Dataset Information

Can Gut Microbiota Be a Good Predictor for Parkinson's Disease? A Machine Learning Approach.

ABSTRACT: The involvement of the gut microbiota in Parkinson's disease (PD), investigated in several studies, identified some common alterations of the microbial community, such as a decrease in Lachnospiraceae and an increase in Verrucomicrobiaceae families in PD patients. However, the results of other bacterial families are often contradictory. Machine learning is a promising tool for building predictive models for the classification of biological data, such as those produced in metagenomic studies. We tested three different machine learning algorithms (random forest, neural networks and support vector machines), analyzing 846 metagenomic samples (472 from PD patients and 374 from healthy controls), including our published data and those downloaded from public databases. Prediction performance was evaluated by the area under curve, accuracy, precision, recall and F-score metrics. The random forest algorithm provided the best results. Bacterial families were sorted according to their importance in the classification, and a subset of 22 families has been identified for the prediction of patient status. Although the results are promising, it is necessary to train the algorithm with a larger number of samples in order to increase the accuracy of the procedure.

SUBMITTER: Pietrucci D

PROVIDER: S-EPMC7226159 | biostudies-literature | 2020 Apr

REPOSITORIES: biostudies-literature

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Can Gut Microbiota Be a Good Predictor for Parkinson's Disease? A Machine Learning Approach.

Pietrucci Daniele D Teofani Adelaide A Unida Valeria V Cerroni Rocco R Biocca Silvia S Stefani Alessandro A Desideri Alessandro A

Brain sciences 20200419 4

The involvement of the gut microbiota in Parkinson's disease (PD), investigated in several studies, identified some common alterations of the microbial community, such as a decrease in <i>Lachnospiraceae</i> and an increase in <i>Verrucomicrobiaceae</i> families in PD patients. However, the results of other bacterial families are often contradictory. Machine learning is a promising tool for building predictive models for the classification of biological data, such as those produced in metagenomi ...[more]

PMID: 32325848

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Project description:Parkinson's disease is a neurodegenerative disorder characterized by the accumulation of intracellular aggregates of misfolded alpha-synuclein along the cerebral axis. Several studies report the association between intestinal dysbiosis and Parkinson's disease, although a cause-effect relationship remains to be established. Herein, the gut microbiota composition of 64 Italian patients with Parkinson's disease and 51 controls was determined using a next-generation sequencing approach. A real metagenomics shape based on gas chromatography-mass spectrometry was also investigated. The most significant changes within the Parkinson's disease group highlighted a reduction in bacterial taxa, which are linked to anti-inflammatory/neuroprotective effects, particularly in the Lachnospiraceae family and key members, such as Butyrivibrio, Pseudobutyrivibrio, Coprococcus, and Blautia The direct evaluation of fecal metabolites revealed changes in several classes of metabolites. Changes were seen in lipids (linoleic acid, oleic acid, succinic acid, and sebacic acid), vitamins (pantothenic acid and nicotinic acid), amino acids (isoleucine, leucine, phenylalanine, glutamic acid, and pyroglutamic acid) and other organic compounds (cadaverine, ethanolamine, and hydroxy propionic acid). Most modified metabolites strongly correlated with the abundance of members belonging to the Lachnospiraceae family, suggesting that these gut bacteria correlate with altered metabolism rates in Parkinson's disease.IMPORTANCE To our knowledge, this is one of the few studies thus far that correlates the composition of the gut microbiota with the direct analysis of fecal metabolites in patients with Parkinson's disease. Overall, our data highlight microbiota modifications correlated with numerous fecal metabolites. This suggests that Parkinson's disease is associated with gut dysregulation that involves a synergistic relationship between gut microbes and several bacterial metabolites favoring altered homeostasis. Interestingly, a reduction of short-chain fatty acid (SCFA)-producing bacteria influenced the shape of the metabolomics profile, affecting several metabolites with potential protective effects in the Parkinson group. On the other hand, the extensive impact that intestinal dysbiosis has at the level of numerous metabolic pathways could encourage the identification of specific biomarkers for the diagnosis and treatment of Parkinson's disease, also in light of the effect that specific drugs have on the composition of the intestinal microbiota.

Dataset Information

Can Gut Microbiota Be a Good Predictor for Parkinson's Disease? A Machine Learning Approach.

Publications

Can Gut Microbiota Be a Good Predictor for Parkinson's Disease? A Machine Learning Approach.

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