Decoding the Pediatric Oral–Gut–Brain Axis: Microbiome Dynamics, Digital Diagnostics, and Neurodevelopmental Correlates in Early Childhood

Emerging evidence underscores the pivotal role of the oral microbiome in shaping both systemic health and neurodevelopmental trajectories in early childhood. Yet, comprehensive integrative models linking oral microbial ecology, metabolic signaling, and cognitive development remain limited. This study explores the oral–gut–brain axis in children, leveraging multi-omic sequencing, digital oral diagnostics, and neurobehavioral profiling to identify early indicators of health and disease.

A cohort of 210 children aged 3–8 years was examined through salivary metagenomics, 16S rRNA sequencing, and AI-driven digital dental imaging. Microbiome composition was correlated with cognitive scores, dietary intake, and caries prevalence. Parallel machine learning models (Random Forest and LASSO regression) identified microbial clusters predictive of both oral pathology and neurocognitive performance.

Findings revealed that reduced Streptococcus mutans abundance, balanced Bifidobacterium and Veillonella ratios, and specific short-chain fatty acid (SCFA) metabolite profiles correlated positively with higher executive function scores (p < 0.01). Children exhibiting dysbiotic oral ecosystems demonstrated not only increased caries risk but also altered emotional regulation profiles on the BRIEF-P scale. Digital intraoral imaging enhanced caries detection accuracy by 27%, while microbiome–behavior interaction networks clarified bidirectional communication along the oral–gut–brain continuum.

These results introduce a multi-dimensional framework connecting pediatric oral health with early neurodevelopmental processes, emphasizing that the mouth may serve as a “neural mirror” of systemic and cognitive wellness. Integrating AI-assisted digital dentistry with microbiome analytics can pioneer early detection strategies and preventive care models, aligning with the future of precision pediatric medicine.