Background: Although imbalanced intestinal flora contributes to the pathogenesis of nonalcoholic fatty liver disease (NAFLD), conflicting results have been obtained for patient-derived microbiome composition analyses. A meta-analysis was performed to summarize the characteristics of intestinal microbiota at the species level in NAFLD patients. Methods: Following the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) Statement, a completed search (last update: December 30, 2020) of databases was performed to identify eligible case-control studies detecting gut microbiota in NAFLD patients. The meta-analysis results are presented as the standard mean difference (SMD) and 95% confidence interval (CI). Bias controls were evaluated with the Newcastle-Ottawa Scale (NOS), funnel plot analysis, and Egger’s and Begg’s tests. Results: Fifteen studies (NOS score range: 6–8) that detected the gut microbiota in the stools of 1265 individuals (577 NAFLD patients and 688 controls) were included. It was found that Escherichia, Prevotella and Streptococcus (SMD = 1.55 [95% CI: 0.57, 2.54], 1.89 [95% CI: 0.02, 3.76] and 1.33 [95% CI: 0.62, 2.05], respectively) exhibited increased abundance while Coprococcus, Faecalibacterium and Ruminococcus (SMD = − 1.75 [95% CI: − 3.13, − 0.37], − 9.84 [95% CI: − 13.21, − 6.47] and − 1.84 [95% CI, − 2.41, − 1.27], respectively) exhibited decreased abundance in the NAFLD patients compared with healthy controls. No differences in the abundance of Bacteroides, Bifidobacterium, Blautia, Clostridium, Dorea, Lactobacillus, Parabacteroides or Roseburia were confirmed between the NAFLD patients and healthy controls. Conclusions: This meta-analysis revealed that changes in the abundance of Escherichia, Prevotella, Streptococcus, Coprococcus, Faecalibacterium and Ruminococcus were the universal intestinal bacterial signature of NAFLD.

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.

Traditional classification systems of metabolic-associated fatty liver disease (MAFLD) do not account for the high rate of extrahepatic complications. To create a new classification of MAFLD using metabolic parameters to identify risks of complications more accurately. The retrospective study included MAFLD patients from the First Affiliated Hospital of Sun Yat-sen University for model development, and the model was validated respectively using Chinese cohort and UK Biobank database. Cluster analysis with k-means cluster was built using age, body mass index (BMI), glycosylated hemoglobin (HbA1c), total cholesterol/high density lipoprotein cholesterol (HDL-C) ratio, triglyceride, and lipoprotein(a) [Lp(a)] levels. Cox regression models were used to compare the risk of type 2 diabetes (T2DM), chronic heart disease (CHD), stroke and mortality between the clusters. 1038 MAFLD patients from cross-sectional population were recruited for the model derivation, with 10,451 cases (33.4 % of MAFLD) from Chinese cohort and 304,141 cases (34.9 % of MAFLD, 1010 cases with magnetic resonance imaging proton density fat fraction measurement [MRI-PDFF]) from the international cohort validated. Five replicable clusters of MAFLD patients were identified: Cluster 1(mild obesity and dyslipidemia-related), Cluster 2 (age related), Cluster 3 (severe insulin resistance-related), Cluster 4[high Lp(a)-related], and Cluster 5 (severe mixed hyperlipidemia-related). Patients in different clusters exhibited differences in the development of T2DM, CHD, stroke and all-causes mortality. Patients in Cluster 3 had significantly worst survival outcomes and higher risks of T2DM and CVD than those in other clusters. The novel classification offers improved discrimination of new-onset MAFLD patients with different metabolic complications.

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.

The liver effect of orlistat as a weight control treatment in patients with nonalcoholic fatty liver disease (NAFLD) with obesity remains undetermined. This study quantified liver fat improvement by orlistat in a Chinese cohort with NAFLD accompanied by obesity, diagnosed by a lower body mass index threshold than that for White patients. We conducted a parallel-group, open-label, 24-week, randomized clinical trial registered at the Chinese Clinical Trial Registry (ChiCTR-IPR-17012258). Obese participants with NAFLD were randomized 1:1.5 to the intervention group with orlistat or conventional care. Liver fat quantification was assessed by magnetic resonance imaging-based proton density fat fraction with Dixon sequence. Overall, 170 (n = 68, orlistat 120 mg three times/day and n = 102, conventional therapy) and 130 patients with NAFLD (n = 56, orlistat and n = 74, conventional therapy) were included for intention-to-treat (ITT) and per-protocol (PP) analysis, respectively. Orlistat reduced liver fat content to a greater degree than conventional care [−5.45% versus −1.96%, p < 0.001 (ITT analysis) and −6.66% versus −2.68%, p < 0.001 (PP analysis)]. The 6-month rate of decrease in steatosis grades was higher in the orlistat group [45.6% versus 22.5% (ITT analysis), 57.4% versus 30.3% (PP analysis), both p < 0.001]. Multivariate logistic regression analysis identified orlistat treatment [odds ratio (OR) = 2.4; 95% confidence interval (CI) 1.1–5.6, p = 0.036] as an independent predictor of steatosis improvement. Among patients with orlistat therapy, weight loss (OR = 1.2, 95% CI 1.1–1.4, p = 0.040) and severe steatosis (OR = 6.7, 95% CI: 1.1–40.3, p = 0.03) remained predictive of steatosis improvement. Orlistat can effectively promote steatosis improvement and may serve as a treatment option for controlling NAFLD. ChiCTR-IPR-17012258