Akkermansia muciniphila improve cognitive dysfunction by regulating BDNF and serotonin pathway in gut-liver-brain axis.

BACKRGROUND: Akkermansia muciniphila, a next-generation probiotic, is known as a cornerstone regulating the gut-organ axis in various diseases, but the underlying mechanism remains poorly understood. Here, we revealed the neuronal and antifibrotic effects of A. muciniphila on the gut-liver-brain axis in liver injury. RESULTS: To investigate neurologic dysfunction and characteristic gut microbiotas, we performed a cirrhosis cohort (154 patients with or without hepatic encephalopathy) and a community cognition cohort (80 participants in one region for three years) and validated the existence of cognitive impairment in a 3,5-diethoxycarbonyl-1,4-dihydrocollidine-induced hepatic injury mouse model. The effects of the candidate strain on cognition were evaluated in animal models of liver injury. The expression of brain-derived neurotrophic factor (BDNF) and serotonin receptors was accessed in patients with fibrosis (100 patients) according to the fibrosis grade and hepatic venous pressure gradient. The proportion of A. muciniphila decreased in populations with hepatic encephalopathy and cognitive dysfunction. Tissue staining techniques confirmed gut-liver-brain damage in liver injury, with drastic expression of BDNF and serotonin in the gut and brain. The administration of A. muciniphila significantly reduced tissue damage and improved cognitive dysfunction and the expression of BDNF and serotonin. Isolated vagus nerve staining showed a recovery of serotonin expression without affecting the dopamine pathway. Conversely, in liver tissue, the inhibition of injury through the suppression of serotonin receptor (5-hydroxytryptamine 2A and 2B) expression was confirmed. The severity of liver injury was correlated with the abundance of serotonin, BDNF, and A. muciniphila. CONCLUSIONS: A. muciniphila, a next-generation probiotic, is a therapeutic candidate for alleviating the symptoms of liver fibrosis and cognitive impairment.

The unfolded features on the synchronized fashion of gut microbiota and Drynaria rhizome against obesity via integrated pharmacology.

Drynaria rhizome (DR) is used as a natural remedy to ameliorate obesity (OB) in East Asia; in parallel, the gut microbiota (GM) might exert a positive impact on OB through their metabolites. This study elucidates the orchestrated effects of DR and GM on OB. DR-GM, - a key signaling pathway-target-metabolite (DGSTM) networks were used to unveil the relationship between DR and GM, and Molecular Docking Test (MDT) and Density Functional Theory (DFT) were adopted to underpin the uppermost molecules. The NR1H3 (target) - 3-Epicycloeucalenol (ligand), and PPARG (target) - Clionasterol (ligand) conjugates from DR, FABP3 (target) - Ursodeoxycholic acid, FABP4 (target) - Lithocholic acid (ligand) or Deoxycholic acid (ligand), PPARA (target) - Equol (ligand), and PPARD (target) - 2,3-Bis(3,4-dihydroxybenzyl)butyrolactone (ligand) conjugates from GM formed the most stable conformers via MDT and DFT. Overall, these findings suggest that DR-GM might be a promising ameliorator on PPAR signaling pathway against OB.

Effect of diabetes on mortality and liver transplantation in alcoholic liver cirrhotic patients with acute decompensation.

BACKGROUND: Previous studies have investigated the influence of diabetes on alcoholic liver cirrhosis patients, leaving its impact unclear. Thus, we conducted a study to reveal the association of diabetes and clinical outcomes of such patients. MATERIALS AND METHODS: We prospectively collected data from multicenter pertaining to 965 patients diagnosed with alcoholic liver cirrhosis, all of whom were admitted due to acute decompensation between 2015 and 2019. Risk of major precipitating factors and incidences of death or liver transplantation in patients with and without diabetes was comparatively assessed. Propensity score (PS) matching was performed at a 1:2 ratio for accurate comparisons. RESULTS: The mean age was 53.4 years, and 81.0% of the patients were male. Diabetes was prevalent in 23.6% of the cohort and was positively correlated with hepatic encephalopathy and upper gastrointestinal bleeding, although not statistically significant. During a median follow-up of 903.5 person-years (PYs), 64 patients with and 171 without diabetes died or underwent liver transplantation, with annual incidence of 33.6/100 PYs and 24.0/100 PYs, respectively. In the PS-matched cohort, the incidence of death or liver transplantation was 36.8/100 PYs and 18.6/100 PYs in the diabetes and matched control group, respectively. After adjusting for various factors, coexisting diabetes significantly heightened the risk of death or liver transplantation in the short and long term, in addition to prolonged prothrombin time, low serum albumin, elevated total bilirubin and creatinine, and decreased serum sodium levels. CONCLUSIONS: Diabetes increases the risk of death or liver transplantation in patients with alcoholic liver cirrhosis.

