University of Oklahoma Health Sciences Center, Oklahoma City, USA
*Corresponding author:
Dharambir K. Sanghera, Ph.D, FSB, FAHA
Associate
Professor of Pediatrics, Department of Pediatrics
Section of Genetics, University
of Oklahoma Health Sciences Center
Oklahoma City, OK 73104, USA Tel: 405-
271-6026 Fax: 405-271-6027 E-mail: dharambir-sanghera@ouhsc.edu
Received May 17, 2012; Accepted June 19, 2012; Published June 23, 2012
The global epidemic of type 2 diabetes mellitus (T2D) is one of the most challenging problems of the 21st century
and the fifth leading cause of death worldwide. Substantial evidence suggests that T2D is a multifactorial disease
with a strong genetic component. Recent genome-wide association studies (GWAS) have successfully identified and
replicated nearly 75 susceptibility loci associated with T2D and related metabolic traits, mostly in Europeans, and
some in African, and South Asian populations. The GWAS serve as a starting point for future genetic and functional
studies since the mechanisms of action by which these associated loci influence disease is still unclear and it is
difficult to predict potential implication of these findings in clinical settings. Despite extensive replication, no study
has unequivocally demonstrated their clinical role in the disease management beyond progression to T2D from
impaired glucose tolerance. However, these studies are revealing new molecular pathways underlying diabetes
etiology, gene-environment interactions, epigenetic modifications, and gene function. This review highlights evolving
progress made in the rapidly moving field of T2D genetics that is starting to unravel the pathophysiology of a complex
phenotype and has potential to show clinical relevance in the near future.
The global epidemic of type 2 diabetes (T2D) is a major public health
problem of 21st century and the fifth leading cause of death worldwide
[1]. The disease is also a leading cause of morbidity and contributes
to development of premature coronary heart disease (CHD), stroke,
peripheral vascular disease, renal failure, and amputation. According
to latest statistics released by the International Diabetes Federation, the
number of people living with diabetes is expected to rise from 366 million
in 2011 to 552 million by 2030; 80% of these people with diabetes
will live in developing countries
(http://www.idf.org/diabetesatlas/5e/the-global-burden). According to these predictions, in three leading
countries with diabetes populations USA, India, and China the approximate
estimate of 23.7, 61.3 and 90 million people with diabetes in US,
India, and China in 2011 will increase to 29.6, 101.2, 129.7 million by
2030 (Figure 1). The global health expenditure on diabetes is expected
to increase from 376 billion dollars in 2010 to 490 billion in 2030 [2].
Figure 1:It compares the prevalence of diabetes for the year 2011 to the projections
for 2030 in USA, China, and India according to the latest projections
by International Diabetes Federation (http://www.idf.org/diabetesatlas/5e/theglobal-
burden).
Substantial evidence suggests that T2D is a multifactorial disease
with a strong genetic component. High concordance rate obtained in
monozygotic twins (96%) supports a substantial contribution of genetic
factors to T2D [3-7]. Furthermore, 40% of first-degree relatives of
T2D patients develop diabetes as compared to 6% in the general population
[8]. Segregation analysis also points to the polygenic nature of
T2D, in addition to the existence of a major genetic component [9,10].
The general estimates of heritability (h2) of T2D is 0.49 and the relative
recurrence risk for a sib of an affected person (λs) to develop T2D is 3.5
[11,12]. However, the findings and the results of recent genome-wide
association studies (GWAS) have significantly underestimated these
heritability estimates and presents a challenge for ongoing and future
investigation.
Clinical Phenotype of T2D
Unlike simple characterization of type 1 diabetes (T1D) which is
primarily due to autoimmune mediated destruction of pancreatic beta
cells resulting in insulin deficiency, the pathogenesis of T2D is more
complex and remains a matter of debate. Hyperglycemia in T2D is a
consequence of complex interplay between insulin resistance (sensitivity)
and abnormal insulin secretion [13]. It is initially characterized
by compensatory insulin secretion associated with insulin resistance.
However, the β-cell’s response is inadequate for the increased demand
during progressive resistance to insulin-mediated glucose disposal, eventually resulting in β-cell failure and overt diabetes. There is also
accumulating evidence that the β-cell is adversely influenced by influx
of fatty acids [14] and cholesterol, which accumulates and exerts toxic
effects when efflux by HDL is limited [15].
The progression of insulin resistance often associated with obesity
and leading to mild glucose intolerance preceding an increase in glu cose levels above defined thresholds for diabetes, has made it difficult
to define the true phenotype, possibly because of early involvement
of multiple tissues such as muscle, fat and pancreatic β-cells. Furthermore,
the insulin resistant state, usually preceding diabetes by several
years, is associated with a cluster of co-morbidities including obesity,
dyslipidemias, and elevated blood pressure [16,17], and the presence of
three or more traits is currently recognized as the metabolic syndrome
which is also predictive of diabetes [18].
