[A machine learning model based on initial gut microbiome data for predicting changes of Bifidobacterium after...

Area of Science:

• Microbiome research
• Nutritional science
• Machine learning applications in biology

Background:

• Prebiotics are known to modulate gut microbiota composition and function.
• Understanding the impact of short-term prebiotic supplementation is crucial for targeted interventions.
• Predictive modeling can enhance personalized nutrition strategies for gut health.

Purpose of the Study:

• To evaluate the effects of fructo-oligosaccharides (FOS) and galacto-oligosaccharides (GOS) on gut microbiota structure and function over 9 days.
• To develop and validate a machine learning model for predicting Bifidobacterium variations post-prebiotic intake based on baseline microbiota data.

Main Methods:

• Randomized, double-blind, self-controlled trial with 35 healthy volunteers consuming FOS or GOS (16 g/day for 9 days).
• 16S rRNA gene sequencing and PICRUSt for analyzing gut microbiota structure and inferring functional potential.
• Random forest model developed using initial microbiota data to predict Bifidobacterium changes, validated with GOS intervention data.

Main Results:

• FOS reduced alpha diversity at day 5, with rebound by day 9; GOS showed progressive alpha diversity decrease.
• No significant changes in beta diversity were observed with either FOS or GOS.
• The machine learning model achieved high predictive accuracy for Bifidobacterium changes (AUC 89.6%, R=0.45, P=0.01; validation R=0.62, P=0.01).

Conclusions:

• Short-term prebiotic interventions can transiently decrease gut microbial alpha diversity.
• A machine learning model effectively predicts Bifidobacterium responses to prebiotics using baseline data.
• This predictive capability supports personalized nutrition and precise gut microbiota modulation.