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015 Gut microbiota and nutritional profiles of Parkinson’s disease patients
  1. Michal Lubomski1,2,
  2. Xiangnan Xu3,
  3. Andrew J Holmes4,
  4. Samuel Mueller3,
  5. Jean Yang3,
  6. Carolyn M Sue1,2 and
  7. Ryan L Davis2
  1. 1Neurology Department, Royal North Shore Hospital, St Leonards, NSW, Australia
  2. 2Department of Neurogenetics, Kolling Institute, University of Sydney and Royal North Shore Hospital, Sydney, NSW, Australia
  3. 3School of Mathematics and Statistics. Sydney Precision Bioinformatics, University of Sydney, Camperdown, NSW, Australia
  4. 4School of Life and Environmental Sciences, The Charles Perkins Centre, University of Sydney, Sydney, NSW, Australia


Objectives Imbalances in the composition of the gut microbiome (GM) from Parkinson’s disease (PD) patients have been reported previously. Collectively, the limited literature indicates a reduction in short-chain fatty-acid–producing bacteria that negatively influence colonic permeability and inflammation. We investigated GM composition in association with various clinical features and nutritional data in a large cross-sectional Australian PD cohort, to determine whether short-chain fatty-acid–producing bacteria representation in the GM was altered in association with clinical or nutritional differences between PD patients and controls.

Methods Clinical outcome measures derived from PD-validated questionnaires and stool samples were collected from 103 PD patients and 81 spousal healthy controls (HCs). GM composition, determined from 16S amplicon sequencing of the V3-V4 region of stool bacterial DNA, was compared between groups and with clinical outcome measures.

Results We identified significant compositional differences in the GM profiles of PD patients compared to HCs, across order, family and genus taxonomic levels. Multiple taxa were associated with a variety of clinical PD characteristics. Predictive models using GM profiles were developed to identify PD and were improved by incorporating nutritional data.

Conclusions We identified notable differences in microbial diversity and GM composition in PD patients compared to HCs that, along with nutritional data, enabled the development of predictive modelling to identify PD. These findings further support the GM as a potentially useful biomarker of PD pathophysiology.

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