Introduction
Parkinson’s disease (PD) is a common progressive multisystem neurogenerative disorder (Rietdijk et al., 2017) that is associated with significant morbidity and healthcare burden, resulting in the deterioration of quality of life (QoL) (Lubomski et al., 2015, 2021b). Gastrointestinal dysfunction is a well-recognized prodromal non-motor symptom (NMS) in PD (Postuma et al., 2012; Lubomski et al., 2020b), with constipation and prolonged intestinal transit times evident many years prior to the development of classical motor symptoms (Savica et al., 2009; Pfeiffer, 2011). With GI dysfunction strongly linked to gut health and the microbiome, causal links have been established between the gut and the brain in PD, with the enteric nervous system (ENS) implicated in early pathogenesis prior to the central nervous system (Hawkes et al., 2007). Consequently, two subtypes of PD pathogenesis have been proposed, brain-first or body-first (Horsager et al., 2020).
There are numerous reports of variations in the gut microbial composition in PD patients, as well as associations to a variety of clinical disease outcome measures (Lubomski et al., 2020b; Romano et al., 2021). Interest in relationships between PD and the gut microbiome (GM) have increased since initial studies were published in 2015 (Hasegawa et al., 2015; Keshavarzian et al., 2015; Scheperjans et al., 2015). Meta-analyses (Nishiwaki et al., 2020; Romano et al., 2021; Shen et al., 2021) of these studies also suggest an enrichment and depletion of various taxa in association with PD. One mechanism thought to contribute to body-first PD pathogenesis is underrepresentation of short-chain fatty-acid (SCFA) producing bacteria considered important in maintaining gut function, integrity and health. Evidence suggests that gut dysbiosis in PD may drive gut and systemic inflammation, leading to impairment of host immune functions that underlie the prevalent gastrointestinal symptoms observed in patients with PD (Bullich et al., 2019; Romano et al., 2021).
More directly, there is evidence of microbe impacts in the gut that are mechanistically relevant to synucleinopathy (Sampson et al., 2016), potentially contributing to α-synuclein (α-syn) aggregation in the ENS that results in the accelerated caudo-rostral neurodegenerative spread observed in PD (Bullich et al., 2019; Lubomski et al., 2020). The GM has also been implicated in the variability of therapeutic outcomes, most notably the influence of Enterococcus faecalis on the metabolism of Levodopa (Maini Rekdal et al., 2019; van Kessel et al., 2019), which could serve as a modifiable target for improving Levodopa efficacy (Lubomski et al., 2019).
The aim of this study was to investigate associations between the GM and clinical parameters to identify relationships that could indicate the presence of PD. Associations between disease characteristics, therapeutic regimes, diet and the GM were explored and used to develop predictive models of PD that may eventually guide diagnosis and clinical management at earlier stages of PD.
Discussion
In this cross-sectional PD GM study, we enrolled a broad clinical spectrum of PD patients, representative of all stages of established PD and uniquely analyzed the influences of DATs, namely DBS and Apomorphine infusions, which have not been reported. When considering the entire GM in our analysis, we identified fourteen genera, two family and one order level variations in GM composition of PD patients when compared to HCs (Table 3 and Figure 3). Through our holistic consideration of the entire GM and extensive clinical parameters, we identified an array of GM associations with various therapeutic, motor and non-motor features. We developed predictive models to identify PD using the GM as a non-invasive fecal biomarker. These study findings provide further experimental evidence in support of GM involvement in body-first PD pathogenesis and disease progression. Iterative development of the foundational models from this study may eventually provide diagnostic indications at earlier stages of PD.
An increasing number of studies, mainly from Western Europe, China, Japan and the United States, have highlighted important patterns in the GM profiles of PD patients (Gorecki et al., 2019; Elfil et al., 2020; Lubomski et al., 2020), with more than 110 differentially abundant taxa described from the level of phylum to ASV or species (Boertien et al., 2019; Bullich et al., 2019; Lubomski et al., 2020; Wallen et al., 2020; Kenna et al., 2021; Romano et al., 2021). Although notable differences exist between the studies (study design, inclusion criteria, ages, gender proportions, disease durations, methodology, etc.), emerging consistencies for PD-specific GM changes suggest enrichment of the genera Lactobacillus, Akkermansia, and Bifidobacterium and depletion of bacteria belonging to the Lachnospiraceae family and the Faecalibacterium genus, as the most consistent PD GM alterations (Romano et al., 2021). One of the pitfalls for previous studies has been to consider only those differentially abundant taxa identified when creating disease discriminant models. We instead performed analyses by considering the entire GM and a diverse range of clinical measures to develop our disease-discriminant models.
