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<journal-id journal-id-type="publisher-id">Front. Public Health</journal-id>
<journal-title>Frontiers in Public Health</journal-title>
<abbrev-journal-title abbrev-type="pubmed">Front. Public Health</abbrev-journal-title>
<issn pub-type="epub">2296-2565</issn>
<publisher>
<publisher-name>Frontiers Media S.A.</publisher-name>
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<article-meta>
<article-id pub-id-type="doi">10.3389/fpubh.2025.1620411</article-id>
<article-categories>
<subj-group subj-group-type="heading">
<subject>Public Health</subject>
<subj-group>
<subject>Original Research</subject>
</subj-group>
</subj-group>
</article-categories>
<title-group>
<article-title>Air pollution increases gastroesophageal reflux disease risk: evidence from a prospective cohort study</article-title>
</title-group>
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<contrib contrib-type="author" equal-contrib="yes">
<name><surname>Ran</surname> <given-names>Yan</given-names></name>
<xref ref-type="aff" rid="aff1"><sup>1</sup></xref>
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<contrib contrib-type="author" equal-contrib="yes">
<name><surname>Lei</surname> <given-names>Jian</given-names></name>
<xref ref-type="aff" rid="aff2"><sup>2</sup></xref>
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<contrib contrib-type="author">
<name><surname>Wang</surname> <given-names>Lianli</given-names></name>
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<contrib contrib-type="author">
<name><surname>Li</surname> <given-names>Laifu</given-names></name>
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<contrib contrib-type="author">
<name><surname>Ye</surname> <given-names>Fangchen</given-names></name>
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<name><surname>Mei</surname> <given-names>Lin</given-names></name>
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<name><surname>Sun</surname> <given-names>Zhuoya</given-names></name>
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<name><surname>Chen</surname> <given-names>Jiamiao</given-names></name>
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<name><surname>Dai</surname> <given-names>Fei</given-names></name>
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<aff id="aff1"><sup>1</sup><institution>Department of Gastroenterology, The Second Affiliated Hospital of Xi'an Jiaotong University</institution>, <addr-line>Xi'an, Shaanxi</addr-line>, <country>China</country></aff>
<aff id="aff2"><sup>2</sup><institution>Key Laboratory of Environment and Genes Releated to Diseases, Department of Occupational and Environmental Health, Ministry of Education, School of Public Health, Xi'an Jiaotong University Health Science Center</institution>, <addr-line>Xi'an, Shaanxi</addr-line>, <country>China</country></aff>
<author-notes>
<fn fn-type="edited-by" id="fn0003">
<p>Edited by: Hasan Mahmud Reza, North South University, Bangladesh</p></fn>
<fn fn-type="edited-by" id="fn0004">
<p>Reviewed by: Zhiheng Yang, Shandong University of Finance and Economics, China</p>
<p>Asim Kumar Bepari, North South University, Bangladesh</p></fn>
<corresp id="c001">&#x002A;Correspondence: Fei Dai, <email>daifei68@xjtu.edu.cn</email></corresp>
<fn fn-type="equal" id="fn0002"><p><sup>&#x2020;</sup>These authors have contributed equally to this work</p></fn>
</author-notes>
<pub-date pub-type="epub">
<day>06</day>
<month>08</month>
<year>2025</year>
</pub-date>
<pub-date pub-type="collection">
<year>2025</year>
</pub-date>
<volume>13</volume>
<elocation-id>1620411</elocation-id>
<history>
<date date-type="received">
<day>29</day>
<month>04</month>
<year>2025</year>
</date>
<date date-type="accepted">
<day>14</day>
<month>07</month>
<year>2025</year>
</date>
</history>
<permissions>
<copyright-statement>Copyright &#x00A9; 2025 Ran, Lei, Wang, Li, Ye, Mei, Sun, Chen and Dai.</copyright-statement>
<copyright-year>2025</copyright-year>
<copyright-holder>Ran, Lei, Wang, Li, Ye, Mei, Sun, Chen and Dai</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 id="sec1">
<title>Background</title>
<p>Gastroesophageal reflux disease (GERD) is one of the most prevalent gastrointestinal disorders with uncertain etiology and high prevalence. Ambient air pollution has been linked to gastrointestinal diseases, but the impact of long-term air pollution exposure on GERD incidence is still unclear.</p>
</sec>
<sec id="sec2">
<title>Methods</title>
<p>We performed a cohort study using the UK Biobank database. Annual mean concentrations of air pollutants, including PM<sub>10</sub>, PM<sub>2.5&#x2013;10</sub>, PM<sub>2.5</sub>, NO<sub>X</sub>, and NO<sub>2</sub>, were obtained from the ESCAPE study using the land use regression model. The Cox proportional hazard regression model was employed to estimate the percentage change of GERD incidence risk related to long-term air pollutant exposures. We further explored the exposure-response relationship curves and identified the vulnerable populations.</p>
</sec>
<sec id="sec3">
<title>Results</title>
<p>During a follow-up period of 14.1&#x202F;&#x00B1;&#x202F;2.4&#x202F;years, a total of 32,413 (11.2%) individuals were diagnosed with GERD among 289,387 participants. We estimated that each interquartile range increase in PM<sub>10</sub>, PM<sub>2.5&#x2013;10</sub>, PM<sub>2.5</sub>, NO<sub>X</sub>, NO<sub>2,</sub> and NO was associated with 1.69, 1.29, 3.57, 2.08, 1.93, and 2.28% higher incidence risks of GERD, respectively. Almost linear exposure-response curves were observed, particularly for GERD without esophagitis. The females, middle-aged, overweight, White ethnicity, and higher socioeconomic status individuals were more vulnerable to GERD when exposed to air pollutants.</p>
</sec>
<sec id="sec4">
<title>Conclusion</title>
<p>This study provided robust evidence supporting the association between long-term exposure to air pollutants and increased risk of GERD incidence. Our research revealed that exposure to both particulate matter and gaseous pollutants was associated with a higher risk of GERD, especially for GERD without esophagitis.</p>
</sec>
</abstract>
<kwd-group>
<kwd>gastroesophageal reflux disease</kwd>
<kwd>air pollution</kwd>
<kwd>particulate matter</kwd>
<kwd>gaseous pollutants</kwd>
<kwd>prospective cohort study</kwd>
</kwd-group>
<counts>
<fig-count count="2"/>
<table-count count="3"/>
<equation-count count="1"/>
<ref-count count="45"/>
<page-count count="10"/>
<word-count count="6967"/>
</counts>
<custom-meta-wrap>
<custom-meta>
<meta-name>section-at-acceptance</meta-name>
<meta-value>Environmental Health and Exposome</meta-value>
</custom-meta>
</custom-meta-wrap>
</article-meta>
</front>
<body>
<sec sec-type="intro" id="sec5">
<label>1</label>
<title>Introduction</title>
<p>Gastroesophageal reflux disease (GERD) is one of the most prevalent gastrointestinal disorders, which is defined as the movement of stomach contents into the esophagus or mouth, causing troublesome symptoms or complications with heartburn and reflux as the most typical symptoms (<xref ref-type="bibr" rid="ref1">1</xref>, <xref ref-type="bibr" rid="ref2">2</xref>). According to estimates from a global epidemiological study, approximately 13.3% of the population worldwide suffered from heartburn or reflux symptoms at least once a week, with prevalence varying from 2.5 to 51.2% across different countries (<xref ref-type="bibr" rid="ref3">3</xref>). Worse still, the number continues to rise (<xref ref-type="bibr" rid="ref4">4</xref>). Due to its high prevalence and chronicity, GERD impairs the patient&#x2019;s quality of life seriously and contributes to substantial economic and medical burdens. GERD has been identified to increase the risk of esophageal strictures and esophageal carcinoma (<xref ref-type="bibr" rid="ref5">5</xref>), which can be life-threatening. Reflux exposure, epithelial resistance, inflammation, motility disorder, and visceral hypersensitivity were all involved in the complicated pathogenesis of GERD (<xref ref-type="bibr" rid="ref6">6</xref>, <xref ref-type="bibr" rid="ref7">7</xref>).</p>
