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<front>
<journal-meta>
<journal-id journal-id-type="publisher-id">Front. Pharmacol.</journal-id>
<journal-title-group>
<journal-title>Frontiers in Pharmacology</journal-title>
<abbrev-journal-title abbrev-type="pubmed">Front. Pharmacol.</abbrev-journal-title>
</journal-title-group>
<issn pub-type="epub">1663-9812</issn>
<publisher>
<publisher-name>Frontiers Media S.A.</publisher-name>
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<article-meta>
<article-id pub-id-type="publisher-id">1665372</article-id>
<article-id pub-id-type="doi">10.3389/fphar.2025.1665372</article-id>
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<article-categories>
<subj-group subj-group-type="heading">
<subject>Original Research</subject>
</subj-group>
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<title-group>
<article-title>Preventive effect of small molecule active substances on septic cardiomyopathy after abdominal trauma: a systematic review and meta-analysis</article-title>
<alt-title alt-title-type="left-running-head">Ouyang et al.</alt-title>
<alt-title alt-title-type="right-running-head">
<ext-link ext-link-type="uri" xlink:href="https://doi.org/10.3389/fphar.2025.1665372">10.3389/fphar.2025.1665372</ext-link>
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<contrib-group>
<contrib contrib-type="author" equal-contrib="yes">
<name>
<surname>Ouyang</surname>
<given-names>Gen</given-names>
</name>
<xref ref-type="aff" rid="aff1">
<sup>1</sup>
</xref>
<xref ref-type="author-notes" rid="fn001">
<sup>&#x2020;</sup>
</xref>
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<contrib contrib-type="author" equal-contrib="yes">
<name>
<surname>Wang</surname>
<given-names>Neng</given-names>
</name>
<xref ref-type="aff" rid="aff2">
<sup>2</sup>
</xref>
<xref ref-type="author-notes" rid="fn001">
<sup>&#x2020;</sup>
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<contrib contrib-type="author">
<name>
<surname>Liu</surname>
<given-names>Yujuan</given-names>
</name>
<xref ref-type="aff" rid="aff3">
<sup>3</sup>
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<contrib contrib-type="author">
<name>
<surname>Yang</surname>
<given-names>Chuang</given-names>
</name>
<xref ref-type="aff" rid="aff3">
<sup>3</sup>
</xref>
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</contrib>
<contrib contrib-type="author">
<name>
<surname>Zeng</surname>
<given-names>Peng</given-names>
</name>
<xref ref-type="aff" rid="aff3">
<sup>3</sup>
</xref>
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</contrib>
<contrib contrib-type="author">
<name>
<surname>Gong</surname>
<given-names>Tao</given-names>
</name>
<xref ref-type="aff" rid="aff3">
<sup>3</sup>
</xref>
<role vocab="credit" vocab-identifier="https://credit.niso.org/" vocab-term="Data curation" vocab-term-identifier="https://credit.niso.org/contributor-roles/data-curation/">Data curation</role>
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</contrib>
<contrib contrib-type="author">
<name>
<surname>Tao</surname>
<given-names>Lu</given-names>
</name>
<xref ref-type="aff" rid="aff3">
<sup>3</sup>
</xref>
<role vocab="credit" vocab-identifier="https://credit.niso.org/" vocab-term="Formal analysis" vocab-term-identifier="https://credit.niso.org/contributor-roles/formal-analysis/">Formal Analysis</role>
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<role vocab="credit" vocab-identifier="https://credit.niso.org/" vocab-term="Writing &#x2013; original draft" vocab-term-identifier="https://credit.niso.org/contributor-roles/writing-original-draft/">Writing - original draft</role>
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<contrib contrib-type="author">
<name>
<surname>Zheng</surname>
<given-names>Ying</given-names>
</name>
<xref ref-type="aff" rid="aff3">
<sup>3</sup>
</xref>
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<contrib contrib-type="author">
<name>
<surname>Ye</surname>
<given-names>Guiying</given-names>
</name>
<xref ref-type="aff" rid="aff3">
<sup>3</sup>
</xref>
<role vocab="credit" vocab-identifier="https://credit.niso.org/" vocab-term="Data curation" vocab-term-identifier="https://credit.niso.org/contributor-roles/data-curation/">Data curation</role>
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<contrib contrib-type="author">
<name>
<surname>Li</surname>
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<xref ref-type="aff" rid="aff3">
<sup>3</sup>
</xref>
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<contrib contrib-type="author">
<name>
<surname>Che</surname>
<given-names>Chi</given-names>
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<xref ref-type="aff" rid="aff3">
<sup>3</sup>
</xref>
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<contrib contrib-type="author" corresp="yes">
<name>
<surname>Wang</surname>
<given-names>Longhai</given-names>
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<xref ref-type="aff" rid="aff4">
<sup>4</sup>
</xref>
<xref ref-type="corresp" rid="c001">&#x2a;</xref>
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<name>
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<given-names>Nai</given-names>
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<xref ref-type="aff" rid="aff3">
<sup>3</sup>
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<xref ref-type="corresp" rid="c001">&#x2a;</xref>
<uri xlink:href="https://loop.frontiersin.org/people/2747485"/>
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<aff id="aff1">
<label>1</label>
<institution>Department of General Surgery, Jiangxi Province Hospital of Integrated Chinese and Western Medicine</institution>, <city>Nanchang</city>, <state>Jiangxi</state>, <country country="CN">China</country>
</aff>
<aff id="aff2">
<label>2</label>
<institution>Graduate School, Jiangxi University of Chinese Medicine</institution>, <city>Nanchang</city>, <state>Jiangxi</state>, <country country="CN">China</country>
</aff>
<aff id="aff3">
<label>3</label>
<institution>Department of Emergency, Jiangxi Province Hospital of Integrated Chinese and Western Medicine</institution>, <city>Nanchang</city>, <state>Jiangxi</state>, <country country="CN">China</country>
</aff>
<aff id="aff4">
<label>4</label>
<institution>Department of Critical Care Medicine, Jiangxi Province Hospital of Integrated Chinese and Western Medicine</institution>, <city>Nanchang</city>, <state>Jiangxi</state>, <country country="CN">China</country>
</aff>
<author-notes>
<corresp id="c001">
<label>&#x2a;</label>Correspondence: Nai Zhang, <email xlink:href="mailto:zhangnai870901@163.com">zhangnai870901@163.com</email>; Longhai Wang, <email xlink:href="mailto:wanglonghai@21cn.com">wanglonghai@21cn.com</email>
</corresp>
<fn fn-type="equal" id="fn001">
<label>&#x2020;</label>
<p>These authors have contributed equally to this work to this study</p>
</fn>
</author-notes>
<pub-date publication-format="electronic" date-type="pub" iso-8601-date="2026-01-06">
<day>06</day>
<month>01</month>
<year>2026</year>
</pub-date>
<pub-date publication-format="electronic" date-type="collection">
<year>2025</year>
</pub-date>
<volume>16</volume>
<elocation-id>1665372</elocation-id>
<history>
<date date-type="received">
<day>23</day>
<month>07</month>
<year>2025</year>
</date>
<date date-type="rev-recd">
<day>14</day>
<month>11</month>
<year>2025</year>
</date>
<date date-type="accepted">
<day>20</day>
<month>11</month>
<year>2025</year>
</date>
</history>
<permissions>
<copyright-statement>Copyright &#xa9; 2026 Ouyang, Wang, Liu, Yang, Zeng, Gong, Tao, Zheng, Ye, Li, Che, Wang and Zhang.</copyright-statement>
<copyright-year>2026</copyright-year>
<copyright-holder>Ouyang, Wang, Liu, Yang, Zeng, Gong, Tao, Zheng, Ye, Li, Che, Wang and Zhang</copyright-holder>
