Early prediction of incident liver disease using conventional risk factors and gut-microbiome-augmented gradient boosting

Exploring non-invasive biomarkers for pulmonary nodule detection based on salivary microbiomics and machine learning algorithms

Microorganisms are one of the most promising biomarkers for cancer, and the relationship between microorganisms and lung cancer occurrence and development provides significant potential for pulmonary nodule (PN) diagnosis from a microbiological perspective. This study aimed to analyze the salivary microbiota features of patients with PN and assess the potential of the salivary microbiota as a non-invasive PN biomarker. We collected saliva smples from 153 patients with PN and 40 controls. Using 16 S rRNA gene sequencing, differences in α- and β-diversity and community composition between the group with PN and controls were analyzed. Subsequently, specific microbial variables were selected using six models were trained on the selected salivary microbial features. The models were evaluated using metrics, such as the area under the receiver operating characteristic curve (AUC), to identify the best-performing model. Furthermore, the Bayesian optimization algorithm was used to optimize this best-performing model. Finally, the SHapley Additive exPlanations (SHAP) interpretability framework was used to interpret the output of the optimal model and identify potential PN biomarkers. Significant differences in α- and β-diversity were observed between the group with PN and controls. Although the predominant genera were consistent between the groups, significant disparities were observed in their relative abundances. By leveraging the random forest algorithm, ten characteristic microbial variables were identified and incorporated into six models, which effectively facilitated PN diagnosis. The XGBoost model demonstrated the best performance. Further optimization of the XGBoost model resulted in a Bayesian Optimization-based XGBoost (BOXGB) model. Based on the BOXGB model, an online saliva microbiota-based PN prediction platform was developed. Lastly, SHAP analysis suggested Defluviitaleaceae_UCG-011, Aggregatibacter, Oribacterium, Bacillus, and Prevotalla are promising non-invasive PN biomarkers. This study proved salivary microbiota as a non-invasive PN biomarker, expanding the clinical diagnostic approaches for PN.

MicrobiomeStatPlots: Microbiome statistics plotting gallery for meta-omics and bioinformatics

The rapid growth of microbiome research has generated an unprecedented amount of multi-omics data, presenting challenges in data analysis and visualization. To address these issues, we present MicrobiomeStatPlots, a comprehensive platform offering streamlined, reproducible tools for microbiome data analysis and visualization. This platform integrates essential bioinformatics workflows with multi-omics pipelines and provides 82 distinct visualization cases for interpreting microbiome datasets. By incorporating basic tutorials and advanced R-based visualization strategies, MicrobiomeStatPlots enhances accessibility and usability for researchers. Users can customize plots, contribute to the platform's expansion, and access a wealth of bioinformatics knowledge freely on GitHub (https://github.com/YongxinLiu/MicrobiomeStatPlot). Future plans include extending support for metabolomics, viromics, and metatranscriptomics, along with seamless integration of visualization tools into omics workflows. MicrobiomeStatPlots bridges gaps in microbiome data analysis and visualization, paving the way for more efficient, impactful microbiome research.

Artificial intelligence applied to 'omics data in liver disease: towards a personalised approach for diagnosis, prognosis and treatment

Advancements in omics technologies and artificial intelligence (AI) methodologies are fuelling our progress towards personalised diagnosis, prognosis and treatment strategies in hepatology. This review provides a comprehensive overview of the current landscape of AI methods used for analysis of omics data in liver diseases. We present an overview of the prevalence of different omics levels across various liver diseases, as well as categorise the AI methodology used across the studies. Specifically, we highlight the predominance of transcriptomic and genomic profiling and the relatively sparse exploration of other levels such as the proteome and methylome, which represent untapped potential for novel insights. Publicly available database initiatives such as The Cancer Genome Atlas and The International Cancer Genome Consortium have paved the way for advancements in the diagnosis and treatment of hepatocellular carcinoma. However, the same availability of large omics datasets remains limited for other liver diseases. Furthermore, the application of sophisticated AI methods to handle the complexities of multiomics datasets requires substantial data to train and validate the models and faces challenges in achieving bias-free results with clinical utility. Strategies to address the paucity of data and capitalise on opportunities are discussed. Given the substantial global burden of chronic liver diseases, it is imperative that multicentre collaborations be established to generate large-scale omics data for early disease recognition and intervention. Exploring advanced AI methods is also necessary to maximise the potential of these datasets and improve early detection and personalised treatment strategies.

Discovery of robust and highly specific microbiome signatures of non-alcoholic fatty liver disease

BACKGROUND

The pathogenesis of non-alcoholic fatty liver disease (NAFLD) with a global prevalence of 30% is multifactorial and the involvement of gut bacteria has been recently proposed. However, finding robust bacterial signatures of NAFLD has been a great challenge, mainly due to its co-occurrence with other metabolic diseases.

RESULTS

Here, we collected public metagenomic data and integrated the taxonomy profiles with in silico generated community metabolic outputs, and detailed clinical data, of 1206 Chinese subjects w/wo metabolic diseases, including NAFLD (obese and lean), obesity, T2D, hypertension, and atherosclerosis. We identified highly specific microbiome signatures through building accurate machine learning models (accuracy = 0.845-0.917) for NAFLD with high portability (generalizable) and low prediction rate (specific) when applied to other metabolic diseases, as well as through a community approach involving differential co-abundance ecological networks. Moreover, using these signatures coupled with further mediation analysis and metabolic dependency modeling, we propose synergistic defined microbial consortia associated with NAFLD phenotype in overweight and lean individuals, respectively.

CONCLUSION

Our study reveals robust and highly specific NAFLD signatures and offers a more realistic microbiome-therapeutics approach over individual species for this complex disease. Video Abstract.

Hither-CMI: Prediction of circRNA-miRNA Interactions Based on a Hybrid Multimodal Network and Higher-Order Neighborhood Information via a Graph Convolutional Network

Numerous studies show that circular RNA (circRNA) functions as a sponge for microRNA (miRNA), significantly regulating gene expression by interacting with miRNA, which in turn affects the progression of human diseases. Traditional experimental approaches for investigating circRNA-miRNA interactions (CMI) are both time-consuming and costly, making computational methods a valuable alternative. Hence, we propose a computational model for predicting CMI, leveraging a hybrid multimodal network and higher-order neighborhood information (Hither-CMI). Specifically, Hither-CMI employs Multiple Kernel Learning (MKL) to integrate sequence, structure, and expression similarity networks of circRNA and miRNA, resulting in a hybrid multimodal network. Next, an enhanced Graph Convolutional Network (GCN) is utilized to combine the circRNA-miRNA hybrid multimodal network with the CMI association network, producing a hybrid higher-order embedding representation. Finally, the XGBoost classifier is applied for training and prediction. The Hither-CMI model achieved a predicted AUC value of 0.9134. In case studies, 25 out of the top 30 predicted CMI were confirmed by recent literature. These extensive experimental results further validate the effectiveness of Hither-CMI in predicting potential CMI, making it a promising prescreening tool for further biological research.

