Predicting Dietary Impact on Multiple Sclerosis-Related Symptoms With the Gut Microbiome: A Pilot Study Using...

Area of Science:

• Microbiome research
• Neuroimmunology
• Nutritional science

Background:

• Multiple sclerosis (MS) is a neurodegenerative condition.
• Dietary interventions, including the modified Paleolithic elimination (MPE) and low-saturated fat (LSF) diets, show promise in managing MS symptoms.
• The mechanisms by which diet influences outcomes in MS, particularly through the gut microbiota, are not well understood.

Purpose of the Study:

• To investigate the role of baseline gut microbiota in predicting treatment response to dietary interventions in people with MS (pwMS).
• To explore the relationship between specific gut microbial profiles and improvements in fatigue and quality of life (QoL) in pwMS undergoing dietary changes.

Main Methods:

• Fecal 16S rRNA sequencing was used to analyze the gut microbiome composition in pwMS.
• Topic modeling, a machine learning approach, was applied to identify predictive microbial features from baseline samples.
• The study included pwMS on either the MPE diet (n=11) or the LSF diet (n=12), with combined analysis of the cohort (n=23).

Main Results:

• Significant changes in specific bacterial genera were observed on both MPE and LSF diets over time.
• Baseline enrichment of Akkermansia, Bacteroides, and Barnesiella predicted a positive response in fatigue levels.
• An initial abundance of Faecalibacterium and Alistipes was associated with a lack of improvement in Mental QoL.

Conclusions:

• Baseline gut microbiota composition can predict improvements in fatigue and Mental QoL in pwMS undergoing dietary interventions.
• These findings underscore the potential of the gut microbiome in personalized nutrition strategies for MS management.
• Further large-scale mechanistic studies are needed to validate these hypothesis-generating results.