Nutritional Intake and Gut Microbiome Composition Predict Parkinson's Disease

Area of Science:

• Microbiome research
• Neurodegenerative disease
• Nutritional science

Background:

• Predictive models for Parkinson's disease (PD) incorporating gut microbiome (GM) alterations have shown variable success.
• Understanding the interplay between GM composition, diet, and PD is crucial for developing effective diagnostic tools.

Purpose of the Study:

• To evaluate the utility of combining GM compositional changes with macronutrient intake for creating a PD predictive model.
• To identify specific GM alterations and dietary factors associated with PD.

Main Methods:

• Cross-sectional analysis of GM and nutritional intake in 103 PD patients and 81 controls.
• 16S amplicon sequencing for GM composition analysis.
• Random Forest and Support Vector Machine (SVM) models were developed for PD prediction.

Main Results:

• Significant GM compositional differences were observed between PD patients and controls, with specific genera over/underrepresented.
• Lactobacillaceae family was notably overrepresented in PD patients.
• A Random Forest model integrating genus-level GM data and carbohydrate intake achieved an AUC of 0.74, indicating good predictive capacity.

Conclusions:

• Distinct GM diversity and composition, alongside clinical and nutritional data, can facilitate PD prediction.
• The combination of GM and nutritional data shows promise as a biomarker for improving PD diagnosis and management.