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<front>
<journal-meta>
<journal-id journal-id-type="publisher-id">Front. Immunol.</journal-id>
<journal-title>Frontiers in Immunology</journal-title>
<abbrev-journal-title abbrev-type="pubmed">Front. Immunol.</abbrev-journal-title>
<issn pub-type="epub">1664-3224</issn>
<publisher>
<publisher-name>Frontiers Media S.A.</publisher-name>
</publisher>
</journal-meta>
<article-meta>
<article-id pub-id-type="doi">10.3389/fimmu.2024.1406234</article-id>
<article-categories>
<subj-group subj-group-type="heading">
<subject>Immunology</subject>
<subj-group>
<subject>Original Research</subject>
</subj-group>
</subj-group>
</article-categories>
<title-group>
<article-title>Dissecting causal relationships between immune cells, plasma metabolites, and COPD: a mediating Mendelian randomization study</article-title>
</title-group>
<contrib-group>
<contrib contrib-type="author">
<name>
<surname>Cao</surname>
<given-names>Zhenghua</given-names>
</name>
<xref ref-type="aff" rid="aff1">
<sup>1</sup>
</xref>
<uri xlink:href="https://loop.frontiersin.org/people/2640351"/>
<role content-type="https://credit.niso.org/contributor-roles/writing-review-editing/"/>
<role content-type="https://credit.niso.org/contributor-roles/writing-original-draft/"/>
<role content-type="https://credit.niso.org/contributor-roles/conceptualization/"/>
</contrib>
<contrib contrib-type="author">
<name>
<surname>Wu</surname>
<given-names>Tong</given-names>
</name>
<xref ref-type="aff" rid="aff1">
<sup>1</sup>
</xref>
<role content-type="https://credit.niso.org/contributor-roles/writing-original-draft/"/>
<role content-type="https://credit.niso.org/contributor-roles/data-curation/"/>
</contrib>
<contrib contrib-type="author">
<name>
<surname>Fang</surname>
<given-names>Yakun</given-names>
</name>
<xref ref-type="aff" rid="aff2">
<sup>2</sup>
</xref>
<role content-type="https://credit.niso.org/contributor-roles/writing-original-draft/"/>
<role content-type="https://credit.niso.org/contributor-roles/data-curation/"/>
</contrib>
<contrib contrib-type="author">
<name>
<surname>Sun</surname>
<given-names>Feng</given-names>
</name>
<xref ref-type="aff" rid="aff2">
<sup>2</sup>
</xref>
<role content-type="https://credit.niso.org/contributor-roles/writing-original-draft/"/>
<role content-type="https://credit.niso.org/contributor-roles/supervision/"/>
<role content-type="https://credit.niso.org/contributor-roles/data-curation/"/>
</contrib>
<contrib contrib-type="author">
<name>
<surname>Ding</surname>
<given-names>Huan</given-names>
</name>
<xref ref-type="aff" rid="aff2">
<sup>2</sup>
</xref>
<role content-type="https://credit.niso.org/contributor-roles/writing-original-draft/"/>
<role content-type="https://credit.niso.org/contributor-roles/data-curation/"/>
</contrib>
<contrib contrib-type="author">
<name>
<surname>Zhao</surname>
<given-names>Lingling</given-names>
</name>
<xref ref-type="aff" rid="aff1">
<sup>1</sup>
</xref>
<role content-type="https://credit.niso.org/contributor-roles/writing-original-draft/"/>
<role content-type="https://credit.niso.org/contributor-roles/data-curation/"/>
</contrib>
<contrib contrib-type="author" corresp="yes">
<name>
<surname>Shi</surname>
<given-names>Li</given-names>
</name>
<xref ref-type="aff" rid="aff2">
<sup>2</sup>
</xref>
<xref ref-type="author-notes" rid="fn001">
<sup>*</sup>
</xref>
<uri xlink:href="https://loop.frontiersin.org/people/1449774"/>
<role content-type="https://credit.niso.org/contributor-roles/writing-review-editing/"/>
<role content-type="https://credit.niso.org/contributor-roles/writing-original-draft/"/>
<role content-type="https://credit.niso.org/contributor-roles/conceptualization/"/>
</contrib>
</contrib-group>
<aff id="aff1">
<sup>1</sup>
<institution>Graduate School, Changchun University of Traditional Chinese Medicine</institution>, <addr-line>Changchun, Jilin</addr-line>, <country>China</country>
</aff>
<aff id="aff2">
<sup>2</sup>
<institution>Respiratory Disease Department, Affiliated Hospital of Changchun University of Traditional Chinese Medicine</institution>, <addr-line>Changchun, Jilin</addr-line>, <country>China</country>
</aff>
<author-notes>
<fn fn-type="edited-by">
<p>Edited by: Simon D. Pouwels, University Medical Center Groningen, Netherlands</p>
</fn>
<fn fn-type="edited-by">
<p>Reviewed by: Saima Rehman, University of Technology Sydney, Australia</p>
<p>Sheng Yang, Nanjing Medical University, China</p>
</fn>
<fn fn-type="corresp" id="fn001">
<p>*Correspondence: Li Shi, <email xlink:href="mailto:shili0648@163.com">shili0648@163.com</email>
</p>
</fn>
</author-notes>
<pub-date pub-type="epub">
<day>28</day>
<month>05</month>
<year>2024</year>
</pub-date>
<pub-date pub-type="collection">
<year>2024</year>
</pub-date>
<volume>15</volume>
<elocation-id>1406234</elocation-id>
<history>
<date date-type="received">
<day>24</day>
<month>03</month>
<year>2024</year>
</date>
<date date-type="accepted">
<day>15</day>
<month>05</month>
<year>2024</year>
</date>
</history>
<permissions>
<copyright-statement>Copyright &#xa9; 2024 Cao, Wu, Fang, Sun, Ding, Zhao and Shi</copyright-statement>
<copyright-year>2024</copyright-year>
<copyright-holder>Cao, Wu, Fang, Sun, Ding, Zhao and Shi</copyright-holder>
<license xlink:href="http://creativecommons.org/licenses/by/4.0/">
<p>This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.</p>
</license>
</permissions>
<abstract>
<sec>
<title>Objective</title>
<p>This study employed Mendelian Randomization (MR) to investigate the causal relationships among immune cells, COPD, and potential metabolic mediators.</p>
</sec>
<sec>
<title>Methods</title>
<p>Utilizing summary data from genome-wide association studies, we analyzed 731 immune cell phenotypes, 1,400 plasma metabolites, and COPD. Bidirectional MR analysis was conducted to explore the causal links between immune cells and COPD, complemented by two-step mediation analysis and multivariable MR to identify potential mediating metabolites.</p>
</sec>
<sec>
<title>Results</title>
<p>Causal relationships were identified between 41 immune cell phenotypes and COPD, with 6 exhibiting reverse causality. Additionally, 21 metabolites were causally related to COPD. Through two-step MR and multivariable MR analyses, 8 cell phenotypes were found to have causal relationships with COPD mediated by 8 plasma metabolites (including one unidentified), with 1-methylnicotinamide levels showing the highest mediation proportion at 26.4%.</p>
</sec>
<sec>
<title>Conclusion</title>
<p>We have identified causal relationships between 8 immune cell phenotypes and COPD, mediated by 8 metabolites. These findings contribute to the screening of individuals at high risk for COPD and offer insights into early prevention and the precocious diagnosis of Pre-COPD.</p>
</sec>
</abstract>
<kwd-group>
<kwd>Mendelian randomization</kwd>
<kwd>immune cells</kwd>
<kwd>plasma metabolites</kwd>
<kwd>COPD</kwd>
<kwd>mediation analysis</kwd>
</kwd-group>
<contract-num rid="cn001">YDZJ202201ZYTS236, 20180101115JC, &#x90ae;&#x7f16;: 20230203189SF</contract-num>
<contract-sponsor id="cn001">Natural Science Foundation of Jilin Province<named-content content-type="fundref-id">10.13039/100007847</named-content>
</contract-sponsor>
<counts>
<fig-count count="5"/>
<table-count count="4"/>
<equation-count count="0"/>
<ref-count count="85"/>
<page-count count="12"/>
<word-count count="5110"/>
</counts>
<custom-meta-wrap>
<custom-meta>
<meta-name>section-in-acceptance</meta-name>
<meta-value>Systems Immunology</meta-value>
</custom-meta>
</custom-meta-wrap>
</article-meta>
</front>
<body>
<sec id="s1" sec-type="intro">
<label>1</label>
<title>Introduction</title>
<p>Chronic Obstructive Pulmonary Disease (COPD) is a heterogeneous disorder primarily characterized by airway pathologies (bronchitis, bronchiolitis) and/or alveolar abnormalities (emphysema), leading to chronic respiratory symptoms (dyspnea, cough, sputum production) and progressively worsening airflow limitation (<xref ref-type="bibr" rid="B1">1</xref>). Globally, COPD accounts for more than half of all chronic respiratory disease cases (<xref ref-type="bibr" rid="B2">2</xref>), gradually becoming the third leading cause of death worldwide (<xref ref-type="bibr" rid="B3">3</xref>). With the increasing prevalence of an aging population, both the incidence and mortality rates of COPD are on the rise annually (<xref ref-type="bibr" rid="B4">4</xref>), imposing a significant economic burden on society (<xref ref-type="bibr" rid="B5">5</xref>).</p>
<p>Immune cells possess multifaceted functions in maintaining homeostasis and facilitating repair after injury (<xref ref-type="bibr" rid="B6">6</xref>). The lungs may serve as a battleground for the interaction between various microbes and the host&#x2019;s innate and adaptive immune defenses (<xref ref-type="bibr" rid="B7">7</xref>). Consequently, the immune system could be a pivotal driving force in the pathogenesis of COPD, with immune responses being significantly associated with acute exacerbations of COPD (<xref ref-type="bibr" rid="B8">8</xref>). However, the detailed physiological mechanisms remain insufficiently explored (<xref ref-type="bibr" rid="B9">9</xref>). Most existing evidence, primarily from observational studies, indicates that compared to healthy controls, individuals with COPD have an increased presence of immune cells in lung tissue (<xref ref-type="bibr" rid="B10">10</xref>, <xref ref-type="bibr" rid="B11">11</xref>) and an upregulated immune cell response (<xref ref-type="bibr" rid="B12">12</xref>). Cells such as CD68+ myeloid antigen-presenting cells, CD4+ T cells, and CD8 T cells are found to proliferate in the lungs of patients with COPD, potentially leading to persistent inflammation (<xref ref-type="bibr" rid="B13">13</xref>, <xref ref-type="bibr" rid="B14">14</xref>). Certain immune cells exhibit a negative correlation with the frequency of COPD exacerbations (<xref ref-type="bibr" rid="B15">15</xref>), such as CD4 T cells and resting natural killer cells (<xref ref-type="bibr" rid="B16">16</xref>). Consequently, the causal relationship and underlying mechanisms between immune cells and COPD remain unclear. Metabolites, as intermediates of metabolic reactions, can influence disease progression (<xref ref-type="bibr" rid="B17">17</xref>) and serve as targets for therapeutic intervention (<xref ref-type="bibr" rid="B18">18</xref>). They have the potential to improve the diagnosis and treatment of COPD (<xref ref-type="bibr" rid="B19">19</xref>) and may play a synergistic role in its pathogenesis (<xref ref-type="bibr" rid="B20">20</xref>), possibly mediating important immunoregulatory functions (<xref ref-type="bibr" rid="B21">21</xref>). Compared to healthy controls, COPD patients exhibit reduced levels of the metabolites 1-methylnicotinamide creatinine, and lactate (<xref ref-type="bibr" rid="B17">17</xref>). Glutamylphenylalanine may serve as a biomarker for acute exacerbations of COPD (<xref ref-type="bibr" rid="B22">22</xref>), while sphingolipids are associated with pulmonary function (<xref ref-type="bibr" rid="B23">23</xref>). Therefore, we hypothesize a causal relationship between immune cells, metabolites, and COPD. Elucidating these associations and understanding the true causal relationships among immune cells, metabolites, and COPD could aid in the early identification, prevention, and management of COPD.</p>
