<?xml version="1.0" encoding="UTF-8"?><!DOCTYPE article PUBLIC "-//NLM//DTD Journal Publishing DTD v3.0 20080202//EN" "http://dtd.nlm.nih.gov/publishing/3.0/journalpublishing3.dtd">
<article xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink" dtd-version="3.0" xml:lang="en" article-type="research article">
 <front>
  <journal-meta>
   <journal-id journal-id-type="publisher-id">
    jbm
   </journal-id>
   <journal-title-group>
    <journal-title>
     Journal of Biosciences and Medicines
    </journal-title>
   </journal-title-group>
   <issn pub-type="epub">
    2327-5081
   </issn>
   <issn publication-format="print">
    2327-509X
   </issn>
   <publisher>
    <publisher-name>
     Scientific Research Publishing
    </publisher-name>
   </publisher>
  </journal-meta>
  <article-meta>
   <article-id pub-id-type="doi">
    10.4236/jbm.2024.128024
   </article-id>
   <article-id pub-id-type="publisher-id">
    jbm-135470
   </article-id>
   <article-categories>
    <subj-group subj-group-type="heading">
     <subject>
      Articles
     </subject>
    </subj-group>
    <subj-group subj-group-type="Discipline-v2">
     <subject>
      Biomedical 
     </subject>
     <subject>
       Life Sciences
     </subject>
    </subj-group>
   </article-categories>
   <title-group>
    Causal Relationship between Chronic Obstructive Pulmonary Disease and Abdominal Aortic Aneurysm: A Mendelian Randomization Study
   </title-group>
   <contrib-group>
    <contrib contrib-type="author" xlink:type="simple">
     <name name-style="western">
      <surname>
       Jiangfeng
      </surname>
      <given-names>
       Tang
      </given-names>
     </name> 
     <xref ref-type="aff" rid="aff1"> 
      <sup>1</sup>
     </xref>
    </contrib>
    <contrib contrib-type="author" xlink:type="simple">
     <name name-style="western">
      <surname>
       Jiangqin
      </surname>
      <given-names>
       Liu
      </given-names>
     </name> 
     <xref ref-type="aff" rid="aff2"> 
      <sup>2</sup>
     </xref>
    </contrib>
   </contrib-group> 
   <aff id="aff1">
    <addr-line>
     aDepartment of Cardiology, The First Affiliated Hospital, Jiangxi Medical College, Nanchang University, Nanchang, China
    </addr-line> 
   </aff> 
   <aff id="aff2">
    <addr-line>
     aDepartment of Emergency, Gaoxin Branch of the First Affiliated Hospital, Jiangxi Medical College, Nanchang University, Nanchang, China
    </addr-line> 
   </aff> 
   <pub-date pub-type="epub">
    <day>
     01
    </day> 
    <month>
     08
    </month>
    <year>
     2024
    </year>
   </pub-date> 
   <volume>
    12
   </volume> 
   <issue>
    08
   </issue>
   <fpage>
    307
   </fpage>
   <lpage>
    319
   </lpage>
   <history>
    <date date-type="received">
     <day>
      16,
     </day>
     <month>
      July
     </month>
     <year>
      2024
     </year>
    </date>
    <date date-type="published">
     <day>
      20,
     </day>
     <month>
      July
     </month>
     <year>
      2024
     </year> 
    </date> 
    <date date-type="accepted">
     <day>
      20,
     </day>
     <month>
      August
     </month>
     <year>
      2024
     </year> 
    </date>
   </history>
   <permissions>
    <copyright-statement>
     © Copyright 2014 by authors and Scientific Research Publishing Inc. 
    </copyright-statement>
    <copyright-year>
     2014
    </copyright-year>
    <license>
     <license-p>
      This work is licensed under the Creative Commons Attribution International License (CC BY). http://creativecommons.org/licenses/by/4.0/
     </license-p>
    </license>
   </permissions>
   <abstract>
    <b>Background:</b> Chronic Obstructive Pulmonary Disease (COPD) is a chronic inflammatory lung condition associated with significant morbidity and mortality. Observational studies indicate a positive correlation between COPD and the risk of abdominal aortic aneurysm (AAA), suggesting individuals with COPD are more likely to develop AAA. However, the causal relationship between COPD and AAA remains unclear. 
    <b>Method:</b> This study employed a bidirectional Mendelian Randomization (MR) approach to assess the causal relationship between COPD and AAA. A two-step MR analysis was conducted to evaluate the mediating effect of 1400 circulating metabolites between COPD and AAA. Expression quantitative trait loci (eQTL) were sourced from the MRC Integrative Epidemiology Unit (MRC-IEU) database, and MR analysis was performed using the TwoSampleMR R package. The results were filtered using the Inverse Variance Weighted (IVW) method to identify genes strongly associated with both COPD and AAA. Furthermore, the Super Exact Test R package was utilized to determine the overlapping genes between COPD and AAA. Enrichment analysis for Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) was conducted using the clusterProfiler R package. Protein-protein interaction (PPI) analysis was carried out using STRING v12.0. 
