\documentclass[10pt]{article}

\usepackage{fullpage}
\usepackage{setspace}
\usepackage{parskip}
\usepackage{titlesec}
\usepackage[section]{placeins}
\usepackage{xcolor}
\usepackage{breakcites}
\usepackage{lineno}
\usepackage{hyphenat}





\PassOptionsToPackage{hyphens}{url}
\usepackage[colorlinks = true,
            linkcolor = blue,
            urlcolor  = blue,
            citecolor = blue,
            anchorcolor = blue]{hyperref}
\usepackage{etoolbox}
\makeatletter
\patchcmd\@combinedblfloats{\box\@outputbox}{\unvbox\@outputbox}{}{%
  \errmessage{\noexpand\@combinedblfloats could not be patched}%
}%
\makeatother


\usepackage{natbib}




\renewenvironment{abstract}
  {{\bfseries\noindent{\abstractname}\par\nobreak}\footnotesize}
  {\bigskip}

\titlespacing{\section}{0pt}{*3}{*1}
\titlespacing{\subsection}{0pt}{*2}{*0.5}
\titlespacing{\subsubsection}{0pt}{*1.5}{0pt}


\usepackage{authblk}


\usepackage{graphicx}
\usepackage[space]{grffile}
\usepackage{latexsym}
\usepackage{textcomp}
\usepackage{longtable}
\usepackage{tabulary}
\usepackage{booktabs,array,multirow}
\usepackage{amsfonts,amsmath,amssymb}
\providecommand\citet{\cite}
\providecommand\citep{\cite}
\providecommand\citealt{\cite}
% You can conditionalize code for latexml or normal latex using this.
\newif\iflatexml\latexmlfalse
\providecommand{\tightlist}{\setlength{\itemsep}{0pt}\setlength{\parskip}{0pt}}%

\AtBeginDocument{\DeclareGraphicsExtensions{.pdf,.PDF,.eps,.EPS,.png,.PNG,.tif,.TIF,.jpg,.JPG,.jpeg,.JPEG}}

\usepackage[utf8]{inputenc}
\usepackage[greek,ngerman,english]{babel}



\usepackage{float}








\begin{document}

\title{The causal relationship between immune-mediated inflammatory diseases
and aortic aneurysm: A bidirectional two-sample Mendelian randomization
study}



\author[1]{Sijia Sun}%
\author[2]{Mengxian Sun}%
\author[2]{Jie He}%
\author[2]{Songtao Tan}%
\author[2]{Ge Wang}%
\author[2]{Yuan Zheng}%
\author[2]{Xiaoping Fan}%
\affil[1]{Guangzhou University of Chinese Medicine}%
\affil[2]{The Second Affiliated Hospital of Guangzhou University of Chinese Medicine}%


\vspace{-1em}



  
  \date{May 15, 2024}


\begingroup
\let\center\flushleft
\let\endcenter\endflushleft
\maketitle
\endgroup





\selectlanguage{english}
\begin{abstract}
\textbf{Introduction:} Many observational studies have identified aortic
aneurysm (AA) as a cardiovascular complication of immune-mediated
inflammatory diseases (IMIDs). However, due to the effects of various
confounders, it is still uncertainty whether this association holds or
whether reverse causality is involved. Here we conducted a two-sample
bidirectional MR study to infer the causal relationships between the two
diseases. \textbf{Method:} We obtained genetic association datasets from
public GWAS databases in populations of European ancestry. Abiding by
the assumptions of Mendelian randomization (MR), we selected valid
instrumental variables from genetic variants. Different statistic
methods were performed for MR analysis and sensitivity analysis, and the
inverse variance weighted (IVW) method was regarded as the most
efficient estimate of the causal effect in this study. \textbf{Results:}
The IVW method found evidence that genetically predicted AA had a causal
effect on rheumatoid arthritis (RA) (OR = 1.06, 95\%CI = 1.01-1.12,
\emph{p} = 0.029), but not of RA or other IMIDs on AA. Besides, no
evidence showed that AA may increase the risk of inflammatory bowel
disease (IBD), Crohn's disease (CD), ulcerative colitis (UC), systemic
lupus erythematosus (SLE) and psoriasis (PSO). The sensitivity analysis
confirmed the absence of heterogeneity or horizontal pleiotropy effect.
\textbf{Conclusion:} In summary, our study discovered that genetically
predicted AA may increase the risk of RA, while no evidence was found
that patients with RA had an increased risk of AA. Furthermore, we
confirmed no evidence of association between IBD, CD, UC, SLE, PSO and
AA. This is in accordance with other reports that demonstrated the human
leukocyte antigen molecule in inflammatory aortic aneurysm was a genetic
risk loci. Our study provides directions for future research on genetic
susceptibility to inflammatory aortic aneurysm.%
\end{abstract}%



\sloppy


\textbf{The causal relationship between immune-mediated inflammatory
diseases and aortic aneurysm: Abidirectional two-sample Mendelian
randomization study}

Sijia Sun\textsuperscript{1}, Mengxian Sun\textsuperscript{2}, Jie
He\textsuperscript{2}, Songtao Tan\textsuperscript{2}, Ge
Wang\textsuperscript{2}, Yuan Zheng\textsuperscript{2}, Xiaoping
Fan\textsuperscript{2*}

1Second Clinical Medical College, Guangzhou University of Chinese
Medicine, Guangzhou, China

2Department of Cardiovascular Surgery, Guangdong Provincial Hospital of
Chinese Medicine, The Second Affiliated Hospital of Guangzhou University
of Chinese Medicine, Guangdong, China

* Correspondence:

Xiaoping Fan

fukui-hanson@hotmail.com.

\textbf{Abstract}

\textbf{Introduction:} Many observational studies have identified aortic
aneurysm (AA) as a cardiovascular complication of immune-mediated
inflammatory diseases (IMIDs). However, due to the effects of various
confounders, it is still uncertainty whether this association holds or
whether reverse causality is involved. Here we conducted a two-sample
bidirectional MR study to infer the causal relationships between the two
diseases.

\textbf{Method:} We obtained genetic association datasets from public
GWAS databases in populations of European ancestry. Abiding by the
assumptions of Mendelian randomization (MR), we selected valid
instrumental variables from genetic variants. Different statistic
methods were performed for MR analysis and sensitivity analysis, and the
inverse variance weighted (IVW) method was regarded as the most
efficient estimate of the causal effect in this study.

\textbf{Results:} The IVW method found evidence that genetically
predicted AA had a causal effect on rheumatoid arthritis (RA) (OR =
1.06, 95\%CI = 1.01-1.12, \emph{p} = 0.029), but not of RA or other
IMIDs on AA. Besides, no evidence showed that AA may increase the risk
of inflammatory bowel disease (IBD), Crohn's disease (CD), ulcerative
colitis (UC), systemic lupus erythematosus (SLE) and psoriasis (PSO).
The sensitivity analysis confirmed the absence of heterogeneity or
horizontal pleiotropy effect.

