Microbiome signature of Parkinson’s disease in healthy and genetically at-risk individuals

Parkinson’s disease (PD) is a major cause of disability. GBA1 variants are the most common genetic risk factor for PD and increase the risk up to 30-fold. Why only approximately 20% of GBA1 variant carriers develop PD remains unknown. Here, by combining clinical and fecal metagenomics data from 271 patients with PD, from 43 carriers of GBA1 variants not manifesting PD symptoms (GBA-NMC) and from 150 healthy controls, and using an innovative microbiome analysis, combining differential abundance of species and coherence of differential abundance variation between the groups as assessed by Cliff’s delta ( δ ), we show that the composition of a large component of the gut microbiome (approximately 25%) in GBA-NMC is intermediate between healthy controls and patients with PD. This component is strongly correlated with disease progression in patients and prodromal symptoms suggestive of future development of PD in both GBA-NMC and healthy individuals. We found microbiome alterations similar to those described here in three independent cohorts from the United States, Korea and Turkey, totaling 638 patients with PD and 319 healthy controls, and we conclude that gut microbiome alterations can identify both genetically and non-genetically at-risk individuals in the general population who may be progressing toward PD, thus serving as an early marker of disease development in the premanifest phase.

Among neurodegenerative disorders, PD is the fastest growing in prevalence, disability and deaths 1 . The economic burden associated with direct medical costs, disability income, costs for paid care and social productivity loss in the United States is estimated to increase from $52 billion in 2017 to $79 billion in 2037 (ref. 2 ). PD is characterized pathologically by loss of dopaminergic neurons of the substantia nigra pars compacta and accumulation of aggregated α-synuclein in the brainstem and several cortical regions 3 . When motor symptoms appear and a clinical diagnosis is possible, the extent of dopaminergic loss is already greater than 50% 4 . Symptomatic treatment is based on dopamine replacement by administration of its precursor levodopa 3 . Slowing, halting or preventing the neurodegenerative process before it translates into clinically evident or disabling symptoms requires early detection of those individuals at risk of, or progressing toward, disease.

The background of PD is likely multifactorial, resulting from both genetic factors, including single gene, risk gene and polygenic contributions, and potentially non-genetic factors 5 . Genetic variants in the GBA1 gene, encoding the lysosomal enzyme glucocerebrosidase, are found in approximately 15% of PD cases 6 , representing the most common genetic risk factor for PD and one of the most appealing targets for new drug development 6 , 7 . However, only a proportion of individuals carrying GBA1 variants will develop PD over their lifetime, with penetrance being estimated at 10% at 60 years up to 19% at 80 years 8 . To date, there are still limited clinical, imaging or biochemical markers that can stratify GBA1 variant carriers for their future risk of developing PD 9 .

Evidence supports a role of the microbiota−gut−brain axis in the pathogenesis of PD. Although motor symptoms remain the core criteria to diagnose PD, non-motor symptoms, such as rapid eye movement (REM) sleep behavior disorder (RBD), hyposmia and autonomic dysfunction, including constipation, can predate the onset of motor symptoms by many years in some patients and, thus, are referred to as prodromal symptoms 5 . Based on presence or absence of prodromal RBD, patients with PD can be classified as body-first or brain-first, with the former predicted to manifest initial signs of neurodegeneration and accumulation of α-synuclein in the autonomic and enteric nervous system with subsequent spread to the central nervous system, whereas the latter is predicted to manifest initial pathology in the brain 10 . Alterations in gut microbiome composition have been detected in individuals with overt PD 11 , 12 , especially in body-first PD 13 . Moreover, gut dysbiosis can precipitate PD in animal models 14 , and exposure to microbial components can induce α-synuclein accumulation in gut enteroendocrine cells 15 .

By comparing gut microbiomes of a cohort of patients with PD ( n = 271), disease-free GBA1 variant carriers ( n = 43) and healthy controls ( n = 150), we found large-scale PD-specific alterations in individuals of the last two groups, which are associated with a PD prodromal clinical profile. We suggest that alterations in gut microbiome composition might help explain the incomplete penetrance of PD in GBA1 variant carriers and identify those at highest risk of conversion to PD. We further suggest that similar changes in healthy individuals without known genetic risk may also help identify those predisposed toward PD development and be used as a marker of early disease development in the premanifest phase.

