背景:代谢相关脂肪性肝病(MAFLD)潜移默化地影响人们的健康,并且已经提出了许多模型来评估肝纤维化。然而,目前仍缺乏在高危人群中筛查MAFLD的无创和敏感模型.
目的:本研究的目的是探索一种对公众进行早期筛查的新方法,并建立一种基于家庭的工具,用于定期进行MAFLD的自我评估和监测。
方法:在这项横断面研究中,有1,758名符合条件的参与者参加训练,200名符合条件的参与者参加测试.血常规,血液生物化学,进行了FibroScan测试,使用身体成分仪器分析身体成分。此外,我们记录了多种因素,包括疾病相关的危险因素,Forns指数得分,肝脏脂肪变性指数(HSI),甘油三酯葡萄糖指数,全身水(TBW),身体脂肪量(BFM),内脏脂肪面积,腰高比(WHtR),和基础代谢率。进行二元logistic回归分析以探索具有预测能力的潜在人体测量指标来筛选MAFLD。一个新的模型,名为MAFLD筛查指数(MFSI),是使用二元逻辑回归分析建立的,和BFM,WHtR,包括TBW。一个简单的评级表,命名为MAFLD等级表(MRT),也是使用这些指标建立的。
结果:HSI的表现(曲线下面积[AUC]=0.873,特异性=76.8%,灵敏度=81.4%),WHtR(AUC=0.866,特异性=79.8%,灵敏度=80.8%),和BFM(AUC=0.842,特异性=76.9%,敏感性=76.2%)在MAFLD组和非脂肪肝组之间进行了评价(P<.001)。包括WHtR在内的组合模型的AUC,HSI,BFM值为0.900(特异性=81.8%,灵敏度=85.6%;P<.001)。MFSI是基于训练集中筛选MAFLD患者的更好表现而建立的(AUC=0.896,特异性=83.8%,灵敏度=82.1%),并在测试集中得到证实(AUC=0.917,特异性=89.8%,灵敏度=84.4%;P<.001)。
结论:使用WHtR建立了新的MFSI模型,BFM,和TBW筛选早期MAFLD。这些身体参数可以在家中使用体脂量表轻松获得,并且移动设备软件可以记录特定值并执行计算。MFSI在早期MAFLD筛查中的表现优于其他模型。新模型显示出强大的功能和稳定性,并在MAFLD检测和自我评估领域显示出希望。MRT是实时评估疾病改变的实用工具。
BACKGROUND: Metabolically associated fatty liver disease (MAFLD) insidiously affects people\'s health, and many models have been proposed for the evaluation of liver fibrosis. However, there is still a lack of noninvasive and sensitive models to screen MAFLD in high-risk populations.
OBJECTIVE: The purpose of this study was to explore a new method for early screening of the public and establish a home-based tool for regular self-assessment and monitoring of MAFLD.
METHODS: In this cross-sectional study, there were 1758 eligible participants in the training set and 200 eligible participants in the testing set. Routine blood, blood biochemistry, and FibroScan tests were performed, and body composition was analyzed using a body composition instrument. Additionally, we recorded multiple factors including disease-related risk factors, the Forns index score, the hepatic steatosis index (HSI), the triglyceride glucose index, total body water (TBW), body fat mass (BFM), visceral fat area, waist-height ratio (WHtR), and basal metabolic rate. Binary logistic regression analysis was performed to explore the potential anthropometric indicators that have a predictive ability to screen for MAFLD. A new model, named the MAFLD Screening Index (MFSI), was established using binary logistic regression analysis, and BFM, WHtR, and TBW were included. A simple rating table, named the MAFLD Rating Table (MRT), was also established using these indicators.
RESULTS: The performance of the HSI (area under the curve [AUC]=0.873, specificity=76.8%, sensitivity=81.4%), WHtR (AUC=0.866, specificity=79.8%, sensitivity=80.8%), and BFM (AUC=0.842, specificity=76.9%, sensitivity=76.2%) in discriminating between the MAFLD group and non-fatty liver group was evaluated (P<.001). The AUC of the combined model including WHtR, HSI, and BFM values was 0.900 (specificity=81.8%, sensitivity=85.6%; P<.001). The MFSI was established based on better performance at screening MAFLD patients in the training set (AUC=0.896, specificity=83.8%, sensitivity=82.1%) and was confirmed in the testing set (AUC=0.917, specificity=89.8%, sensitivity=84.4%; P<.001).
CONCLUSIONS: The novel MFSI model was built using WHtR, BFM, and TBW to screen for early MAFLD. These body parameters can be easily obtained using a body fat scale at home, and the mobile device software can record specific values and perform calculations. MFSI had better performance than other models for early MAFLD screening. The new model showed strong power and stability and shows promise in the area of MAFLD detection and self-assessment. The MRT was a practical tool to assess disease alterations in real time.