Predicting the Role of the Human Gut Microbiome in Constipation Using Machine-Learning Methods: A Meta-Analysis

Microbial imbalance in Chinese children with diarrhea or constipation

Diarrhea and constipation are common health concerns in children. Numerous studies have identified strong association between gut microbiota and digestive-related diseases. But little is known about the gut microbiota that simultaneously affects both diarrhea and constipation or their potential regulatory mechanisms. Stool samples from 618 children (66 diarrhea, 138 constipation, 414 healthy controls) aged 0-3 years were collected to investigate gut microbiota changes using 16S rRNA sequencing. Compared with healthy, children with diarrhea exhibited a significant decrease in microbial diversity, while those with constipation showed a marked increase (p < 0.05). Significantly, our results firstly Ruminococcus increased in constipation (p = 0.03) and decreased in diarrhea (p < 0.01) compared to healthy controls. Pathway analysis revealed that Ruminococcus highly involved in the regulation of five common pathways (membrane transport, nervous system, energy metabolism, signal transduction and endocrine system pathways) between diarrhea and constipation, suggesting a potential shared regulatory mechanism. Our finding firstly reveals one core microorganisms that may affect the steady balance of the gut in children with diarrhea or constipation, providing an important reference for potential diagnosis and treatment of constipation and diarrhea.

Artificial intelligence-powered early identification of refractory constipation in children

Background

Children experiencing refractory constipation, resistant to conventional pharmacological approaches, develop severe symptoms that persist into adulthood, leading to a substantial decline in their quality of life. Early identification of refractory constipation may improve their management. We aimed to describe the characteristics of colonic anatomy in children with different types of constipation and develop a supervised machine-learning model for early identification.

Methods

In this retrospective study, patient characteristics and standardized colon size (SCS) ratios by barium enema (BE) were studied in patients with functional constipation (n=77), refractory constipation (n=63), and non-constipation (n=65). Statistical analyses were performed and a supervised machine learning (ML) model was developed based on these data for the classification of the three groups.

Results

Significant differences in rectum diameter, sigmoid diameter, descending diameter, transverse diameter, and rectosigmoid length were found in the three groups. A linear support vector machine was utilized to build the early detection model. Using five features (SCS ratios of sigmoid colon, descending colon, transverse colon, rectum, and rectosigmoid), the model demonstrated an accuracy of 81% [95% confidence interval (CI): 79.17% to 83.19%].

Conclusions

The application of using a supervised ML strategy obtained an accuracy of 81% in distinguishing children with refractory constipation. The combination of BE and ML model can be used for practical implications, which is important for guiding management in children with refractory constipation.

MaLiAmPi enables generalizable and taxonomy-independent microbiome features from technically diverse 16S-based microbiome studies

For studies using microbiome data, the ability to robustly combine data from technically and biologically distinct microbiome studies is a crucial means of supporting more robust and clinically relevant inferences. Formidable technical challenges arise when attempting to combine data from technically diverse 16S rRNA gene variable region amplicon sequencing (16S) studies. Closed operational taxonomic units and taxonomy are criticized as being heavily dependent upon reference sets and with limited precision relative to the underlying biology. Phylogenetic placement has been demonstrated to be a promising taxonomy-free manner of harmonizing microbiome data, but it has lacked a validated count-based feature suitable for use in machine learning and association studies. Here we introduce a phylogenetic-placement-based, taxonomy-independent, compositional feature of microbiota: phylotypes. Phylotypes were predictive of clinical outcomes such as obesity or pre-term birth on technically diverse independent validation sets harmonized post hoc. Thus, phylotypes enable the rigorous cross-validation of 16S-based clinical prognostic models and associative microbiome studies.

Overview of data preprocessing for machine learning applications in human microbiome research

Although metagenomic sequencing is now the preferred technique to study microbiome-host interactions, analyzing and interpreting microbiome sequencing data presents challenges primarily attributed to the statistical specificities of the data (e.g., sparse, over-dispersed, compositional, inter-variable dependency). This mini review explores preprocessing and transformation methods applied in recent human microbiome studies to address microbiome data analysis challenges. Our results indicate a limited adoption of transformation methods targeting the statistical characteristics of microbiome sequencing data. Instead, there is a prevalent usage of relative and normalization-based transformations that do not specifically account for the specific attributes of microbiome data. The information on preprocessing and transformations applied to the data before analysis was incomplete or missing in many publications, leading to reproducibility concerns, comparability issues, and questionable results. We hope this mini review will provide researchers and newcomers to the field of human microbiome research with an up-to-date point of reference for various data transformation tools and assist them in choosing the most suitable transformation method based on their research questions, objectives, and data characteristics.

Functional constipation and the effect of prebiotics on the gut microbiota: a review

Functional constipation is a significant health issue impacting the lives of an estimated 14 % of the global population. Non-pharmaceutical treatment advice for cases with no underlying medical conditions focuses on exercise, hydration and an increase in dietary fibre intake. An alteration in the composition of the gut microbiota is thought to play a role in constipation. Prebiotics are non-digestible food ingredients that selectively stimulate the growth of a limited number of bacteria in the colon with a benefit for host health. Various types of dietary fibre, though not all, can act as a prebiotic. Short-chain fatty acids produced by these microbes play a critical role as signalling molecules in a range of metabolic and physiological processes including laxation, although details are unclear. Prebiotics have a history of safe use in the food industry spanning several decades and are increasingly used as supplements to alleviate constipation. Most scientific research on the effects of prebiotics and gut microbiota has focussed on inflammatory bowel disease rather than functional constipation. Very few clinical studies evaluated the efficacy of prebiotics in the management of constipation and their effect on the microbiota, with highly variable designs and conflicting results. Despite this, broad health claims are made by manufacturers of prebiotic supplements. This narrative review provides an overview of the literature on the interaction of prebiotics with the gut microbiota and their potential clinical role in the alleviation of functional constipation.

