Добавил:
Sekretar
kiopkiopkiop18@yandex.ru
t.me/Prokururor I Вовсе не секретарь, но почту проверяю
Опубликованный материал нарушает ваши авторские права? Сообщите нам.
Вуз:
Предмет:
Файл:Ординатура / Хирургия / Библиотека им академика М.И. Перельмана / Книга_3916_Библиотеки_им_академика_М_И_Перельмана
.pdf
Genetic Risk Factors for Abdominal Aortic Aneurysms (AAA)
7
aneurysm is accurately derived from the aortic duplex studies as seen in Table 3. Ongoing
research into the genetics of AAA will be strengthened by the knowledge of family history.
5. AAA AS A COMPLEX GENETIC DISEASE
Whereas simple Mendelian diseases are characterized by easily recognizable modes of
inheritance, such as autosomal dominant or autosomal recessive, and a strong correlation
between the presence of a mutation in a gene and the disease phenotype, complex diseases are
characterized by a weak correlation and poorly recognizable modes of inheritance [45, 46].
Frequently multiple genetic factors contribute to a common disease, either independently or
through gene-gene interaction. In addition, environmental or non-genetic risk factors often
contribute to the disease and further weaken the gene-phenotype correlation. The most
common genetic model for complex disease is the multifactorial model in which at least one
environmental factor and one genetic factor contribute to the disease process.
Characterization of the mechanisms of complex genetic diseases therefore involves
identifying both environmental and genetic risk factors.
5.1. Environmental Risk Factors for AAA
AAAs were once thought to be an atherosclerotic disease process because of the many
shared risk factors and findings of concurrent atherosclerosis of the coronary and peripheral
arteries. It is now understood that atherosclerosis and aneurysmal disease are different disease
processes with important differences in risk factors [47, 48].
Risk factors for the development of AAA include cigarette smoking, advanced age,
family history, the presence of coronary or peripheral artery occlusive disease, hypertension,
hypercholesterolemia, and being tall. Of all the known risk factors for the development of
AAA, cigarette smoking has been shown to be the strongest risk factor. In the Aneurysm
Detection and Management (ADAM) study, over 70,000 veterans without a history of AAA
underwent ultrasound measurement of their infrarenal aortic diameter. Smoking had the
strongest positive association with an odds ratio (OR) of 5.57 [49]. They also showed that the
association between smoking and AAA increased significantly with the number of years of
smoking and decreased significantly with the number of years after quitting smoking.
A systematic review of 10 different studies with over 3 million patients demonstrated that
besides lung cancer and chronic obstructive pulmonary disease, the development of AAA has
the closest relationship to cigarette smoking than any other disease process [50]. This
association is 2.5 times greater than the association of smoking with the development of
coronary artery disease and 3.5 times greater than the association of smoking with the
development of cerebrovascular disease [50]. Current smoking status has also been shown to
increase the expansion rate of AAA [47].
To explore the biological mechanism on how smoking contributes to AAA development
and growth, a mouse model was developed [51, 52]. Experiments using this model
demonstrated that tobacco smoke increased elastin degradation and aneurysm size without
affecting matrix metalloproteinase 9 (MMP9) and 12 (MMP12) expression. They also
https://t.me/med1917
Helena Kuivaniemi, Evan J. Ryer, H. Richard Yoon et al.
8
demonstrated that the effect of smoking was not dependent on elastolytic enzymes in AAA
development and persisted for long periods despite cessation [51, 52].
The ADAM study also showed that age (OR = 1.65), height (OR = 1.21), and family
history (OR = 1.95) of AAA were positively associated with AAA as was a history of
hypertension (OR = 1.16), hypercholesterolemia (OR = 1.54), and coronary artery disease
(OR = 1.62) [
40]. Negative associations were found with female sex (OR = 0.22) and African
American ethnicity (OR = 0.49).
