{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,8,28]],"date-time":"2026-08-28T11:33:37Z","timestamp":1787916817837,"version":"build-2784847793"},"reference-count":28,"publisher":"Frontiers Media SA","license":[{"start":{"date-parts":[[2025,10,24]],"date-time":"2025-10-24T00:00:00Z","timestamp":1761264000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":["frontiersin.org"],"crossmark-restriction":true},"short-container-title":["Front. Nutr."],"abstract":"\n Introduction<\/jats:title>\n Obesity is an increasingly serious global health concern and is closely associated with gut dysbiosis and metabolic imbalance. Despite the considerable research conducted on the gut microbiota and metabolism over recent years, studies focusing on their correlation with obesity remain limited. In this study, we sought to characterize the gut microbiota and serum metabolic profiles of patients with obesity, aiming to identify potential biomarkers and therapeutic targets for this condition, and explore possible links between altered amino acid levels and gut microbial composition in its pathophysiology. The findings may offer novel insights into obesity prevention and treatment through microbiota modulation or amino acid regulation.<\/jats:p>\n <\/jats:sec>\n \n Methods<\/jats:title>\n \n Forty adult volunteers with obesity (BMI = 30.9 \u00b1 2.9 kg\/m\n 2<\/jats:sup>\n ) who met the diagnostic criteria were enrolled in this study. Pregnant or lactating women and individuals with severe comorbidities were excluded. The control group comprised 20 subjects with normal weight (BMI = 21.9 \u00b1 1.7 kg\/m\n 2<\/jats:sup>\n ) and without metabolic disorders, recruited from among outpatients during the same period and matched for age and sex. Fecal microbiota profiling was performed using 16S rRNA sequencing. DNA was extracted from stool samples, and the V3\u2013V4 region was amplified and sequenced on the Illumina platform. After rigorous quality control (QC) and chimera removal, effective tags were clustered into Operational Taxonomic Units (OTUs) based on sequence similarity. Alpha and beta diversity and intergroup differential abundance were assessed, with statistical significance determined by Welch's\n t<\/jats:italic>\n -test. Serum metabolomic analysis was performed using standardized sample preparation and QC procedures, followed by LC\u2013MS\/MS-based targeted and untargeted metabolomics. Calibration curves with\n R<\/jats:italic>\n 2<\/jats:sup>\n &gt; 0.99 were established, and relative metabolite concentrations were calculated from peak areas. In total, 28 amino acid metabolites were quantified and used for subsequent statistical analysis.\n <\/jats:p>\n <\/jats:sec>\n \n Results<\/jats:title>\n \n Significant differences in microbial composition were observed across multiple taxonomic levels between the controls and patients with obesity. At the phylum level, Proteobacteria was enriched in the obesity group (AUC = 0.709, 95% CI: 0.569\u20130.848;\n p<\/jats:italic>\n = 0.006). At the class level, Gammaproteobacteria (AUC = 0.712, 95% CI: 0.573\u20130.852;\n p<\/jats:italic>\n = 0.009) and Erysipelotrichia (AUC = 0.614, 95% CI: 0.471\u20130.757;\n p<\/jats:italic>\n = 0.02) were found to be enriched in obesity. At the order level, enrichment was observed for Enterobacteriales (AUC = 0.734, 95% CI: 0.597\u20130.871;\n p<\/jats:italic>\n = 0.008) and Erysipelotrichia (AUC = 0.614, 95% CI: 0.471\u20130.757;\n p<\/jats:italic>\n = 0.029). At the family level, Enterobacteriaceae (AUC = 0.614, 95% CI: 0.471\u20130.757;\n p<\/jats:italic>\n = 0.003) showed enrichment in obesity. Finally, at the genus level,\n Escherichia-Shigella<\/jats:italic>\n (AUC = 0.71, 95% CI: 0.565\u20130.855;\n p<\/jats:italic>\n = 0.028) was enriched in obesity, while at the species level,\n Bacteroides fragilis<\/jats:italic>\n (AUC = 0.733, 95% CI: 0.593\u20130.873;\n p<\/jats:italic>\n = 0.016) and\n Parabacteroides distasonis<\/jats:italic>\n (AUC = 0.61, 95% CI: 0.466\u20130.754;\n p<\/jats:italic>\n = 0.033) were noted to be enriched. Metabolomic analysis revealed that in patients with obesity, the abundance of carnosine (log2FC = 1.16, FDR = 0.0016, VIP = 0.707), creatinine (log2FC = 0.21, FDR = 0.0009, VIP = 2.02), and cystine (log2FC = 0.55, FDR = 0.009, VIP = 1.47) was significantly increased compared with that in the controls; in contrast, that of ornithine (log2FC = \u22120.59, FDR = 0.0009, VIP = 1.19), citrulline (log2FC = \u22120.59, FDR = 0.0003, VIP = 0.707), glycine (log2FC = \u22120.54, FDR = 0.0003, VIP = 1.41), and serine (log2FC = \u22120.38, FDR = 0.0019, VIP = 1.62) was significantly decreased. This suggested that these metabolites may have potential as early diagnostic biomarkers for obesity.\n <\/jats:p>\n <\/jats:sec>\n \n Conclusions<\/jats:title>\n Obesity is associated with coordinated shifts in specific gut taxa and serum metabolites, with measurable effect sizes and strong discriminatory performance. Modulating amino acid levels or gut microbiota composition may represent a promising strategy for obesity prevention and treatment.<\/jats:p>\n <\/jats:sec>","DOI":"10.3389\/fnut.2025.1648469","type":"journal-article","created":{"date-parts":[[2025,10,24]],"date-time":"2025-10-24T04:14:40Z","timestamp":1761279280000},"update-policy":"https:\/\/doi.org\/10.3389\/crossmark-policy","source":"Crossref","is-referenced-by-count":8,"title":["Integrated gut microbiota and metabolomic profiling reveals key associations between amino acid levels and gut microbial composition in patients with 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