More than 80% of diabetic subjects are obese and these individuals
typically have an android body type (upper body obesity) manifesting
as an increased waist circumference [19]. Obesity, the metabolic
syndrome, and T2D are becoming increasingly prevalent even in children
and adolescents living in rapidly developing countries in different
parts of world [20-24]. The prevalence of T2D in the US is higher
among minorities and ethnic populations like African Americans, Native
Americans, Hispanic Americans, Asian Americans, and Pacific
Islanders than in the general population [25-28]. High prevalence is
even seen in children belonging to ethnic minorities [29-32]. Although
environmental factors play an important role in determining the risk
of disease, overwhelming data support that genetic factors influence
the disease susceptibility [33]. Lifestyle modifications (weight reduction
and physical activity) delay or even prevent the development of
T2D [34,35], such changes are exceedingly difficult to sustain outside
the research setting and seem to have contributed little to control the
diabetes epidemic [36].
T2D remains undiagnosed for many years because hyperglycemia
is usually not severe enough to provoke noticeable symptoms, unlike
T1D which often presents with keto-acidosis requiring admission to
hospital for correction and initiation of treatment. But hyperglycemia
can cause significant pathological and functional changes, which can
cause organ damage before the diagnosis of T2D is made. The long
term effects of diabetes include micro-vascular and macro-vascular
complications and those who have genetic susceptibility are at greater
risk [37]. Micro-vascular complications are progressive development
of disease in fine capillaries supplying blood to the kidneys and retina
of the eye that results in blindness. Macro-vascular complications include
hypertension, coronary artery disease, peripheral vascular disease,
cerebral vascular disease, and hyperlipidemia. Diabetic patients
also have neuropathy, which may lead to foot ulcers, amputations, sexual
dysfunction, and non-healing skin wounds. Certain infections such
as staphylococcal sepsis is more common in diabetics and infections of
the ear, nose, throat as well as reactivation of tuberculosis associated
with high rate of mortality and morbidity are more likely with poor
blood glucose control. Consequently, diabetes is a disproportionably
expensive disease. The economic impact of diabetes in the US is enormous,
and is expected to increase from $113 billion to $336 billion [38].
Linkage and Candidate Gene Association Studies
Progress in identifying the genetic basis of simple Mendelian
(monogenic) diseases during the past decade has been substantial.
More than 3000 monogenic disorders have been successfully mapped
by linkage and family based studies
(http://www.ncbi.nlm.nih.gov/omim). On the other hand, complex diseases like T2D do not segregate
in simple Mendelian fashion but rather are affected by multiple genetic
and environmental factors. Linkage and candidate-gene focused studies
were successful in identifying some rare familial forms of T2D presenting
at young ages called maturity onset diabetes of young (MODY),
mitochondrial diabetes and neonatal diabetes. However, linkage and
association studies on the common form of T2D provided inconsistent
results and failed replication in multiple populations [39]. Only PPARG, KCNJ11 and TCF7L2 were identified as established genes associated
with common forms of T2D [40]. Apparently, inconsistencies
across populations were due to the heterogeneity of the disease itself or
its pathogenesis, incorrect candidate selection because of incomplete
knowledge of molecular mechanisms, variation in study design, sample
size, population-specific linkage disequilibrium, choice, analytical
methods, or over-interpretation of results [40-43].
Genome-Wide Association Studies (GWAS)
Completion of the Human Genome Project in 2003 [44] led to
subsequent advances in biomedical research. Since 2007, a new technology
in the form of ‘genome-wide chips’ has facilitated remarkable
progress in T2D genetic research with the first publication of five large
GWA scans within the span of four months, showing that more than
500,000 SNP markers distributed across the genome [45-49]. This approach
has been successful in locating genes for other diseases besides
T2D and obesity [40] namely, type 1 diabetes [50], prostate cancer [51],
rheumatoid arthritis [52], Crohns disease [53,54], and cardiovascular
disease [55] and is being applied to other complex disorders. Use of
this ‘hypothesis-free’ approach involved in GWAS has opened new areas
of biology to explore as discoveries of more than seventy entirely
new T2D loci clearly suggest that associations are not limited to candidate
genes and by applying GWAS and re-sequencing approaches, new
genes involved in disease pathogenesis can be identified [56] (Table 1).
Table 1:Summary of type 2 diabetes genes discovered in GWA studies (in order of gene region).
