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Background: Unrevealing the interplay between diet, the microbiome, and the health state could enable the design of personalized intervention strategies and improve the health and well-being of individuals. A common approach to this is to divide the study population into smaller cohorts based on dietary preferences in the hope of identifying specific microbial signatures. However, classification of patients based solely on diet is unlikely to reflect the microbiome-host health relationship or the taxonomic microbiome makeup. Results: We present a novel approach, the Nutrition-Ecotype Mixture of Experts (NEMoE) model, for establishing associations between gut microbiota and health state that accounts for diet-specific cohort variability using a regularized mixture of experts model framework with an integrated parameter sharing strategy to ensure data-driven diet-cohort identification consistency across taxonomic levels. The success of our approach was demonstrated through a series of simulation studies, in which NEMoE showed robustness with regard to parameter selection and varying degrees of data heterogeneity. Further application to real-world microbiome data from a Parkinson's disease cohort revealed that NEMoE is capable of not only improving predictive performance for Parkinson's Disease but also for identifying diet-specific microbial signatures of disease. Conclusion: In summary, NEMoE can be used to uncover diet-specific relationships between nutritional-ecotype and patient health and to contextualize precision nutrition for different diseases. 7dNoGdMUXMX1VEgXMiTdDJ Video Abstract [ABSTRACT FROM AUTHOR]

Food supply is one of the major drivers of animal behavior, and the gut microbiome is an important mediator between food supply and its effects on physiology. However, predicting the outcome of diet change on microbiome and consequences for the animal has proven extremely challenging. We propose this reflects processes occurring at different scales. Inadequate accounting for the multi‐level complexity of nutrition (nutrients, foods, diets) obscures the diet influence on microbiome and subsequently animal. Here, we present a detailed year‐round, multi‐level analysis of diet and microbiome changes in a wild population of a temperate primate, the rhesus macaque (Macaca mulatta). Total daily food and nutrient intake of 6 male and 6 female macaques was monitored in each of the 4 seasons (total 120 days observations). For each individual, we found significant variation in the microbiome between all 4 seasons. This response was more strongly correlated with changes in macronutrient intake than with food items and much of the response could be explained at the level of 6 ecological guilds—sets of taxa sharing similar responses to nutrient intake. We conclude that study of diet, microbiome, and animal performance in ecology will more effectively identify patterns if diet is recorded at the level of nutrient intake. Although microbiome response to diet does show variation in species‐level taxa in response to food items, there is greater commonality in response at the level of guilds. A goal for microbiome researchers should be to identify genes encoding microbial attributes that can define such guilds. [ABSTRACT FROM AUTHOR]

Background: Altered gut microbiome (GM) composition has been established in Parkinson's disease (PD). However, few studies have longitudinally investigated the GM in PD, or the impact of device-assisted therapies. Objectives: To investigate the temporal stability of GM profiles from PD patients on standard therapies and those initiating device-assisted therapies (DAT) and define multivariate models of disease and progression. Methods: We evaluated validated clinical questionnaires and stool samples from 74 PD patients and 74 household controls (HCs) at 0, 6, and 12 months. Faster or slower disease progression was defined from levodopa equivalence dose and motor severity measures. 19 PD patients initiating Deep Brain Stimulation or Levodopa-Carbidopa Intestinal Gel were separately evaluated at 0, 6, and 12 months post-therapy initiation. Results: Persistent underrepresentation of short-chain fatty-acid-producing bacteria, Butyricicoccus, Fusicatenibacter, Lachnospiraceae ND3007 group , and Erysipelotrichaceae UCG-003 , were apparent in PD patients relative to controls. A sustained effect of DAT initiation on GM associations with PD was not observed. PD progression analysis indicated that the genus Barnesiella was underrepresented in faster progressing PD patients at t = 0 and t = 12 months. Two-stage predictive modeling, integrating microbiota abundances and nutritional profiles, improved predictive capacity (change in Area Under the Curve from 0.58 to 0.64) when assessed at Amplicon Sequence Variant taxonomic resolution. Conclusion: We present longitudinal GM studies in PD patients, showing persistently altered GM profiles suggestive of a reduced butyrogenic production potential. DATs exerted variable GM influences across the short and longer-term. We found that specific GM profiles combined with dietary factors improved prediction of disease progression in PD patients. [ABSTRACT FROM AUTHOR]