The general species diversity across the cohort was not dissimilar, as indicated by a lack of statistical significance in alpha diversity. When comparing species richness between PD and HC groups, the statistical significance in beta diversity was supportive of a PD-specific GM profile. Furthermore, beta diversity differences were identified in association with constipation (Rome-IV criteria and CSS), cognitive profiles (MoCA Total Score), physical activity (IPAQ score), chronic pain, utilization of levodopa and COMT inhibitor medications, as well as an apparent trend for the young onset PD phenotype. This supports the multifactorial nature of the GM and disease interplay and indicates that a more global consideration of GM and clinical parameters is necessary. Of note, the longer mean disease duration of 9.2 years in this study, compared to many earlier PD GM studies, may explain why beta diversity changes were associated with so many clinical variables in our PD cohort, as divergence in beta diversity increases with disease progression (Barichella et al., 2018). Other studies have identified beta diversity differences associated with male sex, RBD, smoking and body-mass-index (Heinzel et al., 2020), although these were not apparent in the current study.
Consistent with beta diversity differences, a number of statistically significant relative abundance differences between the PD and HC groups were apparent, defining a PD-specific GM profile. At the family level, increased abundances of Lactobacillaceae (Scheperjans et al., 2015; Hill-Burns et al., 2017; Hopfner et al., 2017; Aho et al., 2019; Barichella et al., 2019; Pietrucci et al., 2019; Nishiwaki et al., 2020; Tan et al., 2021) and Enterobacteriaceae (Unger et al., 2016; Lin et al., 2018; Barichella et al., 2019; Pietrucci et al., 2019) identified here (Table 3), are consistent with reports from several earlier studies. At the genera level, overrepresentation of Bifidobacterium (Unger et al., 2016; Hill-Burns et al., 2017; Petrov et al., 2017; Aho et al., 2019; Barichella et al., 2019; Wallen et al., 2020) and Butyricimonas (Heintz-Buschart et al., 2018; Jin et al., 2019; Ren et al., 2020) and underrepresentation of Lachnospiraceae ND3007 group (Nishiwaki et al., 2020; Romano et al., 2021) identified here, have been repeatedly associated with PD in prior studies. The underrepresentation of Butyricicoccus and Fusicatenibacter in our cohort have been variably reported (Weis et al., 2019; Wallen et al., 2020), and in one study of Chinese PD patients, Butyricicoccus was reported to be overrepresented (Qian et al., 2018). This highlights the evident inconsistency across GM studies in PD and emphasizes the difficulties in differentiating between genuine inherent biological variability in small sample sizes and inconsistent study methodology. To our knowledge, the differentially represented genera Candidatus Soleaferre, Flavonifractor, Faecalibacterium sp. UBA1819, Erysipelotrichaceae UCG-003, [Eubacterium] xylanophilum group, [Ruminococcus] gauvreauii group, and Firmacutes bacterium CAG:56 identified here, have not previously been described in association with a PD-specific GM profile and warrant consideration in subsequent studies. While we acknowledge the significant variation between studies, a recent consideration is the evolving taxonomic assignment nomenclature used to assign taxa from ASVs, which makes comparison with older studies more difficult but will improve the resolution of GM taxonomy and analysis in future studies.
A considerable body of evidence suggests underrepresentation of typically abundant SCFA-producing bacteria in PD can lead to SCFA imbalances that may have detrimental impacts on disease progression, including increased colonic inflammation, gut leakiness, risk of α-syn deposition in the gastrointestinal tract, and microglial activation in the brain (Bullich et al., 2019; Romano et al., 2021). In this study, we found overrepresentation of Bifidobacterium and Butyricimonas in our PD cohort. Bifidobacterium produces acetate and formate by metabolizing carbohydrates in plants and dairy (Wallen et al., 2020). They are a common component of probiotic supplements and have been shown to induce remission when supplemented in patients with Inflammatory Bowel Disease (Parada Venegas et al., 2019). On the other hand, Butyricimonas produces butyrate, which has been shown to reduce inflammation and maintain gut health (Parada Venegas et al., 2019). The increased relative abundance of these bacteria may be a protective attempt in response to systemic PD pathogenesis or the accompanying underrepresentation of other SCFA-producing bacteria. The underrepresented genera Butyricicoccus, Fusicatenibacter and Lachnospiracaea ND3007 group identified in this study all produce butyrate (Geirnaert et al., 2014), with Lachnospiraceae ND3007 group also producing acetate and alcohols (Boutard et al., 2014) that are believed to collectively exert anti-inflammatory effects, helping to maintain integrity of the gut membrane (Keshavarzian et al., 2020; Nishiwaki et al., 2020; Wallen et al., 2020). While the impact of variability in SCFA-producing bacteria in PD is still not entirely clear (Mulak, 2018; Dalile et al., 2019), there is strong suggestion of a role in pathogenesis and potential as therapy (Metzdorf and Tonges, 2021).