<p>Mendelian Randomization (MR) represents a potential method for causal inference, designed to estimate the causal effects of exposure factors on outcomes, with the capability to control for potential confounding factors and circumvent reverse causation biases (<xref ref-type="bibr" rid="B24">24</xref>). Utilizing the methodology of MR, we are poised to conduct a bidirectional MR study concerning immune cells and COPD, concurrently undertaking two mediating analyses to dissect the causal relationships among immune cells, metabolites, and COPD.</p>
</sec>
<sec id="s2">
<label>2</label>
<title>Methods</title>
<sec id="s2_1">
<label>2.1</label>
<title>Study design</title>
<p>Grounded in two-sample Mendelian Randomization, our study initially assessed the causal relationship between 731 immune cell phenotypes across 7 panels (B cell, cDC, TBNK, Treg, Myeloid cell, Maturation stages of T cell and Monocyte) and COPD. Proceeding with COPD as the exposure factor and employing the Inverse Variance Weighted (IVW) method to select immune cells as the outcome factor, we conducted reverse Mendelian Randomization to ascertain the presence of a reverse causal relationship. Utilizing both the two-step MR (TSMR) and multivariable MR approaches, with 1400 plasma metabolites serving as mediating factors, we aimed to elucidate the significant mediatory role that plasma metabolites may play in the causal pathway between immune cells and COPD (<xref ref-type="fig" rid="f1">
<bold>Figure&#xa0;1</bold>
</xref>).</p>
<fig id="f1" position="float">
<label>Figure&#xa0;1</label>
<caption>
<p>Research ideas.</p>
</caption>
<graphic mimetype="image" mime-subtype="tiff" xlink:href="fimmu-15-1406234-g001.tif"/>
</fig>
</sec>
<sec id="s2_2">
<label>2.2</label>
<title>Data sources</title>
<p>The genetic information pertinent to COPD was sourced from the GWAS database (<ext-link ext-link-type="uri" xlink:href="https://gwas.mrcieu.ac.uk/">https://gwas.mrcieu.ac.uk/</ext-link>), with the selected dataset bearing the identifier ebi-a-GCST90018807, encompassing 468,475 samples and 24,180,654 SNPs, all of which pertain to the European population. The genetic data related to 731 immune cell phenotypes were derived from a 2020 study (<xref ref-type="bibr" rid="B25">25</xref>), all pertaining to the European demographic, with the catalog identifiers ranging from ebi-a-GCST90001391 to ebi-a-GCST90002121. The GWAS data for 1,400 plasma metabolites, hailing from a 2023 study (<xref ref-type="bibr" rid="B18">18</xref>), are accessible from the GWAS database, with identifiers spanning from GCST90199621 to GCST90201020, all associated with the European population.</p>
</sec>
<sec id="s2_3">
<label>2.3</label>
<title>Instrumental variable selection</title>
<p>The selection of instrumental variables necessitates adherence to several assumptions (<xref ref-type="bibr" rid="B26">26</xref>), to fulfill their relevance (<xref ref-type="bibr" rid="B27">27</xref>), we conducted an association analysis on 731 immune cell phenotypes and 1,400 plasma metabolites, uniformly applying a threshold of P&lt;1&#xd7;10<sup>&#x2212;5</sup> (<xref ref-type="bibr" rid="B28">28</xref>, <xref ref-type="bibr" rid="B29">29</xref>). Subsequently, SNPs exhibiting linkage disequilibrium were filtered out using criteria of R^2&lt;0.001 and Kb=10,000 (<xref ref-type="bibr" rid="B30">30</xref>), followed by the calculation of the F-statistic for the selected SNPs to eliminate weak instrumental variables. An F-statistic greater than 10 is considered indicative of the absence of weak instrumental variables (<xref ref-type="bibr" rid="B31">31</xref>, <xref ref-type="bibr" rid="B32">32</xref>).</p>
</sec>
<sec id="s2_4">
<label>2.4</label>
<title>Statistical analysis</title>
<p>We employed five methodologies to assess causality: Inverse Variance Weighted (IVW), MR-Egger, Weighted Median, Simple Mode, and Weighted Mode methods, with IVW serving as the primary approach (<xref ref-type="bibr" rid="B33">33</xref>, <xref ref-type="bibr" rid="B34">34</xref>). P&lt;0.05 was indicative of a causal relationship (<xref ref-type="bibr" rid="B35">35</xref>), while the other four methods served as supplementary analyses (<xref ref-type="bibr" rid="B36">36</xref>). To evaluate the robustness of our results, we conducted sensitivity analysis using the &#x201c;leave-one-out&#x201d; approach, further examining pleiotropy and heterogeneity, P&gt;0.05 suggesting the absence of both (<xref ref-type="bibr" rid="B37">37</xref>, <xref ref-type="bibr" rid="B38">38</xref>). Utilizing the TSMR approach, we first calculated the total effect from immune cells to COPD, the effect of immune cells on metabolites (&#x3b2;1), and the effect of metabolites on COPD (&#x3b2;2), followed by the calculation of the mediating effect (&#x3b2;1*&#x3b2;2), with the direct effect being the total effect minus the mediating effect (<xref ref-type="bibr" rid="B39">39</xref>). All analyses were conducted using the R language (version 4.3.2), with the TwoSampleMR package at version 0.6.0.</p>
</sec>
</sec>
<sec id="s3" sec-type="results">
<label>3</label>
<title>Results</title>
<sec id="s3_1">
<label>3.1</label>
<title>Genetic causality between immune cells and COPD</title>
<p>Through the selection of quantitative tools, we conducted an associative analysis, eliminating linkage disequilibrium and weak instrumental variables, thereby identifying 13,318 SNPs associated with immune cells, with the smallest F-value being 19.53. Preliminary investigations via the Inverse Variance Weighted (IVW) method revealed 41 immune cell phenotypes correlated with COPD, including but not limited to IgD+ CD38br %B cell, CD19 on IgD- CD38br, CD19 on PB/PC, and CD24 on memory B cell within the B cell category; TCRgd %T cell, HLA DR+ T cell%T cell, NKT %lymphocyte, and HLA DR+ NK AC within the TBNK category; CCR2 on plasmacytoid DC, CCR2 on CD62L+ plasmacytoid DC, CD80 on granulocyte, and CD62L on monocyte within the cDC category; and CD28+ CD45RA- CD8br %T cell, CD45RA+ CD28- CD8br %CD8br, CD25hi %T cell, and CD25++ CD8br %CD8br within the Treg category. In our study, we conducted a reverse Mendelian randomization analysis with COPD as the exposure factor and 41 immune cell phenotypes as the outcome factors. Our findings revealed that COPD does not exhibit a reverse causal relationship with 35 of the immune cell phenotypes (r-Pvalue &gt; 0.05). However, a reverse causality was observed in six immune cell phenotypes (r-Pvalue &lt; 0.05), specifically CD14+ CD16+ monocyte AC, CD4+ CD8dim %lymphocyte, CD4+ CD8dim %leukocyte, CD3- lymphocyte AC, CD3 on EM CD8br, and CD45 on Im MDSC. Furthermore, 21 immune cell phenotypes demonstrated a negative correlation with COPD, while 20 showed a positive correlation. Concurrently, tests for pleiotropy and heterogeneity yielded results (P&gt; 0.05), with the direction of OR values being consistent, and leave-one-out sensitivity analysis confirmed the robustness of the MR findings (<xref ref-type="table" rid="T1">
<bold>Table&#xa0;1</bold>
</xref>, <xref ref-type="fig" rid="f2">
<bold>Figure&#xa0;2</bold>
</xref>).</p>
<table-wrap id="T1" position="float">
<label>Table&#xa0;1</label>
<caption>
<p>MR analysis of immune cells and COPD.</p>
</caption>
<table frame="hsides">
<thead>
<tr>
<th valign="middle" align="center">Exposure</th>
<th valign="middle" align="center">Method</th>
<th valign="middle" align="center">Nsnp</th>
<th valign="middle" align="center">Beta</th>
<th valign="middle" align="center">Se</th>
<th valign="middle" align="center">P-val</th>
<th valign="middle" align="center">Pleiotropy</th>
<th valign="middle" align="center">Heterogeneity</th>
</tr>
</thead>
<tbody>
<tr>
<td valign="middle" align="center">IgD+ CD38br %B cell</td>
<td valign="middle" align="center">IVW</td>
<td valign="middle" align="center">27</td>
<td valign="middle" align="center">0.037</td>
<td valign="middle" align="center">0.015</td>
<td valign="middle" align="center">0.014</td>
<td valign="middle" align="center">0.780</td>
<td valign="middle" align="center">0.789</td>
</tr>
<tr>
<td valign="middle" align="center">Myeloid DC AC</td>
<td valign="middle" align="center">IVW</td>
<td valign="middle" align="center">23</td>
<td valign="middle" align="center">0.037</td>
<td valign="middle" align="center">0.012</td>
<td valign="middle" align="center">0.001</td>
<td valign="middle" align="center">0.605</td>
<td valign="middle" align="center">0.733</td>
</tr>
<tr>
<td valign="middle" align="center">CD62L- myeloid DC AC</td>
<td valign="middle" align="center">IVW</td>
<td valign="middle" align="center">17</td>