The number of risk alleles for complex diseases identified by
GWAS since 2007 exceeded those identified in the entire preceding decade,
and these studies can effectively detect multiple common variants
with small effects with odds ratios (ORs) <1.2 [36,39,57] but offers limited
power to detect rare variants with stronger effects [58]. However,
GWAS have the best chance of detecting and identifying additional
genes for T2D and are being pursued in different population groups,
to provide a more realistic ‘genetic risk landscape’ of the disease [59],
and account for variation in population-specific environmental interactions.
Notably, most immediate advances from GWA studies are not
disease prediction or prevention, rather these studies are providing a
better understanding of the disease pathophysiology since further studies
can lead to defining the role of the newly identified genes in key
metabolic pathways involved in pathogenesis. Also GWAS, together
with targeted resequencing of the identified loci allows recovery of a
more complete inventory of the sequence variation leading to identification
of novel metabolic pathways and proteins as potential targets
for interventions [57]. Since substantial numbers, maybe thousands,
of genes with small effects are operative in human disease and can be
identified by this approach, their combined effect may be substantial
[56]. Furthermore, gene-gene interaction may not only be additive but
effects of a mutation can be more than doubled when compounding
mutations are present. The phenomenon known as epistasis was first
shown to occur in a rodent knockout model in which the insulin receptor
when combined with IRS-1 knockout resulted in 40% increase
in diabetes onset compared to isolated knock-outs [60]. The phenomenon
could contribute to effect size and in part account for the low
heritability accounted for by classic GWAS. It follows that multi locus
association testing could be effective in the presence of epistasis. With
this in mind the Welcome Trust Case Control Consortium used twolocus
tests of association and detected epistatic signaling of association
at 79 SNP pairs [61]. However the large number of possible associations
in GWAS requires filtration strategies based on knowledge of the
biochemistry and known interacting pathways [62]. However, several
models for analysis have been proposed and consensus on the optimal
methodology has not been reached [63].
The common strategy used for elucidating the inherited components
of complex disease is based on common disease-common variant
hypothesis [64,65]. The hypothesis has held true in the case of putative
causal variants in APOE, including APOE ε4, association with
Alzheimer’s disease, interleukin 23 receptor association with Crohns
disease (the at-risk allele has a frequency 93% in the general population),
and PPARG2 association with T2D (at-risk (pro) allele frequency
~85% in general population). The assumption with the use of common
variants is that either they will identify the association with the common
disease, or a quantitative trait (QT) directly, or indirectly being
in linkage disequilibrium (LD) with a functional variant. Most of the
T2D loci identified using GWAS approach are common variants with
small effects. The first GWAS in T2D was published by a French group
of investigators who identified a zinc transporter and member of solute
carrier family SLC30A8 and HHEX along with confirming the association
of TCF7L2 and KCNJ11 with T2D [49]. Three additional GWAS in
their jointly published findings confirmed TCF7L2, KCNJ11, PPARG,
SLC30A8, and HHEX. They also discovered novel T2D loci called CDKAL1
and a variant near CDKN2A-B [47,48,66]. These findings were
also simultaneously confirmed by GWAS performed on an Icelandic
sample by the deCODE researchers [67]. Association of FTO with T2D
was also first discovered by GWAS for obesity [OR for T2D being 1.27,
p < 10-8] [68] and was later confirmed in replication studies [46].
Additionally, a joint meta-analysis of three previously conducted
T2D GWA scans by the Diabetes Genetics Replication And Meta-analysis
(DIAGRAM) consortium [69] has detected an additional six novel
loci with strong evidence of association; JAZF1, CDC123, TSPAN8,
THADA, ADAMTS9, and NOTCH2 [69] in 10,128 individuals using
2.2 million SNPs. Also, another European study reported a strong association
of an intronic SNP (rs560887) in G6PC2 with fasting plasma
glucose levels by screening 392,935 SNPs in 654 non-T2D participants
using GWAS [70]. Association of G6PC2 with fasting plasma glucose
levels was replicated in the FUSION study [71]. Further, a variant near
MTNR1B has been identified to increase FBG levels in two separate
GWA studies [72,73]. Association of MTNR1B variants (rs10830963
and rs1387153) with FBG or T2D in our Punjabi cohort from India
has not been confirmed. Instead a less common variant on 5’ UTR
(rs1374645) of MTNR1B revealed a strong association with low FBG
levels in normoglycemic individuals with low BMI (<25 kg/m2). Furthermore,
our data showed strong interaction of this variant with BMI
with respect to FBG levels [74]. Most recently, a meta-analysis was performed
on 21 GWAS comprising 46,186 participants to identify loci influencing
glycemic traits. They performed follow-up studies on 25 loci
in up to 76,558 additional subjects and identified nine loci associated
with fasting glucose and HOMA-B (ADCY5, MADD, ADRA2A, CRY2,
FADS1, GLIS3, SLC2A2, PROX1 and C2CD4B) and one influencing
fasting insulin and HOMA-IR (IGF1) in European populations [75]. Another study analyzed combined GWAS data from 8,130 individuals
with T2D and 38,987 controls of European descent, and in follow-up
meta-analysis found signals in a further 34,412 cases and 59,925 controls
and identified 12 new T2D associations including new signals at
KCNQ1, KLF14, DUSP9, CENTD2, HMGA2, HNF1A and PRC1 [76].
