Combined metabolomic and metagenomic analysis reveals inflammatory bowel disease diversity in pediatric and adult patients.

BACKGROUND: The gut microbiota displays pronounced compositional differences between pediatric and adult populations, both under normal conditions and during the development of inflammatory bowel disease (IBD). These structural variations are accompanied by substantial changes in microbial metabolic activity. AIM: To identify novel early diagnostic biomarkers of IBD, we performed an integrated multi-omics analysis that included assessing microbial community structure and profiling microbial metabolic activity in pediatric and adult cohorts with ulcerative colitis (UC) and Crohn's disease (CD). METHODS: The study cohort consisted of two distinct age groups with confirmed IBD diagnoses: Adult patients (aged 45 to 70) and pediatric patients (aged 5 to 15), each diagnosed with either CD or UC. 16S rRNA gene sequencing was performed using the MinION™ Mk1B platform, with data acquisition carried out via MinKNOW software version 22.12.7 (Oxford Nanopore Technologies). Stool samples were analyzed using a Shimadzu QP2010 Ultra GC/MS system equipped with a Shimadzu HS-20 headspace extractor. RESULTS: Comparative analysis revealed significant age-related differences in the abundance of Bacteroidota, with pediatric IBD patients showing a lower prevalence compared to adults. Microbial profiling identified Streptococcus salivarius and Escherichia coli as potential biomarkers for assessing IBD risk in children. Furthermore, metagenomic analysis uncovered five microbial signatures with diagnostic potential for CD: Ralstonia insidiosa, Stenotrophomonas maltophilia, Erysipelatoclostridium ramosum, Blautia spp., and Coprococcus comes. Using comprehensive metabolomic profiling, we developed and validated novel risk prediction algorithms for pediatric IBD. The CD risk stratification model identifies high-risk patients based on two key biomarkers: An elevated IBD risk coefficient score and reduced levels of 1H-indole-3-methyl. The UC risk prediction model incorporates three metabolic biomarkers indicative of increased disease risk: An elevated risk coefficient score, increased acetate levels, decreased pentanoic acid, and altered excretion of p-cresol (4-methylphenol). CONCLUSION: Functional metabolomics holds transformative potential for IBD diagnostics across all age groups, with especially significant implications for pediatric patients. The distinct metabolic and metagenetic profiles observed in the pediatric cohort may represent primary alterations in IBD, providing valuable insights for exploring novel mechanisms underlying disease pathogenesis.