DNA variations, impaired insulin secretion and type 2
diabetes
7398 words
Valeriya Lyssenko, M.D., Ph.D and Leif Groop, M.D., Ph.D
Department of Clinical Sciences / Diabetes & Endocrinology and Lund University
Diabetes Centre, Lund University, University Hospital Malmö, Malmö, Sweden
Address:
Valeriya Lyssenko, M.D, Ph.D,
Department of Clinical Sciences/Diabetes & Endocrinology
Lund University
University Hospital Malmö
20502 Malmö
Sweden
Phone: 46-40-391214
E-mail: Valeri.Lyssenko@med.lu.se
1
Abstract
The success in dissecting the genetics of complex polygenic diseases like type 2
diabetes (T2D) until now has not been a trivial task. The picture has dramatically
changed during the past years with the introduction of genome wide association
studies (GWAS). Today we know about 30 genetic variants increasing risk of T2D or
influencing glucose/insulin levels. Most of them seem to influence the capacity of cells to increase insulin secretion to meet demands imposed by increase in body
weight and insulin resistance. This probably only represents the tip of the iceberg and
refined tools will over the next few years provide a more complete picture of the
genetic complexity of T2D. This will not only include the current dissection of
common variants increasing susceptibility for the disease but also rare variants with
stronger effects. For the first time we can with some confidence anticipate that the
genetics of a complex disease like T2D really can be dissected.
Key words: genetics, complex disease, polygenic, linkage study, genome wide scan,
association study, single nucleotide polymorphism, insulin section, beta cell function.
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1 Introduction
1.1. Evidence that type 2 diabetes is inherited
There is ample evidence that type 2 diabetes (T2D) has a strong genetic component.
The risk of developing T2D is approximately 3-fold increased in first-degree relatives
of a patient with T2D compared to subjects without family history of diabetes, this
value is often referred to as the sibling-relative risk, s value around 3.5 [1]. If one
parent has diabetes the risk that offspring will develop the disease is about 40%, and if
both parents have diabetes the risk is approximately 70% [2]. Intriguingly, the risk in
the offspring seems to be greater if the mother rather than the father has T2D [3].
Very high concordance rate rates of T2D have been reported in monozygotic twins of
70% compared with 20-30% in dizygotic twins [4, 5]. Thus, approximately of 70% of
the variability of diabetes maybe heritable.
It is clear that the change in the environment towards a more affluent Western life
style plays a key role in the epidemic increase in the prevalence of T2D worldwide.
This change has occurred during the last 50 years, during which period our genes
have not changed. This does not exclude an important role for genes in the rapid
increase in T2D, since genes or variation in them explain how we respond to changes
in the environment and the environment always imposes a selective pressure on
genes. An example of such selective pressure is the mutation of Lac gene causing
lactase persistance, i.e. ability to drink and absorb cow milk as adults with the
domestication of cattle [6]. The hypothesis that thrifty genes could be a plausible
explanation for the interaction between genes and environment was first introduced
by Neel in 1962 [7]. During a long time of evolution humans have been subjected to
long periods of famine and unpredictable food supplies. Neel proposed that
individuals living in an environment with unstable food supply (as for hunters and
nomads) would maximize their probability of survival if they could maximize storage
of energy. Genetic selection would thus favor energy-conserving genotypes in such
environments.
An alternative explanation has been proposed by which these changes can be the
consequence of intrauterine programming, the so called thrifty phenotype
hypothesis [8]. The reason for this could be that poor intrauterine nutrition
3
permanently programs the body to a constant starvation-state, which would lead to a
low birth weight and increased risk of T2D later in life. However, a low birth weight
could also be a consequence of impaired -cell function. Children with a glucokinase
defect have a decrease in insulin secretion and a low birth weight if they inherit
mutation from the father [9]. Carriers of glucokinase mutation can secrete insulin, but
their beta-cells have a higher glucose threshold for glucose-stimulated insulin
secretion. This was particularly apparent in children of diabetic mothers, since these
children would be expected to have a high birth weight as a consequence of high
glucose concentrations passing the placenta and thereby stimulating the fetal pancreas
to produce increasing amounts of anabolic insulin.