<p>Ambient air pollution is a prominent environmental risk factor for various diseases. Particulate matter has been the leading contributor to the Global Burden of Disease Study 2021 (GBD 2021) (<xref ref-type="bibr" rid="ref8">8</xref>). Inhalable particulate matter (PM<sub>10</sub>) could be mainly classified into coarse particulate matter (PM<sub>2.5&#x2013;10</sub>) and fine particulate matter (PM<sub>2.5</sub>) (<xref ref-type="bibr" rid="ref9">9</xref>). According to the Integrated Science Assessment (ISA), approximately 71% of the particulate matter deposited in the nose is subsequently transported to the gastrointestinal tract (<xref ref-type="bibr" rid="ref9">9</xref>), underlining the significance of the gastrointestinal exposure pathway for air pollutants. Nitrogen oxides (NO<sub>X</sub>) are one of the typical types of gaseous pollutants, consisting of nitrogen dioxide (NO<sub>2</sub>) and nitric oxide (NO). NO<sub>X</sub> is mainly sourced from vehicle emissions, industrial processes, and fuel combustion, and has specific adverse health effects (<xref ref-type="bibr" rid="ref10">10</xref>, <xref ref-type="bibr" rid="ref11">11</xref>). It is predicted that NO<sub>X</sub> is emitted into the atmosphere predominantly as NO (more than 90%) (<xref ref-type="bibr" rid="ref10">10</xref>), which is a highly reactive free radical as a noxious air pollutant (<xref ref-type="bibr" rid="ref12">12</xref>). While previous research has largely focused on respiratory and cardiovascular diseases, only limited studies have examined its role in gastrointestinal disorders (<xref ref-type="bibr" rid="ref13 ref14 ref15">13&#x2013;15</xref>). In particular, the potential association between long-term exposure to air pollutants and the risk of GERD incidence has not been investigated.</p>
<p>To address existing knowledge gaps, our study aimed to evaluate the impact of long-term particulate matter (PM<sub>10</sub>, PM<sub>2.5&#x2013;10</sub>, and PM<sub>2.5</sub>) and gaseous pollutants (NO<sub>X</sub>, NO<sub>2</sub>, and NO) exposures on incident GERD as well as its subtypes (GERD without esophagitis and GERD with esophagitis). Furthermore, we sought to identify populations that may be more susceptible to the association between air pollution and incident GERD.</p>
</sec>
<sec sec-type="methods" id="sec6">
<label>2</label>
<title>Methods</title>
<sec id="sec7">
<label>2.1</label>
<title>Study population</title>
<p>We performed this cohort study using data from the UK Biobank database, which is a population-based longitudinal study of around 0.5 million participants aged 40 to 69&#x202F;years enrolled from 22 health assessment centers in the UK between 2006 and 2010 (<xref ref-type="bibr" rid="ref16">16</xref>). During baseline assessment, a wide range of health-related information was collected from touchscreen questionnaires, physical measurements, and biological samples. The UK Biobank study was approved by the North West Multicenter Research Ethics Committee, and all participants provided written informed consent before data collection. Our research utilized data from this approved project (application ID: 99732) within the UK Biobank.</p>
<p>In this study, we aimed to investigate the associations between air pollutant exposures and the risk of GERD incidence. Among a total of 502,271 participants, individuals with a diagnosis of cancer (<italic>n</italic>&#x202F;=&#x202F;38,647) or GERD (<italic>n</italic>&#x202F;=&#x202F;4,326) at baseline were excluded. To reduce the impact of other related factors, we excluded participants with GERD-related conditions (including esophagitis, other diseases of the esophagus, peptic ulcer, gastritis and duodenitis, other diseases of stomach and duodenum) (<italic>n</italic>&#x202F;=&#x202F;602) or take acid inhibitors medicine (proton pump inhibitors or H<sub>2</sub> antagonist) (<italic>n</italic>&#x202F;=&#x202F;139,678). Participants with incomplete socioeconomic data or air pollutants data (<italic>n</italic>&#x202F;=&#x202F;29,631) were also excluded. Finally, a total of 289,387 participants were included in the statistical analysis (<xref ref-type="supplementary-material" rid="SM1">Supplementary Figure S1</xref>).</p>
</sec>
<sec id="sec8">
<label>2.2</label>
<title>Assessment of exposure</title>
<p>The ambient air pollutants examined in this study included PM<sub>10</sub>, PM<sub>2.5&#x2013;10</sub>, PM<sub>2.5</sub>, NO<sub>X</sub>, NO<sub>2</sub>, and NO. The land use regression (LUR) model, developed as part of the European Study of Cohorts for Air Pollution Effects (ESCAPE) project, was employed to estimate the annual mean air pollutant concentrations in 2010 (<xref ref-type="bibr" rid="ref17">17</xref>, <xref ref-type="bibr" rid="ref18">18</xref>). Annual average concentrations of air pollutants were evaluated using pollutant-specific LUR models, which utilize predictor variables derived from the Geographic Information System and linked to participants&#x2019; residential addresses obtained from baseline information collection. The exposure data of PM<sub>2.5&#x2013;10</sub>, PM<sub>2.5</sub>, and NO<sub>X</sub> were collected in 2010, while annual concentration data of PM<sub>10</sub> and NO<sub>2</sub> were available for several years (2007 and 2010 for PM<sub>10</sub>; 2005&#x2013;2007, and 2010 for NO<sub>2</sub>). Following the official guidelines provided by the UK Biobank, data from various air pollution models should not be averaged. Consequently, for our analysis, we utilized the air pollution data for 2010 from the ESCAPE project to represent long-term exposure, consistent with methodologies employed in related prior research (<xref ref-type="bibr" rid="ref19">19</xref>). NO<sub>X</sub> refers specifically to the sum of nitrogen dioxide (NO<sub>2</sub>) and nitric oxide (NO). We estimated the NO concentration by subtracting the concentration of NO<sub>2</sub> from NO<sub>X</sub>.</p>
</sec>
<sec id="sec9">
<label>2.3</label>
<title>Assessment of outcome</title>
<p>The outcome was defined as the first occurrence of a GERD diagnosis, encoded as K21 according to the International Classification of Diseases 10th Revision (ICD-10). Based on ICD-10, the first occurrence of K21 (GERD), including K21.0 (GERD with esophagitis) and K21.9 (GERD without esophagitis), was considered the outcome of this study. The follow-up period extended from the date of baseline assessment to the date of GERD diagnosis. For individuals who did not develop GERD, the endpoint was defined as the earliest of the following events: death, loss to follow-up, or the end of the study (May 2024).</p>
</sec>
<sec id="sec10">
<label>2.4</label>
<title>Assessment of covariates</title>
<p>The covariates related to air pollution and/or GERD were initially determined by reviews of relevant studies (<xref ref-type="bibr" rid="ref1">1</xref>, <xref ref-type="bibr" rid="ref3">3</xref>, <xref ref-type="bibr" rid="ref13">13</xref>), including age, sex, body mass index (BMI), ethnicity, education level, dietary habits, physical activity, smoking status, alcohol consumption, mental health disorders, the Townsend Deprivation Index (TDI), and assessment centers. To determine which covariates should be adjusted in our model, we introduced a graphical tool called directed acyclic graphs (DAGs). DAGs are widely used to identify confounding variables that require adjusting to estimate causal effects (<xref ref-type="bibr" rid="ref20">20</xref>). The DAGitty online tool<xref ref-type="fn" rid="fn0001"><sup>1</sup></xref> was utilized to construct a DAG for our study. We identified six confounders&#x2014;age, sex, ethnicity, education level, the TDI, and assessment centers&#x2014;which required adjustment in the main model (<xref ref-type="supplementary-material" rid="SM1">Supplementary Figure S2</xref>). For more detailed information on methodology, please refer to the <xref ref-type="supplementary-material" rid="SM1">Supplementary Methods</xref>.</p>