Another interesting finding in the ADAM study was the negative association between
diabetes and AAA (OR = 0.54) [40, 53], especially considering that diabetes is a known risk
factor for atherosclerosis and other cardiovascular diseases. Several studies have confirmed
this negative association [8, 49, 54-61]. Furthermore, diabetes is also a negative risk factor for
intracranial aneurysms [62]. In addition, several studies have shown that diabetics were less
likely to have a ruptured AAA at the time of repair and that AAAs enlarged at a slower rate
[
47, 63-67]. One possible mechanism for this negative association includes the finding that
diabetes induces cross-linking of collagen lattices in the aortic media layer which resists
proteolysis and inhibits secretion of MMPs [65]. Another possible explanation is that diabetes
suppresses plasmin, an activator of MMPs [68]. These effects act to decrease aortic wall
degradation leading to thicker abdominal aortic wall and reduced aortic wall stress [69].
5.2. Genetic Risk Factors for AAA
5.2.1. Segregation Analysis: A Formal Test for Genetic Inheritance
Complex segregation analysis using statistical methods can answer two important
questions about a disease: 1) is the disease genetic; and 2) what is the most likely mode of
inheritance [46, 70]. In order to carry out a proper segregation analysis, the patients must be
sampled in an unbiased manner, e.g. collecting information from every AAA patient during a
specific time period. Using an existing collection of AAA families is therefore, not an
appropriate starting point for segregation analysis. The first step in segregation analysis
involves testing whether a genetic model can explain the aggregation of the disease in
families. Segregation analysis can also be used to examine which mode of inheritance best
fits with the data by determining the proportion of affected to unaffected individuals, also
known as the segregation ratio. In diseases with an autosomal recessive pattern, the
segregation ratio should be 1 in 4. The analysis, however, becomes more complicated for
non-Mendelian complex traits. Here, different genetic models for disease are fit to the data
under a maximum likelihood framework to determine which model fits best [45].
Early reports on collections of families discussed the possibility of both a single gene
effect and a multifactorial mode of inheritance for AAA [16, 71]. In 1991, Majumder and
colleagues [70] published the first formal segregation analysis for AAA by analyzing the data
on first-degree relatives of 91 AAA patients. The possibility of a non-genetic model was
rejected, since statistically significant evidence for a genetic model was found. An autosomal
recessive model for a major gene effect gave the best fit [70]. Verloes and colleagues [72]
also performed a segregation analysis on 313 pedigrees and concluded that an autosomal
dominant model for a major gene was the best fit for the familial aggregation of AAA in their
sample. Their study suggested that the disease allele frequency was 1:250, with an age-related
penetrance of 0.4 [72]. It is worth noting that both studies agreed on a major gene effect, and
https://t.me/med1917
Genetic Risk Factors for Abdominal Aortic Aneurysms (AAA)
9
that the evidence for autosomal dominant and autosomal recessive models was almost equal.
This can occur when there is genetic heterogeneity, so that the model that fits best differs
between samples. The AAA segregation results [70, 72] may therefore be interpreted as AAA
being a disease with a major gene effect and presence of genetic heterogeneity.
5.2.2. Genome-Wide Linkage Analysis to Identify Susceptibility Loci for AAA
DNA linkage analysis is an unbiased genome-wide method used to identify susceptibility
loci by following inheritance patterns of the disease of interest in a family through the use of
genetic markers [
45, 46]. Typically, linkage between a specific marker, or several markers,
and the disease of interest can be determined using complex statistical analyses [45, 46, 73].
Such studies require large collections of multigenerational families. AAA is a difficult disease
to study using the traditional family-based DNA linkage approach due to the fact that it is a
disease of the aging and information on parents or children is limited. The parents of most
AAA patients have already died by the time the patient is diagnosed with an AAA, and their
children are not yet old enough to show the disease. An alternative approach is affected-
relative-pairs (ARP) linkage analysis, which is a robust method used to determine shared
chromosomal segments in families. In this analysis, an assumption is made that related
individuals share alleles identity-by-descent (IBD) from a common ancestor, e.g., sibling
pairs are expected to share 0, 1, or 2 parental haplotypes with the probabilities of 0.25, 0.50,
and 0.25, respectively. If both sibs are affected, it is more likely that they will share at least
one parental risk allele or haplotype at the disease risk locus [45, 73-75]. This analysis makes
the null hypothesis assumption that the recombination events are independent on the same
chromosome and that the marker and disease allele are in linkage equilibrium. If a marker and
disease risk allele are linked, ARPs are expected to share more alleles IBD than by chance
[76].