<license>
<ali:license_ref start_date="2026-01-06">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>Objective</title>
<p>This systematic review and meta-analysis aimed to comprehensively evaluate the preventive effects and mechanisms of active small molecules against septic cardiomyopathy (SCM) induced by abdominal trauma or abdominal-origin sepsis in animal models.</p>
</sec>
<sec>
<title>Methods</title>
<p>The PubMed, Embase, Web of Science, Scopus and Cochrane Library databases were searched in January 2000- May 2025 for studies assessing active small molecules in animal SCM models. The standardised mean difference ([SMD], Hedges&#x2019; g) with 95% confidence intervals (CIs) was calculated using random-effects meta-analysis. Subgroup analyses were conducted according to molecular categories, sepsis induction methods, and outcome measures. Methodological quality was assessed using the Systematic Review of Laboratory Animal Experiments risk-of-bias tool.</p>
</sec>
<sec>
<title>Results</title>
<p>Seventeen studies met the inclusion criteria. The pooled meta-analysis showed that active small molecules had an overall beneficial but heterogeneous effect on SCM (SMD &#x3d; 1.47, 95% confidence interval [CI]: &#x2212;0.52&#x2013;3.47, I<sup>2</sup> &#x3d; 92.9%). Subgroup analyses revealed large but imprecise effects for polyphenols (SMD &#x3d; 4.81, 95% CI: &#x2212;5.25&#x2013;14.87) and neutral effects for flavonoids (SMD &#x3d; &#x2212;0.45, 95% CI: &#x2212;3.64&#x2013;2.73). The efficacy was greater in lipopolysaccharide-induced sepsis models (SMD &#x3d; 2.86) compared with cecal ligation and puncture models (SMD &#x3d; 0.16). Outcomes involving cardiac injury biomarkers (creatine kinase and creatine kinase isozymes) showed consistently positive and robust effects, while functional outcomes (e.g., left ventricular ejection fraction) exhibited inconsistent results. Sensitivity analyses confirmed robustness, while funnel plots indicated possible publication bias (Egger&#x2019;s test, p &#x3d; 0.123). Methodological limitations, including incomplete reporting of randomisation, blinding and allocation concealment, were commonly observed.</p>
</sec>
<sec>
<title>Conclusion</title>
<p>Active small molecules demonstrate generally positive yet heterogeneous preventive efficacy against SCM in animal models, with polyphenolic compounds in particular showing notable potential. Variability across molecular categories, sepsis models and measured outcomes highlights the need for standardised methodologies in future studies.</p>
</sec>
</abstract>
<kwd-group>
<kwd>septic cardiomyopathy</kwd>
<kwd>abdominal sepsis</kwd>
<kwd>abdominal trauma</kwd>
<kwd>small molecules</kwd>
<kwd>meta-analysis</kwd>
</kwd-group>
<funding-group>
<funding-statement>The authors declare that no financial support was received for the research and/or publication of this article.</funding-statement>
</funding-group>
<counts>
<fig-count count="8"/>
<table-count count="1"/>
<equation-count count="0"/>
<ref-count count="49"/>
<page-count count="15"/>
</counts>
<custom-meta-group>
<custom-meta>
<meta-name>section-at-acceptance</meta-name>
<meta-value>Cardiovascular and Smooth Muscle Pharmacology</meta-value>
</custom-meta>
</custom-meta-group>
</article-meta>
</front>
<body>
<sec sec-type="intro" id="s1">
<label>1</label>
<title>Introduction</title>
<p>Sepsis is a life-threatening clinical syndrome characterised by a dysregulated host response to infection, resulting in extensive tissue damage, multi-organ dysfunction and high global mortality (<xref ref-type="bibr" rid="B35">Suzuki et al., 2025</xref>; <xref ref-type="bibr" rid="B11">Jinzhong Wang and Jian Fu, 2025</xref>). Among the various complications caused by sepsis, septic cardiomyopathy (SCM) is a particularly severe manifestation and a significant factor in morbidity and mortality in critically ill patients (<xref ref-type="bibr" rid="B20">Mohammad et al., 2022</xref>; <xref ref-type="bibr" rid="B40">Wang et al., 2024</xref>). The condition is defined as acute myocardial dysfunction associated with sepsis, typically manifested by reduced left ventricular ejection fraction (LVEF), impaired myocardial contractility and elevated cardiac biomarkers in the absence of coronary artery obstruction or direct myocardial injury (<xref ref-type="bibr" rid="B45">Yuan et al., 2019</xref>).</p>
<p>Abdominal trauma and subsequent abdominal sepsis are common clinical conditions leading to SCM, with unique pathophysiological mechanisms that exacerbate myocardial injury and dysfunction (<xref ref-type="bibr" rid="B24">Pan et al., 2024</xref>; <xref ref-type="bibr" rid="B32">Shen et al., 2024</xref>; <xref ref-type="bibr" rid="B33">Su et al., 2024</xref>). Mechanistically, abdominal sepsis induces systemic inflammation, characterised by significant elevations in proinflammatory cytokines (e.g., tumour necrosis factor-&#x3b1; [TNF-&#x3b1;], interleukin-6 [IL-6]), excessive oxidative stress, mitochondrial dysfunction and subsequent cardiac cell apoptosis and dysfunction (<xref ref-type="bibr" rid="B13">Latifi and Smiley, 2023</xref>; <xref ref-type="bibr" rid="B1">Adenuga and Adeyeye, 2023</xref>). Despite advances in sepsis management, including early antibiotic treatment, fluid resuscitation and vasoactive drug support, targeted therapies specifically targeting SCM remain limited, highlighting the urgent need for new effective preventive or therapeutic strategies.</p>
<p>In recent years, increasing preclinical evidence has shown that active small molecules, including polyphenols, flavonoids, saponins, antioxidants, glycosides and other bioactive compounds, may have significant cardioprotective potential against septic myocardial injury through multiple mechanisms, including anti-inflammatory effects, anti-oxidative stress response, mitochondrial protection and reduced cardiomyocyte apoptosis (<xref ref-type="bibr" rid="B37">Wang et al., 2021</xref>; <xref ref-type="bibr" rid="B2">Bayly-Jones et al., 2022</xref>; <xref ref-type="bibr" rid="B14">Lee et al., 2022</xref>). For example, resveratrol, a phenotypic polysaccharide, has been shown to have mitochondrial protective effects and the ability to significantly reduce cardiac injury biomarkers in rodent models of endotoxemia. Flavonoids such as quercetin and luteolin have also been reported to reduce cardiac dysfunction and inflammation in animal sepsis models, although their efficacy varies, depending on the experimental setting (<xref ref-type="bibr" rid="B50">Zhao et al., 2023</xref>). Despite preliminary evidence of the promising efficacy of these small molecules, a comprehensive quantitative review of their efficacy and mechanistic characteristics in animal models of SCM remains limited.</p>
<p>Abdominal trauma and subsequent intra-abdominal sepsis are major causes of critical illness in surgical and trauma populations and are associated with a high incidence of multiple organ dysfunction, including SCM (<xref ref-type="bibr" rid="B30">Sartelli et al., 2024</xref>; <xref ref-type="bibr" rid="B21">Mure&#x15f;an et al., 2018</xref>). Abdominal-origin sepsis has pathophysiological characteristics such as ongoing peritoneal contamination, bacterial translocation and the potential for intra-abdominal hypertension/abdominal compartment physiology, which may produce distinct inflammatory and haemodynamic profiles compared with other sepsis sources (<xref ref-type="bibr" rid="B21">Mure&#x15f;an et al., 2018</xref>; <xref ref-type="bibr" rid="B19">MacFie, 2004</xref>).</p>