Effects of data transformation and model selection on feature importance in microbiome classification data

BACKGROUND

Accurate classification of host phenotypes from microbiome data is crucial for advancing microbiome-based therapies, with machine learning offering effective solutions. However, the complexity of the gut microbiome, data sparsity, compositionality, and population-specificity present significant challenges. Microbiome data transformations can alleviate some of the aforementioned challenges, but their usage in machine learning tasks has largely been unexplored.

RESULTS

Our analysis of over 8500 samples from 24 shotgun metagenomic datasets showed that it is possible to classify healthy and diseased individuals using microbiome data with minimal dependence on the choice of algorithm or transformation. Presence-absence transformations performed comparably to abundance-based transformations, and only a small subset of predictors is necessary for accurate classification. However, while different transformations resulted in comparable classification performance, the most important features varied significantly, which highlights the need to reevaluate machine learning-based biomarker detection.

CONCLUSIONS

Microbiome data transformations can significantly influence feature selection but have a limited effect on classification accuracy. Our findings suggest that while classification is robust across different transformations, the variation in feature selection necessitates caution when using machine learning for biomarker identification. This research provides valuable insights for applying machine learning to microbiome data and identifies important directions for future work.

Development and evaluation of statistical and artificial intelligence approaches with microbial shotgun metagenomics data as an untargeted screening tool for use in food production

The increasing knowledge of microbial ecology in food products relating to quality and safety and the established usefulness of machine learning algorithms for anomaly detection in multiple scenarios suggests that the application of microbiome data in food production systems for anomaly detection could be a valuable approach to be used in food systems. These methods could be used to identify ingredients that deviate from their typical microbial composition, which could indicate food fraud or safety issues. The objective of this study was to assess the feasibility of using shotgun sequencing data as input into anomaly detection algorithms using fluid milk as a model system. Contrastive principal component analysis (PCA), cluster-based methods, and explainable artificial intelligence (AI) were evaluated for the detection of two anomalous sample classes using longitudinal metagenomic profiling of fluid milk compared to baseline (BL) samples collected under comparable circumstances. Traditional methods (alpha and beta diversity, clustering-based contrastive PCA, multidimensional scaling, and dendrograms) failed to differentiate anomalous sample classes; however, explainable AI was able to classify anomalous vs baseline samples and indicate microbial drivers in association with antibiotic use. We validated the potential for explainable AI to classify different milk sources using larger publicly available fluid milk 16S rDNA sequencing data sets and demonstrated that explainable AI is able to differentiate between milk storage methods, processing stages, and seasons. Our results indicate that the application of artificial intelligence continues to hold promise in the realm of microbiome data analysis and could present further opportunities for downstream analytic automation to aid in food safety and quality.

IMPORTANCE

We evaluated the feasibility of using untargeted metagenomic sequencing of raw milk for detecting anomalous food ingredient content with artificial intelligence methods in a study specifically designed to test this hypothesis. We also show through analysis of publicly available fluid milk microbial data that our artificial intelligence approach is able to successfully predict milk in different stages of processing. The approach could potentially be applied in the food industry for safety and quality control.

Machine learning-based characterization of the gut microbiome associated with the progression of primary biliary cholangitis to cirrhosis

BACKGROUND

Primary biliary cholangitis (PBC) is associated closely with the gut microbiota. This study aimed to explore the characteristics of the gut microbiota after the progress of PBC to cirrhosis.

METHOD

This study focuses on utilizing the 16S rRNA gene sequencing method to screen for differences in gut microbiota in PBC patients who progress to cirrhosis. Then, we divided the data into training and verification sets and used seven different machine learning (ML) models to validate them respectively, calculating and comparing the accuracy, F1 score, precision, and recall, and screening the dominant intestinal flora affecting PBC cirrhosis.

RESULT

PBC cirrhosis patients showed decreased diversity and richness of gut microbiota. Additionally, there are alterations in the composition of gut microbiota in PBC cirrhosis patients. The abundance of Faecalibacterium and Gemmiger bacteria significantly decreases, while the abundance of Veillonella and Streptococcus significantly increases. Furthermore, machine learning methods identify Streptococcus and Gemmiger as the predominant gut microbiota in PBC patients with cirrhosis, serving as non-invasive biomarkers (AUC = 0.902).

CONCLUSION

Our study revealed that PBC cirrhosis patients gut microbiota composition and function have significantly changed. Streptococcus and Gemmiger may become a non-invasive biomarker for predicting the progression of PBC progress to cirrhosis.

Integrating Multi-Organ Imaging-Derived Phenotypes and Genomic Information for Predicting the Occurrence of Common Diseases

As medical imaging technologies advance, these tools are playing a more and more important role in assisting clinical disease diagnosis. The fusion of biomedical imaging and multi-modal information is profound, as it significantly enhances diagnostic precision and comprehensiveness. Integrating multi-organ imaging with genomic information can significantly enhance the accuracy of disease prediction because many diseases involve both environmental and genetic determinants. In the present study, we focused on the fusion of imaging-derived phenotypes (IDPs) and polygenic risk score (PRS) of diseases from different organs including the brain, heart, lung, liver, spleen, pancreas, and kidney for the prediction of the occurrence of nine common diseases, namely atrial fibrillation, heart failure (HF), hypertension, myocardial infarction, asthma, type 2 diabetes, chronic kidney disease, coronary artery disease (CAD), and chronic obstructive pulmonary disease, in the UK Biobank (UKBB) dataset. For each disease, three prediction models were developed utilizing imaging features, genomic data, and a fusion of both, respectively, and their performances were compared. The results indicated that for seven diseases, the model integrating both imaging and genomic data achieved superior predictive performance compared to models that used only imaging features or only genomic data. For instance, the Area Under Curve (AUC) of HF risk prediction was increased from 0.68 ± 0.15 to 0.79 ± 0.12, and the AUC of CAD diagnosis was increased from 0.76 ± 0.05 to 0.81 ± 0.06.

