Imagine your body is a bustling city, with different neighborhoods representing different parts of your health. Now, imagine that one neighborhood, the gut microbiome, holds the secret to predicting Parkinson’s disease! Scientists have discovered that changes in the composition of gut bacteria, combined with dietary habits, can help create a model to identify those at risk of developing Parkinson’s disease. By studying the stool samples of both Parkinson’s patients and healthy controls, researchers found significant differences in the types of bacteria present. Some genera were overrepresented in Parkinson’s patients, while others were underrepresented. Interestingly, Lactobacillaceae, a family of bacteria, was found to be particularly abundant in those with Parkinson’s disease. The model that incorporated taxonomic data and information about carbohydrate intake had the highest predictive capacity. This research opens up new possibilities for using gut bacteria and diet as biomarkers for Parkinson’s disease diagnosis and management. If you’re interested in learning more about how your gut microbiome influences your health and potential predictive tools for diseases like Parkinson’s, check out the original research!

BackgroundModels to predict Parkinson’s disease (PD) incorporating alterations of gut microbiome (GM) composition have been reported with varying success.ObjectiveTo assess the utility of GM compositional changes combined with macronutrient intake to develop a predictive model of PD.MethodsWe performed a cross-sectional analysis of the GM and nutritional intake in 103 PD patients and 81 household controls (HCs). GM composition was determined by 16S amplicon sequencing of the V3-V4 region of bacterial ribosomal DNA isolated from stool. To determine multivariate disease-discriminant associations, we developed two models using Random Forest and support-vector machine (SVM) methodologies.ResultsUsing updated taxonomic reference, we identified significant compositional differences in the GM profiles of PD patients in association with a variety of clinical PD characteristics. Six genera were overrepresented and eight underrepresented in PD patients relative to HCs, with the largest difference being overrepresentation of Lactobacillaceae at family taxonomic level. Correlation analyses highlighted multiple associations between clinical characteristics and select taxa, whilst constipation severity, physical activity and pharmacological therapies associated with changes in beta diversity. The random forest model of PD, incorporating taxonomic data at the genus level and carbohydrate contribution to total energy demonstrated the best predictive capacity [Area under the ROC Curve (AUC) of 0.74].ConclusionThe notable differences in GM diversity and composition when combined with clinical measures and nutritional data enabled the development of a predictive model to identify PD. These findings support the combination of GM and nutritional data as a potentially useful biomarker of PD to improve diagnosis and guide clinical management.

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