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Author Spotlight: Exploring Non-Motor Symptoms in Parkinson's Disease
Published on: September 22, 2023
Gut Microbiome as a Source of Probiotic Drugs for Parkinson's Disease
Elena U Poluektova1, Alla Stavrovskaya2, Anastasia Pavlova2
1Vavilov Institute of General Genetics, Russian Academy of Sciences, Moscow 119991, Russia.
Abstract:
Parkinson's disease (PD) is a progressive, irreversible neurodegenerative disorder characterized by motor impairments and a wide spectrum of non-motor symptoms, including gastrointestinal dysfunction, sleep disturbances, depression, and cognitive decline. These manifestations arise from disturbances across multiple systems-gastrointestinal, neuroendocrine, immune, enteric, and central nervous systems. Alterations in the gut microbiota may play a causal role in PD onset and frequently accompany disease progression. The gut-brain axis, particularly the vagus nerve, is increasingly recognized as a key communication pathway whose dysregulation contributes to systemic dysfunction and the breakdown of homeostasis, ultimately driving PD pathology. Currently, there is no cure for PD, and existing treatments primarily target symptom relief. Effective management of PD requires a comprehensive approach that integrates multiple pharmacologically active agents aimed at restoring impaired organ functions and, when possible, neutralizing toxic factors that accelerate disease progression. One promising therapeutic avenue lies in functional gut bacteria, which form the basis for developing live biotherapeutic products, postbiotics, and bacterial vesicles. In this review, we summarize current data on the effects of probiotics in PD, drawing on both animal models and clinical studies. We highlight the role of probiotics in modulating PD pathophysiology and discuss their potential as adjunctive therapeutic agents. To provide a broader perspective, we also include sections describing the clinical manifestations of PD, gut microbiota alterations associated with the disease, and the role of artificial intelligence, particularly machine learning, in constructing functional models of PD.
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