Models incorporating physical, laboratory and gut metabolite markers can be used to predict severe hepatic steatosis in MAFLD patients.

Yi-Hsuan Lin, Ching-Hsiang Wang, Yen-Hsun Huang, Hsiao-Chin Shen, Wei-Kai Wu, Hsiao-Yun Yeh, Chia-Chang Huang, Chien-Wei Su, Ying-Ying Yang, Ming-Shiang Wu, Han-Chieh Lin, Ming-Chih Hou
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Abstract

Metabolic-associated fatty liver disease (MAFLD) induced-severe hepatic steatosis poses significant health risks. Early prediction of this condition is crucial for prompt intervention. Short-chain fatty acids (SCFAs) and tryptophan are gut metabolites correlated with MAFLD pathogenesis in the gut-liver axis. This study aims to construct prediction models for severe hepatic steatosis by including SCFAs and tryptophan metabolites. This study enrolled 83 participants from the outpatient department in 2023. Physical measurements, serum metabolic and inflammatory markers, metabolites of serum SCFAs and tryptophan were collected. Severe hepatic steatosis was diagnosed using vibration-controlled transient elastography and abdominal sonography. All 40 (48.2%) participants diagnosed with severe hepatic steatosis had MAFLD, while approximately three-quarters of those without severe hepatic steatosis had MAFLD. In comparison to the non-severe hepatic steatosis group, individuals with severe hepatic steatosis exhibited higher levels of waist and arm circumference, serum triglyceride (TG), and lower levels of serum high-density lipoprotein cholesterol (HDL-C) and AST/ALT ratio. They also had higher serum levels of lipopolysaccharide-binding protein, isovaleric acid, and propionic acid, and lower levels of 3-methylvaleric acid, indole-3-propionic acid, and indoxyl sulfate. Models incorporating these markers predicted severe hepatic steatosis. One model additionally included waist circumference and triglyceride-glucose index, while the other incorporated arm circumference and TG/HDL-C ratio. The area under the curve reached 0.958 and 0.938, respectively (p < 0.001). SCFAs and tryptophan metabolites are valuable in predicting severe hepatic steatosis. Further research is needed to investigate the roles of these metabolites in MAFLD.

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结合物理、实验室和肠道代谢物标记物的模型可用于预测 MAFLD 患者的严重肝脂肪变性。
代谢相关性脂肪肝(MAFLD)诱发的严重肝脏脂肪变性对健康构成重大威胁。对这种疾病的早期预测对于及时干预至关重要。短链脂肪酸(SCFAs)和色氨酸是肠道代谢物,与肠道-肝轴的 MAFLD 发病机制相关。本研究旨在通过纳入 SCFAs 和色氨酸代谢物来构建严重肝脂肪变性的预测模型。本研究于 2023 年从门诊部招募了 83 名参与者。研究人员收集了体格测量数据、血清代谢和炎症指标、血清 SCFAs 和色氨酸代谢物。通过振动控制瞬态弹性成像和腹部超声波检查诊断出重度肝脂肪变性。所有 40 名(48.2%)被确诊为重度肝脂肪变性的参与者都患有 MAFLD,而没有重度肝脂肪变性的参与者中约有四分之三患有 MAFLD。与非重度肝脂肪变性组相比,重度肝脂肪变性患者的腰围和臂围、血清甘油三酯(TG)水平较高,而血清高密度脂蛋白胆固醇(HDL-C)和谷草转氨酶/谷丙转氨酶比值水平较低。他们的血清中脂多糖结合蛋白、异戊酸和丙酸的水平也较高,而 3-甲基戊酸、吲哚-3-丙酸和硫酸吲哚啶的水平较低。包含这些指标的模型可预测严重的肝脂肪变性。一个模型还包括腰围和甘油三酯-葡萄糖指数,另一个模型则包括臂围和 TG/HDL-C 比率。曲线下面积分别达到 0.958 和 0.938(p
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