Clinical and Translational Gastroenterology最新文献

Introduction: Biomarkers to guide clinical decision making in active ulcerative colitis (UC) patients are urgently needed. This study aims to identify metabolites associated with UC treatment escalation and establish prediction models based on untargeted metabolomics and machine learning algorithms.

Methods: Liquid chromatography-mass spectrometry-based untargeted metabolomics analysis was performed on 88 plasma samples (44 active UC patients requiring treatment escalation and 44 active UC patients not requiring treatment escalation). Univariate and multivariate analyses were applied to identify metabolic biomarkers for UC treatment escalation. Metabolic pathway enrichment analysis was performed to reveal the disturbed metabolic pathways related to UC treatment escalation. Four machine learning algorithms, including Support Vector Machine, Random Forest, k-Nearest Neighbor, and logistic regression were used to build diagnostic models for UC treatment escalation.

Results: Nine significantly differential metabolites were identified as the candidate biomarkers for UC treatment escalation. Pathway analysis revealed that phenylalanine metabolism and ether lipid metabolism are the disturbed metabolic pathways related to treatment escalation. The protein-metabolite interaction network identified 21 proteins are associated with 9 treatment escalation related metabolites. The areas under the receiver operating characteristic curve of the Support Vector Machine, Random Forest, k-Nearest Neighbor, and logistic regression models based on metabolic biomarkers were 0.923, 0.966, 0.897 and 0.803, respectively.

Discussion: The plasma metabolome represents a promising source of biomarkers for the prediction of treatment escalation in active UC. Metabolic biomarkers, combined with machine learning algorithms, could be efficient for risk assessment and early identification of UC treatment escalation.

Introduction: Early identification of hepatocellular carcinoma (HCC) is critical to reduce mortality. Diagnostic tools are limited for early disease. Intestinal microbiota may contribute to HCC risk directly and through metabolites, particularly bile acids (BAs), offering potential noninvasive biomarkers.

Methods: This was a case-control study of patients with cirrhosis with or without early-stage HCC, matched based on liver disease severity. Comprehensive analyses of fecal microbiota composition and function were performed.

Results: There were 98 patients in the study (49 patients per group). Subjects with HCC were older (median 64 vs 60 years, P < 0.01) and more likely to have hepatitis C (78% vs 43%, P < 0.01). Alpha diversity, beta diversity, and genes and pathways related to BA metabolism did not differ between groups overall, but alpha diversity did differ within the subset of patients with metabolic-associated steatotic liver disease. There was differential abundance of multiple taxa between groups, including higher abundance of Klebsiella pneumoniae in cases. Increased concentration of secondary BA, which are microbiota-dependent, was associated with higher odds of HCC (adjusted odds ratio 2.4, P = 0.02); however, addition of microbial or BA features to a model with clinical data alone did not improve HCC prediction.

Discussion: When accounting for liver disease severity, there were limited differences in intestinal microbiota composition and BA metabolism between subjects with or without early-stage HCC. Promising areas for future study of microbiota-based HCC biomarkers were identified, including a focus on the subpopulation of patients with metabolic-associated steatotic liver disease.

Nonalcoholic fatty liver disease is the most prevalent chronic liver disease worldwide. It is now updated as metabolic dysfunction-associated steatotic liver disease (MASLD). The progression of MASLD to hepatocellular carcinoma (HCC) involves complex mechanisms, with the gut microbiota (GM) and its metabolites playing a pivotal role in this transformation through the "gut-liver axis." This review systematically summarizes the characteristics of GM dysbiosis in patients with MASLD and the regulatory mechanisms of its metabolites (e.g., short-chain fatty acids, secondary bile acids, trimethylamine N-oxide, and lipopolysaccharides) in the progression from MASLD to HCC. Short-chain fatty acids exert protective effects in the early stages by enhancing the intestinal barrier and modulating immune and metabolic responses. However, metabolic disturbances, such as the "paradoxical effect" of butyrate and the lipogenic effect of acetate, may promote the formation of a tumor microenvironment in the later stages. Secondary bile acids (e.g., deoxycholic acid) exacerbate liver fibrosis and carcinogenesis by activating inflammatory pathways (nuclear factor-κB and mitogen-activated protein kinase), inducing oxidative stress, and inhibiting foresaid X receptor signaling. Trimethylamine N-oxide directly drives HCC progression by activating the mitogen-activated protein kinase/nuclear factor-κB pathway, promoting epithelial-mesenchymal transition, and creating an immunosuppressive microenvironment. Lipopolysaccharide accelerates fibrosis and metabolic reprogramming through toll-like receptor 4-mediated chronic inflammation and hepatic stellate cell activation. This review highlights that the dynamic changes in GM metabolites are closely associated with MASLD-HCC progression. Specific monitoring of these metabolites may serve as potential biomarkers for early detection. Furthermore, gut-targeted therapies (e.g., fecal microbiota transplantation) have shown translational potential. Future studies are needed to further validate their clinical value and develop precise prevention and treatment strategies.