    <b>Results:</b> The IVW method indicated a causal relationship between the risk increase of COPD and AAA (OR: 1.47, 95% CI: 1.16 - 1.86, p = 0.001). Among 1400 circulating metabolites, plasma-free proline was identified as mediating the relationship between COPD and AAA, with a mediation effect proportion of −4.6% (95% CI: −9.032%, −0.164%, p = 0.042). Additionally, PPI analysis revealed 20 functionally interrelated genes mediating the linkage between COPD and AAA. KEGG enrichment analysis showed functional enrichment of these genes in the pathway of aldosterone synthesis and secretion. 
    <b>Conclusion:</b> Our study supports a causal relationship between COPD and an increased risk of AAA. Specifically, plasma-free proline and pathways related to aldosterone synthesis and secretion may play key roles in the connection between COPD and AAA.
   </abstract>
   <kwd-group> 
    <kwd>
     COPD
    </kwd> 
    <kwd>
      Abdominal Aortic Aneurysm
    </kwd> 
    <kwd>
      Circulating Metabolites
    </kwd> 
    <kwd>
      Mendelian Randomization
    </kwd>
   </kwd-group>
  </article-meta>
 </front>
 <body>
  <sec id="s1">
   <title>1. Introduction</title>
   <p>Chronic Obstructive Pulmonary Disease (COPD) represents a prevalent and debilitating chronic lung condition characterized by persistent airflow limitation, leading to respiratory symptoms and functional impairment <xref ref-type="bibr" rid="scirp.135470-1">
     [1]
    </xref>. It encompasses clinical phenotypes such as chronic bronchitis and emphysema and stands as a leading cause of morbidity and mortality globally <xref ref-type="bibr" rid="scirp.135470-2">
     [2]
    </xref> <xref ref-type="bibr" rid="scirp.135470-3">
     [3]
    </xref>. COPD imposes a significant burden on healthcare systems and individuals, with symptoms including cough, sputum production, dyspnea, and reduced exercise tolerance <xref ref-type="bibr" rid="scirp.135470-4">
     [4]
    </xref>. Effective management of COPD involves pharmacological treatments, non-pharmacological interventions, and lifestyle modifications. Pharmacological treatments primarily include bronchodilators, such as beta-agonists and anticholinergics, and inhaled corticosteroids to reduce inflammation and prevent exacerbations <xref ref-type="bibr" rid="scirp.135470-5">
     [5]
    </xref>. Non-pharmacological interventions, including pulmonary rehabilitation and oxygen therapy, have been shown to improve the quality of life and exercise capacity in COPD patients <xref ref-type="bibr" rid="scirp.135470-6">
     [6]
    </xref>. Lifestyle changes, particularly smoking cessation, are critical in slowing disease progression and improving overall outcomes for patients with COPD <xref ref-type="bibr" rid="scirp.135470-7">
     [7]
    </xref>. Abdominal Aortic Aneurysm (AAA) is a potentially life-threatening vascular condition characterized by the local dilation of the abdominal aorta, which can lead to rupture and fatal bleeding <xref ref-type="bibr" rid="scirp.135470-8">
     [8]
    </xref>. Primarily affecting the elderly, the prevalence of AAA increases with age <xref ref-type="bibr" rid="scirp.135470-9">
     [9]
    </xref>, with risk factors including smoking, being male, a family history, and atherosclerosis <xref ref-type="bibr" rid="scirp.135470-10">
     [10]
    </xref> <xref ref-type="bibr" rid="scirp.135470-11">
     [11]
    </xref>.</p>
   <p>Smoking is a major shared risk factor for both diseases, with a clear link between long-term smoking and increased incidence of COPD and AAA <xref ref-type="bibr" rid="scirp.135470-12">
     [12]
    </xref>. Furthermore, age, gender, and other cardiovascular diseases, such as hypertension and coronary artery disease, are considered common risk factors influencing the occurrence of COPD and AAA. The presence of these common risk factors may further complicate the association between COPD and AAA. Retrospective studies have explored the potential association between COPD and AAA <xref ref-type="bibr" rid="scirp.135470-13">
     [13]
    </xref>. For instance, Takagi, H et al. found that individuals with COPD had a higher risk of developing AAA compared to those without COPD <xref ref-type="bibr" rid="scirp.135470-14">
     [14]
    </xref>. Similarly, Xiong, J. et al. reported a significant association between the severity of COPD and the incidence of AAA <xref ref-type="bibr" rid="scirp.135470-15">
     [15]
    </xref>. However, retrospective studies are susceptible to potential confounders and selection biases, leading to uncertainty in this relationship <xref ref-type="bibr" rid="scirp.135470-16">
     [16]