<td valign="middle" align="center">0.043</td>
<td valign="middle" align="center">0.017</td>
<td valign="middle" align="center">0.013</td>
<td valign="middle" align="center">0.731</td>
<td valign="middle" align="center">0.363</td>
</tr>
<tr>
<td valign="middle" align="center">CD62L- CD86+ myeloid DC %DC</td>
<td valign="middle" align="center">IVW</td>
<td valign="middle" align="center">19</td>
<td valign="middle" align="center">0.023</td>
<td valign="middle" align="center">0.011</td>
<td valign="middle" align="center">0.042</td>
<td valign="middle" align="center">0.156</td>
<td valign="middle" align="center">0.762</td>
</tr>
<tr>
<td valign="middle" align="center">CD25hi %T cell</td>
<td valign="middle" align="center">IVW</td>
<td valign="middle" align="center">20</td>
<td valign="middle" align="center">-0.034</td>
<td valign="middle" align="center">0.016</td>
<td valign="middle" align="center">0.032</td>
<td valign="middle" align="center">0.901</td>
<td valign="middle" align="center">0.468</td>
</tr>
<tr>
<td valign="middle" align="center">CD33dim HLA DR+ CD11b- %CD33dim HLA DR+</td>
<td valign="middle" align="center">IVW</td>
<td valign="middle" align="center">25</td>
<td valign="middle" align="center">0.017</td>
<td valign="middle" align="center">0.007</td>
<td valign="middle" align="center">0.016</td>
<td valign="middle" align="center">0.487</td>
<td valign="middle" align="center">0.840</td>
</tr>
<tr>
<td valign="middle" align="center">CD14+ CD16+ monocyte AC</td>
<td valign="middle" align="center">IVW</td>
<td valign="middle" align="center">21</td>
<td valign="middle" align="center">-0.032</td>
<td valign="middle" align="center">0.015</td>
<td valign="middle" align="center">0.036</td>
<td valign="middle" align="center">0.843</td>
<td valign="middle" align="center">0.487</td>
</tr>
<tr>
<td valign="middle" align="center">CD4+ CD8dim %lymphocyte</td>
<td valign="middle" align="center">IVW</td>
<td valign="middle" align="center">22</td>
<td valign="middle" align="center">-0.058</td>
<td valign="middle" align="center">0.022</td>
<td valign="middle" align="center">0.007</td>
<td valign="middle" align="center">0.716</td>
<td valign="middle" align="center">0.101</td>
</tr>
<tr>
<td valign="middle" align="center">CD4+ CD8dim %leukocyte</td>
<td valign="middle" align="center">IVW</td>
<td valign="middle" align="center">17</td>
<td valign="middle" align="center">-0.059</td>
<td valign="middle" align="center">0.027</td>
<td valign="middle" align="center">0.033</td>
<td valign="middle" align="center">0.867</td>
<td valign="middle" align="center">0.053</td>
</tr>
<tr>
<td valign="middle" align="center">TCRgd %T cell</td>
<td valign="middle" align="center">IVW</td>
<td valign="middle" align="center">19</td>
<td valign="middle" align="center">-0.036</td>
<td valign="middle" align="center">0.015</td>
<td valign="middle" align="center">0.018</td>
<td valign="middle" align="center">0.363</td>
<td valign="middle" align="center">0.523</td>
</tr>
<tr>
<td valign="middle" align="center">HLA DR+ T cell%T cell</td>
<td valign="middle" align="center">IVW</td>
<td valign="middle" align="center">33</td>
<td valign="middle" align="center">-0.023</td>
<td valign="middle" align="center">0.010</td>
<td valign="middle" align="center">0.020</td>
<td valign="middle" align="center">0.348</td>
<td valign="middle" align="center">0.622</td>
</tr>
<tr>
<td valign="middle" align="center">NKT %lymphocyte</td>
<td valign="middle" align="center">IVW</td>
<td valign="middle" align="center">36</td>
<td valign="middle" align="center">0.041</td>
<td valign="middle" align="center">0.015</td>
<td valign="middle" align="center">0.006</td>
<td valign="middle" align="center">0.225</td>
<td valign="middle" align="center">0.180</td>
</tr>
<tr>
<td valign="middle" align="center">CD3- lymphocyte AC</td>
<td valign="middle" align="center">IVW</td>
<td valign="middle" align="center">18</td>
<td valign="middle" align="center">-0.057</td>
<td valign="middle" align="center">0.022</td>
<td valign="middle" align="center">0.009</td>
<td valign="middle" align="center">0.954</td>
<td valign="middle" align="center">0.947</td>
</tr>
<tr>
<td valign="middle" align="center">HLA DR+ NK AC</td>
<td valign="middle" align="center">IVW</td>
<td valign="middle" align="center">19</td>
<td valign="middle" align="center">-0.050</td>
<td valign="middle" align="center">0.019</td>
<td valign="middle" align="center">0.007</td>
<td valign="middle" align="center">0.648</td>
<td valign="middle" align="center">0.720</td>
</tr>
<tr>
<td valign="middle" align="center">CD25++ CD8br %CD8br</td>
<td valign="middle" align="center">IVW</td>
<td valign="middle" align="center">23</td>
<td valign="middle" align="center">0.042</td>
<td valign="middle" align="center">0.020</td>
<td valign="middle" align="center">0.039</td>
<td valign="middle" align="center">0.792</td>
<td valign="middle" align="center">0.523</td>
</tr>
<tr>
<td valign="middle" align="center">CD28+ CD45RA- CD8br %T cell</td>
<td valign="middle" align="center">IVW</td>
<td valign="middle" align="center">27</td>
<td valign="middle" align="center">0.014</td>
<td valign="middle" align="center">0.007</td>
<td valign="middle" align="center">0.045</td>
<td valign="middle" align="center">0.488</td>
<td valign="middle" align="center">0.355</td>
</tr>
<tr>
<td valign="middle" align="center">CD45RA+ CD28- CD8br %CD8br</td>
<td valign="middle" align="center">IVW</td>
<td valign="middle" align="center">35</td>
<td valign="middle" align="center">0.001</td>
<td valign="middle" align="center">0.000</td>
<td valign="middle" align="center">0.020</td>
<td valign="middle" align="center">0.937</td>
<td valign="middle" align="center">0.721</td>
</tr>
<tr>
<td valign="middle" align="center">CD19 on CD20- CD38-</td>
<td valign="middle" align="center">IVW</td>
<td valign="middle" align="center">20</td>
<td valign="middle" align="center">0.080</td>
<td valign="middle" align="center">0.022</td>
<td valign="middle" align="center">0.000</td>
<td valign="middle" align="center">0.587</td>
<td valign="middle" align="center">0.116</td>
</tr>
<tr>
<td valign="middle" align="center">CD19 on IgD- CD38br</td>
<td valign="middle" align="center">IVW</td>
<td valign="middle" align="center">17</td>
<td valign="middle" align="center">-0.046</td>
<td valign="middle" align="center">0.020</td>
<td valign="middle" align="center">0.019</td>
<td valign="middle" align="center">0.778</td>
<td valign="middle" align="center">0.452</td>
</tr>
<tr>
<td valign="middle" align="center">CD19 on PB/PC</td>
<td valign="middle" align="center">IVW</td>
<td valign="middle" align="center">24</td>
<td valign="middle" align="center">-0.062</td>
<td valign="middle" align="center">0.020</td>
<td valign="middle" align="center">0.002</td>
<td valign="middle" align="center">0.101</td>
<td valign="middle" align="center">0.258</td>
</tr>
<tr>
<td valign="middle" align="center">CD24 on memory B cell</td>
<td valign="middle" align="center">IVW</td>
<td valign="middle" align="center">32</td>
<td valign="middle" align="center">0.027</td>
<td valign="middle" align="center">0.012</td>
<td valign="middle" align="center">0.024</td>
<td valign="middle" align="center">0.270</td>
<td valign="middle" align="center">0.263</td>
</tr>
<tr>
<td valign="middle" align="center">CD25 on transitional</td>
<td valign="middle" align="center">IVW</td>
<td valign="middle" align="center">22</td>
<td valign="middle" align="center">0.033</td>
<td valign="middle" align="center">0.016</td>
<td valign="middle" align="center">0.042</td>
<td valign="middle" align="center">0.579</td>
<td valign="middle" align="center">0.927</td>
</tr>
<tr>
<td valign="middle" align="center">CD27 on CD24+ CD27+</td>
<td valign="middle" align="center">IVW</td>
<td valign="middle" align="center">31</td>
<td valign="middle" align="center">0.039</td>
<td valign="middle" align="center">0.012</td>
<td valign="middle" align="center">0.001</td>
<td valign="middle" align="center">0.975</td>
<td valign="middle" align="center">0.196</td>
</tr>
<tr>
<td valign="middle" align="center">CD27 on unsw mem</td>
<td valign="middle" align="center">IVW</td>
<td valign="middle" align="center">31</td>
<td valign="middle" align="center">0.036</td>
<td valign="middle" align="center">0.015</td>
<td valign="middle" align="center">0.014</td>
<td valign="middle" align="center">0.521</td>
<td valign="middle" align="center">0.340</td>
</tr>
<tr>
<td valign="middle" align="center">CD27 on sw mem</td>
<td valign="middle" align="center">IVW</td>
<td valign="middle" align="center">30</td>
<td valign="middle" align="center">0.032</td>
<td valign="middle" align="center">0.014</td>
<td valign="middle" align="center">0.017</td>
<td valign="middle" align="center">0.265</td>
<td valign="middle" align="center">0.306</td>
</tr>
<tr>
<td valign="middle" align="center">IgD on IgD+ CD24-</td>
<td valign="middle" align="center">IVW</td>
<td valign="middle" align="center">30</td>
<td valign="middle" align="center">-0.034</td>
<td valign="middle" align="center">0.015</td>
<td valign="middle" align="center">0.023</td>
<td valign="middle" align="center">0.810</td>
<td valign="middle" align="center">0.459</td>
</tr>
<tr>
<td valign="middle" align="center">CD62L on monocyte</td>
<td valign="middle" align="center">IVW</td>
<td valign="middle" align="center">25</td>
<td valign="middle" align="center">0.028</td>
<td valign="middle" align="center">0.013</td>
<td valign="middle" align="center">0.036</td>
<td valign="middle" align="center">0.238</td>
<td valign="middle" align="center">0.883</td>
</tr>
<tr>
<td valign="middle" align="center">CD62L on granulocyte</td>
<td valign="middle" align="center">IVW</td>
<td valign="middle" align="center">18</td>
<td valign="middle" align="center">-0.058</td>
<td valign="middle" align="center">0.020</td>
<td valign="middle" align="center">0.004</td>
<td valign="middle" align="center">0.230</td>
<td valign="middle" align="center">0.476</td>
</tr>
<tr>
<td valign="middle" align="center">CD3 on naive CD8br</td>
