We characterized distinct gut microbial community clusters associated with metabolic syndrome (MetS) and identified key genera underlying these patterns. Health-associated clusters were dominated by beneficial genera such as Bacteroides and Prevotella with known metabolic protective functions. Environmental exposures (air pollution and temperature) were associated with gut microbiota composition, and may increase the risk of MetS. Our findings further suggest that specific bacterial genera, particularly Megamonas and Megasphaera, may statistically mediate the associations of long-term exposure to nitrogen dioxide (NO2), fine particulate matter (PM2.5), and ambient temperature with MetS risk. Integrating environmental exposure assessment with gut microbiota profiling offers a promising strategy for metabolic syndrome prevention and precision intervention. Metabolic syndrome (MetS) has become a major public health concern, affecting nearly one quarter of the global population, and its global prevalence is projected to continue rising, reaching approximately 53% by 2035 1, 2. MetS confers increased risks of cardiovascular disease, type 2 diabetes, sleep apnea, malignancies, and all-cause mortality 3. In addition to genetic and lifestyle factors, accumulating evidence suggests that environmental exposures play an important role in the development of MetS 4, 5. Long-term exposure to air pollutants such as fine particulate matter (PM2.5) and nitrogen dioxide (NO2), as well as meteorological factors, particularly ambient temperature, has been associated with an increased risk of MetS 4, 5. However, the biological mechanisms linking environmental exposures to MetS remain incompletely understood. Recent studies have increasingly highlighted the gut microbiota as a key regulator of host metabolic homeostasis 6. Gut microbial dysbiosis can impair intestinal barrier function, promote chronic low-grade inflammation, and disturb glucose and lipid metabolism, thereby contributing to MetS 7. Emerging evidence from the One Health Microbiome perspective further suggests that environmental stressors, including air pollution and weather fluctuations, can reshape gut microbial composition and function 8. Nevertheless, it remains unclear whether environmentally induced alterations in the gut microbiota contribute to the development of MetS. We hypothesized that alterations in the gut microbiota may represent an important biological pathway through which environmental exposures contribute to the risk of MetS. Using data from the Guangdong Gut Microbiome Project, we investigated the associations of long-term exposure to NO2, PM2.5, and ambient temperature with MetS, and performed exploratory mediation analyses to assess potential indirect associations through the gut microbiota. Our study aims to provide population-based evidence that the gut microbiota represents a key biological pathway linking environmental exposures to metabolic dysfunction. Among the overall 6785 participants, 1258 individuals (18.5%) were diagnosed with MetS, and 3035 (44.7%) were males. High blood pressure (51.1%) and low high-density lipoprotein cholesterol (HDL-C) (26.7%) were the most prevalent MetS components, whereas elevated fasting blood glucose (15.2%) was the least frequent. Males showed slightly higher prevalence of high blood pressure and high triglycerides, whereas females had a higher prevalence of abdominal obesity (Figure S1 and Table S1). Compared with controls, MetS cases were older, had lower incomes and educational attainment, engaged in less physical activity, and had higher prevalences of smoking and alcohol consumption (Table S1). In addition, MetS cases were exposed to higher levels of NO2 and PM2.5, as well as to lower ambient temperatures (Figure S2). Based on 16S rRNA gene sequencing data, we compared the overall gut microbial composition between MetS cases and controls. At both the phylum and genus levels, the overall taxonomic profiles were broadly similar between two groups (Figure S3). To capture more subtle MetS-related microbiome differences, we conducted downstream analyses to characterize microbiome features associated with MetS. Compared with non-MetS participants, those with MetS exhibited lower gut microbial alpha diversity across all indices, including Shannon, phylogenetic diversity (PD whole tree), Chao1, and observed species (all p-values < 0.05; Figure 1A). An elbow plot of the within-cluster sum of squares supported a four-cluster solution as best capturing the underlying community structure (Figure S4). In the resulting Uniform Manifold Approximation and Projection (UMAP) ordination, the four clusters were clearly separated. Pseudotime analysis provided a data-driven ordering of samples along the main microbiome manifold. Along this