A total of 540 participants were included in the full analysis set for clinical data. Participants’ characteristics are shown in Table 1 . Participants with PD ( n = 314) included both carriers ( n = 128) and non-carriers ( n = 186) of GBA1 variants (Appendix A ). The individuals without PD included healthy controls non-carriers of GBA1 variants ( n = 175, HC) and non-manifesting GBA1 variant carriers ( n = 51, GBA-NMC). When age and sex of GBA-NMC were compared to the other groups, no differences were detected. Participants with PD were significantly older than HC ( P = 0.006) and more frequently males ( P = 0.028). More than half of the HC participants were partners of people with PD, to mitigate the effect of diet or other lifestyle-associated variables on gut microbiome composition.

To identify possible clinical elements that could stratify GBA-NMC for their risk of developing PD, we compared the severity of motor and non-motor symptoms between HC and GBA-NMC using a wide range of clinical scales and questionnaires (Table 1 ). We found worse motor symptoms in the GBA-NMC group, either subjectively reported (Movement Disorder Society Unified Parkinson’s Disease Rating Scale (MDS-UPDRS) part II, P = 0.0008, q = 0.0076) or objectively assessed (MDS-UPDRS part III, P = 0.0153, q = 0.0627), with the former remaining significant even after adjustment for multiple testing. In terms of non-motor symptoms, no differences in constipation or global autonomic function were detected; however, GBA-NMC showed significantly higher scores in the MDS-UPDRS part I ( P = 0.0036, q = 0.0228) and more severe urinary symptoms ( P = 0.0198) and cognitive impairment ( β = 0.92, P = 0.0197, odds ratio = 2.5, 95% confidence interval: 1.2−5.5), although these did not survive to multiple testing adjustment (both q = 0.0627). No differences in depression, anxiety or olfactory function were observed. Within the GBA-NMC, 10 individuals reached the threshold for estimated probability according to the MDS prodromal criteria calculated based on available information (listed in Methods ) 16 .

Overall, our clinical data suggest that, in our GBA-NMC cohort, there may be a group of individuals who exhibit some prodromal symptoms (for example, subthreshold parkinsonism and some dysautonomia) and, thus, might be in their prodromal phase of PD. Additional biological markers indicating PD proximity are needed to better refine individualized risk of PD.

Microbiome profiles were successfully generated for 464 individuals (150 HC, 43 GBA-NMC and 271 PD). We first compared microbiomes of patients with PD, carriers ( n = 109) or non-carriers ( n = 162) of GBA1 variants, and found 44 Metagenomic Species Pan-genomes (MSPs) out of 627 with prevalence of at least 10% in our cohort that were different in abundance at P q q n = 150) revealed numerous MSPs significant at q β diversity analysis of Bray−Curtis distances by PERMANOVA confirmed that there was no significant microbiome difference between carrier and non-carrier groups (adonis P = 0.46), whereas the difference between HC and either group was very significant ( P = 0.001). We conclude that, in overt PD, GBA1 genetic status impacts the microbiome composition much less than the disease itself.

We, therefore, pooled all patients with PD regardless of their genetic status ( n = 271) to identify the PD gut microbiome signature at maximal statistical power and compared them with HC ( n = 150). A total of 176 species were differentially abundant at P 1 and Extended Data Fig. 1a ; 103 at q Bifidobacteriaceae at family level (Supplementary Table 2 ), as previously reported 12 , 17 , 18 . By contrast, species enriched in HC belonged to Lachnospiraceae C and Ruminococcaceae . These families include butyrate producers such as Roseburia and Dysosmobacter , which may be antiinflammatory. Bifidobacterium and Faecalibacterium genera were enriched in PD and HC, respectively. Enrichment of Bifidobacterium and depletion of butyrate producers such as Faecalibacterium in patients with PD were previously reported 19 , 20 . Species that showed the greatest increase in PD included Streptococcus mutans , Bifidobacterium longum , Bifidobacterium dentium and Lactobacillus paragasseri , whereas species that showed the greatest depletion in PD included Roseburia intestinalis , Roseburia inulinivorans and an unclassified Faecalibacterium . Further analyses were based on the 176 species differentially abundant at P < 0.05.