1.2. Risk factors predicting future T2D
Although risk factors for T2D seem to differ between different ethnic populations, to
a family history of diabetes consistently confers an increased risk of future T2D
diabetes [1], however its relative effect decreases with increasing frequency of T2D in
the population. Its predictive value is also relatively poor in young subjects whose
parents have not yet developed the disease. Abdominal obesity and presence of the
metabolic syndrome, and low level of physical activity confer an increased risk of
T2D [10, 11]. However an impaired insulin secretion, especially when adjusted for
the degree of insulin resistance, so called disposition index, is the strongest predictor
of future T2D. We have demonstrated this using two large prospective studies, the
Malmö Preventive Project (MPP) and the Botnia Prospective Study (BPS) over a
more than 25-year follow-up period. In both studies, obesity and a low insulin
secretion when adjusted for insulin resistance was strongly associated with increased
risk of future T2D, and this risk was doubled in individuals with a family history of
diabetes (Figure 1) [12]. Whether a family history of diabetes can be replaced by
genetic testing in the prediction of T2D will be shown later.
1.3. Genetic variability
Mapping of an inherited disease implies the identification of the genetic variability
contributing to the disease. Such variability can be deletions, insertions or changes in
a single nucleotide in the genome, single nucleotide polymorphism (SNP). If a SNP
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results in a change in the amino acid sequence it is a called a non- synonymous SNP.
There are about 10 million SNPs in the human 3 billion bp genome, which means one
SNP at about 300 bp intervals. SNPs in coding sequences (exons) are seen at 1250 bp
intervals. Microsatellites are short tandem repeats of nucleotide sequences (e.g. CA)
found at about 5000 bp intervals. Whereas SNPs are frequently bi-allelic
microsatellites have multiple alleles and are thus much more polymorphic than SNPs.
Several public databases provide information on SNPs in different genes (e.g.
www.ncbi.nlm.nig.gov/SNP. A SNP in a database is often referred to as dbSNP; at
moment (build 123) about 60% of all SNPs are in public databases.
A SNP can either be the cause of the disease (causative SNP) or it can be a marker of
the disease. This occurs when the disease susceptibility allele and the marker allele
are so close to each other that they are inherited together, a situation called linkage
disequilibrium (LD or allelic association). Such a combination of tightly linked
alleles on a discrete chromosome is called a haplotype. While this region is
characterized by little or no recombination (haplotype block) regions with high
recombination rate usually separate haplotype blocks. LD thus describes the nonrandom correlation between alleles at a pair of SNPs; it is usually defined by D’ or r2
values. A D’ value of 1 indicates that the two alleles are in complete LD, whereas
values below 0.5 indicate low LD and a high recombination rate. LD extends over
longer distances in isolated populations, but is also in European compared with
African populations. This is considered to reflect a population bottleneck at the time
when humans first left Africa.
An international joint effort to create a genome-wide map of LD and haplotype blocks
is called the HapMap project (http://www.hapmap.org/groups.html). The hope is that
by knowing the haplotype block structure of the genome, one could capture the
genetic variability of the genome by genotyping a much smaller number of SNPs that
describe the haplotype block (haplotype tag or htg SNPs).
1.4. Mapping genetic variability
Profiling genetic variation aims to correlate biological variation (phenotype) with
variation in DNA sequences (genotype). The ultimate goal of mapping genetic
variability is to identify the SNP causing a monogenic disease or the SNPs increasing
susceptibility to a polygenic disease. The most straightforward approach would be to
5
sequence the whole genome in affected and unaffected individuals but this is for
practical reasons not yet possible. Many indirect methods have been developed to
achieve the goal like linkage and association approaches.
