Effects of Fecal Microbiota Transplantation on Intestinal Microbial Characteristics and Clinical Phenotypes in Patients with Parkinson's Disease

Summary

This exploratory study of six patients with Parkinson’s disease who underwent fecal microbiota transplantation observed temporal changes in gut microbial diversity, taxonomic composition, and predicted functional profiles, alongside improvements in motor and non-motor symptoms. These findings provide preliminary evidence supporting further investigation of microbiota-based interventions.

Abstract

Alterations in the gut microbiota have been associated with Parkinson’s disease (PD), but longitudinal microbial changes after fecal microbiota transplantation (FMT) and their clinical associations remain poorly understood. This single-center retrospective observational study included 6 patients with PD, stratified into high- and low-severity subgroups based on disease duration (>6 years vs ≤6 years). Thirty-six fecal samples were collected before FMT and monthly for five months afterward. Microbial diversity, community structure, taxonomic composition, and predicted functional profiles were assessed using 16S ribosomal RNA gene sequencing. Analyses included alpha and beta diversity, taxonomic abundance, linear discriminant analysis effect size, Tax4Fun2-based functional prediction, and Spearman rank correlations between microbial features and clinical indicators. Descriptive analyses indicated differences in microbial richness, diversity, community structure, and predicted functions between severity subgroups and across post-FMT time points. At baseline, the low-severity subgroup had greater microbial richness and diversity than the high-severity subgroup, with relatively higher abundances of taxa including Bifidobacterium and Lactobacillus. One month after FMT, richness and diversity increased from baseline in the high-severity subgroup, accompanied by changes in taxonomic composition. Both subgroups showed time-associated variation in microbial diversity and predicted Kyoto Encyclopedia of Genes and Genomes pathway enrichment after FMT. Predicted functions included carbohydrate and amino acid metabolism, secondary metabolite biosynthesis, membrane transport, and signal transduction. Several operational taxonomic units correlated with indicators of motor impairment, constipation, sleep quality, functional status, and neuropsychiatric symptoms. FMT was therefore associated with longitudinal changes in gut microbial diversity, composition, and predicted functions, and specific microbial features were associated with motor and non-motor indicators. Given the small retrospective cohort, these findings are preliminary and warrant confirmation in larger controlled studies. Future studies should determine whether these microbial alterations are reproducible, persist beyond five months, reflect donor engraftment, and correspond to measurable clinical improvement after transplantation in PD.

Introduction

Parkinson’s disease (PD) is a common progressive neurodegenerative disorder characterized by bradykinesia, rigidity, resting tremor, and postural instability¹. While its pathogenesis remains incompletely elucidated, multiple factors, including neuroinflammation, environmental exposures, and genetic susceptibility, have been implicated in disease onset and progression². Epidemiological evidence indicates that PD is among the most common neurological disorders in older adults and is associated with a substantial burden of disability, with prevalence increasing with age³. Current treatments, particularly dopamine replacement therapy, can relieve motor symptoms to some extent, but their effects on disease progression and many non-motor symptoms remain limited, and treatment responses vary across individuals⁴. Therefore, further investigation of PD-related biological changes and potential therapeutic targets remains clinically important.

The gut microbial community plays an important role in maintaining intestinal barrier integrity, regulating immune responses, and supporting metabolic homeostasis⁵. Fecal microbiota transplantation (FMT) involves the transfer of fecal microbiota from a healthy donor to a recipient to restore gut microbial balance^6,7. Multiple animal studies have suggested that gut microbiota transplantation can influence neurobehavioral phenotypes^8,9. Given that microbiota from patients with depression or anxiety can induce corresponding behavioral changes in mice, microbiota from healthy donors might improve behavioral outcomes¹⁰. In addition, preliminary clinical observations have reported symptom improvement after FMT in some patients with PD and autism, suggesting that microbiota-based interventions may have potential relevance in neurological and neuropsychiatric disorders^11,12. Nevertheless, the mechanisms underlying these effects remain unclear, and clinical studies are required to establish the safety and efficacy of FMT in PD.

Although previous studies have suggested a relationship between the gut microbiota and PD, the longitudinal changes in gut microbial composition following FMT and their associations with clinical characteristics remain poorly understood. It remains unclear whether patients with different clinical severities show distinct microbial responses after FMT.

In this single-center retrospective observational study, we reviewed patients with PD who underwent FMT in routine clinical practice. Clinical scores before and after FMT were analyzed, and available fecal samples were further analyzed using 16S rRNA gene sequencing to explore gut microbial diversity, taxonomic composition, predicted functional profiles, and correlations with clinical indicators. This study aimed to characterize clinical outcomes and gut microbiota profiles after FMT in PD and to provide preliminary evidence for future prospective controlled studies.