<p>Accordingly, this systematic review and meta-analysis aimed to comprehensively evaluate the preventive effects of various active small molecules on SCM induced by abdominal trauma or abdominal sepsis in animal models. Specifically, we aimed to systematically evaluate the overall efficacy of these small molecules, identify potential sources of heterogeneity through subgroup analyses (e.g., molecular class, sepsis model, outcome measures), explore potential protective mechanisms and rigorously evaluate the methodological quality of different studies. We hypothesised that active small molecules could significantly prevent SCM through anti-inflammatory, antioxidant and mitochondrial protective mechanisms, although the extent of their effects varied, based on the specific molecular class and experimental model. Our findings aim to provide strong preclinical evidence to guide future translational research and clinical trials targeting SCM.</p>
</sec>
<sec sec-type="methods" id="s2">
<label>2</label>
<title>Methods</title>
<sec id="s2-1">
<label>2.1</label>
<title>Protocol and registration</title>
<p>This systematic review and meta-analysis were conducted according to the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines.</p>
</sec>
<sec id="s2-2">
<label>2.2</label>
<title>Search strategy</title>
<p>A systematic literature search was conducted in 5 electronic databases (time: January 2000- May 2025), regardless of language: PubMed, Embase, Web of Science, Scopus, and Cochrane Library. The search strategy included: a combination of relevant keywords and MeSH terms related to sepsis (&#x2018;sepsis&#x2019;, &#x2018;endotoxaemia&#x2019;, &#x2018;cecal ligation and puncture&#x2019;, &#x2018;CLP&#x2019;), cardiomyopathy (&#x2018;cardiomyopathy&#x2019;, &#x2018;myocardial dysfunction&#x2019;, &#x2018;cardiac dysfunction&#x2019;, &#x2018;cardiac injury&#x2019;) and active small molecules (e.g., &#x2018;polyphenols&#x2019;, &#x2018;flavonoids&#x2019;, &#x2018;saponins&#x2019;, &#x2018;glycosides&#x2019;, &#x2018;antioxidants&#x2019;, &#x2018;phenylpropanoids&#x2019;, &#x2018;indolamines&#x2019;).</p>
</sec>
<sec id="s2-3">
<label>2.3</label>
<title>Inclusion criteria</title>
<p>Studies that met all of the following inclusion criteria were included.</p>
<p>Subjects: Animal models (mice or rats) with intra-abdominal sepsis or traumatic sepsis.</p>
<p>Interventions: <italic>In vivo</italic> administration of active small molecules as preventive or therapeutic interventions for SCM.</p>
<p>Controls: The study included appropriate control groups (sepsis or model groups without active molecules).</p>
<p>Outcomes: Clear reporting of myocardial outcomes, including cardiac function measures (LVEF), mean arterial pressure (MAP), left ventricular developed pressure [LVDP]) and myocardial injury biomarkers (creatine kinase [CK], CK isozymes [CK-MB], adenosine triphosphate (ATP), cytochrome c oxidase [CcO]).</p>
<p>Study design: Controlled animal studies providing quantitative outcome data (mean, standard deviation [SD] or standard error and the number of animals per group).</p>
<p>Studies were excluded based on the following criteria: non-abdominal sepsis or irrelevant models; cellular or clinical studies lacking animal data; reviews, editorials, comments or conference abstracts; studies lacking clear quantitative outcome measures.</p>
</sec>
<sec id="s2-4">
<label>2.4</label>
<title>Study selection</title>
<p>Two reviewers independently screened titles and abstracts from the search results against the pre-specified inclusion and exclusion criteria. Full texts were retrieved for records judged potentially eligible by either reviewer and were independently assessed by both reviewers. Discrepant judgments (&#x3c;5%) at any stage were discussed and resolved by consensus; persistent disagreements were resolved by consulting a third reviewer. We documented the reasons for exclusion at full-text review. The complete selection process is presented in the PRISMA flow diagram (<xref ref-type="fig" rid="F1">Figure 1</xref>).</p>
<fig id="F1" position="float">
<label>FIGURE 1</label>
<caption>
<p>PRISMA 2020 flow diagram.</p>
</caption>
<graphic xlink:href="fphar-16-1665372-g001.tif">
<alt-text content-type="machine-generated">Flowchart detailing the process of study identification via databases and registers. Initially, 1127 records were found, with 223 removed as duplicates. After screening 904 records, 830 were excluded. Seventy-four reports were sought for retrieval; five were not retrieved. Sixty-nine reports were assessed for eligibility, and 52 were excluded for various reasons. Seventeen studies were included in the final review. The chart highlights the steps: identification, screening, and inclusion.</alt-text>
</graphic>
</fig>
</sec>
<sec id="s2-5">
<label>2.5</label>
<title>Data extraction</title>
<p>Two reviewers independently extracted study-level data using a pre-piloted and standardised data extraction form. Extracted items included:</p>
<p>Study number: author and year of publication.</p>
<p>Animal characteristics: species, strain, age, and sex.</p>
<p>Sepsis model: lipopolysaccharide (LPS) dose, cecal ligation and puncture (CLP) procedure and trauma method.</p>
<p>Intervention details: name and molecular class of active molecule, dose (mg/kg), route of administration and time and frequency of administration.</p>
<p>Outcomes: cardiac function parameters (LVEF, MAP, LVDP), biochemical indices (CK, CK-MB, ATP, CCO), inflammatory markers (e.g., TNF-&#x3b1;, IL-6), oxidative stress markers (malondialdehyde(MAD), GPx4) and survival data.</p>
<p>Study design details: randomisation, blinding and sample size.</p>
<p>Discrepancies in extracted values were cross-checked and resolved by consensus; if a consensus could not be reached, a third reviewer adjudicated.</p>
</sec>
<sec id="s2-6">
<label>2.6</label>
<title>Risk of bias assessment</title>
<p>Two reviewers independently assessed methodological quality and risk of bias using the Systematic Review of Laboratory Animal Experiments (SYRCLE) risk of bias tool. The tool assesses 10 aspects as follows: (1) sequence generation, (2) baseline comparability, (3) allocation concealment, (4) randomisation housing, (5) caregiver blinding, (6) randomised outcome assessment, (7) assessor blinding, (8) incomplete outcome data, (9) selective outcome reporting and (10) other sources of bias. Each domain was judged as &#x2018;low risk&#x2019;, &#x2018;unclear risk&#x2019; or &#x2018;high risk&#x2019;, based on the information reported in the article. To translate domain-level judgments into an overall study-level classification we applied a pre-specified conservative rule: (1) studies with any &#x2018;high risk&#x2019; rating in 1 of 3 key domains (random sequence generation, allocation concealment or outcome assessor blinding) were classified as overall having high-risk; (2) if none of the key domains were &#x2018;high&#x2019; but 2 or more domains were rated as &#x2018;unclear&#x2019;, the study was classified as overall having unclear risk; (3) studies with no &#x2018;high&#x2019; domains and at most 1 &#x2018;unclear&#x2019; domain were classified as overall having low risk. This approach prioritised the identification of studies with critical design/reporting weaknesses and reduced the chance of overestimating internal validity due to incomplete reporting.</p>
</sec>
<sec id="s2-7">
<label>2.7</label>
<title>Statistical analysis</title>
<p>Statistical analysis was performed using the metafor package in R software (version 4.3.2, R Foundation for Statistical Computing, Vienna, Austria). For continuous outcomes, effect sizes were calculated as SMD (Hedges&#x2019; g) and their 95% confidence intervals (CIs). Due to expected heterogeneity, we pre-selected a random-effects model using restricted maximum likelihood. Statistical heterogeneity between studies was quantified using Cochran&#x2019;s Q test and Higgins&#x2019; I<sup>2</sup> statistic (where I<sup>2</sup> &#x3e; 50% indicated significant heterogeneity). Subgroup analyses were performed based on molecular class, sepsis model (LPS, CLP, trauma &#x2b; LPS), and outcome measures (cardiac function vs. biochemical indicators) to explore the sources of heterogeneity.</p>