Gut microbiota-based machine-learning signature for the diagnosis of alcohol-associated and metabolic dysfunction-associated steatotic liver disease

Alcoholic-associated liver disease (ALD) and metabolic dysfunction-associated steatotic liver disease (MASLD) show a high prevalence rate worldwide. As gut microbiota represents current state of ALD and MASLD via gut-liver axis, typical characteristics of gut microbiota can be used as a potential diagnostic marker in ALD and MASLD. Machine learning (ML) algorithms improve diagnostic performance in various diseases. Using gut microbiota-based ML algorithms, we evaluated the diagnostic index for ALD and MASLD. Fecal 16S rRNA sequencing data of 263 ALD (control, elevated liver enzyme [ELE], cirrhosis, and hepatocellular carcinoma [HCC]) and 201 MASLD (control and ELE) subjects were collected. For external validation, 126 ALD and 84 MASLD subjects were recruited. Four supervised ML algorithms (support vector machine, random forest, multilevel perceptron, and convolutional neural network) were used for classification with 20, 40, 60, and 80 features, in which three nonsupervised ML algorithms (independent component analysis, principal component analysis, linear discriminant analysis, and random projection) were used for feature reduction. A total of 52 combinations of ML algorithms for each pair of subgroups were performed with 60 hyperparameter variations and Stratified ShuffleSplit tenfold cross validation. The ML models of the convolutional neural network combined with principal component analysis achieved areas under the receiver operating characteristic curve (AUCs) > 0.90. In ALD, the diagnostic AUC values of the ML strategy (vs. control) were 0.94, 0.97, and 0.96 for ELE, cirrhosis, and liver cancer, respectively. The AUC value (vs. control) for MASLD (ELE) was 0.93. In the external validation, the AUC values of ALD and MASLD (vs control) were > 0.90 and 0.88, respectively. The gut microbiota-based ML strategy can be used for the diagnosis of ALD and MASLD.ClinicalTrials.gov NCT04339725.

Harnessing human microbiomes for disease prediction

The human microbiome has been increasingly recognized as having potential use for disease prediction. Predicting the risk, progression, and severity of diseases holds promise to transform clinical practice, empower patient decisions, and reduce the burden of various common diseases, as has been demonstrated for cardiovascular disease or breast cancer. Combining multiple modifiable and non-modifiable risk factors, including high-dimensional genomic data, has been traditionally favored, but few studies have incorporated the human microbiome into models for predicting the prospective risk of disease. Here, we review research into the use of the human microbiome for disease prediction with a particular focus on prospective studies as well as the modulation and engineering of the microbiome as a therapeutic strategy.

When smoke meets gut: deciphering the interactions between tobacco smoking and gut microbiota in disease development

Tobacco smoking is a prevalent and detrimental habit practiced worldwide, increasing the risk of various diseases, including chronic obstructive pulmonary disease (COPD), cardiovascular disease, liver disease, and cancer. Although previous research has explored the detrimental health effects of tobacco smoking, recent studies suggest that gut microbiota dysbiosis may play a critical role in these outcomes. Numerous tobacco smoke components, such as nicotine, are found in the gastrointestinal tract and interact with gut microbiota, leading to lasting impacts on host health and diseases. This review delves into the ways tobacco smoking and its various constituents influence gut microbiota composition and functionality. We also summarize recent advancements in understanding how tobacco smoking-induced gut microbiota dysbiosis affects host health. Furthermore, this review introduces a novel perspective on how changes in gut microbiota following smoking cessation may contribute to withdrawal syndrome and the degree of health improvements in smokers.

Effect of Laryngopharyngeal Reflux and Potassium-Competitive Acid Blocker (P-CAB) on the Microbiological Comprise of the Laryngopharynx

OBJECTIVE

To probe the microbiota composition progressing from healthy individuals to those with laryngopharyngeal reflux disease (LPRD) and subsequently undergoing potassium-competitive acid inhibitor (P-CAB) therapy.

STUDY DESIGN

Prospective case-control study.

SETTING

Academic Medical Center.

METHODS

Forty patients with LPRD and 51 patients without LPRD were recruited. An 8-week P-CAB therapy was initiated (post-T-LPRD), and 39 had return visits. In total, 130 laryngopharyngeal saliva samples were collected and sequenced by targeting the V3-V4 region of the 16S ribosomal RNA (rRNA) gene using an Illumina MiSeq. Amplicon sequence variants (ASVs) and clinical indices were analyzed.

RESULTS

Alpha and beta diversities were compared among the non-LPRD, LPRD, and post-T-LPRD groups, and the Observed_ASVs were not significantly different. At the same time, the Shannon and Simpson indices, unweighted Unifrac, weighted Unifrac, and binary Jaccard distance were significantly different between non-LPRD and LPRD groups. In addition, significant differences were found in the abundance of Streptococcus, Prevotella, and Prevotellaceae in the LPRD versus non-LPRD groups, and Neisseria, Leptotrichia, and Allprevotella in the LPRD versus post-T-LPRD groups. The genera model was used to distinguish patients with LPRD from those without, and a better receiver operating characteristic curve was formed after combining the clinical indices of reflux symptom index, reflux finding score, and pepsin, with an area under the curve of 0.960.

CONCLUSION

Laryngopharyngeal microbial communities changed after laryngopharyngeal reflux and were modified further after P-CAB treatment, which provides a potential diagnostic value for LPRD, especially when combined with clinical indices.

Integration of polygenic and gut metagenomic risk prediction for common diseases

Multiomics has shown promise in noninvasive risk profiling and early detection of various common diseases. In the present study, in a prospective population-based cohort with ~18 years of e-health record follow-up, we investigated the incremental and combined value of genomic and gut metagenomic risk assessment compared with conventional risk factors for predicting incident coronary artery disease (CAD), type 2 diabetes (T2D), Alzheimer disease and prostate cancer. We found that polygenic risk scores (PRSs) improved prediction over conventional risk factors for all diseases. Gut microbiome scores improved predictive capacity over baseline age for CAD, T2D and prostate cancer. Integrated risk models of PRSs, gut microbiome scores and conventional risk factors achieved the highest predictive performance for all diseases studied compared with models based on conventional risk factors alone. The present study demonstrates that integrated PRSs and gut metagenomic risk models improve the predictive value over conventional risk factors for common chronic diseases.