Protocol

The study was conducted in accordance with the Declaration of Helsinki, and the protocol was approved by the Ethics Committee of The Second Affiliated Hospital of Nanjing Medical University (approval number: 2021-KY-100-01). Written informed consent was obtained from all participants.

This single-center retrospective observational study was conducted at the Department of Neurology, The Second Affiliated Hospital of Nanjing Medical University. From January 2022 to December 2023, a total of 32 patients with primary PD who met the Diagnostic Criteria for Parkinson’s Disease (2020 edition) were included. Ultimately, six patients who met strict selection criteria and completed all study procedures were included in subsequent analysis. Details of screening, exclusion, and follow-up are shown in Supplementary Figure 1.

Fecal sample collection and microbial community analysis

Fecal microbiota was obtained from healthy volunteers. The donor screening process was strictly conducted in accordance with the Chinese Expert Consensus on the Clinical Application of Fecal Microbiota Transplantation. Donor inclusion criteria: Age 18–40 years, with no history of chronic diseases; No history of gastrointestinal diseases, metabolic disorders, autoimmune diseases, or psychiatric disorders; No use of antibiotics, probiotics, immunosuppressants, or proton pump inhibitors in the past 3 months; No travel to epidemic areas, blood transfusions, or surgical history in the past 6 months. Blood and fecal screening to exclude infectious diseases and pathogens includes: Human immunodeficiency virus (HIV), Hepatitis B virus (HBV), Hepatitis C virus (HCV), Treponema pallidum, Clostridium difficile, Salmonella, Shigella, Campylobacter, pathogenic Escherichia coli, enteric viruses, and parasites. The volume of a single valid stool sample should be at least 50 g. Using an automated fecal microbiota purification system (see Table of Materials), 500 mL of sterile saline was added to every 100 g of fresh stool, three rounds of standardized centrifugation and washing (700 × g, 3 min), and immediately inoculated after preparing a suspension with sterile saline in a 1:2 volume ratio. Subjects received a single injection of 150 mL of suspension containing approximately 5 × 10^13 viable bacteria¹³.

All recipients received FMT via colonoscopy, with the scope advanced to the cecum and ascending colon; the suspension was infused slowly and evenly through the biopsy channel to promote preferential colonization of the right colon. After infusion, patients remained in the right lateral decubitus position for at least 2 h. Preprocedurally, a low-residue diet was followed for 3 days, then a full liquid diet for 24 h before the procedure, with fasting from solids for 6 h and from liquids for 2 h prior to the infusion. On the evening before transplantation, a standard whole-bowel cleansing with polyethylene glycol electrolyte solution was performed until clear, stool-free effluent was obtained. Medication restrictions included withholding systemic antibiotics for at least 72 h before FMT and avoiding them for 1 week afterward. Proton pump inhibitors (PPIs) are discontinued 24–48 h before the procedure, and laxatives and probiotics are suspended on the day of transplantation.

During the procedure, heart rate, blood pressure, and oxygen saturation were continuously monitored, and any symptoms, such as abdominal distension, pain, or nausea, were observed; the infusion was immediately stopped if severe abdominal pain or hypotension occurred. For the first 7 days after transplantation, daily follow-up was conducted to record bowel movement frequency, stool consistency, and all adverse events, including abdominal pain, diarrhea, fever, and hematochezia. Long-term follow-up extended to 5 months, with scheduled stool sampling for gut microbiota analysis and clinical assessments using the Unified Parkinson’s Disease Rating Scale (UPDRS) and Wexner Constipation Score, alongside documentation of symptom changes and concomitant medication use, to comprehensively evaluate both efficacy and safety.

Fecal samples were collected at six time points: before FMT and at 1, 2, 3, 4, and 5 months after FMT. All participants received standardized instructions for stool sample collection. Approximately 10–20 g of mid-portion stool was collected using sterile forceps and placed into sterile collection containers. Samples were transported to the laboratory within 2–3 h after collection and aliquoted into sterile 1.5 mL microcentrifuge tubes (see Table of Materials) by trained researchers under aseptic conditions. All aliquots were immediately stored at −80 °C until further analysis.