</sec>
<sec id="sec11">
<label>2.5</label>
<title>Statistical analysis</title>
<p>In the present study, the Cox proportional hazard regression model was applied to estimate the association between long-term exposure to air pollutants and GERD incidence. Results are expressed as the percentage change (%) in risk, which is calculated based on the hazard ratios (HRs) after adjusting for potential confounders (age, sex, ethnicity, education level, the TDI, and assessment centers). Specifically, the percentage change was calculated as [(HR&#x202F;&#x2212;&#x202F;1)/1]&#x202F;&#x00D7;&#x202F;100%, representing the relative change in GERD risk associated with per interquartile range (IQR) increase in air pollutant. Statistical significance is determined based on whether the 95% CI crosses zero for the percentage change (equivalent to crossing one for the HR). The proportional hazard assumption was tested using Schoenfeld residuals and was not violated.</p>
<p>We used single-pollutant models (including each pollutant separately) to estimate the air pollution-related GERD risk. The linear exposure-response relationship between each air pollutant exposure and GERD incidence risk was assessed by calculating the trend <italic>p</italic>-values for each pollutant exposure. Furthermore, we investigated the exposure-response association using a natural cubic spline with 2 degrees of freedom.</p>
<p>Subgroup analyses were conducted by sex (male vs. female), age [&#x003C;60&#x202F;years (middle-aged) vs. &#x2265;60&#x202F;years (older adults)], BMI (&#x003C; 25 vs. &#x2265; 25&#x202F;kg/m<sup>2</sup>), ethnicity (White vs. others), education level (low vs. high), TDI (low SES vs. high SES), and assessment centers (England vs. others) to identify the vulnerable populations. The statistical significance of the difference between strata was assessed using a two-sample <italic>Z</italic> test, applied according to the following formula:</p><disp-formula id="E1">
<mml:math id="M1">
<mml:mrow>
<mml:mi mathvariant="normal">Z</mml:mi>
<mml:mo>=</mml:mo>
<mml:mfrac>
<mml:mrow>
<mml:msub>
<mml:mi>Q</mml:mi>
<mml:mn>1</mml:mn>
</mml:msub>
<mml:mo>&#x2212;</mml:mo>
<mml:msub>
<mml:mi>Q</mml:mi>
<mml:mn>2</mml:mn>
</mml:msub>
</mml:mrow>
<mml:mrow>
<mml:msqrt>
<mml:mrow>
<mml:mi>S</mml:mi>
<mml:msubsup>
<mml:mi>E</mml:mi>
<mml:mn>1</mml:mn>
<mml:mn>2</mml:mn>
</mml:msubsup>
<mml:mo>+</mml:mo>
<mml:mi>S</mml:mi>
<mml:msubsup>
<mml:mi>E</mml:mi>
<mml:mn>2</mml:mn>
<mml:mn>2</mml:mn>
</mml:msubsup>
</mml:mrow>
</mml:msqrt>
</mml:mrow>
</mml:mfrac>
</mml:mrow>
</mml:math>
</disp-formula><p>where Q<sub>1</sub> and Q<sub>2</sub> are the strata-specific regression coefficients, and SE<sub>1</sub> and SE<sub>2</sub> are the corresponding standard errors (<xref ref-type="bibr" rid="ref21">21</xref>).</p>
<p>Additionally, we conducted a series of sensitivity analyses to assess the robustness and reliability of our findings: (1) additional adjustment for behavioral factors (BMI, diet, physical activity, smoking, alcohol, and mental health disorders); (2) excluding cases diagnosed within the first 1&#x2013;3&#x202F;years of follow-up to minimize potential reverse causation; (3) restricting the analysis to participants residing at their current address for at least 10&#x202F;years to reduce exposure misclassification; and (4) using transport accidents (ICD: V01&#x2013;V99) as a negative control outcome to evaluate unmeasured confounding.</p>
<p>Statistical analyses for this study were performed using R (version 4.2.1; R Foundation for Statistical Computing). The Cox proportional hazards regression model was implemented via the <italic>survival</italic> package. The two-sided <italic>p</italic>-values &#x003C; 0.05 were statistically significant.</p>
</sec>
</sec>
<sec sec-type="results" id="sec12">
<label>3</label>
<title>Results</title>
<sec id="sec13">
<label>3.1</label>
<title>Baseline characteristics</title>
<p><xref ref-type="table" rid="tab1">Table 1</xref> displays the baseline characteristics of participants in this study. Of 289,387 participants, the mean (SD) age was 57.2&#x202F;&#x00B1;&#x202F;8.0&#x202F;years, and more than half were females (56.2%). During a follow-up period of 14.1&#x202F;&#x00B1;&#x202F;2.4&#x202F;years, 32,413 (11.2%) incident cases of GERD were identified, including 27,669 cases of GERD without esophagitis (K21.9) and 8,897 cases of GERD with esophagitis (K21.0). The concentration of pollutants (mean, SD, minimum, IQR, percentile, and maximum) is presented in <xref ref-type="table" rid="tab2">Table 2</xref>. The mean estimates of annual average concentrations for PM<sub>10</sub>, PM<sub>2.5&#x2013;10</sub>, PM<sub>2.5</sub>, NO<sub>X</sub>, NO<sub>2</sub> and NO were 16.23, 6.42, 9.99, 43.99, 26.60, and 17.39&#x202F;&#x03BC;g/m<sup>3</sup>, respectively (<xref ref-type="table" rid="tab2">Table 2</xref>), which exceeded the threshold recommended by the World Health Organization Global Air Quality Guidelines (AQG 2021: PM<sub>10</sub>, 15&#x202F;&#x03BC;g/m<sup>3</sup>; PM<sub>2.5</sub>, 5&#x202F;&#x03BC;g/m<sup>3</sup>; NO<sub>2</sub>, 10&#x202F;&#x03BC;g/m<sup>3</sup>) (<xref ref-type="bibr" rid="ref22">22</xref>). Spearman correlation analysis among pollutants showed correlation coefficients below 0.8 (<xref ref-type="supplementary-material" rid="SM1">Supplementary Table S1</xref>).</p>
<table-wrap position="float" id="tab1">
<label>Table 1</label>
<caption>
<p>Baseline characteristics of the included participants.</p>
</caption>
<table frame="hsides" rules="groups">
<thead>
<tr>
<th/>
<th align="center" valign="top">GERD</th>
<th align="center" valign="top">GERD without esophagitis</th>
<th align="center" valign="top">GERD with esophagitis</th>
<th align="center" valign="top">Total population</th>
</tr>
</thead>
<tbody>
<tr>
<td align="left" valign="bottom">Number of participants (<italic>N</italic>, %)</td>
<td align="center" valign="middle">32,413</td>
<td align="center" valign="middle">27,669</td>
<td align="center" valign="middle">8,897</td>
<td align="center" valign="middle">289,387</td>
</tr>
<tr>
<td align="left" valign="bottom">Follow-up period (years)</td>
<td align="center" valign="middle">7.9&#x202F;&#x00B1;&#x202F;3.7</td>
<td align="center" valign="top">8.2&#x202F;&#x00B1;&#x202F;3.6</td>
<td align="center" valign="middle">7.2&#x202F;&#x00B1;&#x202F;3.9</td>
<td align="center" valign="middle">14.1&#x202F;&#x00B1;&#x202F;2.4</td>
</tr>
<tr>
<td align="left" valign="bottom">Age at recruitment</td>
<td align="center" valign="middle">58.8&#x202F;&#x00B1;&#x202F;7.4</td>
<td align="center" valign="top">58.9&#x202F;&#x00B1;&#x202F;7.4</td>
<td align="center" valign="middle">58.7&#x202F;&#x00B1;&#x202F;7.4</td>
<td align="center" valign="middle">57.2&#x202F;&#x00B1;&#x202F;8.0</td>
</tr>
<tr>
<td align="left" valign="bottom" colspan="5">Sex</td>
</tr>
<tr>
<td align="left" valign="bottom">Male</td>
<td align="center" valign="middle">13,525 (41.7)</td>
<td align="center" valign="top">11,285 (40.8)</td>
<td align="center" valign="middle">4,065 (45.7)</td>
<td align="center" valign="middle">126,719 (43.8)</td>
</tr>
<tr>
<td align="left" valign="bottom">Female</td>
<td align="center" valign="middle">18,888 (58.3)</td>
<td align="center" valign="top">16,384 (59.2)</td>
<td align="center" valign="middle">4,832 (54.3)</td>
<td align="center" valign="middle">162,668 (56.2)</td>
</tr>
<tr>
<td align="left" valign="bottom" colspan="5">Age (years)</td>
</tr>
<tr>
<td align="left" valign="bottom">&#x003C;60</td>
<td align="center" valign="middle">14,624 (45.1)</td>
<td align="center" valign="top">12,265 (44.3)</td>
<td align="center" valign="middle">4,089 (46.0)</td>
<td align="center" valign="middle">152,850 (52.8)</td>
</tr>
<tr>
<td align="left" valign="bottom">&#x2265;60</td>
<td align="center" valign="middle">17,789 (54.9)</td>
<td align="center" valign="top">15,404 (55.7)</td>
<td align="center" valign="middle">4,808 (54.0)</td>