1.4. Linkage
The traditional way of mapping a disease gene has been to search for linkage between
a chromosomal region and a disease by genotyping a large number (about 400-500) of
polymorphic markers (microsatellites) in affected family members. If the affected
family members would share an allele more often than expected by non-random
Mendelian inheritance, there is evidence of excess allele sharing. The most likely
explanation for excess allele sharing is that a disease-causing gene is in close
proximity to the genotyped marker. Ideally, such a genome wide scan would be
carried out in large pedigrees where mode of inheritance and penetrance would be
known. Since these parameters are not known and parents are rarely available in
a
complex disease with late-onset, most genome wide scans are performed in affected
siblings with no assumptions on mode of inheritance and penetrance (non-parametric
linkage).
The LOD score defines the strength of linkage. This takes into account the
recombination fraction (), which is the likelihood that a parent will produce a
recombinant in an offspring. If the parental genotype is intact in the offspring, the
recombination fraction is 0 (loci are linked), for completely unlinked loci it
approaches 0.5. The probability test of linkage is called the LOD score (logarithm of
odds). Two loci are considered linked when the probability of linkage as opposed to
the probability against linkage is equal to or greater than the ratio of 1000/1. A LOD
score of 3 corresponds to an odds ratio of 1000/1 (p<10-4). In a study of affected sib
pairs a non-parametric LOD score (NPL) is presented. Although this threshold was
developed for linkage mapping of monogenic disorders with complete information of
genotype and phenotype, the situation for mapping complex disorders is much more
complex. Lander and Kruglyak (5) have proposed that the LOD threshold for
significant genome-wide linkage should be raised to 3.6 (p<2x10-5) while that for
suggestive linkage (would occur one time at random in a genome wide scan) can be
set at 2.2 (p<7x2-4). In addition, they suggest to report all nominal p values < 0.5
without any claim for linkage. In reality each data set will have different thresholds
6
based upon information on affection status, marker density, marker informativeness
etc. Therefore, these thresholds should be simulated using the existing data set before
any claims of linkage can be made.
Accuracy of genotyping and exclusion of Mendel errors are important for the success
but also the careful definition of affection status. This may not always be easy for
disease like asthma, schizophrenia or systematic lupus erythematosus (SLE). Even for
diabetes the definition is based upon man-made cut-offs of plasma glucose.
Dichotomizing variables may result in loss of power. One alternative is therefore to
search linkage to a qualitative trait, e.g. blood glucose, blood pressure body mass
index instead of diabetes, hypertension and obesity. Heritability (h2) is often used as a
measure of the genetic component of a quantitative trait. The higher the heritability,
the more likely it is to find the genetic cause to the trait. Several statistical programs
have been developed to support genome wide scans of quantitative trait loci (QTL)
like
the
variance
component
(www.sfbr.org/sfbr/public/software/solarR)
models
and
SOLAR
Merlin
(www.sph.umich.edu/csg/abecasis/Merlin/tour).
Linkage will only identify relatively large chromosomal regions (often > 20 cM) with
more than 100 genes. Fine mapping with additional markers can narrow the region
further but at the end the causative SNP or a SNP in LD with the causative SNP has to
be identified by an association study. Several approaches have been described to
estimate whether an observed association can account for linkage [13]. Without
functional support it is not always possible to know whether linkage and association
represent the genetic cause of the disease. This can for many complex disorders
require a cumbersome sequence of in vitro and in vivo studies.
