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<journal-meta>
<journal-id journal-id-type="publisher-id">Front. Cell. Infect. Microbiol.</journal-id>
<journal-title-group>
<journal-title>Frontiers in Cellular and Infection Microbiology</journal-title>
<abbrev-journal-title abbrev-type="pubmed">Front. Cell. Infect. Microbiol.</abbrev-journal-title>
</journal-title-group>
<issn pub-type="epub">2235-2988</issn>
<publisher>
<publisher-name>Frontiers Media S.A.</publisher-name>
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<article-meta>
<article-id pub-id-type="doi">10.3389/fcimb.2025.1663272</article-id>
<article-version article-version-type="Version of Record" vocab="NISO-RP-8-2008"/>
<article-categories>
<subj-group subj-group-type="heading">
<subject>Original Research</subject>
</subj-group>
</article-categories>
<title-group>
<article-title>Association between <italic>Mycoplasma pneumoniae</italic> infection and adverse pregnancy outcome: a propensity score weighting study</article-title>
</title-group>
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<name><surname>Yang</surname><given-names>Caihua</given-names></name>
<xref ref-type="aff" rid="aff1"><sup>1</sup></xref>
<xref ref-type="author-notes" rid="fn003"><sup>&#x2020;</sup></xref>
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<contrib contrib-type="author" equal-contrib="yes">
<name><surname>Jiang</surname><given-names>Haoxuan</given-names></name>
<xref ref-type="aff" rid="aff2"><sup>2</sup></xref>
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<name><surname>Li</surname><given-names>Linyan</given-names></name>
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<contrib contrib-type="author">
<name><surname>Zheng</surname><given-names>Ping</given-names></name>
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<contrib contrib-type="author" corresp="yes">
<name><surname>Li</surname><given-names>Yilei</given-names></name>
<xref ref-type="aff" rid="aff1"><sup>1</sup></xref>
<xref ref-type="corresp" rid="c001"><sup>*</sup></xref>
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<contrib contrib-type="author" corresp="yes">
<name><surname>Wu</surname><given-names>Ying</given-names></name>
<xref ref-type="aff" rid="aff3"><sup>3</sup></xref>
<xref ref-type="aff" rid="aff4"><sup>4</sup></xref>
<xref ref-type="corresp" rid="c001"><sup>*</sup></xref>
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<aff id="aff1"><label>1</label><institution>Clinical Pharmacy Center, Nanfang Hospital, Southern Medical University</institution>, <city>Guangzhou</city>,&#xa0;<country country="cn">China</country></aff>
<aff id="aff2"><label>2</label><institution>School of Biomedical Engineering, Hainan University</institution>, <city>Sanya</city>,&#xa0;<country country="cn">China</country></aff>
<aff id="aff3"><label>3</label><institution>Department of Biostatistics, School of Public Health, Southern Medical University</institution>, <city>Guangzhou</city>,&#xa0;<country country="cn">China</country></aff>
<aff id="aff4"><label>4</label><institution>Hainan Lecheng Institute of Real World Study, The Administration of Boao Lecheng International Medical Tourism Pilot Zone</institution>, <city>Hainan</city>, <state>Qionghai</state>,&#xa0;<country country="cn">China</country></aff>
<author-notes>
<corresp id="c001"><label>*</label>Correspondence: Ying Wu, <email xlink:href="mailto:wuying19890321@gmail.com">wuying19890321@gmail.com</email>; Yilei Li, <email xlink:href="mailto:liyilei1975@163.com">liyilei1975@163.com</email></corresp>
<fn fn-type="equal" id="fn003">
<label>&#x2020;</label>
<p>These authors have contributed equally to this work and share first authorship</p></fn>
</author-notes>
<pub-date publication-format="electronic" date-type="pub" iso-8601-date="2025-12-24">
<day>24</day>
<month>12</month>
<year>2025</year>
</pub-date>
<pub-date publication-format="electronic" date-type="collection">
<year>2025</year>
</pub-date>
<volume>15</volume>
<elocation-id>1663272</elocation-id>
<history>
<date date-type="received">
<day>10</day>
<month>07</month>
<year>2025</year>
</date>
<date date-type="accepted">
<day>08</day>
<month>12</month>
<year>2025</year>
</date>
<date date-type="rev-recd">
<day>05</day>
<month>12</month>
<year>2025</year>
</date>
</history>
<permissions>
<copyright-statement>Copyright &#xa9; 2025 Yang, Jiang, Li, Zheng, Li and Wu.</copyright-statement>
<copyright-year>2025</copyright-year>
<copyright-holder>Yang, Jiang, Li, Zheng, Li and Wu</copyright-holder>
<license>
<ali:license_ref start_date="2025-12-24">https://creativecommons.org/licenses/by/4.0/</ali:license_ref>
<license-p>This is an open-access article distributed under the terms of the <ext-link ext-link-type="uri" xlink:href="https://creativecommons.org/licenses/by/4.0/">Creative Commons Attribution License (CC BY)</ext-link>. 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.</license-p>
</license>
</permissions>
<abstract>
<sec>
<title>Introduction</title>
<p>After COVID-19 pandemic, there has been an upward trend in <italic>Mycoplasma pneumoniae</italic> (<italic>M. pneumoniae</italic>) infections across Asia. The COVID-19-induced immunological impairment may increase the risk of adverse outcomes in <italic>M. pneumoniae</italic>-infected patients, yet studies in this area remain limited. We investigated the association between <italic>M. pneumoniae</italic> infection and adverse pregnancy outcomes in the post-COVID-19 era.</p>
</sec>
<sec>
<title>Methods</title>
<p>We conducted a single-center cohort study in Guangzhou, China, from February 2023 to June 2024, involving pregnant women. A total of 186 participants were included, with 49 in the <italic>M. pneumoniae</italic> group (tested positive for <italic>M. pneumoniae</italic> immunoglobulin M antibody (MP IgM)) and 137 in the control group. Propensity score weighting analysis was performed to control bias and estimate the effect size.</p>
</sec>
<sec>
<title>Results</title>
<p>The incidence of adverse pregnancy outcomes in the <italic>M. pneumoniae</italic> group was not significantly different from that in the control group. The odds ratio (OR) for adverse maternal events after propensity score weighting (PSW) was 1.25 (95% confidence interval [CI], 0.62 to 2.55; <italic>p</italic>&#xa0;=&#xa0;0.530), and the PSW OR for adverse neonatal events was 0.95 (95% CI, 0.49 to 1.84; <italic>p</italic>&#xa0;=&#xa0;0.884). However, in the subgroups of advanced maternal age (AMA, age &#x2265; 35, n=29) and primiparous women (n=80), the incidence of adverse pregnancy outcomes was significantly higher in the <italic>M. pneumoniae</italic> group. Additionally, the clinical manifestations of <italic>M. pneumoniae</italic> infection in the post-COVID-19 era were consistent with those observed prior to the pandemic.</p>
</sec>
<sec>
<title>Conclusions</title>
<p>In the post-COVID-19 era, evidence remains insufficient to conclude that <italic>M. pneumoniae</italic> infection increases the risk of adverse pregnancy outcomes in the general pregnant population. Exploratory subgroup analyses suggest possible signals of risk within subgroups of AMA and primiparous women.</p>
</sec>
</abstract>
<kwd-group>
<kwd>advanced maternal age</kwd>
<kwd>COVID-19</kwd>
<kwd><italic>Mycoplasma pneumoniae</italic></kwd>
<kwd>pregnancy</kwd>
<kwd>propensity score</kwd>
</kwd-group>
<funding-group>
<funding-statement>The author(s) declared that financial support was received for this work and/or its publication. This work was supported by the National Natural Science Foundation of China (grant number 82273732); the Hainan Province Science and Technology Special Fund (ZDYF2025LCLH003); the Real World Research Project Grant Fund from the Hainan Institute of Real World data (HNLC2022RWS018); 2024 Key Science Popularization Research Projects of the Science Communication Professional Committee, Chinese Pharmaceutical Association (grant number CMEI2024KPYJ (ZAMM)00301); Provincial College Students&#x2019; Innovation and Entrepreneurship Training Program of Southern Medical University (No. S202412121123).</funding-statement>
