Unraveling the fatigue puzzle: insights into the pathogenesis and management of IBD-related fatigue including the role of the gut-brain axis

A significant percentage of patients with an inflammatory bowel disease (IBD) encounter fatigue which can profoundly diminish patients' quality of life, particularly during periods of disease remission when gastrointestinal symptoms have receded. Various contributing risk factors have been identified including active inflammation, anemia, psychological, lifestyle and drug-related factors. While addressing these risk factors has been suggested as the initial approach to managing fatigue, a considerable number of patients still experience persisting symptoms, the primary causes of which remain incompletely understood. Recent insights suggest that dysfunction of the gut-brain axis may play a pathogenic role. This review provides an overview of established risk factors for fatigue, alongside emerging perspectives on the role of the gut-brain axis, and potential treatment strategies.

Microbiota Alters and Its Correlation with Molecular Regulation Underlying Depression in PCOS Patients

Depression is one of the complications in patients with polycystic ovary syndrome (PCOS) that leads to considerable mental health. Accumulating evidence suggests that human gut microbiomes are associated with the progression of PCOS and depression. However, whether microbiota influences depression development in PCOS patients is still uncharacterized. In this study, we employed metagenomic sequencing and transcriptome sequencing (RNA-seq) to profile the composition of the fecal microbiota and gene expression of peripheral blood mononuclear cells in depressed women with PCOS (PCOS-DP, n = 27) in comparison to mentally healthy women with PCOS (PCOS, n = 18) and compared with healthy control (HC, n = 27) and patients with major depressive disorder (MDD, n = 29). Gut microbiota assessment revealed distinct patterns in the relative abundance in the PCOS-DP compared to HC, MDD, and PCOS groups. Several gut microbes exhibited uniquely and significantly higher abundance in the PCOS-DP compared to PCOS patients, inducing EC Ruminococcus torques, Coprococcus comes, Megasphaera elsdenii, Acidaminococcus intestini, and Barnesiella viscericola. Bacteroides eggerthii was a potential gut microbial biomarker for the PCOS-DP. RNA-seq profiling identified that 35 and 37 genes were significantly elevated and downregulated in the PCOS-DP, respectively. The enhanced differential expressed genes (DEGs) in the PCOS-DP were enriched in pathways involved in signal transduction and endocrine and metabolic diseases, whereas several lipid metabolism pathways were downregulated. Intriguingly, genes correlated with the gut microbiota were found to be significantly enriched in pathways of neurodegenerative diseases and the immune system, suggesting that changes in the microbiota may have a systemic impact on the expression of neurodegenerative diseases and immune genes. Gut microbe-related DEGs of CREB3L3 and CCDC173 were possible molecular biomarkers and therapeutic targets of women with PCOS-DP. Our multi-omics data indicate shifts in the gut microbiome and host gene regulation in PCOS patients with depression, which is of possible etiological and diagnostic importance.

Gut Microbiota in Anxiety and Depression: Unveiling the Relationships and Management Options

The gut microbiota is critical for maintaining human health and the immunological system. Several neuroscientific studies have shown the significance of microbiota in developing brain systems. The gut microbiota and the brain are interconnected in a bidirectional relationship, as research on the microbiome-gut-brain axis shows. Significant evidence links anxiety and depression disorders to the community of microbes that live in the gastrointestinal system. Modified diet, fish and omega-3 fatty acid intake, macro- and micro-nutrient intake, prebiotics, probiotics, synbiotics, postbiotics, fecal microbiota transplantation, and 5-HTP regulation may all be utilized to alter the gut microbiota as a treatment approach. There are few preclinical and clinical research studies on the effectiveness and reliability of various therapeutic approaches for depression and anxiety. This article highlights relevant research on the association of gut microbiota with depression and anxiety and the different therapeutic possibilities of gut microbiota modification.

Cocoa seeds and chocolate products interaction with gut microbiota; mining microbial and functional biomarkers from mechanistic studies, clinical trials and 16S rRNA amplicon sequencing

In recent years, gut microbiome has evolved as a focal point of interest with growing recognition that a well-balanced gut microbiota is highly relevant to an individual's health status. The present review provides a mechanistic insight on the effects of cocoa chemicals on the gut microbiome and further reveals in silico biomarkers, taxonomic and functional features that distinguish gut microbiome of cocoa consumers and controls by using 16S rRNA gene sequencing data. The polyphenols in cocoa can change the gut microbiota either by inhibiting the growth of pathogenic bacteria in the gut such as Clostridium perfringens or by increasing the growth of beneficial microbiota in the gut such as Lactobacillus and Bifidobacterium. This paper demonstrates the holistic effect of gut microbiota on cocoa chemicals and how it impacts human health. We present herein the first comprehensive review and analysis of how raw and roasted cocoa and its products can specifically influence gut homeostasis, and likewise, how microbiota metabolizes cocoa chemicals. In addition to that, our 16S rRNA amplicon sequencing analysis revealed that the flavone and flavonols metabolism, aminobenzoate degradation and fatty acid elongation pathways represent the three most important signatures of microbial functions associated with cocoa consumption.