Genetic Diversity among Populations
The majority of GWAS so far (96%) have been predominantly performed
in Caucasians [77,78]. Given the existence of a strong genetic
diversity among the world communities due to biological traits, cultural
histories, languages, caste system, physical appearance, food habits
etc., the information from GWA findings on Caucasians should not be
transferred and used to predict risk in other populations [79,80]. Even
the largest and best executed GWAS is unlikely to provide a complete
assessment of “the genetic risk landscape” of the disease [59]. These loci
neither account for all cases of T2D, nor do they sufficiently account
for the variation in quantitative sub-phenotypes of T2D. Extending the
use of new methodologies such as GWA to study non-European ancestries
with different mutational spectra and demographic and cultural
histories is important for identifying the population-specific patterns
for allele frequency and LD relationship of the susceptibility loci, and
population-specific environmental factors for disease risk or protection
[57]. Several GWAS in other ethnic groups are currently ongoing
and will be capable of revealing novel susceptibility loci. GWAS have
proven more successful in small homogenous populations like Finland,
Iceland and Costa Rica, and have potential to detect regionally common
variants which may be missed in outbred populations [78]. It is
of interest to note that many of the common loci originally associated
with diabetes in European populations have not been replicated in
other non-European populations [49,67,77,81]. For example, loci associated
with Crohns disease in Caucasian GWAS were not associated
in Japanese [81], also the association of CDKAL1 locus with T2D was
not seen in African Americans [67]. As discussed earlier, the common
intron 1 FTO variant (rs9939609) was initially associated with diabetes
in Europeans, and phenotypic interactions appear to be diabetogenic,
but the associations were due to obesity [68,69]. However, the question
has been explored in independent association and meta-analyses
of South Asian populations in whom BMI and waist association with
FTO is similar to that seen in Europeans, but a strong association with
diabetes is only partly accounted for by BMI [82,83]. A recent large
scale meta-analysis conducted on 96,551 individuals from East and
South Asia confirmed that the association of rs9939609 with T2D was
independent of obesity [84].
There is now a strong consensus that a single population is not sufficient
to uncover all the variants underlying complex diseases [77].
Recent GWAS studies in non-European populations have yielded intriguing
new variants, for instance, the KCNQ1 signal (first noticed in
Japanese) provided insight into novel etiological pathways leading to
insulin resistance and T2D. Joint meta-analysis of Caucasian GWA
studies later discovered a second independent signal at the KCNQ1 locus
(rs231362) and an overlap between loci implicated in monogenic
and multifactorial forms of diabetes at HNF1A [76]. The new Caucasian
signal at KCNQ1 was later confirmed in our study on Asian Indians
[85]. These findings provide new insights into the pathophysiology
of this extremely complex disease by discovering previously unknown
variants and often replicating the findings but not always. Association
of UBE2E2 with T2D was first described in East Asians [86]. Recent
larger meta-analysis using East Asian GWAS cohorts yielded eight new
loci for T2D in (GLIS3, PEPD, FITM2-R3HDML-HNF4A, KCNK16,
MAEA, GCC1-PAX4, PSMD6 and ZFAND3) [87]. Six novel loci for T2D (GRB14, ST6GAL1, VPS26A, HMG20A, AP3S2 and HNF4A) were
first discovered in our South Asian population GWAS by Kooner et al.
[88]. To date, GWAS and meta-analyses in T2D and related quatitative
traits have together identified approximately 75 susceptibility variants
which together explain about 10% of the observed familial aggregation
(Table 1). Although these effect sizes detected so far do not reflect biological
or clinical significance of these variants, they highlight a particular
genomic region likely to be associated with the trait. Of all the
complex traits studied by GWAS , Crohns disease, has so far had the
strongest association with genetic components of risk; up to 20% being
explained by variants detected by GWAS [89]. Furthermore, GWAS
provided important biological insight with direct clinical relevance
adding to significant understanding of the disease compared to what
was known about Crohns disease five years back.
