Nursing Science
Vol. 12  No. 06 ( 2023 ), Article ID: 77741 , 8 pages
10.12677/NS.2023.126148

非侵入性检查诊断非酒精性脂肪性肝病 研究进展

周娟1,白帅婷2,董鸿智3*

1西藏民族大学附属医院,重症医学科,陕西 咸阳

2西藏民族大学附属医院,内科,陕西 咸阳

3西藏民族大学附属医院,中医康复科,陕西 咸阳

收稿日期:2023年11月15日;录用日期:2023年12月12日;发布日期:2023年12月22日

摘要

非酒精性脂肪性肝病(Non-Alcoholic Fatter Liver Disease, NAFLD)在全球的发病率约为25%,是肝硬化和肝癌的主要病因,其亚型——非酒精性脂肪性肝炎(Non-Alcoholic Steatohepatitis, NASH),可进一步发展至晚期肝纤维化、肝硬化、肝细胞癌,并增加肝相关疾病的发病率和死亡率。因此,NAFLD患者的关键问题是早期识别非酒精性脂肪性肝炎。到目前为止,病理学检查仍然是诊断肝损伤的“金标准”,但它是一种侵入性操作,具有操作风险高、成本高、可接受性差等缺点,无法广泛应用于临床,故非侵入性评估越来越受到关注。在这篇综述中,我们对无创评估的工具及方法进行总结,希望可以将这些无创工具应用于临床实践。

关键词

非酒精性脂肪性肝病,非酒精性脂肪性肝炎,非侵入性性检查

Non-Invasive Examination to Diagnose Non-Alcoholic Fatty Liver Disease (NAFLD): An Update

Juan Zhou1, Shuaiting Bai2, Hongzhi Dong3*

1Department of Critical Care Medicine, Affiliated Hospital of Xizang Minzu University, Xianyang Shaanxi

2Department of Internal Medicine, Affiliated Hospital of Xizang Minzu University, Xianyang Shaanxi

3Rehabilitation Department of Chinese Medicine, Affiliated Hospital of Xizang Minzu University, Xianyang Shaanxi

Received: Nov. 15th, 2023; accepted: Dec. 12th, 2023; published: Dec. 22nd, 2023

ABSTRACT

The prevalence of NAFLD was approximately 25% wordwide, which has been the major cause of cirrhosis and liver cancer. Its subtype, non-alcoholic steatohepatitis (NASH) can progress to advanced liver fibrosis, cirrhosis, hepatocellular carcinoma, and increase the liver-related diseases’ incidence and mortality. Therefore, early identification of non-alcoholic steatohepatitis is the key issue for NAFLD patients. Histopathological sampling is the gold standard for the diagnosis of liver injury, but it is an invasive operation, with high operational risk, high cost, poor acceptability ways. As a result, it cannot be used widely and non-invasive evaluation was attracted more and more attention. In this review, we summarize non-invasive assessment methods, hoping to apply these non-invasive tools to clinical practice.

Keywords:Non-Alcoholic Fatter Liver Disease, Non-Alcoholic Steatohepatitis, Non-Invasive Examination

Copyright © 2023 by author(s) and Hans Publishers Inc.

This work is licensed under the Creative Commons Attribution International License (CC BY 4.0).

http://creativecommons.org/licenses/by/4.0/

1. 前言

非酒精性脂肪性肝病(Non-Alcoholic Fatty Liver Disease)是脂肪在肝内过度堆积所导致的一种疾病,NAFLD常伴有肥胖、胰岛素抵抗、2型糖尿病(Type 2 Diabetes, T2DM)等代谢性疾病,而这些合并症患病率的增加导致了NAFLD和NASH患病率在全球、全国甚至儿童中的平行增加。因此,专家们认为,代谢(功能障碍)相关的脂肪肝疾病(Metabolic-Associated Fatty Liver Disease, MAFLD) 被认为是一个更合适的首要术语 [1] 。随着肥胖和糖尿病患病率的逐渐增加,NAFLD的患病率随之增长 [2] [3] 。2019年,Younossi ZM等 [4] 的研究显示,全球T2DM中NAFLD的患病率高达55.5%、NASH患病率为37.3%。到2023年,T2DM中NAFLD的患病率已上升至65.04%、NASH患病率为31.5%,T2DM合并NAFLD的患者中35.54% 出现明显的临床纤维化(F2-F4) [5] 。Ajmera V等 [6] 的前瞻性、横断面研究也显示,2T2DM患者的NAFLD、晚期纤维化和肝硬化的患病率分别为65%、14%和6%。超重/肥胖成年人的全球NAFLD患病率大约为50.7%,且男性患病率(59%)明显高于女性(47.5%) [7] ,母亲的肥胖也会增加后代NAFLD发生的风险和严重程度 [8] 。研究显示,NAFLD是美国唯一一个患病率持续增长的慢性肝病,它和癌症、肥胖、糖尿病及心血管疾病一样,已经成为一种全球流性疾病,影响着全球约25%的人群健康 [9] [10] [11] 。