ADRA2A: The alpha2A adrenergic receptor (ADRA2A) has been known as a
physiological mediator of adrenergic suppression of insulin release. It has previously
been shown that Adra2A knock-out mice present with enhanced insulin secretion and
hypoglycemia [14], and animals with cell-specific overexpression of Adra2A are
glucose intolerant [15]. The diabetic Goto-Kakizaki (GK) rats display a major
diabetes susceptibility locus on rat chromosome 1, called Niddm1 [16]. A 16 Mb
portion of Niddm1, termed Niddm1i, confers defective insulin secretion and is
homologous to a region on human chromosome 10 that has been strongly associated
7
with T2D [17, 18]. In a recent study, we have identified ADRA2A being
overexpressed at the Niddm1i locus, and rats displayed elevated glucose levels which
were paralleled by a profound reduction in insulin secretion [19]. Furthermore, we
demonstrated that genetic variants in the ADRA2A gene in humans were associated
with impaired early and second phase of insulin secretion, increased expression of
both transcript and protein in human islets, and increased risk of T2D (Figure 2) [19].
This was the first time it was possible to merge genetic information from an animal
model of T2D with genetic information in human T2D.
Independently of these
findings, a different variant in the ADRA2A gene has recently been associated with
increased fasting glucose levels [20]. These findings could open up exciting
possibilities for specific and tailor-made therapeutic intervention.
1.5. Association studies – candidate genes
If there is a prior strong candidate gene for the disease, the best approach is to search
for association between SNPs in the gene and the disease. This can either be a case
control or nested cohort study. In a case–control study the inclusion criteria for the
cases are predefined and thereafter matched individual controls are searched
representing the same ethnic group as the cases. In a cohort study affected and
unaffected groups are matched, not individuals. Ideally cohorts are population-based
but often they represent consecutive patients from an outpatient clinic. It is preferable
that controls are older than cases to exclude the possibility that they still will develop
the disease. If cases and controls are not drawn from the same ethnic group, a
spurious association can be detected due to ethnic stratification.
One way to circumvent this problem is to perform a family-based association study.
Distorted transmission of alleles from parents to affected offspring would indicate that
the allele showing excess transmission is associated with the disease. The
untransmitted alleles serve as control. This transmission disequilibrium test (TDT)
represents the most unbiased association study approach but suffers from the
drawback of low power, only transmissions from heterozygous parents are
informative. The prerequisite of DNA from parents usually enrich for individuals with
an earlier onset of the disease.
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PPARG: Even screening only one gene for SNPs can represent a huge and expensive
undertaking. The peroxisome proliferator-activated receptor-gamma (PPARG) gene
on the short arm of chromosome 3 spans 83,000 nucleotides with 231 SNPs in public
databases, 7 of them are coding SNPs. The gene encodes for a nuclear receptor, which
is predominantly expressed in adipose tissue where it regulates transcription of genes
involved in adipogenesis. In the 5’ untranslated end of the gene is an extra exon B that
contains a SNP changing a proline in position 12 of the protein to alanine. The rare
Ala allele is seen in about 15% of Europeans and was in an initial study shown to be
associated with increased transcriptional activity, increased insulin sensitivity and
protective against T2D [21]. Subsequently, there were a number of studies, which
could not replicate the initial finding. Using the TDT approach we could show excess
transmission of the Pro allele to the affected offspring [22]. We thereafter performed a
meta-analysis combining the results from all published studies showing a highly
significant association with T2D. The Pro12Ala polymorphism of the PPARG gene is
until now the best replicated gene for T2D (p< 2x10-10) (Table 1). It also predicts
future T2D, especially in individuals with body mass index > 30 kg/m2 and fasting
plasma glucose > 5.5 mmol/l and in carriers of risk variants in the CAPN10 gene [23].
There is also a strong interaction with nutritional factors and the protective effect of
the Ala allele is enhanced with a high intake of unsaturated fat [24]. This may not be
too surprising as free fatty acids have been proposed as natural ligands for PPARG.
KCNJ11: The ATP-sensitive potassium channel Kir 6.2 (KCNJ11) forms together
with the sulfonylurea receptor SUR1 (ABCC8) an octamer protein that regulates
transmembrane potential and thereby glucose-stimulated insulin secretion in
pancreatic beta-cells. Closure of the K- channel is a prerequisite for insulin secretion.