The first genome-wide DNA linkage analysis was carried out using 119 AAA families
and an ARP approach with sex and number of affected relatives, and their interactions as
covariates, allowing for genetic heterogeneity [77]. Clinical data and blood samples were
collected from AAA patients as well as their relatives. Using a 2-phase/2-stage study design,
a whole genome scan was completed using approximately 400 microsatellite markers, which
are highly variable genetic markers and thus provide the means to distinguish different
parental alleles. The first phase analyzed 36 multiplex families with 78 ARPs using sex and
the number of first-degree relatives as covariates. Twelve regions on human chromosomes
were identified with p < 0.05 and used for follow-up. The second stage of phase 1 was to
genotype additional markers in regions giving positive results. Phase 2 included testing of
positive markers in 83 new multiplex families, which included 157 ARPs. After detailed
follow up analysis with a combined set of 235 ARPs, significant linkage was found on
chromosomes 4q31 (LOD score = 3.73, p = 0.0012) and 19q13 (LOD score = 4.75, p =
0.00014). These genomic regions have been designated as AAA2 and AAA1 susceptibility
loci, respectively, in the Online Mendelian Inheritance in Man (OMIM) (available at
http://omim.org). In addition, a second locus on chromosome 4, at 4q12, had a LOD of 3.13
(p = 0.0042; [77]). The 19q13 locus was found also in another DNA linkage study for AAA
using Dutch AAA families and a different statistical algorithm [78].
The chromosome 4 and 19 candidate regions contain a large number of plausible and
physiologically relevant positional candidate genes including low density lipoprotein
receptor-related protein 3 (LRP3), peptidase D (PEPD), hepsin (HPN), interleukin 15 (IL15),
https://t.me/med1917
Helena Kuivaniemi, Evan J. Ryer, H. Richard Yoon et al.
10
GRB2-associated binding protein 1 (GAB1) and endothelin receptor type A (EDNRA).
Recently a genetic association study for ten of the chromosome 19 candidate genes was
carried out and putative associations with single nucleotide polymorphisms (SNPs) located in
the PEPD gene were identified [79]. Further analysis of the PEPD gene with DNA
sequencing, however, failed to identify mutations responsible for AAA [79]. The results
demonstrate the difficulty in making educated guesses about targets in candidate gene studies.
Recent advances in whole-exome and whole-genome sequencing make it feasible to identify
all sequence variants in the linkage regions. Proving their contributing to AAA is, however,
more challenging.
5.2.3. Candidate Gene Analysis
Candidate gene analysis is the study design used most commonly to analyze the genetic
variation behind AAAs [29, 80, 81]. Candidate genes are selected based on their biological
function and their potential role in the disease pathogenesis. Characteristics of AAA
pathobiology are summarized in Figure 1 and include inflammation, autoimmunity, vascular
smooth muscle cell apoptosis, oxidative stress, and ECM degradation, which are prominently
present in aneurysmal aortic tissue in histological analyses [82-84].
One approach to candidate gene analysis is to sequence the DNA of the candidate gene
including the complete coding region, adjacent parts of the introns, and the promoter [45, 46].
Candidate genes that have been analyzed by DNA sequencing include ECM genes and ECM-
degrading proteases along with their inhibitors [80]. Details on these studies have been
recently described by Hinterseher and colleagues [80].
The other approach to candidate gene analysis is to use known genetic markers
(polymorphisms) in or near the genes of interest and perform genetic association studies on
patients with AAA and controls as a case-control design. Using this method, DNA is obtained
from blood samples and analyzed using PCR-based genotyping assays. The genetic markers
most commonly used are SNPs because of their detectability using high-throughput
techniques.
Genetic association studies, using known polymorphisms, have focused on several
different groups of proteins based on their potential role in the pathophysiology of AAA.
