Parkinson's Disease and the Gut Microbiome in Rural California

Understanding Parkinson's: The microbiome and machine learning approach

OBJECTIVE

Given that Parkinson's disease is a progressive disorder, with symptoms that worsen over time, our goal is to enhance the diagnosis of Parkinson's disease by utilizing machine learning techniques and microbiome analysis. The primary objective is to identify specific microbiome signatures that can reproducibly differentiate patients with Parkinson's disease from healthy controls.

METHODS

We used four Parkinson-related datasets from the NCBI repository, focusing on stool samples. Then, we applied a DADA2-based script for amplicon sequence processing and the Recursive Ensemble Feature Selection (REF) algorithm for biomarker discovery. The discovery dataset was PRJEB14674, while PRJNA742875, PRJEB27564, and PRJNA594156 served as testing datasets. The Extra Trees classifier was used to validate the selected features.

RESULTS

The Recursive Ensemble Feature Selection algorithm identified 84 features (Amplicon Sequence Variants) from the discovery dataset, achieving an accuracy of over 80%. The Extra Trees classifier demonstrated good diagnostic accuracy with an area under the receiver operating characteristic curve of 0.74. In the testing phase, the classifier achieved areas under the receiver operating characteristic curves of 0.64, 0.71, and 0.62 for the respective datasets, indicating sufficient to good diagnostic accuracy. The study identified several bacterial taxa associated with Parkinson's disease, such as Lactobacillus, Bifidobacterium, and Roseburia, which were increased in patients with the disease.

CONCLUSION

This study successfully identified microbiome signatures that can differentiate patients with Parkinson's disease from healthy controls across different datasets. These findings highlight the potential of integrating machine learning and microbiome analysis for the diagnosis of Parkinson's disease. However, further research is needed to validate these microbiome signatures and to explore their therapeutic implications in developing targeted treatments and diagnostics for Parkinson's disease.

Gut microbiota and Parkinson's disease: potential links and the role of fecal microbiota transplantation

Parkinson's disease (PD) is the second most common neurodegenerative disease worldwide and seriously affects the quality of life of elderly patients. PD is characterized by the loss of dopaminergic neurons in the substantia nigra as well as abnormal accumulation of α-synuclein in neurons. Recent research has deepened our understanding of the gut microbiota, revealing that it participates in the pathological process of PD through the gut-brain axis, suggesting that the gut may be the source of PD. Therefore, studying the relationship between gut microbiota and PD is crucial for improving our understanding of the disease's prevention, diagnosis, and treatment. In this review, we first describe the bidirectional regulation of the gut-brain axis by the gut microbiota and the mechanisms underlying the involvement of gut microbiota and their metabolites in PD. We then summarize the different species of gut microbiota found in patients with PD and their correlations with clinical symptoms. Finally, we review the most comprehensive animal and human studies on treating PD through fecal microbiota transplantation (FMT), discussing the challenges and considerations associated with this treatment approach.

Effects of antimicrobial exposure on the risk of Parkinson's disease

BACKGROUND

We aimed to assess how antimicrobial exposure affects Parkinson's disease (PD) risk.

METHODS

A nested case-control study was performed to examine the association between antimicrobial exposure and newly diagnosed PD using the Clinical Practice Research Datalink (CPRD). Each PD case was matched by age, sex, and year of diagnosis (index date) to up to 15 controls. Number of prescribed antimicrobial courses was assessed 1-5, 6-10, and 11-15 years prior to the index date. Logistic regression with generalized estimating equations (GEE) was used to estimate odds ratios (ORs) and false discovery rate-adjusted p-values between antimicrobial exposure and risk of PD.