We then investigated whether some of the PD-related gut microbiome alterations were detectable in the GBA-NMC group ( n = 43) relative to HC ( n = 150) by comparing species abundance in the two groups. A total of 43 species were significantly different at P 3 and Extended Data Fig. 1b ), fewer than in the comparison of HC with PD, possibly, at least in part, because of the loss of statistical power due to the low number of GBA-NMC. Of these, 21 were common with the 176 species significantly altered in patients with PD compared to HC, and all but one were enriched or depleted in GBA-NMC individuals coherently with enrichment or depletion in patients with PD, using the direction of Cliff’s δ as a guide (positive or negative, respectively), which is unlikely to happen by chance ( χ 2 test, P = 5.7 × 10 −3 ). The effect sizes observed for the common MSPs in the comparisons between GBA-NMC versus HC and PD versus HC were highly correlated (red dots, Fig. 1a ).

a , Correlations of Cliff’s δ (CD) of 142 coherent species found by comparing HC individuals with GBA-NMC or patients with PD. Negative CD values are species found enriched in HC, and positive CD values are species found enriched in both GBA-NMC and PD. Red dots highlight the 20 significant and coherently altered species (two-sided Wilcoxon P ρ denotes Spearmanʼs correlation coefficient when considering the 142 or 20 species. b , Average CD of coherently enriched ( n = 81) or depleted ( n = 61) species in GBA-NMC (yellow) and PD (mauve) relative to HC. c , Correlation of CD values of 34 species altered significantly (two-sided Wilcoxon P ρ denotes Spearmanʼs correlation coefficient. d , e , Abundance and proportion of coherent altered species in HC, GBA-NMC and PD and corresponding P values (determined by one-sided Studentʼs t -test for different comparisons); orange and blue refer to coherent enriched and depleted species, respectively. f , g , Abundance and proportion of non-coherent altered species in the three study groups and corresponding P values (determined by one-sided Studentʼs t -test); orange and blue refer to non-coherent enriched and depleted species, respectively. In the box plots, central line denotes the median, boxes the central quartiles, whiskers the extreme quartiles and dots the outliers.

Notably, using the direction of Cliff’s δ , examination of the coherence of variation of the 176 MSPs significantly different between HC and PD individuals revealed that 142 were coherently altered in GBA-NMC and PD relative to HC (Supplementary Table 4 ), which is very unlikely to happen by chance ( χ 2 test, P = 3.9 × 10 −16 ). Of these, 81 MSPs were enriched and 61 were depleted in PD; we term these coherent enriched and coherent depleted species, respectively. Effect sizes of these species in GBA-NMC and PD relative to HC were highly correlated (Fig. 1a ) and were, on average, significantly lower in GBA-NMC than in PD (Fig. 1b ), suggesting that the GBA-NMC microbiome may be in an intermediate state between that of HC and PD. The remaining 34 species were found not to vary coherently in GBA-NMC and PD relative to HC and are hereafter termed non-coherent species; 18 were non-coherent enriched and 16 were non-coherent depleted in PD (Supplementary Table 4 ). Their Cliff’s δ values in GBA-NMC and HC relative to PD were highly correlated (Fig. 1c ). Among the coherent species, we observed enrichment of oral residents ( S. mutans and L. paragasseri ) and proinflammatory Ruminococcus gnavus and the depletion of butyrate producers ( Roseburia or Faecalibacterium prausnitzii ). By contrast, non-coherent enriched species included Bifidobacteria , one of the characteristic alterations of the PD gut microbiome 17 , suggesting that their enrichment may take place at the clinical onset of the disease or develop during its course.