Introduction: Pancreatitis-associated osteopathy is a clinically significant but mechanistically underexplored complication of pancreatic disease. We aimed to characterize stage-specific alterations in bone remodeling biomarkers across the spectrum of disease: recurrent acute pancreatitis (RAP) and chronic pancreatitis (CP).

Methods: In a cross-sectional analysis of North American Pancreatitis Study 2 participants, we measured serum mechanistic (sclerostin, dickkopf-1, receptor activator of nuclear factor κβ ligand, and osteoprotegerin), hormonal (fibroblast growth factor 23, insulin, and leptin), and modulatory (osteopontin, oncostatin, and osteoactivin) markers in controls (n = 30), RAP (n = 40), and CP (n = 40) using a multiplex assay. Group differences were assessed with ANOVA, Fisher exact test, and Kruskal-Wallis; multivariable regression identified predictors of biomarker variation.

Results: Eight of 10 biomarkers differed significantly among groups. Sclerostin, dickkopf-1, receptor activator of nuclear factor κβ ligand, and osteoprotegerin were elevated in RAP and CP vs controls, with the highest values in CP. The RANKL/OPG ratio was greatest in CP. Fibroblast growth factor 23 was increased in RAP, while insulin was reduced in CP. Osteopontin and oncostatin were elevated in pancreatitis groups, with osteopontin increasing progressively from control to RAP to CP. Several bone biomarker patterns varied by sex, tobacco, and alcohol use. Stepwise regression identified several significant predictors.

Discussion: These findings represent the most comprehensive bone metabolism biomarker profiling in pancreatitis to date, revealing stage-specific dysregulation of bone remodeling. Findings suggest a shift toward increased bone resorption and impaired formation with disease progression. Larger longitudinal studies are needed for marker validation, to clarify mechanisms, and guide targeted interventions to reduce bone loss and fracture risk in this high-risk population.

Introduction: Functional dyspepsia (FD), a disorder of gut-brain interaction defined by Rome IV criteria, overlaps frequently with irritable bowel syndrome (IBS) and reflux symptoms. Overlapping gastrointestinal (GI) disorders are associated with more psychological comorbidities and decreased quality of life, but data on physiological parameters in overlapping syndromes are scarce. The aim of this study was to evaluate the impact of overlapping disorders and FD subtypes on psychological and GI symptoms, as well as relevant physiological outcomes.

Methods: In total, 202 patients with predominant Rome IV FD symptoms and overlapping IBS and/or reflux were pooled from 7 studies conducted at University Hospitals Leuven. GI and psychological symptoms, as well as GI-related quality of life were recorded. Physiological measurements included salivary cortisol, gastric emptying, systemic and duodenal immune activation, and duodenal permeability.

Results: GI (Patient Assessment of Upper GI Symptom Severity and Leuven Postprandial Distress Scale [ P < 0.0001]) and psychological (anxiety [ P = 0.0014], depression [ P = 0.0017] and extraintestinal somatic symptoms [ P < 0.0001]) were more pronounced in FD with overlapping disorders, whereas all physiological parameters were similar. Patients with epigastric pain syndrome reported a milder symptom pattern compared with other FD subtypes, with similar physiological alterations. Duodenal eosinophils were associated with anxiety in FD, independent of overlapping GI disorders or subtype ( P = 0.043).

