Università Cattolica del Sacro Cuore, Rome, Italy.
Department of Medical and Surgical Sciences, Fondazione Policlinico Universitario A. Gemelli IRCCS, Rome, Italy.
Gut. 2023 Feb;72(2):392-403. doi: 10.1136/gutjnl-2022-327498. Epub 2022 Jul 12.
Clinical diagnosis and approval of new medications for non-alcoholic steatohepatitis (NASH) require invasive liver biopsies. The aim of our study was to identify non-invasive biomarkers of NASH and/or liver fibrosis.
This multicentre study includes 250 patients (discovery cohort, n=100 subjects (Bariatric Surgery Versus Non-alcoholic Steato-hepatitis - BRAVES trial); validation cohort, n=150 (Liquid Biopsy for NASH and Liver Fibrosis - LIBRA trial)) with histologically proven non-alcoholic fatty liver (NAFL) or NASH with or without fibrosis. Proteomics was performed in monocytes and hepatic stellate cells (HSCs) with iTRAQ-nano- Liquid Chromatography - Mass Spectrometry/Mass Spectrometry (LC-MS/MS), while flow cytometry measured perilipin-2 (PLIN2) and RAB14 in peripheral blood CD14CD16 monocytes. Neural network classifiers were used to predict presence/absence of NASH and NASH stages. Logistic bootstrap-based regression was used to measure the accuracy of predicting liver fibrosis.
The algorithm for NASH using PLIN2 mean florescence intensity (MFI) combined with waist circumference, triglyceride, alanine aminotransferase (ALT) and presence/absence of diabetes as covariates had an accuracy of 93% in the discovery cohort and of 92% in the validation cohort. Sensitivity and specificity were 95% and 90% in the discovery cohort and 88% and 100% in the validation cohort, respectively.The area under the receiver operating characteristic (AUROC) for NAS level prediction ranged from 83.7% (CI 75.6% to 91.8%) in the discovery cohort to 97.8% (CI 95.8% to 99.8%) in the validation cohort.The algorithm including RAB14 MFI, age, waist circumference, high-density lipoprotein cholesterol, plasma glucose and ALT levels as covariates to predict the presence of liver fibrosis yielded an AUROC of 95.9% (CI 87.9% to 100%) in the discovery cohort and 99.3% (CI 98.1% to 100%) in the validation cohort, respectively. Accuracy was 99.25%, sensitivity 100% and specificity 95.8% in the discovery cohort and 97.6%, 99% and 89.6% in the validation cohort. This novel biomarker was superior to currently used FIB4, non-alcoholic fatty liver disease fibrosis score and aspartate aminotransferase (AST)-to-platelet ratio and was comparable to ultrasound two-dimensional shear wave elastography.
The proposed novel liquid biopsy is accurate, sensitive and specific in diagnosing the presence and severity of NASH or liver fibrosis and is more reliable than currently used biomarkers.
Discovery multicentre cohort: Bariatric Surgery versus Non-Alcoholic Steatohepatitis, BRAVES, ClinicalTrials.gov identifier: NCT03524365.Validation multicentre cohort: Liquid Biopsy for NASH and Fibrosis, LIBRA, ClinicalTrials.gov identifier: NCT04677101.
非酒精性脂肪性肝炎（NASH）的临床诊断和新药批准需要进行有创性肝活检。本研究的目的是确定非酒精性脂肪性肝炎和/或肝纤维化的非侵入性生物标志物。
这项多中心研究纳入了 250 例经组织学证实的非酒精性脂肪性肝病（NAFL）或伴有或不伴有纤维化的 NASH患者（发现队列，n=100 例（减肥手术与非酒精性脂肪性肝炎 - BRAVES 试验）；验证队列，n=150 例（用于 NASH 和肝纤维化的液体活检 - LIBRA 试验））。使用 iTRAQ-nano-液相色谱-质谱/质谱联用技术（LC-MS/MS）对单核细胞和肝星状细胞（HSCs）进行蛋白质组学分析，同时通过流式细胞术测量外周血 CD14CD16 单核细胞中的 perilipin-2（PLIN2）和 RAB14。使用神经网络分类器预测 NASH 和 NASH 分期的存在与否。基于逻辑 bootstrap 的回归用于测量预测肝纤维化的准确性。
使用 PLIN2 平均荧光强度（MFI）结合腰围、甘油三酯、丙氨酸氨基转移酶（ALT）和糖尿病的存在/不存在作为协变量的 NASH 算法在发现队列中的准确性为 93%，在验证队列中的准确性为 92%。在发现队列中的灵敏度和特异性分别为 95%和 90%，在验证队列中的灵敏度和特异性分别为 88%和 100%。用于预测 NAS 水平的受试者工作特征（ROC）曲线下面积（AUROC）在发现队列中为 83.7%（CI 75.6%至 91.8%），在验证队列中为 97.8%（CI 95.8%至 99.8%）。包括 RAB14 MFI、年龄、腰围、高密度脂蛋白胆固醇、血浆葡萄糖和 ALT 水平作为协变量以预测肝纤维化存在的算法在发现队列中的 AUROC 为 95.9%（CI 87.9%至 100%），在验证队列中的 AUROC 为 99.3%（CI 98.1%至 100%）。在发现队列中的准确性为 99.25%，灵敏度为 100%，特异性为 95.8%，在验证队列中的准确性为 97.6%，灵敏度为 99%，特异性为 89.6%。这种新的生物标志物在诊断 NASH 或肝纤维化的存在和严重程度方面具有准确性、敏感性和特异性，并且比目前使用的生物标志物更可靠。
发现队列多中心队列：减肥手术与非酒精性脂肪性肝炎，BRAVES，临床试验.gov 标识符：NCT03524365.验证队列多中心队列：用于 NASH 和纤维化的液体活检，LIBRA，临床试验.gov 标识符：NCT04677101.