The species coherently altered in GBA-NMC and PD relative to HC represented a similar fraction (slightly over 25%) of the microbiome in all study groups, but the abundance and proportion of PD-enriched species significantly increased from HC over GBA-NMC to PD, whereas the abundance and proportion of PD-depleted species significantly decreased (Fig. 1d,e ). The non-coherent species represented approximately 7–9% of microbiome abundance and 7% of species proportion. This part of the microbiome varied little between HC and GBA-NMC but very significantly increased and decreased for enriched and depleted species, respectively, in PD relative to HC or GBA-NMC (Fig. 1f,g ), supporting the view that it evolves mostly once PD is manifest.

In sum, over a quarter of the gut microbiome significantly changes in PD relative to HC, of which we distinguished two components. A major one evolves consistently from HC across GBA-NMC to PD, the extent of changes being lower in GBA-NMC than in PD. A minor component changes abruptly in overt PD. Because similar coherent changes in gut microbiome composition are observed in at-risk individuals, such as GBA-NMC, and in patients with PD, we suggest that the coherently altered species of the PD gut microbiome may represent a prodromal feature of PD (the ‘prodromal-PD microbiome’), possibly contributing to PD development.

We estimated microbiome alterations by eight different tests, which capture related but different aspects of microbiome composition. The first four tests were based on abundance of different species types (coherent enriched or depleted; non-coherent enriched or depleted); the other four were based on proportion of the same species types. Abundance and proportion were computed as described in the Methods . Intuitively, an individual could have a higher proportion of coherent enriched species but a lower overall abundance of these species than another; the microbiome of the former would appear more altered than that of the latter by the test of proportion but less altered by the test of abundance. We ordered patients with PD by the fraction of the microbiome estimated by each of the eight tests and found that different quartiles vary greatly in all cases—Q1 and Q4 differed 10−20-fold by abundance and 2−3-fold by proportion (Extended Data Fig. 2a–d ). This analysis shows considerable heterogeneity of microbiome composition among patients with PD.

We hypothesized that higher gut microbiome alterations were associated with worse clinical features in patients with PD. To test this hypothesis, we compared clinical variables of individuals from quartiles with highest ( n = 68) and lowest ( n = 68) microbiome alterations, estimated by each of the eight tests. Extended Data Table 1 shows the results from the comparison using the abundance of coherent depleted species as test of microbiome alterations; all comparisons are displayed in Supplementary Table 5 . Some variables were significantly different in less than 25% of comparisons (≤2/8 comparisons; for example, age or body mass index (BMI)); we considered them to be either weakly correlated or even not correlated with microbiome alterations. By contrast, certain variables were significantly different in more than 75% of comparisons (≥6/8 comparisons), and we suggest that they are highly correlated with microbiome alterations (Fig. 2 and in bold in Extended Data Table 1 ). They fell in two classes.

Comparison of individuals with least (L) and more (M) altered microbiome across the study groups: for PD individuals, top and bottom quartiles of the distribution according to the abundance of coherent depleted species ( n = 68 each); for GBA-NMC individuals, those below and above the median according to the proportion of coherent enriched species ( n = 21 and n = 22, respectively); for HC individuals, top and bottom quartiles of the distribution according to the proportion of coherent enriched species (HC_L and HC_M, n = 38 each). HC individuals having PDMS-16 ≤ −5 (HC16_L, n = 21) and PDMS16 ≥ 3 (HC16_M, n = 27) are also shown. a , Disease-associated variables of patients with PD (disease duration and medication dosage). b , Clinical variables for all groups (automatic dysfunction, constipation, depression and non-motor symptoms). c , Health-related variables for all groups (DQS; appendectomy colored in gray if individuals did not undergo appendectomy and colored in black if individuals underwent appendectomy). The values of clinical variables are the means listed in Extended Data Table 1 . Standard deviations and statistical significance are indicated by the thin bars, and stars indicate the significance level of the one-sided Studentʼs t -test for each comparison, except for appendectomy where comparisons are tested using two-sided χ 2 test. Significance of P values: NS (not significant): P ≥ 0.05; * P P P P P values are listed in Extended Data Table 1 . BDI, Beck Depression Inventory; SCOPA-AUT, Scales for Outcomes in Parkinsonʼs Disease-Autonomic Dysfunction.