Gut Microbiota Differs Between Parkinson’s Disease Patients and Healthy Controls in Northeast China Original paper
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Dr. Umar
Read MoreClinical Pharmacist and Clinical Pharmacy Master’s candidate focused on antibiotic stewardship, AI-driven pharmacy practice, and research that strengthens safe and effective medication use. Experience spans digital health research with Bloomsbury Health (London), pharmacovigilance in patient support programs, and behavioral approaches to mental health care. Published work includes studies on antibiotic use and awareness, AI applications in medicine, postpartum depression management, and patient safety reporting. Developer of an AI-based clinical decision support system designed to enhance antimicrobial stewardship and optimize therapeutic outcomes.
Microbiome Signatures identifies and validates condition-specific microbiome shifts and interventions to accelerate clinical translation. Our multidisciplinary team supports clinicians, researchers, and innovators in turning microbiome science into actionable medicine.
Karen Pendergrass is a microbiome researcher specializing in microbiome-targeted interventions (MBTIs). She systematically analyzes scientific literature to identify microbial patterns, develop hypotheses, and validate interventions. As the founder of the Microbiome Signatures Database, she bridges microbiome research with clinical practice. In 2012, based on her own investigative research, she became the first documented case of FMT for Celiac Disease—four years before the first published case study.
What was studied?
This original research article examined how the gut microbiota differs between Parkinson’s disease patients and healthy controls, focusing on whether gut microbiota differs between Parkinson’s disease patients and healthy controls in Northeast China and identifying clinical or dietary factors associated with these microbial changes. Using 16S rRNA sequencing of stool samples, the investigators mapped community-wide shifts in diversity, richness, and differential taxa. They contextualized these microbial signatures with clinical scoring systems related to motor impairment, non-motor symptoms, autonomic dysfunction, and sleep behavior, and they evaluated potential dietary confounders. Rarefaction curves visually confirmed adequate sequencing depth, while abundance and β-diversity plots depicted the contraction of microbial diversity in Parkinson’s disease. Differential abundance results were supported by both t-tests and LEfSe analysis, with the cladogram on page 7 highlighting distinct microbial clusters enriched or depleted in Parkinson’s disease.
Who was studied?
The study enrolled 51 Parkinson’s disease patients and 48 healthy controls, all of Han Chinese ethnicity and residing in Northeast China. Controls included spouses of patients to minimize lifestyle variation, supplemented by age-matched community members. Strict exclusion criteria limited confounders such as recent antibiotics, gastrointestinal disease, alcohol use, smoking, or medications known to alter gut microbiota. A dietary intake subset included 42 patients and 23 controls, allowing analysis of fiber, fat, and carbohydrate intake, which ultimately showed no significant group differences. Participants were generally well matched for age and BMI, reducing demographic bias. Clinical assessments included the Movement Disorder Society UPDRS-III, NMSQ, SCOPA-AUT, RBDQ-HK, and Wexner constipation scale, enabling correlation of microbial patterns with symptom burden.
Most important findings
The study demonstrated that gut microbiota differs between Parkinson’s disease patients and healthy controls in several key ways. Alpha-diversity analyses showed reduced species richness and phylogenetic diversity in Parkinson’s disease, while β-diversity analyses indicated more tightly clustered microbial profiles, implying reduced community variability. Differential abundance analysis revealed consistent enrichment of Akkermansia and depletion of Lactobacillus, findings supported by the LEfSe cladogram. These shifts suggest weakening of the mucosal barrier and reduction of lactic-acid–producing, potentially beneficial taxa. Several genera—including Bacteroidales_S24-7 group, Ruminococcaceae groups, and Prevotella species—also differed between groups, though regional variation and constipation severity influenced some comparisons. Correlation heatmaps showed that motor and non-motor clinical scores were the strongest predictors of microbial shifts, especially affecting Bacillales, Lactobacillales, Acidaminococcaceae, Phascolarctobacterium, Akkermansia, and Coprococcus.
Key implications
These results reinforce the concept of a gut–brain axis in Parkinson’s disease, emphasizing that gut microbiota differs between Parkinson’s disease patients and healthy controls in ways that may reflect neuroinflammation, impaired barrier integrity, and altered metabolic signaling. The consistent depletion of Lactobacillus suggests potential therapeutic relevance for dietary or probiotic strategies aimed at restoring lactic-acid–producing bacteria. Increased Akkermansia abundance may promote mucus thinning and intestinal permeability, potentially contributing to early gut-driven pathology. Region-specific dietary factors, such as common consumption of Lactobacillus-rich fermented foods, may influence microbial baselines and should be considered in clinical translation.
Citation
Li C, Cui L, Yang Y, Miao J, Zhao X, Zhang J, Cui G, Zhang Y. Gut microbiota differs between Parkinson’s disease patients and healthy controls in Northeast China. Front Mol Neurosci. 2019;12:171. doi:10.3389/fnmol.2019.00171
Parkinson’s disease is increasingly recognized as a systemic disorder involving coordinated disturbances across the gut–brain axis, rather than a condition confined to dopaminergic neurodegeneration alone. Converging evidence implicates gut dysbiosis, altered microbial metabolites, impaired intestinal barrier integrity, and metal dyshomeostasis as upstream drivers of neuroinflammation and alpha-synuclein pathology. These interconnected microbiome, metabolomic, and metallomic signals provide a mechanistic framework for understanding disease initiation, progression, and therapeutic targeting beyond the central nervous system.