<td valign="middle" align="center">IVW</td>
<td valign="middle" align="center">25</td>
<td valign="middle" align="center">-0.035</td>
<td valign="middle" align="center">0.012</td>
<td valign="middle" align="center">0.004</td>
<td valign="middle" align="center">0.948</td>
<td valign="middle" align="center">0.972</td>
</tr>
<tr>
<td valign="middle" align="center">CD3 on EM CD8br</td>
<td valign="middle" align="center">IVW</td>
<td valign="middle" align="center">20</td>
<td valign="middle" align="center">-0.033</td>
<td valign="middle" align="center">0.015</td>
<td valign="middle" align="center">0.026</td>
<td valign="middle" align="center">0.953</td>
<td valign="middle" align="center">0.507</td>
</tr>
<tr>
<td valign="middle" align="center">CD3 on CM CD8br</td>
<td valign="middle" align="center">IVW</td>
<td valign="middle" align="center">19</td>
<td valign="middle" align="center">-0.046</td>
<td valign="middle" align="center">0.017</td>
<td valign="middle" align="center">0.008</td>
<td valign="middle" align="center">0.549</td>
<td valign="middle" align="center">0.956</td>
</tr>
<tr>
<td valign="middle" align="center">CD127 on CD28+ CD45RA- CD8br</td>
<td valign="middle" align="center">IVW</td>
<td valign="middle" align="center">19</td>
<td valign="middle" align="center">-0.030</td>
<td valign="middle" align="center">0.013</td>
<td valign="middle" align="center">0.021</td>
<td valign="middle" align="center">0.237</td>
<td valign="middle" align="center">0.618</td>
</tr>
<tr>
<td valign="middle" align="center">CD127 on CD28- CD8br</td>
<td valign="middle" align="center">IVW</td>
<td valign="middle" align="center">20</td>
<td valign="middle" align="center">-0.044</td>
<td valign="middle" align="center">0.022</td>
<td valign="middle" align="center">0.041</td>
<td valign="middle" align="center">0.246</td>
<td valign="middle" align="center">0.618</td>
</tr>
<tr>
<td valign="middle" align="center">HLA DR on monocyte</td>
<td valign="middle" align="center">IVW</td>
<td valign="middle" align="center">18</td>
<td valign="middle" align="center">-0.026</td>
<td valign="middle" align="center">0.013</td>
<td valign="middle" align="center">0.041</td>
<td valign="middle" align="center">0.558</td>
<td valign="middle" align="center">0.077</td>
</tr>
<tr>
<td valign="middle" align="center">CCR2 on plasmacytoid DC</td>
<td valign="middle" align="center">IVW</td>
<td valign="middle" align="center">19</td>
<td valign="middle" align="center">0.035</td>
<td valign="middle" align="center">0.014</td>
<td valign="middle" align="center">0.013</td>
<td valign="middle" align="center">0.765</td>
<td valign="middle" align="center">0.423</td>
</tr>
<tr>
<td valign="middle" align="center">CCR2 on CD62L+ plasmacytoid DC</td>
<td valign="middle" align="center">IVW</td>
<td valign="middle" align="center">19</td>
<td valign="middle" align="center">0.039</td>
<td valign="middle" align="center">0.014</td>
<td valign="middle" align="center">0.006</td>
<td valign="middle" align="center">0.447</td>
<td valign="middle" align="center">0.550</td>
</tr>
<tr>
<td valign="middle" align="center">CD80 on granulocyte</td>
<td valign="middle" align="center">IVW</td>
<td valign="middle" align="center">33</td>
<td valign="middle" align="center">-0.033</td>
<td valign="middle" align="center">0.012</td>
<td valign="middle" align="center">0.008</td>
<td valign="middle" align="center">0.110</td>
<td valign="middle" align="center">0.798</td>
</tr>
<tr>
<td valign="middle" align="center">CD45 on Im MDSC</td>
<td valign="middle" align="center">IVW</td>
<td valign="middle" align="center">11</td>
<td valign="middle" align="center">-0.036</td>
<td valign="middle" align="center">0.013</td>
<td valign="middle" align="center">0.007</td>
<td valign="middle" align="center">0.271</td>
<td valign="middle" align="center">0.391</td>
</tr>
<tr>
<td valign="middle" align="center">SSC-A on HLA DR+ T cell</td>
<td valign="middle" align="center">IVW</td>
<td valign="middle" align="center">23</td>
<td valign="middle" align="center">-0.044</td>
<td valign="middle" align="center">0.017</td>
<td valign="middle" align="center">0.009</td>
<td valign="middle" align="center">0.673</td>
<td valign="middle" align="center">0.511</td>
</tr>
<tr>
<td valign="middle" align="center">CD11b on CD66b++ myeloid cell</td>
<td valign="middle" align="center">IVW</td>
<td valign="middle" align="center">18</td>
<td valign="middle" align="center">0.032</td>
<td valign="middle" align="center">0.015</td>
<td valign="middle" align="center">0.027</td>
<td valign="middle" align="center">0.209</td>
<td valign="middle" align="center">0.540</td>
</tr>
<tr>
<td valign="middle" align="center">HLA DR on plasmacytoid DC</td>
<td valign="middle" align="center">IVW</td>
<td valign="middle" align="center">23</td>
<td valign="middle" align="center">0.019</td>
<td valign="middle" align="center">0.009</td>
<td valign="middle" align="center">0.024</td>
<td valign="middle" align="center">0.146</td>
<td valign="middle" align="center">0.339</td>
</tr>
</tbody>
</table>
</table-wrap>
<fig id="f2" position="float">
<label>Figure&#xa0;2</label>
<caption>
<p>Circle plot of five Mendelian randomized methods (P&lt;0.05) <bold>(A)</bold> Forest plot of causality between 35 immune cells and COPD (r-Pvalue is the result of reverse MR) <bold>(B)</bold> Forest plot of causality between six immune cells and COPD <bold>(C)</bold> Scatter plot of six immune cells reducing COPD risk <bold>(D)</bold>.</p>
</caption>
<graphic mimetype="image" mime-subtype="tiff" xlink:href="fimmu-15-1406234-g002.tif"/>
</fig>
</sec>
<sec id="s3_2">
<label>3.2</label>
<title>Genetic causality between metabolites and COPD</title>
<p>Through the selection of instrumental variables, we conducted an association analysis, eliminating linkage disequilibrium and weak instrumental variables, thereby identifying 29,302 SNPs associated with plasma metabolites, with the smallest F-statistic being 19.50. The IVW method preliminarily identified 21 plasma metabolites causally related to COPD, comprising 16 known metabolites and 5 unknown. Among the known metabolites, 7 were potentially associated with an increased risk of COPD, namely Stearidonate (18:4n3), Alpha-hydroxyisovalerate, Epiandrosterone sulfate, Cinnamoylglycine, 1-methylnicotinamide, the Arachidonate (20:4n6) to pyruvate ratio, and the Histidine to alanine ratio. Conversely, 9 metabolites were potentially inversely correlated with COPD risk, including 4-vinylphenol sulfate, 16a-hydroxy DHEA 3-sulfate, 1-palmitoyl-GPG (16:0), N-oleoylserine, Alpha-tocopherol, Taurochenodeoxycholate, the Adenosine 5&#x2019;-diphosphate (ADP) to fructose ratio, the Uridine to cytidine ratio, and the Cysteinylglycine to glutamate ratio (<xref ref-type="table" rid="T2">
<bold>Table&#xa0;2</bold>
</xref>, <xref ref-type="fig" rid="f3">
<bold>Figure&#xa0;3</bold>
</xref>).</p>
<table-wrap id="T2" position="float">
<label>Table&#xa0;2</label>
<caption>
<p>MR analysis of metabolites and COPD.</p>
</caption>
<table frame="hsides">
<thead>
<tr>
<th valign="middle" align="center">Exposure</th>
<th valign="middle" align="center">Method</th>
<th valign="middle" align="center">Nsnp</th>
<th valign="middle" align="center">Beta</th>
<th valign="middle" align="center">Se</th>
<th valign="middle" align="center">P-val</th>
<th valign="middle" align="center">Pleiotropy</th>
<th valign="middle" align="center">Heterogeneity</th>
</tr>
</thead>
<tbody>
<tr>
<td valign="middle" align="center">Stearidonate (18:4n3) levels</td>
<td valign="middle" align="center">IVW</td>
<td valign="middle" align="center">27</td>
<td valign="middle" align="center">0.097</td>
<td valign="middle" align="center">0.034</td>
<td valign="middle" align="center">0.004</td>
<td valign="middle" align="center">0.751</td>
<td valign="middle" align="center">0.304</td>
</tr>
<tr>
<td valign="middle" align="center">Alpha-hydroxyisovalerate levels</td>
<td valign="middle" align="center">IVW</td>
<td valign="middle" align="center">22</td>
<td valign="middle" align="center">0.106</td>
<td valign="middle" align="center">0.036</td>
<td valign="middle" align="center">0.003</td>
<td valign="middle" align="center">0.591</td>
<td valign="middle" align="center">0.055</td>
</tr>
<tr>
<td valign="middle" align="center">Epiandrosterone sulfate levels</td>
<td valign="middle" align="center">IVW</td>
<td valign="middle" align="center">23</td>
<td valign="middle" align="center">0.055</td>
<td valign="middle" align="center">0.019</td>
<td valign="middle" align="center">0.004</td>
<td valign="middle" align="center">0.650</td>
<td valign="middle" align="center">0.393</td>
</tr>
<tr>
<td valign="middle" align="center">4-vinylphenol sulfate levels</td>
<td valign="middle" align="center">IVW</td>
<td valign="middle" align="center">25</td>
<td valign="middle" align="center">-0.080</td>
<td valign="middle" align="center">0.027</td>
<td valign="middle" align="center">0.003</td>
<td valign="middle" align="center">0.692</td>
<td valign="middle" align="center">0.224</td>
</tr>
<tr>
<td valign="middle" align="center">16a-hydroxy DHEA 3-sulfate levels</td>
<td valign="middle" align="center">IVW</td>
<td valign="middle" align="center">24</td>
<td valign="middle" align="center">-0.061</td>
<td valign="middle" align="center">0.021</td>
<td valign="middle" align="center">0.005</td>
<td valign="middle" align="center">0.667</td>
<td valign="middle" align="center">0.407</td>
</tr>
<tr>
<td valign="middle" align="center">Cinnamoylglycine levels</td>
<td valign="middle" align="center">IVW</td>
<td valign="middle" align="center">33</td>
<td valign="middle" align="center">0.071</td>
<td valign="middle" align="center">0.025</td>
<td valign="middle" align="center">0.005</td>
<td valign="middle" align="center">0.946</td>
<td valign="middle" align="center">0.984</td>
</tr>
<tr>
<td valign="middle" align="center">1-palmitoyl-GPG (16:0) levels</td>
<td valign="middle" align="center">IVW</td>
<td valign="middle" align="center">24</td>
<td valign="middle" align="center">-0.078</td>