After fecal sample collection, all specimens were transported on dry ice to a commercial sequencing provider (see Table of Materials) for 16S rRNA gene sequencing. Library preparation, sequencing, and initial quality control were performed according to the provider’s standard protocol. Genomic DNA was extracted from each fecal sample, and the V3–V4 hypervariable regions of the bacterial 16S rRNA gene were amplified by polymerase chain reaction (PCR) using primers 341F and 805R. Sequencing was performed on a benchtop short-read sequencing platform (see Table of Materials) using 2 × 300 bp paired-end chemistry to characterize the gut microbial profiles of patients before FMT and at different post-FMT follow-up time points. After sequencing, raw reads were processed to obtain high-quality sequences for downstream microbial analysis. Taxonomic classification was performed using a commercial 16S taxonomic assignment package and its associated microbial identification database (see Table of Materials). The resulting taxonomic profiles were used for subsequent analyses of microbial diversity, community composition, differential abundance, and predicted microbial function.

Clinical assessments and analytic labels

Patients with PD were stratified into two severity groups according to disease duration: a high-severity group (>6 years, designated “_H”) and a low-severity group (≤6 years, designated “_L”). Fecal samples collected before FMT were defined as baseline samples and labeled as FB_H or FB_L according to the corresponding severity group. Samples collected at 1, 2, 3, 4, and 5 months after FMT were labeled as FP1M_H, FP2M_H, FP3M_H, FP4M_H, and FP5M_H for the high-severity group, and FP1M_L, FP2M_L, FP3M_L, FP4M_L, and FP5M_L for the low-severity group. The assessed clinical parameters included the Wexner constipation score, Non-Motor Symptoms Scale (NMSS), Non-Motor Symptoms Questionnaire (NMSQ), Neuropsychiatric Inventory (NPI), Hoehn–Yahr stage, Activities of Daily Living scale (ADL), Pittsburgh Sleep Quality Index (PSQI), the original (UPDRS I–VI), and total UPDRS score. A combination of descriptive and inferential statistical methods was used to analyze the changes in clinical indicators of PD patients in the high-severity group (_H group) and the low-severity group (_L group) at six time points. To preliminarily explore group differences across all time points, the distributions of indicators between the _H and _L groups were compared at each time point using the Wilcoxon rank-sum test, and a more rigorous Linear Mixed Model (LMM) with patient-specific random intercepts was applied. Due to the limited sample size, the results of the mixed-effects model are provided only as exploratory references. The primary endpoint of this exploratory study was the change in gut microbial diversity and composition from baseline to post-fecal microbiota transplantation time points, with clinical changes evaluated as secondary endpoints.

OTU clustering, diversity analysis, and taxonomic abundance

Operational taxonomic units (OTUs) were used to summarize microbial community composition, and sequences with ≥97% similarity were clustered into the same OTU. Sequencing depth was evaluated using Shannon-based rarefaction curves, with curve plateaus indicating generally sufficient coverage for downstream diversity analyses.

Alpha diversity was assessed using the Chao1 and abundance-based coverage estimator (ACE) indices for microbial richness, and the Shannon and Simpson indices for overall diversity and evenness. Beta diversity was evaluated using weighted and unweighted UniFrac distances, followed by principal coordinate analysis (PCoA) to visualize differences in microbial community composition among groups. Between-group differences in alpha and beta diversity were assessed using non-parametric tests and Permutational Multivariate Analysis of Variance (PERMANOVA), respectively, as described in the statistical analysis section.

Taxonomic composition was summarized at the phylum, class, order, family, and genus levels based on 16S rRNA gene sequencing data. Using a community-ecology analysis package for R (see Table of Materials), identify and visualize the top 10 most abundant taxa at each taxonomic level. Taxonomic profiles were displayed using bar plots, and selected taxa were further compared among groups using box plots.

Differential abundance analysis of microbial taxa

Differentially abundant taxa among predefined groups were identified using linear discriminant analysis effect size (LEfSe). LEfSe integrates the Kruskal–Wallis test, Wilcoxon test, and linear discriminant analysis (LDA) to identify taxa with both statistical significance and discriminatory value. Taxa with P < 0.05 and an LDA score (log₁₀) > 3 were considered differentially abundant. The relative abundances of discriminative taxa were visualized using taxonomic bar plots.

Functional prediction and clinical correlation analysis

Predicted functional profiles of the gut microbiota were inferred from 16S rRNA gene sequencing data using Tax4Fun2 and annotated according to the Kyoto Encyclopedia of Genes and Genomes (KEGG) database. Differentially enriched predicted pathways were identified using LEfSe, with P < 0.05 and an LDA score (log₁₀) > 3 as thresholds. Because these functional profiles were inferred rather than directly measured by metagenomic or metabolomic approaches, they were interpreted as predicted functional differences. Spearman’s rank correlation analysis was performed to explore associations between microbial features and clinical indicators.

