ReviewWorld journal of gastroenterology2024
Inflammatory biomarkers as cost-effective predictive tools in metabolic dysfunction-associated fatty liver disease.
Review in World journal of gastroenterology, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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Who cites it
4 citing papers in PubMed.
- Machine Learning-Based Comparative Analysis of Blood Cell-Derived Inflammatory Indices for Predicting MAFLD and Liver Fibrosis: Evidence From NHANES 2017-2020.Journal of clinical laboratory analysis · 2026Article
- Rheumatoid Factor Beyond Rheumatoid Arthritis: A Potential Marker of Cardiometabolic and Hepatic Risk.Biologics : targets & therapy · 2026Review
- Machine learning models for predicting metabolic dysfunction-associated steatotic liver disease prevalence using basic demographic and clinical characteristics.Journal of translational medicine · 2025Article
- Non-Alcoholic Fatty Liver Disease in Everyday Clinical Practice: From Diagnosis to Therapy.Life (Basel, Switzerland) · 2025Article
Corrections and comments
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Authors and funding
3 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Qu and Li emphasize a fundamental aspect of metabolic dysfunction-associated fatty liver disease in their manuscript, focusing on the critical need for non-invasive diagnostic tools to improve risk stratification and predict the progression to severe liver complications. Affecting approximately 25% of the global population, metabolic dysfunction-associated fatty liver disease is the most common chronic liver condition, with higher prevalence among those with obesity. This letter stresses the importance of early diagnosis and intervention, especially given the rising incidence of obesity and metabolic syndrome. Research advancements provide insight into the potential of biomarkers (particularly inflammation-related) as predictive tools for disease progression and treatment response. This overview addresses pleiotropic biomarkers linked to chronic inflammation and cardiometabolic disorders, which may aid in risk stratification and treatment efficacy monitoring. Despite progress, significant knowledge gaps remain in the clinical application of these biomarkers, necessitating further research to establish standardized protocols and validate their utility in clinical practice. Understanding the complex interactions among these factors opens new avenues to enhance risk assessment, leading to better patient outcomes and addressing the public health burden of this worldwide condition.
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Registered trials
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