RESULTS

We compared 12,557 PD cases with 80,804 matched controls. We found an inverse dose-response relationship between number of penicillin courses and PD risk across multiple time periods (5+ courses, 1-5 years prior: OR 0.85, 95 % CI 0.76-0.95, p = 0.043; 6-10 years prior: OR 0.84, 95 % CI: 0.73-0.95, p = 0.059; 11-15 years prior: OR 0.87, 95 % CI 0.74-1.02, p = 0.291). The number of macrolide courses was inversely but not significantly associated with PD risk 1-5 years prior to the index date (OR 0.89-0.91, 95 % CI: 0.79-0.99, adjusted p = 0.140-0.167). Exposure to ≥2 courses of antifungals 1-5 years prior was associated with an increased risk of PD (OR 1.16, 95 % CI: 1.06-1.27, p = 0.020).

CONCLUSIONS

In a large UK-representative population, the risk of PD was modestly lower among adults who had previously received multiple courses of penicillins in the last 15 years and modestly higher among those exposed to antifungal medicines in recent years.

The role of gut-derived short-chain fatty acids in Parkinson's disease

The emerging function of short-chain fatty acids (SCFAs) in Parkinson's disease (PD) has been investigated in this article. SCFAs, which are generated via the fermentation of dietary fiber by gut microbiota, have been associated with dysfunction of the gut-brain axis and, neuroinflammation. These processes are integral to the development of PD. This article examines the potential therapeutic implications of SCFAs in the management of PD, encompassing their capacity to modulate gastrointestinal permeability, neuroinflammation, and neuronal survival, by conducting an extensive literature review. As a whole, this article emphasizes the potential therapeutic utility of SCFAs as targets for the management and treatment of PD.

Fecal microbiota from patients with Parkinson's disease intensifies inflammation and neurodegeneration in A53T mice

AIMS

We evaluated the potential of Parkinson's disease (PD) fecal microbiota transplantation to initiate or exacerbate PD pathologies and investigated the underlying mechanisms.

METHODS

We transplanted the fecal microbiota from PD patients into mice by oral gavage and assessed the motor and intestinal functions, as well as the inflammatory and pathological changes in the colon and brain. Furthermore, 16S rRNA gene sequencing combined with metabolomics analysis was conducted to assess the impacts of fecal delivery on the fecal microbiota and metabolism in recipient mice.

RESULTS

The fecal microbiota from PD patients increased intestinal inflammation, deteriorated intestinal barrier function, intensified microglia and astrocyte activation, abnormal deposition of α-Synuclein, and dopaminergic neuronal loss in the brains of A53T mice. A mechanistic study revealed that the fecal microbiota of PD patients stimulated the TLR4/NF-κB/NLRP3 pathway in both the brain and colon. Additionally, multiomics analysis found that transplantation of fecal microbiota from PD patients not only altered the composition of the gut microbiota but also influenced the fecal metabolic profile of the recipient mice.

CONCLUSION

The fecal microbiota from PD patients intensifies inflammation and neurodegeneration in A53T mice. Our findings demonstrate that imbalance and dysfunction in the gut microbiome play significant roles in the development and advancement of PD.

Diet and the gut microbiome in patients with Parkinson's disease

It has been suggested that gut microbiota influence Parkinson's disease (PD) via the gut-brain axis. Here, we examine associations between diet and gut microbiome composition and its predicted functional pathways in patients with PD. We assessed gut microbiota in fecal samples from 85 PD patients in central California using 16S rRNA gene sequencing. Diet quality was assessed by calculating the Healthy Eating Index 2015 (HEI-2015) based on the Diet History Questionnaire II. We examined associations of diet quality, fiber, and added sugar intake with microbial diversity, composition, taxon abundance, and predicted metagenomic profiles, adjusting for age, sex, race/ethnicity, and sequencing platform. Higher HEI scores and fiber intake were associated with an increase in putative anti-inflammatory butyrate-producing bacteria, such as the genera Butyricicoccus and Coprococcus 1. Conversely, higher added sugar intake was associated with an increase in putative pro-inflammatory bacteria, such as the genera Klebsiella. Predictive metagenomics suggested that bacterial genes involved in the biosynthesis of lipopolysaccharide decreased with higher HEI scores, whereas a simultaneous decrease in genes involved in taurine degradation indicates less neuroinflammation. We found that a healthy diet, fiber, and added sugar intake affect the gut microbiome composition and its predicted metagenomic function in PD patients. This suggests that a healthy diet may support gut microbiome that has a positive influence on PD risk and progression.