NAFLD通常被认为是一种无症状且可能可逆的疾病,它可分为单纯性脂肪肝(Non-Alcoholic Fatty Liver, NAFL)和非酒精性脂肪性肝炎(Non-Alcoholic Steatohepatitis, NASH)。然而,NASH是组织病理上表现为脂肪变性、小叶炎症、肿胀,伴或不伴肝窦周围纤维化,可发展为晚期纤维化、肝硬化、肝细胞癌,NAFLD现在也已经成为造成终末期肝病、肝细胞癌、肝移植的主要病因 [9] [12] 。在NAFL患者中约4%会发展成肝硬化,但在NASH患者中有超过20%的最终会发为肝硬化 [13] ,Noureddin M等 [14] 对美国肝移植的研究发现,自2004年至2016年,美国男性和女性NASH患者肝移植的增长率分别为114%和80%。近年来,人们发现纤维化的阶段是判断肝脏预后最重要的预测指标,因此,确定NAFLD和NASH患者纤维化更晚期对于最佳治疗至关重要。到目前为止,组织病理学分析仍然是诊断肝损伤的“金标准”,然而其为侵入性操作,有操作风险高、对操作者要求高、患者可接受性差、成本高等缺点。此外,肝活检分析的观察者间对结果的评判具有差异,导致在临床试验中测量治疗效果的可靠性较差 [12] [15] 。因此,对于这种流行性疾病,使用快速、廉价、低风险的非侵入性方法来识别NAFLD是一件至关重要的事情,以便评估风险并预防疾病进展,从而预防并发症。我们对几种应用血清标志物进行评估的方法进行了总结。

2. 影像学检查

2.1. 磁共振质子密度脂肪分数 (Magnetic Resonance Imaging Proton Density Fat Fraction, MRS-PDFF)

基于超声的方法对肝脏脂肪含量进行判断诊断准确性为中等,适合于筛查。MRI是诊断肝脏脂肪变性的非侵入方法,它基于水质子和脂肪质子之间的共振频率的差异直接定量脂肪含量分数,我们把这种分数称为。在非侵入性成像方法中,MRI-DFF具有最高的诊断准确性,它是一种可以准确、客观地定量全肝脂肪含量的指标,可重组观察,具有对NAFLD和NASH肝脂肪变性分级的能力,且具有把因T1偏倚、T2衰减和脂肪中质子的多频率信号干扰效应等因素造成的误差最小化的能力,并可用于试验登记和评估NASH早期临床试验的治疗效果 [16] [17] 。Bien Van Tran等 [18] 对超声肝肾指数(Ultrasound Hepatorenal Index, US-HRI)和MRI-PDFF在脂肪肝诊断中的敏感性和特异性进行对比分析,研究发现,US-HRI、MRI-PDFF的敏感性和特异性分别为50%、91.7%和100%、88.9%,组内相关系数(ICCs)分别为0.7和0.85,提示MRI-PDFF诊断肝脏脂肪变性比US-HRI更可靠。Garteiser P等 [19] 的研究发现,在接受减肥手术的可疑NAFLD的病态肥胖患者中,CAP对脂肪变分级1级、2级、3级及NASH的受试者工作特征曲线面积(Area under Receiver Operating Characteristics Curve, AUROC)分别为0.83,0.79,0.73和0.69,瞬时弹性成像对显著纤维化的AUROC为0.8,MRI-PDFF对脂肪变分级1级、2级、3级及NASH的AUROC分别为0.97、0.95、0.92和0.84,提示MRI-PDFF在诊断NAFLD、分级脂肪变性和排除NASH方面优于衰减参数(Controlled Attenuation Parameter, CAP) (98% vs 79%, P < 0.001)。基于它的优点,越来越多的研究以MRI-PDFF基础,进一步查看基因对NAFLD的影响 [20] [21] 。Ajmera V等 [6] 使用MRI-PDFF对T2DM患者进行筛查发现,≥50岁的T2DM患者发生晚期纤维化/肝硬化的风险高。Middleton MS等 [22] 将其应用于诊断儿童NAFLD也发现,MAI-PDFF对儿童的组织学脂肪变性分级的分类和预测,以及组织学脂肪变性分级的变化,也都具有较高的诊断准确性。这项技术也被应用于糖尿病临床药物治疗反应性中 [23] ,一项对NAFLD临床试验中使用MRI-PDFF治疗效果预测的荟萃分析中显示 [24] ,相对于MRI-PFDD无反应者(肝脏脂肪下降<30%),有反应者(肝脏脂肪下降≥30%)的患者更有可能出现组织学应答、组织学NASH的阳性率也更高,表明在NASH早期临床试验中可使用MRI-PDFF对治疗反应进行无创监测。Tianyi Xia等 [25] 应用孟德尔随机化、中介分析和多基因风险评分等方法对MRI-PDFF是否可以作为慢性肝脏疾病预防的有效工具进行探讨,研究结果基于两样本孟德尔随机化分析显示,遗传基因预测的MRI PDFF升高与肝恶性肿瘤(OR 4.5, P < 0.001)、酒精性肝病(OR 1.9, P < 0.001)、肝纤维化(OR 3.6, P = 0.002)、肝硬化(OR 3.8, P < 0.001)、NASH (OR 7.7, P < 0.001)、NAFLD (OR 4.4, P < 0.001)的风险增加有关。中介分析显示,基因预测的高密度脂蛋白胆固醇、2型糖尿病和腰臀比(中介效应,25.1%~46.3%)与肝脏MRI PDFF肝纤维化和肝硬化、肝硬化和NAFLD的发生相关(均P < 0.05),饮食和运动等干预措施可能对减少肝脏脂肪、预防和治疗肝脏疾病有益。以上结果表明,即使没有症状或其他危险因素,肝脏MRI-PDFF升高的人可能会从进行其他肝脏疾病的MRI-PDFF筛查中受益,表明肝脏脂肪是代谢功能障碍相关脂肪性肝病(MASLD)的主要危险因素,并可能在MASLD的发展中扮演遗传决定的角色。另外,这项技术目前也被应用于预测肝炎患者肝细胞癌的不良结局中 [26] ,是否可以在其预测NAFLD肝纤维化基础上进行NAFLD相关肝细胞癌的预测仍值得我们进一步思考,也希望有相关研究陆续发表。然而,执行这种技术需要添加一个特殊的软件包,通常在默认情况下不可用,同时它也是一个这是一项耗时的技术,所以阻碍了它的广泛应用。