    </xref>. Therefore, robust epidemiological methods are necessary to elucidate the causal relationship between COPD and AAA. Mendelian Randomization (MR) analysis, utilizing genetic variants as instrumental variables for exposures, offers a potent approach to assessing causality. MR analysis minimizes confounders and reduces the possibility of reverse causality, addressing limitations inherent in retrospective studies <xref ref-type="bibr" rid="scirp.135470-17">
     [17]
    </xref>-<xref ref-type="bibr" rid="scirp.135470-19">
     [19]
    </xref>.</p>
   <p>In this study, we conducted a bidirectional MR analysis to assess the causal relationship between COPD and AAA. A two-step MR was employed to evaluate the mediating effect of 1400 circulating metabolites between COPD and AAA. Additionally, Expression quantitative trait loci (eQTL) were sourced from the the MRC Integrative Epidemiology Unit (MRC-IEU) database, and results were filtered using the IVW method to identify disease-related overlapping genes. The forestploter R package was utilized for the visual representation of the findings through forest plots. Overlapping genes were further analyzed for gene ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG), as well as Protein-protein interaction (PPI) network analysis.</p>
  </sec><sec id="s2">
   <title>2. Method</title>
   <sec id="s2_1">
    <title>2.1. Study Design and Genetic Instrument Selection</title>
    <p>This study utilized summary statistics from publicly available Genome-wide association study (GWAS) datasets, predominantly focusing on European populations. Ethical approval and informed consent were obtained in the original studies. Valid instrumental variables (IVs) were selected based on their robust association with exposure, independence from confounding factors, and absence of effects on the outcome apart from the exposure itself. The criteria for selecting instrumental variables were as follows: Instrumental variables achieving genome-wide significance (p &lt; 5 × 10<sup>−8</sup>), and ensured minimal linkage disequilibrium (r<sup>2</sup> &lt; 0.001) within a clump window exceeding 10,000 kb to ensure independence. LD levels were estimated based on the European population from the 1000 Genomes Project <xref ref-type="bibr" rid="scirp.135470-20">
      [20]
     </xref>. To ensure a strong association between instrumental variables and the exposure, the F-statistic of single nucleotide polymorphisms (SNPs) was used to assess the strength of association, with an F-statistic &gt; 10 indicating no bias from weak instrumental variables, calculated as F-statistic = (β/SE)<sup>2</sup>.</p>
   </sec>
   <sec id="s2_2">
    <title>2.2. Data Sources for Exposure, Mediators, and Outcomes</title>
    <p>COPD data were obtained from the MRC-IEU (<xref ref-type="bibr" rid="scirp.135470-https://gwas.mrcieu.ac.uk/">
      https://gwas.mrcieu.ac.uk/
     </xref>), with the GWAS ID being finn-b-J10_COPD, including 203,824 European individuals (6915 cases and 186,723 controls) and 16,380,382 SNPs. The 1400 circulating metabolites came from 8210 individuals of European ancestry. This dataset includes absolute concentrations of 1091 biomarkers and ratios of 309 biomarkers. Complete GWAS summary statistics for all 1091 blood metabolites and 309 metabolite ratios are directly downloadable from the NHGRI-EBI GWAS Catalog (<xref ref-type="bibr" rid="scirp.135470-https://www.ebi.ac.uk/gwas/">
      https://www.ebi.ac.uk/gwas/
     </xref>), with the IDs ranging from GCST90199621 to GCST902010209. Furthermore, Data on abdominal aortic aneurysms were acquired from the NHGRI-EBI GWAS Catalog (<xref ref-type="bibr" rid="scirp.135470-https://www.ebi.ac.uk/gwas/">
      https://www.ebi.ac.uk/gwas/
     </xref>), with the GWAS ID being GCST90080047, including 387,930 European individuals (1184 cases and 86,746 controls).</p>
   </sec>
   <sec id="s2_3">
    <title>2.3. Statistical Analysis</title>
    <p>Mendelian Randomization analysis was performed using R software (version 4.1.2, <xref ref-type="bibr" rid="scirp.135470-http://www.R-project.org">
      http://www.R-project.org
     </xref>) and the TwoSampleM packages <xref ref-type="bibr" rid="scirp.135470-21">
      [21]
     </xref>. The bidirectional causal relationship between COPD and abdominal aortic aneurysm was assessed primarily using the Inverse Variance Weighted (IVW) method. The IVW method assumes all instrumental variables are valid; any SNP not meeting this assumption introduces bias <xref ref-type="bibr" rid="scirp.135470-22">
      [22]
     </xref>. Hence, additional analyses were conducted using the Weighted Median and MR-Egger methods. The Weighted Median approach requires at least 50% of the SNPs to be valid <xref ref-type="bibr" rid="scirp.135470-23">