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.

Gut microbiome and metabolome signatures in liver cirrhosis-related complications.

Background/Aims: Shifts in the gut microbiota and metabolites are interrelated with liver cirrhosis progression and complications. However, causal relationships have not been evaluated comprehensively. Here, we identified complication-dependent gut microbiota and metabolic signatures in patients with liver cirrhosis. Methods: Microbiome taxonomic profiling was performed on 194 stool samples (52 controls and 142 cirrhosis patients) via V3-V4 16S rRNA sequencing. Next, 51 samples (17 controls and 34 cirrhosis patients) were selected for fecal metabolite profiling via gas chromatography mass spectrometry and liquid chromatography coupled to time-of-flight-mass spectrometry. Correlation analyses were performed targeting the gut- microbiota, metabolites, clinical parameters, and presence of complications (varices, ascites, peritonitis, encephalopathy, hepatorenal syndrome, hepatocellular carcinoma, and deceased). Results: Veillonella bacteria, Ruminococcus gnavus, and Streptococcus pneumoniae are cirrhosis-related microbiotas compared with control group. Bacteroides ovatus, Clostridium symbiosum, Emergencia timonensis, Fusobacterium varium, and Hungatella_uc were associated with complications in the cirrhosis group. The areas under the receiver operating characteristic curve (AUROCs) for the diagnosis of cirrhosis, encephalopathy, hepatorenal syndrome, and deceased were 0.863, 0.733, 0.71, and 0.69, respectively. The AUROCs of mixed microbial species for the diagnosis of cirrhosis and complication were 0.808 and 0.847, respectively. According to the metabolic profile, 5 increased fecal metabolites in patients with cirrhosis were biomarkers (AUROC > 0.880) for the diagnosis of cirrhosis and complications. Clinical markers were significantly correlated with the gut microbiota and metabolites. Conclusion: Cirrhosis-dependent gut microbiota and metabolites present unique signatures that can be used as noninvasive biomarkers for the diagnosis of cirrhosis and its complications.

The synchronized feature of Saururus chinensis and gut microbiota against T2DM, NAFLD, obesity and hypertension via integrated pharmacology.

Type 2 diabetes mellitus (T2DM), nonalcoholic fatty liver disease (NAFLD), obesity (OB) and hypertension (HT) are categorized as metabolic disorders (MDs), which develop independently without distinct borders. Herein, we examined the gut microbiota (GM) and Saururus chinensis (SC) to confirm their therapeutic effects via integrated pharmacology. The overlapping targets from the four diseases were determined to be key protein coding genes. The protein-protein interaction (PPI) networks, and the SC, GM, signalling pathway, target and metabolite (SGSTM) networks were analysed via RPackage. Additionally, molecular docking tests (MDTs) and density functional theory (DFT) analysis were conducted to determine the affinity and stability of the conformer(s). TNF was the main target in the PPI analysis, and equol derived from Lactobacillus paracasei JS1 was the most effective agent for the formation of the TNF complex. The SC agonism (PPAR signalling pathway), and antagonism (neurotrophin signalling pathway) by SC were identified as agonistic bioactives (aromadendrane, stigmasta-5,22-dien-3-ol, 3,6,6-trimethyl-3,4,5,7,8,9-hexahydro-1H-2-benzoxepine, 4α-5α-epoxycholestane and kinic acid), and antagonistic bioactives (STK734327 and piclamilast), respectively, via MDT. Finally, STK734327-MAPK1 was the most favourable conformer according to DFT. Overall, the seven bioactives from SC and equol that can be produced by Lactobacillus paracasei JS1 can exert synergistic effects on these four diseases.

Validation of MELD 3.0 in patients with alcoholic liver cirrhosis using prospective KACLiF cohort.