2.2. 肝脏瞬时弹性成像技术(Fibroscan)

常规超声检查可以对脂肪肝进行定性,但是受检查的主观性影响,更适合用于脂肪肝的筛查,而Fibroscan包括肝脏硬度测量(The Liver Stiffness Measurement, LSM)和控制衰减参数(Controlled Attenuation Parameter, CAP),均可量化,是基于证据的肝纤维化和脂肪变性的非侵入性测量 [27] 。CAP代表超声在肝脏中的衰减程度,反应的是肝脏脂肪含量,用来评估脂肪肝的严重程度,脂肪细胞占比越大提示脂肪肝程度越重:11%~33%、34%~66%、>67%分别代表轻度、中度和重度脂肪肝。LSM的测量范围在1.5 kpa~75 kpa之间,肝脏硬度程度可以定程度反应肝脏的纤维化程度,其纤维化程度值F0、F1、F2、F3、F4分别代表正常、轻度纤维化、显著纤维化、进展期肝纤维化和肝硬化。Fibroscan Lemoine M等 [20] 应用MRI-PDFF和CAP对有NAFLD风险的HIV患者进行筛查发现:以MRI-PDFF作为参考,CAP对诊断中度至重度脂肪变性具有良好的准确性(AUC 0.86, 95% CI 0.82~0.90)。Seki K等 [28] 对171名肝脏活检证实为NAFLD的患者应用Fibroscan进行肝硬度检查发现,LSM与纤维化分期显著相关(P < 0.001),LSM对≥1期和≥3期纤维化的AUROC分别为0.85和0.91,均高于AST/ALT比值、APRI、FIB-4指数和NAFLD纤维化评分;纤维化≥期1和≥期3的最佳临界值分别为7.2 kPa (敏感性78.5%,特异性78.3%)和10.0 kPa (敏感性89.5%,特异性87.6%);LSM (≥10 kPa)和IV型胶原(≥6.0 ng/ml)联合治疗对晚期纤维化的特异性为97.6%。Arun J Sanyal等 [29] 在Fibroscan检查的基础上,联合应用Agile评分(包含性别、年龄、糖尿病状态、AST/ALT比值)是NAFLD患者肝硬化或心房颤动的存在,结果显示,Fibroscan联合血清学检查可以有助于提高就诊于肝脏诊所的NAFLD患者的肝硬化或房颤的识别,并减少了该人群对肝活检的需求。然而,LSM除纤维化外,还受到炎症、充血和胆汁淤积的影响,而CAP值除受脂肪变性外,还受到体重指数的影响 [27] [28] 。