</funding-group>
<counts>
<fig-count count="3"/>
<table-count count="3"/>
<equation-count count="0"/>
<ref-count count="33"/>
<page-count count="10"/>
<word-count count="5173"/>
</counts>
<custom-meta-group>
<custom-meta>
<meta-name>section-at-acceptance</meta-name>
<meta-value>Clinical Infectious Diseases</meta-value>
</custom-meta>
</custom-meta-group>
</article-meta>
</front>
<body>
<sec id="s1" sec-type="intro">
<title>Introduction</title>
<p>During the COVID-19 pandemic, the prevention and control measures, or non-pharmaceutical interventions (NPIs), blocked the transmission of many respiratory pathogens, including <italic>M. pneumoniae</italic> (<xref ref-type="bibr" rid="B23">Meyer Sauteur et&#xa0;al., 2022</xref>). Therefore, during the period of NPIs, the detection rate of <italic>M. pneumoniae</italic> was extremely low, until the first half of 2023, when there was an observable resurgence, indicating a new outbreak of <italic>M. pneumoniae</italic>, particularly in Asia (<xref ref-type="bibr" rid="B22">Meyer Sauteur et&#xa0;al., 2024</xref>).</p>
<p>It is estimated that 97% of the population in China had been infected with SARS-CoV-2 before January 2023 (<xref ref-type="bibr" rid="B10">Goldberg et&#xa0;al., 2023</xref>). Growing evidence indicates that SARS-CoV-2 infection can induce a prolonged phase of immune suppression and inflammatory injury, characterized by reduced counts of natural killer (NK) cells, lymphocytes, and monocytes, alongside elevated levels of inflammatory cytokines (<xref ref-type="bibr" rid="B25">Peluso et&#xa0;al., 2021</xref>; <xref ref-type="bibr" rid="B27">Ryan et&#xa0;al., 2022</xref>). Notably, immune dysregulation and inflammatory damage are recognized as key pathogenic mechanisms in severe <italic>M. pneumoniae</italic> pneumonia (SMPP) (<xref ref-type="bibr" rid="B32">Zhang et&#xa0;al., 2024</xref>; <xref ref-type="bibr" rid="B31">Yang et&#xa0;al., 2024</xref>). This suggests that the COVID-19-induced immunological impairment may potentially increase the risk of <italic>M. pneumoniae</italic>-infected patients progressing to severe disease. Patients with coinfection (COVID-19 and <italic>M. pneumoniae</italic>) have higher mortality compared with patients with just COVID-19 disease (<xref ref-type="bibr" rid="B1">Amin et&#xa0;al., 2021</xref>). This raises our interest in whether the prognosis of <italic>M. pneumoniae</italic> infection would become worse in the post-COVID-19 era (the period after January 2023 (<xref ref-type="bibr" rid="B18">Li et&#xa0;al., 2024</xref>)).</p>
<p>Pneumonia in pregnancy also results in low-birth-weight neonates in 33.9% of cases compared with 13.6% of controls (<xref ref-type="bibr" rid="B11">Goodnight and Soper, 2005</xref>). Tang et&#xa0;al. observed high incidences of adverse fetal outcomes in patients with severe pneumonia (<xref ref-type="bibr" rid="B29">Tang et&#xa0;al., 2018</xref>). Similarly, Chen et&#xa0;al. found that women with pneumonia during pregnancy had significantly higher risk of low birth weight, preterm birth, small for gestational age (SGA), low Apgar scores, cesarean section (CS), and preeclampsia/eclampsia, compared to without pneumonia (<xref ref-type="bibr" rid="B8">Chen et&#xa0;al., 2012</xref>). In the pregnant patient, pneumonia is the most frequent cause of fatal non-obstetric infection and <italic>M. pneumoniae</italic> is a common organism identified (<xref ref-type="bibr" rid="B19">Lim et&#xa0;al., 2001</xref>). From a biological perspective, a prior SARS-CoV-2 infection could potentially modify the maternal immune response to a subsequent <italic>M. pneumoniae</italic> infection, possibly leading to a more pronounced inflammatory response or an altered clinical course. Such a shift in host-pathogen interaction might result in maternal-fetal outcomes that differ from those observed in the pre-pandemic era, when the immune system had not been primed by SARS-CoV-2. Nevertheless, it remains unclear whether infection with <italic>M. pneumoniae</italic> affects adverse pregnancy outcomes in pregnant women in the post-COVID-19 era.</p>
<p>This paper aims to investigate the association between infection with <italic>M. pneumoniae</italic> and adverse pregnancy outcomes in pregnant women in the post-COVID-19 era, as well as to report the clinical manifestations of pneumonia caused by <italic>M. pneumoniae.</italic></p>
</sec>
<sec id="s2">
<title>Methods</title>
<sec id="s2_1">
<title>Study design and population</title>
<p>This study, conducted at Nanfang Hospital (Guangzhou, China) between February 1, 2023 and June 21, 2024, utilized a prospectively collected cohort for the exposure group combined with a retrospectively selected control group. The inclusion criteria were as follows: (1) Pregnant women receiving antenatal care at the study site during the study period. (2) Provision of informed consent to participate in the study. The exclusion criteria were: (1) Known significant maternal medical comorbidities existing prior to pregnancy, including but not limited to autoimmune diseases, severe cardiac or renal dysfunction, and poorly controlled diabetes and hypertension. (2) The presence of any other concurrent acute infection during pregnancy. (3) Patients who experienced spontaneous or induced abortion, from whom subsequent outcome data could not be collected. (4) Withdrawal of consent or loss to follow-up during the study period. Patients who met the inclusion criteria and tested positive for <italic>M. pneumoniae</italic> immunoglobulin M antibody (MP IgM) during pregnancy were enrolled as the exposure group. Data for the exposure group were prospectively collected. Controls were retrospectively identified as patients without <italic>M. pneumoniae</italic> infection who met the inclusion criteria during the same study period. Given a 1:3 exposure-to-control ratio, participants with complete delivery records were randomly sampled from the electronic medical record (EMR) system. The complete patient selection flow chart is shown in <xref ref-type="fig" rid="f1"><bold>Figure&#xa0;1</bold></xref>. Finally, 186 pregnant women remained in the final analysis, with 49 in the <italic>M. pneumoniae</italic> group and 137 in the control group. This study was approved by the Medical Ethics Committee of Nanfang Hospital affiliated to Southern Medical University (ID: NFEC- 2023-116).</p>
<fig id="f1" position="float">
<label>Figure&#xa0;1</label>
<caption>
<p>Selection flow chart. EMR, electronic medical record; MP IgM, <italic>M. pneumoniae</italic> immunoglobulin M antibody; PSW, Propensity Score Weighting.</p>
</caption>
<graphic mimetype="image" mime-subtype="tiff" xlink:href="fcimb-15-1663272-g001.tif">
<alt-text content-type="machine-generated">Flowchart detailing the selection process of pregnant women for a study. The left branch starts with 82 women enrolled who tested positive for MP IgM. After 33 exclusions, 49 eligible women formed the exposure group. The right branch begins with 147 women without M. pneumoniae infection, leading to 10 exclusions and 137 eligible for the control group. Both groups contribute to a total of 186 in the Propensity Score Weighting cohort, split into 49 PSW exposure and 137 PSW control groups.</alt-text>
</graphic></fig>
</sec>
<sec id="s2_2">
<title>Data collection and measurement</title>
<p>For the exposure group, demographic and baseline characteristics were collected from the participants at the time of recruitment (i.e., at the diagnosis of infection). Among them, 77.6% (n=38) of participants were infected from 28 weeks of pregnancy to delivery, while 22.4% (n=11) were infected between 0 and 28 weeks of pregnancy. Participants were prospectively followed up from recruitment until delivery. Clinical manifestations of <italic>M. pneumoniae</italic> infection were recorded as they developed during follow-up, and final adverse pregnancy outcomes were collected at the time of delivery. For the control group, demographics, baseline characteristics, and adverse pregnancy outcomes were systematically extracted from the EMR. Demographic and baseline characteristics included age, BMI, gravidity, history of abortion (encompassing both spontaneous and induced abortions), and history of chronic diseases.</p>
