting glucose
Type 2 diabetes
Fasting glucose
BMI
Fasting glucose
2 hour glucose
Fasting glucose
Type 2 diabetes
Type 2 diabetes
Type 2 diabetes
Fasting insulin
Type 2 diabetes
JAZF1
KCNJ11
KCNQ1
KCNQ1
KLF14
MADD
MTNR1B
7
11
11
11
7
11
11
rs864745
rs5219
rs2237895
rs231362
rs972283
rs7944584
rs10830963
A
T
C
G
G
A
G
0.47
0.49
0.49
0.49
0.51
0.81
0.30
Type 2 diabetes
Type 2 diabetes
Type 2 diabetes
Type 2 diabetes
Type 2 diabetes
Fasting glucose
Fasting glucose
NOTCH2
PPARG
PRC1
PROX1
SLC2A2
SLC30A8
TCF7L2
THADA
TP53INP1
TSPAN8
VPS13C
WFS1
ZBED3
ZFAND6
1
3
15
1
3
8
10
2
8
12
15
4
2
15
rs10923931
rs1801282
rs8042680
rs340874
rs11920090
rs13266634
rs7903146
rs7578597
rs896854
rs7961581
rs17271305
rs10010131
rs4457053
rs11634397
T
C
A
C
T
C
T
T
T
G
G
G
G
G
0.14
0.86
0.31
0.48
0.87
0.60
0.17
0.94
0.48
0.23
0.51
0.57
0.25
0.67
Type 2 diabetes
Type 2 diabetes
Type 2 diabetes
Fasting glucose
Fasting glucose
Type 2 diabetes
Type 2 diabetes
Type 2 diabetes
Type 2 diabetes
Type 2 diabetes
2 hour glucose
Type 2 diabetes
Type 2 diabetes
Type 2 diabetes
Reference
Zeggini et al. [1]
Dupuis et al. [2]
Dupuis et al. [2]
Voight et al. [3]
Voight et al. [3]
Zeggini et al. [1]
Saxena et al [4], Zeggini et al. [5],
Scott et al. [6]
Saxena et al [4], Zeggini et al. [5],
Scott et al. [6]
Voight et al. [3]
Dupuis et al. [2]
Dupuis et al. [2]
Voight et al. [3]
Dupuis et al. [2]
Frayling et al. [7]
Dupuis et al. [2]
Saxena et al. [8]
Dupuis et al. [2]
Sladek et al. [9]
Voight et al. [3]
Voight et al. [3]
Dupuis et al. [2]
Saxena et al [4], Zeggini et al. [5],
Scott et al. [6]
Zeggini et al. [1]
Gloyn et al. [10]
Yasuda et al. [11], Unoki et al. [12]
Voight et al. [3]
Voight et al. [3]
Dupuis et al. [2]
Prokopenko et al. [13], Lyssenko et al.[14],
Bouatia-Naji et al. [15]
Zeggini et al. [1]
Altshuler et al. [16]
Voight et al. [3]
Dupuis et al. [2]
Dupuis et al. [2]
Sladek et al. [9]
Grant et al. [17]
Zeggini et al. [1]
Voight et al. [3]
Zeggini et al. [1]
Saxena et al. [8]
Sandhu et al. [18]
Voight et al. [3]
Voight et al. [3]
Page 29 of 34
Diabetes
Homozygous non-risk
300
300
280
280
280
260
260
260
ADRA2A
MTNR1B
MADD
PPARG
220
SLC2A2
CIR
IGF1
200
CHCHD2P9
DUSP9(m)
ADAMTS9
240
240
220
220
CIR
ADCY5
240
CIR
Homozygous risk
Heterozygous
300
200
HMGA2
DUSP9(f)
ZBED3
IGF2BP2
200
160
160
140
140
DUSP9(m)
GIPR
TSPAN8
180
180
160
TCF7L2
PRC1
MTNR1B
180
CDKAL1
MTNR1B
GCK
THADA
140
120
120
120
100
150
100
150
100
150
155
160
165
170
175
ISI
180
185
190
195
200
155
160
165
170
175
180
185
190
ISI
195
200
155
160
165
170
175
180
185
190
195
200
ISI
Supplementary Figure 1. Relationship between insulin secretion (CIR) and insulin sensitivity (ISI).
Whereas most SNPs showed effects which kept the relationship between ISI and CIR within the confidence interval of 4,654 non-diabetic subjects, there were some obvious
outliers. MTNR1B, CDKAL1 and GCK risk genotype carriers had CIR values below the values expected from the ISI values, whereas TT-carriers of TCF7L2 showed rather high
insulin sensitivity. N=4,654 non-diabetic participant of the Botnia-PPP study.
