Liver biopsy-based validation, confirmation and comparison of the diagnostic performance of established and novel non-invasive non-alcoholic fatty liver disease indexes:Results from a large multi-center study

Background: Non-invasive tools (NIT) for non-alcoholic fatty liver disease (NAFLD) screening or diagnosis need to be thoroughly validated using liver biopsies.Purpose: To externally validate NITs designed to differentiate the presence or absence of liver steatosis as well as more advanced disease stages, to confirm fully validated indexes (n = 7 NITs), to fully validate partially validated indexes (n = 5 NITs), and to validate for the first time one new index (n = 1 NIT).Methods: This is a multi-center study from two Gastroenterology-Hepatology Departments (Greece and Australia) and one Bariatric-Metabolic Surgery Department (Italy). Overall, n = 455 serum samples of patients with biopsy-proven NAFLD (n = 374, including 237 patients with non-alcoholic steatohepatitis (NASH)) and Controls (n = 81) were recruited. A complete validation analysis was performed to differentiate the presence of NAFLD vs. Controls, NASH vs. NAFL, histological features of NASH, and fibrosis stages.Results: The index of NASH (ION) demonstrated the highest differentiation ability for the presence of NAFLD vs. Controls, with the area under the curve (AUC) being 0.894. For specific histological characterization of NASH, no NIT demonstrated adequate performance, while in the case of specific features of NASH, such as hepatocellular ballooning and lobular inflammation, ION demonstrated the best performance with AUC being close to or above 0.850. For fibrosis (F) classification, the highest AUC was reached by the aspartate aminotransferase to platelet ratio index (APRI) being ~0.850 yet only with the potential to differentiate the severe fibrosis stages (F3, F4) vs. mild or moderate fibrosis (F0–2) with an AUC > 0.900 in patients without T2DM. When we excluded patients with morbid obesity, the differentiation ability of APRI was improved, reaching AUC = 0.802 for differentiating the presence of fibrosis F2–4 vs. F0–1. The recommended by current guidelines index FIB-4 seemed to differentiate adequately between severe (i.e., F3–4) and mild or moderate fibrosis (F0–2) with an AUC = 0.820, yet this was not the case when FIB-4 was used to classify patients with fibrosis F2–4 vs. F0–1. Trying to improve the predictive value of all NITs, using Youden's methodology, to optimize the suggested cut-off points did not materially improve the results.Conclusions: The validation of currently available NITs using biopsy-proven samples provides new evidence for their ability to differentiate between specific disease stages, histological features, and, most importantly, fibrosis grading. The overall performance of the examined NITs needs to be further improved for applications in the clinic

Liver biopsy-based validation, confirmation and comparison of the diagnostic performance of established and novel non-invasive non-alcoholic fatty liver disease indexes:Results from a large multi-center study

Background: Non-invasive tools (NIT) for non-alcoholic fatty liver disease (NAFLD) screening or diagnosis need to be thoroughly validated using liver biopsies.Purpose: To externally validate NITs designed to differentiate the presence or absence of liver steatosis as well as more advanced disease stages, to confirm fully validated indexes (n = 7 NITs), to fully validate partially validated indexes (n = 5 NITs), and to validate for the first time one new index (n = 1 NIT).Methods: This is a multi-center study from two Gastroenterology-Hepatology Departments (Greece and Australia) and one Bariatric-Metabolic Surgery Department (Italy). Overall, n = 455 serum samples of patients with biopsy-proven NAFLD (n = 374, including 237 patients with non-alcoholic steatohepatitis (NASH)) and Controls (n = 81) were recruited. A complete validation analysis was performed to differentiate the presence of NAFLD vs. Controls, NASH vs. NAFL, histological features of NASH, and fibrosis stages.Results: The index of NASH (ION) demonstrated the highest differentiation ability for the presence of NAFLD vs. Controls, with the area under the curve (AUC) being 0.894. For specific histological characterization of NASH, no NIT demonstrated adequate performance, while in the case of specific features of NASH, such as hepatocellular ballooning and lobular inflammation, ION demonstrated the best performance with AUC being close to or above 0.850. For fibrosis (F) classification, the highest AUC was reached by the aspartate aminotransferase to platelet ratio index (APRI) being ~0.850 yet only with the potential to differentiate the severe fibrosis stages (F3, F4) vs. mild or moderate fibrosis (F0–2) with an AUC > 0.900 in patients without T2DM. When we excluded patients with morbid obesity, the differentiation ability of APRI was improved, reaching AUC = 0.802 for differentiating the presence of fibrosis F2–4 vs. F0–1. The recommended by current guidelines index FIB-4 seemed to differentiate adequately between severe (i.e., F3–4) and mild or moderate fibrosis (F0–2) with an AUC = 0.820, yet this was not the case when FIB-4 was used to classify patients with fibrosis F2–4 vs. F0–1. Trying to improve the predictive value of all NITs, using Youden's methodology, to optimize the suggested cut-off points did not materially improve the results.Conclusions: The validation of currently available NITs using biopsy-proven samples provides new evidence for their ability to differentiate between specific disease stages, histological features, and, most importantly, fibrosis grading. The overall performance of the examined NITs needs to be further improved for applications in the clinic

Liver biopsy-based validation, confirmation and comparison of the diagnostic performance of established and novel non-invasive non-alcoholic fatty liver disease indexes:Results from a large multi-center study

