People carrying a high-risk gene variant had gut microbiome changes that resembled an intermediate state between healthy individuals and those with established PD.
Changes in gut bacteria among people carrying a genetic variant linked to Parkinson’s disease may help identify individuals who are in the earliest stages of disease development, according to an international study published in Nature Medicine.
Researchers found that people carrying a GBA1 gene variant – the most common genetic risk factor for PD – had gut microbiome changes that appeared to sit midway between those of healthy individuals and people with established PD.
The findings suggest that biological changes associated with PD may be detectable before the onset of clinical symptoms, although the researchers emphasised that the study cannot determine whether the microbiome changes predict future disease.
PD is a progressive neurodegenerative disorder characterised by motor symptoms such as tremor and rigidity, as well as non-motor symptoms including constipation, sleep disturbances, and cognitive changes. Symptoms typically appear only after substantial loss of dopamine-producing neurons has occurred.
GBA1 variants increase the risk of PD by up to 30-fold, but only about 20% of carriers develop the disease, and the factors determining who progresses remain unclear.
The researchers analysed clinical and faecal microbiome data from participants in the UK and Italy, including 271 people with PD, 43 GBA1 variant carriers without PD, and 150 healthy controls. Microbiome profiles were successfully generated for 464 participants.
They identified 176 microbial species that differed in abundance between those with PD and controls, with changes involving a substantial proportion of the gut microbiome. Of these, 142 showed changes in the same direction in symptom-free GBA1 variant carriers, with their microbiome displaying an intermediate pattern between controls and patients with PD.
The researchers then validated the findings in three independent cohorts from the US, Korea, and Turkey, involving an additional 638 people with PD and 319 controls.
The extent of this microbiome pattern was strongly associated with prodromal symptoms suggestive of future PD development, including non-motor features that can precede diagnosis by several years.
The study identified specific bacterial groups associated with disease status. Species belonging to Lachnospiraceae and Ruminococcaceae, which include butyrate-producing bacteria with potential anti-inflammatory effects, were depleted in PD, while Bifidobacterium species were enriched.
Species showing the greatest increases in PD included Streptococcus mutans, Bifidobacterium longum, Bifidobacterium dentium, and Lactobacillus paragasseri, while Roseburia intestinalis, Roseburia inulinivorans, and Faecalibacterium species were among those most depleted.
Importantly, the researchers found no significant microbiome differences between people with PD who carried GBA1 variants and those who did not, suggesting that the disease itself had a much greater effect on microbiome composition than genetic status once PD was established.
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The key distinction emerged in people who carried the genetic variant but had not yet developed clinical PD.
The authors said the findings support the possibility that gut microbiome changes may reflect early biological processes associated with PD and could potentially contribute to identifying both genetically and non-genetically at-risk individuals.
However, they stressed that the study was cross-sectional, meaning it could not establish whether microbiome changes cause PD, result from early disease processes, or simply occur alongside them.
The researchers said that if validated, microbiome-based biomarkers could potentially help identify people in the premanifest phase of PD, when future disease-modifying therapies may have the greatest opportunity to alter disease progression.
A separate project from Adelaide University’s School of Pharmacy and Biomedical Sciences, Australian Institute for Machine Learning and School of Psychology, is currently exploring the potential of biomarkers to help improve the prediction of PD progression.
They included biomarkers measured in the cerebrospinal fluid or through neuroimaging near time of diagnosis, with the hope of improving the prediction of both motor and non-motor symptoms five years later.
Although they are still actively recruiting volunteers who have been diagnosed with PD for their research, they’ve published findings from their study series in the Journal of Geriatric Psychiatry and Neurology.
“Across both studies, biomarkers had utility for improving prediction of how an individual’s symptoms would present at the five-year follow-up, beyond clinical symptom presentation alone,” said Associate Professor Lyndsey Collins-Praino, who leads the Cognition, Ageing and Neurodegenerative Disease Laboratory and is the senior author of the papers.
“For clusters based on cognitive and mood symptoms, membership in the more impaired cluster was related to lower baseline CSF levels of amyloid-beta and higher baseline CSF levels of phosphorylated tau, which are established biomarkers linked to neurocognitive function in other disorders, such as dementia.
“Conversely, for clusters based largely on motor function, higher CSF levels of phosphorylated tau and lower CSF levels of alpha synuclein at baseline were predictive of membership in the more impaired cluster.”
Professor Collins-Praino told media that the findings indicate that different combinations of biomarkers may have utility for predicting motor versus non-motor symptoms progression.
“Excitingly, using both statistical and machine learning techniques, we showed in both studies that utilising a multi-modal panel of prognostic markers, beyond clinical symptom presentation alone, significantly improved prediction of cluster membership at year five follow-up. This may have utility for informing prognosis of both motor and non-motor outcomes in PD,” she said.
“This might allow for improved prediction of disease trajectory in PD, currently an unmet need, given that disease presentation can differ significantly between individuals.
“Such enhanced understanding has the potential to directly impact clinical management of PD, leading to enhanced monitoring, such as earlier specialist referral and more personalised management strategies.”
