Annals of hepatology最新文献

Metabolic dysfunction-associated steatotic liver disease (MASLD), formerly known as non-alcoholic fatty liver disease (NAFLD), has emerged as a major comorbidity among patients with severe mental illness (SMI), particularly those treated with second-generation antipsychotics (SGAs). These agents induce systemic metabolic disturbances through mechanisms involving adipose tissue dysfunction, mitochondrial injury, and dysregulation of hepatic lipid metabolism. Increasing evidence identifies SGAs as significant contributors to hepatic dysfunction, acting through activation of sterol regulatory element-binding proteins (SREBPs), impairment of mitochondrial respiratory function, low-grade inflammation, alterations in the AMPK signaling pathway, and gut microbiota dysbiosis. Collectively, these processes promote hepatic lipid accumulation, insulin resistance, and progression toward non-alcoholic steatohepatitis (NASH). Furthermore, non-invasive biomarkers such as the Fatty Liver Index (FLI) and FIB-4 score have demonstrated potential utility for early screening and risk stratification in psychiatric populations. Overall, SGAs play a central role in the pathogenesis of MASLD by disrupting mitochondrial homeostasis, lipid metabolism, and gut-liver axis communication. Routine liver monitoring should be integrated into psychiatric care, and future research must focus on preventive and therapeutic strategies that protect hepatic function without compromising mental stability.

Introduction and objectives: Unhealthy sleep patterns have been associated with an increased risk of liver-related events, including the development of advanced liver disease. While prior studies linked sleep patterns to cirrhosis and mortality, the relationship between sleep behaviors and overall liver-related events (LRE) remains underexplored. This study examines the association between healthy sleep patterns (HSP) and the incidence of LRE, including cirrhosis and liver cancer.

Patients and methods: This prospective cohort study included 356,501 European participants from the UK Biobank. A healthy sleep pattern was assessed using five key parameters: sleep duration, chronotype, insomnia, daytime sleepiness, and snoring. Associations between the healthy sleep score (HSS) and the risk of LRE, cirrhosis, and liver cancer were evaluated using Cox proportional hazards models.

Results: Over a median follow-up of 12.8 years, 2441 incident liver-related events (LRE), 2197 cirrhosis cases, and 661 liver cancer cases were documented. After multivariable adjustment, a healthy sleep score (HSS) of 5 was significantly associated with a 44% reduction in LRE risk (HR=0.56; 95% CI: 0.46-0.70), a 46% reduction in cirrhosis risk (HR=0.54; 95% CI: 0.43-0.67), and a 45% reduction in liver cancer risk (HR=0.55; 95% CI: 0.37-0.80), compared to HSS 0-1. The inverse association between HSS and LRE was more pronounced among younger participants, individuals with prolonged sedentary behavior, and those with diabetes mellitus (P < 0.05).

Conclusions: Adherence to a healthy sleep pattern is independently associated with a reduced risk of liver-related events, cirrhosis, and liver cancer. This association is especially pronounced in younger adults, individuals with prolonged sedentary behavior (>4 h/day), and patients with diabetes.

Introduction and objectives: Primary liver cancer (PLC), comprising hepatocellular carcinoma (HCC) and cholangiocarcinoma (CCA), is a leading cause of cancer mortality globally. The combined hepatocellular-cholangiocarcinoma (cHCCCC) subtype may be less common but is relevant to treatment efficacy. We therefore evaluated the diagnostic accuracy of various approaches in distinguishing these liver cancers.

Materials and methods: Patients diagnosed with HCC, CCA, and cHCCCC at Beijing University Cancer Hospital and Institute, China were included. Radiologists of varying expertise independently assessed MRI scans, and we measured their diagnostic consistency. Radiomic features were extracted from MRI scans, and machine learning was applied to differentiate the cancer types.

Results: Standard imaging was insufficient to reliably characterize cHCCCC. Abdominal imaging experts (AIEs) had a higher mean sensitivity for HCC and CCA, 88% and 84% respectively, while non-experts (NIEs) had a lower sensitivity of 50% for HCC and 38% for CCA (HCC: p = 0.03, CCA: p = 0.008). Radiomic analysis found 'Sphericity' and 'ClusterShade' as the most relevant features. However, radiomics algorithms were also not sufficient to distinguish cHCCCC from either HCC or CCA. Regarding sensitivity, the radiomic-based model was not better than radiologists for any of the three classes (p = 0.065 for HCC, p = 0.426 for CCA, and p = 1.0 for cHCCCC). The random forest algorithm yielded an accuracy of 76% in the test set, since it correctly classified most HCC and CCA, while only one quarter of cHCCCC tumors.

Conclusions: Histopathological analysis, complemented by imaging as indicated, remains essential for accurate detection, diagnosis, and treatment of liver cancers.