<td valign="middle" align="center">0.029</td>
<td valign="middle" align="center">0.008</td>
<td valign="middle" align="center">0.462</td>
<td valign="middle" align="center">0.583</td>
</tr>
<tr>
<td valign="middle" align="center">N-oleoylserine levels</td>
<td valign="middle" align="center">IVW</td>
<td valign="middle" align="center">20</td>
<td valign="middle" align="center">-0.075</td>
<td valign="middle" align="center">0.027</td>
<td valign="middle" align="center">0.006</td>
<td valign="middle" align="center">0.568</td>
<td valign="middle" align="center">0.984</td>
</tr>
<tr>
<td valign="middle" align="center">Alpha-tocopherol levels</td>
<td valign="middle" align="center">IVW</td>
<td valign="middle" align="center">28</td>
<td valign="middle" align="center">-0.086</td>
<td valign="middle" align="center">0.029</td>
<td valign="middle" align="center">0.003</td>
<td valign="middle" align="center">0.845</td>
<td valign="middle" align="center">0.656</td>
</tr>
<tr>
<td valign="middle" align="center">Taurochenodeoxycholate levels</td>
<td valign="middle" align="center">IVW</td>
<td valign="middle" align="center">18</td>
<td valign="middle" align="center">-0.124</td>
<td valign="middle" align="center">0.040</td>
<td valign="middle" align="center">0.002</td>
<td valign="middle" align="center">0.563</td>
<td valign="middle" align="center">0.521</td>
</tr>
<tr>
<td valign="middle" align="center">1-methylnicotinamide levels</td>
<td valign="middle" align="center">IVW</td>
<td valign="middle" align="center">18</td>
<td valign="middle" align="center">0.168</td>
<td valign="middle" align="center">0.039</td>
<td valign="middle" align="center">0.000</td>
<td valign="middle" align="center">0.486</td>
<td valign="middle" align="center">0.594</td>
</tr>
<tr>
<td valign="middle" align="center">X-12100 levels</td>
<td valign="middle" align="center">IVW</td>
<td valign="middle" align="center">21</td>
<td valign="middle" align="center">0.099</td>
<td valign="middle" align="center">0.038</td>
<td valign="middle" align="center">0.008</td>
<td valign="middle" align="center">0.869</td>
<td valign="middle" align="center">0.293</td>
</tr>
<tr>
<td valign="middle" align="center">X-19438 levels</td>
<td valign="middle" align="center">IVW</td>
<td valign="middle" align="center">24</td>
<td valign="middle" align="center">-0.081</td>
<td valign="middle" align="center">0.029</td>
<td valign="middle" align="center">0.005</td>
<td valign="middle" align="center">0.335</td>
<td valign="middle" align="center">0.861</td>
</tr>
<tr>
<td valign="middle" align="center">X-23654 levels</td>
<td valign="middle" align="center">IVW</td>
<td valign="middle" align="center">39</td>
<td valign="middle" align="center">0.062</td>
<td valign="middle" align="center">0.023</td>
<td valign="middle" align="center">0.006</td>
<td valign="middle" align="center">0.131</td>
<td valign="middle" align="center">0.944</td>
</tr>
<tr>
<td valign="middle" align="center">X-24243 levels</td>
<td valign="middle" align="center">IVW</td>
<td valign="middle" align="center">20</td>
<td valign="middle" align="center">0.094</td>
<td valign="middle" align="center">0.034</td>
<td valign="middle" align="center">0.006</td>
<td valign="middle" align="center">0.645</td>
<td valign="middle" align="center">0.834</td>
</tr>
<tr>
<td valign="middle" align="center">X-24947 levels</td>
<td valign="middle" align="center">IVW</td>
<td valign="middle" align="center">27</td>
<td valign="middle" align="center">0.047</td>
<td valign="middle" align="center">0.018</td>
<td valign="middle" align="center">0.008</td>
<td valign="middle" align="center">0.714</td>
<td valign="middle" align="center">0.446</td>
</tr>
<tr>
<td valign="middle" align="center">Adenosine 5&#x2019;-diphosphate (ADP) to fructose ratio</td>
<td valign="middle" align="center">IVW</td>
<td valign="middle" align="center">32</td>
<td valign="middle" align="center">-0.079</td>
<td valign="middle" align="center">0.020</td>
<td valign="middle" align="center">0.000</td>
<td valign="middle" align="center">0.409</td>
<td valign="middle" align="center">0.622</td>
</tr>
<tr>
<td valign="middle" align="center">Arachidonate (20:4n6) to pyruvate ratio</td>
<td valign="middle" align="center">IVW</td>
<td valign="middle" align="center">16</td>
<td valign="middle" align="center">0.098</td>
<td valign="middle" align="center">0.031</td>
<td valign="middle" align="center">0.002</td>
<td valign="middle" align="center">0.848</td>
<td valign="middle" align="center">0.868</td>
</tr>
<tr>
<td valign="middle" align="center">Uridine to cytidine ratio</td>
<td valign="middle" align="center">IVW</td>
<td valign="middle" align="center">22</td>
<td valign="middle" align="center">-0.110</td>
<td valign="middle" align="center">0.035</td>
<td valign="middle" align="center">0.002</td>
<td valign="middle" align="center">0.374</td>
<td valign="middle" align="center">0.477</td>
</tr>
<tr>
<td valign="middle" align="center">Cysteinylglycine to glutamate ratio</td>
<td valign="middle" align="center">IVW</td>
<td valign="middle" align="center">25</td>
<td valign="middle" align="center">-0.082</td>
<td valign="middle" align="center">0.031</td>
<td valign="middle" align="center">0.009</td>
<td valign="middle" align="center">0.228</td>
<td valign="middle" align="center">0.569</td>
</tr>
<tr>
<td valign="middle" align="center">Histidine to alanine ratio</td>
<td valign="middle" align="center">IVW</td>
<td valign="middle" align="center">32</td>
<td valign="middle" align="center">0.076</td>
<td valign="middle" align="center">0.029</td>
<td valign="middle" align="center">0.009</td>
<td valign="middle" align="center">0.080</td>
<td valign="middle" align="center">0.280</td>
</tr>
</tbody>
</table>
</table-wrap>
<fig id="f3" position="float">
<label>Figure&#xa0;3</label>
<caption>
<p>Circle plot of five Mendelian randomization methods (p &lt;0.05) <bold>(A)</bold> Forest plot of causality of 21 metabolites and COPD <bold>(B)</bold> Scatter plot of 7 metabolites with increased COPD risk <bold>(C)</bold> Scatter plot of the 9 metabolites reducing the risk of COPD <bold>(D)</bold>.</p>
</caption>
<graphic mimetype="image" mime-subtype="tiff" xlink:href="fimmu-15-1406234-g003.tif"/>
</fig>
</sec>
<sec id="s3_3">
<label>3.3</label>
<title>Mediated Mendelian randomization analysis</title>
<p>Building upon the previously identified immune cells and plasma metabolites, we employed the TSMR approach to further compute mediation through Mendelian randomization. Utilizing the 35 selected immune cell phenotypes as exposure factors and the 21 plasma metabolites as outcome measures, we conducted a MR analysis from immune cell phenotypes to plasma metabolites. This analysis revealed causal relationships between 20 immune cell phenotypes and 14 plasma metabolites, yielding the effect size &#x3b2;1 from immune cell phenotypes to metabolites. Our research has uncovered that there is a negative correlation between CD62L- myeloid DC AC and 1-palmitoyl-GPG (16:0), a positive correlation between TCRgd %T cell and 1-methylnicotinamide, and a positive correlation between HLA DR+ T cell%T cell and Alpha-tocopherol levels, among other findings. Further analysis has revealed that a single immune cell phenotype can have causal relationships with multiple metabolites. For instance, HLA DR+ NK AC not only exhibits a negative correlation with Cinnamoylglycine but also with the Uridine to cytidine ratio. Similarly, CD19 on PB/PC shows a positive correlation with Stearidonate (18:4n3) as well as with 4-vinylphenol sulfate. Moreover, CD24 on memory B cell not only negatively correlates with 1-palmitoyl-GPG (16:0) and the Adenosine 5&#x2019;-diphosphate (ADP) to fructose ratio but also positively correlates with 1-methylnicotinamide, among others. When considering the 14 plasma metabolites as exposure factors and COPD as the outcome, an MR analysis was conducted along with an MR-PRESSO test (p &gt; 0.05), indicating no pleiotropy and unbiased SNPs. This led to the determination of the effect size &#x3b2;2 from metabolites to COPD, and subsequently, the overall effect from immune cells to COPD was calculated (<xref ref-type="fig" rid="f4">
<bold>Figure&#xa0;4</bold>
</xref>, <xref ref-type="table" rid="T3">
<bold>Table&#xa0;3</bold>
</xref>).</p>
<fig id="f4" position="float">
<label>Figure&#xa0;4</label>
<caption>
<p>Forest plot of immune cells and metabolites <bold>(A)</bold> Partial scatter plot of immune cells and metabolites <bold>(B)</bold> Test of MR-PRESSO of metabolites to COPD <bold>(C)</bold> Partial leave-one-out method sensitivity analysis of metabolites to COPD <bold>(D)</bold>.</p>
</caption>
<graphic mimetype="image" mime-subtype="tiff" xlink:href="fimmu-15-1406234-g004.tif"/>
</fig>
<table-wrap id="T3" position="float">
<label>Table&#xa0;3</label>
<caption>
<p>MR analysis of immune cells and metabolites.</p>
</caption>
<table frame="hsides">
<thead>
<tr>
<th valign="middle" align="center">Exposure</th>
<th valign="middle" align="center">Outcome</th>
<th valign="middle" align="center">Method</th>
<th valign="middle" align="center">Nsnp</th>
<th valign="middle" align="center">Beta</th>
<th valign="middle" align="center">Se</th>
<th valign="middle" align="center">P-val</th>
<th valign="middle" align="center">Pleiotropy</th>
<th valign="middle" align="center">Heterogeneity</th>
</tr>
</thead>
<tbody>
<tr>
<td valign="middle" align="center">CD62L- myeloid DC AC</td>
<td valign="middle" align="center">1-palmitoyl-GPG (16:0) levels</td>
<td valign="middle" align="center">IVW</td>
<td valign="middle" align="center">15</td>
<td valign="middle" align="center">-0.055</td>
<td valign="middle" align="center">0.022</td>
<td valign="middle" align="center">0.012</td>
<td valign="middle" align="center">0.676</td>
<td valign="middle" align="center">0.764</td>
</tr>
<tr>
<td valign="middle" align="center">TCRgd %T cell</td>
<td valign="middle" align="center">1-methylnicotinamide levels</td>
<td valign="middle" align="center">IVW</td>
<td valign="middle" align="center">17</td>