Modulation of the gut microbiome and Firmicutes phylum reduction by a nutraceutical blend in the obesity mouse model and overweight humans: A double-blind clinical trial

Overweight and obesity are closely linked to gut dysbiosis/dysmetabolism and disrupted De-Ritis ratio [aspartate aminotransferase (AST)/alanine aminotransferase (ALT) ratio], which may contribute to chronic noncommunicable diseases onset. Concurrently, extensive research explores nutraceuticals, and health-enhancing supplements, for disease prevention or treatment. Thus, sedentary overweight volunteers were double-blind randomized into two groups: Novel Nutraceutical_(S) (without silymarin) and Novel Nutraceutical (with silymarin). Experimental formulations were orally administered twice daily over 180 consecutive days. We evaluated fecal gut microbiota, based on partial 16S rRNA sequences, biochemistry and endocrine markers, steatosis biomarker (AST/ALT ratio), and anthropometric parameters. Post-supplementation, only the Novel Nutraceutical group reduced Clostridium clostridioforme (Firmicutes), Firmicutes/Bacteroidetes ratio (F/B ratio), and De-Ritis ratio, while elevating Bacteroides caccae and Bacteroides uniformis (Bacteroidetes) in Brazilian sedentary overweight volunteers after 180 days. In summary, the results presented here allow us to suggest the gut microbiota as the action mechanism of the Novel Nutraceutical promoting metabolic hepatic recovery in obesity/overweight non-drug interventions.

Ferrostatin-1 improves prognosis and regulates gut microbiota of steatotic liver transplantation recipients in rats

Aims: To investigate the effects of Ferrostatin-1 (Fer-1) on improving the prognosis of liver transplant recipients with steatotic liver grafts and regulating gut microbiota in rats. Methods: We obtained steatotic liver grafts and established a liver transplantation model. Recipients were divided into sham, liver transplantation and Fer-1 treatment groups, which were assessed 1 and 7 days after surgery (n = 6). Results & conclusion: Fer-1 promotes recovery of the histological structure and function of steatotic liver grafts and the intestinal tract, and improves inflammatory responses of recipients following liver transplantation. Fer-1 reduces gut microbiota pathogenicity, and lowers iron absorption and improves fat metabolism of recipients, thereby protecting steatotic liver grafts.

Nine (not so simple) steps: a practical guide to using machine learning in microbial ecology

Due to the complex nature of microbiome data, the field of microbial ecology has many current and potential uses for machine learning (ML) modeling. With the increased use of predictive ML models across many disciplines, including microbial ecology, there is extensive published information on the specific ML algorithms available and how those algorithms have been applied. Thus, our goal is not to summarize the breadth of ML models available or compare their performances. Rather, our goal is to provide more concrete and actionable information to guide microbial ecologists in how to select, run, and interpret ML algorithms to predict the taxa or genes associated with particular sample categories or environmental gradients of interest. Such microbial data often have unique characteristics that require careful consideration of how to apply ML models and how to interpret the associated results. This review is intended for practicing microbial ecologists who may be unfamiliar with some of the intricacies of ML models. We provide examples and discuss common opportunities and pitfalls specific to applying ML models to the types of data sets most frequently collected by microbial ecologists.

Integrating clinical and cross-cohort metagenomic features: a stable and non-invasive colorectal cancer and adenoma diagnostic model

Background: Dysbiosis is associated with colorectal cancer (CRC) and adenomas (CRA). However, the robustness of diagnostic models based on microbial signatures in multiple cohorts remains unsatisfactory. Materials and Methods: In this study, we used machine learning models to screen metagenomic signatures from the respective cross-cohort datasets of CRC and CRA (selected from CuratedMetagenomicData, each disease included 4 datasets). Then select a CRC and CRA data set from the CuratedMetagenomicData database and meet the requirements of having both metagenomic data and clinical data. This data set will be used to verify the inference that integrating clinical features can improve the performance of microbial disease prediction models. Results: After repeated verification, we selected 20 metagenomic features that performed well and were stably expressed within cross-cohorts to represent the diagnostic role of bacterial communities in CRC/CRA. The performance of the selected cross-cohort metagenomic features was stable for multi-regional and multi-ethnic populations (CRC, AUC: 0.817-0.867; CRA, AUC: 0.766-0.833). After clinical feature combination, AUC of our integrated CRC diagnostic model reached 0.939 (95% CI: 0.932-0.947, NRI=30%), and that of the CRA integrated model reached 0.925 (95%CI: 0.917-0.935, NRI=18%). Conclusion: In conclusion, the integrated model performed significantly better than single microbiome or clinical feature models in all cohorts. Integrating cross-cohort common discriminative microbial features with clinical features could help construct stable diagnostic models for early non-invasive screening for CRC and CRA.

Multi-omics approaches to studying gastrointestinal microbiome in the context of precision medicine and machine learning

The human gastrointestinal (gut) microbiome plays a critical role in maintaining host health and has been increasingly recognized as an important factor in precision medicine. High-throughput sequencing technologies have revolutionized -omics data generation, facilitating the characterization of the human gut microbiome with exceptional resolution. The analysis of various -omics data, including metatranscriptomics, metagenomics, glycomics, and metabolomics, holds potential for personalized therapies by revealing information about functional genes, microbial composition, glycans, and metabolites. This multi-omics approach has not only provided insights into the role of the gut microbiome in various diseases but has also facilitated the identification of microbial biomarkers for diagnosis, prognosis, and treatment. Machine learning algorithms have emerged as powerful tools for extracting meaningful insights from complex datasets, and more recently have been applied to metagenomics data via efficiently identifying microbial signatures, predicting disease states, and determining potential therapeutic targets. Despite these rapid advancements, several challenges remain, such as key knowledge gaps, algorithm selection, and bioinformatics software parametrization. In this mini-review, our primary focus is metagenomics, while recognizing that other -omics can enhance our understanding of the functional diversity of organisms and how they interact with the host. We aim to explore the current intersection of multi-omics, precision medicine, and machine learning in advancing our understanding of the gut microbiome. A multidisciplinary approach holds promise for improving patient outcomes in the era of precision medicine, as we unravel the intricate interactions between the microbiome and human health.