2.3. 其他影像学检查方法

振动控制瞬时弹性成像(Vibration-Controlled Transient Elastography, VCTE)、点横波弹性成像(Point Shear Wave Elastography, pSWE) 2-Dimensional Shear Wave Elastography, pSWE)、二维横波弹性成像(2-Dimensional Shear Wave Elastography, 2DSWE)、瞬时成像技术(Transient Elastography, TE)、磁共振弹性成像(Magnetic Resonance Elastography, MRE)和磁共振成像(Magnetic Resonance Imaging, MRI)也已被提出作为NAFLD患者的无创检查。Jiang W等 [30] 的Meta分析显示:对于显著纤维化、晚期纤维化、肝硬化,pSWE (纳入9项研究,982名患者)的AUC分别为0.86 (95% CI 0.83~0.89),0.94 (95% CI 0.91~0.95)和0.95 (95% CI 0.93~0.97)、TE (纳入11项研究、1753名患者)的AUC分别为0.85 (95% CI 0.82~0.88)、0.92 (95% CI 0.89)和0.94 (95% CI 0.93~0.97),提示pSWE和TE可以对NAFLD后肝纤维化进行精确分期,尤其是晚期纤维化和肝硬化尤为敏感。Selvaraj EA等 [31] (纳入了82项研究14,609名患者) Meta分析显示:在诊断明显纤维化的患者中,VCTE、MRE、pSWE、2DSWE的sAUC分别为0.83、0.91、0.86和0.75;在诊断晚期纤维化的患者中,VCTE、MRE、pSWE、2DSWE的sAUC分别为0.85、0.92、0.89、0.72;在诊断肝硬化时,VCTE、MRE、pSWE、2DSWE的sAUC分别为0.89、0.90、0.90、0.88;MRE诊断NASH的AUC为0.83。这些指标对晚期纤维化和肝硬化具有可接受的诊断准确性。然而,由于缺乏意向诊断分析和预先指定阈值的验证,潜在临床影响无法得到充分评估,未来需要更多的研究去解决上述问题,以更好地应用于临床。

3. 血清学检查

FIB-4和非酒精性脂肪性肝病纤维化评分(NFS)是两种最常用的无创血液血清检测方法,广泛的纤维化筛查,但在应用于NAFLD纤维化低风险人群中,特在高危人群中假阴性率、一般人群中假阳性率均高,不适用于筛查目的 [32] [33] 。新标志物或模型的出现为血清学无创检查特异性、敏感性带来希望。

3.1. CK18

越来越多的血清生物标志物已被研究用于诊断NASH,但细胞角蛋白(CK)-18是迄今为止研究最广泛的标志物 [34] 。CK-18片段来自于肝细胞凋亡的半胱天冬酶-3 (caspase 3酶)裂解产生的,可通过免疫分析法在血清中检测。M30酶联免疫吸附试验检测caspase裂解的K18片段并检测细胞凋亡,这是脂肪性肝炎的一个标志,而M65酶联免疫吸附试验检测总细胞死亡 [34] 。2009年,自从Feldstein AE等 [35] 研究发现,CK-18可预测NASH (AUROC 0.83,敏感性0.75、特异性0.81),越来越多的研究也验证了这一结果 [36] [37] 。他们还在儿童中(平均年龄10.7 ± 2.5岁)应用CK-18来诊断NASH [38] :该研究共纳入201名受试者,研究结果显示,NASH患者的CK18水平显著高于非NASH患者[分别为322.1 U/l ± 104.8 vs. 164.2 U/l ± 62), P < 0.001]。肝活检中发生NASH的风险随着CK18水平的升高而增加(P < 0.001)。Zhao C等 [39] 对糖尿病老年患者研究发现,CK-18可能与老年T2DM合并NAFLD患者的糖脂代谢相关。研究结果显示,与对照组(健康人群)相比,合并NAFLD的糖尿病患者和未合并NAFLD患者的CK-18水平均升高,且合并NAFLD的糖尿病患者的CK-18水平高于未合并NAFLD患者,CK-18水平与空腹血糖、糖化血红蛋白、空腹胰岛素、胰岛素抵抗指数、总胆固醇、甘油三酯、高密度脂蛋白、低密度脂蛋白水平呈负性相关,其预测NAFLD的AUROC为0.857。动物试验也显示 [40] ,CK-18水平越高,NASH病理程度越严重。我国2018版指南提出,对于CK18持续升高的NAFLD患者,建议行肝活检建议明确是否存在NASH [41] 。还有研究发现,CK-18和内脏性肥胖可作为肝脏损伤的早期标志物 [42] [43] 。CK-18水平越高,提示脂肪炎症反应越重,NASH可能性也越大,但它不能区分单纯性脂肪变性和NASH。

3.2. 非侵入性血液生物标志物面板(NIS-4)