Hinterseher and colleagues summarized all genetic studies on AAA reported in 2010 or
before [80]. The first group of proteins studied included the genes of the ECM for their role in
the structure and remodeling of the aorta [80]. Another group is the genes of the
cardiovascular system. These genes regulate blood pressure, are involved in blood cell
development and coagulation, or are genes known to be associated with other cardiovascular
diseases such as genes encoding proteins in lipid metabolism. Genes of the immune system
constitute the third group as aneurysmal disease is associated with inflammation and
infiltration of leukocytes and increased amounts of inflammatory cytokines. Yet another
group includes genes involved in cellular signaling such as TGFB, which promotes growth of
vascular smooth muscle cells as well as expression of ECM proteins. Other candidate genes
investigated which do not belong to any of these groups of proteins include the estrogen and
progesterone receptors [80]. Most of the candidate gene studies were too small to draw any
firm conclusions. Multiple studies by different research groups also gave conflicting results
mostly due to small sample sizes. Here we will concentrate on the polymorphisms in the 11
genes summarized in Table 4 with a strong evidence for association with AAA. These results
https://t.me/med1917
Genetic Risk Factors for Abdominal Aortic Aneurysms (AAA)
11
were obtained with large sample sets (with a minimum of 400 cases and 400 controls) or in
meta-analyses combining results from several different studies.
Table 4. Most Significant Associations with AAA Discovered in Candidate Gene Studies
Gene Name
and Literature
Citation
Functional Class
Polymorphism
AAA
Cases
N
Controls
N
OR
[95% CI]
Association
P-value
a
SORT1 [85]
b
Lipid metabolism
rs599839
7,048
75,976
0.81
[0.76 0.85]
7.2 x 10
-14
IL6R [92]
Inflammation
rs7529229
4,524
15,710
0.84
[0.80 0.89]
2.7 x 10
-11
LPA [86]
Lipid metabolism
rs10455872 with
rs3798220
4,572
33,520
1.23
[1.11 1.36]
6.0 x 10
-5
AGTR1 [96]
b
Renin-angiotensin
system
rs5186
1,226
1,723
1.60
[1.32 1.93]
1.1 x 10
-6
TGFBR2 [100]
b
TGFB signaling
rs1036095
1,904
2,616
1.59
[1.23 2.07]
4.8 x 10
-4
TGFBR2 [100]
b
TGFB signaling
rs764522
1,904
2,616
1.69
[1.28 2.25]
2.7 x 10
-4
ACE [95]
b
Renin-angiotensin
system
rs4646994
1,415
1,677
1.35
[1.17 1.56]
<0.0001
MMP3 [104]
b
Degradation of ECM
rs3025058
1,258
1,406
1.48
[1.23 1.78]
NA
MMP13 [104]
b
Degradation of ECM
rs2252070
800
843
1.37
[1.04 1.82]
NA
MTHFD1 [112]
Methionine metabolism
rs8003379
423
423
0.41
[0.26 0.65]
<0.0001
MTRR [112]
Methionine metabolism
rs326118
423
423
0.47
[0.29 0.77]
0.003
OR, odds ratio; 95% CI, 95% confidence interval; ECM, extracellular matrix; TGFB, transforming
growth factor beta; NA, not available. Gene symbols are available from
http://www.ncbi.nlm.nih.gov/gene/
a
P-values were taken from either a meta-analysis or the largest report demonstrating association with
AAA and cited in the first column.
b
Meta-analysis.
Two highly significant associations have been found with genes encoding proteins in
lipid metabolism (Table 4). The most significant of these was with a SNP (rs599839) located
in the sortilin-1 (SORT1) gene and discovered in a pathway-based analysis [85]. The
investigators analyzed over 100 SNPs known to be associated with either blood lipid profile
or coronary artery disease. The final analyses were carried out with over 7,000 AAA cases
and nearly 76,000 controls yielding to the highly significant results (Table 4).
