Gut Microbiome

Longitudinal Interaction Between Individualized Gut Microbial Dynamics and Diet Is Associated with Metabolic Health in School-Aged Children.

TL;DR

Fecal microbiota dynamics exhibit inter-individual variation among school-aged children, with low-stability microbiota associated with adverse blood lipid profiles and linked to dietary fiber and amino acid consumption, suggesting gut microbial dynamics as a possible complementary biomarker for individualized dietary interventions aimed at improving metabolic health in childhood.

Key Findings

Gut microbiota dynamics in school-aged children can be classified into high- and low-stability subgroups based on intra-individual β-diversity variability.

  • 204 fecal samples were collected from 51 children (aged 8.90 ± 0.78 years) at four timepoints over 52 weeks
  • Classification into high- and low-stability subgroups was based on intra-individual β-diversity variability
  • The study used 16S rRNA gene amplicon sequencing for fecal microbiota characterization
  • The dynamics exhibited inter-individual variation among children

Children with low-stability microbiota had adverse blood lipid profiles compared to those with high-stability microbiota.

  • Adverse blood lipid profiles were significantly associated with low microbial stability (p < 0.05)
  • 153 blood samples were analyzed for biochemical tests across the study period
  • Blood samples were collected from 51 children at multiple timepoints over 52 weeks
  • The relationship between microbial stability and lipid profiles was assessed longitudinally

Low-stability microbiota was significantly associated with low dietary fiber intake and highly variable amino acid consumption.

  • Associations met thresholds of |r| > 0.3 and q < 0.05
  • Compared to the high-stability group, low-stability microbiota demonstrated significant associations with both dietary fiber and amino acid intake patterns
  • Dietary intake was assessed as part of the broader characterization including physical activity, clinical phenotypes, early-life factors, and fecal characteristics
  • The low-stability group showed highly variable amino acid consumption specifically

Phocaeicola vulgatus exhibited marked fluctuations in the low-stability microbiota group and was strongly linked to blood triglycerides, lipoprotein(a) levels, dietary fiber, and amino acid intake.

  • P. vulgatus fluctuations were a distinguishing feature of low-stability microbiota
  • Associations were found with both blood triglycerides and lipoprotein(a) levels
  • P. vulgatus was also linked to dietary fiber and amino acid intake
  • Short-chain fatty acid quantification was performed on fecal samples as part of the metabolic assessment

Baseline depletion of Phocaeicola vulgatus and Faecalibacterium duncaniae, combined with physiological status, lifestyle behaviors, and early-life factors, predicted microbial stability classification with high accuracy.

  • The predictive model achieved an AUROC of 0.93 for classifying children into high- or low-stability microbiota groups
  • Predictors included baseline depletion of both P. vulgatus and F. duncaniae
  • Additional predictors encompassed children's physiological status, lifestyle behaviors, and early-life factors
  • The model was developed using data from 51 children over the 52-week longitudinal study

The study design was a longitudinal observational study collecting fecal and blood samples from school-aged children at four timepoints over one year.

  • 51 children participated, aged 8.90 ± 0.78 years
  • Four timepoints were assessed over 52 weeks
  • 204 fecal samples and 153 blood samples were collected in total
  • Assessments included dietary intake, physical activity, clinical phenotypes, early-life factors, and fecal characteristics
  • Fecal analysis included 16S rRNA gene amplicon sequencing and short-chain fatty acid quantification

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Citation

Feng C, Yang M, Yang Z, Liao X, Jiang S, Li L, et al.. (2026). Longitudinal Interaction Between Individualized Gut Microbial Dynamics and Diet Is Associated with Metabolic Health in School-Aged Children.. Nutrients. https://doi.org/10.3390/nu18020187