{"title":"Association of cardiovascular health metrics and metabolic associated fatty liver disease: Methodological limitations, and future directions.","authors":"Arunkumar Krishnan, Diptasree Mukherjee","doi":"10.4254/wjh.v17.i3.105635","DOIUrl":null,"url":null,"abstract":"<p><p>Metabolic-associated fatty liver disease (MAFLD), formerly known as nonalcoholic fatty liver disease, is an increasing global health challenge with substantial implications for metabolic and cardiovascular health (CVH). A recent study by Fu <i>et al</i> investigated the relationship between CVH metrics, specifically Life's Simple 7 and Life's Essential 8, and the prevalence of MAFLD. While this study offered important insights into the relationship between CVH and MAFLD, several methodological limitations, unaddressed confounding factors, and potential biases that could impact the interpretation of their findings should be considered. The study's cross-sectional nature restricted the ability to draw causal conclusions, and it did not fully account for potential confounding factors such as dietary habits, genetic predispositions, and medication use. Furthermore, relying on transient elastography to diagnose MAFLD introduces certain diagnostic limitations. Longitudinal study designs, advanced statistical modeling techniques, and diverse population groups should be utilized to strengthen future research. Exploring the mechanistic pathways that link CVH metrics to MAFLD through multi-omics approaches and interventional studies will be essential in formulating targeted prevention and treatment strategies. Structural equation modeling and machine learning techniques could provide a more refined analysis of these interrelated factors. Additionally, future research should employ longitudinal study designs and explore genetic and epigenetic influences to enhance our understanding of CVH and MAFLD interactions.</p>","PeriodicalId":23687,"journal":{"name":"World Journal of Hepatology","volume":"17 3","pages":"105635"},"PeriodicalIF":2.5000,"publicationDate":"2025-03-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11959666/pdf/","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"World Journal of Hepatology","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.4254/wjh.v17.i3.105635","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q2","JCRName":"GASTROENTEROLOGY & HEPATOLOGY","Score":null,"Total":0}
引用次数: 0
Abstract
Metabolic-associated fatty liver disease (MAFLD), formerly known as nonalcoholic fatty liver disease, is an increasing global health challenge with substantial implications for metabolic and cardiovascular health (CVH). A recent study by Fu et al investigated the relationship between CVH metrics, specifically Life's Simple 7 and Life's Essential 8, and the prevalence of MAFLD. While this study offered important insights into the relationship between CVH and MAFLD, several methodological limitations, unaddressed confounding factors, and potential biases that could impact the interpretation of their findings should be considered. The study's cross-sectional nature restricted the ability to draw causal conclusions, and it did not fully account for potential confounding factors such as dietary habits, genetic predispositions, and medication use. Furthermore, relying on transient elastography to diagnose MAFLD introduces certain diagnostic limitations. Longitudinal study designs, advanced statistical modeling techniques, and diverse population groups should be utilized to strengthen future research. Exploring the mechanistic pathways that link CVH metrics to MAFLD through multi-omics approaches and interventional studies will be essential in formulating targeted prevention and treatment strategies. Structural equation modeling and machine learning techniques could provide a more refined analysis of these interrelated factors. Additionally, future research should employ longitudinal study designs and explore genetic and epigenetic influences to enhance our understanding of CVH and MAFLD interactions.