<td align="center" valign="middle">136,537 (47.2)</td>
</tr>
<tr>
<td align="left" valign="bottom" colspan="5">Body mass index (kg/m<sup>2</sup>)</td>
</tr>
<tr>
<td align="left" valign="bottom">&#x003C;25</td>
<td align="center" valign="middle">7,697 (23.7)</td>
<td align="center" valign="top">6,384 (23.1)</td>
<td align="center" valign="middle">2,168 (24.4)</td>
<td align="center" valign="middle">87,561 (30.3)</td>
</tr>
<tr>
<td align="left" valign="bottom">&#x2265;25</td>
<td align="center" valign="middle">24,568 (75.8)</td>
<td align="center" valign="top">21,147 (76.4)</td>
<td align="center" valign="middle">6,696 (75.3)</td>
<td align="center" valign="middle">200,366 (69.2)</td>
</tr>
<tr>
<td align="left" valign="bottom">Missing data</td>
<td align="center" valign="middle">148 (0.5)</td>
<td align="center" valign="top">138 (0.5)</td>
<td align="center" valign="middle">33 (0.4)</td>
<td align="center" valign="middle">1,460 (0.5)</td>
</tr>
<tr>
<td align="left" valign="bottom" colspan="5">Ethnic background</td>
</tr>
<tr>
<td align="left" valign="bottom">White</td>
<td align="center" valign="middle">29,815 (92.0)</td>
<td align="center" valign="top">25,422 (91.9)</td>
<td align="center" valign="middle">8,209 (92.3)</td>
<td align="center" valign="middle">263,216 (91.0)</td>
</tr>
<tr>
<td align="left" valign="bottom">Mixed</td>
<td align="center" valign="middle">1,096 (3.4)</td>
<td align="center" valign="top">939 (3.4)</td>
<td align="center" valign="middle">313 (3.5)</td>
<td align="center" valign="middle">10,508 (3.6)</td>
</tr>
<tr>
<td align="left" valign="bottom">Asian or Asian British</td>
<td align="center" valign="middle">904 (2.8)</td>
<td align="center" valign="top">767 (2.8)</td>
<td align="center" valign="middle">246 (2.8)</td>
<td align="center" valign="middle">9,590 (3.3)</td>
</tr>
<tr>
<td align="left" valign="bottom">Black or Black British</td>
<td align="center" valign="middle">173 (0.5)</td>
<td align="center" valign="top">157 (0.6)</td>
<td align="center" valign="middle">43 (0.5)</td>
<td align="center" valign="middle">1,686 (0.6)</td>
</tr>
<tr>
<td align="left" valign="bottom">Others</td>
<td align="center" valign="middle">425 (1.3)</td>
<td align="center" valign="top">384 (1.4)</td>
<td align="center" valign="middle">86 (1.0)</td>
<td align="center" valign="middle">4,387 (1.5)</td>
</tr>
<tr>
<td align="left" valign="bottom" colspan="5">Education level<sup>1</sup></td>
</tr>
<tr>
<td align="left" valign="bottom">Low</td>
<td align="center" valign="middle">16,477 (50.8)</td>
<td align="center" valign="top">13,971 (50.5)</td>
<td align="center" valign="middle">4,561 (51.3)</td>
<td align="center" valign="middle">146,012 (50.5)</td>
</tr>
<tr>
<td align="left" valign="bottom">High</td>
<td align="center" valign="middle">7,287 (22.5)</td>
<td align="center" valign="top">6,056 (21.9)</td>
<td align="center" valign="middle">2,046 (23.0)</td>
<td align="center" valign="middle">85,593 (29.6)</td>
</tr>
<tr>
<td align="left" valign="bottom">Missing data</td>
<td align="center" valign="middle">8,649 (26.7)</td>
<td align="center" valign="top">7,642 (27.6)</td>
<td align="center" valign="middle">2,290 (25.7)</td>
<td align="center" valign="middle">57,782 (20.0)</td>
</tr>
<tr>
<td align="left" valign="bottom" colspan="5">Townsend Deprivation Index<sup>2</sup></td>
</tr>
<tr>
<td align="left" valign="bottom">Low SES</td>
<td align="center" valign="middle">16,217 (50.0)</td>
<td align="center" valign="top">13,824 (50.0)</td>
<td align="center" valign="middle">4,462 (50.2)</td>
<td align="center" valign="middle">145,073 (50.1)</td>
</tr>
<tr>
<td align="left" valign="bottom">High SES</td>
<td align="center" valign="middle">16,196 (50.0)</td>
<td align="center" valign="top">13,845 (50.0)</td>
<td align="center" valign="middle">4,435 (49.8)</td>
<td align="center" valign="middle">144,314 (49.9)</td>
</tr>
<tr>
<td align="left" valign="bottom" colspan="5">Smoking status</td>
</tr>
<tr>
<td align="left" valign="bottom">Never</td>
<td align="center" valign="middle">15,972 (49.3)</td>
<td align="center" valign="top">13,629 (49.3)</td>
<td align="center" valign="middle">4,328 (48.6)</td>
<td align="center" valign="middle">153,812 (53.2)</td>
</tr>
<tr>
<td align="left" valign="bottom">Previous smoking</td>
<td align="center" valign="middle">12,851 (39.7)</td>
<td align="center" valign="top">11,006 (39.8)</td>
<td align="center" valign="middle">3,509 (39.4)</td>
<td align="center" valign="middle">104,614 (36.2)</td>
</tr>
<tr>
<td align="left" valign="bottom">Current smoking</td>
<td align="center" valign="middle">3,408 (10.5)</td>
<td align="center" valign="top">2,874 (10.4)</td>
<td align="center" valign="middle">1,012 (11.4)</td>
<td align="center" valign="middle">29,770 (10.3)</td>
</tr>
<tr>
<td align="left" valign="bottom">Missing data</td>
<td align="center" valign="middle">182 (0.6)</td>
<td align="center" valign="top">160 (0.6)</td>
<td align="center" valign="middle">48 (0.5)</td>
<td align="center" valign="middle">1,191 (0.4)</td>
</tr>
<tr>
<td align="left" valign="bottom" colspan="5">Drinking status</td>
</tr>
<tr>
<td align="left" valign="bottom">Never</td>
<td align="center" valign="middle">1,768 (5.5)</td>
<td align="center" valign="top">1,589 (5.7)</td>
<td align="center" valign="middle">427 (4.8)</td>
<td align="center" valign="middle">13,653 (4.7)</td>
</tr>
<tr>
<td align="left" valign="bottom">Previous drinking</td>
<td align="center" valign="middle">1,657 (5.1)</td>
<td align="center" valign="top">1,490 (5.4)</td>
<td align="center" valign="middle">431 (4.8)</td>
<td align="center" valign="middle">11,640 (4.0)</td>
</tr>
<tr>
<td align="left" valign="bottom">Current drinking</td>
<td align="center" valign="middle">28,933 (89.3)</td>
<td align="center" valign="top">24,543 (88.7)</td>
<td align="center" valign="middle">8,023 (90.2)</td>
<td align="center" valign="middle">263,700 (91.1)</td>
</tr>
<tr>
<td align="left" valign="bottom">Missing data</td>
<td align="center" valign="middle">55 (0.2)</td>
<td align="center" valign="top">47 (0.2)</td>
<td align="center" valign="middle">16 (0.2)</td>
<td align="center" valign="middle">394 (0.1)</td>
</tr>
<tr>
<td align="left" valign="bottom" colspan="5">Diet<sup>3</sup></td>
</tr>
<tr>
<td align="left" valign="bottom">Healthy</td>
<td align="center" valign="middle">28,525 (88.0)</td>
<td align="center" valign="top">24,345 (88.0)</td>
<td align="center" valign="middle">7,787 (87.5)</td>
<td align="center" valign="middle">256,512 (88.6)</td>
</tr>
<tr>
<td align="left" valign="bottom">Unhealthy</td>
<td align="center" valign="middle">3,888 (12.0)</td>
<td align="center" valign="top">3,324 (12.0)</td>
<td align="center" valign="middle">1,110 (12.5)</td>
<td align="center" valign="middle">32,875 (11.4)</td>
</tr>
<tr>
<td align="left" valign="bottom" colspan="5">Physical activity<sup>4</sup></td>
</tr>
<tr>
<td align="left" valign="bottom">Low</td>
<td align="center" valign="middle">5,040 (15.5)</td>
<td align="center" valign="top">4,284 (15.5)</td>
<td align="center" valign="middle">1,380 (15.5)</td>
<td align="center" valign="middle">42,999 (14.9)</td>
</tr>
<tr>
<td align="left" valign="bottom">Moderate</td>
<td align="center" valign="middle">9,416 (29.1)</td>
<td align="center" valign="top">7,971 (28.8)</td>
<td align="center" valign="middle">2,600 (29.2)</td>
<td align="center" valign="middle">89,668 (31.0)</td>
</tr>
<tr>
<td align="left" valign="bottom">High</td>
<td align="center" valign="middle">9,134 (28.2)</td>
<td align="center" valign="top">7,731 (27.9)</td>
<td align="center" valign="middle">2,595 (29.2)</td>
<td align="center" valign="middle">87,938 (30.4)</td>
</tr>
<tr>
<td align="left" valign="bottom">Missing data</td>
<td align="center" valign="middle">8,823 (27.2)</td>
<td align="center" valign="top">7,683 (27.8)</td>
<td align="center" valign="middle">2,322 (26.1)</td>
<td align="center" valign="middle">68,782 (23.8)</td>
</tr>
<tr>
<td align="left" valign="bottom" colspan="5">Mental disorders<sup>5</sup></td>
