Robust Microbial Markers for Non-Invasive Inflammatory Bowel Disease Identification

The Current Role of Imaging in the Diagnosis of Inflammatory Bowel Disease and Detection of Its Complications: A Systematic Review

Inflammatory bowel disease (IBD) encompasses complex gastrointestinal (GI) conditions, primarily Crohn's disease (CD) and ulcerative colitis (UC), requiring precise imaging for effective diagnosis and management of complications. This systematic review aimed to evaluate the current role of imaging modalities in diagnosing IBD and detecting related complications. The review adhered to the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines. We performed a literature search using text words and controlled vocabulary applying Boolean operators "AND," "OR," with various combinations on databases such as PubMed, Embase, and Cochrane Library. The search targeted open-access articles involving humans, with full-text available, and published in the English Language from 2005 to 2024. The quality of the included studies was assessed using the Cochrane Risk-of-Bias (RoB) checklist. Our search process identified 127 records from Cochrane (39), Embase (29), and PubMed (59). After removing 98 irrelevant records, 29 underwent further screening. Five were excluded as they involved irrelevant problems or outcomes, leaving us with 24 reports with full text, all of which were accessible. Following the eligibility assessment, two more reports were excluded due to inaccessibility, and 22 studies were included in the final analysis. The risk of bias and methodological quality assessment revealed that out of 22 studies analyzed, five (23%) had a high risk of bias, while 13 (59%) were classified as moderate risk, and four (18%) showed low risk. This distribution highlights a predominance of moderate-risk studies in research on imaging in IBD, emphasizing the need for enhanced study designs in future investigations. Our findings revealed the varying effectiveness of imaging modalities in diagnosing complications of CD and UC. Magnetic resonance enterography (MRE) stands out as the preferred method for CD due to its high sensitivity and noninvasive nature. In contrast, colonoscopy remains the gold standard for UC, providing direct visualization of mucosal lesions. While techniques like ultrasound and capsule endoscopy offer valuable insights, they have limitations that may affect their utility in certain cases.

Camu-camu decreases hepatic steatosis and liver injury markers in overweight, hypertriglyceridemic individuals: A randomized crossover trial

Non-alcoholic fatty liver disease (NAFLD) affects 25% of the adult population with no effective drug treatments available. Previous animal studies reported that a polyphenol-rich extract from the Amazonian berry camu-camu (CC) prevented hepatic steatosis in a mouse model of diet-induced obesity. This study aims to determine the impact of CC on hepatic steatosis (primary outcome) and evaluate changes in metabolic and gut microbiota profiles (exploratory outcomes). A randomized, double-blind, placebo-controlled crossover trial is conducted on 30 adults with overweight and hypertriglyceridemia, who consume 1.5 g of CC capsules or placebo daily for 12 weeks. CC treatment decreases liver fat by 7.43%, while it increases by 8.42% during the placebo intervention, showing a significant difference of 15.85%. CC decreases plasma aspartate and alanine aminotransferases levels and promotes changes in gut microbiota composition. These findings support that polyphenol-rich prebiotic may reduce liver fat in adults with overweight, reducing the risk of developing NAFLD.

Prediction of Smoking Habits From Class-Imbalanced Saliva Microbiome Data Using Data Augmentation and Machine Learning

Human microbiome research is moving from characterization and association studies to translational applications in medical research, clinical diagnostics, and others. One of these applications is the prediction of human traits, where machine learning (ML) methods are often employed, but face practical challenges. Class imbalance in available microbiome data is one of the major problems, which, if unaccounted for, leads to spurious prediction accuracies and limits the classifier's generalization. Here, we investigated the predictability of smoking habits from class-imbalanced saliva microbiome data by combining data augmentation techniques to account for class imbalance with ML methods for prediction. We collected publicly available saliva 16S rRNA gene sequencing data and smoking habit metadata demonstrating a serious class imbalance problem, i.e., 175 current vs. 1,070 non-current smokers. Three data augmentation techniques (synthetic minority over-sampling technique, adaptive synthetic, and tree-based associative data augmentation) were applied together with seven ML methods: logistic regression, k-nearest neighbors, support vector machine with linear and radial kernels, decision trees, random forest, and extreme gradient boosting. K-fold nested cross-validation was used with the different augmented data types and baseline non-augmented data to validate the prediction outcome. Combining data augmentation with ML generally outperformed baseline methods in our dataset. The final prediction model combined tree-based associative data augmentation and support vector machine with linear kernel, and achieved a classification performance expressed as Matthews correlation coefficient of 0.36 and AUC of 0.81. Our method successfully addresses the problem of class imbalance in microbiome data for reliable prediction of smoking habits.

Artificial intelligence applications in inflammatory bowel disease: Emerging technologies and future directions

Inflammatory bowel disease (IBD) is a complex and multifaceted disorder of the gastrointestinal tract that is increasing in incidence worldwide and associated with significant morbidity. The rapid accumulation of large datasets from electronic health records, high-definition multi-omics (including genomics, proteomics, transcriptomics, and metagenomics), and imaging modalities (endoscopy and endomicroscopy) have provided powerful tools to unravel novel mechanistic insights and help address unmet clinical needs in IBD. Although the application of artificial intelligence (AI) methods has facilitated the analysis, integration, and interpretation of large datasets in IBD, significant heterogeneity in AI methods, datasets, and clinical outcomes and the need for unbiased prospective validations studies are current barriers to incorporation of AI into clinical practice. The purpose of this review is to summarize the most recent advances in the application of AI and machine learning technologies in the diagnosis and risk prediction, assessment of disease severity, and prediction of clinical outcomes in patients with IBD.