10
Diabetes
Page 30 of 34
A
ADCY5
5.3
THADA
GCK
IGF1
CHCHD2P9
5.2
PPARG
ARAP1
CRY2
MTNR1B
MADD
5.5
5.5
5.4
5.4
MTNR1B
GCK
5.3
Fasting glucose (mmol/l)
Fasting glucose (mmol/l)
5.4
Fasting glucose (mmol/l)
Homozygous risk
Heterozygous
Homozygous non-risk
5.5
CDKAL1
5.2
THADA
5.0
33
34
35
36
37
38
39
40
41
42
43
44
5.0
33
45
HMGA2
GCK
DGKB
5.3
IGF2BP2
HNF1A
CDKAL1
TCF7L2
5.2
GIPR
5.1
5.1
5.1
MTNR1B
34
35
36
37
38
39
40
41
42
43
44
5.0
33
45
34
35
36
37
38
39
40
41
42
43
44
45
Fasting insulin (pmol/l)
Fasting insulin (pmol/l)
Fasting insulin (pmol/l)
B
Homozygous risk
Heterozygous
Homozygous non-risk
5.6
5.6
5.6
5.5
5.5
5.5
GCK
PPARG
5.1
IGF1
ADCY5
5.0
2hr glucose (mmol/l)
ADAMTS9
GLIS3
5.2
2hr glucose (mmol/l)
2hr glucose (mmol/l)
5.4
5.4
5.4
5.3
5.3
CDKAL1
GIPR
IGF1
5.2
5.1
CDKAL1
IGF2BP2
VPS13C
5.3
KCNJ11
GIPR
5.2
WFS1
HNF1A
FTO
ZBED3 CAMK1D
JAZF1
KLF14
MTNR1B
KCNQ1†
TCF7L2
FADS1
5.1
HMGA2
5.0
5.0
4.9
4.9
CDKN2A/2B
MADD
4.9
4.8
150
160
170
180
190
200
2hr Insulin (pmol/l)
210
220
230
4.8
150
160
170
180
190
200
2hr Insulin (pmol/l)
210
220
230
4.8
150
160
170
180
190
200
210
220
230
2hr Insulin (pmol/l)
Supplementary Figure 2. Relationship between glucose and insulin levels during OGTT in 4,654 non-diabetic participants of the PPP-Botnia study.
Panel A shows the fasting values. Homozygous carriers of the risk allele in TCF7L2 had the lowest, whereas homozygous carriers of HMGA2 showed the highest insulin levels.
Panel B shows the 2hr values. The GCK risk allele showed the highest postprandial glucose concentrations.
11
Page 31 of 34
Diabetes
A
Homozygous risk
Heterozygous
Homozygous non-risk
5.35
5.35
5.35
5.30
5.30
MTNR1B
HMGA2
ADRA2A
5.25
KLF14
5.20
5.15
CDKN2A/2B
CHCHD2P9
MTNR1B
5.10
Fasting glucose (mmol/l)
5.30
Fasting glucose (mmol/l)
Fasting glucose (mmol/l)
GCK
ADCY5
MTNR1B
GCK
5.25
CDKAL1
5.20
5.15
5.10
IGF2BP2
5.25
GLIS3
FADS1
KCNQ1†
HNF1A
NOTCH2
5.20
5.15
5.10
SLC2A2
THADA
5.05
5.05
5.05
MADD
5.00
5.00
5.00
66
68
70
72
74
76
78
80
82
66
84
68
70
72
74
76
78
80
82
66
84
68
70
72
74
76
78
80
82
84
Fasting glucagon (pg/ml)
Fasting glucagon (pg/ml)
Fasting glucagon (pg/ml)
B
Homozygous non-risk
Homozygous risk
Heterozygous
5.6
5.6
5.6
5.5
5.5
5.5
5.4
5.4
5.4
5.3
ADAMTS9
5.2
KCNJ11
PPARG
5.1
IGF1
ADCY5
SLC2A2
2hr glucose (mmol/l)
2hr glucose (mmol/l)
2hr glucose (mmol/l)
GCK
5.3
5.2
5.1
IGF2BP2
CDKAL1
HNF1A
FTO
5.3
ZBED3
5.2
5.1
NOTCH2
HMGA2
ADCY5
5.0
THADA
CDKN2A/2B
5.0
5.0
4.9
4.9
ADRA2A
MADD
4.9
4.8
60
62
64
66
68
70
72
74
2hr glucagon (pg/ml)
76
78
80
82
4.8
60
62
64
66
68
70
72
74
2hr glucagon (pg/ml)
76
78
80
82
4.8
60
62
64
66
68
70
72
74
76
78
80
82
2hr glucagon (pg/ml)
Supplementary Figure 3. Relationship between glucose and glucagon levels during OGTT in 4,654 non-diabetic participants of the PPP-Botnia study.
Panel A shows the fasting values. HMGA2 and NOTCH2 showed both high glucose and glucagon levels, while MTNR1B, GCK and IGF2BP2 showed high glucose and low
glucagon levels. Panel B shows the 2hr values. The HMGA2 variant showed the highest postprandial glucagon level.