A Glu23Lys polymorphism (E23K) has been associated with T2D and a modest
impairment in insulin secretion [25, 26]. In addition, an activating mutation in the
gene causes a severe form of neonatal diabetes [27]. Whereas these neonatal
mutations result in a 10-fold activation of the ATP-dependent potassium channel, the
E23K variant results in only a 2-fold increase in activity [28].
TCF7L2: By far the strongest association with T2D is seen for SNPs in the gene
encoding for the transcription factor-7-like 2 (TCF7L2) [17, 29]. TCF7L2 encodes
for a transcription factor involved in Wnt signalling. Heterodimerization of TCF7L2
9
with -catenin induces transcription of a number of genes including intestinal
proglucagon. It is clear that risk variants in TCF7L2 are associated with impaired
insulin secretion, and an impaired incretin effect, i.e. impaired stimulatory effect of
incretin hormones like GLP-1 and GIP on insulin secretion (Figure 3) [30, 31]. It is
also possible that the gene is involved in proliferation of -cells in response to
increased demands. At onset of diabetes, T2D patients show a markedly increased
expression of TCF7L2 in their islets [30]. Since over-expression of TCF7L2 in human
islets resulted in impaired insulin secretion it is unlikely that the increased expression
is a consequence of a defect in the downstream pathway, it rather reflects a defect in
transcription or translation of TCF7L2 itself. There is a unique splicing pattern of the
TCF7L2 gene in human islets, where especially exon 4 including isoforms are
abundant [32]. Intriguingly, there was a positive correlation between the amount of
exon 4 in human islets and HbA1c. Despite the increased expression of TCF7L2 in
islets it is not known whether risk variants in the gene cause increased or decreased
Wnt signaling. On the other hand, in rodent islets disruption of TCF7L2 in islets also
result in impaired insulin secretion [33]. It will be one of the greatest challenges to
identify these mechanisms, as TCF7L2 undoubtedly represent a potential novel drug
target in T2D.
WSF1: The Wolfram syndrome 1 (WFS1) gene encodes for wolframin, a protein,
which is defective in individuals with the Wolfram syndrome. This syndrome is
characterized by diabetes insipidus, juvenile diabetes, optic atrophy and deafness.
WFS1 gene was identified through candidate gene search of 1536 SNPs in 84 genes,
and further replication in 9,533 cases and 11,389 controls [34]. Genetic variants in the
WFS1 have been associated with impaired glucose- and GLP1 (glucagon-like peptide1) -stimulated insulin secretion [35-37]. Wfs1 knockout mice showed progressive beta
cell loss and impaired insulin secretion [38]. We have recently demonstrated that
expression of WFS1 gene is also perturbed in pancreatic islets and skeletal muscle
[39]. However, WFS1 discovery also highlights some of the difficulties of candidate
gene studies. We are limited by our own imagination and only 1 out of 84 candidate
genes gave a positive result!
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1.6. Genome wide association studies
A problem in the field of genetics of complex diseases is the difficulty to replicate an
initial association. The International HapMap Project (http://www.hapmap.org) and a
public-private SNP consortium (http://www.ncbi.nlm.nih.gov) have provided a
catalogue of 10 million common genetic variants in the human genome. With the
improvement of genotyping technology it has become technically possible to
genotype a large number of SNPs at affordable costs, which paved the way for the so
called genome wide association studies (GWAS). In 2007 GWAS made a real
breakthrough in the genetics of T2D applying DNA chips with > 500,000 SNPs in a
large number of patients with T2D and controls [40]. In our collaborative study with
the Broad Institute and Novartis (Diabetes Genetic Initiative, DGI) we performed a
WGAS in 1464 patients with T2D and 1467 non-diabetic control subjects from
Finland and Sweden. Prior to publication we shared the results with researchers from
the FUSION (Finnish USA Study of NIDDM) and WTCCC (Welcome Trust Case
Control Consortium) groups [41]. We only considered positive results, which were
seen and replicated in all three studies, i.e. together with replication samples the
results were based upon DNA from 32,000 individuals! Two other WGAS [42, 43] in
T2D have been published in the past year supporting and complementing our results.