<p>Publication bias was visually assessed using funnel plots and statistically tested using Egger&#x2019;s regression test, with p-values &#x3c;0.05 indicating significant bias. Sensitivity analyses included leave-one-out analysis to assess the robustness of the pooled effect.</p>
</sec>
</sec>
<sec sec-type="results" id="s3">
<label>3</label>
<title>Results</title>
<sec id="s3-1">
<label>3.1</label>
<title>Literature search and study screening</title>
<p>The database search initially identified a total of 1,127 articles. After deduplication using EndNote X9 (Clarivate Analytics, Connecticut, United States), 223 duplicate records were removed, leaving 904 unique records. The titles and abstracts of these 904 articles were independently screened by two reviewers based on predefined inclusion and exclusion criteria as follows: (i) animal models of abdominal sepsis or abdominal trauma sepsis; (ii) interventions involving active small molecules; and (iii) clear reporting of myocardial outcomes, such as cardiac function or myocardial injury markers. Disagreements between reviewers (&#x3c;5%) were resolved through discussion and consensus. After initial screening, 830 records were excluded due to irrelevant topics, leaving 74 articles eligible for full-text search. Of these, 5 reports were inaccessible despite multiple attempts to obtain the full text from institutional libraries and authors. Therefore, 69 full-text articles were evaluated in detail to determine their eligibility for the search. At this stage, 52 articles were excluded for the following reasons: 25 studies did not use SCM animal models, 18 studies lacked appropriate small molecule interventions or used irrelevant exposure conditions, and 9 studies were reviews, <italic>in vitro</italic> experiments only, or did not report relevant outcome data. Ultimately, 17 studies met all of the inclusion criteria and were included in the quantitative comprehensive analysis (meta-analysis). <xref ref-type="fig" rid="F1">Figure 1</xref> shows the PRISMA flow chart, detailing the study screening process.</p>
</sec>
<sec id="s3-2">
<label>3.2</label>
<title>Study characteristics</title>
<p>
<xref ref-type="table" rid="T1">Table 1</xref> (<xref ref-type="bibr" rid="B11">Jinzhong Wang and Jian Fu, 2025</xref>; <xref ref-type="bibr" rid="B45">Yuan et al., 2019</xref>; <xref ref-type="bibr" rid="B50">Zhao et al., 2023</xref>; <xref ref-type="bibr" rid="B47">Zeng et al., 2023</xref>; <xref ref-type="bibr" rid="B9">Huajie, 2015</xref>; <xref ref-type="bibr" rid="B5">Cui et al., 2024</xref>; <xref ref-type="bibr" rid="B7">He et al., 2015</xref>; <xref ref-type="bibr" rid="B10">Jing et al., 2016</xref>; <xref ref-type="bibr" rid="B15">Li et al., 2017</xref>; <xref ref-type="bibr" rid="B27">Qu and Meng-fei, 2020</xref>; <xref ref-type="bibr" rid="B18">Liu et al., 2021</xref>; <xref ref-type="bibr" rid="B51">Zhou et al., 2022</xref>; <xref ref-type="bibr" rid="B25">Peng et al., 2022</xref>; <xref ref-type="bibr" rid="B3">Bin et al., 2022</xref>; <xref ref-type="bibr" rid="B48">Zhang et al., 2019</xref>; <xref ref-type="bibr" rid="B16">Lin et al., 2023</xref>; <xref ref-type="bibr" rid="B17">Liu and Su, 2023</xref>) summarises the characteristics of the 17 included studies. Ten studies (59%) used rats and seven (41%) used mice (mainly C57BL/6). The most common sepsis model was cecal ligation and puncture (CLP, n &#x3d; 10, 59%), followed by lipopolysaccharide (LPS)-induced endotoxemia (n &#x3d; 6, 35%), with one study (6%) using a trauma &#x2b; LPS model. The active small molecules investigated fell into six pharmacological classes: flavonoids (n &#x3d; 9, including one isoflavone), polyphenols (n &#x3d; 3), saponins (n &#x3d; 2). phenylpropanoids (n &#x3d; 1), glycosides (n &#x3d; 1), and antioxidants (n &#x3d; 1). Doses ranged from 0.2&#xa0;mg/kg (luteolin) to 500&#xa0;mg/kg (vitamin C), administered via intraperitoneal, oral, or intravenous routes. Primary outcomes included cardiac function indices (LVEF, MAP, LVDP) and biochemical injury markers (CK, CK-MB, ATP, CCO). Most studies (n &#x3d; 9) reported LVEF, and six reported CK or CK-MB.</p>
<table-wrap id="T1" position="float">
<label>TABLE 1</label>
<caption>
<p>Study characteristics.</p>
</caption>
<table>
<thead valign="top">
<tr>
<th align="left">Study ID</th>
<th align="left">Year</th>
<th align="left">Active molecule</th>
<th align="left">Category</th>
<th align="left">Species</th>
<th align="left">Model</th>
<th align="left">Dose (mg/kg)</th>
<th align="left">Route</th>
<th align="left">Measurement timepoint (h)</th>
<th align="left">Primary outcomes</th>
</tr>
</thead>
<tbody valign="top">
<tr>
<td align="left">
<xref ref-type="bibr" rid="B7">He et al. (2015)</xref>
</td>
<td align="left">2015</td>
<td align="left">Salidroside</td>
<td align="left">Glycoside</td>
<td align="left">Rat</td>
<td align="left">LPS</td>
<td align="left">40</td>
<td align="left">p.o</td>
<td align="left">6</td>
<td align="left">CK-MB</td>
</tr>
<tr>
<td align="left">
<xref ref-type="bibr" rid="B9">Huajie (2015)</xref>
</td>
<td align="left">2015</td>
<td align="left">Resveratrol</td>
<td align="left">Polyphenol</td>
<td align="left">Rat</td>
<td align="left">LPS</td>
<td align="left">8</td>
<td align="left">i.p</td>
<td align="left">6</td>
<td align="left">ATP, CCO</td>
</tr>
<tr>
<td align="left">
<xref ref-type="bibr" rid="B10">Jing et al. (2016)</xref>
</td>
<td align="left">2016</td>
<td align="left">Quercetin</td>
<td align="left">Flavonoid</td>
<td align="left">Rat</td>
<td align="left">Trauma &#x2b; LPS</td>
<td align="left">20</td>
<td align="left">i.p</td>
<td align="left">12</td>
<td align="left">LVDP</td>
</tr>
<tr>
<td align="left">
<xref ref-type="bibr" rid="B15">Li et al. (2017)</xref>
</td>
<td align="left">2017</td>
<td align="left">Apigenin</td>
<td align="left">Flavonoid</td>
<td align="left">Mouse</td>
<td align="left">LPS</td>
<td align="left">50</td>
<td align="left">i.p</td>
<td align="left">18</td>
<td align="left">LVEF</td>
</tr>
<tr>
<td align="left">
<xref ref-type="bibr" rid="B45">Yuan et al. (2019)</xref>
</td>
<td align="left">2019</td>
<td align="left">Polydatin</td>
<td align="left">Phenylpropanoid</td>
<td align="left">Rat</td>
<td align="left">CLP</td>
<td align="left">30</td>
<td align="left">i.p</td>
<td align="left">48</td>
<td align="left">LVEF</td>
</tr>
<tr>
<td align="left">
<xref ref-type="bibr" rid="B27">Qu and Meng-fei. (2020)</xref>
</td>
<td align="left">2020</td>
<td align="left">Astragaloside IV</td>
<td align="left">Saponin</td>
<td align="left">Mouse</td>
<td align="left">LPS</td>
<td align="left">80</td>
<td align="left">i.p</td>
<td align="left">8</td>
<td align="left">LVEF</td>
</tr>
<tr>
<td align="left">
<xref ref-type="bibr" rid="B18">Liu et al. (2021)</xref>
</td>
<td align="left">2021</td>
<td align="left">Quercetin</td>
<td align="left">Flavonoid</td>
<td align="left">Mouse</td>
<td align="left">CLP</td>
<td align="left">200</td>
<td align="left">p.o</td>
<td align="left">24</td>
<td align="left">MAP</td>
</tr>
<tr>
<td align="left">
<xref ref-type="bibr" rid="B51">Zhou et al. (2022)</xref>
</td>
<td align="left">2022</td>
<td align="left">Puerarin</td>
<td align="left">Isoflavone</td>
<td align="left">Rat</td>
<td align="left">LPS</td>
<td align="left">100</td>
<td align="left">i.p</td>
<td align="left">24</td>
<td align="left">LVEF, CK-MB</td>
</tr>
<tr>
<td align="left">
