Nutritional Intake and Gut Microbiome Composition Predict Parkinson's Disease

The Future of Obesity Management through Precision Nutrition: Putting the Individual at the Center

PURPOSE OF REVIEW: The prevalence of obesity continues to rise steadily. While obesity management typically relies on dietary and lifestyle modifications, individual responses to these interventions vary widely. Clinical guidelines for overweight and obesity stress the importance of personalized approaches to care. This review aims to underscore the role of precision nutrition in delivering tailored interventions for obesity management. RECENT FINDINGS: Recent technological strides have expanded our ability to detect obesity-related genetic polymorphisms, with machine learning algorithms proving pivotal in analyzing intricate genomic data. Machine learning algorithms can also predict postprandial glucose, triglyceride, and insulin levels, facilitating customized dietary interventions and ultimately leading to successful weight loss. Additionally, given that adherence to dietary recommendations is one of the key predictors of weight loss success, employing more objective methods for dietary assessment and monitoring can enhance sustained long-term compliance. Biomarkers of food intake hold promise for a more objective dietary assessment. Acknowledging the multifaceted nature of obesity, precision nutrition stands poised to transform obesity management by tailoring dietary interventions to individuals' genetic backgrounds, gut microbiota, metabolic profiles, and behavioral patterns. However, there is insufficient evidence demonstrating the superiority of precision nutrition over traditional dietary recommendations. The integration of precision nutrition into routine clinical practice requires further validation through randomized controlled trials and the accumulation of a larger body of evidence to strengthen its foundation.

Role of Artificial Intelligence in Multinomial Decisions and Preventative Nutrition in Alzheimer's Disease

Alzheimer's disease (AD) affects 50 million people worldwide, an increase of 35 million since 2015, and it is known for memory loss and cognitive decline. Considering the morbidity associated with AD, it is important to explore lifestyle elements influencing the chances of developing AD, with special emphasis on nutritional aspects. This review will first discuss how dietary factors have an impact in AD development and the possible role of Artificial Intelligence (AI) and Machine Learning (ML) in preventative care of AD patients through nutrition. The Mediterranean-DASH diets provide individuals with many nutrient benefits which assists the prevention of neurodegeneration by having neuroprotective roles. Lack of micronutrients, protein-energy, and polyunsaturated fatty acids increase the chance of cognitive decline, loss of memory, and synaptic dysfunction among others. ML software has the ability to design models of algorithms from data introduced to present practical solutions that are accessible and easy to use. It can give predictions for a precise medicine approach to evaluate individuals as a whole. There is no doubt the future of nutritional science lies on customizing diets for individuals to reduce dementia risk factors, maintain overall health and brain function.

Association Analysis of Gut Microbiota and Prognosis of Patients with Acute Ischemic Stroke in Basal Ganglia Region

Previous studies have implied the potential impact of gut microbiota on acute ischemic stroke (AIS), but the relationships of gut microbiota with basal ganglia region infarction (BGRI) and the predictive power of gut microbiota in BGRI prognosis is unclear. The aim of this study was to ascertain characteristic taxa of BGRI patients with different functional outcomes and identify their predictive value. Fecal samples of 65 BGRI patients were collected at admission and analyzed with 16s rRNA gene sequencing. Three-month functional outcomes of BGRI were evaluated using modified Rankin Scale (mRS), and patients with mRS score of 0-1 were assigned to good-BGRI group while others were assigned to poor-BGRI group. We further identified characteristic microbiota using linear discriminant analysis effect size, and receiver operating characteristic (ROC) curve was used to determine the predictive value of differential bacteria. According to the mRS score assessed after 3 months of stroke onset, 22 patients were assigned to poor-BGRI group, while 43 patients were assigned to good-BGRI group. Short chain fatty acids-producing bacteria, Romboutsia and Fusicatenibacter, were characteristic microbiota of the good-BGRI group, while pro-inflammatory taxa, Acetanaerobacterium, were characteristic microbiota of the poor-BGRI group. Furthermore, the differential bacteria showed extensive associations with clinical indices. ROC curves, separately plotted based on Romboutsia and Fusicatenibacter, achieved area under the curve values of 0.7193 and 0.6839, respectively. This study identified the efficient discriminative power of characteristic microbiota in BGRI patients with different outcomes and provided novel insights into the associations of gut microbiota with related risk factors.

Altered gut microbiota in patients with idiopathic Parkinson's disease: an age-sex matched case-control study

OBJECTIVE

The investigations related to how gut microbiota changes the brain-gut axis in idiopathic Parkinson's disease (PD) attract growing interest. We aimed to determine whether gut microbiota is altered in PD patients and whether non-motor symptoms of PD and disease duration had any relation with alterations of microbiota profiles among patients.

METHODS

Microbial taxa in stool samples obtained from 84 subjects (42-PD patients and 42-healthy spouses) were analyzed using 16S rRNA amplicon-sequencing.