Notably, TCF7L2 was on top of each WGAS with a joint p value in the three scans of
10
–50
[29]. Several of the new genes seem to influence -cell proliferation by
interfering with the cell cycle e.g. CDKAL1 and CDKN2A/CDKN2B. It has also
become evident that some of the same SNPs which increase risk of or protect against
T2D increase susceptibility to certain cancers like in the prostate and in colon [44].
This has led to a hypothesis that genes increasing cell proliferation predispose to
cancers but protect against T2D, and vice versa inability of -cells to increase cell
proliferation predispose to diabetes, and if occurring in other organs, protect against
cancer [45]. In general, the risk associated with these variants was modest with Odds
ratios in the range of 1.2-1.5. This was only the beginning, an extended meta-analysis
of more than 60,000 GWAS data (DIAGRAM) identified variants in or around 6
additional genes (JAZF1, THADA, CDC23, LGR5, ADAMTS9, NOTCH2) [29]
with even more modest effect sizes than those seen for the initial variants. A third
meta-analysis of even more individuals (> 100,000) is soon to be published.
11
Most of the genetic variants described to date result in impaired -cell function. When
we compared high –risk and low-risk genotypes, i.e. those belonging to highest and
lowest 20%, high risk genotypes did not influence BMI or insulin sensitivity but they
could not increase their -cell function to compensate for the decrease in insulin
sensitivity imposed by an increase in BMI [12] (Figure 4, 6). In addition, the GWAS
have been extended to quantitative traits like glucose and insulin and have recently
identified variants in three genes (G6PC2, GKCR and MTNR1B) to be consistently
associated with fasting glucose concentrations [46-48].
KCNQ1: KCNQ1 is an ATP-dependent potassium channel expressed in most tissues.
Mutations in the gene cause the long QT- syndrome characterized by severe
arrhythmias [49]. Two small (100K chips) Japanese WGAS identified variants in this
gene being associated with T2D [50, 51]. We have further replicated these findings in
Scandinavian populations and also shown that variants predict future T2D, which
partially can be explained by an effect on insulin secretion [52]. The question rose
why it was missed in the previous European WGAS. The most likely explanation is
that the risk genotype was seen in > 90% of Europeans thereby limiting the power to
detect an association.
MTNR1B: An intriguing observation was that a common variant in the gene encoding
the melatonin receptor 1B (MTNR1B) was associated with impaired insulin secretion,
elevated glucose concentrations and increased risk of future T2D (Figure 5) [47].
There is a well-established link between sleep disorders and T2D. Preliminary data
suggest that variants in the MTNR1B gene can partially explain this association. We
could also show that the MTNR1B gene is expressed in human islets. However, we do
not know whether melatonin is produced in islets from serotonin or transported to the
islets. The MTNR1B gene was up-regulated in carriers of risk genotypes suggesting a
gain-of-function effect increasing risk of T2D. In support of this adding melatonin to
clonal -cells inhibited glucose-stimulated insulin secretion (Figure 5). Inhibition of
melatonin effects in islets could thus represent a novel therapeutic target.