A Systematic Review of Artificial Intelligence and Machine Learning Applications to Inflammatory Bowel Disease, with Practical Guidelines for Interpretation

BACKGROUND

Inflammatory bowel disease (IBD) is a gastrointestinal chronic disease with an unpredictable disease course. Computational methods such as machine learning (ML) have the potential to stratify IBD patients for the provision of individualized care. The use of ML methods for IBD was surveyed, with an additional focus on how the field has changed over time.

METHODS

On May 6, 2021, a systematic review was conducted through a search of MEDLINE and Embase databases, with the search structure ("machine learning" OR "artificial intelligence") AND ("Crohn* Disease" OR "Ulcerative Colitis" OR "Inflammatory Bowel Disease"). Exclusion criteria included studies not written in English, no human patient data, publication before 2001, studies that were not peer reviewed, nonautoimmune disease comorbidity research, and record types that were not primary research.

RESULTS

Seventy-eight (of 409) records met the inclusion criteria. Random forest methods were most prevalent, and there was an increase in neural networks, mainly applied to imaging data sets. The main applications of ML to clinical tasks were diagnosis (18 of 78), disease course (22 of 78), and disease severity (16 of 78). The median sample size was 263. Clinical and microbiome-related data sets were most popular. Five percent of studies used an external data set after training and testing for additional model validation.

DISCUSSION

Availability of longitudinal and deep phenotyping data could lead to better modeling. Machine learning pipelines that consider imbalanced data and that feature selection only on training data will generate more generalizable models. Machine learning models are increasingly being applied to more complex clinical tasks for specific phenotypes, indicating progress towards personalized medicine for IBD.

Inflammatory bowel disease biomarkers of human gut microbiota selected via different feature selection methods

The tremendous boost in next generation sequencing and in the "omics" technologies makes it possible to characterize the human gut microbiome-the collective genomes of the microbial community that reside in our gastrointestinal tract. Although some of these microorganisms are considered to be essential regulators of our immune system, the alteration of the complexity and eubiotic state of microbiota might promote autoimmune and inflammatory disorders such as diabetes, rheumatoid arthritis, Inflammatory bowel diseases (IBD), obesity, and carcinogenesis. IBD, comprising Crohn's disease and ulcerative colitis, is a gut-related, multifactorial disease with an unknown etiology. IBD presents defects in the detection and control of the gut microbiota, associated with unbalanced immune reactions, genetic mutations that confer susceptibility to the disease, and complex environmental conditions such as westernized lifestyle. Although some existing studies attempt to unveil the composition and functional capacity of the gut microbiome in relation to IBD diseases, a comprehensive picture of the gut microbiome in IBD patients is far from being complete. Due to the complexity of metagenomic studies, the applications of the state-of-the-art machine learning techniques became popular to address a wide range of questions in the field of metagenomic data analysis. In this regard, using IBD associated metagenomics dataset, this study utilizes both supervised and unsupervised machine learning algorithms, (i) to generate a classification model that aids IBD diagnosis, (ii) to discover IBD-associated biomarkers, (iii) to discover subgroups of IBD patients using k-means and hierarchical clustering approaches. To deal with the high dimensionality of features, we applied robust feature selection algorithms such as Conditional Mutual Information Maximization (CMIM), Fast Correlation Based Filter (FCBF), min redundancy max relevance (mRMR), Select K Best (SKB), Information Gain (IG) and Extreme Gradient Boosting (XGBoost). In our experiments with 100-fold Monte Carlo cross-validation (MCCV), XGBoost, IG, and SKB methods showed a considerable effect in terms of minimizing the microbiota used for the diagnosis of IBD and thus reducing the cost and time. We observed that compared to Decision Tree, Support Vector Machine, Logitboost, Adaboost, and stacking ensemble classifiers, our Random Forest classifier resulted in better performance measures for the classification of IBD. Our findings revealed potential microbiome-mediated mechanisms of IBD and these findings might be useful for the development of microbiome-based diagnostics.