MIND and Mediterranean Diets Associated with Later Onset of Parkinson's Disease.

BACKGROUND: The MIND diet has been linked with prevention of Alzheimer's disease and cognitive decline but has not been fully assessed in the context of Parkinson's disease (PD). The objective of the present study was to determine whether MIND diet adherence is associated with the age of Parkinson's disease onset in a manner superior to that of the Mediterranean diet. METHODS: Food Frequency Questionnaires from 167 participants with PD and 119 controls were scored for MIND and 2 versions of Mediterranean diet adherence. Scores were compared between sex and disease subgroups, and PD diet adherence was correlated with age at onset using univariate and multivariate linear models. RESULTS: The female subgroup adhered more closely to the MIND diet than the male subgroup, and diet scores were not modified by disease status. Later age of onset correlated most strongly with MIND diet adherence in the female subgroup, corresponding to differences of up to 17.4 years (P < 0.001) between low and high dietary tertiles. Greek Mediterranean adherence was also significantly associated with later PD onset across all models (P = 0.05-0.03). Conversely, only Greek Mediterranean diet adherence remained correlated with later onset across all models in men, with differences of up to 8.4 years (P = 0.002). CONCLUSIONS: This cross-sectional study found a strong correlation between age of onset of PD and dietary habits, suggesting that nutritional strategies may be an effective tool to delay PD onset. Further studies may help to elucidate potential nutrition-related sex-specific pathophysiological mechanisms and differential prevalence rates in PD. © 2021 The Authors. Movement Disorders published by Wiley Periodicals LLC on behalf of International Parkinson and Movement Disorder Society.

Microbiota Composition and Metabolism Are Associated With Gut Function in Parkinson's Disease.

BACKGROUND: Parkinson's disease is characterized by a high burden of gastrointestinal comorbidities, especially constipation and reduced colonic transit time, and by gut microbiota alterations. The diverse metabolites produced by the microbiota are broadly relevant to host health. How microbiota composition and metabolism relate to gastrointestinal function in Parkinson's disease is largely unknown. The objectives of the current study were to assesses associations between microbiota composition, stool consistency, constipation, and systemic microbial metabolites in Parkinson's disease to better understand how intestinal microbes contribute to gastrointestinal disturbances commonly observed in patients. METHODS: Three hundred participants (197 Parkinson's patients and 103 controls) were recruited for this cross-sectional cohort study. Participants supplied fecal samples for microbiota sequencing (n = 300) and serum for untargeted metabolomics (n = 125). Data were collected on motor and nonmotor Parkinson's symptoms, medications, diet, and demographics. RESULTS: Significant microbiota taxonomic differences were observed in Parkinson's patients, even when controlling for gastrointestinal function. Parkinson's microbiota was characterized by reduced carbohydrate fermentation and butyrate synthesis capacity and increased proteolytic fermentation and production of deleterious amino acid metabolites, including p-cresol and phenylacetylglutamine. Taxonomic shifts and elevated proteolytic metabolites were strongly associated with stool consistency (a proxy for colonic transit time) and constipation among patients. CONCLUSIONS: Compositional and metabolic alterations in the Parkinson's microbiota are highly associated with gut function, suggesting plausible mechanistic links between altered bacterial metabolism and reduced gut health in this disease. The systemic detection of elevated deleterious proteolytic microbial metabolites in Parkinson's serum suggests a mechanism whereby microbiota dysbiosis contributes to disease etiology and pathophysiology. © 2020 International Parkinson and Movement Disorder Society.

Data-Driven Prediction of Fatigue in Parkinson's Disease Patients.

Introduction: Numerous non-motor symptoms are associated with Parkinson's disease (PD) including fatigue. The challenge in the clinic is to detect relevant non-motor symptoms while keeping patient-burden of questionnaires low and to take potential subgroups such as sex differences into account. The Fatigue Severity Scale (FSS) effectively detects clinically significant fatigue in PD patients. Machine learning techniques can determine which FSS items best predict clinically significant fatigue yet the choice of technique is crucial as it determines the stability of results. Methods: 182 records of PD patients were analyzed with two machine learning algorithms: random forest (RF) and Boruta. RF and Boruta calculated feature importance scores, which measured how much impact an FSS item had in predicting clinically significant fatigue. Items with the highest feature importance scores were the best predictors. Principal components analysis (PCA) grouped highly related FSS items together. Results: RF, Boruta and PCA demonstrated that items 8 ("Fatigue is among my three most disabling symptoms") and 9 ("Fatigue interferes with my work, family or social life") were the most important predictors. Item 5 ("Fatigue causes frequent problems for me") was an important predictor for females, and item 6 ("My fatigue prevents sustained physical functioning") was important for males. Feature importance scores' standard deviations were large for RF (14-66%) but small for Boruta (0-5%). Conclusion: The clinically most informative questions may be how disabling fatigue is compared to other symptoms and interference with work, family and friends. There may be some sex-related differences with frequency of fatigue-related complaints in females and endurance-related complaints in males yielding significant information. Boruta but not RF yielded stable results and might be a better tool to determine the most relevant components of abbreviated questionnaires. Further research in this area would be beneficial in order to replicate these findings with other machine learning algorithms, and using a more representative sample of PD patients.

The Gut Mycobiome in Parkinson's Disease.

The gut microbiome has been increasingly implicated in Parkinson's disease (PD); however, most existing studies employ bacterial-specific sequencing, and have not investigated non-bacterial microbiome constituents. Here, we use fungal-specific internal transcribed spacer (ITS)-2 amplicon sequencing in a cross-sectional PD cohort to investigate associations between the fungal gut microbiome and PD. Fungal load among participants was extremely low, and genera identified were almost exclusively of proposed dietary or environmental origin. We observed significantly lower fungal DNA relative to bacterial DNA among PD patients. No fungi differed in abundance between patients and controls, nor were any associated with motor, cognitive, or gastrointestinal features among patients.