1.7. Common variants in MODY genes
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Maturity-onset diabetes of the young (MODY) is an autosomal dominant form of
diabetes, where a mutation in a single gene causes the disease. There are at least six
forms of MODY caused by mutations in a distinct genes have been found. Most forms
of MODY are caused by mutations in different transcription factors, i.e. HNF4
(MODY1), HNF1 (MODY3), IPF-1 (MODY4), HNF1 (MODY5), and NeuroD
(MODY6), and only MODY2 caused by mutations in the gene encoded for
glucokinase enzyme [53]. Common to all MODY carriers phenotype is that they are
characterized by impaired insulin secretion and usually show strong allelic variability,
i.e. different mutations cause the disease in different families. We therefore looked at
whether common variations in these genes could contribute to common late-onset
T2D. This turned out to be the case, and we indeed demonstrated that common
variants in HNF1, HNF1 (TCF2) and HNF4 were associated with impaired betacell function [54-56]. It was, however, not easy to detect these subtle effects, which
seem to be unmasked in insulin-resistant obese elderly individuals. As mentioned
above, the same variants in the HNF1 gene which increases risk of prostate cancer
protects against T2D [44].
1.8. Personalized prediction of T2D risk?
Combined information of genetic and clinical risk factors ultimately might aid
in personalized prediction of disease risk. Recently we have evaluated effect of
clinical and genetic factors to predict progression to diabetes in two prospective
cohorts [12]. When we replaced family history with increasing number of T2D
associated risk alleles, the risk of T2D gradually increased with increasing number of
risk alleles and increasing quartiles of body mass index. The risk was highest in
subjects with high genetic risk and highest quartile of BMI yeilding an OR of 8.0 as
compared to those with low genetic risk and lowest quartile of BMI (Figure 1). As
these studies comprised a large number of participants with long follow-up we were
in a unique position to address the question whether genetic risk factors added to
clinical risk factors could improve prediction of future diabetes. The results
demonstrated that adding genetic markers to the clinical risk factors modestly
improved the discriminatory power as assessed by the area under the Receiver
Operating Characteristic (ROC) curves (from AUC 0.73 to 0.74) [12]. An important
factor defining the discriminative power of clinical and genetic risk factors is duration
13
of follow-up. We also assessed the area under the ROC curves to determine the
discriminative ability of clinical and genetic risk factors in relation to quintiles of
follow-up time. We observed a decrease in AUC for the clinical model (P=0.01) and
an increase in the AUC for the genetic risk score (P=0.01) with increasing duration of
follow-up. These findings suggest that an individual genetic profile could be valuable
from birth, long before exposure to most environmental risk factors takes place. It has
been suggested that using a larger number of common variants can make possible an
accurate prediction of genetic risk [57, 58] . Simulation studies have estimated that for
an AUC of 0.80 to predict cardiovascular disease it will be necessary to genotype 50
risk variants with allele frequencies of 10% and ORs of 1.5 [59]. However, this figure
might look different if we find rare variants with stronger effect size, e.g. with odds
ratio > 2-3, possibly requiring about 10 rare variants with strong effect size to explain
the genetic risk of T2D. This figure might change completely if biomarkers involved
in the key pathway could be identified which together with genetic markers would
markedly increase the risk prediction. Taken together, genetic tests cannot be offered
yet to predict disease. The main reason is the marginally increased risk associated
with each risk variant. However, it may be possible to use them on a population level
to reduce the number of individuals needed to be included in trails aiming at
prevention of T2D.
1.9. Pharmacogenetics
An important goal of genetics is to use the information to improve treatment, i.e. to
identify individuals who are more likely than others to respond to a specific therapy.
While there are some intriguing examples on the potential of pharmacogenetics for
monogenic forms of diabetes [60, 61], its role in the more common forms is still
unclear. Patients with neonatal diabetes due to mutations in the KCJN11 gene do
much better on treatment with sulfonylureas than with insulin [60]; in fact the
sometimes accompanying developmental defects improve. Patients with mutations in
glucokinase (MODY 2) can be taken of all treatment including insulin as the disease
does not progress and patients with mutations in HNF1 (MODY 3) respond
extremely well to sulfonylureas [62]. It has also recently been shown that individuals
with the risk genotype in TCF7L2 respond poorly to treatment with sulfonylureas,
eventually as a consequence of their more severe impairment in beta-cell function
14
[63]. It is still an open issue whether variants in the TCF7L2 would modify response
to incretin mimetics.
