Clinical and Translational Gastroenterology最新文献

Introduction: Esophageal adenocarcinoma (EAC) is an aggressive cancer with poor prognosis. Barrett's esophagus (BE) is a critical precursor of EAC. Patients with BE undergo endoscopic surveillance to monitor disease progression although only a small fraction develop EAC. These procedures are invasive and have limited accuracy in predicting BE progression. We evaluated the utility of an 8-protein mass spectrometry panel in predicting progression in patients with BE.

Methods: Eighty untreated controls and 20 cases were selected from our institutional tissue registry. Quantitative mass-spectrometry was performed on microdissected tissue sections. Data were split into 80% training and 20% test sets. We used Least Absolute Shrinkage and Selection Operator-regularized regression to train a logistic classifier on training data. Classifier performance was evaluated in test data.

Results: Ninety-two samples had sufficient tissue for mass spectrometry analysis (18 progressors, 74 nonprogressors). The multivariable regression model produced a sensitivity of 100% and a specificity of 39% in the overall cohort, with AUCs of 0.75 and 0.89 in the overall and test cohorts, respectively. Cox proportional hazards time-to-progression (TTP) showed a hazard ratio of 66.1 (95% CI 7.79-561, P = 0.00012) for the model prediction.

Discussion: The promising performance of the model generated here suggests that the test may aid in selecting patients most likely to benefit from active BE surveillance. Moreover, the association of this model's prediction with time-to-progression may offer decision support for management of patients likely to progress quickly. These results support continued development of this proteomic panel as a risk stratification tool for patients with BE.

Introduction: Clinically relevant esophagogastric junction metrics on functional lumen imaging probe (FLIP) in postfundoplication patients remain unclear.

Methods: Sixty-three symptomatic postfundoplication patients underwent FLIP, barium esophagram, and high-resolution manometry. Logistic regressions and receiver-operating characteristic curves for distensibility index (DI) at 60 mL and maximal diameter were generated to predict impaired clearance.

Results: Maximal diameter (odds ratio: 0.77, confidence interval: 0.62-0.96, P = 0.02, area under receiver-operating characteristic curve = 0.73), but not DI, independently predicted impaired clearance. Diameter >16.5 mm achieved >90% sensitivity for normal clearance; DI < 2.0 mm 2 /mm Hg and diameter <8 mm were >90% specific for impaired clearance.

Discussion: Maximal diameter on postfundoplication FLIP predicts impaired clearance and discriminates better than DI.

Introduction: Chitinase-3-like protein 1 (CHI3L1) is a glycoprotein involved in inflammation and fibrosis, but its association with nonalcoholic fatty liver disease (NAFLD) remains unclear. This study investigated the association between serum CHI3L1 levels and the risk of severe NAFLD in a large prospective cohort.

Methods: A prospective cohort study was conducted using UK Biobank data from 50,334 participants. Serum CHI3L1 levels were measured at baseline. Severe NAFLD cases were identified using hospital records. Cox proportional hazards models evaluated the association between CHI3L1 levels and severe NAFLD risk. Restricted cubic spline analysis assessed potential nonlinearity. Subgroup and mediation analyses were conducted to explore effect modifiers and underlying pathways.

Results: Over a median follow-up of 16 years, 766 severe NAFLD cases were identified. Higher CHI3L1 levels were significantly associated with increased severe NAFLD risk (hazard ratio 1.45, 95% confidence interval 1.34-1.58, P < 0.001). Restricted cubic spline analysis revealed a linear positive association without evidence of nonlinearity. Stratified analyses showed consistent associations across subgroups, with no significant interactions. Mediation analysis identified high-density lipoprotein cholesterol and alanine aminotransferase as partial mediators, explaining 6.38% and 2.86% of the total effect, respectively, whereas the direct effect of CHI3L1 remained dominant.

Discussion: Elevated CHI3L1 levels are associated with an increased risk of severe NAFLD. These findings suggest that CHI3L1 may serve as a novel biomarker and potential contributor to NAFLD progression, offering insights into the inflammatory and metabolic mechanisms underlying the disease.

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.