<p>Adverse pregnancy outcomes were categorized into maternal and neonatal outcomes. Adverse maternal outcomes included six variables: CS, postpartum hemorrhage (PPH), polyhydramnios or oligohydramnios, amniotic fluid contamination, placental abruption, and premature rupture of membranes (PROM). Adverse neonatal outcomes included eight variables: fetal heart rate variability (HRV), preterm birth, fetal distress, other neonatal infection, neonatal length, neonatal weight, neonatal head circumference, and the one-minute Apgar score. To increase the number of outcome events, the primary adverse maternal outcome in this study was defined as adverse maternal events, which is a composite outcome, encompassing CS, PPH, polyhydramnios or oligohydramnios, amniotic fluid contamination, placental abruption, and PROM. Similarly, the primary adverse neonatal outcome was defined as adverse neonatal events, including fetal HRV, preterm birth, birth asphyxia (defined as a one-minute Apgar score &#x2264; 7), low birth weight (defined as neonatal weight&lt; 2.5&#xa0;kg), fetal distress, and other neonatal infections. Each individual variable was considered a secondary outcome. The encoding of each variable is presented in <xref ref-type="supplementary-material" rid="SM1"><bold>Supplementary Table S1</bold></xref>.</p>
</sec>
<sec id="s2_3">
<title>Statistical analysis</title>
<p>Descriptive analyses were performed for all the demographic and baseline characteristics. Continuous variables were expressed as the means with standard deviations. And categorical variables were expressed as counts and percentages. Missing values in demographic and baseline characteristics were imputed via multiple imputation (5 imputation datasets) (<xref ref-type="bibr" rid="B14">Harel and Zhou, 2007</xref>). Continuous variables were imputed using predictive mean matching, while categorical variables had no missing value. The proportion of missing data for each variable in each group are reported in <xref ref-type="supplementary-material" rid="SM1"><bold>Supplementary Table S1</bold></xref>.</p>
<p>Propensity scores (<xref ref-type="bibr" rid="B3">Austin, 2011</xref>), which represent the probability of infection with <italic>M. pneumoniae</italic>, were estimated with a multivariable logistic regression model incorporating all demographics and baseline characteristics. Specifically, age and BMI were included as continuous variables, history of abortion and history of chronic diseases were treated as binary variables, while gravidity was treated as a three-level categorical variable. Stabilized propensity score weighting (PSW) based on propensity scores was used to balance the baseline characteristics between the <italic>M. pneumoniae</italic> group and the control group (<xref ref-type="bibr" rid="B4">Austin and Stuart, 2015</xref>). The balance of all baseline characteristics was evaluated via the absolute standardized mean difference (ASMD) in both unadjusted data and PSW data (<xref ref-type="table" rid="T1"><bold>Table&#xa0;1</bold></xref>). An ASMD of&lt; 0.10 was defined as an acceptable balance (<xref ref-type="bibr" rid="B2">Austin, 2009</xref>). After applying PSW, weighted generalized linear models were employed to compare the difference of multiple adverse pregnancy outcomes between the <italic>M. pneumoniae</italic> and control groups (identity link function for continuous outcome variables and probit link function for binary outcomes). For continuous outcome variables, mean differences were used to quantify the exposure effect, whereas for binary outcomes, odds ratios (OR) were applied to measure the effect. Propensity score matching (PSM) was performed as a sensitivity analysis due to the loss of sample size.</p>
<table-wrap id="T1" position="float">
<label>Table&#xa0;1</label>
<caption>
<p>Baseline characteristics of participants before and after propensity score weighting.</p>
</caption>
<table frame="hsides">
<thead>
<tr>
<th valign="middle" rowspan="2" align="left">Baseline</th>
<th valign="middle" colspan="3" align="left">Unadjusted</th>
<th valign="middle" colspan="3" align="left">PSW</th>
</tr>
<tr>
<th valign="middle" align="left">Control (n=137)</th>
<th valign="middle" align="left"><italic>M. pneumoniae</italic> (n=49)</th>
<th valign="middle" align="left">ASMD</th>
<th valign="middle" align="left">Control (n=137)</th>
<th valign="middle" align="left"><italic>M. pneumoniae</italic> (n=49)</th>
<th valign="middle" align="left">ASMD</th>
</tr>
</thead>
<tbody>
<tr>
<td valign="middle" align="left">Age, Mean &#xb1; SD</td>
<td valign="middle" align="left">30.42&#xa0;&#xb1;&#xa0;4.37</td>
<td valign="middle" align="left">29.18&#xa0;&#xb1;&#xa0;3.79</td>
<td valign="middle" align="left">0.301</td>
<td valign="middle" align="left">30.04&#xa0;&#xb1;&#xa0;4.37</td>
<td valign="middle" align="left">29.65&#xa0;&#xb1;&#xa0;3.62</td>
<td valign="middle" align="left">0.098</td>
</tr>
<tr>
<td valign="middle" align="left">BMI, Mean &#xb1; SD</td>
<td valign="middle" align="left">26.5&#xa0;&#xb1;&#xa0;3.74</td>
<td valign="middle" align="left">25.65&#xa0;&#xb1;&#xa0;3.51</td>
<td valign="middle" align="left">0.238</td>
<td valign="middle" align="left">26.28&#xa0;&#xb1;&#xa0;3.67</td>
<td valign="middle" align="left">26.12&#xa0;&#xb1;&#xa0;3.62</td>
<td valign="middle" align="left">0.044</td>
</tr>
<tr>
<td valign="middle" align="left">Gravidity, n (%)</td>
<td valign="middle" align="left"/>
<td valign="middle" align="left"/>
<td valign="middle" align="left">0.198</td>
<td valign="middle" align="left"/>
<td valign="middle" align="left"/>
<td valign="middle" align="left">0.027</td>
</tr>
<tr>
<td valign="middle" align="left">&#x2003;1</td>
<td valign="middle" align="left">58 (42.34)</td>
<td valign="middle" align="left">22 (44.90)</td>
<td valign="middle" align="left"/>
<td valign="middle" align="left">58 (42.45)</td>
<td valign="middle" align="left">20 (41.80)</td>
<td valign="middle" align="left"/>
</tr>
<tr>
<td valign="middle" align="left">&#x2003;2</td>
<td valign="middle" align="left">46 (33.58)</td>
<td valign="middle" align="left">19 (38.78)</td>
<td valign="middle" align="left"/>
<td valign="middle" align="left">48 (35.28)</td>
<td valign="middle" align="left">18 (36.56)</td>
<td valign="middle" align="left"/>
</tr>
<tr>
<td valign="middle" align="left">&#x2003;3</td>
<td valign="middle" align="left">33 (24.09)</td>
<td valign="middle" align="left">8 (16.33)</td>
<td valign="middle" align="left"/>
<td valign="middle" align="left">31 (22.27)</td>
<td valign="middle" align="left">11 (21.64)</td>
<td valign="middle" align="left"/>
</tr>
<tr>
<td valign="middle" align="left">History of abortion, n (%)</td>
<td valign="middle" align="left">40 (29.20)</td>
<td valign="middle" align="left">15 (30.61)</td>
<td valign="middle" align="left">0.031</td>
<td valign="middle" align="left">41 (30.35)</td>
<td valign="middle" align="left">16 (33.55)</td>
<td valign="middle" align="left">0.069</td>
</tr>
<tr>
<td valign="middle" align="left">History of chronic diseases, n (%)</td>
<td valign="middle" align="left">22 (16.06)</td>
<td valign="middle" align="left">17 (34.69)</td>
<td valign="middle" align="left">0.438</td>
<td valign="middle" align="left">29 (21.29)</td>
<td valign="middle" align="left">10 (21.34)</td>
<td valign="middle" align="left">0.001</td>
</tr>
</tbody>
</table>
<table-wrap-foot>
<fn>
<p>An ASMD of&lt; 0.10 was defined as an acceptable covariate balance. ASMD, absolute standardized mean difference; BMI, body mass index; PSW, propensity score weighting; SD, standard deviation.</p></fn>
</table-wrap-foot>
</table-wrap>
<p>Firth&#x2019;s penalized logistic regression (<xref ref-type="bibr" rid="B7">Chaudhry et&#xa0;al., 2025</xref>) was used to correct for small sample bias (also known as sparse data bias (<xref ref-type="bibr" rid="B13">Greenland et&#xa0;al., 2016</xref>)) due to limited sample size and complete separation (<xref ref-type="bibr" rid="B12">Gosho et&#xa0;al., 2023</xref>).</p>