Harrison SA [9] 等的一项前瞻性研究,以来自三组独立队列的血液样本、临床资料、肝脏细胞活检为样本基础,开发了一种基于血液生物标志物的模型,该模型包括:miR-34a-5p、Alpha-2巨球蛋白(A2M)、YKL-40 [也称为几丁质酶3样蛋白1 (CHI3L1)]和糖化血红蛋白(HbA1c),被称为NIS-4。该研究将纳入的941名患者分为探索组(239名患者)和两个外部验证组(RESOLVE-IT诊断组475名患者,Angers回顾组227名患者),研究结果显示,NIS4算法的AUROC为0.80 (95% CI 0.73~0.85),并且不需要对年龄、性别、体重指数(BMI)或转氨酶浓度进行调整。NIS4在RESOLVE-IT诊断组(AUROC 0.83, 95% CI 0.79~0.86)和Angers队列(0.76, 0.69~0.82)中得到验证。在合并验证队列中,NIS4值 < 0.36的患者被归类为无高危NASH (排除),敏感性为81.5% (95% CI 76.9%~85.3%),特异性为63% (57.8%~68%),阴性预测值为77.9% (72.52%~82.4%),而NIS4值大于0.63被列为高危NASH (统治),87.1% (83.1~90.3)特异性,50.7% (45.3%~56.1%)敏感性,阳性预测值为79.2% (73.1%~84.2%)。NIS4是一种新型的基于血液的诊断方法,为有代谢危险因素和疑似疾病的患者提供了一种有效的、无创的可排除高危NASH的方法。在临床试验或临床中使用NIS4有可能大大减少疾病进展风险较低的患者不必要的肝活检,但是这种模型的建立需要特定的标志物模板,且尚未有更多研究对其进行验证。

3.3. 内脏脂肪指数(Visceral Adiposity Index, VAI)

目前国内外指南中,尚缺乏对VAI的建议。VAI是评价内脏脂肪的可靠指标,肥胖是NAFLD患者中常见的危险因素,肝脏脂肪变性与脂肪营养不良、脂肪组织功能障碍、代谢综合征、2型糖尿病均相关。其计算公式如下:VAI (男) = WC (cm)/(39.68 + 1.88 × BMI) × TG/1.03 × 1.31/HDL-C,VAI (女) = WC (cm)/(36.58 + 1.89 × BMI) × TG/0.81 × 1.52/HDL-C (WC:腰围;BMI:体质指数;HDL-C:高密度脂蛋白)。Xu C等 [44] 的一项前瞻性研究,纳入4809名受试者并联系随访4年,研究结果显示:该队列中4年NAFLD的累积发病率为13.9%。VAI第二、第三和第四个四分位数与第一个四分位数相比,NAFLD的风险比(95%置信区间,95% CI)分别为1.42 (95% CI: 1.24~1.64)、1.73 (95% CI: 1.51~1.99)和2.13 (95% CI: 1.86~2.45)。Kaplan-Meier生存分析提示,VAI水平越高,NAFLD发生率越高,呈剂量依赖性关系。结果表明,VAI水平是NAFLD的独立危险因素。Tang M等 [45] 的对2型糖尿病患者的研究也发现,VAI与MAFLD呈正相关,可能是2型糖尿病患者MAFLD诊断价值的指标。Ismaiel A [46] 等对24篇有量化标准的研究(总人数70519人)进行Meta分析后显示:NAFLD和NASH患者之间的VAI有明显统计学差异(P < 0.01)、糖尿病前期及糖尿病患者与未合并糖尿病的NAFLD患者VAI之间也有明显统计学差异。结果表明,VAI对NAFLD和NASH有很好地预测作用,但是不能评估NASH后纤维化的严重程度。

关于NAFLD患者肝病的无创评估已经取得了显著进展。无创检测的使用应根据环境(初级医疗保健、三级转诊中心、试验)和临床需求(筛查、纤维化分期、随访)而量身定制。关于脂肪变性的检测和分级,MRI-PDFF是最准确的方法,但似乎更适合评估和随访选定患者的临床试验。关于NASH,没有高度敏感和特异性的血液检测可以区分NASH和单纯脂肪变性,但是基于MRI的模式已显示出了前景,我们期待进一步的研究。

基金项目

项目类别:西藏自治区科技计划–厅校联合基金项目;项目编号:XZ2019ZRG-30(Z)。

利益冲突声明

本人作者声明,不存在任何利益冲突!

文章引用

周 娟,白帅婷,董鸿智. 非侵入性检查诊断非酒精性脂肪性肝病研究进展
Non-Invasive Examination toDiagnose Non-Alcoholic Fatty LiverDisease (NAFLD): An Update[J]. 护理学, 2023, 12(06): 1063-1070. https://doi.org/10.12677/NS.2023.126148

参考文献

  1. 1. Eslam, M., Sanyal, A.J. and George, J. (2020) MAFLD: A Consensus-Driven Proposed Nomenclature for Metabolic Associated Fatty Liver Disease. Gastroenterology, 158, 1999-2014.e1. https://doi.org/10.1053/j.gastro.2019.11.312

  2. 2. Younossi, Z.M. (2019) Non-Alcoholic Fatty Liver Disease—A Global Public Health Perspective. Journal of Hepatology, 70, 531-544. https://doi.org/10.1016/j.jhep.2018.10.033

  3. 3. Younossi, Z.M., Noureddin, M., Bernstein, D., et al. (2021) Role of Noninvasive Tests in Clinical Gastroenterology Practices to Identify Patients with Nonalcoholic Steatohepatitis at High Risk of Adverse Outcomes: Expert Panel Recommendations. American Journal of Gastroenterology, 116, 254-262. https://doi.org/10.14309/ajg.0000000000001054