We identified many clinically important associations between the GM and clinical measures. With respect to DATs, after controlling for age, gender and BMI, two moderate correlations were identified between Enterococcus and ASV_155/Klebsiella spp and LCIG use. Associations with overrepresentation of Enterococcus signifies important implications for patients using large doses of levodopa and may result in positive selection of bacterium with tyrosine decarboxylase activity that can convert levodopa to dopamine in the gut (Lubomski et al., 2019; Maini Rekdal et al., 2019; van Kessel et al., 2019). This has implications for the treatment efficacy of levodopa administration in PD and could be exploited to improve dose efficiency. LCIG use was also associated with overrepresentation of Enterobacteriaceae and Klebsiella spp., which are genera of the family Enterobacteriaceae that is also independently associated with increased daily LED, Hoehn, and Yahr scores and motor fluctuations, consistent with motor disease severity, as previously identified (Li et al., 2017). The influence of DBS has been sparsely reported in regards to the GM (Lubomski et al., 2021c), but was associated here with overrepresentation of Streptococcaceae and Streptococcus spp. and underrepresentation of Rikenellaceae, potentially suggestive of peripheral effects of DBS on the GM that warrant further investigation. Uniquely, we identified positive associations between continuous subcutaneous Apomorphine infusion (a DAT that has not previously been studied in terms of the GM in PD) and the genera Intestinibacter, Parasutterella, and Actinomyces, again justifying further investigation.
In terms of standard therapies, oral Levodopa use was associated with the families Sutterellaceae and Rikenellaceae in our cohort, rather than Bacillaceae, as was reported in another study (Heintz-Buschart et al., 2018). Anticholinergic use was associated with ASV_166/Bifidobacterium spp., a beneficial bacterium known to digest dietary fiber and maintain healthy gut function (Castelli et al., 2021), although its association with this therapy has not been previously reported. Furthermore, COMT inhibitor use was associated with overrepresentation of Bifidobacteriaceae, Enterococcaceae, and Lactobacillaceae in our cohort. This is contrary to a previous report of a negative association with Lachnospiraceae abundance (Hill-Burns et al., 2017), although the relationships between the use of COMT inhibitors and gut microbiota have yielded many discordant findings in several other studies (Unger et al., 2016; Hill-Burns et al., 2017; Barichella et al., 2019). Furthermore, overrepresentation of Lactobacillus and Lactobacillaceae have often been seen as indicators of increased PD severity and duration, associating with PD duration, UPDRS-III total score, Hoehn and Yahr score, daily LED, adjuvant therapies for advanced disease (like COMT inhibitors and apomorphine), as well as decreased QoL, physical activity and increased depression severity (Lubomski et al., 2020b; Nishiwaki et al., 2020; Romano et al., 2021). Barichella et al. (2019) also identified a significant relationship between increased Lactobacillaceae and UPDRS-III total score. Lactobacillaceae overrepresentation in more advanced disease may be a relative finding in light of the underrepresentation of other genera, particularly from the family Lachnospiraceae, known for their beneficial SCFA production (Romano et al., 2021).
In terms of NMS in our PD cohort, better physical health, indicated by a higher physical component score from the SF-36 assessment, showed positive associations with Fusicatenibacter, Butyricicoccus, and ASV_32/Blautia spp. Whilst the clinical significance of these taxa associations are still unclear, important implications arising from favorable SCFA metabolism could be implicated (Aho et al., 2021), and warrants further study. Depression severity was positively associated with Veillonella, Klebsiella, and Pseudoflavonifractor, taxa that have not previously been described in association with mood changes in PD. Chronic pain was negatively associated with Enterobacteriaceae, whilst positively with Bacteroidaceae and Synergistaceae abundances, although these NMS have not been widely studied in prior PD GM studies. GI dysfunctions, practically constipation severity inferred from the BSS score and ROME-IV score, were most markedly associated with Butyricicoccus and ASV_151/Bacteroides spp., whilst other taxa including ASV_350/Clostridium_XIVa spp., Faecalibacterium, Coprococcus, and Roseburia spp. were also implicated. These bacteria have also previously been associated with chronic constipation in the general population (Zhao and Yu, 2016), although are different from some earlier PD studies reporting associations with Bradyrhizobiaceae, Verrucomicrobiaceae (Scheperjans et al., 2015), and Bifidobacteria (Baldini et al., 2020) in constipated PD patients. Distinctively, we also show moderate associations between upper GI dysfunction with overrepresentation of Desulfomicrobiaceae and Desulfomicrobium, associations not previously reported with regard to PD GM profiles.
