OBJECTIVES:Discharged cirrhotic patients hospitalized for acute decompensation (AD) without acute-on-chronic liver failure (ACLF) have heterogeneous long-term prognosis; however, reliable prognostic tools are lacking. We aimed to identify the risk factors associated with 1-year adverse outcomes including mortality and liver transplantation (LT) after patient's discharge and to develop an evidence-based prognostic model. METHODS:We enrolled 1212 and 621 cirrhotic patients who clinically improved after AD without ACLF before discharge from the Chinese AcuTe-on-CHronic LIver FailurE (CATCH-LIFE) derivation and validation cohorts, respectively. The primary outcome was 1-year adverse outcomes (all-cause mortality or LT) post-discharge. Independent risk factors for 1-year post-discharge adverse outcomes were identified using multivariable analysis, and a prognostic model was derived and validated. RESULTS:In the derivation cohort, 203 (16.7%) patients experienced 1-year adverse outcomes and 370 (30.5%) were readmitted within 90 days post-discharge. Age, prior decompensation, total bilirubin, international normalized ratio, albumin, and hemoglobin on discharge were independently associated with 1-year post-discharge adverse outcomes. The prognostic model achieved an area under the receiver operating characteristic curve (AUC) of 0.7767 (95% confidence interval [CI] 0.7408-0.8125) and 0.7274 (95% CI 0.6656-0.7892) in the derivation and validation cohorts. Using a risk threshold of ≥ 0.27, 17.70% of discharged non-ACLF cirrhotic patients with AD in the validation cohort were classified as high-risk, with 1-year adverse outcome rate being 43.96%. CONCLUSION:A prognostic model for predicting 1-year adverse outcomes in discharged non-ACLF cirrhotic patients was developed and validated, with a high-risk cut-off ≥ 0.27, which may effectively stratify adverse outcome risks at 1-year post-discharge and guide intensive post-discharge management.

BACKGROUND & AIMS:Acute-on-chronic liver failure (ACLF) is characterised by multiorgan failure and high short-term mortality in hospitalised patients with acute decompensation of cirrhosis. Although the EASL-CLIF criteria are widely used for diagnosis and prognostication, evolving definitions of organ dysfunction and emerging therapies require updated, tailored criteria to improve diagnostic accuracy, treatment assessment, and applicability in clinical trials. We aimed to develop and validate the A-TANGO organ failure (OF) score to refine ACLF diagnosis and enhance its utility for treatment response evaluation and risk stratification. METHODS:We performed a retrospective analysis of prospective observational cohorts. The derivation cohort comprised three EF-CLIF consortium studies conducted in Europe and Latin America (CANONIC, PREDICT, ACLARA; n = 3,896). Validation cohorts included one study from India (Ambi-spective study n = 2,055) and one from China (CATCH-LIFE; n = 2,568). Patients were enrolled between 2011 and 2023, with follow-up completed in 2023. The primary objective was to redefine thresholds for organ dysfunction and failure using three subscores per organ, with subscore 3 corresponding to ≥15% 28-day mortality and defining organ failure. RESULTS:Compared with the CLIF-C OF score, the A-TANGO OF score introduced revised thresholds for organ failure and added an ACLF grade 4 to address the wide mortality variation within CLIF-C OF grade 3. A-TANGO identified more organ failures, increasing ACLF diagnosis from 24% to 36% and improving the net reclassification index by 16%, while maintaining similar predictive accuracy for 28- and 90-day mortality. Two additional prognostic models (A-TANGO ACLF-WBC and A-TANGO ACLF-CRP) demonstrated strong associations with 28- and 90-day mortality and improved prognostic performance. Findings were confirmed in external validation cohorts. CONCLUSIONS:The A-TANGO OF score is a reproducible and comprehensive tool for ACLF diagnosis with preserved prognostic performance, validated across large international cohorts. It provides a robust framework for clinical trials by enabling more accurate diagnosis, reducing required sample sizes, and offering clinically meaningful endpoints such as ACLF resolution for treatment response assessment. IMPACT AND IMPLICATIONS:The A-TANGO organ failure (OF) score provides a scientifically justified advancement in ACLF research by refining organ-specific dysfunction thresholds and introducing a new grade 4, thereby addressing limitations in current EASL-CLIF criteria and improving identification of high-risk patients. These findings are important for clinicians, researchers, and healthcare systems globally, as they increase detection of organ failure, enhance risk stratification, and enable more accurate prediction of short-term mortality in hospitalized patients with cirrhosis. The A-TANGO OF score and its associated prognostic scores (ACLF-WBC and ACLF-CRP) can be applied in clinical practice to guide treatment decisions, and serve as reliable, measurable endpoints in clinical trials evaluating emerging therapies. Although limitations such as missing data, cohort-specific recruitment differences, and historical classification criteria exist, the consistent and robust performance of the A-TANGO scores across large, multinational cohorts highlights their potential applicability and utility on a global scale.

