The relationships between fine particulate matter (PM2.5) exposure and health effects are complex and incompletely understood. Evidence suggests that PM2.5 exposure alters gut microbiota composition and metabolites, but the connections between these changes remain unclear. The aim of our study was to investigate how gut microbiota are involved in the systemic metabolic changes following PM2.5 gastrointestinal exposure. We used multi-omics approaches, including 16S rRNA sequencing and serum metabolomics, to identify alterations in gut microbes and metabolites of PM2.5-exposed rats. We then explored correlations between perturbed gut microbiota and metabolic changes, and conducted pathway analyses to determine critical metabolic pathways impacted by PM2.5 exposure. To verify links between gut microbiome and metabolome disruptions, we performed fecal microbiota transplantation (FMT) experiment. A total of 30 differential gut microbe taxa were identified between PM2.5 and control groups, primarily in Firmicutes, Acidobacteria, and Proteobacteria phyla. We also identified 30 differential metabolites, including glycerophospholipids, fatty acyls, amino acids and others. Pathway analysis revealed disruptions in glycerophospholipid metabolism, steroid hormone biosynthesis, and linoleic acid metabolism. Through FMT, we confirmed PM2.5 altered phosphatidylcholine and linoleic acid metabolism by changing specific gut bacteria. Our results suggest that PM2.5 gastrointestinal exposure triggers systemic metabolic changes by disrupting the gut microbiome, especially glycerophospholipid and linoleic acid metabolism pathways.

Background: Resveratrol is a potent phytochemical known for its potential in treating cardiometabolic multimorbidity. However, its underlying mechanisms remain unclear. Our study systematically investigates the effects of resveratrol on cardiometabolic multimorbidity and elucidates its mechanisms using network pharmacology and molecular docking techniques. Methods: We screened cardiometabolic multimorbidity-related targets using the OMIM, GeneCards, and DisGeNET databases, and utilized the DSigDB drug characterization database to predict resveratrol’s effects on cardiometabolic multimorbidity. Target identification for resveratrol was conducted using the TCMSP, SymMap, DrugBank, Swiss Target Prediction, CTD, and UniProt databases. SwissADME and ADMETlab 2.0 simulations were used to predict drug similarity and toxicity profiles of resveratrol. Protein–protein interaction (PPI) networks were constructed using Cytoscape 3.9.1 software. Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) functional enrichment analyses were performed via the DAVID online platform, and target-pathway networks were established. Molecular docking validated interactions between core targets and resveratrol, followed by molecular dynamics simulations on the optimal core proteins identified through docking. Differential analysis using the GEO dataset validated resveratrol as a core target in cardiometabolic multimorbidity. Results: A total of 585 cardiometabolic multimorbidity target genes were identified, and the predicted results indicated that the phytochemical resveratrol could be a major therapeutic agent for cardiometabolic multimorbidity. SwissADME simulations showed that resveratrol has potential drug-like activity with minimal toxicity. Additionally, 6703 targets of resveratrol were screened. GO and KEGG analyses revealed that the main biological processes involved included positive regulation of cell proliferation, positive regulation of gene expression, and response to estradiol. Significant pathways related to MAPK and PI3K-Akt signaling pathways were also identified. Molecular docking and molecular dynamics simulations demonstrated strong interactions between resveratrol and core targets such as MAPK and EGFR. Conclusions: This study predicts potential targets and pathways of resveratrol in treating cardiometabolic multimorbidity, offering a new research direction for understanding its molecular mechanisms. Additionally, it establishes a theoretical foundation for the clinical application of resveratrol.

Background: Metabolic syndrome (MS) is combination of medical disorders that increase people's risk for cardiovascular disease and diabetes mellitus. Little data exists on the prevalence of MS of rural original adults in Ningxia of China. Methods: A cross-sectional survey method was used and the participants were interviewed by trained health workers under a structured questionnaire in rural of Ningxia in 2008. The number of research subjects was 1612. MS was defined by International Diabetes Federation IDF (2005). Results: The age-adjusted prevalence of the metabolic syndrome was 11.8%, whereas ethnic-specific prevalence was 10.3% in Han ethnic group and 13.7% in Hui ethnic group. Components of MS and MS were more common in Hui ethnic group than Han ethnic group. The mean levels and prevalence of abnormal value increased with increasing age in both ethnic groups (Cochran-Artimage test for trend, Hui ethnic group P < 0.05, Han ethnic group P < 0.01). Conclusions: The prevalence of MS was high in rural residents' adults in Ningxia. Clustering of MS components and MS was increased with age. The components of MS have big differences among different ethnic groups.

Objective: This study evaluated the associations of serum ferritin (SF) concentration during pregnancy with the risk of adverse maternal and fetal pregnancy outcomes. Methods: We conducted a retrospective study of 2327 pregnant women from 2015 to 2020 in Guangdong, China. SF concentrations were measured at 16– 18th and 28– 32th week of gestation. Logistic regression models were applied to estimate the association between SF concentration and the risk of adverse pregnancy outcomes. Results: After multivariable adjustment, the odds ratio (OR) of the highest quartile of SF concentration at 16– 18th week of gestation was 1.43 (95% confidence interval [CI]: 1.09, 1.89) for gestational diabetes mellitus (GDM) and 1.79 (95% CI: 1.15, 2.79) for small for gestational age (SGA) when compared with the lowest quartile. At 28– 32th week of gestation compared with the lowest quartile, women with SF in the highest quartile had an increased risk of SGA (OR: 1.62; 95% CI: 1.01, 2.62). Moreover, the lowest quartile of SF concentration decreased risk of SGA by 90% (95% CI: 0.01, 0.80) when compared with the highest quartile among pregnancy women with GDM. Conclusion: Elevated SF concentrations increased the risk of GDM and SGA during pregnancy. Maintaining an appropriately low level of maternal SF at 28– 32th week of gestation in women with GDM could reduce the risk of SGA.