Background: Models to predict Parkinson's disease (PD) incorporating alterations of gut microbiome (GM) composition have been reported with varying success. Objective: To assess the utility of GM compositional changes combined with macronutrient intake to develop a predictive model of PD. Methods: We performed a cross-sectional analysis of the GM and nutritional intake in 103 PD patients and 81 household controls (HCs). GM composition was determined by 16S amplicon sequencing of the V3-V4 region of bacterial ribosomal DNA isolated from stool. To determine multivariate disease-discriminant associations, we developed two models using Random Forest and support-vector machine (SVM) methodologies. Results: Using updated taxonomic reference, we identified significant compositional differences in the GM profiles of PD patients in association with a variety of clinical PD characteristics. Six genera were overrepresented and eight underrepresented in PD patients relative to HCs, with the largest difference being overrepresentation of Lactobacillaceae at family taxonomic level. Correlation analyses highlighted multiple associations between clinical characteristics and select taxa, whilst constipation severity, physical activity and pharmacological therapies associated with changes in beta diversity. The random forest model of PD, incorporating taxonomic data at the genus level and carbohydrate contribution to total energy demonstrated the best predictive capacity [Area under the ROC Curve (AUC) of 0.74]. Conclusion: The notable differences in GM diversity and composition when combined with clinical measures and nutritional data enabled the development of a predictive model to identify PD. These findings support the combination of GM and nutritional data as a potentially useful biomarker of PD to improve diagnosis and guide clinical management. [ABSTRACT FROM AUTHOR]

An important public health question is understanding how changes in human environments can drive changes in the gut microbiota that influence risks associated with human health and wellbeing. It is well-documented that the modernization of societies is strongly correlated with intergenerational change in the frequency of nutrition-related chronic diseases in which microbial dysbiosis is implicated. The population of Bali, Indonesia, is well-positioned to study the interconnection between a changing food environment and microbiome patterns in its early stages, because of a recent history of modernization. Here, we characterize the fecal microbiota and diet history of the young adult women in Bali, Indonesia (n = 41) in order to compare microbial patterns in this generation with those of other populations with different histories of a modern food environment (industrialized supply chain). We found strong support for two distinct fecal microbiota community types in our study cohort at similar frequency: a Prevotella-rich (Type-P) and a Bacteroides-rich (Type-B) community (p < 0.001, analysis of similarity, Wilcoxon test). Although Type-P individuals had lower alpha diversity (p < 0.001, Shannon) and higher incidence of obesity, multivariate analyses with diet data showed that community types significantly influenced associations with BMI. In a multi-country dataset (n = 257), we confirmed that microbial beta diversity across subsistent and industrial populations was significantly associated with Prevotella and Bacteroides abundance (p < 0.001, generalized additive model) and that the prevalence of community types differs between societies. The young adult Balinese microbiota was distinctive in having an equal prevalence of two community types. Collectively, our study showed that the incorporation of community types as an explanatory factor into study design or modeling improved the ability to identify microbiome associations with diet and health metrics. [ABSTRACT FROM AUTHOR]

Background: Microbiome feedbacks are proposed to influence Parkinson's disease (PD) pathophysiology. A number of studies have evaluated the impact of oral medication on the gut microbiome (GM) in PD. However, the influence of PD device-assisted therapies (DATs) on the GM remains to be investigated. Objectives: To profile acute gut microbial community alterations in response to PD DAT initiation. Methods: Clinical data and stool samples were collected from 21 PD patients initiating either deep brain stimulation (DBS) or levodopa–carbidopa intestinal gel (LCIG) and ten spousal healthy control (HC) subjects. 16S amplicon sequencing of stool DNA enabled comparison of temporal GM stability between groups and with clinical measures, including disease alterations relative to therapy initiation. Results: We assessed GM response to therapy in the PD group by comparing pre-therapy (− 2 and 0 weeks) with post-therapy initiation timepoints (+ 2 and + 4 weeks) and HCs at baseline (0 weeks). Altered GM compositions were noted between the PD and HC groups at various taxonomic levels, including specific differences for DBS (overrepresentation of Clostridium_XlVa, Bilophila, Parabacteroides, Pseudoflavonifractor and underrepresentation of Dorea) and LCIG therapy (overrepresentation of Pseudoflavonifractor, Escherichia/Shigella, and underrepresentation of Gemmiger). Beta diversity changes were also found over the 4 week post-treatment initiation period. Conclusions: We report on initial short-term GM changes in response to the initiation of PD DATs. Prior to the introduction of the DAT, a PD-associated GM was observed. Following initiation of DAT, several DAT-specific changes in GM composition were identified, suggesting DATs can influence the GM in PD. [ABSTRACT FROM AUTHOR]

Summary Diet influences health and patterns of disease in populations. How different diets do this and why outcomes of diets vary between individuals are complex and involve interaction with the gut microbiome. A major challenge for predicting health outcomes of the host-microbiome dynamic is reconciling the effects of different aspects of diet (food composition or intake rate) on the system. Here we show that microbial community assembly is fundamentally shaped by a dichotomy in bacterial strategies to access nitrogen in the gut environment. Consequently, the pattern of dietary protein intake constrains the host-microbiome dynamic in ways that are common to a very broad range of diet manipulation strategies. These insights offer a mechanism for the impact of high protein intake on metabolic health and form the basis for a general theory of the impact of different diet strategies on host-microbiome outcomes. [ABSTRACT FROM AUTHOR]