BACKGROUND AND AIMS:To establish an early and quick model for diagnosing infections in patients with acute-on-chronic liver disease (AoCLD). APPROACH:This study analysed 3949 patients from two multicenter prospective cohorts of the Chinese Acute-on-Chronic Liver Failure (CATCH-LIFE) study. The dataset was randomly divided into training and validation cohorts in a 7:3 ratio. In the training cohort, logistic regression and least absolute shrinkage and selection operator regression analyses were used to identify predictive risk factors for infection in patients with AoCLD, and a simple nomogram was established. Two different cutoff values were determined to stratify infection risk in AoCLD patients. RESULTS:The developed diagnostic model included six variables: cirrhosis, ascites, neutrophil count (N), total bilirubin, C-reactive protein (CRP) and blood sodium levels. The area under the receiver operating characteristic curve for the training and validation cohorts was 0.818 and 0.809, respectively, significantly higher than using CRP, procalcitonin or N alone. Additionally, in the training cohort, we set a low cutoff value of 0.2028, resulting in a sensitivity of 80.15%, specificity of 68.25% and a negative predictive value of 92.7% for rule-out diagnosis. A high cutoff value of 0.4045 results in a specificity of 90.1%, sensitivity of 52.7% and a positive predictive value of 64% for rule-in diagnosis. These cutoff values were validated in the validation cohort. CONCLUSIONS:We established a nomogram model to assist clinicians in diagnosing infections in patients with AoCLD, effectively improving the accuracy and timeliness of diagnosis.

Background and Aims:Bacterial infection is a key cause of mortality in patients with acute-on-chronic liver failure (ACLF). In this study, we aimed to identify metabolite biomarkers and develop a novel machine learning model for early identification of bacterial infection in ACLF. Methods:Based on a prospective multicenter cohort from 14 centers, 1,314 patients with acute-on-chronic liver disease were enrolled, including those with ACLF and non-ACLF. Plasma samples at admission were collected for metabolomics profiling. Patients were randomly divided into discovery (n = 921) and validation (n = 393) sets. Machine learning was used to develop diagnostic models. The win ratio method was employed to assess the risk stratification capability of the models. Results:Bacterial infection occurred in 198 of the 451 ACLF patients and 132 of the 863 non-ACLF patients. Infection altered the plasma metabolome, especially in lipid, amino acid, and xenobiotic metabolic pathways. Models for bacterial infection in ACLF (five metabolites) and non-ACLF (six metabolites) demonstrated superior discrimination in the discovery (AUCs: 0.881 and 0.935, respectively) and validation sets (AUCs: 0.835 and 0.889, respectively) compared with C-reactive protein, white blood cell count, procalcitonin, and the best composite clinical model. Metabolic risk stratification based on the models effectively predicted 90-day outcomes (all-cause death, organ failure, sepsis, new-onset acute decompensation, and systemic inflammatory response syndrome). Conclusions:Our models based on novel metabolic biomarkers enable identification of patients at high risk of bacterial infection and support risk stratification of 90-day outcomes.