Potential effects of the most prescribed drugs on the microbiota-gut-brain-axis: A review

The link between drug-induced dysbiosis and its influence on brain diseases through gut-residing bacteria and their metabolites, named the microbiota-gut-brain axis (MGBA), remains largely unexplored. This review investigates the effects of commonly prescribed drugs (metformin, statins, proton-pump-inhibitors, NSAIDs, and anti-depressants) on the gut microbiota, comparing the findings with altered bacterial populations in major brain diseases (depression, multiple sclerosis, Parkinson's and Alzheimer's). The report aims to explore whether drugs can influence the development and progression of brain diseases via the MGBA. Central findings indicate that all explored drugs induce dysbiosis. These dysbiosis patterns were associated with brain disorders. The influence on brain diseases varied across different bacterial taxa, possibly mediated by direct effects or through bacterial metabolites. Each drug induced both positive and negative changes in the abundance of bacteria, indicating a counterbalancing effect. Moreover, the above-mentioned drugs exhibited similar effects, suggesting that they may counteract or enhance each other's effects on brain diseases when taken together by comorbid patients. In conclusion, the interplay of bacterial species and their abundances may have a greater impact on brain diseases than individual drugs or bacterial strains. Future research is needed to better understand drug-induced dysbiosis and the implications for brain disease pathogenesis, with the potential to develop more effective therapeutic options for patients with brain-related diseases.

Gut microbiome correlates with plasma lipids in amyotrophic lateral sclerosis

Amyotrophic lateral sclerosis (ALS) is a complex, fatal neurodegenerative disease. Disease pathophysiology is incompletely understood but evidence suggests gut dysbiosis occurs in ALS, linked to impaired gastrointestinal integrity, immune system dysregulation and altered metabolism. Gut microbiome and plasma metabolome have been separately investigated in ALS, but little is known about gut microbe-plasma metabolite correlations, which could identify robust disease biomarkers and potentially shed mechanistic insight. Here, gut microbiome changes were longitudinally profiled in ALS and correlated to plasma metabolome. Gut microbial structure at the phylum level differed in ALS versus control participants, with differential abundance of several distinct genera. Unsupervised clustering of microbe and metabolite levels identified modules, which differed significantly in ALS versus control participants. Network analysis found several prominent amplicon sequence variants strongly linked to a group of metabolites, primarily lipids. Similarly, identifying the features that contributed most to case versus control separation pinpointed several bacteria correlated to metabolites, predominantly lipids. Mendelian randomization indicated possible causality from specific lipids related to fatty acid and acylcarnitine metabolism. Overall, the results suggest ALS cases and controls differ in their gut microbiome, which correlates with plasma metabolites, particularly lipids, through specific genera. These findings have the potential to identify robust disease biomarkers and shed mechanistic insight into ALS.

Untargeted serum metabolomics reveals novel metabolite associations and disruptions in amino acid and lipid metabolism in Parkinson's disease

BACKGROUND

Untargeted high-resolution metabolomic profiling provides simultaneous measurement of thousands of metabolites. Metabolic networks based on these data can help uncover disease-related perturbations across interconnected pathways.

OBJECTIVE

Identify metabolic disturbances associated with Parkinson's disease (PD) in two population-based studies using untargeted metabolomics.

METHODS

We performed a metabolome-wide association study (MWAS) of PD using serum-based untargeted metabolomics data derived from liquid chromatography with high-resolution mass spectrometry (LC-HRMS) using two distinct population-based case-control populations. We also combined our results with a previous publication of 34 metabolites linked to PD in a large-scale, untargeted MWAS to assess external validation.