</tr>
<tr>
<td align="left" valign="bottom">Yes</td>
<td align="center" valign="middle">5,440 (16.8)</td>
<td align="center" valign="top">4,767 (17.2)</td>
<td align="center" valign="middle">1,485 (16.7)</td>
<td align="center" valign="middle">37,539 (13.0)</td>
</tr>
<tr>
<td align="left" valign="bottom">No</td>
<td align="center" valign="middle">26,781 (82.6)</td>
<td align="center" valign="top">22,732 (82.2)</td>
<td align="center" valign="middle">7,364 (82.8)</td>
<td align="center" valign="middle">250,338 (86.5)</td>
</tr>
<tr>
<td align="left" valign="bottom">Missing data</td>
<td align="center" valign="middle">192 (0.6)</td>
<td align="center" valign="top">170 (0.6)</td>
<td align="center" valign="middle">48 (0.5)</td>
<td align="center" valign="middle">1,510 (0.5)</td>
</tr>
</tbody>
</table>
<table-wrap-foot>
<p><sup>1</sup>Education level was dichotomized as high (college or university degree) and low (high school or below).</p>
<p><sup>2</sup>High SES was defined as Townsend Deprivation Index &#x003C; &#x2212;2.17 (median value) while low SES was defined as Townsend Deprivation Index&#x202F;&#x2265;&#x202F;&#x2212;2.17.</p>
<p><sup>3</sup>Healthy diet was defined as at least two of the healthy foods (fruit and vegetable intake: &#x003E;4.5 pieces or servings a week; fish intake: &#x003E;2 per week; meat intake: processed meat &#x2264; 2 per week and red meat &#x2264; 5 per week), otherwise unhealthy.</p>
<p><sup>4</sup>Physical activity was categorized as low, moderate, and high based on the International Physical Activity Questionnaire (IPAQ).</p>
<p><sup>5</sup>Mental disorders were assessed according to the touchscreen question (&#x201C;Have you ever seen a psychiatrist for nerves, anxiety, tension, or depression?&#x201D;).</p>
<p>SES, socioeconomic status.</p>
</table-wrap-foot>
</table-wrap>
<table-wrap position="float" id="tab2">
<label>Table 2</label>
<caption>
<p>Descriptive statistics for the annual concentrations of ambient air pollutants in the United Kingdom in 2010.</p>
</caption>
<table frame="hsides" rules="groups">
<thead>
<tr>
<th align="left" valign="top" rowspan="2">Air pollutants</th>
<th align="center" valign="top" rowspan="2">Mean</th>
<th align="center" valign="top" rowspan="2">SD</th>
<th align="center" valign="top" rowspan="2">IQR</th>
<th align="center" valign="top" rowspan="2">Min</th>
<th align="center" valign="top" colspan="5">Percentile</th>
<th align="center" valign="top" rowspan="2">Max</th>
</tr>
<tr>
<th align="center" valign="top">P1</th>
<th align="center" valign="top">P25</th>
<th align="center" valign="top">P50</th>
<th align="center" valign="top">P75</th>
<th align="center" valign="top">P99</th>
</tr>
</thead>
<tbody>
<tr>
<td align="left" valign="bottom" colspan="11">Particulate matter</td>
</tr>
<tr>
<td align="left" valign="bottom">PM<sub>10</sub></td>
<td align="center" valign="middle">16.23</td>
<td align="center" valign="middle">1.89</td>
<td align="center" valign="middle">1.75</td>
<td align="center" valign="middle">11.78</td>
<td align="center" valign="middle">11.98</td>
<td align="center" valign="middle">15.25</td>
<td align="center" valign="middle">16.02</td>
<td align="center" valign="middle">17.00</td>
<td align="center" valign="middle">21.47</td>
<td align="center" valign="middle">30.65</td>
</tr>
<tr>
<td align="left" valign="bottom">PM<sub>2.5&#x2013;10</sub></td>
<td align="center" valign="middle">6.42</td>
<td align="center" valign="middle">0.90</td>
<td align="center" valign="middle">0.79</td>
<td align="center" valign="middle">5.57</td>
<td align="center" valign="middle">5.59</td>
<td align="center" valign="middle">5.84</td>
<td align="center" valign="middle">6.11</td>
<td align="center" valign="middle">6.63</td>
<td align="center" valign="middle">9.19</td>
<td align="center" valign="middle">12.82</td>
</tr>
<tr>
<td align="left" valign="bottom">PM<sub>2.5</sub></td>
<td align="center" valign="middle">9.99</td>
<td align="center" valign="middle">1.06</td>
<td align="center" valign="middle">1.27</td>
<td align="center" valign="middle">8.17</td>
<td align="center" valign="middle">8.17</td>
<td align="center" valign="middle">9.29</td>
<td align="center" valign="middle">9.93</td>
<td align="center" valign="middle">10.56</td>
<td align="center" valign="middle">13.17</td>
<td align="center" valign="middle">21.31</td>
</tr>
<tr>
<td align="left" valign="bottom" colspan="11">Gaseous pollutants</td>
</tr>
<tr>
<td align="left" valign="bottom">NO<sub>X</sub></td>
<td align="center" valign="middle">43.99</td>
<td align="center" valign="middle">15.55</td>
<td align="center" valign="middle">16.43</td>
<td align="center" valign="middle">19.74</td>
<td align="center" valign="middle">20.61</td>
<td align="center" valign="middle">34.25</td>
<td align="center" valign="middle">42.20</td>
<td align="center" valign="middle">50.68</td>
<td align="center" valign="middle">95.63</td>
<td align="center" valign="middle">265.94</td>
</tr>
<tr>
<td align="left" valign="bottom">NO<sub>2</sub></td>
<td align="center" valign="middle">26.60</td>
<td align="center" valign="middle">7.57</td>
<td align="center" valign="middle">9.73</td>
<td align="center" valign="middle">12.93</td>
<td align="center" valign="middle">13.02</td>
<td align="center" valign="middle">21.38</td>
<td align="center" valign="middle">26.07</td>
<td align="center" valign="middle">31.11</td>
<td align="center" valign="middle">47.78</td>
<td align="center" valign="middle">108.49</td>
</tr>
<tr>
<td align="left" valign="bottom">NO</td>
<td align="center" valign="middle">17.39</td>
<td align="center" valign="middle">9.07</td>
<td align="center" valign="middle">9.01</td>
<td align="center" valign="middle">0.00</td>
<td align="center" valign="middle">5.48</td>
<td align="center" valign="middle">11.69</td>
<td align="center" valign="middle">16.05</td>
<td align="center" valign="middle">20.70</td>
<td align="center" valign="middle">49.68</td>
<td align="center" valign="middle">160.06</td>
</tr>
</tbody>
</table>
<table-wrap-foot>
<p>Air pollutant concentrations are measured in &#x03BC;g/m<sup>3</sup>.</p>
<p>PM<sub>10</sub>, inhalable particulate matter; PM<sub>2.5&#x2013;10</sub>, coarse particulate matter; PM<sub>2.5</sub>, fine particulate matter; NO<sub>X</sub>, nitrogen oxides; NO<sub>2</sub>, nitrogen dioxide; NO, nitric oxide. SD, standard deviation; IQR, interquartile range; Min, minimum; P, Percentile; Max, maximum.</p>
</table-wrap-foot>
</table-wrap>
</sec>
<sec id="sec14">
<label>3.2</label>
<title>Associations of air pollutants with incident GERD</title>
<p>Assessing by Cox proportional hazard regression models, significantly positive associations of particulate matter (PM<sub>10</sub>, PM<sub>2.5&#x2013;10</sub>, and PM<sub>2.5</sub>) and gaseous pollutants (NO<sub>X</sub>, NO<sub>2</sub>, and NO) with incident GERD were observed (<xref ref-type="table" rid="tab3">Table 3</xref> and <xref ref-type="fig" rid="fig1">Figure 1</xref>). Overall, for each IQR increase in long-term PM<sub>10</sub>, PM<sub>2.5&#x2013;10</sub>, PM<sub>2.5</sub>, NO<sub>X</sub>, NO<sub>2</sub>, and NO exposure, the risk of GERD increased 1.69% (95% CI: 0.58, 2.81%), 1.29% (95% CI: 0.29, 2.29%), 3.57% (95% CI: 1.97, 5.19%), 2.08% (95% CI: 0.56, 3.63%), 1.93% (95% CI: 0.06, 3.83%), and 2.28% (0.88, 3.70%), individually. When stratified by GERD subtype, the positive associations were predominantly observed for GERD without esophagitis (K21.9), with no significant association for GERD with esophagitis (K21.0).</p>
<table-wrap position="float" id="tab3">
<label>Table 3</label>
<caption>
<p>Percentage change in the risk of incident GERD per IQR increase in air pollutants.</p>
</caption>
<table frame="hsides" rules="groups">
<thead>
<tr>
<th align="left" valign="top" rowspan="2">Air pollutants</th>
<th align="center" valign="top" colspan="3">Particulate matter</th>
<th align="center" valign="top" colspan="3">Gaseous pollutants</th>
</tr>
<tr>
<th align="center" valign="top">PM<sub>10</sub></th>