<xref ref-type="bibr" rid="B25">Peng et al. (2022)</xref>
</td>
<td align="left">2022</td>
<td align="left">Rosmarinic acid</td>
<td align="left">Polyphenol</td>
<td align="left">Mouse</td>
<td align="left">LPS</td>
<td align="left">100</td>
<td align="left">p.o</td>
<td align="left">12</td>
<td align="left">LVEF</td>
</tr>
<tr>
<td align="left">
<xref ref-type="bibr" rid="B3">Bin et al. (2022)</xref>
</td>
<td align="left">2022</td>
<td align="left">Luteolin</td>
<td align="left">Flavonoid</td>
<td align="left">Mouse</td>
<td align="left">CLP</td>
<td align="left">0.2</td>
<td align="left">i.p</td>
<td align="left">24</td>
<td align="left">LVEF</td>
</tr>
<tr>
<td align="left">
<xref ref-type="bibr" rid="B48">Zhang et al. (2019)</xref>
</td>
<td align="left">2019</td>
<td align="left">Melatonin</td>
<td align="left">Indoleamine</td>
<td align="left">Rat</td>
<td align="left">LPS</td>
<td align="left">30</td>
<td align="left">i.p</td>
<td align="left">8</td>
<td align="left">LVEF,CK</td>
</tr>
<tr>
<td align="left">
<xref ref-type="bibr" rid="B16">Lin et al. (2023)</xref>
</td>
<td align="left">2023</td>
<td align="left">Quercetin</td>
<td align="left">Flavonoid</td>
<td align="left">Rat</td>
<td align="left">CLP</td>
<td align="left">40</td>
<td align="left">i.p</td>
<td align="left">24</td>
<td align="left">CK-MB</td>
</tr>
<tr>
<td align="left">
<xref ref-type="bibr" rid="B50">Zhao et al. (2023)</xref>
</td>
<td align="left">2023</td>
<td align="left">Quercetin</td>
<td align="left">Flavonoid</td>
<td align="left">Rat</td>
<td align="left">CLP</td>
<td align="left">40</td>
<td align="left">i.p</td>
<td align="left">24</td>
<td align="left">CK-MB</td>
</tr>
<tr>
<td align="left">
<xref ref-type="bibr" rid="B17">Liu and Su (2023)</xref>
</td>
<td align="left">2023</td>
<td align="left">Luteolin</td>
<td align="left">Flavonoid</td>
<td align="left">Mouse</td>
<td align="left">CLP</td>
<td align="left">0.2</td>
<td align="left">i.p</td>
<td align="left">96</td>
<td align="left">Survival, MAP</td>
</tr>
<tr>
<td align="left">
<xref ref-type="bibr" rid="B47">Zeng et al. (2023)</xref>
</td>
<td align="left">2023</td>
<td align="left">Resveratrol</td>
<td align="left">Polyphenol</td>
<td align="left">Rat</td>
<td align="left">CLP</td>
<td align="left">50</td>
<td align="left">i.p</td>
<td align="left">24</td>
<td align="left">LVEF</td>
</tr>
<tr>
<td align="left">
<xref ref-type="bibr" rid="B5">Cui et al. (2024)</xref>
</td>
<td align="left">2024</td>
<td align="left">Vitamin C</td>
<td align="left">Antioxidant</td>
<td align="left">Rat</td>
<td align="left">CLP</td>
<td align="left">500</td>
<td align="left">i.v</td>
<td align="left">24</td>
<td align="left">LVEF, CK</td>
</tr>
<tr>
<td align="left">
<xref ref-type="bibr" rid="B11">Jinzhong Wang and Jian Fu. (2025)</xref>
</td>
<td align="left">2025</td>
<td align="left">Ginsenoside Rc</td>
<td align="left">Saponin</td>
<td align="left">Mouse</td>
<td align="left">CLP</td>
<td align="left">20</td>
<td align="left">p.o</td>
<td align="left">24</td>
<td align="left">LVEF, CK-MB</td>
</tr>
</tbody>
</table>
</table-wrap>
</sec>
<sec id="s3-3">
<label>3.3</label>
<title>Risk of bias assessment</title>
<p>
<xref ref-type="fig" rid="F2">Figure 2</xref> presents the SYRCLE risk-of-bias assessment for the 17 included animal studies. Most items were rated as low risk, although several domains showed incomplete reporting.</p>
<fig id="F2" position="float">
<label>FIGURE 2</label>
<caption>
<p>Risk of bias assessment.</p>
</caption>
<graphic xlink:href="fphar-16-1665372-g002.tif">
<alt-text content-type="machine-generated">A heatmap showing risk assessments across various criteria for multiple studies. Green indicates low risk, and yellow indicates unclear risk. Rows represent studies conducted by Zhao2015, Wu2021, Liu2022, and others, while columns include sequence generation, baseline comparability, allocation concealment, and additional criteria. The map visually contrasts risk levels among the studies.</alt-text>
</graphic>
</fig>
<p>Specifically, 9 studies (53%) clearly described adequate random sequence generation random sequence generation methods (e.g., random number tables or computer-generated lists), while 8 studies (47%) provided insufficient detail and were rated as &#x201c;unclear risk.&#x201d;</p>
<p>Baseline comparability was generally adequate across groups, with 100% of studies providing balanced characteristics.</p>
<p>For allocation concealment, 88% of studies (15/17) were rated low risk, while the remainder lacked clear information.</p>
<p>Random housing was adequately performed in 65% of studies (11/17), with 35% (6/17) rated as unclear.</p>
<p>Random outcome assessment was well reported in 9 studies (53%), though 8 studies (47%) lacked specific blinding details.</p>
<p>Incomplete outcome data were rarely an issue, with all studies (100%) fully reporting analyzed animals and exclusions.</p>
<p>Selective outcome reporting was low risk in 65% of studies (11/17), but 6 studies (35%) did not predefine outcomes or lacked accessible protocols.</p>
<p>Overall, the methodological quality was moderate, with the main limitations related to insufficient reporting of randomization and blinding procedures.</p>
</sec>
<sec id="s3-4">
<label>3.4</label>
<title>Quantitative synthesis</title>
<sec id="s3-4-1">
<label>3.4.1</label>
<title>Overall summary effect</title>
<p>The overall summary effect of active small molecules on abdominal trauma or SCM was summarised using a random-effects meta-analysis. As shown in <xref ref-type="fig" rid="F3">Figure 3</xref>, the SMD (Hedges&#x2019; g) across all studies was 1.47 (95% CI: &#x2212;0.52&#x2013;3.47), indicating a potential beneficial effect, although the confidence interval exceeded 0. There was significant statistical heterogeneity among the studies (I<sup>2</sup> &#x3d; 92.9%, p &#x3c; 0.0001), indicating that the results were significantly different among the included studies. The wide prediction interval (&#x2212;4.75&#x2013;7.70) indicated that the true effect was significantly different between the studies.</p>
<fig id="F3" position="float">
<label>FIGURE 3</label>
<caption>
<p>Overall summary effect Random-effects meta-analysis of all included studies showing standardized mean difference (Hedges&#x2019; g) and 95% confidence intervals (CIs). The model uses restricted maximum likelihood (REML). Heterogeneity statistics are reported as I<sup>2</sup>, &#x3c4;<sup>2</sup>, and p-value for Cochran&#x2019;s Q test. A 95% prediction interval is also shown to illustrate expected dispersion of true effects across similar studies. Individual study weights are indicated.</p>
</caption>
<graphic xlink:href="fphar-16-1665372-g003.tif">
<alt-text content-type="machine-generated">Forest plot illustrating standardized mean differences (SMD) with 95% confidence intervals for various studies. Each study is represented by a square, with the size indicating the weight in the meta-analysis. A diamond indicates combined results from random and common effect models. Heterogeneity is noted at I&#xB2; = 92.9%, &#x3C4;&#xB2; = 8.4324, p &#x3C; 0.0001. Results range from -10 to 10 on the x-axis.</alt-text>
</graphic>
</fig>
</sec>
<sec id="s3-4-2">
<label>3.4.2</label>
<title>Subgroup analysis</title>