Hidden Heritability and Rare Variant Hypothesis
Despite these successes, results of multiple GWAS and meta-analysis
studies (based on the common disease-common variant hypothesis
[64,65]) show that common variants discovered in these studies
explain only a small fraction with odds ratios (ORs) ranging from 1.1-
1.2 (p values <10-8) of the heritable component of T2D risk, and these
studies have not pinpointed with certainty the causal variants at the
associated loci [90]. Importantly, commercial arrays used in GWAS
have captured much less than 60% of common SNP information in the
majority of the GWAS conducted to date [91]. One approach could be
to expand the sample sizes to detect more variants of moderate effects,
however, the GWAS approach is usually underpowered to locate those
variants with a frequency of <5% [57], and is considered unlikely to
yield more information on the heritable control of disease than what
has already been revealed by GWAS [92]. Moreover, association studies,
based on the common disease-common variant hypothesis would
have less relevance if the disease is caused by less common mutations at
multiple sites and each arising on a different ancestral haplotype [93].
Many studies have suggested the large role of rare variants in
complex diseases [93,94], and that the genes carrying common variants
with small effects also are likely to carry rare variants with large
effects [69,90] which can only be detected by targeted resequencing
[57,95]. Also, several independently and dominantly acting rare variants
from different genes, together can confer a detectable relative risk
for a given “common disease-rare variant” hypothesis [96]. These low
frequency variants with stronger penetrance would be expected if trait
alleles have undergone purifying selection [94]. Resequencing of certain
candidate genes have shown that certain rare variants can be identified
in individuals with extreme phenotypes [97]. These rare variants
when clustered together can produce a significant genetic effect in a
complex disease [56]. For instance, rare functional recessive mutations
contribute to HDL and triglyceride levels [98,99], suggesting the possibility
of discovering such variants in a common disease such as T2D
[100]. Fearhead and colleagues [101] detected several rare functional
variants in five genes among 124 subjects with multiple adenomatous
polyps showing combined ORs of 2.2; p = 0.0001. Furthermore, 25% of
these individuals had a single rare variant and none of them had two or
more. Their findings suggested that mainly missense and possibly also
nonsense, promoter, and splicing variants can collectively explain a significant
portion of inherited susceptibility to colorectal adenomas. It is
realistic to expect similar findings in other chronic diseases [94,96] including
diabetes. It is also important to note that rare variants are often
founder variants caused by genetic drift and can be population-specific
and less cosmopolitan. For instance, the rare variants discovered in the
APC gene for colorectal adenomas in Ashkenazi Jews were not present in Koreans [102]. These findings therefore suggest that the search for
genetic variation in diabetes-susceptible populations should be carried
out in multiple ethnicities in carefully chosen candidate genes, and in
clearly phenotyped disease groups. Further use of whole genome rare
variants from the 1000 Genomes Project (www.1000genomes.org) and
T2D-GENES Consortium with data from multiple ethnic groups will
substantially improve power of these studies and capture heritability
associated with rare variants from specific regions or even from the
other regions of the genome, especially those (deletions/insertions, duplications,
inversions) that have escaped detection in GWAS [103]. Additionally,
imputation from 1000 Genomes Project will allow discovery
of new association signals from the missing “dark matter”. It will also
assist in post-GWAS fine mapping and functional characterization of
known associations and enhance our ability to fine map signals of local
adaptation as has been successfully achieved for refining the locus for
smoking quantity at 15q25 (P = 9.4 × 10−19) using imputed data from
1000 Genomes which increased marker density to five-fold compared
to HapMap2 [104].
In addition to major gene mutations and SNPs, changes in the normal
genome sequence can result in significant miscoding with disease
association [105]. Submicroscopic deletions, duplications, insertions,
inversions and translocations can occur in a small DNA segment and
are known as copy number variations (CNV) [106]. Since methodology
for CNV detection is relatively new [107], few studies have looked
for CNV associations with T2D. Since copy number variation in the
leptin receptor gene (LEPR) is associated with T2D [108], it is possible
that genome-wide detection of CNV could improve association with
T2D as shown for insulin resistance in the HyperGEN study, in which
joint tests of association for SNPs and CNVs were performed resulting
in detection of association in the T-cell receptor gene (TCRVB) in
African Americans [109]. Furthermore, generation of CNV maps [110]
supports potential for studying CNV for missing variation in T2D and
possibly accounting for the inter-ethnic variation found among diabetes-
prone populations [111]; however, common CNVs may be tagged
by SNPs and have been indirectly explored in previous SNP-based
studies [112]. Further, the event resulting in a CNV may also disrupt
regional genes and their regulatory elements accounting for disease association
[106].