<td valign="middle" align="center">0.049</td>
<td valign="middle" align="center">0.022</td>
<td valign="middle" align="center">0.021</td>
<td valign="middle" align="center">0.360</td>
<td valign="middle" align="center">0.855</td>
</tr>
<tr>
<td valign="middle" align="center">HLA DR+ T cell%T cell</td>
<td valign="middle" align="center">Alpha-tocopherol levels</td>
<td valign="middle" align="center">IVW</td>
<td valign="middle" align="center">31</td>
<td valign="middle" align="center">0.034</td>
<td valign="middle" align="center">0.013</td>
<td valign="middle" align="center">0.007</td>
<td valign="middle" align="center">0.575</td>
<td valign="middle" align="center">0.709</td>
</tr>
<tr>
<td valign="middle" align="center">HLA DR+ NK AC</td>
<td valign="middle" align="center">Cinnamoylglycine levels</td>
<td valign="middle" align="center">IVW</td>
<td valign="middle" align="center">17</td>
<td valign="middle" align="center">-0.049</td>
<td valign="middle" align="center">0.024</td>
<td valign="middle" align="center">0.046</td>
<td valign="middle" align="center">0.700</td>
<td valign="middle" align="center">0.764</td>
</tr>
<tr>
<td valign="middle" align="center">HLA DR+ NK AC</td>
<td valign="middle" align="center">Uridine to cytidine ratio</td>
<td valign="middle" align="center">IVW</td>
<td valign="middle" align="center">17</td>
<td valign="middle" align="center">-0.053</td>
<td valign="middle" align="center">0.026</td>
<td valign="middle" align="center">0.039</td>
<td valign="middle" align="center">0.553</td>
<td valign="middle" align="center">0.371</td>
</tr>
<tr>
<td valign="middle" align="center">CD25++ CD8br %CD8br</td>
<td valign="middle" align="center">Taurochenodeoxycholate levels</td>
<td valign="middle" align="center">IVW</td>
<td valign="middle" align="center">21</td>
<td valign="middle" align="center">0.055</td>
<td valign="middle" align="center">0.024</td>
<td valign="middle" align="center">0.023</td>
<td valign="middle" align="center">0.753</td>
<td valign="middle" align="center">0.957</td>
</tr>
<tr>
<td valign="middle" align="center">CD28+ CD45RA- CD8br %T cell</td>
<td valign="middle" align="center">Alpha-hydroxyisovalerate levels</td>
<td valign="middle" align="center">IVW</td>
<td valign="middle" align="center">26</td>
<td valign="middle" align="center">0.019</td>
<td valign="middle" align="center">0.009</td>
<td valign="middle" align="center">0.033</td>
<td valign="middle" align="center">0.552</td>
<td valign="middle" align="center">0.830</td>
</tr>
<tr>
<td valign="middle" align="center">CD19 on IgD- CD38br</td>
<td valign="middle" align="center">Alpha-tocopherol levels</td>
<td valign="middle" align="center">IVW</td>
<td valign="middle" align="center">15</td>
<td valign="middle" align="center">-0.052</td>
<td valign="middle" align="center">0.025</td>
<td valign="middle" align="center">0.033</td>
<td valign="middle" align="center">0.471</td>
<td valign="middle" align="center">0.782</td>
</tr>
<tr>
<td valign="middle" align="center">CD19 on PB/PC</td>
<td valign="middle" align="center">Stearidonate (18:4n3) levels</td>
<td valign="middle" align="center">IVW</td>
<td valign="middle" align="center">23</td>
<td valign="middle" align="center">0.071</td>
<td valign="middle" align="center">0.026</td>
<td valign="middle" align="center">0.006</td>
<td valign="middle" align="center">0.888</td>
<td valign="middle" align="center">0.244</td>
</tr>
<tr>
<td valign="middle" align="center">CD19 on PB/PC</td>
<td valign="middle" align="center">4-vinylphenol sulfate levels</td>
<td valign="middle" align="center">IVW</td>
<td valign="middle" align="center">23</td>
<td valign="middle" align="center">0.052</td>
<td valign="middle" align="center">0.025</td>
<td valign="middle" align="center">0.038</td>
<td valign="middle" align="center">0.938</td>
<td valign="middle" align="center">0.531</td>
</tr>
<tr>
<td valign="middle" align="center">CD24 on memory B cell</td>
<td valign="middle" align="center">1-palmitoyl-GPG (16:0) levels</td>
<td valign="middle" align="center">IVW</td>
<td valign="middle" align="center">28</td>
<td valign="middle" align="center">-0.034</td>
<td valign="middle" align="center">0.015</td>
<td valign="middle" align="center">0.024</td>
<td valign="middle" align="center">0.673</td>
<td valign="middle" align="center">0.723</td>
</tr>
<tr>
<td valign="middle" align="center">CD24 on memory B cell</td>
<td valign="middle" align="center">1-methylnicotinamide levels</td>
<td valign="middle" align="center">IVW</td>
<td valign="middle" align="center">28</td>
<td valign="middle" align="center">0.042</td>
<td valign="middle" align="center">0.016</td>
<td valign="middle" align="center">0.011</td>
<td valign="middle" align="center">0.323</td>
<td valign="middle" align="center">0.175</td>
</tr>
<tr>
<td valign="middle" align="center">CD24 on memory B cell</td>
<td valign="middle" align="center">Adenosine 5&#x2019;-diphosphate (ADP) to fructose ratio</td>
<td valign="middle" align="center">IVW</td>
<td valign="middle" align="center">28</td>
<td valign="middle" align="center">-0.040</td>
<td valign="middle" align="center">0.020</td>
<td valign="middle" align="center">0.041</td>
<td valign="middle" align="center">0.306</td>
<td valign="middle" align="center">0.589</td>
</tr>
<tr>
<td valign="middle" align="center">CD25 on transitional</td>
<td valign="middle" align="center">Cinnamoylglycine levels</td>
<td valign="middle" align="center">IVW</td>
<td valign="middle" align="center">21</td>
<td valign="middle" align="center">0.069</td>
<td valign="middle" align="center">0.024</td>
<td valign="middle" align="center">0.004</td>
<td valign="middle" align="center">0.329</td>
<td valign="middle" align="center">0.338</td>
</tr>
<tr>
<td valign="middle" align="center">CD27 on unsw mem</td>
<td valign="middle" align="center">Taurochenodeoxycholate levels</td>
<td valign="middle" align="center">IVW</td>
<td valign="middle" align="center">29</td>
<td valign="middle" align="center">-0.054</td>
<td valign="middle" align="center">0.019</td>
<td valign="middle" align="center">0.004</td>
<td valign="middle" align="center">0.828</td>
<td valign="middle" align="center">0.486</td>
</tr>
<tr>
<td valign="middle" align="center">CD3 on naive CD8br</td>
<td valign="middle" align="center">Taurochenodeoxycholate levels</td>
<td valign="middle" align="center">IVW</td>
<td valign="middle" align="center">25</td>
<td valign="middle" align="center">0.033</td>
<td valign="middle" align="center">0.016</td>
<td valign="middle" align="center">0.045</td>
<td valign="middle" align="center">0.753</td>
<td valign="middle" align="center">0.770</td>
</tr>
<tr>
<td valign="middle" align="center">CD3 on CM CD8br</td>
<td valign="middle" align="center">X-12100 levels</td>
<td valign="middle" align="center">IVW</td>
<td valign="middle" align="center">16</td>
<td valign="middle" align="center">0.041</td>
<td valign="middle" align="center">0.021</td>
<td valign="middle" align="center">0.049</td>
<td valign="middle" align="center">0.987</td>
<td valign="middle" align="center">0.750</td>
</tr>
<tr>
<td valign="middle" align="center">CD127 on CD28+ CD45RA- CD8br</td>
<td valign="middle" align="center">Cinnamoylglycine levels</td>
<td valign="middle" align="center">IVW</td>
<td valign="middle" align="center">19</td>
<td valign="middle" align="center">-0.039</td>
<td valign="middle" align="center">0.020</td>
<td valign="middle" align="center">0.048</td>
<td valign="middle" align="center">0.525</td>
<td valign="middle" align="center">0.155</td>
</tr>
<tr>
<td valign="middle" align="center">CD127 on CD28+ CD45RA- CD8br</td>
<td valign="middle" align="center">Uridine to cytidine ratio</td>
<td valign="middle" align="center">IVW</td>
<td valign="middle" align="center">19</td>
<td valign="middle" align="center">-0.051</td>
<td valign="middle" align="center">0.018</td>
<td valign="middle" align="center">0.006</td>
<td valign="middle" align="center">0.315</td>
<td valign="middle" align="center">0.310</td>
</tr>
<tr>
<td valign="middle" align="center">CD127 on CD28- CD8br</td>
<td valign="middle" align="center">Uridine to cytidine ratio</td>
<td valign="middle" align="center">IVW</td>
<td valign="middle" align="center">19</td>
<td valign="middle" align="center">-0.047</td>
<td valign="middle" align="center">0.023</td>
<td valign="middle" align="center">0.043</td>
<td valign="middle" align="center">0.697</td>
<td valign="middle" align="center">0.379</td>
</tr>
<tr>
<td valign="middle" align="center">CCR2 on plasmacytoid DC</td>
<td valign="middle" align="center">N-oleoylserine levels</td>
<td valign="middle" align="center">IVW</td>
<td valign="middle" align="center">18</td>
<td valign="middle" align="center">-0.043</td>
<td valign="middle" align="center">0.018</td>
<td valign="middle" align="center">0.018</td>
<td valign="middle" align="center">0.802</td>
<td valign="middle" align="center">0.876</td>
</tr>
<tr>
<td valign="middle" align="center">CCR2 on plasmacytoid DC</td>
<td valign="middle" align="center">Arachidonate (20:4n6) to pyruvate ratio</td>
<td valign="middle" align="center">IVW</td>
<td valign="middle" align="center">18</td>
<td valign="middle" align="center">-0.037</td>
<td valign="middle" align="center">0.017</td>
<td valign="middle" align="center">0.034</td>
<td valign="middle" align="center">0.480</td>
<td valign="middle" align="center">0.668</td>
</tr>
<tr>
<td valign="middle" align="center">CCR2 on CD62L+ plasmacytoid DC</td>
<td valign="middle" align="center">N-oleoylserine levels</td>
<td valign="middle" align="center">IVW</td>
<td valign="middle" align="center">18</td>
<td valign="middle" align="center">-0.040</td>
<td valign="middle" align="center">0.018</td>
<td valign="middle" align="center">0.027</td>
<td valign="middle" align="center">0.838</td>
<td valign="middle" align="center">0.775</td>
</tr>
<tr>
<td valign="middle" align="center">CCR2 on CD62L+ plasmacytoid DC</td>
<td valign="middle" align="center">Arachidonate (20:4n6) to pyruvate ratio</td>