\textbf{Conclusion:} In summary, our study discovered that genetically
predicted AA may increase the risk of RA, while no evidence was found
that patients with RA had an increased risk of AA. Furthermore, we
confirmed no evidence of association between IBD, CD, UC, SLE, PSO and
AA. This is in accordance with other reports that demonstrated the human
leukocyte antigen molecule in inflammatory aortic aneurysm was a genetic
risk loci. Our study provides directions for future research on genetic
susceptibility to inflammatory aortic aneurysm.

\textbf{Keywords:} aortic aneurysm, immune-mediated inflammatory
diseases, Mendelian randomization, inflammatory aortic aneurysm,
rheumatoid arthritis.

\textbf{Introduction}

Aortic aneurysm (AA) is a chronic condition of the aorta characterized
by localized distension or weakening of its wall, which may lead to
rupture and severe internal bleeding resulting in shock(1). With an
incidence rate of approximately 3 per 100,000 individuals worldwide(2),
AA ranks second only to atherosclerosis in prevalence and represents the
most common disease affecting the aorta(3). AA is considered to be a
chronic degenerative disease caused by local damage to the aortic wall,
mainly related to chronic inflammatory infiltration(4). Ruptured aortic
aneurysms can result in immediate fatality. For patients with apparent
symptoms or large diameter aortic aneurysms, open surgery or
endovascular repair remains the sole treatment option(4, 5).

Immune-mediated inflammatory diseases (IMIDs) are a collection of
chronic inflammatory disorders that involve multiple systems and organs,
characterized by common mechanisms of inflammation and immune
dysregulation(6). IMIDs comprise a spectrum of disorders, and the
presence of multiple systemic symptoms impose a significant burden on
healthcare resources(7). Individuals with immune-mediated inflammatory
diseases (IMIDs) are at a significantly increased risk of cardiovascular
complications, due to both the disease itself and certain corticosteroid
medications used in its treatment(8, 9). Of note, atherosclerosis(10)
and large vessel vasculitis(11), which are prone to the development of
dilated aortic aneurysms, are considered to be immune system-mediated
processes. This suggests a potential common genetic etiology or
pathogenesis between AA and IMIDs. Although observational studies(12-20)
have linked IMIDs with cardiovascular disease (CVD), it is still unclear
whether patients with AA are at increased risk of IMIDs. Reverse
causation may also account for the association, whereby AA increases the
risk of IMIDs rather than vice versa(21). Confounding factors can easily
distort the interpretation from observational studies, leading to a
fallacious inference of causality. Meanwhile, despite being the standard
epidemiological design for establishing causal relationships, randomized
controlled trials (RCTs) have some limitations. These include the
significant investment of time, manpower and financial resources
required to conduct them, as well as strict inclusion and exclusion
criteria that can limit the representativeness and external validity of
research findings. Therefore, more robust methods are required to assess
causality using available data.

Mendelian randomization (MR) experiments use variants of genetic loci as
instrumental variables (IVs) to investigate the association between
exposure and health outcomes. Unlike conventional RCTs and observational
studies, MR studies are not suffered to confounders. In MR, genetic
variants serve as IVs for exposures to help the identification potential
causal effect with disease-related outcomes(22, 23). Generally, the IVs
must fulfill these three assumptions: (1) the IVs are strongly
associated with the exposure; (2) the IVs are independent of other
factors that may affect the inference of causal relationship; and (3)
the IVs do not affect the outcome except with its potential effect on
the exposure(24). Besides, germ-line genotypes are established during
conception and precede the observed variables in temporal sequence, thus
avoiding issues of reverse causality(25). Here, we obtained public
datasets from various genome-wide association studies (GWASs) of IMIDs
(including IBD, CD, UC, RA, SLE, and PSO) and AA in populations of
European ancestry and performed a two-sample bidirectional MR study to
infer the magnitude and direction of causal associations between the two
diseases.

\textbf{Methods}

\textbf{2.1 Data sources and study design}

To explore the causal relationship between IMIDs and AA, we utilized
summary genetic association datasets of European ancestry for both IMIDs
and AA from the IEU OpenGWAS Database Project
(https://gwas.mrcieu.ac.uk/). The GWAS summary statistics for AA (GWAS
ID: finn-b-I9\_AORTANEUR) were obtained from the FinnGen cohorts' Aortic
aneurysm GWAS summary statistics, which included 2,825 cases and 206,541
controls with 16,380,417 SNPs
(https://r5.finngen.fi/pheno/I9\_AORTANEUR). In FinnGen Release 5, the
diagnostic criteria for AA are defined in the following link
(https://r5.risteys.finngen.fi/phenocode/I9\_AORTANEUR). The GWAS
datasets for IBD, CD, and UC were established by FinnGen Biobank with
the respective GWAS ID numbers of ''finn-b-K11\_IBD'',
''finn-b-K11\_CROHN'' and ''finn-b-K11\_ULCER''. Specifically, the GWAS
study related to IBD involved 5,673 cases and 213,119 controls and was
further divided into K11\_CROHN (2,056 cases and 210,300 controls) and
K11\_ULCER (4,320 cases and 210,300controls) endpoints. The PSO dataset,
identified by the GWAS ID ''finn-b-L12\_PSORIASIS'', was also received
from FinnGen Biobank and comprises a sample size of 4,510 cases and
212,242 controls. The GWAS datasets associated with RA and SLE were
obtained from European Bioinformatics Institute under the ID numbers
''ebi-a-GCST90013534'' (14,361 cases and 43,923 controls) (26) and
''ebi-a-GCST003156'' (5,201 cases and 9,066 controls) (27),
respectively. Detailed information of these GWAS datasets are provided
in Supplementary Table S1. Based on requirements of STROBE-MR
standards(28), we report the design, methods and results analysis of
this study. Schematic representation of this MR study is presented in
Figure 1A.

