Integrating Metabolomics and Gut Microbiota to Identify Key Biomarkers and Regulatory Pathways Underlying Metabolic Heterogeneity in Childhood Obesity

• Published in: Nutrients Vol. 17; no. 11; p. 1876
• Main Authors: Xia, Zhiwei, Li, Yan, Yin, Jiyong, Gong, Zhaolong, Sun, Jing, Shen, Shi, Yang, Yi, Liu, Tingting, Wang, Liyuan, Huo, Junsheng
• Format: Journal Article
• Language: English
• Published: Switzerland MDPI AG 30.05.2025; MDPI
• Subjects: Amino acids; Antibiotics; Biological markers; Biomarkers; Biomarkers - metabolism; Blood lipids; Blood pressure; Body mass index; Child; Child, Preschool; Childhood; Children; Children & youth; China; Constipation; Diabetes; Diarrhea; Diet; Female; Gastrointestinal Microbiome - physiology; Glucose; Gut microbiota; Health aspects; Health care; Humans; Lipids; Male; Membrane lipids; Metabolism; Metabolites; Metabolomics - methods; Microbiota; Microbiota (Symbiotic organisms); Nervous system; Nutrition research; Obesity; Obesity in children; Pediatric Obesity - metabolism; Pediatric Obesity - microbiology; Pediatrics; Probiotics; Questionnaires; RNA; RNA, Ribosomal, 16S - genetics; Type 2 diabetes; China; California; United States; Massachusetts; multi-omics; metabolic heterogeneity; metabolomics; gut microbiota; childhood obesity
• ISSN: 2072-6643; 2072-6643
• DOI: 10.3390/nu17111876

Background/Objectives: Individuals with childhood obesity exhibit significant metabolic heterogeneity, necessitating precise biomarkers for risk stratification and assessment. This multi-omics investigation characterizes metabolic and microbial signatures underlying divergent metabolic phenotypes in the context of pediatric obesity. Methods: We analyzed 285 Chinese children (5–7 years) stratified into five groups: wasting (WAS, n = 55), metabolically healthy/unhealthy and normal weight (MHWH, n = 54; MUWH, n = 67), and metabolically healthy/unhealthy obesity (MHO, n = 36; MUO, n = 73). Untargeted metabolomics (Orbitrap ID-X Tribrid™) and 16S rRNA sequencing were integrated with multivariate analyses (OPLS-DA with VIP > 1, FDR < 0.05; Maaslin 2 with TSS normalization and BH correction, FDR < 0.10). Results: Analysis identified 225 differential metabolites and 12 bacterial genera. The proportion of steroids and their derivatives among differential metabolites in the MUO/MHO group was significantly lower than that in the OVOB/NOR and OVOB/WAS groups (2.12% vs. 7.9–14.1%). MUO displayed elevated C17 sphinganine and LysoPC (O-18:0) levels but reduced PI (16:0/14:1) levels. In contrast, OVOB showed upregulated glycerol phospholipids (LPCs and PSs) and downregulated PE species (e.g., PE(16:0/16:0)) as well as gut microbiota dysbiosis characterized by a higher Firmicutes/Bacteroidetes (F/B) ratio (2.07 vs. 1.24 in controls, p = 0.009) and reduced α diversity (Ace index, Chao1 index, and Shannon index values were lower in the OVOB group, Shannon index: 2.96 vs. 3.45, p = 0.03). SCFA-producing genera were negatively correlated with the OVOB group, while positively associated with PE(16:0/16:0). Internal validation showed differential metabolites had potential predictive efficacy for MUO/MHO (AUC = 0.967) and OVOB/NOR (AUC = 0.888). Conclusions: We identified distinct lipid disruptions characterizing obesity subtypes, including steroid/terpene deficits and sphingolipid/ether lipid dysregulation in the MUO/MHO groups as well as phospholipid imbalance (↑LPC/PS↓PE) in the OVOB/NOR groups. The gut microbiota exhibited a profile characterized by low diversity, an increased F/B ratio, and a reduced abundance of SCFA-producing genera. These findings suggest potential biomarkers for childhood obesity stratification, though further validation is warranted.