<p>We also performed subgroup analyses to investigate the homogeneity of <italic>M. pneumoniae</italic> infection for adverse pregnancy outcomes across clinically important subgroups (age, gravidity, history of abortion, history of chronic diseases). In addition, subgroup analysis can help identify potential high-risk populations, providing guidance for disease prevention and treatment. The two-tailed Wald test was used to assess the significance of regression coefficients, and statistical significance was defined as a <italic>p</italic>&#xa0;&lt;&#xa0;0.05. All the statistical analyses were conducted via R software (version 4.3.1) and a detailed list of all R packages and versions used in this study is provided in <xref ref-type="supplementary-material" rid="SM1"><bold>Supplementary Table S2</bold></xref>.</p>
</sec>
</sec>
<sec id="s3" sec-type="results">
<title>Results</title>
<sec id="s3_1">
<title>Demographic and baseline characteristics</title>
<p>The demographics and baseline characteristics of patients with exposure and control groups were compared in unadjusted and PSW-adjusted data (<xref ref-type="table" rid="T1"><bold>Table&#xa0;1</bold></xref>). In the unadjusted data, significant disparities were noted in the demographic and baseline variables between the two groups. These differences were markedly reduced after PSW, with the ASMD decreasing significantly, indicating a successful balance of demographic and baseline variables (all ASMDs &#x2264; 0.10). For example, the age was comparable between the exposure (29.65) and control (30.04) groups after PSW. The BMI, gravidity, the history of abortion and chronic diseases also showed small differences after PSW. The characteristics after PSM were displayed in <xref ref-type="supplementary-material" rid="SM1"><bold>Supplementary Table S3</bold></xref>.</p>
</sec>
<sec id="s3_2">
<title>Clinical manifestations and laboratory findings of infection with <italic>M. pneumoniae</italic></title>
<p>We analyzed the clinical manifestations and laboratory test results of 49 pregnant women in the <italic>M. pneumoniae</italic> infection group (<xref ref-type="table" rid="T2"><bold>Table&#xa0;2</bold></xref>). The clinical manifestations of <italic>M. pneumoniae</italic> infection were predominantly upper respiratory tract symptoms, including fever in 33 patients (67.35%), cough in 34 patients (69.39%), expectoration in 20 patients (40.82%), pharyngodynia in 20 patients (40.82%), nasal congestion in 13 patients (26.53%), and rhinorrhea in 11 patients (22.45%). In addition, systemic toxic symptoms were reported, including headache (16.33%) and asthenia (12.24%). Furthermore, three patients (6.12%) exhibited dyspnea.</p>
<table-wrap id="T2" position="float">
<label>Table&#xa0;2</label>
<caption>
<p>Clinical manifestations and laboratory test results of infection with <italic>M. pneumoniae</italic>.</p>
</caption>
<table frame="hsides">
<thead>
<tr>
<th valign="middle" align="left">Characteristics</th>
<th valign="middle" align="left">M. pneumoniae group(n=49)</th>
</tr>
</thead>
<tbody>
<tr>
<td valign="middle" align="left">Fever, n (%)</td>
<td valign="middle" align="left">33 (67.35)</td>
</tr>
<tr>
<td valign="middle" align="left">Cough, n (%)</td>
<td valign="middle" align="left">34 (69.39)</td>
</tr>
<tr>
<td valign="middle" align="left">Expectoration, n (%)</td>
<td valign="middle" align="left">20 (40.82)</td>
</tr>
<tr>
<td valign="middle" align="left">Pharyngodynia, n (%)</td>
<td valign="middle" align="left">20 (40.82)</td>
</tr>
<tr>
<td valign="middle" align="left">Nasal congestion, n (%)</td>
<td valign="middle" align="left">13 (26.53)</td>
</tr>
<tr>
<td valign="middle" align="left">Rhinorrhea, n (%)</td>
<td valign="middle" align="left">11 (22.45)</td>
</tr>
<tr>
<td valign="middle" align="left">Headache, n (%)</td>
<td valign="middle" align="left">8 (16.33)</td>
</tr>
<tr>
<td valign="middle" align="left">Asthenia, n (%)</td>
<td valign="middle" align="left">6 (12.24)</td>
</tr>
<tr>
<td valign="middle" align="left">Dyspnea, n (%)</td>
<td valign="middle" align="left">3 (6.12)</td>
</tr>
<tr>
<td valign="middle" align="left">Number of symptoms</td>
<td valign="middle" align="left">3.29 &#xb1; 1.38</td>
</tr>
<tr>
<td valign="middle" align="left">Duration of symptoms, days</td>
<td valign="middle" align="left">8.63 &#xb1; 8.62</td>
</tr>
<tr>
<td valign="middle" align="left">Duration of hospitalization, days</td>
<td valign="middle" align="left">7.41 &#xb1; 10.16</td>
</tr>
<tr>
<td valign="middle" align="left">Any complication of <italic>M. pneumoniae</italic> infection, n (%)</td>
<td valign="middle" align="left">2 (4.1)</td>
</tr>
<tr>
<td valign="middle" align="left">WBC, <inline-formula>
<mml:math display="inline" id="im1"><mml:mrow><mml:mn>1</mml:mn><mml:mo>&#xd7;</mml:mo><mml:msup><mml:mrow><mml:mn>10</mml:mn></mml:mrow><mml:mn>9</mml:mn></mml:msup><mml:mo stretchy="false">/</mml:mo><mml:mi>L</mml:mi></mml:mrow></mml:math></inline-formula></td>
<td valign="middle" align="left">9.35 &#xb1; 2.77</td>
</tr>
<tr>
<td valign="middle" align="left">LYM, <inline-formula>
<mml:math display="inline" id="im2"><mml:mrow><mml:mn>1</mml:mn><mml:mo>&#xd7;</mml:mo><mml:msup><mml:mrow><mml:mn>10</mml:mn></mml:mrow><mml:mn>9</mml:mn></mml:msup><mml:mo stretchy="false">/</mml:mo><mml:mi>L</mml:mi></mml:mrow></mml:math></inline-formula></td>
<td valign="middle" align="left">1.09 &#xb1; 0.64</td>
</tr>
<tr>
<td valign="middle" align="left">PLT, <inline-formula>
<mml:math display="inline" id="im3"><mml:mrow><mml:mn>1</mml:mn><mml:mo>&#xd7;</mml:mo><mml:msup><mml:mrow><mml:mn>10</mml:mn></mml:mrow><mml:mn>9</mml:mn></mml:msup><mml:mo stretchy="false">/</mml:mo><mml:mi>L</mml:mi></mml:mrow></mml:math></inline-formula></td>
<td valign="middle" align="left">237.71 &#xb1; 62.93</td>
</tr>
<tr>
<td valign="middle" align="left">Hb, <inline-formula>
<mml:math display="inline" id="im4"><mml:mrow><mml:mn>1</mml:mn><mml:mo>&#xd7;</mml:mo><mml:msup><mml:mrow><mml:mn>10</mml:mn></mml:mrow><mml:mn>9</mml:mn></mml:msup><mml:mo stretchy="false">/</mml:mo><mml:mi>L</mml:mi></mml:mrow></mml:math></inline-formula></td>
<td valign="middle" align="left">111.65 &#xb1; 10.41</td>
</tr>
<tr>
<td valign="middle" align="left">CRP, <inline-formula>
<mml:math display="inline" id="im5"><mml:mrow><mml:mi>m</mml:mi><mml:mi>g</mml:mi><mml:mo stretchy="false">/</mml:mo><mml:mi>L</mml:mi></mml:mrow></mml:math></inline-formula></td>
<td valign="middle" align="left">26.76 &#xb1; 26.79</td>
</tr>
<tr>
<td valign="middle" align="left">PCT, <inline-formula>
<mml:math display="inline" id="im6"><mml:mrow><mml:mi>n</mml:mi><mml:mi>g</mml:mi><mml:mo stretchy="false">/</mml:mo><mml:mi>m</mml:mi><mml:mi>o</mml:mi><mml:mi>l</mml:mi></mml:mrow></mml:math></inline-formula></td>
<td valign="middle" align="left">2.10 &#xb1; 12.91</td>
</tr>
<tr>
<td valign="middle" align="left">IL-6, <inline-formula>
<mml:math display="inline" id="im7"><mml:mrow><mml:mi>p</mml:mi><mml:mi>g</mml:mi><mml:mo stretchy="false">/</mml:mo><mml:mi>m</mml:mi><mml:mi>L</mml:mi></mml:mrow></mml:math></inline-formula></td>
<td valign="middle" align="left">35.63 &#xb1; 58.29</td>
</tr>
<tr>
<td valign="middle" align="left">ALT, <inline-formula>
<mml:math display="inline" id="im8"><mml:mrow><mml:mi>U</mml:mi><mml:mo stretchy="false">/</mml:mo><mml:mi>L</mml:mi></mml:mrow></mml:math></inline-formula></td>
<td valign="middle" align="left">16.00 &#xb1; 14.75</td>
</tr>
<tr>
<td valign="middle" align="left">AST, <inline-formula>
<mml:math display="inline" id="im9"><mml:mrow><mml:mi>U</mml:mi><mml:mo stretchy="false">/</mml:mo><mml:mi>L</mml:mi></mml:mrow></mml:math></inline-formula></td>
<td valign="middle" align="left">21.04 &#xb1; 8.34</td>
</tr>
<tr>
<td valign="middle" align="left">SCr, <inline-formula>