  4. 4. Younossi, Z.M., Golabi, P., de Avila, L., et al. (2019) The Global Epidemiology of NAFLD and NASH in Patients with Type 2 Diabetes: A Systematic Review and Meta-Analysis. Journal of Hepatology, 71, 793-801. https://doi.org/10.1016/j.jhep.2019.06.021

  5. 5. En Li Cho, E., Ang, C.Z., Quek, J., et al. (2023) Global Prevalence of Non-Alcoholic Fatty Liver Disease in Type 2 Diabetes Mellitus: An Updated Systematic Review and Meta-Analysis. Gut, 72, 2138-2148. https://doi.org/10.1136/gutjnl-2023-330110

  6. 6. Ajmera, V., Cepin, S., Tesfai, K., et al. (2023) A Prospective Study on the Prevalence of NAFLD, Advanced Fibrosis, Cirrhosis and Hepatocellular Carcinoma in People with Type 2 Diabetes. Journal of Hepatology, 78, 471-478. https://doi.org/10.1016/j.jhep.2022.11.010

  7. 7. Liu, J., Mu, C., Li, K., Luo, H., Liu, Y. and Li, Z. (2021) Estimat-ing Global Prevalence of Metabolic Dysfunction- Associated Fatty Liver Disease in Overweight or Obese Children and Adolescents: Systematic Review and Meta-Analysis. International Journal of Public Health, 66, Article ID: 1604371. https://doi.org/10.3389/ijph.2021.1604371

  8. 8. Hagström, H., Simon, T.G., Roelstraete, B., Stephansson, O., Söderling, J. and Ludvigsson, J.F. (2021) Maternal Obesity Increases the Risk and Severity of NAFLD in Offspring. Journal of Hepatology, 75, 1042-1048. https://doi.org/10.1016/j.jhep.2021.06.045

  9. 9. Harrison, S.A., Ratziu, V., Boursier, J., et al. (2020) A Blood-Based Biomarker Panel (NIS4) for Non-Invasive Diagnosis of Non-Alcoholic Steatohepatitis and Liver Fibrosis: A Prospective Derivation and Global Validation Study. The Lancet Gastroenterology and Hepatology, 5, 970-985. https://doi.org/10.1016/S2468-1253(20)30252-1

  10. 10. Stefan, N. and Cusi, K. (2022) A Global View of the Inter-play between Non-Alcoholic Fatty Liver Disease and Diabetes. The Lancet Diabetes & Endocrinology.

  11. 11. Powell, E.E., Wong, V.W. and Rinella, M. (2021) Non-Alcoholic Fatty Liver Disease. The Lancet, 397, 2212-2224. https://doi.org/10.1016/S0140-6736(20)32511-3

  12. 12. Vilar-Gomez, E. and Chalasani, N. (2018) Non-Invasive Assessment of Non-Alcoholic Fatty Liver Disease: Clinical Prediction Rules and Blood-Based Biomarkers. Journal of Hepatology, 68, 305-315. https://doi.org/10.1016/j.jhep.2017.11.013

  13. 13. Sheka, A.C., Adeyi, O., Thompson, J., Hameed, B., Crawford, P.A. and Ikramuddin, S. (2020) Nonalcoholic Steatohepatitis: A Review. JAMA, 323, 1175-1183. https://doi.org/10.1001/jama.2020.2298

  14. 14. Noureddin, M., Vipani, A., Bresee, C., et al. (2018) NASH Leading Cause of Liver Transplant in Women: Updated Analysis of Indications for Liver Transplant and Ethnic and Gender Var-iances. American Journal of Gastroenterology, 113, 1649-1659. https://doi.org/10.1038/s41395-018-0088-6

  15. 15. Dinani, A.M., Kowdley, K.V. and Noureddin, M. (2021) Appli-cation of Artificial Intelligence for Diagnosis and Risk Stratification in NAFLD and NASH: The State of the Art. Hepa-tology, 74, 2233-2240. https://doi.org/10.1002/hep.31869

  16. 16. Tamaki, N., Ajmera, V. and Loomba, R. (2022) Non-Invasive Methods for Imaging Hepatic Steatosis and Their Clinical Importance in NAFLD. Nature Reviews Endocrinology, 18, 55-66. https://doi.org/10.1038/s41574-021-00584-0

  17. 17. Piazzolla, V.A. and Mangia, A. (2020) Noninvasive Diagnosis of NAFLD and NASH. Cells, 9, Article No. 1005. https://doi.org/10.3390/cells9041005

  18. 18. Tran, B.V., Ujita, K., Taketomi-Takahashi, A., Hirasawa, H., Suto, T. and Tsushima, Y. (2021) Reliability of Ultrasound Hepatorenal Index and Magnetic Resonance Imaging Proton Density Fat Fraction Techniques in the Diagnosis of Hepatic Steatosis, with Magnetic Resonance Spectroscopy as the Reference Standard. PLOS ONE, 16, e0255768. https://doi.org/10.1371/journal.pone.0255768