The second highly significant association in the lipid metabolism was discovered by
Helgadottir et al. [86] when they investigated the effects of variants in the apolipoprotein(a)
(LPA) with many different cardiovascular phenotypes including 4,752 AAA patients. For
simplicity, these investigators assessed the association between a disease and the total number
of minor alleles of two different SNPs, rs3798220-C and rs10455872-G, which they called a
LPA score. Associations were found between the LPA score and ischemic stroke subtype
large artery atherosclerosis (OR =1.27; p = 6.7 x 10
4
), peripheral artery disease (OR = 1.47;
https://t.me/med1917
Helena Kuivaniemi, Evan J. Ryer, H. Richard Yoon et al.
12
p = 2.9 x 10
14
), and AAA (OR = 1.23; p = 6.0 x 10
5
), but not with the ischemic stroke
subtypes cardioembolism or small vessel disease.
Figure 1. Summary of the pathogenesis of AAA. Several biological processes and risk factors have
been identified that contribute to AAA pathogenesis. Genes in the biological pathways have been used
in candidate gene studies. VSMC, vascular smooth muscle cell; ECM, extracellular matrix; ROS,
reactive oxygen species; MMPs, matrix metalloproteinases. Reproduced with permission from Boddy
et al. Drug News Perspect 2008, 21(3): 142-148 [82]. Copyright © 2011 Prous Science, S.A.U. or its
licensors. All rights reserved.
AAA disease typically involves inflammation with leukocyte infiltrates in the aortic wall
and increased levels of cytokines both in the serum and the aorta [87-91]. Several immune-
related genes have been studied in genetic association studies for their potential role in AAA
pathogenesis [80]. One SNP in the interleukin 6 receptor (IL6R) gene (rs7529229) was
identified as a highly significant (p = 2.7 x 10
5
) independent risk factor for AAA in an
analysis of 4,524 AAA cases and 15,710 controls (Table 4) [92]. As shown by a recent meta-
analysis [92] the biological relevance of this association is supported by the finding that
serum IL6 levels are higher in AAA patients than controls.
Clinical studies on patients with AAA have shown that angiotensin 1 converting enzyme
(ACE) inhibitors augment collagen synthesis, reduce stiffness of the aortic wall [93], and
reduce the risk of AAA rupture [94]. Thus, different genes of the renin-angiotensin-
aldosterone system have been investigated for their role in AAA pathogenesis [80]. Several
research groups have studied the insertion/deletion polymorphism (rs4646994) located in
intron 16 of the ACE gene and its association with AAA [80]. A meta-analysis on ACE results
on 1,415 AAA cases and 1,677 controls revealed an association (p<0.0001; Table 4) [95].
https://t.me/med1917
Genetic Risk Factors for Abdominal Aortic Aneurysms (AAA)
13
A polymorphism (rs5186) in the angiotensinogen II receptor, type 1 (AGTR1), one of the
other genes of the renin-angiotensin-aldosterone system also showed an association with
AAA in patients from New Zealand, United Kingdom and Australia (Table 4) [96]. The
association, however, could not be replicated in two other large studies [97, 98].
Genetic association studies on SNPs in the genes part of the TGFB signaling pathway
also found significant results (Table 4). First one of these studies was carried out in the Dutch
population and it investigated 32 different SNPs in the TGFBR1 and TGFBR2 genes [
99].
Three of the SNPs (rs1626340 in TGFBR1, and rs1036095 and rs4522809 in TGFBR2)
showed an association with AAA [99]. A meta-analysis confirmed the association with one of
the SNPs (rs1036095) and found another significant association with another SNP, rs764522
in TGFBR2 (Table 4) [100].
Much attention has focused on the MMPs, the major family of proteins involved in the
breakdown of ECM in the aortic wall, and their association with AAA [
101]. The MMP3
subtype is an enzyme capable of degrading collagen types II, III, IV, IX, and X as well as
proteoglycans, fibronectin, laminin and elastin. The gene coding for MMP3 is located on the
long arm of chromosome 11 and contains a large number of polymorphisms. The 5A/6A
polymorphism in the promoter region (1612 base pairs upstream from exon 1) has been most
extensively investigated as this polymorphism increases the activity of the MMP3 promoter
in cultured cells [102]. Deguara et al. [103] showed a significant association of the 5A allele
of the 5A/6A polymorphism (OR = 1.32; p = 0.005) with AAA when 405 patients were
compared to 123 controls. Additionally, this study identified increased levels of MMP3 RNA
and protein in the aortic wall of 5A homozygotes compared to heterozygotes. Another study
which found an association between AAA and this same variant was carried out by Saracini
and coworkers [104]. While these findings are noteworthy, it is important to mention that
other investigators have found contradictory results for MMP3 [105-110]. A meta-analysis
[104] on the results of 1,258 AAA cases and 1,406 controls found a significant association
(Table 4).