<th align="center" valign="top">PM<sub>2.5&#x2013;10</sub></th>
<th align="center" valign="top">PM<sub>2.5</sub></th>
<th align="center" valign="top">NO<sub>X</sub></th>
<th align="center" valign="top">NO<sub>2</sub></th>
<th align="center" valign="top">NO</th>
</tr>
</thead>
<tbody>
<tr>
<td align="left" valign="top">K21</td>
<td align="center" valign="top">1.69 (0.58, 2.81)</td>
<td align="center" valign="top">1.29 (0.29, 2.29)</td>
<td align="center" valign="top">3.57 (1.97, 5.19)</td>
<td align="center" valign="top">2.08 (0.56, 3.63)</td>
<td align="center" valign="top">1.93 (0.06, 3.83)</td>
<td align="center" valign="top">2.28 (0.88, 3.70)</td>
</tr>
<tr>
<td align="left" valign="top">K21.9</td>
<td align="center" valign="top">2.52 (1.31, 3.73)</td>
<td align="center" valign="top">1.89 (0.80, 2.99)</td>
<td align="center" valign="top">4.78 (3.03, 6.56)</td>
<td align="center" valign="top">3.46 (1.80, 5.15)</td>
<td align="center" valign="top">4.00 (1.94, 6.10)</td>
<td align="center" valign="top">3.38 (1.86, 4.93)</td>
</tr>
<tr>
<td align="left" valign="top">K21.0</td>
<td align="center" valign="top">&#x2212;0.30 (&#x2212;2.39, 1.83)</td>
<td align="center" valign="top">&#x2212;1.40 (&#x2212;3.33, 0.56)</td>
<td align="center" valign="top">1.18 (&#x2212;1.78, 4.23)</td>
<td align="center" valign="top">&#x2212;0.42 (&#x2212;3.25, 2.49)</td>
<td align="center" valign="top">&#x2212;2.26 (&#x2212;5.68, 1.29)</td>
<td align="center" valign="top">0.50 (&#x2212;2.11, 3.17)</td>
</tr>
</tbody>
</table>
<table-wrap-foot>
<p>Results are expressed as the percentage change (%) in risk, and percentage signs (%) are omitted for clarity. The interquartile range (IQR) values for PM<sub>10</sub>, PM<sub>2.5&#x2013;10</sub>, PM<sub>2.5</sub>, NO<sub>X</sub>, NO<sub>2</sub>, and NO were 1.75, 0.79, 1.27, 16.43, 9.73, and 9.01&#x202F;&#x03BC;g/m<sup>3</sup>, respectively. The model was adjusted for age, sex, ethnicity, education level, Townsend deprivation index, and assessment centers.</p>
<p>PM<sub>10</sub>, inhalable particulate matter; PM<sub>2.5&#x2013;10</sub>, coarse particulate matter; PM<sub>2.5</sub>, fine particulate matter; NO<sub>X</sub>, nitrogen oxides; NO<sub>2</sub>, nitrogen dioxide; NO, nitric oxide; K21, gastroesophageal reflux disease (GERD); K21.9, GERD without esophagitis; K21.0, GERD with esophagitis; IQR, interquartile range.</p>
</table-wrap-foot>
</table-wrap>
<fig position="float" id="fig1">
<label>Figure 1</label>
<caption>
<p>The estimated association between percentage change (%) in GERD incidence risk associated with each IQR in air pollutants. The interquartile range (IQR) values for PM<sub>10</sub>, PM<sub>2.5&#x2013;10</sub>, PM<sub>2.5</sub>, NO<sub>X</sub>, NO<sub>2</sub>, and NO were 1.75, 0.79, 1.27, 16.43, 9.73, and 9.01&#x202F;&#x03BC;g/m<sup>3</sup>, respectively. The model was adjusted for age, sex, ethnicity, education level, Townsend deprivation index, and assessment centers. Abbreviation: PM<sub>10</sub>, inhalable particulate matter; PM<sub>2.5&#x2013;10</sub>, coarse particulate matter; PM<sub>2.5</sub>, fine particulate matter; NO<sub>X</sub>, nitrogen oxides; NO<sub>2</sub>, nitrogen dioxide; NO, nitric oxide; K21, gastroesophageal reflux disease (GERD); K21.9, GERD without esophagitis; K21.0, GERD with esophagitis; IQR, interquartile range.</p>
</caption>
<graphic xlink:href="fpubh-13-1620411-g001.tif">
<alt-text content-type="machine-generated">Bar chart showing percentage change in air pollutants (PM&#x2081;&#x2080;, PM&#x2082;.&#x2085;&#x208B;&#x2081;&#x2080;, PM&#x2082;.&#x2085;, NO&#x2093;, NO&#x2082;, NO). Symbols represent data sets K21 (triangle), K21.9 (circle), and K21.0 (square). Error bars depict variability. Data points for K21 and K21.9 are mostly above zero, while K21.0 varies around zero.</alt-text>
</graphic>
</fig>
<p>Furthermore, exposure-response curves (<xref ref-type="fig" rid="fig2">Figure 2</xref>) demonstrated an almost linear relationship between PM<sub>2.5&#x2013;10</sub> and the incidence risks of GERD (K21). The risk of GERD followed approximately linearly increasing associations with other pollutants (PM<sub>10</sub>, PM<sub>2.5</sub>, NO<sub>X</sub>, NO<sub>2</sub>, and NO) at lower exposure levels, with subtle downward trends at higher exposures. Similar trends were shown between air pollutants and GERD without esophagitis (K21.9) (<xref ref-type="supplementary-material" rid="SM1">Supplementary Figure S3</xref>), while there was no linear exposure-response relationship between air pollution and GERD with esophagitis (K21.0) (<xref ref-type="supplementary-material" rid="SM1">Supplementary Figure S4</xref>).</p>
<fig position="float" id="fig2">
<label>Figure 2</label>
<caption>
<p>The exposure and response curves of long-term ambient air pollutant exposures and the percentage change of GERD (K21) incident risk. Note: the model was adjusted for age, sex, ethnicity, education level, Townsend deprivation index, and assessment centers. PM<sub>10</sub>, inhalable particulate matter; PM<sub>2.5&#x2013;10</sub>, coarse particulate matter; PM<sub>2.5</sub>, fine particulate matter; NO<sub>X</sub>, nitrogen oxides; NO<sub>2</sub>, nitrogen dioxide; NO, nitric oxide; GERD, gastroesophageal reflux disease.</p>
</caption>
<graphic xlink:href="fpubh-13-1620411-g002.tif">
<alt-text content-type="machine-generated">Six scatter plots show the percentage change in health-related data against air pollutant concentrations including PM&#x2081;&#x2080;, PM&#x2082;.&#x2085;&#x208B;&#x2081;&#x2080;, PM&#x2082;.&#x2085;, NO&#x2093;, NO&#x2082;, and NO. Each plot displays a nonlinear trend with solid black lines representing the model fit and dashed lines indicating confidence intervals. Below each plot, histograms depict the distribution of pollutant concentrations.</alt-text>
</graphic>
</fig>
</sec>
<sec id="sec15">
<label>3.3</label>
<title>Subgroup and sensitivity analyses</title>
<p>Subgroup analyses revealed that the middle-aged (&#x003C;60&#x202F;years), White ethnicity, those with higher socioeconomic status (SES), and residents of England were more susceptible to particulate matter exposure. No statistically significant effect was observed when stratified by sex, BMI, or education level. For gaseous pollutants, increased risk was observed among females, the middle-aged (&#x003C;60&#x202F;years), overweight (BMI&#x202F;&#x2265;&#x202F;25&#x202F;kg/m<sup>2</sup>) individuals, and those of White ethnicity and higher SES. No statistically significant differences were observed when stratified by education level or assessment center (<xref ref-type="supplementary-material" rid="SM1">Supplementary Tables S2, S3</xref>).</p>
<p>Sensitivity analyses verified the robustness of the associations observed between air pollutants and GERD in the main model analysis. When adding other covariates to the main model, excluding participants whose GERD diagnosis occurred in the 1/2/3&#x202F;years of follow-up, or restricting the analysis to participants who lived in their current address for at least 10&#x202F;years, the association between air pollutants (except for NO<sub>2</sub>) exposure with GERD incidence did not significantly change (<xref ref-type="supplementary-material" rid="SM1">Supplementary Table S4</xref>). In addition, we did not observe statistically significant associations between air pollutant exposures and the risk of traffic accidents (negative control group) (<xref ref-type="supplementary-material" rid="SM1">Supplementary Table S5</xref>).</p>
</sec>
</sec>
<sec sec-type="discussion" id="sec16">
<label>4</label>
<title>Discussion</title>