      [23]
     </xref>, while MR-Egger regression provides an unbiased estimate even without considering pleiotropy among instrumental variable SNP. The MR-Egger intercept test was employed to evaluate pleiotropic associations between genetic variations and potential confounders <xref ref-type="bibr" rid="scirp.135470-24">
      [24]
     </xref>. Heterogeneity among SNPs was evaluated using Cochran’s Q test and funnel plots <xref ref-type="bibr" rid="scirp.135470-25">
      [25]
     </xref>. The leave-one-out approach was used to determine if any specific SNP significantly altered the results by excluding SNPs one at a time <xref ref-type="bibr" rid="scirp.135470-26">
      [26]
     </xref>. A two-step Mendelian Randomization was used to assess the mediating effect of 1400 circulating metabolites between COPD and AAA, with MR-Egger methods employed to validate the robustness of the IVW results in the MR analysis.</p>
   </sec>
   <sec id="s2_4">
    <title>2.4. Knowledge-Based Analysis</title>
    <p>All eQTL data were retrieved from the MRC-IEU database (<xref ref-type="bibr" rid="scirp.135470-https://gwas.mrcieu.ac.uk/">
      https://gwas.mrcieu.ac.uk/
     </xref>). Initially, association analyses were conducted, filtering data based on a P-value threshold of less than 5<sup>−</sup><sup>8</sup> to identify SNPs associated with the exposure factor. SNPs in linkage disequilibrium were excluded using a threshold of kb = 10,000 and r<sup>2</sup> = 0.001. Subsequently, data underwent F-testing to filter SNPs with an F-test value greater than 10, mitigating the impact of weak instrumental variables. MR analysis was then performed using the TwoSampleMR R package, filtering results with IVW &lt; 0.05 to identify genes strongly associated with these diseases. The Super Exact Test R package was used to identify overlapping genes between COPD and AAA gene sets. Finally, the cluster profile R package was employed for GO and KEGG enrichment analysis. PPI analysis was conducted using STRING v12.0.</p>
   </sec>
  </sec><sec id="s3">
   <title>3. Results</title>
   <p>The Mendelian Randomization analysis, employing the IVW model, revealed a significant genetic correlation between the risk of developing COPD and AAA (OR: 1.47, 95% CI: 1.16 - 1.86, p = 0.001). This finding underscores a causal link between COPD and an increased risk of AAA. The results from the MR-Egger model were consistent with those from the IVW model. Our analysis found no evidence of pleiotropy and heterogeneity in any exposures (<xref ref-type="table" rid="table1">
     Table 1
    </xref>).</p>
   <table-wrap id="table1">
    <label>
     <xref ref-type="table" rid="table1">
      Table 1
     </xref></label>
    <caption>
     <title>
      <xref ref-type="bibr" rid="scirp.135470-"></xref>Table 1. MR estimates of the effect of COPD on AAA.</title>
    </caption>
    <table class="MsoTableGrid custom-table" border="0" cellspacing="0" cellpadding="0"> 
     <tr> 
      <td class="custom-bottom-td acenter" width="11.63%">Outcome<p style="text-align:center"></p></td> 
      <td class="custom-bottom-td acenter" width="13.37%">method<p style="text-align:center"></p></td> 
      <td class="custom-bottom-td acenter" width="14.64%">OR(95% CI)<p style="text-align:center"></p></td> 
      <td class="custom-bottom-td acenter" width="7.44%">p<p style="text-align:center"></p></td> 
      <td class="custom-bottom-td acenter" width="9.79%">Qstatistic<p style="text-align:center"></p></td> 
      <td class="custom-bottom-td acenter" width="18.52%">P-heterogeneity<p style="text-align:center"></p></td> 
      <td class="custom-bottom-td acenter" width="10.64%">Egger intercept<p style="text-align:center"></p></td> 
      <td class="custom-bottom-td acenter" width="13.96%">P-intercept<p style="text-align:center"></p></td> 
     </tr> 
     <tr> 
      <td rowspan="5" class="custom-top-td acenter" width="11.63%">AAA<p style="text-align:center"></p></td> 
      <td class="custom-top-td acenter" width="13.37%">IVW<p style="text-align:center"></p></td> 
      <td class="custom-top-td acenter" width="14.64%">1.47<p style="text-align:center"></p>(1.16 – 1.86)<p style="text-align:center"></p></td> 
      <td class="custom-top-td acenter" width="7.44%">0.001<p style="text-align:center"></p></td> 
      <td class="custom-top-td acenter" width="9.79%">48.526<p style="text-align:center"></p></td> 
      <td class="custom-top-td acenter" width="18.52%">0.980<p style="text-align:center"></p></td> 
      <td class="custom-top-td acenter" width="10.64%"><p style="text-align:center"></p></td> 
      <td class="custom-top-td acenter" width="13.96%"><p style="text-align:center"></p></td> 