Role of Epigenetics in Missing Heritability
The assumption of SNP-based GWAS has been that genomic sequence
variations account for disease phenotypes, however recent developments
in the field of epigenetics has shown altered expression of
genes that could lead to enhanced prediction of obesity and diabetes
[113]. The modifications include DNA methylation and histone modifications
such as ubiquitination, adenosine diphosphate ribosylation,
phosphorylation, acetylation and methylation. Since the modifications
are often observed during cellular growth and differentiation, it follows
that the changes may influence embryological development of cells
involved in carbohydrate homeostasis such as the β-cells, adipocytes
and myocytes. For example, DNA methylation occurs in the leptin
gene promotor and in GLUT4 in 3T3-L1 cells [114]. The epigenetic
mechanism mediate the influence of the environment on gene expression
[115]. For instance, leptin plays an important role in programming
of obesity-related traits [116,117], change in expression could be
involved in the pathogenesis of T2D, in part supporting epidemiological
findings that fetal growth and early nutrition in childhood influence
adult disease incl , HNF1B (rs 4430796 uding T2D [118]. For example,
increased methylation of PDX-1 results in decreased expression in
pancreatic islets from patients with T2D suggesting that modification of PDX-1 plays a role in T2D pathogenesis [119]. Besides, epigenetic
modifications are sensitive to change by nutrients, hormones and toxins
that can operate during developmental windows according to tissue-
specific and sex-specific mechanisms [120], which potentially can
be reversed with treatment and prevention [121,122].
Gene-Environment Interaction
Evidence from the epidemiology of T2D overwhelmingly supports
a strong environmental influence interacting with genetic predisposition
in a synergistic fashion as has been recently reviewed [123], however
current state-of-the-art methods for measuring environmental
effects lack precision and can result in changes in statistical power to
detect interaction [123,124]. Since lifestyle factors are important in
preventing diabetes [125,126], interaction of gene variants with measures
of dietary intake and exercise have been selected for studies on
gene-environment interaction. For example, HNF1B (rs 4430796) was
shown to interact with exercise; low levels of activity enhanced the risk
of T2D in association with absence of the risk allele, but there was no
protective effect of exercise when the allele was present. It follows that
subgrouping by genotype may serve to enhance risk prediction while
considering gene-environment interaction as has been done for exercise
[127]. Also lifestyle including exercise modified the effect of a
CDKN2A/B variant on 2-hour glucose levels in the Diabetes Prevention
Program [128] but was not confirmed in the HERITAGE study using
different measurements and phenotypes involving insulin sensitivity
and β-cell function [129]. The pro12ala PPARG variant also interacts
with physical activity for effect on 2-hour glucose levels [130], which
was confirmed in the smaller HERITAGE study [129]. In addition, a
relationship of dietary fat intake with plasma insulin and BMI differs
by the pro12ala PPARG genotype [131].
It is possible that there are genes that because of their known
metabolic involvement are likely to interact with specific nutrients.
For example, SLC30A8 which encodes a zinc transporter localized in
secretory granules, interacted with dietary zinc to effect fasting insulin
levels [132]. However, the majority of GWAS variants have not
shown interaction with environmental factors for effect on diabetes
or related traits. Therefore, it is likely that prospective future studies
will utilize improved assessment methods to increase power and avoid
false interpretation [133,134]. This could be enhanced by prioritizing
variants that are most likely to have effects [135] or selective sampling
according to extremes of the environmental factor could reduce the
requirement for sample size [136]. These and other strategies such as
meta-analysis, nested case control and genotype-based studies have
been recently reviewed [123,133] and the difficulties in measuring environmental
exposures have been emphasized, including the application
of analyses based on logistic regression [124] and problems with
instruments such as physical activity questionnaires [137]. Validated
food frequency questionnaires are popular instruments for evaluation
diabetes risk and are often used in conjunction with food analysis software
[138,139]. Similar methodology has been adapted to assess two
predominant food consumption patterns by Prudent and Western
[140], and demonstrated synergistic interaction with genotype and a
less healthy Western dietary pattern in determining male risk for T2D
by showing that the gene-diet interaction was higher in men with a
high genetic risk score determined by a gene counting method [141].
Also the effects of diet may predominate at specific developmental periods
[142] suggesting that age and associated physiological changes
are important as well as differences between genders. It has also been
observed that homogeneity of an environmental factor such as physical
activity in an Asian Indian study, may reduce ability to detect interac tion, but could be solved by subgrouping by the level of activity [143],
but increased recruitment would be needed to maintain power.