\textbf{2.2 Genetic instrumental variables selection}

To obtain robust instrumental variables that are sufficiently correlated
with the exposure of interest, SNPs related to IMIDs were chosen with a
genome-wide significance threshold of \emph{p} \textless{} 5e-08. Only
two potential instrumental variables associated with AA were identified
at the threshold of \emph{p} \textless{} 5e-08. Therefore, we modified a
more lenient threshold of \emph{p} \textless{} 5e-06 for genome-wide
significance to search for additional SNPs. Then, we set parameters
related to linkage disequilibrium (r2 = 0.001, distance = 10000 kb) to
clump these genetic loci, in which all loci exhibit complete
independence in order to avoid double-count the contribution of any
particular variants(29). Next, with the help of the online tool
PhenoScanner v2 (\emph{http://www.phenoscanner.medschl.cam.ac.uk/} ), we
checked genotype-phenotype associations between potential IVs and
outcome(30), and removed SNPs associated with confounders. Then, SNP
harmonization was conducted to correct the orientation of the
alleles(31). Finally, we assessed IVs strength by calculating the
genetic instruments (R\textsuperscript{2}) and F-statistic for each
instrumental variable. R\textsuperscript{2} and F-statistic was
calculated as following formula:
R\textsuperscript{2}={[}2\selectlanguage{greek}β\selectlanguage{english}\textsuperscript{2} \selectlanguage{ngerman}× MAF × (1-MAF){]} /
{[}2\selectlanguage{greek}β\selectlanguage{english}\textsuperscript{2} \selectlanguage{ngerman}× MAF × (1-MAF)+2\selectlanguage{greek}β\selectlanguage{english}\textsuperscript{2} \selectlanguage{ngerman}× MAF ×
(1-MAF) × N × SE{]}, and F={[}R\textsuperscript{2} × (N-k-1)
/k(1-R\textsuperscript{2}){]} (where \selectlanguage{greek}β \selectlanguage{english}= effect estimate of the SNP in
the exposure, MAF = minor allele frequency, N = sample size of the GWAS
dataset, k = number of IVs, SE=the standard error of genetic effect)
(32). Theoretically, all selected genetic IVs should have F-statistics
greater than 10.

\textbf{2.3 Statistical analysis}

We used the ``TwoSampleMR'' and ``MRPRESSO'', ``mr.raps'' packages of
the R software (version 4.2.2) to perform MR analysis. Multiple MR
approaches were utilized to explore causal relationships between
exposure and outcome. Causal effect estimates from the IVW was
considered the criterion for establishing a causal relationship between
exposure and outcome(33). It is suggested that if the IVs meet the three
major assumption of MR and the selected SNPs are all valid IVs, result
from IVW model provides an efficient and reliable estimate for the
causal effect(34). As an extension to IVW, MR-PRESSO approach attempts
to remove outliers based on their contributions to heterogeneity, while
other MR Methods were regarded as a supplement and reference(35). After
MR analysis, we assessed the quality of the harmonization and whether
the variants were difficult to harmonize by performing Cochran's Q test
and MR Egger regression analysis, as well as leave-one-out method. The
IVW model was applied to the heterogeneity analysis, where Cochran's Q
statistic provided evidence for heterogeneity and invalid instruments,
with \emph{p} value greater than 0.05 indicating the absence of
heterogeneity(34). The intercept of MR-Egger regression was used to
explore and account for the impact of horizontal pleiotropy, and
\emph{p} value greater than 0.05 is evidence for credible instrumental
variables(36). To further assess the reliability of the MR results, we
evaluated the impact of one single SNP for estimates by performing the
leave-one-out method, in which new MR results was recalculated after
leaving out each SNP in turn. Finally, we visualized the main results of
the MR analysis using scatter plots, forest plots, and funnel plots.
Figure 1B shows a detailed flowchart of this MR study.

\textbf{Results}

\textbf{3.1 The causal effect of IMIDs on AA}

A total of 179 independent SNPs associated with IMIDs were collected
following the established screening criteria for the MR relevance
hypothesis. In order to meet MR independence and exclusion assumption,
we conducted a literature review and found smoking(1), high blood
pressure(37), obesity(38), and alcohol consumption(39) as established
risk factors for AA. After excluding palindromic SNPs and SNPs
associated with these confounding variables, 154 independent SNPs
remained (10 SNPs for IBD on AA, 6 SNPs for CD on AA, 7 SNPs for UC on
AA, 77 SNPs for RA on AA, 39 SNPs for SLE on AA, 15 SNPs for PSO on AA).
Most of the F-statistics of these IVs are greater than 10, indicating a
low risk of weak instrument bias. Complete information about the IVs is
summarized in Supplementary Table S3. In the MR analysis, the result of
IVW with fixed effects model indicated that no causal relationship was
found between IBD and AA (OR = 0.91, 95\%CI = 0.81-1.01, \emph{p} =
0.072), as well as CD and AA (OR = 0.97, 95\%CI = 0.88-1.07, \emph{p} =
0.544), UC and AA (OR = 0.95, 95\%CI = 0.84-1.07, \emph{p} = 0.363), RA
and AA (OR = 0.97, 95\%CI = 0.92-1.02, \emph{p} = 0.204), SLE and AA (OR
= 0.99, 95\%CI = 0.96-1.02, \emph{p} = 0.547), and PSO and AA (OR =
0.96, 95\%CI = 0.91-1.03, \emph{p} = 0.245). Meanwhile, the results of
other MR methods were also similar to IVW-FE mode (Figure 2). In MR
sensitivity analysis, \emph{p} value of Cochran Q-test revealed no
significant heterogeneity was observed (all \emph{p} \textgreater{}
0.05, Table 2). The intercept of MR-Egger regression showed there was no
intercept deviated from zero and all \emph{p} values were greater than
0.05, explaining no impact of horizontal pleiotropy (Table 2). Indeed,
these results were also confirmed in the funnel plot with symmetrical
distribution (Supplementary Figure S2). Further, the leave-one-out
method indicated that no single SNP differed apparently from the overall
odds ratio (Supplementary Figure S1).

\textbf{3.2 The causal effect of AA on IMIDs}

The initial screening identified 138 potentially independent SNPs that
meet the inclusion criteria. Genetic variables associated with
depression, anxiety, smoking, glucocorticoids and non-steroidal
anti-inflammatory drugs use, obesity or malnutrition(12, 40, 41), as
known to have causal associations with IMIDs, were removed. After
harmonizing the exposure and outcome datasets, a total of 112 SNPs were
obtained with most F-statistics greater than 10 (21 SNPs for AA on IBD,
21 SNPs for AA on CD, 21 SNPs for AA on UC, 15 SNPs for AA on RA, 13
SNPs for AA on SLE, 21 SNPs for AA on PSO, Supplementary Table S2). 112
independent SNPs associated with AA were selected as IVs to explore the
effect of AA on IMIDs. As shown in Figure 2 and the scatter plot
(Supplementary Figure S3), the IVW method indicated that patients with
AA had a 6\% increased risk of RA (OR = 1.06, 95\%CI =
1.01-1.12,\emph{p} = 0.029). MR-PRESSO method also presented unanimous
evidence of causality between AA and RA (OR = 1.06, 95\% CI = 1.01-1.11,
\emph{p} = 0.019). Cochran's Q test showed there was no heterogeneity in
IVs related to RA (Q = 11.82, \emph{p} = 0.621). Moreover, both results
of MR-PRESSO (no outlier was found, Table 1) and MR-Egger regression
(intercept = -0.01, \emph{p} = 0.570, Figure 2) provided no evidence of
horizontal pleiotropy. However, the effect of genetically predicted AA
on IBD, CD, UC, SLE, PSO did not reach statistical significance in
either IVW or MR-PRESSO method (Figure 2 and Table 1). Sensitivity
analysis of the above MR results suggested that there was no underlying
heterogeneity and horizontal pleiotropy (all \emph{p} value of Cochran's
Q and Egger-intercept \textgreater{} 0.05, Table 2).