     </tr> 
     <tr> 
      <td class="acenter" width="13.37%">MR-Egger<p style="text-align:center"></p></td> 
      <td class="acenter" width="14.64%">2.15<p style="text-align:center"></p>(1.21 - 3.80)<p style="text-align:center"></p></td> 
      <td class="acenter" width="7.44%">0.010<p style="text-align:center"></p></td> 
      <td class="acenter" width="9.79%">46.485<p style="text-align:center"></p></td> 
      <td class="acenter" width="18.52%">0.986<p style="text-align:center"></p></td> 
      <td class="acenter" width="10.64%">−0.033<p style="text-align:center"></p></td> 
      <td class="acenter" width="13.96%">0.16<p style="text-align:center"></p></td> 
     </tr> 
     <tr> 
      <td class="acenter" width="13.37%">Weighted median<p style="text-align:center"></p></td> 
      <td class="acenter" width="14.64%">1.38<p style="text-align:center"></p>(0.95 - 2.00)<p style="text-align:center"></p></td> 
      <td class="acenter" width="7.44%">0.089<p style="text-align:center"></p></td> 
      <td class="acenter" width="9.79%"><p style="text-align:center"></p></td> 
      <td class="acenter" width="18.52%"><p style="text-align:center"></p></td> 
      <td class="acenter" width="10.64%"><p style="text-align:center"></p></td> 
      <td class="acenter" width="13.96%"><p style="text-align:center"></p></td> 
     </tr> 
     <tr> 
      <td class="acenter" width="13.37%">Simple mode<p style="text-align:center"></p></td> 
      <td class="acenter" width="14.64%">1.08<p style="text-align:center"></p>(0.44 - 2.61)<p style="text-align:center"></p></td> 
      <td class="acenter" width="7.44%">0.860<p style="text-align:center"></p><p style="text-align:center"></p></td> 
      <td class="acenter" width="9.79%"><p style="text-align:center"></p></td> 
      <td class="acenter" width="18.52%"><p style="text-align:center"></p></td> 
      <td class="acenter" width="10.64%"><p style="text-align:center"></p></td> 
      <td class="acenter" width="13.96%"><p style="text-align:center"></p></td> 
     </tr> 
     <tr> 
      <td class="acenter" width="13.37%">Weighted mode<p style="text-align:center"></p></td> 
      <td class="acenter" width="14.64%">0.99<p style="text-align:center"></p>(0.47 - 2.08)<p style="text-align:center"></p></td> 
      <td class="acenter" width="7.44%">0.985<p style="text-align:center"></p></td> 
      <td class="acenter" width="9.79%"><p style="text-align:center"></p></td> 
      <td class="acenter" width="18.52%"><p style="text-align:center"></p></td> 
      <td class="acenter" width="10.64%"><p style="text-align:center"></p></td> 
      <td class="acenter" width="13.96%"><p style="text-align:center"></p></td> 
     </tr> 
    </table>
   </table-wrap>
   <p>However, the results indicate no significant genetic correlation between the risk of AAA and an increased risk of COPD (OR: 1.00, 95% CI: 0.96 - 1.04, p = 0.808). The consistency of results across different statistical models suggests that AAA does not significantly increase the risk of COPD, further validating the reliability of our conclusions (<xref ref-type="table" rid="table2">
     Table 2
    </xref>).</p>
   <table-wrap id="table2">
    <label>
     <xref ref-type="table" rid="table2">
      Table 2
     </xref></label>
    <caption>
     <title>
      <xref ref-type="bibr" rid="scirp.135470-"></xref>Table 2. MR estimates of the effect of AAA on COPD.</title>
    </caption>
    <table class="MsoTableGrid custom-table" border="0" cellspacing="0" cellpadding="0"> 
     <tr> 
      <td class="custom-bottom-td acenter" width="11.54%">Outcome<p style="text-align:center"></p></td> 
      <td class="custom-bottom-td acenter" width="12.99%">method<p style="text-align:center"></p></td> 
      <td class="custom-bottom-td acenter" width="15.10%">OR(95% CI)<p style="text-align:center"></p></td> 
      <td class="custom-bottom-td acenter" width="7.24%">p<p style="text-align:center"></p></td> 
      <td class="custom-bottom-td acenter" width="9.79%">Qstatistic<p style="text-align:center"></p></td> 
      <td class="custom-bottom-td acenter" width="18.31%">P-heterogeneity<p style="text-align:center"></p></td> 
      <td class="custom-bottom-td acenter" width="11.28%">Egger intercept<p style="text-align:center"></p></td> 
      <td class="custom-bottom-td acenter" width="13.75%">P-intercept<p style="text-align:center"></p></td> 
     </tr> 
     <tr> 
      <td rowspan="5" class="custom-top-td acenter" width="11.54%">COPD<p style="text-align:center"></p></td> 
      <td class="custom-top-td acenter" width="12.99%">IVW<p style="text-align:center"></p></td> 
      <td class="custom-top-td acenter" width="15.10%">1.00<p style="text-align:center"></p>(0.96 - 1.04)<p style="text-align:center"></p></td> 
