Background: Previous studies have suggested that bile acids (BAs) may participate in the development and/or progression of metabolic dysfunction-associated steatotic liver disease (MASLD). The present study aimed to define whether specific BA molecular species are selectively associated with MASLD development, disease severity, or geographic region. Methods: We comprehensively identified all eligible studies reporting circulating BAs in both MASLD patients and healthy controls through 30 July 2023. The pooled results were expressed as the standard mean difference (SMD) and 95% confidence interval (CI). Subgroup, sensitivity, and meta-regression analyses were performed to address heterogeneity. Results: Nineteen studies with 154,807 individuals were included. Meta-analysis results showed that total BA levels in MASLD patients were higher than those in healthy controls (SMD = 1.03, 95% CI: 0.63–1.42). When total BAs were divided into unconjugated and conjugated BAs or primary and secondary BAs, the pooled results were consistent with the overall estimates except for secondary BAs. Furthermore, we examined each individual BA and found that 9 of the 15 BAs were increased in MASLD patients, especially ursodeoxycholic acids (UDCA), taurococholic acid (TCA), chenodeoxycholic acids (CDCA), taurochenodeoxycholic acids (TCDCA), and glycocholic acids (GCA). Subgroup analysis revealed that different geographic regions or disease severities led to diverse BA profiles. Notably, TCA, taurodeoxycholic acid (TDCA), taurolithocholic acids (TLCA), and glycolithocholic acids (GLCA) showed a potential ability to differentiate metabolic dysfunction-associated steatohepatitis (MASH) (all p < 0.05). Conclusions: An altered profile of circulating BAs was shown in MASLD patients, providing potential targets for the diagnosis and treatment of MASLD.

Background: As the nonalcoholic fatty liver disease (NAFLD) epidemic matures, understanding how metabolic changes in NAFLD development vary over the age distribution is important to guide precise prevention. We aimed to clarify metabolic trends in age-specific NAFLD incidence. Methods: We conducted a 4-year longitudinal retrospective cohort study enrolling 10,240 consecutive healthy individuals who received annual physical examination during 2012–2019. Baseline and dynamic changes in metabolism and hepatic steatosis determined with ultrasound were collected and analyzed stratified by age into the following groups: 20–34, 35–49, 50–64, and over 65 years. Results: Overall, 1701 incident NAFLD participants (16.6%) were identified. Adjusted Cox regression analysis showed that the baseline and increased body mass index were the main risk factors for NAFLD in people ≤ 65 years old. Baseline high-density lipoprotein (HR = 0.56; 95% CI 0.39–0.78) was a protective factor for newly onset NAFLD in the 50-to-64-year-old group, while baseline SBP (HR = 1.03; 95% CI 1.01–1.05), baseline uric acid (HR = 1.04; 95% CI 1.01–1.07), triglyceride increase (HR = 4.76; 95% CI 3.69–6.14), fasting blood glucose increase (HR = 1.32; 95% CI 1.06–1.65) were independently associated with incident NAFLD in over-65-year-old group. Conclusions: NAFLD incidence attributable to potentially metabolic risk factors varied substantially across age groups in a cohort of Chinese people. The adoption of age targeted metabolic prevention strategies might reduce the burden of NAFLD.

Losing weight by lifestyle interventions is the first-line treatment for metabolic-associated fatty liver disease (MAFLD) but is limited by low compliance. This study aimed to compare the effects of orlistat or an experimental high-protein/lower-carbohydrate diet with a control diet in Asian patients with obesity and MAFLD. A total of 118 Asian patients with obesity and MAFLD confirmed with MRI-based proton density fat fraction with Dixon sequence were enrolled and allocated to the control group, the orlistat group, or the experimental diet group for 24 wk. The primary endpoint was the relative change in liver fat content (LFC) assessed by MRI-based proton density fat fraction. A total of 118 subjects with obesity and MAFLD were randomly assigned to the control group (n = 39), the orlistat group (n = 40), or the experimental diet group (n = 39). All 3 groups demonstrated improvement in liver steatosis at wk 24. The absolute decrease in LFC in the orlistat group was 9.1% and 5.4% in the experimental diet group, both significantly higher than that in the control group (P < 0.05). The relative reduction in LFC was 30.2% in the experimental diet group, which was significantly higher than the 12.2% observed in the control group (P = 0.01). Orlistat and the experimental diet group reduced liver steatosis compared to the control group. This trial was registered at Chinese Clinical Trial Registry (ChiCTR-1900027172). http://www.chictr.org.cn.