RESULTS

LC-HRMS detected 4,762 metabolites for analysis (HILIC: 2716 metabolites; C18: 2046 metabolites). We identified 296 features associated with PD at FDR<0.05, 134 having a log2 fold change (FC) beyond ±0.5 (228 beyond ±0.25). Of these, 104 were independently associated with PD in both discovery and replication studies at p<0.05 (170 at p<0.10), while 27 were associated with levodopa-equivalent dose among the PD patients. Intriguingly, among the externally validated features were the microbial-related metabolites, p-cresol glucuronide (FC=2.52, 95% CI=1.67, 3.81, FDR=7.8e-04) and p-cresol sulfate. P-cresol glucuronide was also associated with motor symptoms among patients. Additional externally validated metabolites associated with PD include phenylacetyl-L-glutamine, trigonelline, kynurenine, biliverdin, and pantothenic acid. Novel associations include the anti-inflammatory metabolite itaconate (FC=0.79, 95% CI=0.73, 0.86; FDR=2.17E-06) and cysteine-S-sulfate (FC=1.56, 95% CI=1.39, 1.75; FDR=3.43E-11). Seventeen pathways were enriched, including several related to amino acid and lipid metabolism.

CONCLUSIONS

Our results revealed PD-associated metabolites, confirming several previous observations, including for p-cresol glucuronide, and newly implicating interesting metabolites, such as itaconate. Our data also suggests metabolic disturbances in amino acid and lipid metabolism and inflammatory processes in PD.

Parkinson's disease and gut microbiota: from clinical to mechanistic and therapeutic studies

Parkinson's disease (PD) is one of the most prevalent neurodegenerative diseases. The typical symptomatology of PD includes motor symptoms; however, a range of nonmotor symptoms, such as intestinal issues, usually occur before the motor symptoms. Various microorganisms inhabiting the gastrointestinal tract can profoundly influence the physiopathology of the central nervous system through neurological, endocrine, and immune system pathways involved in the microbiota-gut-brain axis. In addition, extensive evidence suggests that the gut microbiota is strongly associated with PD. This review summarizes the latest findings on microbial changes in PD and their clinical relevance, describes the underlying mechanisms through which intestinal bacteria may mediate PD, and discusses the correlations between gut microbes and anti-PD drugs. In addition, this review outlines the status of research on microbial therapies for PD and the future directions of PD-gut microbiota research.

Metagenomics of the Gut Microbiome in Parkinson's Disease: Prodromal Changes

OBJECTIVE

Prior studies on the gut microbiome in Parkinson's disease (PD) have yielded conflicting results, and few studies have focused on prodromal (premotor) PD or used shotgun metagenomic profiling to assess microbial functional potential. We conducted a nested case-control study within 2 large epidemiological cohorts to examine the role of the gut microbiome in PD.

METHODS

We profiled the fecal metagenomes of 420 participants in the Nurses' Health Study and the Health Professionals Follow-up Study with recent onset PD (N = 75), with features of prodromal PD (N = 101), controls with constipation (N = 113), and healthy controls (N = 131) to identify microbial taxonomic and functional features associated with PD and features suggestive of prodromal PD. Omnibus and feature-wise analyses identified bacterial species and pathways associated with prodromal and recently onset PD.

RESULTS

We observed depletion of several strict anaerobes associated with reduced inflammation among participants with PD or features of prodromal PD. A microbiome-based classifier had moderate accuracy (area under the curve [AUC] = 0.76 for species and 0.74 for pathways) to discriminate between recently onset PD cases and controls. These taxonomic shifts corresponded with functional shifts indicative of carbohydrate source preference. Similar, but less marked, changes were observed in participants with features of prodromal PD, in both microbial features and functions.

INTERPRETATION

PD and features of prodromal PD were associated with similar changes in the gut microbiome. These findings suggest that changes in the microbiome could represent novel biomarkers for the earliest phases of PD. ANN NEUROL 2023;94:486-501.