<p>Subgroup analysis was performed according to molecular classification, sepsis modelling method and outcome measure to further explore the source of heterogeneity. The molecular category (<xref ref-type="fig" rid="F4">Figure 4</xref>) subgroup analysis showed that the effect size varied. The effect size for polyphenols was large but imprecise (g &#x3d; 4.81, 95% CI: &#x2212;5.25&#x2013;14.87), whereas the effect for flavonoids was relatively neutral (g &#x3d; &#x2212;0.45, 95% CI: &#x2212;3.64&#x2013;2.73). Subgroup heterogeneity remained significant within each subgroup (I<sup>2</sup> &#x3e; 90%), suggesting that the molecular class could not fully explain the variability. Subgroup analysis of sepsis modelling approaches (<xref ref-type="fig" rid="F5">Figure 5</xref>) showed differences in effect estimates. The LPS-induced sepsis model showed a medium effect size (g &#x3d; 2.86, 95% CI: &#x2212;0.74&#x2013;6.46), while the CLP-induced model subgroup had a smaller effect size (g &#x3d; 0.16, 95% CI: &#x2212;2.60&#x2013;2.91). Only 1 study used the trauma &#x2b; LPS model, with an effect size of 3.27 (95% CI: 1.08&#x2013;5.46). There were no statistically significant differences between subgroups (p &#x3d; 0.449). The analysis of outcome measures (<xref ref-type="fig" rid="F6">Figure 6</xref>) showed significant differences between specific outcomes. Notably, outcomes related to myocardial biochemical markers, such as CK and CK-MB, had higher and more precise effect sizes compared with functional measures such as LVEF and MAP. For example, the CK result showed a robust effect (g &#x3d; 6.74, 95% CI: 4.99&#x2013;8.48), while the LVEF result showed a small and imprecise negative effect (g &#x3d; &#x2212;0.50, 95% CI: &#x2212;2.58&#x2013;1.57). The overall test of subgroup differences was statistically significant (p &#x3c; 0.0001).</p>
<fig id="F4" position="float">
<label>FIGURE 4</label>
<caption>
<p>The molecular category subgroup analysis Random-effects subgroup meta-analyses by molecular class (polyphenols, flavonoids, saponins, antioxidants, glycosides, others). Subgroup p-value indicates test for subgroup differences.</p>
</caption>
<graphic xlink:href="fphar-16-1665372-g004.tif">
<alt-text content-type="machine-generated">Forest plot illustrating the standardized mean difference (SMD) and 95% confidence intervals (CI) for various studies across subgroups: Polyphenol, Flavonoid, Saponin, Phenylpropanoid, Indoleamine, Antioxidant, Isoflavone, and Glycoside. Each subgroup contains individual study results, a diamond representing the random effects model, and noted heterogeneity statistics. The overall prediction interval and subgroup differences are highlighted, showing significant heterogeneity and differences among studies, with p-values all less than 0.0001.</alt-text>
</graphic>
</fig>
<fig id="F5" position="float">
<label>FIGURE 5</label>
<caption>
<p>Subgroup analysis of sepsis modeling analysis Forest plot with subgroup-specific pooled estimates (SMD, 95% CI), I<sup>2</sup> within subgroup and counts (k).</p>
</caption>
<graphic xlink:href="fphar-16-1665372-g005.tif">
<alt-text content-type="machine-generated">Forest plot showing standardized mean differences (SMD) with 95% confidence intervals for several studies across three subgroups: LPS, CLP, and Trauma+LPS. Each study is represented by a gray square and horizontal line indicating the SMD and its confidence interval. The overall effects and heterogeneity statistics are summarized with diamonds. LPS shows SMD 2.86, CLP 0.16, and Trauma+LPS 1.47. Heterogeneity is high across all subgroups. A red prediction interval line is also included.</alt-text>
</graphic>
</fig>
<fig id="F6" position="float">
<label>FIGURE 6</label>
<caption>
<p>Analysis of outcome measures.</p>
</caption>
<graphic xlink:href="fphar-16-1665372-g006.tif">
<alt-text content-type="machine-generated">Forest plot illustrating standardized mean differences (SMD) with 95% confidence intervals for various subgroups including ATP, CCO, MAP, LVEF, CK-MB, LVDP, and CK. Each study within the subgroups is represented by a square indicating the SMD, and a horizontal line for the confidence interval. Diamonds indicate aggregated effects for random effects models, along with heterogeneity statistics. The plot ranges from -10 to 10 on the x-axis.</alt-text>
</graphic>
</fig>
</sec>
<sec id="s3-4-3">
<label>3.4.3</label>
<title>Publication bias</title>
<p>Publication bias was assessed using funnel plots and Egger regression tests (<xref ref-type="fig" rid="F7">Figure 7</xref>). Visual inspection of the funnel plots showed slight asymmetry, suggesting the possibility of publication bias. However, the Egger regression test did not show statistically significant asymmetry (p &#x3d; 0.123). Given the large heterogeneity of the studies and the limited number of studies included, the interpretation of potential publication bias should be carefully considered.</p>
<fig id="F7" position="float">
<label>FIGURE 7</label>
<caption>
<p>Funnel Plot and Egger&#x2019;s regression. Funnel plot for assessment of small-study effects; Egger&#x2019;s regression test p-value reported. Visual asymmetry should be interpreted cautiously given high heterogeneity.</p>
</caption>
<graphic xlink:href="fphar-16-1665372-g007.tif">
<alt-text content-type="machine-generated">Funnel plot displaying the standard error plotted against Hedges&#x27; g. Dots representing individual studies are dispersed asymmetrically, with some outliers. Dashed lines show confidence intervals, and Egger&#x27;s test p-value is 0.123.</alt-text>
</graphic>
</fig>
</sec>
<sec id="s3-4-4">
<label>3.4.4</label>
<title>Sensitivity analysis</title>
<p>The robustness of the overall pooled effect was assessed using a leave-one-out sensitivity analysis (<xref ref-type="fig" rid="F8">Figure 8</xref>). Excluding any one study did not significantly change the pooled SMD, with effect sizes ranging from 0.03 to 0.52 in different iterations. These results support the robustness and stability of the conclusions of the primary meta-analysis.</p>
<fig id="F8" position="float">
<label>FIGURE 8</label>
<caption>
<p>Leave-one-out sensitivity analysis.</p>
</caption>
<graphic xlink:href="fphar-16-1665372-g008.tif">
<alt-text content-type="machine-generated">Forest plot of a leave-one-out meta-analysis showing standardized mean differences (SMD) and 95% confidence intervals (CI) for each study omitted. The pooled effect size is 0.27 with a 95% CI of -0.06 to 0.61. Individual studies are listed vertically, with corresponding SMD and CI values. Gray squares represent point estimates, and horizontal lines depict CIs for each study. A diamond at the bottom indicates the overall effect size with CIs.</alt-text>
</graphic>
</fig>
<p>In summary, the quantitative meta-analysis showed that active small molecules had generally positive but imprecise efficacy in animal models of abdominal trauma or sepsis-induced cardiomyopathy, with high statistical heterogeneity. Subgroup analyses suggested that molecular classification, sepsis model and specific outcome measures partially explained the observed heterogeneity.</p>
</sec>
</sec>
</sec>
<sec sec-type="discussion" id="s4">
<label>4</label>
<title>Discussion</title>
<p>In this systematic review and meta-analysis, we comprehensively evaluated the protective effects of various active small molecules on SCM induced by abdominal trauma or sepsis in animal models. We included 17 studies covering multiple molecular classes and animal models, and performed quantitative analysis, which showed that SCM had an overall positive effect. Specifically, the pooled SMD for cardiac function and injury outcomes was 1.47 (95% CI: &#x2212;0.52&#x2013;3.47). Although the pooled effect suggested a potential cardioprotective effect, the wide confidence interval and high heterogeneity (I<sup>2</sup> &#x3d; 92.9%) highlighted significant differences in the strength and consistency of these effects among the included studies.</p>