<p>In this large-scale and long-term prospective cohort study, we estimated a robust association between several air pollutants and the risk of GERD. Our findings indicated that long-term exposure to both particulate matter (PM<sub>10</sub>, PM<sub>2.5&#x2013;10</sub>, and PM<sub>2.5</sub>) and gaseous pollutants (NO<sub>X</sub>, NO<sub>2</sub>, and NO) is associated with an increased risk of GERD incidence, particularly for GERD without esophagitis (K21.9). We observed almost linear exposure-response curves for the association between long-term exposure to air pollutants and the risk of GERD. Subgroup analyses revealed a stronger association with particulate matter among individuals under 60&#x202F;years old, while females, individuals under 60&#x202F;years old, and the overweight individuals (BMI&#x202F;&#x2265;&#x202F;25&#x202F;kg/m<sup>2</sup>) were more sensitive to gaseous pollutants.</p>
<p>Most previous research has concentrated on the effects of air pollution on respiratory and cardiovascular diseases (<xref ref-type="bibr" rid="ref23 ref24 ref25">23&#x2013;25</xref>), with relatively limited attention to the associations between air pollution and gastrointestinal diseases. Several previous epidemiological studies have revealed associations between air pollutant exposure and gastrointestinal diseases. In accordance with previous research examining the relationship between long-term PM<sub>2.5</sub> exposure and the risk of esophageal cancer incidence (<xref ref-type="bibr" rid="ref13">13</xref>), our study also observed nearly linear exposure-response curves for the association between long-term PM<sub>2.5</sub> and PM<sub>10</sub> exposure and GERD. Although it is well established that GERD is a major risk factor for esophageal cancer (<xref ref-type="bibr" rid="ref5">5</xref>), current evidence is insufficient to confirm whether air pollution indirectly increases the risk of esophageal cancer by promoting GERD progression. Further studies are needed to confirm this potential linkage. Only a few studies have investigated the potential association between air pollution and GERD. One study from Korea found that GERD-related medical utilization increased with the levels of PM<sub>2.5</sub> and carbon monoxide (<xref ref-type="bibr" rid="ref26">26</xref>), which only reflected healthcare utilization patterns rather than the incidence risk of GERD. Furthermore, the study did not differentiate GERD subtypes. Another cohort study examined the relationships of air pollutants and the risk of multiple gastrointestinal diseases (<xref ref-type="bibr" rid="ref27">27</xref>), which included GERD in their investigation, and found an association between PM<sub>2.5</sub> exposure and GERD. In contrast to these two studies, our study offers a more comprehensive and disease-specific perspective by analyzing the associations between multiple air pollutants and GERD incidence, including distinct analyses by GERD subtypes. Moreover, we conducted comprehensive subgroup analyses to identify vulnerable populations and estimated exposure-response relationships, which provide novel insights into pollutant-specific and subtype-specific risks of GERD.</p>
<p>Additionally, we observed a stronger association between air pollution and GERD without esophagitis (K21.9) compared to GERD with esophagitis (K21.0). This difference is more likely attributable to the different pathophysiological mechanisms underlying these two subtypes. GERD with esophagitis is characterized by visible mucosal injury on endoscopy and is primarily associated with prolonged acid exposure and overt epithelial damage (<xref ref-type="bibr" rid="ref28">28</xref>), which may be less directly influenced by pollutant-induced pathways. In contrast, GERD without esophagitis lacks macroscopic mucosal erosion and involves impaired mucosal resistance, increased epithelial permeability, and enhanced visceral hypersensitivity (<xref ref-type="bibr" rid="ref29">29</xref>, <xref ref-type="bibr" rid="ref30">30</xref>), which could be more susceptible to pollution-induced oxidative stress and inflammatory responses.</p>
<p>The associations between air pollutant exposures and GERD present a biological mechanism of rationality. In addition to the respiratory tract, the gastrointestinal tract is another important exposure route for air pollutants (<xref ref-type="bibr" rid="ref9">9</xref>). Particulate matter can enter the gastrointestinal tract through multiple pathways, including ingestion of contaminated food and water, mucociliary clearance from the respiratory tract, and the systemic blood circulation (<xref ref-type="bibr" rid="ref31 ref32 ref33">31&#x2013;33</xref>). Besides, gaseous pollutants might also affect the digestive tract through swallowed air. Although NO<sub>X</sub> itself is unlikely to enter the bloodstream directly, its various reaction products may migrate into the blood and spread to other tissues or organs (<xref ref-type="bibr" rid="ref10">10</xref>). All the above indicate that the gastrointestinal tract serves as an important pathway for exposure to air pollutants.</p>
<p>Although direct evidence linking air pollution to GERD remains limited, existing studies have demonstrated that air pollutants can induce oxidative stress, systemic inflammation, and epithelial barrier dysfunction in the gastrointestinal tract (<xref ref-type="bibr" rid="ref34 ref35 ref36 ref37">34&#x2013;37</xref>), which provides important biological plausibility for the observed associations in our study. Experimental studies indicated that exposure to particulate matter may enhance the generation of reactive oxygen species (ROS), cause damage to epithelial cells, and lead to the disruption and increased permeability of the gastrointestinal barrier (<xref ref-type="bibr" rid="ref35">35</xref>). Furthermore, particulate matter exposure could activate immune and inflammatory responses in the gastrointestinal tract. It has been reported that exposure to particulate matter is associated with increased infiltration of inflammatory cells, heightened expression of inflammation-related genes (such as IL-1&#x03B2;, IL-6, and TNF-<italic>&#x03B1;</italic>), and exacerbation of mucosal inflammation in the colon (<xref ref-type="bibr" rid="ref36">36</xref>, <xref ref-type="bibr" rid="ref37">37</xref>). Barrier disruption, immune and inflammation activation contributed to the pathogenesis of GERD, highlighting potential mechanisms that may underlie the association between particulate matter exposure and GERD. Regarding gaseous pollutants, this is the first cohort study to evaluate the association between ambient NO<sub>X</sub> exposure and the risk of GERD, which revealed that both NO<sub>2</sub> and NO exposures increase the risk of GERD without esophagitis (K21.9). The association between NO<sub>X</sub> exposure and GERD is biologically plausible. NO<sub>X</sub> can convert into various reactive nitrogen oxide species (RNOS) in the human body. RNOS at the human gastro-esophageal junction can damage the barrier function of the adjacent tissue by disrupting the tight junction (<xref ref-type="bibr" rid="ref38">38</xref>). Studies suggested that the products of NO<sub>2</sub>, such as nitrite, may migrate into the blood and induce systemic inflammation and oxidative stress, providing a potential mechanism by which NO<sub>2</sub> exposure could lead to health effects beyond the respiratory system (<xref ref-type="bibr" rid="ref10">10</xref>). Additionally, swallowed NO<sub>2</sub> might cause nitration of different compounds, including nitrate and nitrite in the stomach, which could induce the redox interplay and inflammatory response (<xref ref-type="bibr" rid="ref14">14</xref>, <xref ref-type="bibr" rid="ref39">39</xref>). Previous studies have provided evidence that NO can inhibit esophageal motility, disrupt epithelial barrier function, exacerbate inflammation, and accelerate columnar transformation in the esophagus (<xref ref-type="bibr" rid="ref39">39</xref>, <xref ref-type="bibr" rid="ref40">40</xref>), suggesting that NO could also contribute to GERD pathogenesis in addition to conventional causative factors. This evidence provided biological plausibility and enhances the reliability of our findings. While direct evidence remains limited, these mechanisms may provide important biological plausibility for the associations observed in our study. Further experimental and longitudinal studies are needed to confirm the causal relationships and to elucidate the exact role of air pollutants in the pathogenesis of GERD.</p>