<mml:math display="inline" id="im10"><mml:mrow><mml:mi>&#x3bc;</mml:mi><mml:mi>m</mml:mi><mml:mi>o</mml:mi><mml:mi>l</mml:mi><mml:mo stretchy="false">/</mml:mo><mml:mi>L</mml:mi></mml:mrow></mml:math></inline-formula></td>
<td valign="middle" align="left">47.02 &#xb1; 10.54</td>
</tr>
</tbody>
</table>
<table-wrap-foot>
<fn>
<p>77.6% (n=38) of participants were infected from 28 weeks of pregnancy to delivery, while 22.4% (n=11) were infected between 0 and 28 weeks of pregnancy. Values are presented as number (%) and mean &#xb1; SD. ALT, alanine aminotransferase; AST, aspartate transaminase; CRP, C-reactive protein; Hb, hemoglobin; IL-6, Interleukin-6; LYM, lymphocyte; PCT, procalcitonin; PLT, platelets; SCr, serum creatinine; SD, standard deviation; WBC, white blood cell.</p></fn>
</table-wrap-foot>
</table-wrap>
<p>Laboratory test results encompassed hematological parameters, inflammatory biomarkers, and hepatorenal function profiles. Hematological analysis revealed white blood cell (WBC, <inline-formula>
<mml:math display="inline" id="im11"><mml:mrow><mml:mn>9.35</mml:mn><mml:mo>&#xd7;</mml:mo><mml:msup><mml:mrow><mml:mn>10</mml:mn></mml:mrow><mml:mn>9</mml:mn></mml:msup><mml:mo stretchy="false">/</mml:mo><mml:mi>L</mml:mi></mml:mrow></mml:math></inline-formula>) and platelet (PLT, <inline-formula>
<mml:math display="inline" id="im12"><mml:mrow><mml:mn>237.71</mml:mn><mml:mo>&#xd7;</mml:mo><mml:msup><mml:mrow><mml:mn>10</mml:mn></mml:mrow><mml:mn>9</mml:mn></mml:msup><mml:mo stretchy="false">/</mml:mo><mml:mi>L</mml:mi></mml:mrow></mml:math></inline-formula>) counts within normal reference ranges. In contrast, lymphocyte (LYM, <inline-formula>
<mml:math display="inline" id="im13"><mml:mrow><mml:mn>1.09</mml:mn><mml:mo>&#xd7;</mml:mo><mml:msup><mml:mrow><mml:mn>10</mml:mn></mml:mrow><mml:mn>9</mml:mn></mml:msup><mml:mo stretchy="false">/</mml:mo><mml:mi>L</mml:mi></mml:mrow></mml:math></inline-formula>) and hemoglobin (Hb, <inline-formula>
<mml:math display="inline" id="im14"><mml:mrow><mml:mn>111.65</mml:mn><mml:mtext>&#xa0;</mml:mtext><mml:mo>&#xd7;</mml:mo><mml:msup><mml:mrow><mml:mn>10</mml:mn></mml:mrow><mml:mn>9</mml:mn></mml:msup><mml:mo stretchy="false">/</mml:mo><mml:mi>L</mml:mi></mml:mrow></mml:math></inline-formula>) levels demonstrated mild decreases compared to normal threshold. As expected, all inflammatory markers in the <italic>M. pneumoniae</italic> group showed significantly elevated average values, including C-reactive protein (CRP, <inline-formula>
<mml:math display="inline" id="im15"><mml:mrow><mml:mn>26.76</mml:mn><mml:mo>&#xa0;</mml:mo><mml:mi>m</mml:mi><mml:mi>g</mml:mi><mml:mo stretchy="false">/</mml:mo><mml:mi>L</mml:mi></mml:mrow></mml:math></inline-formula>), procalcitonin (PCT, <inline-formula>
<mml:math display="inline" id="im16"><mml:mrow><mml:mn>2.10</mml:mn><mml:mo>&#xa0;</mml:mo><mml:mi>m</mml:mi><mml:mi>g</mml:mi><mml:mo stretchy="false">/</mml:mo><mml:mi>L</mml:mi></mml:mrow></mml:math></inline-formula>), and interleukin-6 (IL-6, <inline-formula>
<mml:math display="inline" id="im17"><mml:mrow><mml:mn>35.63</mml:mn><mml:mo>&#xa0;</mml:mo><mml:mi>m</mml:mi><mml:mi>g</mml:mi><mml:mo stretchy="false">/</mml:mo><mml:mi>L</mml:mi></mml:mrow></mml:math></inline-formula>). Hepatorenal function indices, however, remained within clinically normal limits, with alanine aminotransferase (ALT, <inline-formula>
<mml:math display="inline" id="im18"><mml:mrow><mml:mn>16.00</mml:mn><mml:mo>&#xa0;</mml:mo><mml:mi>U</mml:mi><mml:mo stretchy="false">/</mml:mo><mml:mi>L</mml:mi></mml:mrow></mml:math></inline-formula>, aspartate aminotransferase (AST, <inline-formula>
<mml:math display="inline" id="im19"><mml:mrow><mml:mn>21.04</mml:mn><mml:mo>&#xa0;</mml:mo><mml:mi>U</mml:mi><mml:mo stretchy="false">/</mml:mo><mml:mi>L</mml:mi></mml:mrow></mml:math></inline-formula>), and serum creatinine (SCr, <inline-formula>
<mml:math display="inline" id="im20"><mml:mrow><mml:mn>47.02</mml:mn><mml:mtext>&#xa0;</mml:mtext><mml:mi>&#x3bc;</mml:mi><mml:mi>m</mml:mi><mml:mi>o</mml:mi><mml:mi>l</mml:mi><mml:mo stretchy="false">/</mml:mo><mml:mi>L</mml:mi></mml:mrow></mml:math></inline-formula>).</p>
</sec>
<sec id="s3_3">
<title>The association of <italic>M. pneumoniae</italic> infection and adverse pregnancy outcome</title>
<p>The primary adverse maternal outcome is adverse maternal events. As shown in <xref ref-type="table" rid="T3"><bold>Table&#xa0;3</bold></xref>, in the unadjusted analysis, a total of 126 pregnant women experienced adverse maternal events, with 49 cases (69.39%) in the <italic>M. pneumoniae</italic> group and 92 cases (67.15%) in the control group. The PSW-adjusted OR was 1.25 (95% CI, 0.62 to 2.55; <italic>p</italic>&#xa0;=&#xa0;0.530), suggesting no significant difference between <italic>M. pneumoniae</italic> infection and non-infection. As for the secondary outcomes, our research revealed that CS, PPH, amniotic fluid contamination, and PROM were almost identical between the control group and <italic>M. pneumoniae</italic> group, while polyhydramnios or oligohydramnios and placental abruption showed considerable differences between the control group and <italic>M. pneumoniae</italic> group, but all of these outcomes showed no statistically significant differences both for unadjusted analysis and PSW analysis.</p>
<table-wrap id="T3" position="float">
<label>Table&#xa0;3</label>
<caption>
<p>Effects of <italic>M. pneumoniae</italic> infection on pregnant women before and after propensity score weighting.</p>
</caption>
<table frame="hsides">
<thead>
<tr>
<th valign="middle" rowspan="2" align="left">Outcomes</th>
<th valign="middle" rowspan="2" align="left">Control (n=137)</th>
<th valign="middle" rowspan="2" align="left"><italic>M. pneumoniae</italic> (n=49)</th>
<th valign="middle" colspan="2" align="left">Unadjusted</th>
<th valign="middle" colspan="2" align="left">PSW</th>
</tr>
<tr>
<th valign="middle" align="left">OR/MD</th>
<th valign="middle" align="left"><italic>p</italic></th>
<th valign="middle" align="left">OR/MD</th>
<th valign="middle" align="left"><italic>p</italic></th>
</tr>
</thead>
<tbody>
<tr>
<td valign="middle" align="left">Adverse maternal events, n (%) #</td>
<td valign="middle" align="left">92 (67.15)</td>
<td valign="middle" align="left">34 (69.39)</td>
<td valign="middle" align="left">1.11 (0.55-2.25)</td>
<td valign="middle" align="left">0.774</td>
<td valign="middle" align="left">1.25 (0.62-2.55)</td>
<td valign="middle" align="left">0.530</td>
</tr>
<tr>
<td valign="middle" align="left">CS, n (%)</td>
<td valign="middle" align="left">60 (43.80)</td>
<td valign="middle" align="left">28 (57.14)</td>
<td valign="middle" align="left">1.71 (0.88-3.32)</td>
<td valign="middle" align="left">0.112</td>
<td valign="middle" align="left">1.83 (0.94-3.55)</td>
<td valign="middle" align="left">0.075</td>
</tr>
<tr>
<td valign="middle" align="left">PPH, n (%) <sup>&#x2020;</sup></td>
<td valign="middle" align="left">1 (0.73)</td>
<td valign="middle" align="left">0 (0.00)</td>
<td valign="middle" align="left">0.92 (0.04-24.09)</td>
<td valign="middle" align="left">0.959</td>
<td valign="middle" align="left">0.91 (0.03-23.74)</td>
<td valign="middle" align="left">0.954</td>
</tr>
<tr>
<td valign="middle" align="left">Polyhydramnios or oligohydramnios, n (%)</td>
<td valign="middle" align="left">16 (11.68)</td>
<td valign="middle" align="left">11 (22.45)</td>
<td valign="middle" align="left">2.19 (0.93-5.15)</td>
<td valign="middle" align="left">0.072</td>
<td valign="middle" align="left">2.19 (0.93-5.17)</td>
<td valign="middle" align="left">0.074</td>
</tr>
<tr>
<td valign="middle" align="left">Amniotic fluid contamination, n (%)</td>
<td valign="middle" align="left">16 (11.68)</td>
<td valign="middle" align="left">6 (12.24)</td>
<td valign="middle" align="left">1.06 (0.39-2.89)</td>
<td valign="middle" align="left">0.916</td>
<td valign="middle" align="left">1.52 (0.59-3.92)</td>
<td valign="middle" align="left">0.388</td>
</tr>
<tr>
<td valign="middle" align="left">Placental abruption, n (%)</td>
<td valign="middle" align="left">1 (0.73)</td>
<td valign="middle" align="left">1 (2.04)</td>