12
Diabetes
A
Page 32 of 34
B
0.15
0.15
ADCY5
MTNR1B
GCK
0.10
CDKN2A/2B
GIPR
KCNQ1†
VPS13C
2hr glucose
Fasting glucose
0.10
GCK
MADD
0.05
THADA
CDKN2A/2B
FADS1
BCL11A
0.00
WFS1
ZFAND6
ZBED3
CAMK1D
-0.05
-0.04
DGKB
GLIS3
TCF7L2
CDKAL1
ARAP1 HHEX
IGF1 SLC30A8
0.00
IGF2BP2
SLC2A2
KCNJ11
ADRA2A DUSP9(f) VPS13C
PRC1
DUSP9(m)
ADCY5
GIPR
NOTCH2
PROX1
FTO
-0.02
PPARG
CHCHD2P9
KCNQ1†
CRY2
HNF1A
JAZF1
TP53INP1
HMGA2
KCNQ1*
0.00
-0.05
ZBED3
MTNR1B
FTO
TP53INP1
KCNQ1*
KLF14
JAZF1
TSPAN8
SLC2A2
CAMK1D
TCF7L2
HMGA2
SLC30A8 PPARG
CRY2
ADRA2A
IGF2BP2
KCNJ11
HNF1A
DUSP9(f)
DGKB
BCL11A
ARAP1
ADAMTS9
IGF1
PROX1
NOTCH2
HHEX
WFS1
GLIS3
ZFAND6
DUSP9(m)
PRC1
FADS1
MADD
0.05
TSPAN8
ADAMTS9
CDKAL1
CHCHD2P9
THADA
KLF14
0.02
Fasting insulin/glucagon
0.04
0.06
-0.10
-0.15
-0.10
-0.05
0.00
0.05
0.10
0.15
2hr insulin/glucagon
Supplementary Figure 4. Graphic representation of effects of genetic variants on relationship between glucose and insulin to glucagon ratios during OGTT in non-diabetic
participants of the PPP-Botnia study.
Effect sizes are betas from linear regression adjusted for age, gender and BMI. KCNQ1† represents rs2237895, while KCNQ1* represents rs231362. Black circles P<0.05; White
circles P>0.05. Panel A shows the relationship between fasting insulin to glucagon ratio and fasting glucose. MTNR1B was a clear outlier with increased fasting glucose despite
high insulin to glucagon ratio. Panel B shows the relationship between 2hr insulin to glucagon ratio and 2hr glucose. Again MTNR1B was a clear outlier with increased 2hr glucose
despite the highest insulin to glucagon ratio.
13
Page 33 of 34
Diabetes
A
Homozygous non-risk
Homozygous risk
Heterozygous
5.35
5.35
5.35
5.30
5.30
HMGA2
MTNR1B
GCK
GCK
5.25
PPARG
5.20
Fasting glucose (mmol/l)
Fasting glucose (mmol/l)
ADRA2A
5.25
5.20
PROX1
5.15
5.10
MTNR1B
Fasting glucose (mmol/l)
ADCY5
5.30
5.15
CHCHD2P9
MTNR1B
5.10
SLC2A2
THADA
5.05
5.05
MADD
5.00
NOTCH2
5.20
TCF7L2
5.15
5.10
5.05
5.00
5.00
4.95
4.95
4.95
4.90
4.90
4.90
0.080
0.085
0.090
0.095
0.100
0.105
IGF2BP2
5.25
0.080
0.085
Fasting insulin/glucagon
0.090
0.095
0.100
0.080
0.105
0.085
0.090
0.095
0.100
0.105
Fasting insulin/glucagon
Fasting insulin/glucagon
B
Heterozygous
Homozygous non-risk
Homozygous risk
5.7
5.7
5.7
5.6
5.6
5.6
5.5
5.5
5.5
DUSP9(f)
5.3
GLIS3
PROX1
ADAMTS9
HHEX
IGF2BP2
5.2
TP53INP1
5.1
ADCY5
IGF1
SLC2A2
2hr glucose (mmol/l)
5.4
2hr glucose (mmol/l)
2hr glucose (mmol/l)
GCK
5.4
5.3
DUSP9(f)
GIPR
5.2
5.1
CDKAL1
5.4
HNF1A
FTO
ZBED3
5.3
GIPR
IGF2BP2
DUSP9(f)
VPS13C
JAZF1
KCNJ11
KCNQ1†
5.2
TCF7L2
NOTCH2
5.1
ADCY5
ADCY5
HMGA2
DUSP9(m)
THADA
CDKN2A/2B
5.0
MADD
5.0
5.0
ADRA2A
4.9
4.9
4.9
4.8
4.8
4.8
0.40
0.45
0.50
0.55
2hr insulin/glucagon
0.60
0.65
0.40
0.45
0.50
0.55
2hr insulin/glucagon
0.60
0.65
0.40
0.45
0.50
0.55
0.60
0.65
2hr insulin/glucagon
Supplementary Figure 5. Relationship between glucose levels and insulin to glucagon ratios during OGTT in 4,654 non-diabetic participants of the PPP-Botnia study.
Panel A shows the fasting values. Homozygous risk allele carriers of TCF7L2 showed high fasting glucose values despite low insulin to glucagon ratio. Panel B shows the 2hr
values. Consistent with the fasting situation, risk allele carriers of TCF7L2 showed high glucose despite high insulin to glucagon ratio.
14
Diabetes
Page 34 of 34
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15