\textbf{Discussion}

We conducted a two-sample bidirectional MR study to investigate the
causal relationship between IMIDs (mainly including IBD, CD, UC, RA,
SLE, and PSO) and AA. We found evidence of a causal effect of AA on RA,
but not of RA or other IMIDs on AA. Although evidence from previous
observational studies supported a causal relationship between some
certain types of IMIDs and AA(12-20), causal inference cannot be made
with great certainty in these studies for the possibility of residual
confounding factors and various bias.

A retrospective case-control study with age- and gender-matched cohort
from Israel, which adjust for different factors including socioeconomic
and smoking, found that AA is more prevalent in patients with RA
compared with controls (OR: 1.406, 95\%CI: 1.094-1.789, \emph{p} =
0.006). It is worth mentioning that the information regarding the
treatment of RA patients and assessment of RA severity had not been
formally investigated, as well as congenital diseases predisposing for
AA(12). Contrary to above findings, a cohort study in England showed no
evidence of a link between RA and AA, even after adjusting for age,
gender, cardiovascular disease risk factors or anti-inflammatory
drugs(13). Nonetheless, these studies lacked detailed information on
daily tobacco use and alcohol consumption, family history of AA, as well
as the imaging data, which all could bias causal inference towards a
positive or negative direction. In addition, selection bias is also a
problem that cannot be ignored. The same problems existed in studies of
the association between SLE and AA(14, 15). People with SLE may
experience a variety of symptoms and receive examination more frequently
than people without SLE, therefore, they are more likely to be diagnosed
with AA. Similarly, the two cohort studies on psoriasis failed to
account for confounders such as body weight, smoking status, detail
imaging findings, and assessment of psoriasis severity(16, 17).
Moreover, the estimates from these observational studies are subject to
substantial uncertainty with a wide confidence interval, indicating
causal effects and their magnitude should be viewed with caution.
Several isolated case reports have claimed that CD and UC were
frequently observed comorbidities in AA patients(18-20), thus, we also
conducted an investigation to explore the causal association of these
diseases in AA individuals. However, either by forward or backward
analysis, we did not fnd causal association between IBD, CD, UC and AA.

Genetic predisposition to AA is present from birth, therefore the MR
results of our study would not be affected by the acquired drug
treatment or other known or unknown exposure factors of AA patients.
However, the exact mechanism by which genetically predisposed AA
increases the risk of RA is still not completely understood. A study
investigated the incidence of autoimmune diseases in patients with
inflammatory aortic aneurysm (IAA). The serologic tests and biopsy
findings were analyzed for the presence of autoimmune diseases before
surgery in all cases. Autoimmune diseases were shown to be statistically
significant in the inflammatory aortic group compared with the
non-inflammatory aortic aneurysm group(21). Previous studies have
provided evidence that a genetic predisposition to IAA exists and
suggested that it may be related to human leukocyte antigen (HLA)
molecule in IAA. HLA molecules play a critical role in the immune
recognition process of specific antigen-presenting cells, which can be
expressed by macrophages and dendritic cells. Amino acid polymorphism of
HLA-DR molecule mediates immune responses to different antigens in
different individuals and have also been found to be associated with the
development of many diseases. A study investigating the genetic risk
associated with HLA-DR gene polymorphism in patients with IAA revealed
that HLA-DR B1*15 and HLA-DR B1*0404 allele were significantly enriched
in these patients compared to control subjects(42). Moreover,
observation with B-cell-enriched lymphoid follicles and CD3-positive T
cells from the aneurysm wall of IAA patient suggested that a potential
shared pathogenesis with IgG4-related diseases(43). The etiology of RA
remains elusive, and several studies have consistently demonstrated its
association with genetic factors. As early as 1976, Stastny(44) reported
a correlation between RA and the HLA-DR4 allele of the human leukocyte
antigen class II. Many studies have confirmed the association between RA
and HLA-DR4 in a various of different ethnic groups, but the subtypes of
HLA-DR4 and other types of HLA-DR vary widely among ethnic groups
(45-48). Given the limited sample size and racial difference in previous
studies, more large-scale research should focus on investigating the
genetic contribution of HLA risk loci in IAA patients worldwide.
Furthermore, the investigation of the genetic determinants and their
role in the pathogenesis of RA necessitates further explore. Hopefully,
the efficacy of autoantigen immunoregulation in treating RA has been
demonstrated in animal studies. Clinical trials of autologous modified
dendritic cells exposed to citrulline peptide by percutaneous injection
are currently underway (49).

Unlike previous observational studies examining the association between
IMIDs and AA, we used MR method to deal with known and unknown
confounding factors such as smoking, alcohol or fallacies caused by
reverse causation. Thus, our results overcome the limitations of
observational studies to reflect causal associations and effects of
IMIDs and AA. Our work also had some limitations. While we concluded
that exposure to genetically predisposed AA increased the lifetime risk
of RA, this does not mean that this conclusion can be used directly to
infer the possible impact of interventions targeting genetically
predisposed AA patients on RA risk. Besides, owing to constraints within
the GWAS database, we did not distinguish the anatomic site of the
aortic aneurysm. Although the thoracic aorta and the abdominal aorta
share certain anatomical features, these two disease subtypes exhibit
marked differences with respect to their pathophysiological mechanisms.
Finally, our study was dependent on data from European population, which
limits generalizability to other racial populations.

\textbf{Conclusion}

In summary, our study has showed genetically predicted AA may increase
the risk of RA, while there was no evidence that RA had an increased
risk of AA. Furthermore, we found no evidence of association between
IBD, CD, UC, SLE, PSO and AA. This finding is in line with other
research that demonstrated the genetic risk determinants are linked to
HLA molecules in IAA patients. IAA may be a separate entity with similar
pathogenesis with IgG4-related disease. Our study provides directions
for future research on genetic susceptibility to IAA. The antigen
binding role of IAA risk genes in the disease origin of RA needs
additional investigation.

\textbf{Ethics approval and consent to participate}

No extra ethical approval or informed consent is required.

\textbf{Consent for publication}

All authors contributed to this article and approved the submitted
version.

\textbf{Availability of data and materials}

The summary genetic association datasets for IMIDs and AA are accessible
through the IEU OpenGWAS Database Project (https://gwas.mrcieu.ac.uk/)
or the published article and its supplementary files. For further
inquiries, please contact the corresponding author.

\textbf{Competing interests}

The authors declare that the research was conducted in the absence of
any commercial or financial relationships that could be construed as a
potential conflict of interest.

