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Evaluating a Microbial Biomarker in Blood Samples for Parkinson’s Disease

Study Rationale: Diagnosing Parkinson’s disease (PD) currently involves evaluation by movement disorder specialists and assessment based on physical examination and questionnaires. This approach is subject to evaluator bias and error, lacks detailed resolution of disease features and provides no insight into PD etiologies. The availability of biomarkers in blood samples would allow objective assessment of PD onset and progression, and would facilitate standardization of results across sites and geographies. Further, molecular diagnostics may yield new information into the biological underpinnings of PD not captured by current assessment tools, and may improve prediction of disease subtype, prognosis and drug response.

Hypothesis: We hypothesize that changes in the gut microbiome can be detected in bacterial DNA sequences obtained from blood samples, and that distinct patterns of microbial abundance will allow us to diagnose people with PD and to monitor the severity of the disease.

Study Design: We will mine whole genome sequencing data from blood samples of people with PD and healthy participants for microbial DNA signatures. To discover the factors that help shape blood microbial profiles, we will use a variety of statistical and machine-learning algorithms to correlate the presence and abundance of distinct microbial signatures in blood with PD status, disease severity and symptoms, immune and genetic profiles and drug responses. Finally, we will apply new computational approaches to predict biological interactions underlying causes of PD and suggest new treatment options with disease-modifying potential.

Impact on Diagnosis/Treatment of Parkinson’s disease: This exploratory research will validate a blood microbial biomarker for PD. The long-term objectives of our research program are to develop and validate objective and implementable biomarkers for PD, and to inform the development of safe, effective, personalized treatment strategies based on robust quantitative biological parameters.

Next Steps for Development: Next steps will compare using analytical metagenomics on blood samples as a diagnostic tool for PD against current methodologies and determine whether computationally predicted interactions impact PD symptoms and pathology in preclinical models, including interventions that may slow or halt neurodegeneration, alpha-synuclein accumulation, neuroinflammation and motor dysfunction.


  • Sarkis K. Mazmanian, PhD

    Pasadena, CA United States

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