Discussion: The common overlap between FD, IBS, and reflux is characterized by a high GI-specific, psychological, and somatic symptom burden. By contrast, key pathophysiological parameters were not different between FD with and without overlapping disorders or between FD subtypes. Central integration of multiple GI manifestations rather than more severely impaired peripheral alterations is more likely to explain the high symptom burden in overlapping disorders.

Introduction: Noncirrhotic portal hypertension (NCPH) refers to a diverse group of disorders that affects the hepatic portosinusoidal vascular system resulting in portal hypertension (PH). Unlike cirrhosis, there are no noninvasive criteria to diagnose PH and varices in NCPH.

Methods: A prospective cohort of patients with NCPH who had transjugular liver biopsy, liver stiffness (LSM) measured using transient elastography, and laboratory and imaging data were included. PH was defined by the presence of one among the following-varices on endoscopy, portosystemic collaterals, or ascites on imaging. Logistic regression was used to identify predictors. The classification tree approach was used to identify cutoff values for the stepwise decision model.

Results: Of the 59 patients, 41 (69%) had PH and 36 (62%) had varices. LSM was higher in patients with PH (11.5 kPa vs 5.7 kPa, P < 0.01) and varices (12 kPa vs 5.9 kPa, P < 0.01). Platelet count was lower in patients with PH (79 vs 218 × 109/L, P < 0.01) and varices (75 vs 209 × 109/L, P < 0.01). Multivariate analysis combining LSM and platelet count predicts PH (area under receiver operating characteristic 96% [91%-99%]) and varices (area under receiver operating characteristic 92% [85%-99%]). A stepwise model combining platelet 140 × 109/L and LSM 7 kPa performed with a sensitivity of 100%, negative predictive value of 100%, and accuracy of 90% to detect PH. The same model performed with sensitivity of 100%, negative predictive value of 100%, and accuracy of 81% to detect varices.

Discussion: Noninvasive model combining LSM with platelet count can aid in identifying NCPH patients with PH and varices. However, this model requires validation in an independent cohort.

Introduction: Severe hepatic steatosis (HS) and significant hepatic fibrosis indicate severity of metabolic dysfunction-associated steatotic liver disease (MASLD), and the models for early detection are important. Recent studies suggest immunoglobulin G (IgG) glycosylation and microbial consortia may involve in the development of MASLD. This study investigates the utility of IgG glycosylation and microbial consortia in early detection models.

Methods: A total of 111 patients with MASLD categorized into high vs low controlled attenuation parameter (for HS) or high vs low FIB-4 (for fibrosis). We analyzed differences in microbial consortia and IgG1/IgG2 glycosylation features, creating scores for identifying severe HS and significant hepatic fibrosis. The combination scores for identifying severe HS and significant hepatic fibrosis in MASLD patients were derived based on the microbial consortia and IgG1/IgG2 glycosylation features accordingly.

Results: Megamonas was linked to severe HS, and Christensenellaceae R-7 , UCG-005 , CAG-56 , and Lachnospira were associated with significant hepatic fibrosis in patients with MASLD. The areas under curve of severe HS- and significant hepatic fibrosis-microbial scores were 0.731 and 0.773, respectively. The ratios of IgG2-N4H5F1/IgG1-N4H5F1 and bisected-IgG2/bisected-IgG1 predicted severe HS, while IgG2-N4H3F1/IgG1-N4H3F1, bisected-IgG1/bisected-IgG2, and IgG1-galactosylation/IgG2-galactosylation index identified significant hepatic fibrosis in MASLD. The combination scores including microbial consortia and glycosylation features achieved areas under curve of 0.776 and 0.810, showing comparable performance with established indices like HS index and FibroAST score.

Discussion: The study highlights the effectiveness of combining fecal microbiota and serum IgG glycosylation features in identifying MASLD severity, which are noninferior to the current regularly used models, and suggests the intervention of glycosylation as a promising therapeutic approach for MASLD.