<td valign="middle" align="left">2.83 (0.17-47.05)</td>
<td valign="middle" align="left">0.466</td>
<td valign="middle" align="left">7.16 (0.62-82.09)</td>
<td valign="middle" align="left">0.113</td>
</tr>
<tr>
<td valign="middle" align="left">PROM, n (%)</td>
<td valign="middle" align="left">34 (24.82)</td>
<td valign="middle" align="left">10 (20.41)</td>
<td valign="middle" align="left">0.78 (0.35-1.73)</td>
<td valign="middle" align="left">0.535</td>
<td valign="middle" align="left">0.64 (0.28-1.49)</td>
<td valign="middle" align="left">0.302</td>
</tr>
<tr>
<td valign="middle" align="left">Adverse neonatal events, n (%) #</td>
<td valign="middle" align="left">65 (47.45)</td>
<td valign="middle" align="left">24 (48.98)</td>
<td valign="middle" align="left">1.06 (0.55-2.05)</td>
<td valign="middle" align="left">0.854</td>
<td valign="middle" align="left">0.95 (0.49-1.84)</td>
<td valign="middle" align="left">0.884</td>
</tr>
<tr>
<td valign="middle" align="left">Fetal HRV, n (%)</td>
<td valign="middle" align="left">26 (18.98)</td>
<td valign="middle" align="left">14 (28.57)</td>
<td valign="middle" align="left">1.71 (0.80-3.64)</td>
<td valign="middle" align="left">0.165</td>
<td valign="middle" align="left">1.44 (0.67-3.11)</td>
<td valign="middle" align="left">0.347</td>
</tr>
<tr>
<td valign="middle" align="left">Preterm infant, n (%)</td>
<td valign="middle" align="left">17 (12.41)</td>
<td valign="middle" align="left">8 (16.33)</td>
<td valign="middle" align="left">1.38 (0.55-3.45)</td>
<td valign="middle" align="left">0.492</td>
<td valign="middle" align="left">1.34 (0.53-3.38)</td>
<td valign="middle" align="left">0.532</td>
</tr>
<tr>
<td valign="middle" align="left">Fetal distress, n (%)</td>
<td valign="middle" align="left">24 (17.52)</td>
<td valign="middle" align="left">10 (20.41)</td>
<td valign="middle" align="left">1.21 (0.53-2.76)</td>
<td valign="middle" align="left">0.654</td>
<td valign="middle" align="left">1.81 (0.84-3.93)</td>
<td valign="middle" align="left">0.131</td>
</tr>
<tr>
<td valign="middle" align="left">Other neonatal infection, n (%)</td>
<td valign="middle" align="left">7 (5.11)</td>
<td valign="middle" align="left">6 (12.24)</td>
<td valign="middle" align="left">2.59 (0.82-8.19)</td>
<td valign="middle" align="left">0.104</td>
<td valign="middle" align="left">2.65 (0.85-8.33)</td>
<td valign="middle" align="left">0.094</td>
</tr>
<tr>
<td valign="middle" align="left">Neonatal length, Mean &#xb1; SD</td>
<td valign="middle" align="left">49.58&#xa0;&#xb1;&#xa0;3.01</td>
<td valign="middle" align="left">49.31&#xa0;&#xb1;&#xa0;2.22</td>
<td valign="middle" align="left">-0.27 (-1.20-0.66)</td>
<td valign="middle" align="left">0.566</td>
<td valign="middle" align="left">-0.13 (-1.04-0.79)</td>
<td valign="middle" align="left">0.785</td>
</tr>
<tr>
<td valign="middle" align="left">Neonatal weight, Mean &#xb1; SD</td>
<td valign="middle" align="left">3.05&#xa0;&#xb1;&#xa0;0.53</td>
<td valign="middle" align="left">3.01&#xa0;&#xb1;&#xa0;0.52</td>
<td valign="middle" align="left">-0.05 (-0.22-0.12)</td>
<td valign="middle" align="left">0.586</td>
<td valign="middle" align="left">0.02 (-0.15-0.19)</td>
<td valign="middle" align="left">0.845</td>
</tr>
<tr>
<td valign="middle" align="left">Neonatal head circumference, Mean &#xb1; SD</td>
<td valign="middle" align="left">33.04&#xa0;&#xb1;&#xa0;1.93</td>
<td valign="middle" align="left">32.96&#xa0;&#xb1;&#xa0;1.55</td>
<td valign="middle" align="left">-0.08 (-0.68-0.53)</td>
<td valign="middle" align="left">0.800</td>
<td valign="middle" align="left">0.09 (-0.50-0.69)</td>
<td valign="middle" align="left">0.755</td>
</tr>
<tr>
<td valign="middle" align="left">One-minute Apgar score, Mean &#xb1; SD</td>
<td valign="middle" align="left">8.93&#xa0;&#xb1;&#xa0;0.30</td>
<td valign="middle" align="left">8.90&#xa0;&#xb1;&#xa0;0.51</td>
<td valign="middle" align="left">-0.04 (-0.16-0.08)</td>
<td valign="middle" align="left">0.554</td>
<td valign="middle" align="left">-0.05 (-0.17-0.08)</td>
<td valign="middle" align="left">0.464</td>
</tr>
</tbody>
</table>
<table-wrap-foot>
<fn>
<p>#The primary outcome. <sup>&#x2020;</sup>Firth&#x2019;s penalized logistic regression to correct sparse data bias.</p></fn>
<fn>
<p>CS, cesarean section; HRV, heart rate variability; PSW, propensity score weighting; MD, mean difference; OR, odds ratio; PPH, postpartum hemorrhage; PROM, premature rupture of membranes; SD, standard deviation.</p></fn>
</table-wrap-foot>
</table-wrap>
<p>Additionally, a total of 186 neonates were delivered. In the PSW analysis, a total of 89 neonates experienced adverse events, with 24 cases (48.98%) in the <italic>M. pneumoniae</italic> group and 65 cases (47.45%) in the control group. The PSW OR between the two groups was 0.95 (95% CI, 0.49 to 1.84; <italic>p</italic>&#xa0;=&#xa0;0.884), suggesting no significant difference between groups. The PSW and unadjusted analyses yielded consistent results, with an unadjusted OR of 1.06 (48.98% vs. 47.45%; 95% CI, 0.55 to 2.05; <italic>p</italic>&#xa0;=&#xa0;0.854). Similarly, for secondary adverse neonatal outcomes, including fetal HRV, preterm birth, fetal distress, other neonatal infections, neonatal length, neonatal weight, neonatal head circumference, and the one-minute Apgar score, there was no significant difference between groups. Similar patterns were found in the PSM analysis (<xref ref-type="supplementary-material" rid="SM1"><bold>Supplementary Table S4</bold></xref>).</p>
</sec>
<sec id="s3_4">
<title>Subgroup analysis</title>
<p>The result of subgroup analysis was presented in <xref ref-type="fig" rid="f2"><bold>Figures&#xa0;2</bold></xref>, <xref ref-type="fig" rid="f3"><bold>3</bold></xref>. Firth&#x2019;s penalized logistic regression was also applied to subgroup analyses. In primiparous women (Gravidity=1), <italic>M. pneumoniae</italic> infection was associated with increased odds of adverse maternal events (PSW OR, 15.94; 95% CI, 1.03 to 247.78; <italic>p</italic>&#xa0;=&#xa0;0.048). With regard to CS, we also found that for primiparous women, infection with <italic>M. pneumoniae</italic> increased the risk of undergoing a CS (PSW OR, 6.07; 95% CI, 1.80 to 20.44; <italic>p</italic>&#xa0;=&#xa0;0.004). A similar result was observed among participants without history of abortion, with an PSW OR of 2.57 (95% CI, 1.12 to 5.88; <italic>p</italic>&#xa0;=&#xa0;0.026).</p>
<fig id="f2" position="float">
<label>Figure&#xa0;2</label>
<caption>
<p><bold>(A)</bold> Subgroup analysis of adverse maternal events. <bold>(B)</bold> Subgroup analysis of cesarean section. <sup>&#x2020;</sup>Firth&#x2019;s penalized logistic regression to correct sparse data bias. PSW, propensity score weighting; OR, odds ratio.</p>
</caption>
<graphic mimetype="image" mime-subtype="tiff" xlink:href="fcimb-15-1663272-g002.tif">
<alt-text content-type="machine-generated">Two panels display forest plots. Panel A illustrates adverse maternal events comparing control and M. pneumoniae groups across subgroups such as age, gravidity, history of abortion, and chronic diseases. A significant outcome is observed in gravidity with one previous child, odds ratio of 15.94. Panel B focuses on cesarean sections with similar subgroup breakdowns. Significant outcomes are seen in gravidity with one previous child, odds ratio of 6.07, and history of abortion indicating no. Both plots show point estimates with confidence intervals and p-values.</alt-text>
</graphic></fig>
<fig id="f3" position="float">
<label>Figure&#xa0;3</label>
<caption>
<p><bold>(A)</bold> Subgroup analysis of preterm infant. <bold>(B)</bold> Subgroup analysis of fetal distress.</p>
</caption>
<graphic mimetype="image" mime-subtype="tiff" xlink:href="fcimb-15-1663272-g003.tif">
<alt-text content-type="machine-generated">Forest plot showing odds ratios for two outcomes: Preterm Infant (A) and Fetal Distress (B). Each subgroup analysis compares control and Mycoplasma pneumoniae groups across age, gravidity, history of abortion, and chronic diseases. Panel A shows a significant odds ratio of 24.39 for age under thirty-five. Panel B shows a significant odds ratio of 84.07 for age thirty-five and up. Confidence intervals and p-values vary across subgroups.</alt-text>