It is also speculated that several rapidly developing communities
lived through centuries of famine and starvation and over the years
have adapted to survival under conditions of unreliable food supply
because they have “thrifty genes” which enable efficient storage
of energy during periods of food deprivation [144]. The genes that
once represented a survival advantage, now, in the modern environment
with continuous low physical activity and dietary excess, result
in obesity and T2D [145,146]. In addition, early protein deprivation,
as indicated by low birth weight is considered as an important contributor
to the high prevalence of T2D especially in people living in
the under-developed world [147-150] possibly when followed by rapid
catch-up growth in infancy and early childhood [151]. The “thrifty
phenotype” hypothesis was based on data showing a positive association
of low birth weight with cardiovascular disease and diabetes in
adulthood [152], but the metabolic basis of gestational programming of
the fetus is poorly understood. In Europeans, the risk of T2D increases
with increase in birth weight whereas in Pima Indians and in South
Asians T2D rates increase with both low and high birth weight [153]
suggesting that the effective birth weight distribution is bimodal. Poor
intra-uterine nutrition also may be associated with abnormal β-cell development
and differentiation [154-156]. Therefore, it is imperative to
develop a deeper understanding of genetic, epigenetic, environmental,
cultural, and social factors that may interact to cause progression to
T2D, and are responsible for the declining health of the fastest developing
nations of the third world.
Prospects of Translation of Genetic Findings into Clinical
Practice
As discussed in more detail in previous sections, investigations on
genetic determinants of T2D have progressed substantially following
increased characterization of the genome and use of GWAS, however
a large portion of the heritability is unaccounted for, and many of the
genes commonly found in GWAS have small effects [157-160]. Thus,
state-of-the-art clinical use of genotyping is currently restricted to investigation.
Also the use SNPs to predict disease onset or response to
treatment is limited but can be done with some monogenic forms of
diabetes [160]. Furthermore, metabolic pathways that have been responsive
to treatment with pharmaceutical agents or exercise, or have
been associated with disease have rarely contained genes that have been
identified by large scale studies such as GWAS.
However, pharmacogenetic studies on association with the effects
of anti-diabetes drugs have provided a significant clinical link with
epidemiological findings [161], and frequently used agents in the treatment
of T2D such as metformin, sulfonylureas, and thiozolidinediones
have been shown to have gene associations. The principal metforminassociated
pathway involves adenosine-monophosphate-activated
kinase (AMPK), a regulator of glucose levels [162], and therefore has
potential as a drug target [163] for the control of T2D by metformin
and possibly other agents. It serves as a sensor of cellular energy status
via its phosphorylation and activation. Liver AMPK controls hepatic
glucose production by inhibiting expression of phosphoenolpyruvate
carboxykinase (PEPCK) and glucose-6-phosphatase (G6Pase) and this
action is down-regulated by the drug, metformin. Candidate genes
encoding enzymes, cofactors and transcription factors involved in
AMPK-associated pathways have potential for studies on therapeutic
responses. Since gene variants coding for steps in metabolic pathways
involving metformin’s action may lead to defining treatment responses, investigators in the United Kingdom conducted a meta-analysis
using the glycemic response to metformin as the phenotype. A variant
at a locus containing ATM, the ataxia telangiectasia mutated gene
(rs11212617) was associated with the response [164]. The importance
of the variant was emphasized by showing that specific inhibition of
ATM attenuated the phosphorylation and activation of AMPK in response
to metformin in a rat hepatoma cell line [164]. Although the
effect attributed to the variant was small, the study identified an important
upstream AMPK regulator adding information on how metformin
works, and in addition identified a possible mechanistic link
with DNA repair and cancer prevention. Shu et al. have investigated
polymorphisms in SLC22A1 which encodes organic cation transporter
1(OCT-1) which functions to facilitate absorption of metformin into
hepatocytes and identified association with reduced responsiveness to
metformin’s effect on glucose levels during an oral glucose tolerance
test [165]. Also association with HbA1c was shown with a variant in SLC47A1
encoding the multidrug and toxin extrusion protein 1 involved
in the excretion of metformin into bile and urine [166]. Furthermore,
the two genes both acting to increase intracellular metformin, have an
interactive effect [167]. The roles of both genes were supported by the
Diabetes Prevention Program investigators [168] who confirmed association
of the glucose-lowering response to metformin and variants in
SLC47A1, and interactions with SLC22A1 and the AMPK genes STK11,
PRKAA1, and PRKAA2.
Genes encoding the receptor and pathways for PPARγ agonists
constituting the thiozolidinediones, are often required for glucose
control because of their role in promoting insulin sensitivity. Of 133
PPARG variants tested in the TRIPOD study, eight showed evidence
for association with response to troglitazone therapy defined as change
in tolerance to intravenous glucose (IVGTT) [169] but no association
with fasting glucose suggesting that the response shown in the IVGTT
represents insulin resistance whereas fasting glucose is more likely to
be a measure of β-cell failure.