Statistical analysis

Statistical analyses were performed using R software (see Table of Materials). Clinical variables were summarized as medians and ranges (minimum–maximum). Alpha diversity indices were compared between groups using the Wilcoxon rank-sum test, while beta diversity was assessed via PERMANOVA with 999 permutations. Differentially abundant taxa and predicted functional pathways were identified using LEfSe, with P < 0.05 and LDA score (log₁₀) > 3 as the significance thresholds. For longitudinal differential-abundance analysis, a mixed-effects model with patient-specific random intercepts was applied, followed by post-hoc pairwise comparisons with Benjamini-Hochberg false discovery rate correction. Spearman's rank correlation analysis was used to evaluate associations between gut microbial features and clinical indicators, with Benjamini–Hochberg false discovery rate correction applied to correlation P values. A two-sided P < 0.05 was considered statistically significant. Multiple-comparison correction was applied to correlation and taxonomic abundance analyses. Given the small sample size and repeated longitudinal sampling design, the statistical analyses were considered exploratory, and the results were interpreted cautiously.

Results

Patient selection and baseline characteristics

Six patients with PD were included in the final analysis, including three males and three females. The median age was 69.0 years (range, 61–75), and the median body mass index was 22.6 kg/m². The cohort included two rigidity-dominant, two tremor-dominant, and two mixed clinical phenotypes. Baseline clinical characteristics are shown in Table 1.

Clinical characteristics differ...

Discussion

FMT is a microbiota-based intervention that transfers fecal microbiota from a healthy donor to the recipient’s gut to restore intestinal microbial balance¹⁴. Growing evidence suggests that the gut microbiota plays an important role in maintaining nervous system homeostasis¹⁵. Evidence from the bidirectional gut-brain axis indicates that the gut microbiota may influence central nervous system inflammation, neurotransmitter metabolism, and behavioral responses in PD

Materials

List of materials used in this article

Name	Company	Catalog Number	Comments
MiSeq System	Illumina, Inc.	SY-410-1003	Benchtop short-read sequencing platform used for 16S rRNA V3–V4 amplicon sequencing with 2 × 300 bp paired-end chemistry.
Metagenome@KIN	World Fusion Co., Ltd.	v2.2.1; https://www.w-fusion.com/metagenome	Commercial software used for 16S rRNA taxonomic assignment and microbial-community analysis.
Microbial Identification Database (bacterial)	TechnoSuruga Laboratory Co., Ltd.	DB-BA, database v10.0; https://www.tecsrg.co.jp/services/enki/	Bacterial 16S rDNA reference database used for microbial identification.
341F primer	BGI Genomics Co., Ltd.	Custom oligonucleotide; no catalogue number	Forward primer sequence: 5′-CCTACGGGNGGCWGCAG-3′.
805R primer	BGI Genomics Co., Ltd.	Custom oligonucleotide; no catalogue number	Reverse primer sequence: 5′-GACTACHVGGGTATCTAATCC-3′.
16S rRNA gene sequencing service	BGI Genomics Co., Ltd. (Wuhan, China)	https://www.bgi.com/us/sequencing-services/rna-sequencing-solutions/16s18s-its-sequencing/	Commercial sequencing provider; performed library preparation, sequencing and initial quality control.
Eppendorf Safe-Lock microcentrifuge tubes, Biopur, 1.5 mL	Eppendorf SE	'0030121589	Sterile polypropylene tubes used to aliquot fecal samples for storage at −80 °C.
LEfSe	Segata Lab/bioBakery	v1.1.2; https://github.com/SegataLab/lefse	Linear discriminant analysis effect size software used to identify differentially abundant taxa and predicted pathways.
Tax4Fun2	Tax4Fun2 development team	v1.1.5; https://github.com/fjossandon/Tax4Fun2	R package used to infer Kyoto Encyclopedia of Genes and Genomes functional profiles from 16S rRNA data.
microeco R package	Chi Liu and contributors	v1.15.0; https://cran.r-project.org/package=microeco	Community-ecology analysis and visualization package for microbial abundance and diversity analyses.
R software	R Foundation for Statistical Computing	Version not reported; https://www.r-project.org/	Statistical computing environment used for clinical and microbiome analyses; the exact version should be confirmed by the authors.
GenFMTer automated fecal microbiota purification system	Nanjing FMT Medical Co., Ltd.	Model: GenFMTer; https://en.fmtmed.com/	Automated system used for standardized centrifugation, washing and preparation of the fecal microbiota suspension.