A polymorphism (rs2252070) in another MMP gene, MMP13, has also been found to be
associated with AAA in a meta-analysis, which combined results from two independent
studies [104, 107] with a total of 800 AAA cases and 843 controls (Table 4) [104]. This MMP
has not been studied as extensively in AAA as MMP9 [101], but its mRNA and protein levels
are elevated in AAA tissue compared to infrarenal aortic samples from age- and sex-matched
individuals without AAA [111].
The last two polymorphisms to be discussed here are located in two genes involved in the
methionine metabolism (Table 4) and were discovered by Giusti and colleagues in a study of
423 AAA cases and 423 controls [112]. The results demonstrated an association between
AAA and a SNP (rs8003379) in the methylenetetrahydrofolate dehydrogenase 1 (MTHFD1)
as well as a SNP (rs326118) in the 5-methyltetrahydrofolate-homocysteine methyltransferase
reductase (MTRR) gene [112].
5.2.4. Genome-Wide Association Studies (GWAS) to Identify Susceptibility Loci for
AAA
Genome-wide association studies (GWAS) examine many common genetic variants in
different individuals to see if a variant is associated with a specific trait. GWAS typically
focuses on associations between SNPs, variations in a single nucleotide within the DNA
sequence, and their influence on health and disease. GWAS is an efficient method to identify
https://t.me/med1917
Helena Kuivaniemi, Evan J. Ryer, H. Richard Yoon et al.
14
reproducible risk alleles predisposing to disease and typically analyzes numerous common
SNPs to assess the association with the disease of interest [80, 113, 114].
Table 5. AAA Genetic Loci Discovered Using Genome-Wide Association Studies
Chromosomal
Location
and Literature
Citation
Nearest Gene
(Gene symbol; Gene ID)
SNP rs#
RAF
OR
[95% CI]
Association
P-value
a
3p12.3 [97]
Contactin 3
(CNTN3; 5067)
rs7635818
0.42
1.33
[1.10 1.21]
0.0028
9p21.3 [115]
CDKN2B antisense RNA 1
(CDKN2BAS1;100048912)
rs10757278
0.45
1.31
[1.22 1.42]
1.2 x 10
-12
9q33.1 [119]
DAB2 interacting protein
(DAB2IP; 153090)
rs7025486
0.25
1.21
[1.14 1.28]
4.6 x 10
-10
12q13.3 [120]
Low density lipoprotein receptor-
related protein 1
(LRP1; 4035)
rs1466535
0.68
1.15
[1.10 1.21]
4.5 x 10
-10
RAF, risk allele frequency in population; OR, odds ratio; 95% CI, 95% confidence interval. Gene
symbols and IDs are available from http://www.ncbi.nlm.nih.gov/gene/
a
P-values were taken from the first report demonstrating association with AAA and cited in the first
column.
To examine the possibility that multiple forms of arterial disease share common risk
factors, Helgadottir et al. [115] examined five different vascular phenotypes (AAA,
intracranial aneurysms, coronary artery disease, peripheral artery disease and atherosclerotic
stroke) and their link to SNPs previously associated with myocardial infarction and type 2
diabetes mellitus. The patient cohort came from seven different geographic regions and
included 2,836 AAA cases and 16,732 controls. In this study, the G allele of rs10757278,
previously reported to be associated with coronary artery disease [116], associated with AAA
(OR = 1.31; p = 1.2 x 10
-12
) and intracranial aneurysm (OR = 1.29; p = 2.5 x 10
-6
). This SNP
is located on chromosome 9p21 in the CDKN2BAS gene. CDKN2BAS, previously known as
ANRIL, encodes an antisense RNA that regulates expression of cyclin-dependent kinase
inhibitor 2B and has been attributed a critical role in cell proliferation and apoptosis [117,
118]. These findings, while quite novel, did not focus exclusively on the AAA patient
population and demonstrated only a moderate increase in AAA risk. Since this 2008
publication, three GWAS focused specifically on AAA have been reported and are
summarized in Table 5.
