Metabolic-Dysfunction-Associated Steatotic Liver Disease: Molecular Mechanisms, Clinical Implications, and Emerging Therapeutic Strategies

International Journal of Molecular Sciences, Journal Year: 2025, Volume and Issue: 26(7), P. 2959 - 2959

Published: March 25, 2025

Metabolic-dysfunction-associated steatotic liver disease (MASLD), previously known as non-alcoholic fatty (NAFLD), is a highly prevalent metabolic disorder characterized by hepatic steatosis in conjunction with at least one cardiometabolic risk factor, such obesity, type 2 diabetes, hypertension, or dyslipidemia. As global rates of obesity and syndrome continue to rise, MASLD becoming major public health concern, projections indicating substantial increase prevalence over the coming decades. The spectrum ranges from simple metabolic-dysfunction-associated steatohepatitis (MASH), fibrosis, cirrhosis, hepatocellular carcinoma, contributing significant morbidity mortality worldwide. This review delves into molecular mechanisms driving pathogenesis, including dysregulation lipid metabolism, chronic inflammation, oxidative stress, mitochondrial dysfunction, gut microbiota alterations. Recent advances research have highlighted role genetic epigenetic factors progression, well novel therapeutic targets peroxisome proliferator-activated receptors (PPARs), fibroblast growth factors, thyroid hormone receptor beta agonists. Given multifaceted nature MASLD, multidisciplinary approach integrating early diagnosis, insights, lifestyle interventions, personalized therapies critical. underscores urgent need for continued innovative treatment strategies precision medicine approaches halt progression improve patient outcomes.

Language: Английский

A Non-Invasive Interpretable NAFLD Diagnostic Method Combining TCM Tongue Features

2021 IEEE International Conference on Bioinformatics and Biomedicine (BIBM), Journal Year: 2023, Volume and Issue: 22, P. 4527 - 4534

Published: Dec. 5, 2023

Non-alcoholic fatty liver disease (NAFLD) is a clinicopathological syndrome characterized by hepatic steatosis resulting from the exclusion of alcohol and other identifiable liver-damaging factors. It has emerged as leading cause chronic worldwide. Currently, conventional methods for NAFLD detection are expensive not suitable users to perform daily diagnostics. To address this issue, study proposes non-invasive interpretable diagnostic method, required user-provided indicators only Gender, Age, Height, Weight, Waist Circumference, Hip tongue image. This method involves merging patients' physiological with features, which then input into fusion network named SelectorNet. SelectorNet combines attention mechanisms feature selection mechanisms, enabling it autonomously learn ability select important features. The experimental results show that proposed achieves an accuracy 77.22% using data, also provides compelling interpretability matrices. contributes early diagnosis intelligent advancement TCM diagnosis. project mentioned in paper currently publicly available ¹ .

Language: Английский