In studies where overt T2D has been the phenotype the majority
of associated polymorphisms have encoded proteins known to be involved
in β-cell metabolism; for example TCF7L2, KCNJ11 and HHEX
have shown robust association [170,171]. This suggests that these genes
could prove useful in predicting β-cell preservation during the course
of T2D. The glucokinase gene (GCK) coding for the initial glucosesensing
step in the β-cell can have activating mutations causing hypoglycemia
that might provide structural and functional models leading
to drug targets for treating T2D [172]. In the GoDARTs study, investigators
examined the medication response of metformin and sulphonylurea
based on the TCF7L2 variants mainly affecting the β-cell. The
carriers of the at risk ‘T’ allele responded less well to sulphonylurea
therapy than metformin [173]. Also it is of significant public health interest
that in the Diabetes Prevention Program, lifestyle modifications
were shown to reduce the risk of diabetes conferred by risk variants of
TCF7L2 at rs7093146, and in placebo participants who carried the homozygous
risk genotype (TT), there was 80% higher risk for developing
diabetes compared to the lifestyle intervention group carrying the
same risk genotypes [35]. These findings could herald significant future
progress in the field of T2D pharmacogenomics, possibly leading to the
development and use of agents tailored on the basis of genotype.
Predictive Power of Genetics beyond Traditional T2D
Risk Factors
Traditional clinical assessment includes a complete history of the
presenting traits including family history of the traits, and followed by laboratory studies that present either as continuous variables or
dichotomous values according to a cut-point. From this basic clinical
information it has been possible to derive risk scores that predict
T2D such as the Finnish Diabetes Risk Score and the Diabetes Risk
Calculator [174,175], which have been used to assess predictive ability
using receiver operating characteristic curves or the area under the
curve (AUC) as an index of discriminative accuracy. Using these scores
prediction has only been considered to be modest [176], leading to the
question whether genetic risk prediction might be superior. Studies
have been based on genetic variants alone and in combination with
environmental factors, but studies have differed in the number and
choice of genes and environmental factors. However, disappointingly
the genetic risk models had lower prediction capacity than the clinical
environment-based models [176]. Differences in populations, BMI, age
and duration of follow-up may offer some explanation [176,177], but
improving genetic methodologies may be the main issue needing to be
addressed.
Conclusions
The escalating current and predicted world-wide prevalence of
T2D underscores the need to develop effective strategy for early detection,
prevention and targeted treatments. In this regard the field of
genetic epidemiology has potential to progress to effective translation
to patient care possibly within the next decade [178]. Current stateof-
the-art approaches that may lead to this goal include defining the
pathogenesis of T2D and the phasic nature of the pre-diabetes phenotype
beginning in utero and progressing to overt T2D associated with
β-cell failure. Thus far, genes identified by GWAS using diagnosed
T2D as the phenotype have mainly been associated with β-cell failure
but have only accounted for less than 5% of the heritability. Although
the greater than 60% heritability appears clinically obvious, environmental
interactions, biochemical DNA modifications (epigenetic), and
the combined effect of rare variants and other genes present potential
challenges in defining risk association. However, the contribution of
GWAS over the past decade should not be minimized because of low
contribution to heritability, since the genes showing association have
been pointers to key metabolic pathways involved in pathogenesis and
in the action of drugs used for T2D treatment. Future functional studies
on model organisms or cell lines will enable functional characterization
of identified risk loci and will provide better understanding of the
biological mechanisms of disease development. Improved SNP- and
expression-array technology, sequencing, and targeted re-sequencing
are likely to enhance current understanding and progress. In addition,
inclusion of “at risk” populations in the developing world is likely to
improve understanding of gene-environment interaction and is more
likely to lead to detection of rare variants and novel variants that associate
with unique phenotypes and disease associations.
Acknowledgements
This work was partly supported by NIH grants -R01DK082766 funded by
the National Institute of Diabetes and Digestive and Kidney Diseases and NOTHG-
11-009 funded by National Genome Research Institute, USA. Superb technical
assistance provided by Latonya Been in manuscript preparation is duly acknowledged.
(2001) CDC at a Glance Diabetes: A Serious Public Health Problem. In. Centers for Disease Control and Prevention, Atlanta, Georgia, Centers for Disease Control and Prevention.
Cockram C JC (1999) Diabetes threat on the rise among U.S. children Chron Dis Notes Reports. Centers for Disease Control and Prevention, National Center for Chronic Disease Prevention and Health Promotion, Centers for Disease Control and Prevention,Atlanta, Georgia: 10-12.
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