<td valign="middle" align="center">IVW</td>
<td valign="middle" align="center">18</td>
<td valign="middle" align="center">-0.041</td>
<td valign="middle" align="center">0.018</td>
<td valign="middle" align="center">0.018</td>
<td valign="middle" align="center">0.894</td>
<td valign="middle" align="center">0.881</td>
</tr>
<tr>
<td valign="middle" align="center">CD80 on granulocyte</td>
<td valign="middle" align="center">Taurochenodeoxycholate levels</td>
<td valign="middle" align="center">IVW</td>
<td valign="middle" align="center">30</td>
<td valign="middle" align="center">0.035</td>
<td valign="middle" align="center">0.017</td>
<td valign="middle" align="center">0.042</td>
<td valign="middle" align="center">0.655</td>
<td valign="middle" align="center">0.478</td>
</tr>
<tr>
<td valign="middle" align="center">CD11b on CD66b++ myeloid cell</td>
<td valign="middle" align="center">4-vinylphenol sulfate levels</td>
<td valign="middle" align="center">IVW</td>
<td valign="middle" align="center">15</td>
<td valign="middle" align="center">-0.042</td>
<td valign="middle" align="center">0.020</td>
<td valign="middle" align="center">0.040</td>
<td valign="middle" align="center">0.558</td>
<td valign="middle" align="center">0.594</td>
</tr>
<tr>
<td valign="middle" align="center">HLA DR on plasmacytoid DC</td>
<td valign="middle" align="center">X-12100 levels</td>
<td valign="middle" align="center">IVW</td>
<td valign="middle" align="center">22</td>
<td valign="middle" align="center">-0.033</td>
<td valign="middle" align="center">0.015</td>
<td valign="middle" align="center">0.025</td>
<td valign="middle" align="center">0.228</td>
<td valign="middle" align="center">0.222</td>
</tr>
<tr>
<td valign="middle" align="center">HLA DR on plasmacytoid DC</td>
<td valign="middle" align="center">X-19438 levels</td>
<td valign="middle" align="center">IVW</td>
<td valign="middle" align="center">22</td>
<td valign="middle" align="center">-0.052</td>
<td valign="middle" align="center">0.015</td>
<td valign="middle" align="center">0.001</td>
<td valign="middle" align="center">0.282</td>
<td valign="middle" align="center">0.696</td>
</tr>
</tbody>
</table>
</table-wrap>
</sec>
<sec id="s3_4">
<label>3.4</label>
<title>Mediation analysis</title>
<p>In our final analysis, we conducted a mediation analysis to elucidate the causal relationship between immune cell phenotypes and COPD, mediated by plasma metabolites. We discovered that 8 plasma metabolites mediated the relationship between 8 immune cell phenotypes and COPD (P &lt; 0.05), among which 7 are known plasma metabolites and one remains unidentified. Notably, the CD24 on memory B cells was mediated by two distinct plasma metabolites. The mediation proportion of 1-methylnicotinamide was found to be the highest at 26.4% (P=0.013), followed by the unidentified metabolite X-19438 with a mediation proportion of 21.8% (P=0.004), Taurochenodeoxycholate at 18.8% (P=0.008), and Alpha-hydroxyisovalerate at 14.2% (P=0.036), among others (<xref ref-type="fig" rid="f5">
<bold>Figure&#xa0;5</bold>
</xref>, <xref ref-type="table" rid="T4">
<bold>Table&#xa0;4</bold>
</xref>).</p>
<fig id="f5" position="float">
<label>Figure&#xa0;5</label>
<caption>
<p>Forest plots of immune cells, plasma metabolites, and COPD <bold>(A)</bold> Plasma metabolites mediate the causal relationship between immune cells and COPD (red is a risk factor, green is a protective factor) <bold>(B)</bold>.</p>
</caption>
<graphic mimetype="image" mime-subtype="tiff" xlink:href="fimmu-15-1406234-g005.tif"/>
</fig>
<table-wrap id="T4" position="float">
<label>Table&#xa0;4</label>
<caption>
<p>Mendelian randomization analyses of the causal effects between immune cells, plasma metabolites and COPD.</p>
</caption>
<table frame="hsides">
<thead>
<tr>
<th valign="middle" align="center">Immune cell</th>
<th valign="middle" align="center">Metabolite</th>
<th valign="middle" align="center">Outcome</th>
<th valign="middle" align="center">Mediated effect</th>
<th valign="middle" align="center">Mediated proportion</th>
<th valign="middle" align="center">Beta<break/>Direct</th>
<th valign="middle" align="center">P-val</th>
</tr>
</thead>
<tbody>
<tr>
<td valign="middle" align="center">CD62L- myeloid DC AC</td>
<td valign="middle" align="center">1-palmitoyl-GPG (16:0) levels</td>
<td valign="middle" align="center">COPD</td>
<td valign="middle" align="center">0.00431</td>
<td valign="middle" align="center">10%</td>
<td valign="middle" align="center">0.039</td>
<td valign="middle" align="center">0.041</td>
</tr>
<tr>
<td valign="middle" align="center">HLA DR+ T cell%T cell</td>
<td valign="middle" align="center">Alpha-tocopherol levels</td>
<td valign="middle" align="center">COPD</td>
<td valign="middle" align="center">-0.0029</td>
<td valign="middle" align="center">12.4%</td>
<td valign="middle" align="center">-0.020</td>
<td valign="middle" align="center">0.012</td>
</tr>
<tr>
<td valign="middle" align="center">CD28+ CD45RA- CD8br %T cell</td>
<td valign="middle" align="center">Alpha-hydroxyisovalerate levels</td>
<td valign="middle" align="center">COPD</td>
<td valign="middle" align="center">0.00205</td>
<td valign="middle" align="center">14.2%</td>
<td valign="middle" align="center">0.012</td>
<td valign="middle" align="center">0.036</td>
</tr>
<tr>
<td valign="middle" align="center">CD24 on memory B cell</td>
<td valign="middle" align="center">1-palmitoyl-GPG (16:0) levels</td>
<td valign="middle" align="center">COPD</td>
<td valign="middle" align="center">0.00267</td>
<td valign="middle" align="center">10%</td>
<td valign="middle" align="center">0.024</td>
<td valign="middle" align="center">0.039</td>
</tr>
<tr>
<td valign="middle" align="center">CD24 on memory B cell</td>
<td valign="middle" align="center">1-methylnicotinamide levels</td>
<td valign="middle" align="center">COPD</td>
<td valign="middle" align="center">0.0070</td>
<td valign="middle" align="center">26.4%</td>
<td valign="middle" align="center">0.020</td>
<td valign="middle" align="center">0.013</td>
</tr>
<tr>
<td valign="middle" align="center">CD25 on transitional</td>
<td valign="middle" align="center">Cinnamoylglycine levels</td>
<td valign="middle" align="center">COPD</td>
<td valign="middle" align="center">0.00491</td>
<td valign="middle" align="center">14.8%</td>
<td valign="middle" align="center">0.028</td>
<td valign="middle" align="center">0.039</td>
</tr>
<tr>
<td valign="middle" align="center">CD27 on unsw mem</td>
<td valign="middle" align="center">Taurochenodeoxycholate levels</td>
<td valign="middle" align="center">COPD</td>
<td valign="middle" align="center">0.00671</td>
<td valign="middle" align="center">18.8%</td>
<td valign="middle" align="center">0.029</td>
<td valign="middle" align="center">0.008</td>
</tr>
<tr>
<td valign="middle" align="center">CCR2 on plasmacytoid DC</td>
<td valign="middle" align="center">N-oleoylserine levels</td>
<td valign="middle" align="center">COPD</td>
<td valign="middle" align="center">0.00321</td>
<td valign="middle" align="center">9.09%</td>
<td valign="middle" align="center">0.032</td>
<td valign="middle" align="center">0.040</td>
</tr>
<tr>
<td valign="middle" align="center">HLA DR on plasmacytoid DC</td>
<td valign="middle" align="center">X-19438 levels</td>
<td valign="middle" align="center">COPD</td>
<td valign="middle" align="center">0.00424</td>
<td valign="middle" align="center">21.8%</td>
<td valign="middle" align="center">0.015</td>
<td valign="middle" align="center">0.004</td>
</tr>
</tbody>
</table>
</table-wrap>
</sec>
</sec>
<sec id="s4" sec-type="discussion">
<label>4</label>
<title>Discussion</title>
<p>In our MR study, the findings indicated a causal relationship between 41 immune cell phenotypes and COPD. However, a reverse MR analysis revealed that 35 immune cell phenotypes bore no causal relationship with COPD, while 6 did exhibit a causal connection. Further employing TSMR and MVMR for mediation analysis, we identified that 8 cell phenotypes could be causally linked to COPD through 8 plasma metabolites (including one unidentified), among which the mediation proportion of 1-methylnicotinamide levels was the highest at 26.4%.</p>
<p>Our research has corroborated the existence of a causal relationship between 41 immune cell phenotypes and COPD, aligning with previous studies that posit chronic inflammation leading to compromised immunity and immunosuppression as pivotal in the pathogenesis of COPD (<xref ref-type="bibr" rid="B40">40</xref>), a condition persistently present in the disease (<xref ref-type="bibr" rid="B41">41</xref>). It has been observed that, compared to healthy individuals, patients with COPD exhibit an increase in B cells and their products in the blood and lungs (<xref ref-type="bibr" rid="B42">42</xref>), alongside an upsurge in the expression of genes related to inflammation, B cell activation, and proliferation. This activation of B cells is associated with an autoimmune-mediated mechanism of COPD pathogenesis (<xref ref-type="bibr" rid="B43">43</xref>, <xref ref-type="bibr" rid="B44">44</xref>). However, the association between B cells and COPD does not imply causality (<xref ref-type="bibr" rid="B45">45</xref>). Our study, however, confirms a causal relationship between specific B cell phenotypes and COPD, with an increase in memory B-cells being linked to impaired lung function and small airway dysfunction (<xref ref-type="bibr" rid="B46">46</xref>), consistent with our findings that CD24 on memory B cells increases the risk of COPD. Furthermore, the subgroups of peripheral blood TBNK lymphocytes in COPD patients show a certain correlation with COPD (<xref ref-type="bibr" rid="B47">47</xref>) and its severity (<xref ref-type="bibr" rid="B48">48</xref>), aligning with our MR results and suggesting a causal relationship. Regulatory T (Treg) cells play a crucial role in the immune system by suppressing excessive immune responses and maintaining immune balance. The relationship between Treg cells and lung function (<xref ref-type="bibr" rid="B49">49</xref>), the