Background: The normalization of liver biochemical parameters usually reflects the histological response to treatment for nonalcoholic fatty liver disease (NAFLD). Researchers have not clearly determined whether different liver enzymes exhibit various metabolic changes during the follow-up period in patients with NAFLD. Methods: We performed a retrospective analysis of patients with NAFLD who were receiving therapy from January 2011 to December 2019. Metabolism indexes, including glucose levels, lipid profiles, uric acid levels and liver biochemical parameters, were measured. Magnetic resonance imaging-based proton density fat fraction (MRI-PDFF) and liver ultrasound were used to evaluate steatosis. All patients received recommendations for lifestyle modifications and guideline-recommended pharmacological treatments with indications for drug therapy for metabolic abnormalities. Results: Overall, 1048 patients with NAFLD were included and received lifestyle modification recommendations and pharmaceutical interventions, including 637 (60.7%) patients with abnormal GGT levels and 767 (73.2%) patients with abnormal ALT levels. Patients with concurrent ALT and GGT abnormalities presented higher levels of metabolism indexes and higher liver fat content than those in patients with single or no abnormalities. After 12 months of follow-up, the cumulative normalization rate of GGT was considerably lower than that of ALT (38% vs. 62%, P < 0.001). Greater weight loss resulted in higher cumulative normalization rates of GGT and ALT. Weight loss (OR = 1.21, 95% CI 1.11–1.32, P < 0.001), ALT normalization (OR = 2.75, 95% CI 1.41–5.36, P = 0.01) and lower TG and HOMA-IR values (OR = 2.03, 95% CI 1.11–3.71, P = 0.02; OR = 2.04, 95% CI 1.07–3.89, P = 0.03) were independent protective factors for GGT normalization. Elevated baseline GGT (OR = 0.99, 95% CI 0.98–0.99, P = 0.01) was a risk factor. Conclusions: For NAFLD patients with concurrently increased ALT and GGT levels, a lower normalization rate of GGT was observed, rather than ALT. Good control of weight and insulin resistance was a reliable predictor of GGT normalization.

INTRODUCTION: The recent US Food and Drug Administration approval of resmetirom for treating metabolic dysfunction-associated steatohepatitis in patients necessitates patient selection for significant fibrosis or higher (≥F2). No existing vibration-controlled transient elastography (VCTE) algorithm targets ≥F2. METHODS: The mAchine Learning ADvanceD fibrosis and rIsk metabolic dysfunction-associated steatohepatitis Novel predictor (ALADDIN) study addressed this gap by introducing a machine-learning-based web calculator that estimates the likelihood of significant fibrosis using routine laboratory parameters with and without VCTE. Our study included a training set of 827 patients, a testing set of 504 patients with biopsy-confirmed metabolic dysfunction-associated steatotic liver disease from 6 centers, and an external validation set of 1,299 patients from 9 centers. Five algorithms were compared using area under the curve (AUC) in the test set: ElasticNet, random forest, gradient boosting machines, XGBoost, and neural networks. The top 3 (random forest, gradient boosting machines, and XGBoost) formed an ensemble model. RESULTS: In the external validation set, the ALADDIN-F2-VCTE model, using routine laboratory parameters with VCTE (AUC 0.791, 95% confidence interval [CI]: 0.764–0.819), outperformed VCTE alone (0.745, 95% CI 0.717–0.772, P < 0.0001), FibroScan-aspartate aminotransferase (0.710, 0.679–0.748, P < 0.0001), and Agile-3 model (0.740, 0.710–0.770, P < 0.0001) regarding the AUC, decision curve analysis, and calibration. The ALADDIN-F2-Lab model, using routine laboratory parameters without VCTE, achieved an AUC of 0.706 (95% CI: 0.668–0.749) and outperformed Fibrosis-4, steatosis-associated fibrosis estimator, and LiverRisk scores. DISCUSSION: Along with the steatosis-associated fibrosis estimator model developed to target significant fibrosis or higher, ALADDIN-F2-VCTE (https://aihepatology.shinyapps.io/ALADDIN1) uniquely supports a refined noninvasive approach to patient selection for resmetirom without the need for liver biopsy. In addition, ALADDIN-F2-Lab (https://aihepatology.shinyapps.io/ALADDIN2) offers an effective alternative when VCTE is unavailable.

Background: The clinical burden and natural history of non-alcoholic fatty liver disease (NAFLD) vary globally. We aimed to investigate NAFLD-related mortality profiles in hospitalized patients in southern China.Methods: A multicenter retrospective investigation with a 10-year study period (2009–2018) analyzed 10,071 deaths during hospitalization (NAFLD: 2,015; other liver diseases: 1,140; without liver diseases: 6,916) was performed using a multiple cause of death analysis. Medical histories and biochemistry and imaging findings were extracted from the electronic medical record system. The underlying causes of death were classified by 10th Revision of the International Classification of Diseases (ICD-10) codes.Results: The distribution of death causes in patients with NAFLD has stabilized over time, with cardio- and cerebral vascular disease (CVD) ranked first (35.6%), followed by extrahepatic malignancies (22.6%), infection (11.0%), kidney disease (7.5%), liver-related diseases (5.2%), respiratory diseases (3.9%), digestive diseases (3.5%), endocrine diseases (3.5%), and other diseases (7.2%). NAFLD patients had more deaths attributable to CVD, extrahepatic malignancies, liver-related diseases (all P < 0.001) and multiorgan failure than the deceased controls. The severity of steatosis was independently associated with these relationships (liver-related diseases: OR = 1.37, 95% CI: 1.20–1.59, cardio- and cerebrovascular diseases: OR = 1.23, 95% CI: 1.19–1.31, infectious diseases: OR = 1.14, 95% CI: 1.04–1.26, and renal diseases: OR = 1.21, 95% CI: 1.02–1.47, all P < 0.05) after adjustment for sex, body mass index (BMI), fasting blood glucose, low-density lipoprotein cholesterol, uric acid, metabolic syndromes and fibrosis index based on the 4 factors.Conclusion : NAFLD patients had higher proportions of death due to underlying CVD and liver-related diseases than the general population in China; these proportions positively correlated with steatosis degree.