Gut Microbiome-Brain Alliance: A Landscape View into Mental and Gastrointestinal Health and Disorders

Gut microbiota includes a vast collection of microorganisms residing within the gastrointestinal tract. It is broadly recognized that the gut and brain are in constant bidirectional communication, of which gut microbiota and its metabolic production are a major component, and form the so-called gut microbiome-brain axis. Disturbances of microbiota homeostasis caused by imbalance in their functional composition and metabolic activities, known as dysbiosis, cause dysregulation of these pathways and trigger changes in the blood-brain barrier permeability, thereby causing pathological malfunctions, including neurological and functional gastrointestinal disorders. In turn, the brain can affect the structure and function of gut microbiota through the autonomic nervous system by regulating gut motility, intestinal transit and secretion, and gut permeability. Here, we examine data from the CAS Content Collection, the largest collection of published scientific information, and analyze the publication landscape of recent research. We review the advances in knowledge related to the human gut microbiome, its complexity and functionality, its communication with the central nervous system, and the effect of the gut microbiome-brain axis on mental and gut health. We discuss correlations between gut microbiota composition and various diseases, specifically gastrointestinal and mental disorders. We also explore gut microbiota metabolites with regard to their impact on the brain and gut function and associated diseases. Finally, we assess clinical applications of gut-microbiota-related substances and metabolites with their development pipelines. We hope this review can serve as a useful resource in understanding the current knowledge on this emerging field in an effort to further solving of the remaining challenges and fulfilling its potential.

Subtyping intractable functional constipation in children using clinical and laboratory data in a classification model

Background

Children with intractable functional constipation (IFC) who are refractory to traditional pharmacological intervention develop severe symptoms that can persist even in adulthood, resulting in a substantial deterioration in their quality of life. In order to better manage IFC patients, efficient subtyping of IFC into its three subtypes, normal transit constipation (NTC), outlet obstruction constipation (OOC), and slow transit constipation (STC), at early stages is crucial. With advancements in technology, machine learning can classify IFC early through the use of validated questionnaires and the different serum concentrations of gastrointestinal motility-related hormones.

Method

A hundred and one children with IFC and 50 controls were enrolled in this study. Three supervised machine-learning methods, support vector machine, random forest, and light gradient boosting machine (LGBM), were used to classify children with IFC into the three subtypes based on their symptom severity, self-efficacy, and quality of life which were quantified using certified questionnaires and their serum concentrations of the gastrointestinal hormones evaluated with enzyme-linked immunosorbent assay. The accuracy of machine learning subtyping was evaluated with respect to radiopaque markers.

Results

Of 101 IFC patients, 37 had NTC, 49 had OOC, and 15 had STC. The variables significant for IFC subtype classification, according to SelectKBest, were stool frequency, the satisfaction domain of the Patient Assessment of Constipation Quality of Life questionnaire (PAC-QOL), the emotional self-efficacy for Functional Constipation questionnaire (SEFCQ), motilin serum concentration, and vasoactive intestinal peptide serum concentration. Among the three models, the LGBM model demonstrated an accuracy of 83.8%, a precision of 84.5%, a recall of 83.6%, a f1-score of 83.4%, and an area under the receiver operating characteristic curve (AUROC) of 0.89 in discriminating IFC subtypes.

Conclusion

Using clinical characteristics measured by certified questionnaires and serum concentrations of the gastrointestinal hormones, machine learning can efficiently classify pediatric IFC into its three subtypes. Of the three models tested, the LGBM model is the most accurate model for the classification of IFC, with an accuracy of 83.8%, demonstrating that machine learning is an efficient tool for the management of IFC in children.

Crosstalk between the Gut Microbiome and Colonic Motility in Chronic Constipation: Potential Mechanisms and Microbiota Modulation

Chronic constipation (CC) is a highly prevalent and burdensome gastrointestinal disorder. Accumulating evidence highlights the link between imbalances in the gut microbiome and constipation. However, the mechanisms by which the microbiome and microbial metabolites affect gut movement remain poorly understood. In this review, we discuss recent studies on the alteration in the gut microbiota in patients with CC and the effectiveness of probiotics in treating gut motility disorder. We highlight the mechanisms that explain how the gut microbiome and its metabolism are linked to gut movement and how intestinal microecological interventions may counteract these changes based on the enteric nervous system, the central nervous system, the immune function, and the ability to modify intestinal secretion and the hormonal milieu. In particular, microbiota-based approaches that modulate the levels of short-chain fatty acids and tryptophan catabolites or that target the 5-hydroxytryptamine and Toll-like receptor pathways may hold therapeutic promise. Finally, we discuss the existing limitations of microecological management in treating constipation and suggest feasible directions for future research.