      <td class="custom-top-td acenter" width="7.24%">0.808<p style="text-align:center"></p></td> 
      <td class="custom-top-td acenter" width="9.79%">23.821<p style="text-align:center"></p></td> 
      <td class="custom-top-td acenter" width="18.31%">0.003<p style="text-align:center"></p></td> 
      <td class="custom-top-td acenter" width="11.28%"><p style="text-align:center"></p></td> 
      <td class="custom-top-td acenter" width="13.75%"><p style="text-align:center"></p></td> 
     </tr> 
     <tr> 
      <td class="acenter" width="12.99%">MR-Egger<p style="text-align:center"></p></td> 
      <td class="acenter" width="15.10%">0.97<p style="text-align:center"></p>(0.91 - 1.05)<p style="text-align:center"></p></td> 
      <td class="acenter" width="7.24%">0.609<p style="text-align:center"></p></td> 
      <td class="acenter" width="9.79%">21.927<p style="text-align:center"></p></td> 
      <td class="acenter" width="18.31%">0.002<p style="text-align:center"></p></td> 
      <td class="acenter" width="11.28%">0.015<p style="text-align:center"></p></td> 
      <td class="acenter" width="13.75%">0.462<p style="text-align:center"></p></td> 
     </tr> 
     <tr> 
      <td class="acenter" width="12.99%">Weighted median<p style="text-align:center"></p></td> 
      <td class="acenter" width="15.10%">1.00<p style="text-align:center"></p>(0.96 - 1.03)<p style="text-align:center"></p></td> 
      <td class="acenter" width="7.24%">0.997<p style="text-align:center"></p></td> 
      <td class="acenter" width="9.79%"><p style="text-align:center"></p></td> 
      <td class="acenter" width="18.31%"><p style="text-align:center"></p></td> 
      <td class="acenter" width="11.28%"><p style="text-align:center"></p></td> 
      <td class="acenter" width="13.75%"><p style="text-align:center"></p></td> 
     </tr> 
     <tr> 
      <td class="acenter" width="12.99%">Simple mode<p style="text-align:center"></p></td> 
      <td class="acenter" width="15.10%">0.98<p style="text-align:center"></p>(0.93 - 1.04)<p style="text-align:center"></p></td> 
      <td class="acenter" width="7.24%">0.708<p style="text-align:center"></p></td> 
      <td class="acenter" width="9.79%"><p style="text-align:center"></p></td> 
      <td class="acenter" width="18.31%"><p style="text-align:center"></p></td> 
      <td class="acenter" width="11.28%"><p style="text-align:center"></p></td> 
      <td class="acenter" width="13.75%"><p style="text-align:center"></p></td> 
     </tr> 
     <tr> 
      <td class="acenter" width="12.99%">Weighted mode<p style="text-align:center"></p></td> 
      <td class="acenter" width="15.10%">1.00<p style="text-align:center"></p>(0.95 - 1.04)<p style="text-align:center"></p></td> 
      <td class="acenter" width="7.24%">0.995<p style="text-align:center"></p></td> 
      <td class="acenter" width="9.79%"><p style="text-align:center"></p></td> 
      <td class="acenter" width="18.31%"><p style="text-align:center"></p></td> 
      <td class="acenter" width="11.28%"><p style="text-align:center"></p></td> 
      <td class="acenter" width="13.75%"><p style="text-align:center"></p></td> 
     </tr> 
    </table>
   </table-wrap>
   <p>Among the 1400 analyzed circulating metabolites, plasma-free proline levels were significantly associated with both COPD and AAA (<xref ref-type="fig" rid="fig1">
     Figure 1
    </xref>). The mediation effect of COPD on AAA through plasma-free proline levels was significant, with a mediation proportion of 4.6% (95% CI: −9.032%, −0.164%, p = 0.042), suggesting that COPD may influence the development of AAA through this pathway (<xref ref-type="table" rid="table3">
     Table 3
    </xref>).</p>
   <fig id="fig1" position="float">
    <label>Figure 1</label>
    <caption>
     <title>Figure 1. The potential causal evidence summarized from the MR analysis of Plasma-free proline levels.</title>
    </caption>
    <graphic mimetype="image" position="float" xlink:type="simple" xlink:href="https://html.scirp.org/file/2152719-rId17.jpeg?20240823032402" />
   </fig>
   <table-wrap id="table3">
    <label>
     <xref ref-type="table" rid="table3">
      Table 3
     </xref></label>
    <caption>
     <title>
      <xref ref-type="bibr" rid="scirp.135470-"></xref>Table 3. Mediated effect of COPD on AAA.</title>
    </caption>
    <table class="MsoTableGrid custom-table" border="0" cellspacing="0" cellpadding="0"> 
     <tr> 
      <td class="custom-bottom-td acenter" width="14.10%">Exposure<p style="text-align:center"></p></td> 
      <td class="custom-bottom-td acenter" width="17.88%">Metabolite<p style="text-align:center"></p></td> 
      <td class="custom-bottom-td acenter" width="19.15%">outcome<p style="text-align:center"></p></td> 