ordering, clusters differed in MetS prevalence, suggesting that variation in gut microbial community composition was aligned with differences in metabolic status across clusters (Figure 1B). To identify genera associated with cluster differentiation, we compared genus-level abundances in each cluster. Cluster 1 was mainly enriched in Bacteroides and Parabacteroides, whereas cluster 2 was characterized by enrichment of Prevotella and Desulfovibrio. Cluster 3 showed a relatively diffuse enrichment pattern across multiple environmental or opportunistic genera, without a single strongly dominant genus, as all differences in centered log-ratio (ΔCLR) values were < 1.5. In contrast, cluster 4 was dominated by members of the family Enterobacteriaceae, with marked overrepresentation of opportunistic taxa such as Serratia, Enterobacter, Citrobacter, and Klebsiella (Figure 1C). We further compared MetS-related clinical indicators across the four clusters. Participants in clusters 3 and 4 generally exhibited a more adverse clinical profile than those in clusters 1 and 2, with higher waist circumference, fasting glucose, triglycerides, and systolic and diastolic blood pressure, together with lower HDL-C (Figure 1D). In contrast, participants in clusters 1 and 2 showed the most favorable metabolic profiles, consistent with their enrichment in Bacteroides and Prevotella. Previous studies have shown that Bacteroides plays an important role in maintaining glucose homeostasis and overall metabolic balance 9. Likewise, Prevotella-dominated communities are characterized by an enhanced capacity to metabolize plant-derived carbohydrates and by robust production of short-chain fatty acids (SCFAs) from diets rich in complex carbohydrates and dietary fibers 10. SCFAs have been implicated in the regulation of glucose and lipid metabolism, insulin sensitivity, and inflammatory responses, which may be relevant to metabolic health 11. We then constructed a genus-level co-variation network based on genera identified by linear discriminant analysis effect size (LEfSe). A dense module was mainly observed among genera enriched in the non-MetS group, including Bacteroides, Faecalibacterium, and Parabacteroides, which showed predominantly positive correlations with one another. In contrast, genera enriched in the MetS group were more dispersed across the network and did not form a comparably dense module (Figure 1E). We first used univariate permutational multivariate analysis of variance (PERMANOVA) to quantify the variance in gut microbial community structure explained by host, environmental, and lifestyle factors. Geographical location explained the largest proportion of between-sample variance in gut microbiota composition. Among non-geographical variables, NO2, ambient temperature, and PM2.5 explained relatively large proportions of variance (ranked first, sixth, and seventh, respectively), indicating that air pollution and meteorological conditions were important correlates of gut microbiota composition (Figure 2A). These findings indicate that, beyond regional background, environmental exposures are important modifiable factors associated with gut microbial community structure. We next assessed associations between key microbial genera and environmental exposures, focusing on genera associated with the previously defined clusters and those identified by LEfSe. Their associations with NO2, PM2.5, and ambient temperature showed two contrasting patterns. Genera such as Megamonas, Megasphaera, Acidaminococcus, and Bacteroides were positively associated with NO2 and PM2.5 but inversely associated with ambient temperature. In contrast, genera including Pseudomonas, Lactobacillus, and Ochrobactrum generally showed negative associations with air pollutants and positive associations with ambient temperature (Figure 2B). Given these exposure-microbiome patterns, we further quantified the associations between environmental exposures and MetS. In logistic regression models, higher NO2 and PM2.5 concentrations were associated with increased odds of MetS, whereas higher ambient temperature was associated with lower odds of MetS: per interquartile-range increase, the odds ratios (ORs) for MetS were 1.114 (95% confidence interval (CI): 0.993–1.250) for NO2, 1.128 (95% CI: 1.000–1.273) for PM2.5, and 0.833 (95% CI: 0.798–0.976) for ambient temperature. Quartile-based analyses showed broadly consistent patterns, with higher quartiles of NO2 and PM2.5 generally associated with higher adjusted odds of MetS, whereas higher ambient temperature quartiles tended to be associated with lower odds (Figure 2C and Table S3). These findings are consistent with previous epidemiological evidence 12-14. Finally, we performed exploratory statistical mediation analyses to clarify the role of the gut microbiota