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Fecal microbiome alterations in treatment-naive de novo Parkinson’s disease Original paper

Researched by:

  • Dr. Umar ID
    Dr. Umar

    User avatarClinical 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.

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November 15, 2025

Researched by:

  • Dr. Umar ID
    Dr. Umar

    User avatarClinical 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.

    Read More

Last Updated: 2022-01-01

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

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.

Location
Finland
Netherlands
Sample Site
Feces
Species
Homo sapiens

What was studied?

This study investigated the composition of the fecal microbiome in treatment-naive, de novo Parkinson’s disease (PD) patients compared to healthy controls, focusing on potential gut microbial alterations at the time of diagnosis and before the start of dopaminergic therapy. Previous research in PD microbiome alterations often involved patients already on medication and with longer disease durations, potentially confounding observed microbial differences due to the impact of medication or disease progression. To address these limitations, the present research analyzed two large, independent, case-control cohorts from the Netherlands and Finland, comprising recently diagnosed, untreated PD patients and geographically matched healthy controls. The study used 16S rRNA gene sequencing of fecal samples and included comprehensive adjustment for relevant confounding factors, including technical variables (such as different DNA extraction methods and batch effects), dietary habits, and constipation—a common non-motor symptom of PD and a known modulator of gut microbiome composition. The primary aim was to characterize the gut microbiome landscape in early, untreated PD and to disentangle disease-related changes from those potentially driven by medication or disease chronicity.

Who was studied?

The NL cohort included 136 treatment-naive de novo PD patients and 85 healthy controls, while the FIN cohort comprised 56 PD patients and 87 healthy controls. All PD participants were newly diagnosed, had not yet initiated dopaminergic therapy, and met strict diagnostic criteria confirmed by neuroimaging or clinical follow-up. Controls were free from neurodegenerative disorders and, in the Dutch cohort, often included spouses or individuals from the same household as PD patients. Both cohorts were geographically localized to minimize environmental variability, although there were methodological differences between them, such as DNA extraction protocols and dietary assessment methods. Comprehensive clinical, dietary, and gastrointestinal data were collected to adjust for potential confounders, and recent antibiotic use was an exclusion criterion. This design enabled the study to focus specifically on microbiome profiles at the earliest clinical stage of PD, prior to the confounding effects of common pharmacotherapy.

Most important findings

The study demonstrated that significant differences in overall fecal microbiome composition exist between treatment-naive de novo PD patients and healthy controls in both the NL and FIN cohorts, even after adjusting for potential confounders. However, the effect sizes were smaller than previously reported in studies involving medicated or advanced PD patients, and no single differentially abundant taxon was robustly replicated across both cohorts at the genus or family level. Despite this, both cohorts showed a consistent reduction in multiple taxa belonging to the family Lachnospiraceae, particularly the genus Roseburia, which are notable producers of short-chain fatty acids (SCFAs) with anti-inflammatory properties. Other SCFA-producing taxa, such as Butyricicoccus and Veillonellaceae, also trended lower in PD. Conversely, an increase in Akkermansia muciniphila—a mucin-degrading species associated with gut barrier disruption and constipation—was observed in the FIN cohort. The study found that previously reported increases in Bifidobacterium and Lactobacillus in PD were absent in these untreated cohorts, supporting the hypothesis that such changes may be linked to dopaminergic medications rather than PD itself. Additionally, methodological differences (notably DNA extraction method) and household matching influenced microbiome profiles, highlighting the complexity and variability inherent in human microbiome research.

Key implications

This study provides critical evidence that gut microbiome alterations are present at the time of PD diagnosis, independent of medication effects or prolonged disease duration. The consistent reduction in SCFA-producing Lachnospiraceae taxa in early, untreated PD suggests a possible role in the disease’s pathophysiology, potentially via impaired gut barrier function or modulation of neuroinflammation. However, the absence of robust, cross-cohort microbiome signatures at higher taxonomic levels indicates that the microbiome’s utility as a diagnostic biomarker for PD may be limited, at least without deeper functional or strain-level characterization. The findings underscore the necessity of careful control for technical, dietary, and clinical confounders in microbiome studies and highlight the value of multi-cohort designs for reproducibility. Further research, especially with longitudinal and functional (metabolomic and metagenomic) approaches, is needed to clarify the causal relationships and mechanistic pathways linking gut microbiota to PD onset and progression, and to explore the potential for subtype-specific microbial signatures.

Citation

Boertien JM, Murtomäki K, Pereira PAB, van der Zee S, Mertsalmi TH, Levo R, Nojonen T, Mäkinen E, Jaakkola E, Laine P, Paulin L, Pekkonen E, Kaasinen V, Auvinen P, Scheperjans F, van Laar T, PPNN Study Group. Fecal microbiome alterations in treatment-naive de novo Parkinson’s disease. npj Parkinson’s Disease. 2022;8:129. doi:10.1038/s41531-022-00395-8

Short-chain Fatty Acids (SCFAs)

Short-chain fatty acids are microbially derived metabolites that regulate epithelial integrity, immune signaling, and microbial ecology. Their production patterns and mechanistic roles provide essential functional markers within microbiome signatures and support the interpretation of MBTIs, MMAs, and systems-level microbial shifts across clinical conditions.

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