Nonalcoholic Fatty Liver Disease and the Intestinal Microbiome: An Inseparable Link

Nonalcoholic fatty liver disease (NAFLD) particularly affects patients with type 2 diabetes and obesity. The incidence of NAFLD has increased significantly over the last decades and is now pandemically across the globe. It is a complex systemic disease comprising hepatic lipid accumulation, inflammation, lipotoxicity, gut dysbiosis, and insulin resistance as main features and with the potential to progress to cirrhosis and hepatocellular carcinoma (HCC). In numerous animal and human studies the gut microbiota plays a key role in the pathogenesis of NAFLD, NAFLD-cirrhosis and NAFLD-associated HCC. Lipotoxicity is the driver of inflammation, insulin resistance, and liver injury. Likewise, western diet, obesity, and metabolic disorders may alter the gut microbiota, which activates innate and adaptive immune responses and fuels hereby hepatic and systemic inflammation. Indigestible carbohydrates are fermented by the gut microbiota to produce important metabolites, such as short-chain fatty acids and succinate. Numerous animal and human studies suggested a pivotal role of these metabolites in the progression of NAFLD and its comorbidities. Though, modification of the gut microbiota and/or the metabolites could even be beneficial in patients with NAFLD, NAFLD-cirrhosis, and NAFLD-associated HCC. In this review we collect the evidence that exogenous and endogenous hits drive liver injury in NAFLD and propel liver fibrosis and the progressing to advanced disease stages. NAFLD can be seen as the product of a complex interplay between gut microbiota, the immune response and metabolism. Thus, the challenge will be to understand its pathogenesis and to develop new therapeutic strategies.

The intersection between alcohol-related liver disease and nonalcoholic fatty liver disease

Nonalcoholic fatty liver disease (NAFLD) and alcohol-related liver disease (ALD) are the leading causes of chronic liver disease worldwide. NAFLD and ALD share pathophysiological, histological and genetic features and both alcohol and metabolic dysfunction coexist as aetiological factors in many patients with hepatic steatosis. A diagnosis of NAFLD requires the exclusion of significant alcohol consumption and other causes of liver disease. However, data suggest that significant alcohol consumption is often under-reported in patients classified as having NAFLD and that alcohol and metabolic factors interact to exacerbate the progression of liver disease. In this Review, we analyse existing data on the interaction between alcohol consumption and metabolic syndrome as well as the overlapping features and differences in the pathogenesis of ALD and NAFLD. We also discuss the clinical implications of the coexistence of alcohol consumption, of any degree, in patients with evidence of metabolic derangement as well as the use of alcohol biomarkers to detect alcohol intake. Finally, we summarize the evolving nomenclature of fatty liver disease and describe a recent proposal to classify patients at the intersection of NAFLD and ALD. We propose that, regardless of the presumed aetiology, patients with fatty liver disease should be evaluated for both metabolic syndrome and alcohol consumption to enable better prognostication and a personalized medicine approach.

PitNETs and the gut microbiota: potential connections, future directions

The role of the gut microbiome has been widely discussed in numerous works of literature. The biggest concern is the association of the gut microbiome with the central nervous system through the microbiome-brain-gut axis in the past ten years. As more and more research has been done on the relationship between the disease of the central nervous system and gut microbes. This fact is being revealed that gut microbes seem to play an important role from the onset and progression of the disease to clinical symptoms, and new treatments. As a special tumor of the central nervous system, pituitary neuroendocrine tumors (PitNETs)are closely related to metabolism, endocrinology, and immunity. These factors are the vectors through which intestinal microbes interact with the central nervous system. However, little is known about the effects of gut microbes on the PitNET. In this review, the relationship of gut microbiota in PitNETs is introduced, the potential effects of the gut-brain axis in this relationship are analyzed, and future research directions are presented.

Alcohol-Associated Liver Disease: Evolving Concepts and Treatments

Alcohol is a prominent cause of liver disease worldwide with higher prevalence in developed nations. The spectrum of alcohol-associated liver disease (ALD) encompasses a diverse range of clinical entities, from asymptomatic isolated steatosis to decompensated cirrhosis, and in some cases, acute or chronic liver failure. Consequently, it is important for healthcare practitioners to maintain awareness and systematically screen for ALD. The optimal evaluation and management of ALD necessitates a collaborative approach, incorporating a multidisciplinary team and accounting for concurrent medical conditions. A repertoire of therapeutic interventions exists to support patients in achieving alcohol cessation and sustaining remission, with complete abstinence being the ultimate objective. This review explores the existing therapeutic options for ALD acknowledging geographical discrepancies in accessibility. Recent innovations, including the inclusion of alcohol consumption biomarkers into clinical protocols and the expansion of liver transplantation eligibility to encompass severe alcohol-associated hepatitis, are explored.

Gut Microbiota Patterns in Patients with Non-Alcoholic Fatty Liver Disease: A Comprehensive Assessment Using Three Analysis Methods

Non-alcoholic fatty liver disease (NAFLD) is considered the most common chronic liver disease worldwide, affecting nearly 25% of the global adult population. Increasing evidence suggests that functional and compositional changes in the gut microbiota may contribute to the development and promote the progression of NAFLD. 16S rRNA gene next-generation sequencing is widely used to determine specific features of the NAFLD microbiome, but a complex system such as the gut microbiota requires a comprehensive approach. We used three different approaches: MALDI-TOF-MS of bacterial cultures, qPCR, and 16S NGS sequencing, as well as a wide variety of statistical methods to assess the differences in gut microbiota composition between NAFLD patients without significant fibrosis and the control group. The listed methods showed enrichment in Collinsella sp. and Oscillospiraceae for the control samples and enrichment in Lachnospiraceae (and in particular Dorea sp.) and Veillonellaceae in NAFLD. The families, Bifidobacteriaceae, Lactobacillaceae, and Enterococcaceae (particularly Enterococcus faecium and Enterococcus faecalis), were also found to be important taxa for NAFLD microbiome evaluation. Considering individual method observations, an increase in Candida krusei and a decrease in Bacteroides uniformis for NAFLD patients were detected using MALDI-TOF-MS. An increase in Gracilibacteraceae, Chitinophagaceae, Pirellulaceae, Erysipelatoclostridiaceae, Muribaculaceae, and Comamonadaceae, and a decrease in Acidaminococcaceae in NAFLD were observed with 16S NGS, and enrichment in Fusobacterium nucleatum was shown using qPCR analysis. These findings confirm that NAFLD is associated with changes in gut microbiota composition. Further investigations are required to determine the cause-and-effect relationships and the impact of microbiota-derived compounds on the development and progression of NAFLD.