The first GWAS for AAA was published in 2009 [97] and was carried out using pooled
DNA samples (6 pools) from 123 AAA cases and 112 controls. The pooled DNA was
hybridized to an Affymetrix SNP array designed to simultaneously measure half a million
SNPs. A SNP (rs763518) located on chromosome 3p12.3, a region upstream of the contactin-
3 (CNTN3) gene showed the strongest association with AAA (p = 0.017). CNTN3 is a lipid
anchored cell adhesion molecule known to be expressed the aortic wall and SNP rs763518
may be a functional variant in the regulatory region of the CNTN3 gene. To further
substantiate the genetic association with AAA, SNP rs7635818 was genotyped in an
independent set of 453 case and 416 control samples from Belgium, Canada and the United
States. SNP rs7635818 was significantly associated with AAA (p = 0.013) in this independent
https://t.me/med1917
Genetic Risk Factors for Abdominal Aortic Aneurysms (AAA)
15
sample. Since smoking is a strong environmental risk factor for AAA and a detailed smoking
history was available, the association of SNP rs7635818 with AAA was examined in a subset
of 391 cases and 241 controls with a smoking history. Indeed, the association was even
stronger when analyzed in subjects with 20 or more years of a smoking history (OR = 1.80;
95%CI: 1.3-2.5; p = 0.00041) [97].
In the second AAA GWAS, 1,292 AAA patients and 30,503 unscreened controls from
Iceland and the Netherlands were examined (Illumina HumanHap 370 or 610 SNP chips) and
~300,000 SNPs were assessed [119]. In this discovery phase, three SNPs associated with the
CDKN2A-CDKN2B locus on chromosome 9p21 achieved genome wide significance (p = 1.6
x 10
-7
to 1.9 x 10
-8
). CDKN2A and CDKN2B are known to have a critical role in cell
proliferation and (as described above) associated with AAA, myocardial infarction, and
intracranial aneurysm in a previous vascular disease GWAS [115]. To search for additional
sequence variants associated with AAA, the investigators selected 22 SNPs with p < 5.5 x 10
-
5
(excluding the 9p21 locus) for genotyping in an additional sample from Belgium, Canada,
New Zealand, and the UK (1,665 cases and 1,931 controls). After the results of these two
cohorts were combined, one SNP, rs7025486 [A] on chromosome 9q33 was associated with
AAA at a genome wide significance level (OR = 1.24; p = 1.8 x 10
-9
). For further validation,
rs7025486 was genotyped in four additional sample sets from Denmark, The Netherlands and
two US populations. When combining all cohorts (4,559 cases and 37,954 controls),
rs7025486 [A] was significantly associated with AAA (OR = 1.21; p = 4.6 x 10
-10
). To
examine a potential broad effect of this variant, these investigators also examined its
association with other vascular diseases. The rs7025486 SNP was associated with early onset
myocardial infarction, peripheral arterial disease and pulmonary embolism but was not
associated with intracranial aneurysm or ischemic stroke. rs7025486[A] is located within
intron 1 of DAB2 interacting protein (DAB2IP), which plays a role in suppressing cell
survival and proliferation, and is, therefore, a plausible candidate gene for AAA development.
