Gut microbiome dysbiosis across early Parkinson’s disease, REM sleep behavior disorder and their first-degree relatives

Abstract

The microbiota-gut-brain axis has been suggested to play an important role in Parkinson’s disease (PD). Here we performed a cross-sectional study to profile gut microbiota across early PD, REM sleep behavior disorder (RBD), first-degree relatives of RBD (RBD-FDR), and healthy controls, which could reflect the gut-brain staging model of PD. We show gut microbiota compositions are significantly altered in early PD and RBD compared with control and RBD-FDR. Depletion of butyrate-producing bacteria and enrichment of pro-inflammatory Collinsella have already emerged in RBD and RBD-FDR after controlling potential confounders including antidepressants, osmotic laxatives, and bowel movement frequency. Random forest modelling identifies 12 microbial markers that are effective to distinguish RBD from control. These findings suggest that PD-like gut dysbiosis occurs at the prodromal stages of PD when RBD develops and starts to emerge in the younger RBD-FDR subjects. The study will have etiological and diagnostic implications.

Altered Impulsivity Across Drug‐Naïve Parkinsonism, Isolated Rapid Eye Movement Sleep Behavior Disorder, and Their High‐Risk Relatives

“…This was a cross-sectional study in which three groups of participants, resembling prodromal and early stages of α-synucleinopathy, including iRBD patients and their FDRs, and early drug-naïve parkinsonism patients, were recruited and compared with control participants (Fig 1 ), 20 in the Li Chiu Kong Family Sleep Assessment Unit at Shatin Hospital, Shatin, Hong Kong, from September 2020 to January 2023. iRBD patients and iRBD-FDRs were recruited from our ongoing RBD and family cohorts. 19,20 All iRBD patients were diagnosed by video polysomnography (vPSG). iRBD-FDRs who did not fulfill the criteria of RBD were included in this study.…”

Untargeted Plasma Metabolomics Unveils Distinct Metabolite Profiles in Parkinson’s Disease Subtypes: A Focus on idiopathic REM Sleep Behavior Disorders

“…Several studies emphasize the intricate link between PD and the gut microbiota (6, 7). Building on the body-first PD subtype, a recent cross-sectional cohort study examined microbial signatures in healthy individuals, first-degree relatives of iRBD, iRBD, and PD with RBD (8). This study indicates that PD-like changes in gut microbial signatures commence in iRBD, supporting body-first PD subtypes (8).…”

“…Building on the body-first PD subtype, a recent cross-sectional cohort study examined microbial signatures in healthy individuals, first-degree relatives of iRBD, iRBD, and PD with RBD (8). This study indicates that PD-like changes in gut microbial signatures commence in iRBD, supporting body-first PD subtypes (8). Given the potential different etiologies between PD with RBD at onset and PD without RBD at onset, further investigation is needed into microbial signatures between two groups of PD.…”

Machine Learning-Assistant Colorimetric Sensor Arrays for Intelligent and Rapid Diagnosis of Urinary Tract Infection

“…36,37 More recently, support vector machine, random forest, and other supervised learning methods have been applied to construct the detection model for microbial analysis and disease diagnosis. 38,39 Thus, the synergistic combination of machine learning algorithms and optical sensing arrays with SANs allows the building of an intelligent nanoplatform for rapid and reliable identification of UTI types.…”

“…With the rapid development of artificial intelligence, the integration of machine learning algorithms has been applied to improve the detection capabilities of these sensor arrays. − The use of unsupervised algorithms such as PCA, T-SNE, and U-MAP allows to reduce the multidimensional features of complex data sets and facilitate the following supervised learning steps. Additionally, they can reduce the training time and improve the learning effectiveness. , More recently, support vector machine, random forest, and other supervised learning methods have been applied to construct the detection model for microbial analysis and disease diagnosis. , Thus, the synergistic combination of machine learning algorithms and optical sensing arrays with SANs allows the building of an intelligent nanoplatform for rapid and reliable identification of UTI types.…”