  19. 19. Garteiser, P., Castera, L., Coupaye, M., et al. (2021) Prospective Comparison of Transient Elastography, MRI and Serum Scores for Grading Ste-atosis and Detecting Non-Alcoholic Steatohepatitis in Bariatric Surgery Candidates. JHEP Reports, 3, Article ID: 100381. https://doi.org/10.1016/j.jhepr.2021.100381

  20. 20. Lemoine, M., Assoumou, L., Girard, P.M., et al. (2022) Screen-ing HIV Patients at Risk for NAFLD Using MRI-PDFF and Transient Elastography: A European Multicenter Prospec-tive Study. Clinical Gastroenterology and Hepatology, 21, 713-722.E3. https://doi.org/10.1016/j.cgh.2022.03.048

  21. 21. Yang, A., Zhu, X., Zhang, L., et al. (2022) Non-Invasive Evaluation of NAFLD and the Contribution of Genes: An MRI-PDFF-Based Cross-Sectional Study. Hepatology International, 16, 1035-1051. https://doi.org/10.1007/s12072-022-10355-2

  22. 22. Middleton, M.S., Van Natta, M.L., Heba, E.R., et al. (2018) Di-agnostic Accuracy of Magnetic Resonance Imaging Hepatic Proton Density Fat Fraction in Pediatric Nonalcoholic Fatty Liver Disease. Hepatology, 67, 858-872. https://doi.org/10.1002/hep.29596

  23. 23. Gastaldelli, A., Cusi, K., Fernández Landó, L., Bray, R., Brouwers, B. and Rodríguez, Á. (2022) Effect of Tirzepatide versus Insulin Degludec on Liver Fat Content and Abdominal Adipose Tissue in People with Type 2 Diabetes (SURPASS-3 MRI): A Substudy of the Randomised, Open-Label, Parallel-Group, Phase 3 SURPASS-3 Trial. The Lancet Diabetes & Endocrinology, 10, 393-406. https://doi.org/10.1016/S2213-8587(22)00070-5

  24. 24. Stine, J.G., Munaganuru, N., Barnard, A., et al. (2021) Change in MRI-PDFF and Histologic Response in Patients with Nonalcoholic Steatohepatitis: A Systematic Review and Meta-Analysis. Clinical Gastroenterology and Hepatology, 19, 2274-2283.e5. https://doi.org/10.1016/j.cgh.2020.08.061

  25. 25. Xia, T., Du, M., Li, H., et al. (2023) Association between Liver MRI Proton Density Fat Fraction and Liver Disease Risk. Radiology, 309, e231007. https://doi.org/10.1148/radiol.231007

  26. 26. Hui, R.W., Chan, A.C., Lo, G., et al. (2022) Magnetic Resonance Elas-tography and Proton Density Fat Fraction Predict Adverse Outcomes in Hepatocellular Carcinoma. Hepatology Interna-tional, 16, 371-380. https://doi.org/10.1007/s12072-022-10305-y

  27. 27. Oeda, S., Tanaka, K., Oshima, A., Matsumoto, Y., Sueoka, E. and Takahashi, H. (2020) Diagnostic Accuracy of FibroScan and Factors Affecting Measurements. Diagnostics (Basel), 10, Article No. 940. https://doi.org/10.3390/diagnostics10110940

  28. 28. Seki, K., Shima, T., Oya, H., Mitsumoto, Y., Mizuno, M. and Okanoue, T. (2017) Assessment of Transient Elastography in Japanese Patients with Non-Alcoholic Fatty Liver Disease. Hepatology Research, 47, 882-889. https://doi.org/10.1111/hepr.12829

  29. 29. Sanyal, A.J., Foucquier, J., Younossi, Z.M., et al. (2023) Enhanced Diag-nosis of Advanced Fibrosis and Cirrhosis in Individuals with NAFLD Using FibroScan-Based Agile Scores. Journal of Hepatology, 78, 247-259. https://doi.org/10.1016/j.jhep.2022.10.034

  30. 30. Jiang, W., Huang, S., Teng, H., et al. (2018) Diagnostic Accuracy of Point Shear Wave Elastography and Transient Elastography for Staging Hepatic Fibrosis in Patients with Non-Alcoholic Fatty Liver Disease: A Meta-Analysis. BMJ Open, 8, e021787. https://doi.org/10.1136/bmjopen-2018-021787

  31. 31. Selvaraj, E.A., Mózes, F.E., Jayaswal, A., et al. (2021) Diag-nostic Accuracy of Elastography and Magnetic Resonance Imaging in Patients with NAFLD: A Systematic Review and Meta-Analysis. Journal of Hepatology, 75, 770-785.