BACKGROUND AND AIM: The Model for End-Stage Liver Disease (MELD) is a reliable prognostic tool for short-term outcome prediction in patients with end-stage liver disease. MELD 3.0 was introduced to enhance the predictive accuracy. This study assessed the performance of MELD 3.0, in comparison to MELD and MELD-Na, in patients with alcoholic liver cirrhosis. METHODS: This multicenter prospective cohort study comprised patients with alcoholic cirrhosis admitted for acute deterioration of liver function in the Republic of Korea between 2015 and 2019. This study compared the predictive abilities of MELD, MELD-Na, and MELD 3.0, for 30-day and 90-day outcomes, specifically death or liver transplantation, and explored the factors influencing these outcomes. RESULTS: A total of 1096 patients were included in the study, with a mean age of 53.3 ± 10.4 years, and 82.0% were male. The mean scores for MELD, MELD-Na, and MELD 3.0 at the time of admission were 18.7 ± 7.2, 20.6 ± 7.7, and 21.0 ± 7.8, respectively. At 30 and 90 days, 7.2% and 14.1% of patients experienced mortality or liver transplantation. The areas under the receiver operating characteristic curves for MELD, MELD-Na, and MELD 3.0 at 30 days were 0.823, 0.820, and 0.828; and at 90 days were 0.765, 0.772, and 0.776, respectively. Factors associated with the 90-day outcome included concomitant chronic viral hepatitis, prolonged prothrombin time, elevated levels of aspartate transaminase, bilirubin, and creatinine, and low albumin levels. CONCLUSION: MELD 3.0 demonstrated improved performance compared to previous models, although the differences were not statistically significant.

Dynamic analysis of acute deterioration in chronic liver disease patients using modified quick sequential organ failure assessment.

BACKGROUND/AIMS: Quick sequential organ failure assessment (qSOFA) is believed to identify patients at risk of poor outcomes in those with suspected infection. We aimed to evaluate the ability of modified qSOFA (m-qSOFA) to identify high-risk patients among those with acutely deteriorated chronic liver disease (CLD), especially those with acute-onchronic liver failure (ACLF). METHODS: We used data from both the Korean Acute-on-Chronic Liver Failure (KACLiF) and the Asian Pacific Association for the Study of the Liver ACLF Research Consortium (AARC) cohorts. qSOFA was modified by replacing the Glasgow Coma Scale with hepatic encephalopathy, and an m-qSOFA ≥2 was considered high. RESULTS: Patients with high m-qSOFA had a significantly lower 1-month transplant-free survival (TFS) in both cohorts and higher organ failure development in KACLiF than those with low m-qSOFA (Ps<0.05). Subgroup analysis by ACLF showed that patients with high m-qSOFA had lower TFS than those with low m-qSOFA. m-qSOFA was an independent prognostic factor (hazard ratios, HR=2.604, 95% confidence interval, CI 1.353-5.013, P=0.004 in KACLiF and HR=1.904, 95% CI 1.484- 2.442, P<0.001 in AARC). The patients with low m-qSOFA at baseline but high m-qSOFA on day 7 had a significantly lower 1-month TFS than those with high m-qSOFA at baseline but low m-qSOFA on day 7 (52.6% vs. 89.4%, P<0.001 in KACLiF and 26.9% vs. 61.5%, P<0.001 in AARC). CONCLUSION: Baseline and dynamic changes in m-qSOFA may identify patients with a high risk of developing organ failure and short-term mortality among CLD patients with acute deterioration.

Proton pump inhibitors increase the risk of carbapenem-resistant Enterobacteriaceae colonization by facilitating the transfer of antibiotic resistance genes among bacteria in the gut microbiome.

Carbapenem-resistant Enterobacteriaceae (CRE) pose a global health threat; however, there is still limited understanding of the risk factors and underlying mechanisms of CRE colonization in the gut microbiome. We conducted a matched case-control study involving 282 intensive care unit patients to analyze influencing covariates on CRE colonization. Subsequently, their effects on the gut microbiome were analyzed in a subset of 98 patients (47 CRE carriers and 51 non-CRE carriers) using whole metagenome sequences. The concomitant use of proton pump inhibitors (PPIs) and antibiotics was a significant risk factor for CRE colonization. The gut microbiome differed according to PPI administration, even within the CRE and non-CRE groups. Moreover, the transfer of mobile genetic elements (MGEs) harboring carbapenem resistance genes (CRGs) between bacteria was higher in the PPI-treated group than in the PPI-not-treated group among CRE carriers. The concomitant use of PPIs and antibiotics significantly alters the gut microbiome and increases the risk of CRE colonization by facilitating the transfer of CRGs among bacteria of the gut microbiome. Based on these findings, improved stewardship of PPIs as well as antibiotics can provide strategies to reduce the risk of CRE colonization, thereby potentially improving patient prognosis.