RESULTS

We observed a significant decrease of Firmicutes and a significant increase of Verrucomicrobiota at the phylum level. At the family level, Lactobacillaceae and Akkermansiaceae were significantly increased and Coriobacteriales Incertae Sedis were significantly decreased in the PD patients compared to their healthy spouses. Genus level comparison inferred significant increase in abundance only in Lactobacillus while the abundance of Lachnospiraceae ND3007 group, Tyzzerella, Fusicatenibacter, Eubacterium hallii group and Ruminococcus gauvreauii group were all decreased. We determined that the abundance of Prevotella genus decreased, but not significantly in PD patients. In addition, we found differences in microbiota composition between patients with and without non-motor symptoms.

CONCLUSION

We observed differences in gut microbiota composition between PD patients and their healthy spouses. Our findings suggest that disease duration influenced microbiota composition, which in turn influenced development of non-motor symptoms in PD. This study is the first in terms of both gut microbiota research in Turkish PD patients and the probable effect of microbiota on non-motor symptoms of PD.

NEMoE: a nutrition aware regularized mixture of experts model to identify heterogeneous diet-microbiome-host health interactions

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. Video Abstract.

Ruminococcus gnavus: friend or foe for human health

Ruminococcus gnavus was first identified in 1974 as a strict anaerobe in the gut of healthy individuals, and for several decades, its study has been limited to specific enzymes or bacteriocins. With the advent of metagenomics, R. gnavus has been associated both positively and negatively with an increasing number of intestinal and extraintestinal diseases from inflammatory bowel diseases to neurological disorders. This prompted renewed interest in understanding the adaptation mechanisms of R. gnavus to the gut, and the molecular mediators affecting its association with health and disease. From ca. 250 publications citing R. gnavus since 1990, 94% were published in the last 10 years. In this review, we describe the biological characterization of R. gnavus, its occurrence in the infant and adult gut microbiota and the factors influencing its colonization of the gastrointestinal tract; we also discuss the current state of our knowledge on its role in host health and disease. We highlight gaps in knowledge and discuss the hypothesis that differential health outcomes associated with R. gnavus in the gut are strain and niche specific.

The Interplay between Gut Microbiota and Parkinson's Disease: Implications on Diagnosis and Treatment

The bidirectional interaction between the gut microbiota (GM) and the Central Nervous System, the so-called gut microbiota brain axis (GMBA), deeply affects brain function and has an important impact on the development of neurodegenerative diseases. In Parkinson’s disease (PD), gastrointestinal symptoms often precede the onset of motor and non-motor manifestations, and alterations in the GM composition accompany disease pathogenesis. Several studies have been conducted to unravel the role of dysbiosis and intestinal permeability in PD onset and progression, but the therapeutic and diagnostic applications of GM modifying approaches remain to be fully elucidated. After a brief introduction on the involvement of GMBA in the disease, we present evidence for GM alterations and leaky gut in PD patients. According to these data, we then review the potential of GM-based signatures to serve as disease biomarkers and we highlight the emerging role of probiotics, prebiotics, antibiotics, dietary interventions, and fecal microbiota transplantation as supportive therapeutic approaches in PD. Finally, we analyze the mutual influence between commonly prescribed PD medications and gut-microbiota, and we offer insights on the involvement also of nasal and oral microbiota in PD pathology, thus providing a comprehensive and up-to-date overview on the role of microbial features in disease diagnosis and treatment.

Microbiota Dysbiosis in Parkinson Disease—In Search of a Biomarker

Numerous studies have highlighted the role of the gastrointestinal system in Parkinson disease pathogenesis. It is likely triggered by proinflammatory markers produced by specific gut bacteria. This review’s aim is to identify gut bacterial biomarkers of Parkinson disease. A comprehensive search for original research papers on gut microbiota composition in Parkinson disease was conducted using the PubMed, Embase, and Scopus databases. Research papers on intestinal permeability, nasal and oral microbiomes, and interventional studies were excluded. The yielded results were categorized into four groups: Parkinson disease vs. healthy controls; disease severity; non-motor symptoms; and clinical phenotypes. This review was conducted in accordance with the PRISMA 2020 statement. A total of 51 studies met the eligibility criteria. In the Parkinson disease vs. healthy controls group, 22 bacteria were deemed potentially important. In the disease severity category, two bacteria were distinguished. In the non-motor symptoms and clinical phenotypes categories, no distinct pathogen was identified. The studies in this review report bacteria of varying taxonomic levels, which prevents the authors from reaching a clear conclusion. Future research should follow a unified methodology in order to identify potential biomarkers for Parkinson disease.

The Gut Microbiome in Parkinson’s Disease: A Longitudinal Study of the Impacts on Disease Progression and the Use of Device-Assisted Therapies

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.