\textbf{Funding}

No financial support was received for this study.

\textbf{Authors' contributions}

XF guided and funded this study. SS designed, conducted the MR analysis
and draft of the manuscript. JH, GW, YZ, ST provided insightful feedback
on both the study and manuscript. MS assisted in organizing the tables
and figures. SS, MS and JH shared equal contribution of this article.

\textbf{Acknowledgments}

We are grateful to the IEU Open GWAS project for the publicly available
data and to all those involved in the FinnGen and European
Bioinformatics Institute studies.

\textbf{Figure legends}

\textbf{Figure 1.} \textbf{(A) Schematic view of this MR study for the
association between IMIDs and AA.} IMIDs, Immune-mediated inflammatory
diseases, including inflammatory bowel disease (IBD), Crohn's disease
(CD), ulcerative colitis (UC), rheumatoid arthritis (RA), systemic lupus
erythematosus (SLE) and psoriasis (PSO). AA, aortic aneurysm.
\textbf{(B) Detailed flowchart of the study design.} IVs, instrumental
variables.

\textbf{Figures 2. Main result of estimates from MR analysis with
forward and reverse direction.} (A) (B), forward analysis. (C) (D),
reverse analysis. IBD, inflammatory bowel disease, CD, Crohn's disease,
UC, ulcerative colitis, RA, rheumatoid arthritis, SLE, systemic lupus
erythematosus, and PSO, psoriasis. CI, confidence interval.

\textbf{Supplementary Figure S1} \textbf{Leave-one-out sensitivity
analysis.} (A) IBD and AA, (B) CD and AA, (C) UC and AA, (D) RA and AA,
(E) SLE and AA, (F) PSO and AA, (G) AA and IBD, (H) AA and CD, (I) AA
and UC, (J) AA and RA, (K) AA and SLE, (L) AA and PSO.

\textbf{Supplementary Figure S2 Funnel plot for IVW and MR-Egger
method.} (A) IBD and AA, (B) CD and AA, (C) UC and AA, (D) RA and AA,
(E) SLE and AA, (F) PSO and AA, (G) AA and IBD, (H) AA and CD, (I) AA
and UC, (J) AA and RA, (K) AA and SLE, (L) AA and PSO.

\textbf{Supplementary Figure S3 Scatter plot with all IVs.} (A) IBD and
AA, (B) CD and AA, (C) UC and AA, (D) RA and AA, (E) SLE and AA, (F) PSO
and AA, (G) AA and IBD, (H) AA and CD, (I) AA and UC, (J) AA and RA, (K)
AA and SLE, (L) AA and PSO.

\textbf{Tables}

\textbf{Table 1.} Results of supplemented MR methods\selectlanguage{english}
\begin{longtable}[]{@{}llllll@{}}
\toprule
\textbf{Exposure} & \textbf{Outcome} & \textbf{MR} & \textbf{MR} &
\textbf{MR} & \textbf{MR}\tabularnewline
& & \textbf{Methods} & \textbf{Beta} & \textbf{OR (95\%CI)} & \emph{P}
\textbf{value}\tabularnewline
IBD & AA & MR PRESSO & -0.06 & 0.94(0.88,1.00) & 0.100\tabularnewline
& & Mr raps & -0.06 & 0.94(0.83,1.06) & 0.320\tabularnewline
CD & AA & MR PRESSO & -0.03 & 0.97(0.90,1.05) & 0.488\tabularnewline
& & Mr raps & / & / & /\tabularnewline
UC & AA & MR PRESSO & -0.06 & 0.94(0.88,1.01) & 0.139\tabularnewline
& & Mr raps & -0.06 & 0.94(0.83,1.06) & 0.336\tabularnewline
RA & AA & MR PRESSO & -0.04 & 0.96(0.92,1.01) & 0.146\tabularnewline
& & Mr raps & -0.04 & 0.96(0.91,1.01) & 0.117\tabularnewline
SLE & AA & MR PRESSO & -0.01 & 0.99(0.96,1.02) & 0.593\tabularnewline
& & Mr raps & -0.01 & 0.99(0.96,1.03) & 0.691\tabularnewline
PSO & AA & MR PRESSO & -0.01 & 0.99(0.94,1.04) & 0.678\tabularnewline
& & Mr raps & 0.00 & 1.00(0.94,1.05) & 0.870\tabularnewline
AA & IBD & MR PRESSO & 0.04 & 1.04(0.99,1.09) & 0.154\tabularnewline
& & Mr raps & 0.04 & 1.04(0.97,1.11) & 0.273\tabularnewline
AA & CD & MR PRESSO & 0.06 & 1.06(0.97,1.16) & 0.207\tabularnewline
& & Mr raps & 0.06 & 1.06(0.95,1.18) & 0.290\tabularnewline
AA & UC & MR PRESSO & 0.02 & 1.02(0.96,1.08) & 0.529\tabularnewline
& & Mr raps & 0.02 & 1.02(0.95,1.10) & 0.613\tabularnewline
AA & RA & MR PRESSO & 0.06 & 1.06(1.01,1.11) & 0.019\tabularnewline
& & Mr raps & 0.06 & 1.06(1.00,1.12) & 0.034\tabularnewline
AA & SLE & MR PRESSO & -0.03 & 0.97(0.88,1.07) & 0.561\tabularnewline
& & Mr raps & -0.04 & 0.96(0.87,1.07) & 0.453\tabularnewline
AA & PSO & MR PRESSO & 0.01 & 1.01(0.95,1.08) & 0.691\tabularnewline
& & Mr raps & 0.01 & 1.01(0.94,1.09) & 0.791\tabularnewline
\bottomrule
\end{longtable}

\textbf{Table 2.} Heterogeneity and pleiotropy analysis\selectlanguage{english}
\begin{longtable}[]{@{}lllllll@{}}
\toprule
\textbf{Exposure} & \textbf{Outcome} & \textbf{Heterogeneity} &
\textbf{Heterogeneity} & \textbf{Heterogeneity} & \textbf{Pleiotropy} &
\textbf{Pleiotropy}\tabularnewline
& & \textbf{Methods} & \textbf{Cochran's Q} & \emph{P} \textbf{value} &
\textbf{Egger-intercept(95\%CI)} & \emph{P}
\textbf{value}\tabularnewline
IBD & AA & IVW & 2.88 & 0.969 & -0.02(-0.06,0.03) & 0.491\tabularnewline
CD & AA & IVW & 3.28 & 0.656 & 0.00(-0.06,0.06) & 0.929\tabularnewline
UC & AA & IVW & 2.48 & 0.871 & 0.00(-0.05,0.06) & 0.911\tabularnewline
RA & AA & IVW & 72.14 & 0.604 & 0.01(-0.01,0.02) & 0.353\tabularnewline
SLE & AA & IVW & 30.16 & 0.810 & 0.02(-0.01,0.05) & 0.157\tabularnewline
PSO & AA & IVW & 11.51 & 0.645 & 0.00(-0.04,0.03) & 0.897\tabularnewline
AA & IBD & IVW & 12.72 & 0.889 & 0.01(-0.02,0.05) & 0.405\tabularnewline
AA & CD & IVW & 16.40 & 0.691 & 0.03(-0.03,0.08) & 0.353\tabularnewline
AA & UC & IVW & 13.22 & 0.868 & 0.01(-0.03,0.05) & 0.596\tabularnewline
AA & RA & IVW & 11.82 & 0.621 & -0.01(-0.04,0.02) & 0.570\tabularnewline
AA & SLE & IVW & 7.44 & 0.827 & 0.03(-0.05,0.10) & 0.507\tabularnewline
AA & PSO & IVW & 18.99 & 0.522 & 0.01(-0.03,0.04) & 0.772\tabularnewline
\bottomrule
\end{longtable}