      <td class="custom-bottom-td acenter" width="25.17%">Mediated effect<p style="text-align:center"></p></td> 
      <td class="custom-bottom-td acenter" width="23.71%">Mediatedproportion<p style="text-align:center"></p></td> 
     </tr> 
     <tr> 
      <td class="custom-top-td acenter" width="14.10%">COPD<p style="text-align:center"></p></td> 
      <td class="custom-top-td acenter" width="17.88%">Plasma-freeprolinelevels<p style="text-align:center"></p></td> 
      <td class="custom-top-td acenter" width="19.15%">Abdominalaorticaneurysm<p style="text-align:center"></p></td> 
      <td class="custom-top-td acenter" width="25.17%">−0.018(−0.035, −6.4E−04)<p style="text-align:center"></p></td> 
      <td class="custom-top-td acenter" width="23.71%">−4.6%(−9.032%, −0.164%)<p style="text-align:center"></p></td> 
     </tr> 
    </table>
   </table-wrap>
   <p>About 302 protein-coding risk genes within the genome were identified for COPD (136 upregulated and 174 downregulated) and 212 for AAA (126 upregulated and 86 downregulated). The overlapping genes were intersected, and Venn diagrams were created to illustrate these findings (<xref ref-type="fig" rid="fig2">
     Figure 2
    </xref> and <xref ref-type="fig" rid="fig3">
     Figure 3
    </xref>). We assessed the gene overlap between the gene sets of COPD and AAA, eight genes were found to overlap between COPD and AAA, including TCL1A, PRKD2, BCL11A, ATP13A4, COPB1, ADARB1, LINC00243, and PARP8. The forestploter R package was used to visualize the results, producing forest plots (<xref ref-type="fig" rid="fig4">
     Figure 4
    </xref> and <xref ref-type="fig" rid="fig5">
     Figure 5
    </xref>).</p>
   <p>PPI analysis identified 20 functionally interrelated genes that mediate the connection between COPD and AAA (<xref ref-type="fig" rid="fig6">
     Figure 6
    </xref>). In the GO enrichment analysis, these shared genes showed functional enrichment in “peptidyl-serine phosphorylation” (<xref ref-type="fig" rid="fig7">
     Figure 7
    </xref>). In the KEGG enrichment analysis, these shared genes demonstrated functional enrichment in pathways related to “aldosterone synthesis and secretion” (<xref ref-type="fig" rid="fig8">
     Figure 8
    </xref>).</p>
   <fig id="fig2" position="float">
    <label>Figure 2</label>
    <caption>
     <title>Figure 2. Venn diagram of up gene between COPD and AAA.</title>
    </caption>
    <graphic mimetype="image" position="float" xlink:type="simple" xlink:href="https://html.scirp.org/file/2152719-rId18.jpeg?20240823032402" />
   </fig>
   <fig id="fig3" position="float">
    <label>Figure 3</label>
    <caption>
     <title>Figure 3. Venn diagram of down gene between COPD and AAA.</title>
    </caption>
    <graphic mimetype="image" position="float" xlink:type="simple" xlink:href="https://html.scirp.org/file/2152719-rId19.jpeg?20240823032402" />
   </fig>
   <fig id="fig4" position="float">
    <label>Figure 4</label>
    <caption>
     <title>Figure 4. The causal effects of the risk gene on COPD.</title>
    </caption>
    <graphic mimetype="image" position="float" xlink:type="simple" xlink:href="https://html.scirp.org/file/2152719-rId20.jpeg?20240823032401" />
   </fig>
   <fig id="fig5" position="float">
    <label>Figure 5</label>
    <caption>
     <title>Figure 5. The causal effects of the risk gene on AAA.</title>
    </caption>
    <graphic mimetype="image" position="float" xlink:type="simple" xlink:href="https://html.scirp.org/file/2152719-rId21.jpeg?20240823032401" />
   </fig>
   <fig id="fig6" position="float">
    <label>Figure 6</label>
    <caption>
     <title>Figure 6. Protein-protein interactions among the risk genes shared between COPD and AAA.</title>
    </caption>
    <graphic mimetype="image" position="float" xlink:type="simple" xlink:href="https://html.scirp.org/file/2152719-rId22.jpeg?20240823032401" />
   </fig>
   <fig id="fig7" position="float">
    <label>Figure 7</label>
    <caption>
     <title>Figure 7. GO among the risk genes shared between COPD and AAA.</title>
    </caption>
    <graphic mimetype="image" position="float" xlink:type="simple" xlink:href="https://html.scirp.org/file/2152719-rId23.jpeg?20240823032402" />
   </fig>
   <fig id="fig8" position="float">
    <label>Figure 8</label>
    <caption>
     <title>Figure 8. KEGG among the risk genes shared between COPD and AAA.</title>
    </caption>
    <graphic mimetype="image" position="float" xlink:type="simple" xlink:href="https://html.scirp.org/file/2152719-rId24.jpeg?20240823032402" />
   </fig>
  </sec><sec id="s4">
   <title>4. Discussion</title>
