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Brief summary of the study 

• The study combines the typical method for testing Parkinson's diseases (PD) i.e. neuroimaging, with 16S rRNA sequencing of stool and saliva to identify biomarkers that can predict cognitive decline among patients. 

• The study uses advanced machine learning (ML) techniques to identify patterns in brain structure and specific gut bacteria associated with faster cognitive decline in PD patients, with better predictive accuracy using combined data than either neuroimaging or microbiome data alone.

Major comments

Plus points

• The study clearly mentions that ethics approval and informed consent was obtained

• Cohort establishment and participants recruitment is clearly explained

• Figures are well made, they are of high quality and text is readable 

• The study utilizes a novel approach combining brain imaging and microbiome data to identify biomarkers that can be predictive of Parkinson's diseases progression

Things to improve

• Information on microbiome data analysis is scant. Description of saliva and stool collection procedure, DNA extraction and handling, DNA sequencing information and initial DNA data analysis including quality control is missing. 

Minor comments 

• Suggestions for future work could go in the discussion section, not in abstract, unless the scientist plan to conduct the studies 

Comments on reporting 

• Statistical analyses are well described 

• Information on availability of genomic data is provided, but not for brain imaging data

Conflicts of interest of reviewers

• None declared 

Competing interests

The authors declare that they have no competing interests.