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探索代谢功能障碍相关脂肪性肝病的非侵入性诊断方法。

Exploring non-invasive diagnostics for metabolic dysfunction-associated fatty liver disease.

机构信息

Department of Clinical Pharmacology, The Second Hospital of Anhui Medical University, Hefei 230601, Anhui Province, China.

Jiangsu Engineering Research Center of Cardiovascular Drugs Targeting Endothelial Cells, College of Health Sciences, School of Life Sciences, Jiangsu Normal University, Xuzhou 221000, Jiangsu Province, China.

出版信息

World J Gastroenterol. 2024 Jul 28;30(28):3447-3451. doi: 10.3748/wjg.v30.i28.3447.

DOI:10.3748/wjg.v30.i28.3447
PMID:39091712
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC11290396/
Abstract

The population with metabolic dysfunction-associated fatty liver disease (MAFLD) is increasingly common worldwide. Identification of people at risk of progression to advanced stages is necessary to timely offer interventions and appropriate care. Liver biopsy is currently considered the gold standard for the diagnosis and staging of MAFLD, but it has associated risks and limitations. This has spurred the exploration of non-invasive diagnostics for MAFLD, especially for steatohepatitis and fibrosis. These non-invasive approaches mostly include biomarkers and algorithms derived from anthropometric measurements, serum tests, imaging or stool metagenome profiling. However, they still need rigorous and widespread clinical validation for the diagnostic performance.

摘要

代谢相关脂肪性肝病(MAFLD)患者在全球范围内日益增多。识别有进展为晚期风险的人群对于及时提供干预和适当的护理是必要的。肝活检目前被认为是 MAFLD 诊断和分期的金标准,但它存在相关风险和局限性。这促使人们探索 MAFLD 的非侵入性诊断方法,特别是针对脂肪性肝炎和纤维化。这些非侵入性方法主要包括来自人体测量、血清检测、影像学或粪便宏基因组分析的生物标志物和算法。然而,它们仍然需要进行严格和广泛的临床验证,以评估其诊断性能。

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本文引用的文献

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Omics-based biomarkers as useful tools in metabolic dysfunction-associated steatotic liver disease clinical practice: How far are we?基于组学的生物标志物作为代谢功能障碍相关脂肪性肝病临床实践中有用的工具:我们走了多远?
World J Gastroenterol. 2024 Apr 14;30(14):1982-1989. doi: 10.3748/wjg.v30.i14.1982.
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Diagnostic performance of circulating biomarkers for non-alcoholic steatohepatitis.循环生物标志物对非酒精性脂肪性肝炎的诊断性能。
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Application of machine learning in predicting non-alcoholic fatty liver disease using anthropometric and body composition indices.应用机器学习预测基于人体测量学和身体成分指数的非酒精性脂肪性肝病。
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Biomarkers for staging fibrosis and non-alcoholic steatohepatitis in non-alcoholic fatty liver disease (the LITMUS project): a comparative diagnostic accuracy study.非酒精性脂肪性肝病中纤维化和非酒精性脂肪性肝炎的分期生物标志物(LITMUS 项目):一项比较诊断准确性研究。
Lancet Gastroenterol Hepatol. 2023 Aug;8(8):714-725. doi: 10.1016/S2468-1253(23)00017-1. Epub 2023 Mar 21.
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