There is some indication that variants in genes metabolizing/transporting metformin
influence its effects [64, 65]. The effect one would like to see is that seen with the
rs149056 variant in the gene for SLCO1B1 gene (a cation transporter) for prediction
of myopathy during high dose statin therapy; the variant was associated with a 6-fold
increased risk of myopathy [66].
Clinical implications and future directions
Although it has been debated whether impaired insulin secretion or increased insulin
resistance is the key defect in the pathogenesis of T2D, the accumulating evidence
today point at the central role of the failing beta cells (Figure 6). Dissecting the
genetic architecture of a complex disease such as T2D is a rather challenging task.
The genetic variants detected represent common variants shared by a large number of
individuals but with modest effects. Each risk allele increases risk of T2D only by
12% [12]. The about 30 T2D genes discovered thus far explain only a small
proportion ( 0.3) of the individual risk of T2D (s of 3). It is still possible that there
are rare variants with stronger effects not detected with current methods. It is unlikely
that high-density DNA arrays can detect these rare variants. Their detection will
rather require sequencing. Sequencing of the whole genome was once a dream, but
with the new technology this dream may become true in a very near future. Dissection
of the genetic complexity of T2D, identifying novel pathways in the pathogenesis of
the disease most likely will pave ways to the new therapeutic strategies.
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Figures legends
Figure 1: Risk of incident T2D in individuals with different risk factors. (A) Family
history of T2D doubled the risk of T2D associated with a low insulin response to oral
glucose. (B) There was an increased risk of T2D with increasing quartiles of BMI and
with increasing number of the risk alleles. The risk was highest in subjects with high
genetic risk and highest quartile of BMI yeilding an OR of 8.0 as compared to those
with low genetic risk and lowest quartile of BMI. The Y axis shows incidence of
diabetes.
Figure 2: Association of ADRA2A rs553668 with insulin secretion in humans. (A)
Effects of rs553668 genotype on insulin levels during IVGTT in 799 individuals. Data
are means ± SEM. (B) Immunoblots of total protein from human islets from 8
individuals using polyclonal alpha(2A)AR antisera. The histogram shows average
alpha(2A)AR signal normalized for β-actin from 4 blots from a total of 11 GG, 7 GA
and 1 AA carriers. (C) Islet ADRA2A mRNA expression in 24 GG, 7 GA and 1 AA
carriers. P < 0.05 for GG versus GA/AA, or P < 0.05 for linear regression of
expression versus number of risk alleles. (D) Islet insulin secretion at 2.8 or 20 mM
glucose with or without alpha(2A)AR antagonist. *P <0.05, **P<0.01, ***P<0.001.
Figure 3: Insulin secretion and incretin effect according to different TCF7L2
rs7903146 genotypes. (A) Change in insulin secretion (disposition index) over time
in subjects who converted to T2D in the Botnia cohort (risk genotypes = solid line).
(B) Risk genotype carriers of the TCF7L2 rs7903146 (risk genotypes = black bars)
show impaired incretin effect as shown by lower insulin response to oral than to
intravenous glucose.
Figure 4: Carriers of high-risk genotypes for T2D (solid line) cannot increase their
insulin secretion to compensate for the increase in insulin resistance compared with
carriers of low-risk genotypes (dashed line). High and low risk was defined as highest
or lowest 20% of risk genotypes.
Figure 5: Risk genotype carriers of the MTRN1B gene (black bars and solid lines)
show (A) impaired -cell function corrected for insulin sensitivity (disposition effect)
16
cross-sectionally, and (B) decline in beta-cell function over 8-year follow-up period,
(C) increased expression of the gene in human islets. (D) Melatonin inhibits glucosestimulated insulin secretion.
Figure 6. Schematic view of a potential role of genetic loci influencing risk of T2D
and/or glycemic traits in beta-cell function.
17
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