imbalance of Treg cells during COPD progression (<xref ref-type="bibr" rid="B50">50</xref>), and the potential of modulating Treg cells to improve COPD (<xref ref-type="bibr" rid="B51">51</xref>) and lung inflammation underscore their significance (<xref ref-type="bibr" rid="B52">52</xref>). Myeloid cells, capable of phagocytosing pathogens, initiating inflammatory responses, and presenting antigens to other immune cells, contribute to tissue repair and remodeling. Our analysis identified six immune cell phenotypes with a causal relationship to COPD in both directions, namely CD14+ CD16+ monocyte AC, CD4+ CD8dim %lymphocyte, CD4+ CD8dim %leukocyte, CD3- lymphocyte AC, CD3 on EM CD8br, and CD45 on Im MDS, all associated with a reduced risk of COPD. The CD14+ CD16+ monocyte AC, a monocyte subgroup expressing CD14 and CD16, plays a role in modulating inflammatory responses and promoting tissue repair, with monocytes being etiologically related to COPD (<xref ref-type="bibr" rid="B53">53</xref>) and influencing its pathogenesis and diagnosis (<xref ref-type="bibr" rid="B54">54</xref>, <xref ref-type="bibr" rid="B55">55</xref>), serving as key drivers of lung inflammation and tissue remodeling (<xref ref-type="bibr" rid="B56">56</xref>). The CD4+ CD8dim %lymphocyte, a unique lymphocyte, plays a role in regulating immune responses and maintaining immune balance, with CD4-regulated T cells controlling autoimmunity and thus managing lung inflammation in COPD (<xref ref-type="bibr" rid="B57">57</xref>, <xref ref-type="bibr" rid="B58">58</xref>), while CD4 and CD8 are related to bronchiolar wall remodeling in COPD (<xref ref-type="bibr" rid="B59">59</xref>) and the reduction of terminal bronchioles (<xref ref-type="bibr" rid="B60">60</xref>).</p>
<p>Metabolites play a crucial role in the early identification of individuals at high risk and in the prevention of diseases (<xref ref-type="bibr" rid="B61">61</xref>). Clinically, they enable us to differentiate the disease characteristics of COPD (<xref ref-type="bibr" rid="B62">62</xref>), identify diagnostic biomarkers (<xref ref-type="bibr" rid="B63">63</xref>, <xref ref-type="bibr" rid="B64">64</xref>), and evaluate the efficacy indicators of COPD treatments (<xref ref-type="bibr" rid="B65">65</xref>). Our study has discovered a causal relationship between CD24 on memory B cells and COPD, mediated by two intermediaries: 1-methylnicotinamide and 1-palmitoyl-GPG (16:0). There is a positive correlation between CD24 on memory B cells and COPD, where an increase in CD24 on memory B cells elevates the risk of COPD. Furthermore, CD24 on memory B cells is positively correlated with 1-methylnicotinamide, which, in turn, is positively associated with COPD. 1-methylnicotinamide, a primary metabolite found in all living organisms and involved in growth, development, or reproduction, possesses various immunomodulatory properties. It is linked to inflammatory responses in lung epithelial cells (<xref ref-type="bibr" rid="B66">66</xref>)and the activation of the NLRP3 inflammasome (<xref ref-type="bibr" rid="B67">67</xref>), a significant mediator in COPD inflammation (<xref ref-type="bibr" rid="B68">68</xref>). Through NLRP3, lung inflammation can be regulated (<xref ref-type="bibr" rid="B69">69</xref>, <xref ref-type="bibr" rid="B70">70</xref>), indicating a certain correlation between 1-methylnicotinamide and COPD. Our research confirms a causal relationship between 1-methylnicotinamide and COPD, with CD24 on memory B cells influencing COPD risk through the mediation of 1-methylnicotinamide. Conversely, CD24 on memory B cells is negatively correlated with 1-palmitoyl-GPG (16:0), which is positively associated with COPD. Research on 1-palmitoyl-GPG (16:0) is limited, but it is generally considered part of lipid metabolism, related to vitamin D deficiency (<xref ref-type="bibr" rid="B71">71</xref>) and pulmonary hypertension (<xref ref-type="bibr" rid="B72">72</xref>). Vitamin D may be a risk factor for COPD (<xref ref-type="bibr" rid="B73">73</xref>), though specific studies on COPD are lacking. Lipid metabolism plays a key role in the adaptive immune response to chronic inflammation (<xref ref-type="bibr" rid="B74">74</xref>) and is associated with lung inflammation in mice (<xref ref-type="bibr" rid="B75">75</xref>), with COPD patients exhibiting higher lipid expression (<xref ref-type="bibr" rid="B76">76</xref>). Our MR analysis concludes a causal relationship between CD24 on memory B cells and COPD through 1-palmitoyl-GPG (16:0).</p>
<p>Taurochenodeoxycholate could potentially serve as an intermediary in the causal relationship between CD27 on unsw mem and COPD, demonstrating a negative correlation with both CD27 on unsw mem and COPD. Taurochenodeoxycholate, a bile acid functionally related to chenodeoxycholic acid, is involved in inflammatory responses (<xref ref-type="bibr" rid="B77">77</xref>), immune cell regulation (<xref ref-type="bibr" rid="B78">78</xref>), endoplasmic reticulum stress inhibition (<xref ref-type="bibr" rid="B79">79</xref>), and is associated with pulmonary fibrosis (<xref ref-type="bibr" rid="B80">80</xref>). However, research specifically targeting its role in COPD is scarce. MR analysis suggests that CD27 on unsw mem may have a causal relationship with COPD through the mediation of taurochenodeoxycholate. Alpha-tocopherol, the most active form of Vitamin E, has been observed in studies to reduce the risk of COPD in women (<xref ref-type="bibr" rid="B81">81</xref>), exhibiting anti-inflammatory and antioxidant properties that improve bronchial epithelial thickening, alveolar destruction, and lung function (<xref ref-type="bibr" rid="B82">82</xref>). Consistent with our MR analysis, alpha-tocopherol is negatively correlated with the risk of COPD, mediating a causal relationship between COPD and the percentage of HLA DR+ T cells among T cells. Alpha-hydroxyisovalerate, initially identified in studies related to human aging and early development (<xref ref-type="bibr" rid="B83">83</xref>), is associated with the severity of bronchiolitis (<xref ref-type="bibr" rid="B84">84</xref>) and, consistent with our MR analysis, positively correlated with the risk of COPD. It mediates a causal relationship between COPD and the phenotype of CD28+ CD45RA- CD8 bright %T cells. Cinnamoylglycine, with limited research related to COPD, has been found through MR analysis to be positively correlated with COPD. CD25 on transitional cells has a causal relationship with COPD mediated by cinnamoylglycine. N-oleoylserine, a secondary metabolite functionally related to oleic acid and with scant research in the context of lung inflammation (<xref ref-type="bibr" rid="B85">85</xref>), has been found through MR analysis to be negatively correlated with COPD, suggesting a protective factor.</p>
</sec>
<sec id="s5" sec-type="conclusions">
<label>5</label>
<title>Conclusion</title>
<p>This study represents a comprehensive assessment of the causal relationships between immune cell phenotypes, plasma metabolites, and COPD. We have identified 8 immune cell phenotypes that exhibit a causal relationship with COPD, mediated by 8 metabolites. These findings illuminate the significance of the underlying mechanisms between immune cells, metabolites, and COPD. They contribute to the screening of individuals at high risk for COPD and offer insights into early prevention and the preemptive diagnosis of Pre-COPD conditions.</p>
</sec>
<sec id="s6" sec-type="data-availability">
<title>Data availability statement</title>
<p>The original contributions presented in the study are included in the article/supplementary material. Further inquiries can be directed to the corresponding author.</p>
</sec>
<sec id="s7" sec-type="ethics-statement">
<title>Ethics statement</title>
<p>Ethical approval was not required for the study involving humans in accordance with the local legislation and institutional requirements. Written informed consent to participate in this study was not required from the participants or the participants&#x2019; legal guardians/next of kin in accordance with the national legislation and the institutional requirements.</p>
</sec>
<sec id="s8" sec-type="author-contributions">
<title>Author contributions</title>
<p>ZC: Writing &#x2013; review &amp; editing, Writing &#x2013; original draft, Conceptualization. TW: Writing &#x2013; original draft, Data curation. YF: Writing &#x2013; original draft, Data curation. FS: Writing &#x2013; original draft, Supervision, Data curation. HD: Writing &#x2013; original draft, Data curation. LZ: Writing &#x2013; original draft, Data curation. LS: Writing &#x2013; review &amp; editing, Writing &#x2013; original draft, Conceptualization.</p>
</sec>
</body>
<back>
<sec id="s9" sec-type="funding-information">
<title>Funding</title>
<p>The author(s) declare financial support was received for the research, authorship, and/or publication of this article. This study was funded by the Natural Science Foundation of Jilin Province (YDZJ202201ZYTS236, 20180101115JC and 20230203189SF), Administration of Traditional Chinese Medicine (2022ZYLCYJ04&#x2013;1 and 202209), and Jilin Provincial Administration of Traditional Chinese Medicine (2022219). We gratefully acknowledge the funding of the above projects. The funders had no role in the study design, data collection and analysis, decision to publish, or preparation of the manuscript.</p>
</sec>
<ack>
<title>Acknowledgments</title>
<p>We thank all the participants and investigators involved in the GWAS, as well as all the authors for their contributions to this article.</p>
</ack>
<sec id="s10" sec-type="COI-statement">
<title>Conflict of interest</title>
<p>The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as potential conflicts of interest.</p>
</sec>
<sec id="s11" sec-type="disclaimer">
<title>Publisher&#x2019;s note</title>
<p>All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher.</p>
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