<p>Our subgroup analyses indicated that the associations between air pollutants and GERD varied in different groups. Specifically, we observed that females displayed greater sensitivity, especially for gaseous pollutants. Several previous studies have reported increased risks among females (<xref ref-type="bibr" rid="ref41">41</xref>, <xref ref-type="bibr" rid="ref42">42</xref>), which may partly be attributed to sex-related differences in susceptibility and the greater exposure to indoor air pollution, particularly from sources such as cooking (<xref ref-type="bibr" rid="ref41">41</xref>). We also observed that the risk of GERD tended to be higher in middle-aged individuals (&#x003C;60&#x202F;years) when exposed to air pollutants. Younger populations tend to experience greater exposure to air pollution due to their increased frequency of outdoor activities compared to older individuals, which may be one of the reasons why the younger population is more vulnerable. Besides, we discovered stronger associations between air pollutants (especially NO<sub>X</sub>, NO<sub>2</sub>, and NO) and GERD in overweight individuals (BMI&#x202F;&#x2265;&#x202F;25&#x202F;kg/m<sup>2</sup>) than those with BMI&#x202F;&#x003C;&#x202F;25&#x202F;kg/m<sup>2</sup>, which is consistent with other studies that air pollution exposure associated with overweight and obesity (<xref ref-type="bibr" rid="ref43">43</xref>, <xref ref-type="bibr" rid="ref44">44</xref>). Additionally, we observed stronger associations in White ethnicity and those with higher SES. A previous study also found that GERD was more prevalent among individuals with higher SES (<xref ref-type="bibr" rid="ref45">45</xref>). The differential vulnerability may be partially explained by SES-related lifestyle factors, such as dietary patterns and obesity. Moreover, individuals with higher SES are more likely to seek medical attention for GERD symptoms and undergo diagnostic testing, which may contribute to increased case detection.</p>
<p>Our study exhibits several significant advantages. First, it represents the first large-scale, population-based, and long-term cohort study revealing the impact of both particulate matter (PM<sub>10</sub>, PM<sub>2.5&#x2013;10</sub>, and PM<sub>2.5</sub>) and gaseous pollutants (NO<sub>X</sub>, NO<sub>2</sub>, and NO) on different subtypes of GERD (including K21.9 and K21.0). The large sample size and long follow-up period greatly enhanced the statistical power. Second, we introduced a DAG to provide a comprehensive and transparent process for more appropriately selecting confounders, which boosts the scientificity of our study. Finally, we conducted several sensitivity analyses to verify the robustness of the main model, further improving the credibility of the results.</p>
<p>It is also important to acknowledge several limitations in our study. First, because of the limited time scale of exposure data available in the UK Biobank, we used the annual average concentration of air pollutants for the year 2010 to represent long-term exposure, which is in accordance with methodologies employed in prior research within the UK Biobank cohort. Second, the annual concentrations of air pollutants in the UK were relatively low, preventing us from exploring the impact of higher concentrations of air pollutant exposures on GERD. Additional research is warranted in developing countries with higher ambient air pollution levels. Third, as the UK Biobank only includes participants aged 40&#x2013;69&#x202F;years at baseline, our results primarily reflect associations in middle-aged and older adults and may not be fully generalizable to younger populations.</p>
</sec>
<sec sec-type="conclusions" id="sec17">
<label>5</label>
<title>Conclusion</title>
<p>In conclusion, this study demonstrated that air pollution may serve as an &#x200C;unignorable environmental risk factor for GERD. Our findings indicated that long-term exposure to both particulate matter (PM<sub>10</sub>, PM<sub>2.5&#x2013;10</sub>, and PM<sub>2.5</sub>) and gaseous pollutants (NO<sub>X</sub>, NO<sub>2</sub>, and NO) may increase the incidence risk of GERD, particularly GERD without esophagitis rather than GERD with esophagitis. Considering the hazardous impact of air pollution, our research suggested the necessity of reducing emissions and restricting the air pollutants standards to alleviate the disease burden of GERD.</p>
</sec>
</body>
<back>
<sec sec-type="data-availability" id="sec18">
<title>Data availability statement</title>
<p>The data used in this study were obtained from the UK Biobank (<ext-link xlink:href="https://www.ukbiobank.ac.uk" ext-link-type="uri">https://www.ukbiobank.ac.uk</ext-link>, application number: 99732). The authors do not have the rights to share the dataset directly.</p>
</sec>
<sec sec-type="ethics-statement" id="sec19">
<title>Ethics statement</title>
<p>The UK Biobank was approved by North West Multicenter Research Ethics Committee (21/NW/0157). Data for this study was based on the approved project (application ID: 99732) from the UK Biobank.</p>
</sec>
<sec sec-type="author-contributions" id="sec20">
<title>Author contributions</title>
<p>YR: Methodology, Conceptualization, Writing &#x2013; review &#x0026; editing, Investigation, Formal analysis, Writing &#x2013; original draft. JL: Conceptualization, Writing &#x2013; review &#x0026; editing, Visualization, Formal analysis, Methodology. LW: Resources, Conceptualization, Writing &#x2013; review &#x0026; editing, Methodology. LL: Methodology, Writing &#x2013; review &#x0026; editing, Conceptualization, Resources. FY: Resources, Conceptualization, Writing &#x2013; review &#x0026; editing. LM: Conceptualization, Resources, Writing &#x2013; review &#x0026; editing. ZS: Writing &#x2013; review &#x0026; editing, Conceptualization, Resources. JC: Resources, Writing &#x2013; review &#x0026; editing. FD: Funding acquisition, Writing &#x2013; review &#x0026; editing, Supervision, Project administration.</p>
</sec>
<sec sec-type="funding-information" id="sec21">
<title>Funding</title>
<p>The author(s) declare that financial support was received for the research and/or publication of this article. This research was supported by the National Natural Science Foundation of China (81770540), the China Postdoctoral Science Foundation (2023TQ0260), the Postdoctoral Research Project of Shaanxi Province (2023BSHYDZZ17), and the Basic Scientific Research Operating Funds of Xi&#x2019;an Jiaotong University (11913224000012).</p>
</sec>
<ack>
<p>We are grateful to UK Biobank participants. This research has been conducted using the UK Biobank Resource under application number 99732.</p>
</ack>
<sec sec-type="COI-statement" id="sec22">
<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 a potential conflict of interest.</p>
</sec>
<sec sec-type="ai-statement" id="sec23">
<title>Generative AI statement</title>
<p>The authors declare that no Gen AI was used in the creation of this manuscript.</p>
</sec>
<sec sec-type="disclaimer" id="sec24">
<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>
</sec>
<sec sec-type="supplementary-material" id="sec25">
<title>Supplementary material</title>
<p>The Supplementary material for this article can be found online at: <ext-link xlink:href="https://www.frontiersin.org/articles/10.3389/fpubh.2025.1620411/full#supplementary-material" ext-link-type="uri">https://www.frontiersin.org/articles/10.3389/fpubh.2025.1620411/full#supplementary-material</ext-link></p>
<supplementary-material xlink:href="Data_Sheet_1.docx" id="SM1" mimetype="application/vnd.openxmlformats-officedocument.wordprocessingml.document" xmlns:xlink="http://www.w3.org/1999/xlink"/>
</sec>
<fn-group>
<fn id="fn0001">
<p><sup>1</sup><ext-link xlink:href="http://www.dagitty.net" ext-link-type="uri">www.dagitty.net</ext-link></p></fn>
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