An analysis of the characteristics of the intestinal flora in patients with Parkinson’s disease complicated with constipation 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 investigated the characteristics of the intestinal microbiota in patients with Parkinson’s disease (PD), specifically comparing those with constipation (C-PD), those without constipation (NC-PD), and healthy controls. Using 16S rRNA sequencing, the study aimed to analyze the diversity and composition of the gut microbiota among these three groups, to better understand the relationship between gut microbial alterations, PD, and the presence of constipation. The researchers also utilized co-occurrence network analyses to explore the ecological interactions among gut microbial genera in each group, and performed correlation analyses to link specific bacterial genera with clinical features of PD, such as disease severity and constipation.
Who was studied?
The study enrolled 44 subjects from the Affiliated BenQ Hospital of Nanjing Medical University between August 2017 and March 2018: 15 idiopathic PD patients with constipation, 14 idiopathic PD patients without constipation, and 15 healthy controls. All PD diagnoses were made according to the International Movement Disorders Association’s 2015 revised criteria. Exclusion criteria included neurological diseases other than PD, severe comorbidities, a history of gastric or bowel surgery, and antibiotic or probiotic use within 3 months prior to enrollment. Healthy controls were also screened to exclude those with similar criteria. Demographic data, clinical severity scores (UPDRSIII, NMSQ, MMSE, PDQ39, Wexner), and medication use were recorded for all participants.
Most important findings
The study found significant differences in the composition of gut microbiota between PD patients (both with and without constipation) and healthy controls, and also between C-PD and NC-PD groups. While alpha diversity (richness and evenness of species) did not differ significantly among the groups, beta diversity (differences in species composition) was significantly altered, as shown by principal coordinate analysis. At the genus level, the abundance of pro-inflammatory genera such as Hungatella and Collinsella was increased in all PD patients compared to controls, whereas Lachnospira and Fusicatenibacter (potentially anti-inflammatory, short-chain fatty acid (SCFA)-producing genera) were reduced. Within PD subgroups, constipated patients exhibited higher levels of Hungatella, Streptococcus, and Anaerotruncus, and lower levels of Megamonas and Holdemanella compared to non-constipated PD patients. Co-occurrence network analyses revealed that gut microbial ecological networks were more complex in PD, especially in constipated PD, indicating altered microbial community interactions. Clinically, the abundance of Streptococcus, Hungatella, Anaerotruncus, Veillonella, Sellimonas, and Faecalitalea positively correlated with higher Wexner constipation scores, while Megamonas and Holdemanella were negatively correlated. Some genera also correlated with non-motor symptom severity (NMSQ) and quality of life (PDQ39).
Key implications
This study provides strong evidence that gut microbiota composition and ecological networks are significantly altered in PD, particularly when constipation is present. The enrichment of pro-inflammatory and depletion of anti-inflammatory or SCFA-producing genera suggest that microbiota-mediated inflammation and impaired gut-brain signaling may contribute to PD pathogenesis and non-motor symptoms such as constipation. The identification of specific genera associated with constipation severity and other clinical features supports the potential utility of microbiome signatures as biomarkers for PD phenotyping. Moreover, these findings highlight the possibility of microbiota-targeted interventions (e.g., probiotics, prebiotics, or fecal transplantation) as adjunctive strategies for managing PD symptoms, particularly gastrointestinal dysfunction. Further research with larger cohorts and inclusion of constipated healthy controls is needed to elucidate causality and the therapeutic potential of gut microbiome modulation in PD.
Citation
Chen W, Bi Z, Zhu Q, Gao H, Fan Y, Zhang C, Liu X, Ye M. An analysis of the characteristics of the intestinal flora in patients with Parkinson’s disease complicated with constipation. Am J Transl Res. 2021;13(12):13710-13722.
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.