in the associations between environmental exposures and MetS. When gut microbial genera were specified as potential mediators, six genera (Megamonas, Megasphaera, Ruminococcus, Acidaminococcus, Butyricimonas, and Fusobacterium) showed evidence of exploratory statistical mediation for the associations of NO2, PM2.5, and ambient temperature with MetS, with a false discovery rate < 0.10 (Figure 2D and Table S4). Because genus-level microbial features are correlated, findings from single-mediator models should be interpreted as exploratory screening results. In addition, the estimated mediated proportions derived from the logistic mediation models are non-additive and should be interpreted as scale-dependent relative indirect contributions, rather than independent contributions across genera or proportions on an absolute risk scale. Among these genera, Megamonas showed the largest estimated mediated proportions in the logistic mediation models, with values of 15.1%, 16.1%, and 4.7% for the associations of PM2.5, NO2, and ambient temperature with MetS, respectively. In addition, Megasphaera showed corresponding estimated mediated proportions of 6.3%, 9.9%, and 3.1% for the associations of PM2.5, NO2, and ambient temperature with MetS, respectively. Ruminococcus showed estimated mediated proportions of 8.4% for the association of PM2.5 with MetS and 4.7% for the association of ambient temperature with MetS, whereas no evidence of mediation was observed for NO2. Similarly, Acidaminococcus showed estimated mediated proportions of 5.9% for the association between PM2.5 and MetS, 5.0% for the association between NO2 and MetS, and 3.3% for the association between ambient temperature and MetS. These results suggest exploratory statistical mediation, indicating that gut microbiota features may account for part of the observed associations between environmental exposures and MetS, with patterns that may vary by exposure type. For air pollution, mediation was dominated by Megamonas, with additional contributions from Megasphaera and Acidaminococcus. Notably, Megamonas has been reported to harbor genes involved in myo-inositol degradation, and experimental evidence shows that colonization with Megamonas rupellensis enhances intestinal lipid absorption and promotes obesity, which supports biological plausibility for a potential microbiome-related pathway 15. A Japanese study reported a positive association between Megasphaera abundance and type 2 diabetes, supporting its adverse metabolic relevance 16. In addition, a meta-analysis reported a higher relative abundance of Acidaminococcus in obese than in non-obese adults, suggesting a potential link with adverse metabolic profiles 17. However, direct mechanistic evidence remains limited, and its role in pollution-related metabolic dysfunction warrants further investigation. For ambient temperature, mediation was mainly driven by Butyricimonas, Megamonas, Ruminococcus, and Acidaminococcus. Notably, Megamonas and Acidaminococcus showed consistent mediating effects for both air pollution and ambient temperature, suggesting a shared set of genera implicated in the exploratory mediation results. Prior studies have reported that Butyricimonas was identified as a differential genus in mice exposed to high temperature and humidity, which was further associated with altered plasma insulin and glucose levels 18. In addition, Ruminococcus has been linked to altered carbohydrate fermentation, impaired gut barrier function, and chronic low-grade inflammation, processes closely related to host energy balance and potentially sensitive to temperature-related physiological perturbations 19. Together, these findings suggest that specific gut microbial genera may mediate part of the metabolic impact of environmental exposures and highlight the gut microbiota as a potential target for MetS prevention and intervention. In summary, this study found that MetS was associated with lower gut microbial α-diversity, distinct community clusters, and differences in key genera, including enrichment of several potentially pathogenic genera. Higher long-term exposure to NO2 and PM2.5 and lower ambient temperature were related to increased odds of MetS, and several genera, such as Megamonas, Megasphaera, Acidaminococcus, and Ruminococcus, were identified in exploratory mediation analyses as accounting for part of the associations. These findings indicate that the gut microbiota may act as a biological interface through which environmental exposures influence MetS risk. The cross-sectional design precludes causal inference among environmental exposures, gut microbiota, and MetS, and longitudinal and experimental studies are needed to clarify temporal and causal relationships. Residual confounding remains possible, particularly from