</graphic></fig>
<p>The PSW OR for most adverse neonatal outcomes among AMA was statistically significant. For example, the PSW OR of preterm infant was 24.39 (95% CI, 2.05 to 290.08; <italic>p</italic>&#xa0;=&#xa0;0.014), and that of fetal distress was 84.07 (95% CI, 2.85 to 2482.75; <italic>p</italic>&#xa0;=&#xa0;0.012), which indicated that, among AMA, infection with <italic>M. pneumoniae</italic> increased the risk of preterm birth and fetal distress. The subgroup analyses based on PSW for the other adverse pregnancy outcomes are presented in <xref ref-type="supplementary-material" rid="SM1"><bold>Supplementary Figure S1</bold></xref>&#x2013;<xref ref-type="supplementary-material" rid="SM1"><bold>S12</bold></xref>. Notably, statistical significance was observed in 6 out of 9 adverse neonatal outcomes among the AMA subgroup (<xref ref-type="fig" rid="f3"><bold>Figure&#xa0;3</bold></xref>, <xref ref-type="supplementary-material" rid="SM1"><bold>Supplementary Figure S6</bold></xref>-<xref ref-type="supplementary-material" rid="SM1"><bold>S12</bold></xref>), suggesting possible signals of risk associated with <italic>M. pneumoniae</italic> infection in AMA.</p>
</sec>
</sec>
<sec id="s4" sec-type="discussion">
<title>Discussion</title>
<p>The COVID-19 pandemic has significantly diminished the populace&#x2019;s immunity (<xref ref-type="bibr" rid="B30">Wu et&#xa0;al., 2024</xref>), which may result in a poorer prognosis when pneumonia occurs post-COVID-19 era compared to pre-COVID-19 era. This study focused on the outbreak of <italic>M. pneumoniae</italic> infections following the COVID-19 pandemic and provided some valuable insights into the potential impact of <italic>M. pneumoniae</italic> infection on adverse pregnancy outcomes among pregnant women, based on data from Guangzhou, China.</p>
<p>We first analyzed the clinical manifestations and laboratory findings of hospitalized pregnant women with <italic>M. pneumoniae</italic> infection after the COVID-19 pandemic (2023). Upper respiratory tract infection (URI) was the most common clinical presentation of <italic>M. pneumoniae</italic> infection, with the majority of pregnant patients exhibiting fever and cough as predominant symptoms (<xref ref-type="bibr" rid="B15">Layani-Milon et&#xa0;al., 1999</xref>). Respiratory manifestations were the most prominent, often accompanied by pronounced pharyngodynia, nasal congestion, headache, and asthenia, indicative of systemic toxicity. In contrast, extrapulmonary manifestations were uncommon and generally mild. These findings were consistent with the Expert Consensus on the Diagnosis and Treatment of <italic>M. pneumoniae</italic> Pneumonia in Adults, developed by the Infection Group of the Respiratory Society of the Chinese Medical Association. These clinical features were also comparable to those reported in adult <italic>M. pneumoniae</italic> infections both before and after the COVID-19 pandemic (<xref ref-type="bibr" rid="B6">Centers for Disease Control and Prevention (CDC), 2013</xref>; <xref ref-type="bibr" rid="B18">Li et&#xa0;al., 2024</xref>). Laboratory findings revealed that pregnant women with <italic>M. pneumoniae</italic> infection had white blood cell (WBC) counts approaching the upper limit of the normal adult range, while lymphocyte counts were slightly below the normal threshold, aligning with previous reports (<xref ref-type="bibr" rid="B18">Li et&#xa0;al., 2024</xref>). <italic>M. pneumoniae</italic> infection has been associated with a broad decline in immune cell populations, including lymphocytes, CD3<sup>+</sup> T cells, CD4<sup>+</sup> T cells, CD8<sup>+</sup> T cells, and B cells, suggesting potential immunological alterations. Regarding inflammatory markers, levels of C-reactive protein (CRP), procalcitonin (PCT), and interleukin-6 (IL-6) were significantly elevated, indicating a robust inflammatory response. This trend is consistent with the findings of Li et&#xa0;al (<xref ref-type="bibr" rid="B18">Li et&#xa0;al., 2024</xref>), who reported similar elevations in CRP, PCT, and IL-6 in pediatric <italic>M. pneumoniae</italic> infections in Guangzhou following the COVID-19 pandemic, with these markers correlating with infection severity. Moreover, our results demonstrated no significant impairment of hepatic or renal function in pregnant women with <italic>M. pneumoniae</italic> infection. However, the potential impact of prolonged or severe <italic>M. pneumoniae</italic> infection on hepatic and renal function requires further investigation.</p>
<p>Following propensity score analysis, our findings suggest that in the post-COVID-19 era, evidence remains insufficient to conclude that <italic>M. pneumoniae</italic> infection increases the risk of adverse pregnancy outcomes in the overall population. However, some subgroups showed statistical significance. This contrast may be attributed to the study population being insufficiently specific to capture potential high-risk groups (e.g., the AMA population), thereby limiting our ability to detect statistical significance.</p>
<p>A possible reason for the lack of observed significance in the overall population is the small sample size of the cohort, with only 49 participants in the exposure group, which may have limited our ability to detect the differences (<xref ref-type="bibr" rid="B28">Sullivan and Feinn, 2012</xref>). Therefore, a <italic>post-hoc</italic> power analysis was conducted to evaluate the statistical power. The results showed that for the logistic regression analysis, a total sample of 186 participants (49 exposure group vs. 137 control group) achieves 54% power (implying a 46% Type II error rate) at a two-tailed 0.05 significance level to detect an OR of 2.00, assuming a baseline risk of 0.4 (<xref ref-type="supplementary-material" rid="SM1"><bold>Supplementary Figure S13</bold></xref><bold>).</bold> A problem accompanying the small sample size was the low number of events (e.g., no participants in the <italic>M. pneumoniae</italic> group experienced postpartum hemorrhage), which can lead to considerable upward or downward bias in estimates obtained from standard ML. Given this unavoidable limitation, Firth&#x2019;s penalized logistic regression was used to correct small sample bias, thereby preventing more extreme and implausible estimates. This issue of small sample size was particularly pronounced in the subgroup analyses. For instance, the AMA subgroup included only 29 participants.</p>
<p>Subgroup analyses for adverse maternal outcomes revealed two findings: <italic>M. pneumoniae</italic> infection significantly increased the risk of adverse maternal events and CS in primiparous women, while women without a history of abortion also exhibited a higher CS rate. These findings would be of substantial importance if the associations are confirmed in adequately powered studies. Prior to the COVID-19 pandemic, multiple clinical studies on pneumonia in pregnant women identified CS as a major adverse pregnancy outcome (<xref ref-type="bibr" rid="B29">Tang et&#xa0;al., 2018</xref>; <xref ref-type="bibr" rid="B8">Chen et&#xa0;al., 2012</xref>; <xref ref-type="bibr" rid="B26">Romanyuk et&#xa0;al., 2011</xref>). Our findings in the post-pandemic era are consistent with these reports, particularly in the subgroup of pregnant women with infection with <italic>M. pneumoniae</italic>. This increased CS rate may be attributed to the elevation of inflammatory cytokines following pathogen infection, which can impair uterine contractility and cervical dilation, subsequently increasing the likelihood of CS. Studies have demonstrated that the activation of inflammatory transcription factors such as NF-&#x3ba;B may directly upregulate genes associated with uterine contractions, thereby promoting the contractility of uterine smooth muscle (<xref ref-type="bibr" rid="B21">Mendelson, 