Objective 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. Design 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 CD14+ CD16− 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. Results 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. Conclusions 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.

Accurate liquid biopsy for the diagnosis of non-alcoholic steatohepatitis and liver fibrosis / Angelini, Giulia; Panunzi, Simona; CASTAGNETO GISSEY, Lidia; Pellicanò, Francesca; De Gaetano, Andrea; Pompili, Maurizio; Riccardi, Laura; Garcovich, Matteo; Raffaelli, Marco; Ciccoritti, Luigi; Verrastro, Ornella; Russo, MARIA FRANCESCA; Maria Vecchio, Fabio; Casella, Giovanni; James, Casella-Mariolo; Papa, Luigi; Luigi Marini, Pier; Rubino, Francesco; W le Roux, Carel; Bornstein, Stefan; Mingrone, Geltrude. - In: GUT. - ISSN 0017-5749. - 72:2(2023), pp. 392-403. [10.1136/gutjnl-2022-327498]

Accurate liquid biopsy for the diagnosis of non-alcoholic steatohepatitis and liver fibrosis

Lidia Castagneto-Gissey;Maria Francesca Russo;Giovanni Casella;James Casella-Mariolo;
2023

Abstract

Objective 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. Design 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 CD14+ CD16− 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. Results 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. Conclusions 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.
2023
gut; bariatric surgery; incretins; nash; fatty liver disease; liver fibrosis
01 Pubblicazione su rivista::01a Articolo in rivista
Accurate liquid biopsy for the diagnosis of non-alcoholic steatohepatitis and liver fibrosis / Angelini, Giulia; Panunzi, Simona; CASTAGNETO GISSEY, Lidia; Pellicanò, Francesca; De Gaetano, Andrea; Pompili, Maurizio; Riccardi, Laura; Garcovich, Matteo; Raffaelli, Marco; Ciccoritti, Luigi; Verrastro, Ornella; Russo, MARIA FRANCESCA; Maria Vecchio, Fabio; Casella, Giovanni; James, Casella-Mariolo; Papa, Luigi; Luigi Marini, Pier; Rubino, Francesco; W le Roux, Carel; Bornstein, Stefan; Mingrone, Geltrude. - In: GUT. - ISSN 0017-5749. - 72:2(2023), pp. 392-403. [10.1136/gutjnl-2022-327498]
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11573/1664435
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