Background and Aims:Acute-on-chronic liver failure (ACLF) lacks a universally accepted definition, and recent efforts have proposed consensus organ failure criteria. In this study, we aimed to compare the clinical validity of a recently proposed consensus ACLF framework with the outcome-calibrated A-TANGO classification. Methods:We performed a multinational cohort study including 2,398 patients from the TIH cohort (India) and 2,568 from the CATCH-LIFE cohort (China) who were hospitalized with acute decompensation of cirrhosis. ACLF was defined using A-TANGO and an operationalized version of the 2025 consensus framework. Outcomes were 28- and 90-day mortality. Analyses assessed case capture, overlap, mortality risk, sensitivity, specificity, and net reclassification improvement (NRI). Results:ACLF prevalence differed substantially by definition. In TIH, A-TANGO classified 79.2% as ACLF versus 42.3% by the consensus definition; in CATCH-LIFE, the corresponding values were 31.4% versus 5.8%, respectively. Most consensus ACLF cases were captured by A-TANGO, which additionally classified 26%-37% of patients as having ACLF. These patients had substantial mortality (28-day: 18.1%-26.9%; 90-day: 33.2%-37.9%), significantly higher than those negative by both frameworks and comparable to established ACLF risk thresholds. A-TANGO showed higher sensitivity for 28-day mortality (TIH: 94.1% vs. 67.8%; CATCH-LIFE: 76.1% vs. 25.6%), whereas consensus criteria were more specific. Reclassification analyses showed improved discrimination with A-TANGO (NRI: 17.1% in TIH; 27.4% in CATCH-LIFE). Within the consensus non-ACLF group, A-TANGO further stratified patients into distinct risk groups with stepwise increase in mortality. Conclusions:In conclusion, the two frameworks identify fundamentally different populations. The consensus definition significantly reduces sensitivity and under-recognizes high-risk patients. Compared with consensus definitions, the outcome-calibrated framework better supports diagnosis, clinical decision-making, risk stratification, and trial design in ACLF.

The growing prevalence of male infertility has become a significant clinical and public health issue, with environmental exposures increasingly recognized as a major modifiable risk factor. This review synthesizes current evidence within the framework of the “male reproductive exposome”, linking lifelong exposure to environmental toxicants—ranging from endocrine-disrupting chemicals to emerging contaminants—to clinically relevant outcomes such as impaired semen parameters, altered reproductive hormone profiles, and an increased risk of testicular dysfunction. We critically evaluate the concept of life-course vulnerability, highlighting how exposures during critical developmental windows—including prenatal, peripubertal, and adult stages—may program distinct pathological trajectories that manifest as reproductive disorders in later life. In addition to classical mechanisms of endocrine disruption, we emphasize oxidative stress and, particularly, epigenetic reprogramming of the germline as key biological pathways contributing to both immediate fertility impairments and potential transgenerational health effects. Furthermore, we discuss the translational importance of these insights, focusing on the development of mechanism-informed biomarker panels for early detection and risk stratification, as well as addressing the persistent challenge of assessing toxicity from complex chemical mixtures. Finally, we underscore the necessity of integrating epidemiological research, mechanistic toxicology, and clinical practice to advance preventive and clinical strategies. This integration requires overcoming methodological challenges in mixture exposure assessment, accelerating biomarker discovery for personalized risk prediction, and formulating evidence-based public health interventions. In a word, this review advocates for a proactive, science-driven approach to mitigate environmental threats to male reproductive health and protect the well-being of future generations.

Abstract Background/Aims Acute-on-chronic liver failure (ACLF) is one of the deadliest complications of chronic liver disease yet treatment options are sparse. In Asia, HBV-reactivation (HBVr) is one of the most common triggers. Recently, HBV-associated ACLF was associated with distinct changes in the metabolome. Here, metabolic impacts of HBVr were analysed in-depth in non-ACLF, pre-ACLF and ACLF. Methods Clinical and metabolic data of 1024 Chinese patients (CATCH-LIFE studies) with chronic HBV mono-infection were analyzed. ACLF was diagnosed according to COSSH criteria. Metabolites in plasma were quantified using LC-MS, compared to non-ACLF and subjected to enrichment and pathway analyses using the database SMPDB via MetaboAnalyst v6. Results In 1024 patients (611: non-ACLF, 72: pre-ACLF, 341: ACLF), HBVr was present in 20.2% (ACLF) or 33.3% (pre-ACLF) of patients. HBVr increased 28-day mortality in pre-ACLF patients (2.1% in HBVnr vs. 25% in HBVr). Energy metabolism generating reactive oxygen species (ROS) was highly induced in the absence of ROS-detoxifying pathways in pre-ACLF. Urea cycle, thus nitric oxide (NO) build-up, the polyamine metabolism and related pathways were highly increased in both HBVr pre-ACLF and ACLF, shifting pre-ACLF close to mature ACLF. Specific metabolites could be identified as putative markers and key-regulators herein. Conclusion HBV reactivation induces inflammation, hepatic injury and mortality especially in pre-ACLF patients. Metabolic disruption in the ROS/NO/polyamine axis favors stress and hinders liver regeneration. Findings help identifying and alleviating HBVr-driven progression towards ACLF via pharmacological or biopharmaceutical intervention. Trial registration Analyses were based on the CATCH-LIFE studies. The CATCH-LIFE studies were registered at Clinical.trials.gov (Clinical Trial Number: NCT02457637, first registered 2015-01-31 and NCT03641872, first registered 2018-08-14).

