This review provides a practical and comprehensive overview of non-pharmacological interventions for metabolic-associated fatty liver disease (MASLD), focusing on dietary and exercise strategies. It highlights the effectiveness of coffee consumption, intermittent fasting, and Mediterranean and ketogenic diets in improving metabolic and liver health. The review emphasizes the importance of combining aerobic and resistance training as a critical approach to reducing liver fat and increasing insulin sensitivity. Additionally, it discusses the synergy between diet and exercise in enhancing liver parameters and the role of gut microbiota in MASLD. The paper underscores the need for a holistic, individualized approach, integrating diet, exercise, gut health, and patient motivation. It also highlights the long-term benefits and minimal risks of lifestyle interventions compared to the side effects of pharmacological and surgical options. The review calls for personalized treatment strategies, continuous patient education, and further research to optimize therapeutic outcomes in MASLD management.

Background: We examined trends in NAFLD-related mortality in the United States from 1999 to 2022, focusing on sex, racial differences, and specific age groups. Methods: We analyzed age-adjusted mortality rates (AAMRs) for NAFLD-related deaths using the Centers for Disease Control and Prevention Wide-Ranging Online Data for Epidemiologic Research database and assessed differences between sex and racial groups. Results: Between 1999 and 2022, NAFLD-related mortality rose from an age-adjusted mortality rate (AAMR) of 0.2 to 1.7 per 100,000, with an average annual percent change (AAPC) of 10.0% (p < 0.001). In all, 85.4% of the cases were reported after 2008. Females (0.2–2 per 100,000, AAPC: 11.7%, p < 0.001) saw a steeper increase than males (0.2–1.3 per 100,000, AAPC: 9.3%, p < 0.001). White individuals’ AAMR rose from 0.2 to 1.9 per 100,000 (AAPC: 10.8%, p < 0.001). Asian or Pacific Islanders (AAPI) increased from 0.2 in 2013 to 0.5 in 2022 (AAPC: 12.13%, p = 0.002), and American Indians or Alaska Natives (AI/AN) from 1 in 2013 to 2.2 in 2022 (AAPC: 7.9%, p = 0.001). African Americans (AA) showed an insignificant change (0.3–0.5 per 100,000, AAPC: 0.7%, p = 0.498). Regarding age, individuals 45–64 saw AAMR rise from 0.3 to 1.2 per 100,000 (AAPC: 6.5%, p < 0.001), and those 65+ from 0.2 to 6 per 100,000 (AAPC: 16.5%, p < 0.001). No change was observed in the 25–44 age group (AAMR: 0.2 per 100,000, AAPC: 0.0%, p = 0.008). Conclusion: We report increased NAFLD-related mortality among both sexes and certain racial groups. The mortality rate increased for older populations, emphasizing the need for targeted public health measures and evidence-based interventions.

We examined trends in alcohol-associated liver disease (ALD)–related mortality in the United States from 1999 to 2022, focusing on sex, racial differences, and specific age groups. We analyzed age-adjusted mortality rates for ALD-related deaths using the CDC WONDER database and assessed differences between sex and racial groups. ALD-related mortality rates increased significantly between 1999 and 2022, with a more pronounced increase in females. White, Asian, Pacific Islander (AAPI), and American Indian or Alaska Native (AI/AN) groups showed significant uptrends in ALD-related mortality, while African Americans (AA) experienced a nonsignificant decline. Age-specific trends revealed substantial increases in crude mortality rates across various age groups, with the largest increase observed in the younger age groups of 25–34 years, with an average percent change of 11.12% from 2006 to 2022 (average annual percent change of 7.1% for the study period), and 35–44 years, which showed an average percent change of 17.2% from 2018 to 2022 (average annual percent change of 3.8% for the study period). This study reveals increased ALD-related mortality rates in the United States from 1999 to 2022, with disparities among sex, racial groups, and younger age groups. Continued monitoring and evidence-based interventions are needed to address the growing burden of ALD-related mortality, particularly in the younger population. Export

Hassam Ali, Joshua Zweigle, Pratik Patel, Brandon Tedder, Rafeh Khan, Saurabh Agrawal. Ann Hepatobiliary Pancreat Surg -0001;0:. https://doi.org/10.14701/ahbps.22-090

(1) Background: Colon polyps are common protrusions in the colon’s lumen, with potential risks of developing colorectal cancer. Early detection and intervention of these polyps are vital for reducing colorectal cancer incidence and mortality rates. This research aims to evaluate and compare the performance of three machine learning image classification models’ performance in detecting and classifying colon polyps. (2) Methods: The performance of three machine learning image classification models, Google Teachable Machine (GTM), Roboflow3 (RF3), and You Only Look Once version 8 (YOLOv8n), in the detection and classification of colon polyps was evaluated using the testing split for each model. The external validity of the test was analyzed using 90 images that were not used to test, train, or validate the model. The study used a dataset of colonoscopy images of normal colon, polyps, and resected polyps. The study assessed the models’ ability to correctly classify the images into their respective classes using precision, recall, and F1 score generated from confusion matrix analysis and performance graphs. (3) Results: All three models successfully distinguished between normal colon, polyps, and resected polyps in colonoscopy images. GTM achieved the highest accuracies: 0.99, with consistent precision, recall, and F1 scores of 1.00 for the ‘normal’ class, 0.97–1.00 for ‘polyps’, and 0.97–1.00 for ‘resected polyps’. While GTM exclusively classified images into these three categories, both YOLOv8n and RF3 were able to detect and specify the location of normal colonic tissue, polyps, and resected polyps, with YOLOv8n and RF3 achieving overall accuracies of 0.84 and 0.87, respectively. (4) Conclusions: Machine learning, particularly models like GTM, shows promising results in ensuring comprehensive detection of polyps during colonoscopies.