Shift of Maternal Gut Microbiota of Tibetan Antelope (Pantholops hodgsonii) During the Periparturition Period

The maternal gut microbiota can influence and be affected by the substantial physiological changes taking place during the periparturition period. However, little information is known about the changes in the maternal gut microbiota and hormonal variations during this period in nonmodel organisms. Tibetan antelope (Pantholops hodgsonii) provide a unique system to address this issue because their summer migration cycle is synchronized with the periparturition period. Here, we used fecal microbiota as a proxy of gut microbiota. We characterized fecal microbial community of female migratory Tibetan antelope in the late pregnancy and postpartum periods using 16S rRNA gene sequencing and quantified fecal glucocorticoids (GCs) and triiodothyronine (T3) metabolite concentrations through enzyme immunoassays to identify the associations between maternal gut microbiota and physiological changes related with reproduction. We found that the fecal microbiota of Tibetan antelope was dominated by Firmicutes and Bacteroidetes. The microbial composition was significantly altered during the transition from late pregnancy to the postpartum period. Fecal T3 concentration was significantly higher in the postpartum period compared to late pregnancy, whereas GC metabolite concentration did not significantly differ between two reproductive states. We identified six genera (Anaerofustis, Bacteroides, Coprococcus_2, Ruminiclostridium_5, Ruminococcaceae_UCG-007, and Tyzzerella) that were significantly associated with reproductive states. We also found two genera (Christensenellaceae_R-7_group and Rikenellaceae_RC9_gut_group) significantly associated with GC metabolite concentration and two genera (Agathobacter and Papillibacter) significantly associated with T3 metabolite concentration, though these correlations were weak with coefficient values ranging from - 0.007 to 0.03. Our results indicate that many members of the gut microbiota are associated with the physiological changes in the transition from late pregnancy to the postpartum period, likely reflecting the metabolic and immune system dynamics during the periparturition period. This study highlights the importance of integrating microbiota, hormones and migration pattern to study the reproductive health of wildlife. By establishing a baseline of the physiological changes during the migration/periparturition period, we can have a better understanding of the impacts of increasing human activities on the Tibetan Plateau on the reproductive health of Tibetan antelope.

Baseline gut microbiome composition predicts metformin therapy short-term efficacy in newly diagnosed type 2 diabetes patients

BACKGROUND

The study was conducted to investigate the effects of metformin treatment on the human gut microbiome's taxonomic and functional profile in the Latvian population, and to evaluate the correlation of these changes with therapeutic efficacy and tolerance.

METHODS

In this longitudinal observational study, stool samples for shotgun metagenomic sequencing-based analysis were collected in two cohorts. The first cohort included 35 healthy nondiabetic individuals (metformin dose 2x850mg/day) at three time-points during metformin administration. The second cohort was composed of 50 newly-diagnosed type 2 diabetes patients (metformin dose-determined by an endocrinologist) at two concordant times. Patients were defined as Responders if their HbA1c levels during three months of metformin therapy had decreased by ≥12.6 mmol/mol (1%), while in Non-responders HbA1c were decreased by <12.6 mmol/mol (1%).

RESULTS

Metformin reduced the alpha diversity of microbiota in healthy controls (p = 0.02) but not in T2D patients. At the species level, reduction in the abundance of Clostridium bartlettii and Barnesiella intestinihominis, as well as an increase in the abundance of Parabacteroides distasonis and Oscillibacter unclassified overlapped between both study groups. A large number of group-specific changes in taxonomic and functional profiles was observed. We identified an increased abundance of Prevotella copri (FDR = 0.01) in the Non-Responders subgroup, and enrichment of Enterococcus faecium, Lactococcus lactis, Odoribacter, and Dialister at baseline in the Responders group. Various taxonomic units were associated with the observed incidence of side effects in both cohorts.

CONCLUSIONS

Metformin effects are different in T2D patients and healthy individuals. Therapy induced changes in the composition of gut microbiome revealed possible mediators of observed short-term therapeutic effects. The baseline composition of the gut microbiome may influence metformin therapy efficacy and tolerance in T2D patients and could be used as a powerful prediction tool.

Environmental microcystin targets the microbiome and increases the risk of intestinal inflammatory pathology via NOX2 in underlying murine model of Nonalcoholic Fatty Liver Disease

With increased climate change pressures likely to influence harmful algal blooms, exposure to microcystin, a known hepatotoxin and a byproduct of cyanobacterial blooms can be a risk factor for NAFLD associated comorbidities. Using both in vivo and in vitro experiments we show that microcystin exposure in NAFLD mice cause rapid alteration of gut microbiome, rise in bacterial genus known for mediating gut inflammation and lactate production. Changes in the microbiome were strongly associated with inflammatory pathology in the intestine, gut leaching, tight junction protein alterations and increased oxidative tyrosyl radicals. Increased lactate producing bacteria from the altered microbiome was associated with increased NOX-2, an NADPH oxidase isoform. Activationof NOX2 caused inflammasome activation as shown by NLRP3/ASCII and NLRP3/Casp-1 colocalizations in these cells while use of mice lacking a crucial NOX2 component attenuated inflammatory pathology and redox changes. Mechanistically, NOX2 mediated peroxynitrite species were primary to inflammasome activation and release of inflammatory mediators. Thus, in conclusion, microcystin exposure in NAFLD could significantly alter intestinal pathology especially by the effects on microbiome and resultant redox status thus advancing our understanding of the co-existence of NAFLD-linked inflammatory bowel disease phenotypes in the clinic.