2009</xref>). Furthermore, monocytes recruited to the cervix and myometrium, upon activation, release pro-inflammatory cytokines including IL-1&#x3b2;, TNF-&#x3b1;, and IL-6, which participate in cervical ripening, membrane rupture, and the initiation of labor (<xref ref-type="bibr" rid="B24">Obeagu, 2025</xref>). <italic>M. pneumoniae</italic> infection has been shown to significantly elevate the levels of cytokines such as IL-6, IL-8, and TNF-&#x3b1; (<xref ref-type="bibr" rid="B33">Zhao et&#xa0;al., 2019</xref>). The findings from Li et&#xa0;al (<xref ref-type="bibr" rid="B18">Li et&#xa0;al., 2024</xref>). indirectly lend support to the potential importance of this mechanism. Notably, within the amniotic fluid environment, IL-6 and IL-8 levels have been established as robust predictors of adverse fetal outcomes (<xref ref-type="bibr" rid="B20">McCartney et&#xa0;al., 2021</xref>). The impact of infection with <italic>M. pneumoniae</italic> on adverse maternal outcomes was more pronounced in primiparous women, potentially due to both psychological and physiological factors. Psychologically, the absence of prior childbirth experience may contribute to heightened fear and anxiety, which could affect stress responses and lead to labor abnormalities. Physiologically, the uterus and birth canal of primiparous women may exhibit increased susceptibility to infection, further predisposing them to adverse outcomes. Similarly, pregnant women without a history of abortion may have a relatively fragile reproductive system, lacking prior immunological adaptation to pregnancy-related inflammatory challenges, which could make them more vulnerable to the adverse effects of <italic>M. pneumoniae</italic>.</p>
<p>In terms of adverse neonatal outcomes, subgroup analyses revealed that, in AMA, the incidence of preterm birth, fetal distress, and other infections was significantly higher in the <italic>M. pneumoniae</italic> group. Moreover, neonates in this subgroup exhibited a marked tendency toward reduced birth length, weight, and head circumference. Although these findings from exploratory subgroup analyses require further validation given the limited sample size, several mechanisms may explain these observations. First, AMA is inherently associated with multiple obstetric risk factors, predisposing to adverse pregnancy outcomes, which is consistent with the meta-analysis findings of Lean et&#xa0;al (<xref ref-type="bibr" rid="B16">Lean et&#xa0;al., 2017a</xref>). A widely accepted explanation is that accelerated placental senescence in AMA pregnancies leads to altered nutrient transport and vascular function, thereby compromising the intrauterine environment for fetal growth. Similar characteristics have been observed in aged murine models (<xref ref-type="bibr" rid="B17">Lean et&#xa0;al., 2017b</xref>). Second, the genetic integrity of oocytes declines with maternal aging (<xref ref-type="bibr" rid="B9">Cimadomo et&#xa0;al., 2018</xref>), and while the relationship between this phenomenon and adverse pregnancy outcomes remains to be fully elucidated, it warrants further investigation. Additionally, in pregnancies at AMA complicated by infection with <italic>M. pneumoniae</italic>, the infection and its associated inflammatory response constitute a major risk factor for preterm birth (<xref ref-type="bibr" rid="B5">Cappelletti et&#xa0;al., 2016</xref>). This heightened inflammatory burden may impose additional stress on the placenta and uterus, increasing the risk of fetal distress and further restricting fetal growth, ultimately leading to reductions in neonatal birth length, weight, and head circumference. These findings align with clinical studies conducted before the COVID-19 pandemic on pneumonia-complicated pregnancies (<xref ref-type="bibr" rid="B8">Chen et&#xa0;al., 2012</xref>).</p>
<p>These findings should be interpreted in the context of specific limitations. First, the single-center design of this study may restrict the generalizability of our findings. Additionally, statistical power was insufficient, as <italic>post-hoc</italic> analysis indicated 54% power overall, implying a 46% Type II error rate. Similarly, the exploratory subgroup analyses were constrained by small sample sizes (particularly in the AMA subgroup), complete separation, and the potential for spurious findings due to multiple comparisons across outcomes and subgroups. Consequently, the subgroup findings are hypothesis-generating and require validation in larger, adequately powered studies.</p>
</sec>
<sec id="s5" sec-type="conclusions">
<title>Conclusions</title>
<p>In the post-COVID-19 era, evidence remains insufficient to conclude that <italic>M. pneumoniae</italic> infection increases the risk of adverse pregnancy outcomes in the general pregnant population. However, our findings suggest potential risks specifically within subgroups of AMA and primiparous women. Despite limitations regarding sample size and the single-center design, it provides clinically relevant insights into patterns of <italic>M. pneumoniae</italic> infection in the post-COVID-19 era. These findings offer valuable guidance for clinical management and inform therapeutic decision-making following infection.</p>
</sec>
</body>
<back>
<sec id="s6" sec-type="data-availability">
<title>Data availability statement</title>
<p>The data and analytical code used in this study are available from the corresponding author upon reasonable request. Requests to access these datasets should be directed to CY, <email xlink:href="mailto:xiaoyang856@163.com">xiaoyang856@163.com</email>.</p></sec>
<sec id="s7" sec-type="ethics-statement">
<title>Ethics statement</title>
<p>The studies involving humans were approved by the Medical Ethics Committee of Nanfang Hospital affiliated to Southern Medical University (ID: NFEC- 2023-116). The studies were conducted in accordance with the local legislation and institutional requirements. The participants provided their written informed consent to participate in this study.</p></sec>
<sec id="s8" sec-type="author-contributions">
<title>Author contributions</title>
<p>CY: Conceptualization, Data curation, Funding acquisition, Investigation, Resources, Writing &#x2013; original draft. HJ: Formal analysis, Methodology, Software, Visualization, Writing &#x2013; original draft. LL: Writing &#x2013; review &amp; editing. PZ: Writing &#x2013; review &amp; editing. YL: Project administration, Supervision, Writing &#x2013; review &amp; editing. YW: Funding acquisition, Methodology, Project administration, Writing &#x2013; review &amp; editing.</p></sec>
<ack>
<title>Acknowledgments</title>
<p>We thank all patients and staffs participating in the study.</p>
</ack>
<sec id="s10" sec-type="COI-statement">
<title>Conflict of interest</title>
<p>The authors declared that this work was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.</p></sec>
<sec id="s11" sec-type="ai-statement">
<title>Generative AI statement</title>
<p>The author(s) declared that generative AI was not used in the creation of this manuscript.</p>
<p>Any alternative text (alt text) provided alongside figures in this article has been generated by Frontiers with the support of artificial intelligence and reasonable efforts have been made to ensure accuracy, including review by the authors wherever possible. If you identify any issues, please contact us.</p></sec>
<sec id="s12" 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></sec>
<sec id="s13" sec-type="supplementary-material">
<title>Supplementary material</title>
<p>The Supplementary Material for this article can be found online at: <ext-link ext-link-type="uri" xlink:href="https://www.frontiersin.org/articles/10.3389/fcimb.2025.1663272/full#supplementary-material">https://www.frontiersin.org/articles/10.3389/fcimb.2025.1663272/full#supplementary-material</ext-link></p>
<supplementary-material xlink:href="DataSheet1.docx" id="SM1" mimetype="application/vnd.openxmlformats-officedocument.wordprocessingml.document"/></sec>
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