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.

Guidelines for microbiome studies in renal physiology

Gut microbiome research has increased dramatically in the last decade, including in renal health and disease. The field is moving from experiments showing mere association to causation using both forward and reverse microbiome approaches, leveraging tools such as germ-free animals, treatment with antibiotics, and fecal microbiota transplantations. However, we are still seeing a gap between discovery and translation that needs to be addressed, so that patients can benefit from microbiome-based therapies. In this guideline paper, we discuss the key considerations that affect the gut microbiome of animals and clinical studies assessing renal function, many of which are often overlooked, resulting in false-positive results. For animal studies, these include suppliers, acclimatization, baseline microbiota and its normalization, littermates and cohort/cage effects, diet, sex differences, age, circadian differences, antibiotics and sweeteners, and models used. Clinical studies have some unique considerations, which include sampling, gut transit time, dietary records, medication, and renal phenotypes. We provide best-practice guidance on sampling, storage, DNA extraction, and methods for microbial DNA sequencing (both 16S rRNA and shotgun metagenome). Finally, we discuss follow-up analyses, including tools available, metrics, and their interpretation, and the key challenges ahead in the microbiome field. By standardizing study designs, methods, and reporting, we will accelerate the findings from discovery to translation and result in new microbiome-based therapies that may improve renal health.

Artificial intelligence-enabled microbiome-based diagnosis models for a broad spectrum of cancer types

Microbiome-based diagnosis of cancer is an increasingly important supplement for the genomics approach in cancer diagnosis, yet current models for microbiome-based diagnosis of cancer face difficulties in generality: not only diagnosis models could not be adapted from one cancer to another, but models built based on microbes from tissues could not be adapted for diagnosis based on microbes from blood. Therefore, a microbiome-based model suitable for a broad spectrum of cancer types is urgently needed. Here we have introduced DeepMicroCancer, a diagnosis model using artificial intelligence techniques for a broad spectrum of cancer types. Built based on the random forest models it has enabled superior performances on more than twenty types of cancers' tissue samples. And by using the transfer learning techniques, improved accuracies could be obtained, especially for cancer types with only a few samples, which could satisfy the requirement in clinical scenarios. Moreover, transfer learning techniques have enabled high diagnosis accuracy that could also be achieved for blood samples. These results indicated that certain sets of microbes could, if excavated using advanced artificial techniques, reveal the intricate differences among cancers and healthy individuals. Collectively, DeepMicroCancer has provided a new venue for accurate diagnosis of cancer based on tissue and blood materials, which could potentially be used in clinics.

Tamarind Xyloglucan Oligosaccharides Attenuate Metabolic Disorders via the Gut-Liver Axis in Mice with High-Fat-Diet-Induced Obesity

Functional oligosaccharides exert obesity-reducing effects by acting at various pathological sites responsible for the development of obesity. In this study, tamarind xyloglucan oligosaccharides (TXOS) were used to attenuate metabolic disorders via the gut-liver axis in mice with high-fat-diet (HFD)-induced obesity, as determined through LC/MS-MS and 16S rRNA sequencing technology. A TXOS dose equivalent to 0.39 g/kg/day in humans restored the gut microbiota in obese mice, which was in part supported by the key microflora, particularly Bifidobacterium pseudolongum. Moreover, TXOS reduced the abundance of opportunistic pathogen species, such as Klebsiella variicola and Romboutsia ilealis. The bodyweight and weight gain of TXOS-treated (4.8 g/kg per day) mice began to decrease at the 14th week, decreasing by 12.8% and 23.3%, respectively. Sixteen fatty acids were identified as potential biomarkers in the liver, and B. pseudolongum and caprylic acid were found to tightly regulate each other. This was associated with reduced inflammation in the liver, circulation, and adipose tissue and protection from metabolic disorders. The findings of this study indicate that TXOS can significantly increase the gut microbiota diversity of obese mice and restore the HFD-induced dysbiosis of gut microbiota.

Galactooligosaccharides ameliorate dietary advanced glycation end product-induced intestinal barrier damage in C57BL/6 mice by modulation of the intestinal microbiome

Advanced glycation end products (AGEs) are increasingly recognized as potentially pathogenic components of processed foods, and long-term consumption of dietary AGEs triggers disruption of the intestinal barrier integrity and increases the risk of chronic diseases. Galactooligosaccharides (GOS) as prebiotics can modulate the intestinal microbiota and improve the intestinal barrier integrity. In this study, we aimed to investigate whether GOS could ameliorate the intestinal barrier damage induced by AGEs. The results showed an increased number of goblet cells (AGEs vs. H-GOS, 133.4 vs. 174.7, p < 0.05) and neutral mucin area (PAS positive area, 7.29% vs. 10.05%, p < 0.05). Upregulated expressions of occludin and claudin-1 and improved intestinal barrier integrity were observed in the H-GOS group. Using 16S rRNA sequencing analysis, we found that GOS significantly reduced the high enrichment of Akkermansia (16.95% vs. 1.29%, p < 0.05) induced by dietary AGEs while increasing the content of short-chain fatty acids. Fecal microbiota transplantation (FMT) showed that AGE-induced damage to the intestinal mucus barrier was reversed in the H-GOS transplanted group. Collectively, GOS ameliorated dietary AGE-induced intestinal barrier damage by reversing the dysregulated state of the intestinal microbiota. Our study lays the foundation for further research on dietary guidelines for populations with high AGE diets.

Clinical features and metabolic complications for non-alcoholic fatty liver disease (NAFLD) in youth with obesity

Pediatric obesity has become in the last forty years the most common metabolic disease in children and adolescents affecting about 25% of the pediatric population in the western world. As obesity worsens, a whole-body insulin resistance (IR) occurs. This phenomenon is more pronounced during adolescence, when youth experience a high degree of insulin resistance due the production of growth hormone. As IR progresses, the blunted control of insulin on adipose tissue lipolysis causes an increased flux of fatty acids with FFA deposition in ectopic tissues and organs such as the liver, leading to the development of NAFLD. In this brief review, we will discuss the clinical implications of IR and NAFLD in the context of pediatric obesity. We will review the pathogenesis and the link between these two entities, the major pathophysiologic underpinnings, including the role of genetics and metagenomics, how these two entities lead to the development of type 2 diabetes, and which are the therapeutic options for NAFLD in youth.