Environmental influences of geography and dietary habits are known to be highly influential on GM composition. We made attempts to mitigate these by using HCs who resided with their respective PD patient. Analysis of the dietary FFQ indicated that significant macro and micronutrient differences did not exist between the two groups, aside from increased free sugar intake in the PD cohort, which is likely a function of dopamine dysregulation (Palavra et al., 2021). Few prior cross-sectional PD GM studies have evaluated the influences of diet in their analyses but is of potential importance given the beta diversity changes associated with clinical parameters and nutrition identified here. Therefore, we undertook predictive multivariate modeling in an effort to better define the GM as a biomarker for PD. Our results using just the GM (AUC 0.71) were comparable to earlier reports, with AUCs of between 0.64 and 0.84 reported (Scheperjans et al., 2015; Bedarf et al., 2017; Hopfner et al., 2017; Qian et al., 2018). A major point of difference was that in this study we were able to assess the entirety of the GM data at various taxonomic levels, rather than using individual taxa showing statistically significant relative abundance changes. Uniquely, our modeling was expanded and optimized as a two-stage model that incorporated additional clinical and nutritional data, in a manner that most accurately predicts PD. We were able to show that the incorporation of carbohydrate contribution to total energy was an important consideration in the ability to accurately predict PD rather than utilizing GM data alone, resulting in an AUC of 0.74. Future predictive metanalyses of the GM in PD should consider incorporating clinical variables, such as macronutrient data, to optimize PD predictability from GM compositions.
Several limitations to this study should be considered, as it does not address certain potential confounding factors, including other comorbidities and other non-PD medication effects. Medication use for GI dysfunction (e.g., laxatives, anti-diarrhea medication, and reflux medication), as well as GI tract medical diagnosis (e.g., inflammatory bowel disease, inflammatory bowel syndrome and coeliac disease), are important modulators in the GI measures, but were not available and were not considered in the analysis, as reported earlier (Lubomski et al., 2020b). Whether these covariates alter the PD-specific GM profiles is yet to be analyzed. PD and HC groups non-optimally matched for age and sex due to spousal recruitment, may have resulted in confounding in the comparative GM analysis, as age and sex are known to influence GM composition, and are classical matching criteria between case and control groups (Baldini et al., 2020). The utilization of cohabitants or spousal HC is generally more suitable to adjust for geographic and environmental confounders, although differences in age, sex distributions and methodological inconsistencies, may potentially account for some heterogeneity in outcomes and observed GM profiles. This highlights the importance for subsequent studies to have much larger sample sizes, be inclusive of highly diverse cohorts, include comprehensive clinical and nutrition data and have consensus study design to allow for large meta-analyses that may identify more absolute microbial signatures, as have recently been reported (Nishiwaki et al., 2020; Romano et al., 2021). The benefit of larger meta-analysis, particularly greater statistical power, may further allow investigation of smaller subgroups of patients, such as those with younger onset or genetically driven PD, those receiving DATs, as well as thoroughly expose the influences of geography and environmental exposures. Larger multicenter analyses may also reveal why Lactobacillaceae and Bifidobacterium have been persistently elevated in PD across so many studies, determining if their overexpression is a beneficial compensatory mechanism to overcome dysbiosis or an unfavorable response to increasing disease progression, motor severity and daily LED. The findings presented in this study should be interpreted with consideration for these and other limitations, including the self-reporting nature of the data and potential selection bias of the study population being drawn from specialist PD clinics. Due to the cross-sectional nature of this study, causal inferences were not possible. Furthermore, our sample only reflected the experience of patients from a single metropolitan area, whereas previous studies from Australia have shown PD patients from regional areas to be comparably older with an older age of diagnosis (Lubomski et al., 2013, 2014).