In the most recently published GWAS approximately half a million SNPs were tested in
1,866 cases (mean AAA size 4.8 cm and median age 72 years) and 5,435 unscreened controls
[120]. Genotyping was performed with the Illumina 1.2M (controls) and 670K (AAA) bead
chips. Nine loci were associated with AAA at a p value of <1 x 10
-5
and assessed for
replication in a further sample of 2,871 cases and 32,687 controls (mixed group of screened
and unscreened). One SNP, rs1466535 located within intron 2 of the gene encoding low
density lipoprotein receptor-related protein 1 (LRP1) on chromosome 12q13.3 was associated
with AAA (p = 4.2 x 10
-3
). This finding was confirmed in another group of 1,491 cases and
11,060 unscreened controls (p = 0.035). When all samples genotyped for rs1466535 were
analyzed together (6,228 cases and 49,182 controls), the combined p value was 4.52x10
-10
(OR = 1.15; 95%CI: 1.10-1.21) [120]. These investigators also assessed whether the
association of rs1466535 with AAA was driven by the presence of clinical risk factors
(hyperlipidemia, smoking or diabetes mellitus) and after adjusting for these risk factors, it
was found that the variant on 12q13.3 was associated with AAA independently of these risk
factors and furthermore did not associate with any of these traits (including coronary artery
disease). LRP1 is a large endocytic trans-membrane receptor named because it contains three
separate clusters of ligand-binding domains that bear structural similarity to that of other LDL
receptors [121]. It is, however, larger and has a more diverse range of functions. In support of
its importance in aneurysm formation, Lrp1 knock-out mice develop aortic aneurysms
secondary to proliferation of vascular smooth muscle cells [122].
https://t.me/med1917
Helena Kuivaniemi, Evan J. Ryer, H. Richard Yoon et al.
16
6. FUTURE DIRECTIONS
Overall the findings described above demonstrate the effectiveness of genetic studies to
uncover novel mechanisms contributing to AAA development. AAA is considered a complex
disease with multiple genetic and environmental risk factors. When estimating the
contribution of all the identified factors to the risk of a person to develop an AAA, the growth
of an AAA and its rupture, the interactions of the various factors should also be considered.
Such analyses require large sample sizes with comprehensive data necessitating collaborative
multi-center projects. The availability of biobanks linked to electronic medical records (EMR)
will facilitate this research and even make it possible to carry out some of the studies
retrospectively [123]. Patients would be identified from the EMR using phenotyping
algorithms based on disease and procedure codes [
123-126]. Information on risk factors
would also be extracted from the EMR. AAA growth information would be available from
aortic duplex, CT or MRI reports using natural language processing (NLP) algorithms.
Plausibility of such EMR-based studies has been demonstrated for several other diseases
[126].
The ultimate goal of the genetic studies is to use the information to design new risk
scoring algorithms [7] r developing an AAA for use in
clinical practice. Another important goal is to design new therapies based on the information
on mechanisms of disease processes to prevent the development or halt the progression of
AAA.
6.1. Genomically-Informed Risk Scores, Screening, and Counseling
As important as the above described genetic discoveries are, genetic testing does not
currently affect the clinical management of patients with aortic aneurysms. There are no
genomically informed risk scores or screening tests that are presently available for clinical
purposes. Furthermore, genetic tests are complex and do not affect the clinical management
of family members, except in Ehlers-Danlos type IV, Marfan or Loeys-Dietz syndrome.
Lastly, there is not enough confidence or experience with genetic patterns to exclude a family
member from regular radiographic imaging. Despite great effort, more genetic studies are
needed as the known genetic polymorphisms only account for a small percentage of familial
aortic aneurysms. It is expected that genetic testing will become more practical and clinically
valuable in the near future when the many on-going genomic medicine initiatives eliminate
some of the hurdles. In the meantime, ultrasound screening for men as early as 55 years of
age or for women at 65 years is appropriate for those with a family history of AAA [127].
Due to the complex nature of aneurysm pathobiology, the authors of this chapter envisage
that future genomically-enhanced AAA screening tools will need to incorporate multiple
polymorphisms involving several genes into risk calculation tools. Furthermore, these
forthcoming risk stratification instruments must easily be incorporated into the clinical
practice of medicine. An example of how investigators are beginning to approach the
combining of multiple polymorphisms, Saracini et al. [104] performed a candidate gene
association study of polymorphisms in genes coding MMPs, tissue inhibitors of MMPs
(TIMPs), and elastin in AAA. In this particular study, DNA samples from 423 AAA patients
https://t.me/med1917
Соседние файлы в папке Библиотека им академика М.И. Перельмана