Using machine learning models to predict the effects of seasonal fluxes on Plesiomonas shigelloides population density

Seasonal variations (SVs) affect the population density (PD), fate, and fitness of pathogens in environmental water resources and the public health impacts. Therefore, this study is aimed at applying machine learning intelligence (MLI) to predict the impacts of SVs on P. shigelloides population density (PDP) in the aquatic milieu. Physicochemical events (PEs) and PDP from three rivers acquired via standard microbiological and instrumental techniques across seasons were fitted to MLI algorithms (linear regression (LR), multiple linear regression (MR), random forest (RF), gradient boosted machine (GBM), neural network (NN), K-nearest neighbour (KNN), boosted regression tree (BRT), extreme gradient boosting (XGB) regression, support vector regression (SVR), decision tree regression (DTR), M5 pruned regression (M5P), artificial neural network (ANN) regression (with one 10-node hidden layer (ANN10), two 6- and 4-node hidden layers (ANN64), and two 5- and 5-node hidden layers (ANN55)), and elastic net regression (ENR)) to assess the implications of the SVs of PEs on aquatic PDP. The results showed that SVs significantly influenced PDP and PEs in the water (p < 0.0001), exhibiting a site-specific pattern. While MLI algorithms predicted PDP with differing absolute flux magnitudes for the contributing variables, DTR predicted the highest PDP value of 1.707 log unit, followed by XGB (1.637 log unit), but XGB (mean-squared-error (MSE) = 0.0025; root-mean-squared-error (RMSE) = 0.0501; R² =0.998; medium absolute deviation (MAD) = 0.0275) outperformed other models in terms of regression metrics. Temperature and total suspended solids (TSS) ranked first and second as significant factors in predicting PDP in 53.3% (8/15) and 40% (6/15), respectively, of the models, based on the RMSE loss after permutations. Additionally, season ranked third among the 7 models, and turbidity (TBS) ranked fourth at 26.7% (4/15), as the primary significant factor for predicting PDP in the aquatic milieu. The results of this investigation demonstrated that MLI predictive modelling techniques can promisingly be exploited to complement the repetitive laboratory-based monitoring of PDP and other pathogens, especially in low-resource settings, in response to seasonal fluxes and can provide insights into the potential public health risks of emerging pathogens and TSS pollution (e.g., nanoparticles and micro- and nanoplastics) in the aquatic milieu. The model outputs provide low-cost and effective early warning information to assist watershed managers and fish farmers in making appropriate decisions about water resource protection, aquaculture management, and sustainable public health protection.

Alcohol consumption and metabolic syndrome: Clinical and epidemiological impact on liver disease

Alcohol use and metabolic syndrome are highly prevalent in the population and frequently co-exist. Both are implicated in a large range of health problems, including chronic liver disease, hepatocellular carcinoma, and liver-related outcomes (i.e. decompensation or liver transplantation). Studies have yielded mixed results regarding the effects of mild-moderate alcohol consumption on the risk of metabolic syndrome and fatty liver disease, possibly due to methodological differences. The few available prospective studies have indicated that mild-moderate alcohol use is associated with an increase in liver-related outcomes. This conclusion was substantiated by systems biology analyses suggesting that alcohol and metabolic syndrome may play a similar role in fatty liver disease, potentiating an already existing dysregulation of common vital homeostatic pathways. Alcohol and metabolic factors are independently and jointly associated with liver-related outcomes. Indeed, metabolic syndrome increases the risk of liver-related outcomes, regardless of alcohol intake. Moreover, the components of metabolic syndrome appear to have additive effects when it comes to the risk of liver-related outcomes. A number of population studies have implied that measures of central/abdominal obesity, such as the waist-to-hip ratio, can predict liver-related outcomes more accurately than BMI, including in individuals who consume harmful quantities of alcohol. Many studies even point to synergistic interactions between harmful alcohol use and many metabolic components. This accumulating evidence showing independent, combined, and modifying effects of alcohol and metabolic factors on the onset and progression of chronic liver disease highlights the multifactorial background of liver disease in the population. The available evidence suggests that more holistic approaches could be useful for risk prediction, diagnostics and treatment planning.

Polychlorinated biphenyls alter hepatic m6A mRNA methylation in a mouse model of environmental liver disease

Exposure to polychlorinated biphenyls (PCBs) has been associated with liver injury in human cohorts and with nonalcoholic steatohepatitis (NASH) in mice fed a high fat diet (HFD). N (6)-methyladenosine (m6A) modification of mRNA regulates transcript fate, but the contribution of m6A modification on the regulation of transcripts in PCB-induced steatosis and fibrosis is unknown. This study tested the hypothesis that PCB and HFD exposure alters the levels of m6A modification in transcripts that play a role in NASH in vivo. Male C57Bl6/J mice were fed a HFD (12 wks) and administered a single oral dose of Aroclor1260, PCB126, or Aroclor1260 + PCB126. Genome-wide identification of m6A peaks was accomplished by m6A mRNA immunoprecipitation sequencing (m6A-RIP) and the mRNA transcriptome identified by RNA-seq. Exposure of HFD-fed mice to Aroclor1260 decreased the number of m6A peaks and m6A-containing genes relative to PCB vehicle control whereas PCB126 or the combination of Aroclor1260 + PCB126 increased m6A modification frequency. ∼41% of genes had one m6A peak and ∼49% had 2-4 m6A peaks. 117 m6A peaks were common in the four experimental groups. The Aroclor1260 + PCB126 exposure group showed the highest number (52) of m6A-peaks. qRT-PCR confirmed enrichment of m6A-containing fragments of the Apob transcript with PCB exposure. A1cf transcript abundance, m6A peak count, and protein abundance was increased with Aroclor1260 + PCB126 co-exposure. Irrespective of the PCB type, all PCB groups exhibited enriched pathways related to lipid/lipoprotein metabolism and inflammation through the m6A modification. Integrated analysis of m6A-RIP-seq and mRNA-seq identified 242 differentially expressed genes (DEGs) with increased or reduced number of m6A peaks. These data show that PCB exposure in HFD-fed mice alters the m6A landscape offering an additional layer of regulation of gene expression affecting a subset of gene responses in NASH.

Hybrid XGBoost model with hyperparameter tuning for prediction of liver disease with better accuracy

BACKGROUND

Liver disease indicates any pathology that can harm or destroy the liver or prevent it from normal functioning. The global community has recently witnessed an increase in the mortality rate due to liver disease. This could be attributed to many factors, among which are human habits, awareness issues, poor healthcare, and late detection. To curb the growing threats from liver disease, early detection is critical to help reduce the risks and improve treatment outcome. Emerging technologies such as machine learning, as shown in this study, could be deployed to assist in enhancing its prediction and treatment.