\textbf{Supplementary tables}

\textbf{Supplementary table S1.} Characteristics of GWAS datasets in the
study

\textbf{Supplementary table S2.} Instrumental variables related to
aortic aneurysm

\textbf{Supplementary table S3.} Instrumental variables related to
Immune-mediated inflammatory diseases

\textbf{References:}

1. Bossone E, Eagle KA. Epidemiology and Management of Aortic Disease:
Aortic Aneurysms and Acute Aortic Syndromes. \emph{Nature reviews
Cardiology} (2021) 18(5):331-48. Epub 2020/12/24. doi:
10.1038/s41569-020-00472-6.

2. Mutlu O, Salman HE, Al-Thani H, El-Menyar A, Qidwai UA, Yalcin HC.
How Does Hemodynamics Affect Rupture Tissue Mechanics in Abdominal
Aortic Aneurysm: Focus on Wall Shear Stress Derived Parameters,
Time-Averaged Wall Shear Stress, Oscillatory Shear Index, Endothelial
Cell Activation Potential, and Relative Residence Time. \emph{Computers
in biology and medicine} (2023) 154:106609. Epub 2023/02/02. doi:
10.1016/j.compbiomed.2023.106609.

3. Erbel R, Aboyans V, Boileau C, Bossone E, Bartolomeo RD, Eggebrecht
H, et al. 2014 Esc Guidelines on the Diagnosis and Treatment of Aortic
Diseases: Document Covering Acute and Chronic Aortic Diseases of the
Thoracic and Abdominal Aorta of the Adult. The Task Force for the
Diagnosis and Treatment of Aortic Diseases of the European Society of
Cardiology (Esc). \emph{European heart journal} (2014) 35(41):2873-926.
Epub 2014/09/01. doi: 10.1093/eurheartj/ehu281.

4. Gao J, Cao H, Hu G, Wu Y, Xu Y, Cui H, et al. The Mechanism and
Therapy of Aortic Aneurysms. \emph{Signal transduction and targeted
therapy} (2023) 8(1):55. Epub 2023/02/04. doi:
10.1038/s41392-023-01325-7.

5. Golledge J. Abdominal Aortic Aneurysm: Update on Pathogenesis and
Medical Treatments. \emph{Nature reviews Cardiology} (2019)
16(4):225-42. Epub 2018/11/18. doi: 10.1038/s41569-018-0114-9.

6. Scherlinger M, Richez C, Tsokos GC, Boilard E, Blanco P. The Role of
Platelets in Immune-Mediated Inflammatory Diseases. \emph{Nature reviews
Immunology} (2023):1-16. Epub 2023/01/29. doi:
10.1038/s41577-023-00834-4.

7. McInnes IB, Gravallese EM. Immune-Mediated Inflammatory Disease
Therapeutics: Past, Present and Future. \emph{Nature reviews
Immunology}(2021) 21(10):680-6. Epub 2021/09/15. doi:
10.1038/s41577-021-00603-1.

8. Conrad N, Verbeke G, Molenberghs G, Goetschalckx L, Callender T,
Cambridge G, et al. Autoimmune Diseases and Cardiovascular Risk: A
Population-Based Study on 19 Autoimmune Diseases and 12 Cardiovascular
Diseases in 22 Million Individuals in the Uk. \emph{Lancet (London,
England)} (2022) 400(10354):733-43. Epub 2022/08/31. doi:
10.1016/s0140-6736(22)01349-6.

9. Agca R, Smulders Y, Nurmohamed M. Cardiovascular Disease Risk in
Immune-Mediated Inflammatory Diseases: Recommendations for Clinical
Practice. \emph{Heart (British Cardiac Society)} (2022) 108(1):73-9.
Epub 2021/03/07. doi: 10.1136/heartjnl-2019-316378.

10. Gister\selectlanguage{ngerman}å A, Hansson GK. The Immunology of
Atherosclerosis.\emph{Nature reviews Nephrology} (2017) 13(6):368-80.
Epub 2017/04/11. doi: 10.1038/nrneph.2017.51.

11. Schmidt J, Sunesen K, Kornum JB, Duhaut P, Thomsen RW. Predictors
for Pathologically Confirmed Aortitis after Resection of the Ascending
Aorta: A 12-Year Danish Nationwide Population-Based Cross-Sectional
Study. \emph{Arthritis research \& therapy} (2011) 13(3):R87. Epub
2011/06/17. doi: 10.1186/ar3360.

12. Shovman O, Tiosano S, Comaneshter D, Cohen AD, Amital H, Sherf M.
Aortic Aneurysm Associated with Rheumatoid Arthritis: A Population-Based
Cross-Sectional Study. \emph{Clinical rheumatology} (2016)
35(11):2657-61. Epub 2016/08/10. doi: 10.1007/s10067-016-3372-0.

13. Pujades-Rodriguez M, Duyx B, Thomas SL, Stogiannis D, Rahman A,
Smeeth L, et al. Rheumatoid Arthritis and Incidence of Twelve Initial
Presentations of Cardiovascular Disease: A Population Record-Linkage
Cohort Study in England. \emph{PloS one} (2016) 11(3):e0151245. Epub
2016/03/16. doi: 10.1371/journal.pone.0151245.

14. Wang SH, Chang YS, Liu CJ, Lai CC, Chen TJ, Chen WS. Incidence and
Risk Analysis of Aortic Aneurysm and Aortic Dissection among Patients
with Systemic Lupus Erythematosus: A Nationwide Population-Based Study
in Taiwan. \emph{Lupus} (2014) 23(7):665-71. Epub 2014/02/21. doi:
10.1177/0961203314523868.

15. Guy A, Tiosano S, Comaneshter D, Tekes-Manova D, Shovman O, Cohen
AD, et al. Aortic Aneurysm Association with Sle - a Case-Control
Study.\emph{Lupus} (2016) 25(9):959-63. Epub 2016/01/27. doi:
10.1177/0961203316628999.