  32. 32. Graupera, I., Thiele, M., Serra-Burriel, M., et al. (2022) Low Accuracy of FIB-4 and NAFLD Fibrosis Scores for Screening for Liver Fibrosis in the Population. Clinical Gastroen-terology and Hepatology, 20, 2567-2576.e6. https://doi.org/10.1016/j.cgh.2021.12.034

  33. 33. Kjaergaard, M., Lindvig, K.P., Thorhauge, K.H., et al. (2023) Using the ELF Test, FIB-4 and NAFLD Fibrosis Score to Screen the Population for Liver Disease. Journal of Hepatology, 79, 277-286. https://doi.org/10.1016/j.jhep.2023.04.002

  34. 34. Castera, L., Friedrich-Rust, M. and Loomba, R. (2019) Noninva-sive Assessment of Liver Disease in Patients with Nonalcoholic Fatty Liver Disease. Gastroenterology, 156, 1264-1281.e4. https://doi.org/10.1053/j.gastro.2018.12.036

  35. 35. Feldstein, A.E., Wieckowska, A., Lopez, A.R., Liu, Y.C., Zein, N.N. and McCullough, A.J. (2009) Cytokeratin-18 Fragment Levels as Noninvasive Biomarkers for Nonalcoholic Steatohepatitis: A Multicenter Validation Study. Hepatology, 50, 1072-1078. https://doi.org/10.1002/hep.23050

  36. 36. Tada, T., Saibara, T., Ono, M., et al. (2021) Predictive Value of Cy-tokeratin-18 Fragment Levels for Diagnosing Steatohepatitis in Patients with Nonalcoholic Fatty Liver Disease. European Journal of Gastroenterology & Hepatology, 33, 1451-1458. https://doi.org/10.1097/MEG.0000000000002176

  37. 37. Eguchi, A., Iwasa, M., Yamada, M., et al. (2022) A New Detection System for Serum Fragmented Cytokeratin 18 as a Biomarker Reflecting Histologic Activities of Human Non-alcoholic Steatohepatitis. Hepatology Communications, 6, 1987-1999. https://doi.org/10.1002/hep4.1971

  38. 38. Feldstein, A.E., Alkhouri, N., De Vito, R., Alisi, A., Lopez, R. and Nobili, V. (2013) Serum Cytokeratin-18 Fragment Levels Are Useful Biomarkers for Nonalcoholic Steatohepatitis in Children. American Journal of Gastroenterology, 108, 1526-1531. https://doi.org/10.1038/ajg.2013.168

  39. 39. Zhao, C., Lou, F., Li, X., et al. (2021) Correlation of CD3+/CD4+, and Serum CK-18 Fragment Levels with Glucose and Lipid Metabo-lism in Elderly Type 2 Diabetes Patients with Nonalcoholic Fatty Liver Disease. The American Journal of Translational Research, 13, 2546-2554.

  40. 40. Dai, G., Tan, Y., Liu, J., et al. (2020) The Significance of IL-28B and CK-18 M30 Lev-els in the Diagnosis of Non- Alcoholic Steatohepatitis in SD Rats. Pathology—Research and Practice, 216, Article ID: 152901. https://doi.org/10.1016/j.prp.2020.152901

  41. 41. 非酒精性脂肪性肝病防治指南(2018更新版) [J]. 中华肝脏病杂志, 2018, 26(3): 195-203.

  42. 42. de Alteriis, G., Pugliese, G., Di Sarno, A., et al. (2023) Visceral Obesity and Cy-tokeratin-18 Antigens as Early Biomarkers of Liver Damage. International Journal of Molecular Sciences, 24, Article No. 10885. https://doi.org/10.3390/ijms241310885

  43. 43. Hempel, F., Roderfeld, M., Müntnich, L.J., et al. (2021) Caspa-se-Cleaved Keratin 18 Measurements Identified Ongoing Liver Injury after Bariatric Surgery. Journal of Clinical Medicine, 10, Article No. 1233. https://doi.org/10.3390/jcm10061233

  44. 44. Xu, C., Ma, Z., Wang, Y., et al. (2018) Visceral Adiposity Index as a Predictor of NAFLD: A Prospective Study with 4-Year Follow-Up. Liver International, 38, 2294-2300. https://doi.org/10.1111/liv.13941

  45. 45. Tang, M., Wei, X.H., Cao, H., et al. (2022) Association between Chinese Visceral Adiposity Index and Metabolic- Associated Fatty Liver Disease in Chinese Adults with Type 2 Diabetes Melli-tus. Frontiers in Endocrinology (Lausanne), 13, Article ID: 935980. https://doi.org/10.3389/fendo.2022.935980

  46. 46. Ismaiel, A., Jaaouani, A., Leucuta, D.C., Popa, S.L. and Du-mitrascu, D.L. (2021) The Visceral Adiposity Index in Non-Alcoholic Fatty Liver Disease and Liver Fibrosis-Systematic Review and Meta-Analysis. Biomedicines, 9, Article No. 1890. https://doi.org/10.3390/biomedicines9121890

  47. NOTES

    *通讯作者。

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