<p>The potential protective mechanisms of active small molecules against SCM involve multiple biological pathways, particularly reducing inflammation, alleviating oxidative stress, protecting mitochondrial function and, subsequently, improving myocardial injury markers and cardiac function parameters. In subgroup analyses, polyphenols showed a significant but imprecise protective effect (SMD &#x3d; 4.81), while the results for flavonoids were more ambiguous (SMD &#x3d; &#x2212;0.45). These differential findings suggest the existence of different molecular mechanisms, bioavailability, pharmacokinetic or dose-related factors that may explain the differences between the categories.</p>
<p>Previous preclinical and clinical studies have highlighted the pleiotropic effects of polyphenolic compounds such as resveratrol and rosmarinic acid, especially their potent antioxidant and anti-inflammatory activities. Zhao et al. reported that resveratrol significantly increased myocardial ATP and mitochondrial CCO activity in a rat LPS-induced sepsis model, highlighting mitochondrial protection as a key mechanism of cardioprotection (<xref ref-type="bibr" rid="B49">Zhao et al., 2015</xref>). These results are consistent with existing evidence from other cardiovascular studies that attribute the efficacy of polyphenols to antioxidant pathways targeting mitochondria, and the subsequent attenuation of oxidative stress-induced myocardial dysfunction (<xref ref-type="bibr" rid="B33">Su et al., 2024</xref>; <xref ref-type="bibr" rid="B43">Xu et al., 2020</xref>; <xref ref-type="bibr" rid="B44">Yang et al., 2024</xref>).</p>
<p>In contrast, the results for flavonoids in this meta-analysis were inconsistent. Although some individual flavonoids, such as luteolin and apigenin, showed moderate efficacy in specific cardiac outcomes, the overall results for flavonoids were relatively neutral. This finding is in stark contrast to previous studies that have highlighted the potent anti-inflammatory properties of flavonoids in various inflammatory disease models (<xref ref-type="bibr" rid="B8">Hu et al., 2015</xref>; <xref ref-type="bibr" rid="B41">Wu et al., 2023a</xref>; <xref ref-type="bibr" rid="B39">Wang et al., 2023</xref>). These variances may reflect differences in the severity of SCM animal models, differences in dosing regimens or inherent bioavailability limitations of flavonoids.</p>
<p>Subgroup analyses based on the method of sepsis induction further demonstrated that the efficacy of active small molecules varied across models. The LPS-induced endotoxemia model showed a greater protective effect (SMD &#x3d; 2.86) than the CLP model (SMD &#x3d; 0.16). This difference may stem from the pathophysiological differences between endotoxemia (acute inflammatory stimulation, systemic cytokine surge) and CLP-induced polymicrobial sepsis, which is closer to the clinical sepsis state (<xref ref-type="bibr" rid="B37">Wang et al., 2021</xref>; <xref ref-type="bibr" rid="B29">Rosado-Franco et al., 2021</xref>; <xref ref-type="bibr" rid="B46">Yuan et al., 2022</xref>). Therefore, molecules with acute anti-inflammatory effects, such as resveratrol and rosmarinic acid, may show higher efficacy in the LPS model, reflecting their rapid modulation of cytokine profiles and acute inflammation, but their sustained effects may be weaker in the more complex and prolonged CLP-induced sepsis environment.</p>
<p>Further analysis based on outcome measures found significant differences between biochemical markers (CK, CK-MB) and functional cardiac parameters (LVEF, MAP). Biochemical indicators such as CK-MB showed sustained protective effects, while functional indicators such as LVEF showed mixed and unclear efficacy. This observation is consistent with existing literature suggesting that biochemical markers are sensitive indicators of early myocardial injury and recovery, whereas echocardiographic parameters such as LVEF may reflect more complex, multifactorial changes in myocardial function and haemodynamics, which evolve over time (<xref ref-type="bibr" rid="B38">Wang et al., 2022</xref>; <xref ref-type="bibr" rid="B42">Wu et al., 2023b</xref>; <xref ref-type="bibr" rid="B34">Sun et al., 2024</xref>).</p>
<p>Comparison with previous literature: Previous systematic reviews have highlighted the potent antioxidant and mitochondrial protective properties of polyphenols, especially resveratrol, which are well aligned with our current findings (<xref ref-type="bibr" rid="B47">Zeng et al., 2023</xref>; <xref ref-type="bibr" rid="B36">Taha et al., 2023</xref>). Similarly, flavonoids, despite their variable overall efficacy in our analysis, have been widely reported to exhibit cardioprotective properties through anti-inflammatory and antioxidant mechanisms, although results have been inconsistent across experimental contexts (<xref ref-type="bibr" rid="B9">Huajie, 2015</xref>).</p>
<p>Septic cardiomyopathy remains an important unmet clinical need. Current clinical management is primarily supportive (including careful fluid resuscitation, vasopressors and inotropes when indicated), and there are no widely accepted, evidence-based pharmacologic agents that specifically prevent or reverse sepsis-induced myocardial dysfunction (<xref ref-type="bibr" rid="B12">L&#x27;Heureux et al., 2020</xref>). Recent preclinical data indicate that certain small molecules (notably, polyphenolic compounds such as resveratrol and related agents) can modulate key pathophysiologic pathways implicated in SCM, including pro-inflammatory signalling, oxidative stress and mitochondrial dysfunction, and thereby protect cardiac tissue in animal models. However, translating these findings to human therapy faces several practical barriers: many polyphenols and small molecules exhibit poor oral bioavailability and limited tissue penetration; robust safety/toxicity and PharmacoKinetics and PharmacoDynamics (PK/PD) characterisation in relevant models is often lacking (<xref ref-type="bibr" rid="B6">Di Lorenzo et al., 2021</xref>); dosing regimens and timing relative to sepsis induction require careful definition; and functional cardiac endpoints (rather than biochemical markers only) must be demonstrated in reproducible, cross-species studies. To facilitate translation, we propose a staged roadmap: (1) standardised, high-quality preclinical studies that adhere to ARRIVE recommendations and include PK/PD and formal toxicity arms; (2) the demonstration of reproducibility across species and in more clinically relevant models (e.g., CLP and larger animal models, where feasible); and (3) early-phase clinical studies that prioritise safety and objective cardiac functional endpoints (e.g., echocardiographic strain measures, haemodynamic indices and clinically relevant outcomes) (<xref ref-type="bibr" rid="B26">Percie du Sert et al., 2020</xref>; <xref ref-type="bibr" rid="B22">Nicol et al., 2021</xref>). These steps aim to bridge promising preclinical signals and rigorous human evaluation.</p>
<p>Lipopolysaccharide-induced endotoxemia and CLP-induced polymicrobial sepsis differ markedly in terms of their kinetics of cytokine responses, bacterial dissemination and clinical course; consequently, therapeutic efficacy observed in LPS models may not translate to CLP models or to human sepsis cases. Previous comparative studies have documented these kinetic and immunologic differences (<xref ref-type="bibr" rid="B28">Remick et al., 2020</xref>; <xref ref-type="bibr" rid="B31">Seemann et al., 2017</xref>) accordingly, we suggest prioritising CLP models for translational studies, while recognising the value of LPS models for testing acute anti-inflammatory effects. Importantly, our review focused on abdominal-origin models (including abdominal trauma and abdominal-source sepsis); therefore, the findings may not be generalisable to sepsis arising from other sources (e.g., pulmonary or urinary tract infections), and it is possible that the inclusion of studies focused on other sepsis origins could yield different pooled estimates due to source-specific pathophysiology.</p>