16. Khalid U, Egeberg A, Ahlehoff O, Smedegaard L, Gislason GH, Hansen
PR. Nationwide Study on the Risk of Abdominal Aortic Aneurysms in
Patients with Psoriasis. \emph{Arteriosclerosis, thrombosis, and
vascular biology} (2016) 36(5):1043-8. Epub 2016/04/16. doi:
10.1161/atvbaha.116.307449.

17. Chiu HY, Lo PC, Huang WF, Tsai YW, Tsai TF. Increased Risk of Aortic
Aneurysm (Aa) in Relation to the Severity of Psoriasis: A National
Population-Based Matched-Cohort Study. \emph{Journal of the American
Academy of Dermatology} (2016) 75(4):747-54. Epub 2016/07/31. doi:
10.1016/j.jaad.2016.06.002.

18. Talathi S, Mannion M, Colvin E, Beck A, Dabal R, Maclin J, et al.
Aortitis with an Aneurysm at Initial Presentation of Crohn
Disease.\emph{Journal of pediatric gastroenterology and nutrition}
(2018) 67(1):e12-e3. Epub 2018/06/22. doi: 10.1097/mpg.0000000000001945.

19. Taddio A, Maschio M, Martelossi S, Barbi E, Ventura A. Crohn's
Disease and Takayasu's Arteritis: An Uncommon Association. \emph{World
journal of gastroenterology} (2013) 19(35):5933-5. Epub 2013/10/15. doi:
10.3748/wjg.v19.i35.5933.

20. Kashima K, Kawasaki D, Yotsumoto G, Hatake S, Yamashita E, Nagayoshi
S, et al. Rapid Progression of Aortic Regurgitation with Thoracic Aortic
Aneurysm Due to Takayasu Arteritis Associated with Ulcerative
Colitis.\emph{Internal medicine (Tokyo, Japan)} (2010) 49(11):1007-11.
Epub 2010/06/04. doi: 10.2169/internalmedicine.49.3122.

21. Haug ES, Skomsvoll JF, Jacobsen G, Halvorsen TB, Saether OD, Myhre
HO. Inflammatory Aortic Aneurysm Is Associated with Increased Incidence
of Autoimmune Disease. \emph{Journal of vascular surgery} (2003)
38(3):492-7. Epub 2003/08/30. doi: 10.1016/s0741-5214(03)00340-9.

22. Tin A, Köttgen A. Mendelian Randomization Analysis as a Tool to Gain
Insights into Causes of Diseases: A Primer. \emph{Journal of the
American Society of Nephrology : JASN} (2021) 32(10):2400-7. Epub
2021/06/18. doi: 10.1681/asn.2020121760.

23. Gupta V, Walia GK, Sachdeva MP. 'Mendelian Randomization': An
Approach for Exploring Causal Relations in Epidemiology. \emph{Public
health} (2017) 145:113-9. Epub 2017/04/01. doi:
10.1016/j.puhe.2016.12.033.

24. Boef AG, Dekkers OM, le Cessie S. Mendelian Randomization Studies: A
Review of the Approaches Used and the Quality of
Reporting.\emph{International journal of epidemiology} (2015)
44(2):496-511. Epub 2015/05/09. doi: 10.1093/ije/dyv071.

25. Zheng J, Baird D, Borges MC, Bowden J, Hemani G, Haycock P, et al.
Recent Developments in Mendelian Randomization Studies. \emph{Current
epidemiology reports} (2017) 4(4):330-45. Epub 2017/12/12. doi:
10.1007/s40471-017-0128-6.

26. Ha E, Bae SC, Kim K. Large-Scale Meta-Analysis across East Asian and
European Populations Updated Genetic Architecture and Variant-Driven
Biology of Rheumatoid Arthritis, Identifying 11 Novel Susceptibility
Loci. \emph{Annals of the rheumatic diseases} (2021) 80(5):558-65. Epub
2020/12/15. doi: 10.1136/annrheumdis-2020-219065.

27. Bentham J, Morris DL, Graham DSC, Pinder CL, Tombleson P, Behrens
TW, et al. Genetic Association Analyses Implicate Aberrant Regulation of
Innate and Adaptive Immunity Genes in the Pathogenesis of Systemic Lupus
Erythematosus. \emph{Nature genetics} (2015) 47(12):1457-64. Epub
2015/10/27. doi: 10.1038/ng.3434.

28. Skrivankova VW, Richmond RC, Woolf BAR, Yarmolinsky J, Davies NM,
Swanson SA, et al. Strengthening the Reporting of Observational Studies
in Epidemiology Using Mendelian Randomization: The Strobe-Mr
Statement.\emph{Jama} (2021) 326(16):1614-21. Epub 2021/10/27. doi:
10.1001/jama.2021.18236.

29. Jansen H, Samani NJ, Schunkert H. Mendelian Randomization Studies in
Coronary Artery Disease. \emph{European heart journal} (2014)
35(29):1917-24. Epub 2014/06/12. doi: 10.1093/eurheartj/ehu208.

30. Staley JR, Blackshaw J, Kamat MA, Ellis S, Surendran P, Sun BB, et
al. Phenoscanner: A Database of Human Genotype-Phenotype
Associations.\emph{Bioinformatics (Oxford, England)} (2016)
32(20):3207-9. Epub 2016/06/19. doi: 10.1093/bioinformatics/btw373.

31. Hemani G, Zheng J, Elsworth B, Wade KH, Haberland V, Baird D, et al.
The Mr-Base Platform Supports Systematic Causal Inference across the
Human Phenome. \emph{eLife} (2018) 7. Epub 2018/05/31. doi:
10.7554/eLife.34408.

32. Papadimitriou N, Dimou N, Tsilidis KK, Banbury B, Martin RM, Lewis
SJ, et al. Physical Activity and Risks of Breast and Colorectal Cancer:
A Mendelian Randomisation Analysis. \emph{Nature communications} (2020)
11(1):597. Epub 2020/02/01. doi: 10.1038/s41467-020-14389-8.

33. Lin Z, Deng Y, Pan W. Combining the Strengths of Inverse-Variance
Weighting and Egger Regression in Mendelian Randomization Using a
Mixture of Regressions Model. \emph{PLoS genetics} (2021)
17(11):e1009922. Epub 2021/11/19. doi: 10.1371/journal.pgen.1009922.

34. Bowden J, Del Greco MF, Minelli C, Zhao Q, Lawlor DA, Sheehan NA, et
al. Improving the Accuracy of Two-Sample Summary-Data Mendelian
Randomization: Moving Beyond the Nome Assumption. \emph{International
journal of epidemiology} (2019) 48(3):728-42. Epub 2018/12/19. doi:
10.1093/ije/dyy258.