Hepatitis E virus (HEV) represents a leading cause of acute viral hepatitis in China, yet large-scale studies characterizing its dynamic epidemiology and enabling early prediction of adverse outcomes remain scarce. We performed a retrospective cohort study comprising 816 patients hospitalized with acute HEV infection (2019–2024). Predictors were identified through multivariable logistic regression, with Firth's penalized likelihood method applied to address potential small-sample bias, and bootstrap resampling (BCa 95

BACKGROUND:This study aimed to develop and validate a non-invasive model for screening advanced liver fibrosis and predicting liver-related outcomes in patients with type 2 diabetes mellitus (T2DM). METHODS:This study included patients with T2DM from five tertiary hospitals for the development and internal validation of a non-invasive model. Advanced liver fibrosis was defined as a liver stiffness measurement ≥12 kPa. An external validation cohort was obtained from the National Health and Nutrition Examination Survey (NHANES), and the model's predictive performance for hepatocellular carcinoma (HCC) and liver-related mortality was assessed in the UK Biobank. FINDINGS:In total, 28,197 patients with T2DM were enrolled. In the derivation cohort (n = 1,129), waist circumference, alanine aminotransferase, aspartate aminotransferase, platelet count, and albumin were identified as independent risk factors for advanced fibrosis and were fit to develop the "DiabetesLiver score." The area under the curve (AUC) was 0.835 (95% confidence interval [CI]: 0.781-0.890), significantly higher than the AUCs of non-invasive tests (all p < 0.01). It maintained high AUCs of 0.870 and 0.823 in the internal validation (n = 1,000), and NHANES cross-sectional (n = 1,432) cohorts, respectively. A dual cutoff of 2.39 and 3.99 with sensitivity ≥90% and specificity ≥90%, respectively, was used to classify patients into low-, middle-, and high-risk groups. In the UK Biobank cohort (n = 24,636), the high-risk group had an elevated risk of liver-related outcomes. CONCLUSIONS:The DiabetesLiver score demonstrated good performance in identifying advanced liver fibrosis and the development of liver-related events in the T2DM population. FUNDING:National Natural Science Foundation.

BACKGROUND AND OBJECTIVES:Early differentiation between infectious and non-infectious systemic inflammatory response syndrome (SIRS) in patients with acute-on-chronic liver disease (AoCLD) remains challenging. This study aimed to develop and validate an early diagnostic model to accurately identify infection status in AoCLD patients with SIRS, thereby guiding targeted anti-infective therapy and improving clinical management. METHODS:Based on two multicenter prospective cohorts from the Chinese Acute-on-Chronic Liver Failure (CATCH-LIFE) study, 515 AoCLD patients with SIRS were randomly divided into a training cohort (n = 361) and a validation cohort (n = 154). In the training cohort, predictive factors for infection were screened using logistic regression, least absolute shrinkage and selection operator (LASSO) regression, multicollinearity analysis, and stepwise regression. Restricted cubic splines (RCS) were employed to explore nonlinear relationships and interaction effects. A nomogram model was subsequently constructed. The model's performance was evaluated using receiver operating characteristic (ROC) curves, calibration curves, and decision curve analysis (DCA), with internal validation via bootstrap method. RESULTS:The final model incorporated five clinical indicators: jaundice, ascites, neutrophil count (N), albumin (ALB), and C-reactive protein (CRP). CRP exhibited a significant nonlinear relationship with infection risk (P < 0.001) and was included as a spline term, while an interaction term between ALB and jaundice was incorporated. The model demonstrated excellent discrimination, with area under the ROC curve (AUC) of 0.840 (95 % CI: 0.788-0.888) in the training cohort and 0.865 (95 % CI: 0.803-0.927) in the validation cohort, significantly outperforming single biomarkers. Calibration curves showed high consistency between predicted and observed probabilities. DCA indicated substantial clinical net benefit across threshold probabilities. A clinical decision pathway was developed based on the nomogram's risk prediction and optimal DCA threshold. CONCLUSION:The early diagnostic model developed in this study effectively differentiates between infectious and non-infectious SIRS in AoCLD patients, potentially reducing unnecessary antimicrobial exposure and improving clinical outcomes.