AIM

To present a more efficient system for timely prediction of liver disease using a hybrid eXtreme Gradient Boosting model with hyperparameter tuning with a view to assist in early detection, diagnosis, and reduction of risks and mortality associated with the disease.

METHODS

The dataset used in this study consisted of 416 people with liver problems and 167 with no such history. The data were collected from the state of Andhra Pradesh, India, through https://www.kaggle.com/datasets/uciml/indian-liver-patient-records. The population was divided into two sets depending on the disease state of the patient. This binary information was recorded in the attribute "is_patient".

RESULTS

The results indicated that the chi-square automated interaction detection and classification and regression trees models achieved an accuracy level of 71.36% and 73.24%, respectively, which was much better than the conventional method. The proposed solution would assist patients and physicians in tackling the problem of liver disease and ensuring that cases are detected early to prevent it from developing into cirrhosis (scarring) and to enhance the survival of patients. The study showed the potential of machine learning in health care, especially as it concerns disease prediction and monitoring.

CONCLUSION

This study contributed to the knowledge of machine learning application to health and to the efforts toward combating the problem of liver disease. However, relevant authorities have to invest more into machine learning research and other health technologies to maximize their potential.

Machine Learning Approach for Metabolic Syndrome Diagnosis Using Explainable Data-Augmentation-Based Classification

Metabolic syndrome (MetS) is a cluster of risk factors including hypertension, hyperglycemia, dyslipidemia, and abdominal obesity. Metabolism-related risk factors include diabetes and heart disease. MetS is also linked to numerous cancers and chronic kidney disease. All of these variables raise medical costs. Developing a prediction model that can quickly identify persons at high risk of MetS and offer them a treatment plan is crucial. Early prediction of metabolic syndrome will highly impact the quality of life of patients as it gives them a chance for making a change to the bad habit and preventing a serious illness in the future. In this paper, we aimed to assess the performance of various algorithms of machine learning in order to decrease the cost of predictive diagnoses of metabolic syndrome. We employed ten machine learning algorithms along with different metaheuristics for feature selection. Moreover, we examined the effects of data augmentation in the prediction accuracy. The statistics show that the augmentation of data after applying feature selection on the data highly improves the performance of the classifiers.

Intestinal Engineered Probiotics as Living Therapeutics: Chassis Selection, Colonization Enhancement, Gene Circuit Design, and Biocontainment

Intestinal probiotics are often used for the in situ treatment of diseases, such as metabolic disorders, tumors, and chronic inflammatory infections. Recently, there has been an increased emphasis on intelligent, customized treatments with a focus on long-term efficacy; however, traditional probiotic therapy has not kept up with this trend. The use of synthetic biology to construct gut-engineered probiotics as live therapeutics is a promising avenue in the treatment of specific diseases, such as phenylketonuria and inflammatory bowel disease. These studies generally involve a series of fundamental design issues: choosing an engineered chassis, improving the colonization ability of engineered probiotics, designing functional gene circuits, and ensuring the safety of engineered probiotics. In this review, we summarize the relevant past research, the progress of current research, and discuss the key issues that restrict the widespread application of intestinal engineered probiotic living therapeutics.

Evaluating the clinical relevance of the enterotypes in the Estonian microbiome cohort

Human gut microbiome is subject to high inter-individual and temporal variability, which complicates building microbiome-based applications, including applications that can be used to improve public health. Categorizing the microbiome profiles into a small number of distinct clusters, such as enterotyping, has been proposed as a solution that can ameliorate these shortcomings. However, the clinical relevance of the enterotypes is poorly characterized despite a few studies marking the potential for using the enterotypes for disease diagnostics and personalized nutrition. To gain a further understanding of the clinical relevance of the enterotypes, we used the Estonian microbiome cohort dataset (n = 2,506) supplemented with diagnoses and drug usage information from electronic health records to assess the possibility of using enterotypes for disease diagnostics, detecting disease subtypes, and evaluating the susceptibility for developing a condition. In addition to the previously established 3-cluster enterotype model, we propose a 5-cluster community type model based on our data, which further separates the samples with extremely high Bacteroides and Prevotella abundances. Collectively, our systematic analysis including 231 phenotypic factors, 62 prevalent diseases, and 33 incident diseases greatly expands the knowledge about the enterotype-specific characteristics; however, the evidence suggesting the practical use of enterotypes in clinical practice remains scarce.

Host and microbial-derived metabolites for Clostridioides difficile infection: Contributions, mechanisms and potential applications

Clostridioides difficile infection (CDI), which mostly occurs in hospitalized patients, is the most common and costly health care-associated disease. However, the biology of C. difficile remains incompletely understood. Current therapeutics are still challenged by the frequent recurrence of CDI. Advances in metabolomics facilitate our understanding of the etiology of CDI, which is not merely an alteration in the structure of the gut microbial community but also a dysbiosis metabolic setting promoting the germination, expansion and virulence of C. difficile. Therefore, we summarized the gut microbial and metabolic profiles for CDI under different conditions, such as those of postantibiotic treatment and postfecal microbiota transplantation. The current understanding of the role of host and gut microbial-derived metabolites as well as other nutrients in preventing or alleviating the disease symptoms of CDI will also be provided in this review. We hope that a specific nutrient-centric dietary strategy or the administration of certain nutrients to the colon could serve as an alternate line of investigation for the prophylaxis and mitigation of CDI in the future. Nevertheless, rigorously designed basic studies and randomized controlled trials need to be conducted to assess the functional mechanisms and effects of such therapeutics.

Small molecules in the big picture of gut microbiome-host cross-talk

Research on the gut microbiome and related diseases is rapidly growing with the development of sequencing technologies. An increasing number of studies offer new perspectives on disease development or treatment. Among these, the mechanisms of gut microbial metabolite-mediated effects merit better understanding. In this review, we first summarize the shifts in gut microbial metabolites within complex diseases, in which metabolites have correlational and occasionally causal effects on diseases and discuss the reported mechanisms. We further investigate the interactions between gut microbes and drugs, providing insights for precision medication as well as limitations of current research. Finally, we provide new research directions and research strategies for the development of drugs from gut microbial metabolites. FUNDING STATEMENT: None.