BACKGROUND AND AIMS
Metabolic dysfunction-associated steatotic liver disease (MASLD) is strongly associated with dyslipidemia, which is a major risk factor for cardiovascular diseases and the leading cause of mortality in MASLD. This study aimed to compare the low-density lipoprotein cholesterol (LDL-C) response to statins in patients with and without hepatic steatosis.
METHODS
We identified subjects from a population screening program for MASLD who were subsequently started on statins. Statin response was defined as the absolute decrease between prestatin LDL-C level and LDL-C levels from months 0 to 3 and from months 3 to 12, modelled using multivariable linear mixed models.
RESULTS
Among 922 subjects who underwent proton-magnetic resonance spectroscopy screening, 286 received statin during follow-up, among whom 132 (46%) had hepatic steatosis (liver fat fraction ≥ 5%) at baseline. The mean defined daily dose of statins was 0.50 ± 0.32 units. The analysis on LDL-C average change per month over 12 months revealed no significant association between hepatic steatosis and statin response. Hepatic steatosis showed a nonsignificant association with poorer statin response over months 0-3 (difference of 0.097 mmol/L per month vs. no hepatic steatosis, p = 0.231) and months 3-12 (-0.037 mmol/L per month, p = 0.141), which remained after adjusting for diabetes, BMI, defined daily dose of statin, use of other lipid-lowering drugs, age, and sex.
CONCLUSIONS
The association between hepatic steatosis and poorer LDL-C response to statins, if any, is not clinically significant. Patients with hepatic steatosis are recommended to receive a standard statin dose according to clinical indications.
Kristopher Cho-Hei Lau, V. W. Wong, Alice P. S. Kong et al.· Journal of Gastroenterology...· 0 citations
Artificial intelligence (AI), particularly foundation and generative models, is reshaping the practice of hepatology through enhanced knowledge synthesis, quantitative and reproducible analysis of multimodal data, and personalized clinical decision support. This narrative review examines the transition from task-specific discrimination AI to large language models (LLMs), multimodal foundation models, and agentic AI. We synthesize evidence from original and validation studies, clinical evaluations, and benchmark studies, as well as expert reviews and regulatory frameworks across metabolic dysfunction-associated steatotic liver disease, chronic hepatitis B, cirrhosis and portal hypertension, hepatocellular carcinoma, and liver transplantation. LLMs can convert free-text notes into structured data, summarize longitudinal electronic health records, support patient education, and retrieve guideline-based information. Retrieval-augmented generation and agentic AI may improve traceability and workflow support, but current evidence is largely retrospective or proof-of-concept. In digital pathology and imaging, discriminative AI has enabled more quantitative and reproducible histologic scoring and biomarker analysis. Pathology and multimodal foundation models offer transferable representations, report generation, and cross-modal reasoning, but hepatology-specific validation remains limited. Key risks include hallucination, automation bias, domain shift across centers and devices, and inequities due to under-representation of patient subgroups. We outline the future directions for safe AI model deployment based on multimodal foundation models, prospective and federated evaluation, lifecycle governance, and continuous monitoring for performance, calibration, and equity. Most generative AI applications in hepatology remain at the proof-of-concept stage, and rigorous prospective validation with human-in-the-loop oversight is required before clinical integration.
Nana Peng, Mary Yue Wang, S. J. Song et al.· Clinical and Molecular Hepat...· 0 citations