   <p>This study elucidated the causal relationship between COPD and AAA through bidirectional Mendelian Randomization. We support a causal association between COPD and an increased risk of AAA. However, AAA does not significantly increase the risk of COPD. Moreover, our analysis indicates the effect of COPD on AAA through a reduction in plasma free proline levels, unveiling a potential mechanistic pathway linking the two conditions. Additionally, our study identified 20 functionally interrelated genes that mediate the link between COPD and AAA, demonstrating functional enrichment in pathways related to aldosterone synthesis and secretion.</p>
   <p>Consistent with previous epidemiological studies, such as that by Sakamaki, F. et al. <xref ref-type="bibr" rid="scirp.135470-27">
     [27]
    </xref>, which reported an increased risk of AAA among patients with COPD. Furthermore, research by Lindholt, J. et al. <xref ref-type="bibr" rid="scirp.135470-28">
     [28]
    </xref> also indicated a higher incidence of AAA among COPD patients compared to non-COPD patients. Recent studies suggest an association between proline levels and vascular structural changes <xref ref-type="bibr" rid="scirp.135470-29">
     [29]
    </xref> <xref ref-type="bibr" rid="scirp.135470-30">
     [30]
    </xref>, indicating that plasma-free proline might increase the risk of AAA through alterations in inflammatory pathways or vascular structure. Furthermore, Liu et al. found that mineralocorticoid receptor agonists combined with a high salt diet induced the formation and rupture of abdominal and thoracic aortic aneurysms in mice. The use of mineralocorticoid receptor antagonists, such as spironolactone or eplerenone, significantly reduced these factors-induced aortic aneurysms, suggesting a potential role for aldosterone in the pathogenesis of aortic aneurysms <xref ref-type="bibr" rid="scirp.135470-31">
     [31]
    </xref>. However, direct studies on the effects of proline on aldosterone synthesis and secretion are lacking.</p>
   <p>In the bidirectional Mendelian Randomization approach, this study provides novel evidence for the potential causal relationship between COPD and AAA. Moreover, we identified plasma-free proline as a potential mediated factor linking COPD with AAA. These findings offer new insights into the potential mechanisms underlying the association between these two diseases, crucial for understanding the complex interplay between chronic respiratory diseases and cardiovascular conditions. By elucidating the involved mechanistic pathways, clinicians can better identify high-risk patients and implement intervention measures to reduce the risk of AAA in this population.</p>
   <p>However, this study has some limitations: Firstly, our analysis relies on GWAS data from European populations, which may limit the generalizability of our findings to other racial groups. Secondly, MR studies have inherent limitations, such as potential interactions between genes and environment that may affect the accuracy of causal inference, and measurement errors in genotype data that could impact result precision. Thirdly, If a genetic variant affects multiple traits (i.e., pleiotropy), and some of these traits are associated with the exposure and outcome, it can introduce bias. Future research should consider replicating this study in diverse racial and ethnic groups to assess the universality and applicability of these results. Additionally, experimental studies are recommended to explore the exact role of proline metabolism in the progression from COPD to AAA.</p>
  </sec><sec id="s5">
   <title>5. Conclusion</title>
   <p>In conclusion, this study supports a causal relationship between COPD and an increased risk of AAA. Specifically, our findings highlight the key role of plasma-free proline and pathways related to aldosterone synthesis and secretion in the link between COPD and AAA. This discovery emphasizes the importance of managing AAA risk among COPD patients and points to plasma-free proline as a potential biomarker, offering new perspectives for future prevention and treatment strategies.</p>
  </sec><sec id="s6">
   <title>Acknowledgements</title>
   <p>The authors thank the Genetic Investigation of the MRC IEU Open GWAS Projec.</p>
  </sec><sec id="s7">
   <title>Data Availability Statement</title>
   <p>Please contact the corresponding author for additional inquiries.</p>
  </sec><sec id="s8">
   <title>Ethics Statement</title>
   <p>Because the GWAS data are accessible to the general public, ethical approval was not necessary.</p>
  </sec><sec id="s9">
   <title>Consent for Publication</title>
   <p>All authors approved the submitted version.</p>
  </sec>
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