<p>Notably, although previous studies have frequently documented significant protective effects of antioxidants such as vitamin C, our meta-analysis included only limited data (<xref ref-type="bibr" rid="B5">Cui et al., 2024</xref>), highlighting the need for further exploration and the validation of such interventions in animal SCM models. Similarly, the favourable effects observed for saponins (astragaloside IV, ginsenoside Rc) are consistent with previous findings documenting anti-inflammatory, anti-apoptotic and mitochondrial protective properties in other experimental models of myocardial injury.</p>
<p>Our meta-analysis has several strengths. First, to our knowledge, this is the first systematic, comprehensive study of active small molecules in animal models of abdominal trauma/sepsis-induced cardiomyopathy. In addition, we used a rigorous and comprehensive approach, including extensive subgroup analyses, sensitivity testing and systematic risk of bias assessment, using the validated SYRCLE tool.</p>
<p>Several important limitations should, however, temper the interpretation of our findings. First, substantial heterogeneity persisted across studies (I<sup>2</sup> &#x3d; 92.9%), which likely reflects multiple sources of clinical and methodological variability, that is, inconsistent reporting of dosing regimens and timing relative to sepsis induction, heterogeneity in sepsis models and model severity, differences in animal species/strains, as well as variable definitions and timing of the outcome measurements. Second, many of the included studies incompletely reported key experimental design features, increasing the risk of selection and detection bias. Moreover, these methodological shortcomings not only threaten internal validity by inflating effect sizes but also reduce the reproducibility and external generalisability of the findings. Third, the modest number of studies overall, combined with sparse and inconsistent reporting of quantitative dosing, route and timing, limits the feasibility and reliability of meta-regression or stratified analyses. Fourth, the available literature placed greater emphasis on biochemical markers than on standardised functional cardiac outcomes; biomarker improvements may not equate to meaningful or durable functional recovery. Fifth, adverse effects and formal toxicity assessments were generally not reported, restricting any assessment of benefit/risk for the compounds studied. Sixth, small-study effects and possible publication bias cannot be excluded, and this may have led to the overestimation of efficacy. Seventh, many studies lacked pre-registration or publicly available raw data, limiting transparency and reproducibility. Finally, several included studies originated from a limited number of research groups or geographic regions, which may have reduced methodological diversity and increased the risk of confirmation bias. Future work should follow ARRIVE recommendations, include PK/PD and toxicity arms, pre-register experimental protocols, use harmonised endpoints, and make raw datasets available to enable reliable synthesis.</p>
<p>Our findings highlight the potential of active small molecules, especially polyphenols, as preventive treatments for SCM and highlight their mechanisms of inflammation reduction, oxidative stress reduction and mitochondrial protection. However, the large differences observed suggest that further standardised, rigorously designed preclinical trials are urgently needed. Future studies should prioritise methodological rigour, including detailed reporting of randomisation, allocation concealment and blinding, as well as systematic investigation of dose-response relationships, timing and long-term cardiac outcomes.</p>
</sec>
<sec sec-type="conclusion" id="s5">
<label>5</label>
<title>Conclusion</title>
<p>In summary, this meta-analysis demonstrates that active small molecules have generally positive but heterogeneous protective effects against abdominal trauma or sepsis-induced cardiomyopathy in animal models. Polyphenols showed particularly promising cardioprotective effects through antioxidant and mitochondrial protective mechanisms, although large variability remained. Future studies should prioritise methodological rigour, improved standardisation and translation-oriented studies to validate these promising preclinical findings and guide clinical application.</p>
</sec>
</body>
<back>
<sec sec-type="data-availability" id="s6">
<title>Data availability statement</title>
<p>The original contributions presented in the study are included in the article/supplementary material, further inquiries can be directed to the corresponding authors.</p>
</sec>
<sec sec-type="author-contributions" id="s7">
<title>Author contributions</title>
<p>GO: Conceptualization, Data curation, Formal Analysis, Investigation, Methodology, Resources, Software, Validation, Writing &#x2013; original draft. NW: Data curation, Formal Analysis, Investigation, Methodology, Resources, Software, Validation, Writing &#x2013; original draft. YL: Data curation, Formal Analysis, Investigation, Methodology, Resources, Validation, Writing &#x2013; original draft. CY: Formal Analysis, Investigation, Methodology, Resources, Software, Visualization, Writing &#x2013; original draft. PZ: Data curation, Formal Analysis, Investigation, Methodology, Resources, Software, Writing &#x2013; original draft. TG: Data curation, Formal Analysis, Investigation, Methodology, Resources, Writing &#x2013; original draft. LT: Formal Analysis, Investigation, Methodology, Resources, Writing &#x2013; original draft. YZ: Data curation, Formal Analysis, Investigation, Methodology, Resources, Software, Validation, Writing &#x2013; original draft. GY: Data curation, Investigation, Methodology, Project administration, Resources, Validation, Writing &#x2013; original draft. HL: Data curation, Formal Analysis, Investigation, Methodology, Resources, Software, Validation, Writing &#x2013; original draft. CC: Data curation, Formal Analysis, Investigation, Resources, Software, Validation, Writing &#x2013; original draft. LW: Data curation, Formal Analysis, Investigation, Methodology, Resources, Validation, Writing &#x2013; original draft. NZ: Conceptualization, Formal Analysis, Investigation, Methodology, Project administration, Resources, Supervision, Writing &#x2013; original draft, Writing &#x2013; review and editing.</p>
</sec>
<sec sec-type="COI-statement" id="s9">
<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="s10">
<title>Generative AI statement</title>
<p>The authors declare that no Generative AI was 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 sec-type="disclaimer" id="s11">
<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>
<fn-group>
<fn fn-type="custom" custom-type="edited-by">
<p>
<bold>Edited by:</bold> <ext-link ext-link-type="uri" xlink:href="https://loop.frontiersin.org/people/94513/overview">Simone Brogi</ext-link>, University of Pisa, Italy</p>
</fn>
<fn fn-type="custom" custom-type="reviewed-by">
<p>
<bold>Reviewed by:</bold> <ext-link ext-link-type="uri" xlink:href="https://loop.frontiersin.org/people/871899/overview">Leonardo Maciel</ext-link>, Federal University of Rio de Janeiro, Brazil</p>
<p>
<ext-link ext-link-type="uri" xlink:href="https://loop.frontiersin.org/people/2291143/overview">Manjusha Biswas</ext-link>, University Hospital Bonn, Germany</p>
<p>
<ext-link ext-link-type="uri" xlink:href="https://loop.frontiersin.org/people/3154048/overview">Lana Mari&#x10d;i&#x107;</ext-link>, Klini&#x10d;ki bolni&#x10d;ki centar Osijek, Croatia</p>
</fn>
</fn-group>
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