unmeasured factors such as medication use and host genetics, which were unavailable in this study and may affect both microbiota composition and MetS. In addition, the lack of detailed information on time-activity patterns, indoor air pollution, environmental noise, and green space may lead to exposure misclassification. Although we examined several major environmental factors, including NO2, PM2.5, and temperature, other relevant exposures were not comprehensively assessed, and the use of a 2-year averaging window may obscure potentially relevant exposure periods. Future studies are warranted to incorporate a broader range of exposures, alongside finer-resolution exposure assessment and alternative exposure windows. Nevertheless, integrating environmental exposures with gut microbiota profiles remains a promising strategy for elucidating MetS and may facilitate more precise risk stratification and prevention. Yixiang Huang: Writing—original draft; visualization; software; methodology. Wei Wu: Writing—review resources; data curation. Yuan Zheng: Writing—review data curation. Hongwei Tu: Writing—review methodology; resources. Yiping Duan: Writing—review data curation. Qijiong Zhu: Writing—review methodology; resources. Zhiqing Chen: Writing—review methodology. Siwen Yu: Writing—review conceptualization; project administration. Yayi Li: Writing—review data curation. Wan Peng: Writing—review software. Wenjun Ma: Writing—review writing—original draft; project administration; methodology; conceptualization. Tao Liu: Writing—review writing—original draft; project administration; methodology; conceptualization. All authors have read the final manuscript and approved it for publication. This work was supported by the National Key Research and Development Program of China (2023YFC3605000), Guangdong Province Special Support Plan (0720240243), the National Natural Science Foundation of China (42075173, 42175181, 42375180), and the National Innovation and Entrepreneurship Training Program for Undergraduate (202510559115). We also acknowledge support from the Undergraduate Medical Innovation Capability Expansion Experimental Teaching Demonstration Center of Jinan University. We apologize for not being able to cite additional work owing to space limitations. The authors declare no conflicts of interest. This study was approved by the Ethical Review Committee of the Chinese Center for Disease Control and Prevention (approval number: 201519-B). All participants provided written informed consent. The initial draft of this manuscript was polished for grammar and sentence structure using ChatGPT and Grammarly (https://app.grammarly.com/). All AI-generated contents were meticulously reviewed and edited by the authors to ensure factual accuracy and prevent any potential AI-induced bias. We hereby confirm that no AI tools were employed in the processes of data generation, analysis, or visual content creation. The raw data for 16S rRNA gene sequences are available from the European Nucleotide Archive (ENA) under the accession code PRJEB18535 (https://www.ebi.ac.uk/ena/browser/view/PRJEB18535). All analysis data and scripts are publicly available on the GitHub website (https://github.com/Hyx20/2026IMO). Supplementary materials (methods, figures, tables, graphical abstract, slides, videos, Chinese translated version, and updated materials) may be found in the online DOI or iMetaOmics Science http://www.imeta.science/imetaomics/. Figure S1: Radar plot showing the prevalence of MetS and its components in the overall population and stratified by sex. Figure S2: Boxplots comparing long-term exposure levels to PM2.5, NO2, and ambient temperature between MetS and non-MetS participants. Figure S3: Phylum and genus -level relative abundance of gut microbiota in non-MetS and MetS groups, based on 16S rRNA gene sequencing. Figure S4: Elbow plot for selecting the optimal number of gut microbial community clusters. Figure S5: The process of long-term exposure assessment to air pollutants. Figure S6: Random forest variable-importance ranking of six ambient air pollutants (PM2.5, NO2, CO, PM10, SO2, and O3) for predicting MetS. Figure S7: SHAP-based feature importance of six ambient air pollutants in the XGBoost model for predicting MetS. Table S1: Prevalence of metabolic syndrome and its diagnostic components. Table S2: General characteristics of study participants by MetS status. Table S3: ORs and 95% CIs of the associations between environmental factors and MetS. Table S4: Mediation of the associations between environmental exposures and MetS by selected gut microbial genera. Table S5: Variables used in the spatiotemporal land use regression (LUR) model. Table S6: The definitions of each category of included variables. 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