35. Verbanck M, Chen CY, Neale B, Do R. Detection of Widespread
Horizontal Pleiotropy in Causal Relationships Inferred from Mendelian
Randomization between Complex Traits and Diseases. \emph{Nature
genetics} (2018) 50(5):693-8. Epub 2018/04/25. doi:
10.1038/s41588-018-0099-7.

36. Bowden J, Davey Smith G, Burgess S. Mendelian Randomization with
Invalid Instruments: Effect Estimation and Bias Detection through Egger
Regression. \emph{International journal of epidemiology} (2015)
44(2):512-25. Epub 2015/06/08. doi: 10.1093/ije/dyv080.

37. Werner N, Nickenig G, Sinning JM. Complex Pci Procedures: Challenges
for the Interventional Cardiologist. \emph{Clinical research in
cardiology : official journal of the German Cardiac Society} (2018)
107(Suppl 2):64-73. Epub 2018/07/07. doi: 10.1007/s00392-018-1316-1.

38. Björck M, Wanhainen A. Pathophysiology of Aaa: Heredity Vs
Environment. \emph{Progress in cardiovascular diseases} (2013)
56(1):2-6. Epub 2013/09/03. doi: 10.1016/j.pcad.2013.05.003.

39. Wood AM, Kaptoge S, Butterworth AS, Willeit P, Warnakula S, Bolton
T, et al. Risk Thresholds for Alcohol Consumption: Combined Analysis of
Individual-Participant Data for 599\selectlanguage{english} 912 Current Drinkers in 83
Prospective Studies. \emph{Lancet (London, England)} (2018)
391(10129):1513-23. Epub 2018/04/21. doi: 10.1016/s0140-6736(18)30134-x.

40. Ananthakrishnan AN, Kaplan GG, Bernstein CN, Burke KE, Lochhead PJ,
Sasson AN, et al. Lifestyle, Behaviour, and Environmental Modification
for the Management of Patients with Inflammatory Bowel Diseases: An
International Organization for Study of Inflammatory Bowel Diseases
Consensus. \emph{The lancet Gastroenterology \& hepatology} (2022)
7(7):666-78. Epub 2022/04/30. doi: 10.1016/s2468-1253(22)00021-8.

41. Pujades-Rodriguez M, Morgan AW, Cubbon RM, Wu J. Dose-Dependent Oral
Glucocorticoid Cardiovascular Risks in People with Immune-Mediated
Inflammatory Diseases: A Population-Based Cohort Study. \emph{PLoS
medicine} (2020) 17(12):e1003432. Epub 2020/12/04. doi:
10.1371/journal.pmed.1003432.

42. Rasmussen TE, Hallett JW, Jr., Metzger RL, Richardson DM, Harmsen
WS, Goronzy JJ, et al. Genetic Risk Factors in Inflammatory Abdominal
Aortic Aneurysms: Polymorphic Residue 70 in the Hla-Dr B1 Gene as a Key
Genetic Element. \emph{Journal of vascular surgery} (1997) 25(2):356-64.
Epub 1997/02/01. doi: 10.1016/s0741-5214(97)70358-6.

43. Sakata N, Tashiro T, Uesugi N, Kawara T, Furuya K, Hirata Y, et al.
Igg4-Positive Plasma Cells in Inflammatory Abdominal Aortic Aneurysm:
The Possibility of an Aortic Manifestation of Igg4-Related Sclerosing
Disease. \emph{The American journal of surgical pathology} (2008)
32(4):553-9. Epub 2008/02/28. doi: 10.1097/PAS.0b013e31815a04db.

44. Ollier B. Shock Revelations About Hla-Dr4 -- a Shortcut to
Rheumatoid Arthritis? \emph{Nature medicine} (1996) 2(3):279-80. Epub
1996/03/01. doi: 10.1038/nm0396-279.

45. Ali AA, Khalid KE, Mohammed SE, Akhtar MS, Saeed OK. Association of
Human Leukocyte Antigen (Hla) Class Ii (Drb1 and Dqb1) Alleles and
Haplotypes with Rheumatoid Arthritis in Sudanese
Patients.\emph{Frontiers in immunology} (2023) 14:1178546. Epub
2023/07/10. doi: 10.3389/fimmu.2023.1178546.

46. Atouf O, Benbouazza K, Brick C, Bzami F, Bennani N, Amine B, et al.
Hla Polymorphism and Early Rheumatoid Arthritis in the Moroccan
Population. \emph{Joint bone spine} (2008) 75(5):554-8. Epub 2008/09/20.
doi: 10.1016/j.jbspin.2008.01.027.

47. Fries JF, Wolfe F, Apple R, Erlich H, Bugawan T, Holmes T, et al.
Hla-Drb1 Genotype Associations in 793 White Patients from a Rheumatoid
Arthritis Inception Cohort: Frequency, Severity, and Treatment
Bias.\emph{Arthritis and rheumatism} (2002) 46(9):2320-9. Epub
2002/10/02. doi: 10.1002/art.10485.

48. Yang M, Kuang X, Li J, Pan Y, Tan M, Lu B, et al. Meta-Analysis of
the Association of Hla-Drb1 with Rheumatoid Arthritis in Chinese
Populations. \emph{BMC musculoskeletal disorders} (2013) 14:307. Epub
2013/10/29. doi: 10.1186/1471-2474-14-307.

49. Benham H, Nel HJ, Law SC, Mehdi AM, Street S, Ramnoruth N, et al.
Citrullinated Peptide Dendritic Cell Immunotherapy in Hla Risk
Genotype-Positive Rheumatoid Arthritis Patients. \emph{Science
translational medicine} (2015) 7(290):290ra87. Epub 2015/06/05. doi:
10.1126/scitranslmed.aaa9301.\selectlanguage{english}
\begin{figure}[H]
\begin{center}
\includegraphics[width=0.70\columnwidth]{figures/Figure-1/Figure-1}

\end{center}
\end{figure}\selectlanguage{english}
\begin{figure}[H]
\begin{center}
\includegraphics[width=0.70\columnwidth]{figures/Figure-2A/Figure-2A}

\end{center}
\end{figure}\selectlanguage{english}
\begin{figure}[H]
\begin{center}
\includegraphics[width=0.70\columnwidth]{figures/Figure-2B/Figure-2B}

\end{center}
\end{figure}\selectlanguage{english}
\begin{figure}[H]
\begin{center}
\includegraphics[width=0.70\columnwidth]{figures/Figure-2C/Figure-2C}

\end{center}
\end{figure}\selectlanguage{english}
\begin{figure}[H]
\begin{center}
\includegraphics[width=0.70\columnwidth]{figures/Figure-2D/Figure-2D}

\end{center}
\end{figure}

\selectlanguage{english}
\FloatBarrier
\end{document}