Background: Non-invasive tools (NIT) for non-alcoholic fatty liver disease (NAFLD) screening or diagnosis need to be thoroughly validated using liver biopsies.Purpose: To externally validate NITs designed to differentiate the presence or absence of liver steatosis as well as more advanced disease stages, to confirm fully validated indexes (n = 7 NITs), to fully validate partially validated indexes (n = 5 NITs), and to validate for the first time one new index (n = 1 NIT).Methods: This is a multi-center study from two Gastroenterology-Hepatology Departments (Greece and Australia) and one Bariatric-Metabolic Surgery Department (Italy). Overall, n = 455 serum samples of patients with biopsy-proven NAFLD (n = 374, including 237 patients with non-alcoholic steatohepatitis (NASH)) and Controls (n = 81) were recruited. A complete validation analysis was performed to differentiate the presence of NAFLD vs. Controls, NASH vs. NAFL, histological features of NASH, and fibrosis stages.Results: The index of NASH (ION) demonstrated the highest differentiation ability for the presence of NAFLD vs. Controls, with the area under the curve (AUC) being 0.894. For specific histological characterization of NASH, no NIT demonstrated adequate performance, while in the case of specific features of NASH, such as hepatocellular ballooning and lobular inflammation, ION demonstrated the best performance with AUC being close to or above 0.850. For fibrosis (F) classification, the highest AUC was reached by the aspartate aminotransferase to platelet ratio index (APRI) being ~0.850 yet only with the potential to differentiate the severe fibrosis stages (F3, F4) vs. mild or moderate fibrosis (F0–2) with an AUC > 0.900 in patients without T2DM. When we excluded patients with morbid obesity, the differentiation ability of APRI was improved, reaching AUC = 0.802 for differentiating the presence of fibrosis F2–4 vs. F0–1. The recommended by current guidelines index FIB-4 seemed to differentiate adequately between severe (i.e., F3–4) and mild or moderate fibrosis (F0–2) with an AUC = 0.820, yet this was not the case when FIB-4 was used to classify patients with fibrosis F2–4 vs. F0–1. Trying to improve the predictive value of all NITs, using Youden's methodology, to optimize the suggested cut-off points did not materially improve the results.Conclusions: The validation of currently available NITs using biopsy-proven samples provides new evidence for their ability to differentiate between specific disease stages, histological features, and, most importantly, fibrosis grading. The overall performance of the examined NITs needs to be further improved for applications in the clinic

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

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. CLINICAL TRIALS 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

Bariatric-metabolic surgery versus lifestyle intervention plus best medical care in non-alcoholic steatohepatitis (BRAVES). a multicentre, open-label, randomised trial

Background: Observational studies suggest that bariatric-metabolic surgery might greatly improve non-alcoholic steatohepatitis (NASH). However, the efficacy of surgery on NASH has not yet been compared with the effects of lifestyle interventions and medical therapy in a randomised trial. Methods: We did a multicentre, open-label, randomised trial at three major hospitals in Rome, Italy. We included participants aged 25-70 years with obesity (BMI 30-55 kg/m2), with or without type 2 diabetes, with histologically confirmed NASH. We randomly assigned (1:1:1) participants to lifestyle modification plus best medical care, Roux-en-Y gastric bypass, or sleeve gastrectomy. The primary endpoint of the study was histological resolution of NASH without worsening of fibrosis at 1-year follow-up. This study is registered at ClinicalTrials.gov, NCT03524365. Findings: Between April 15, 2019, and June 21, 2021, we biopsy screened 431 participants; of these, 103 (24%) did not have histological NASH and 40 (9%) declined to participate. We randomly assigned 288 (67%) participants with biopsy-proven NASH to lifestyle modification plus best medical care (n=96 [33%]), Roux-en-Y gastric bypass (n=96 [33%]), or sleeve gastrectomy (n=96 [33%]). In the intention-to-treat analysis, the percentage of participants who met the primary endpoint was significantly higher in the Roux-en-Y gastric bypass group (54 [56%]) and sleeve gastrectomy group (55 [57%]) compared with lifestyle modification (15 [16%]; p<0·0001). The calculated probability of NASH resolution was 3·60 times greater (95% CI 2·19-5·92; p<0·0001) in the Roux-en-Y gastric bypass group and 3·67 times greater (2·23-6·02; p<0·0001) in the sleeve gastrectomy group compared with in the lifestyle modification group. In the per protocol analysis (236 [82%] participants who completed the trial), the primary endpoint was met in 54 (70%) of 77 participants in the Roux-en-Y gastric bypass group and 55 (70%) of 79 participants in the sleeve gastrectomy group, compared with 15 (19%) of 80 in the lifestyle modification group (p<0·0001). No deaths or life-threatening complications were reported in this study. Severe adverse events occurred in ten (6%) participants who had bariatric-metabolic surgery, but these participants did not require re-operations and severe adverse events were resolved with medical or endoscopic management. Interpretation: Bariatric-metabolic surgery is more effective than lifestyle interventions and optimised medical therapy in the treatment of NASH. Funding: Fondazione Policlinico Universitario A Gemelli, Policlinico Universitario Umberto I and S Camillo Hospital, Rome, Italy

Development and validation of an interpretable machine learning-based calculator for predicting 5-year weight trajectories after bariatric surgery: a multinational retrospective cohort SOPHIA study

Background Weight loss trajectories after bariatric surgery vary widely between individuals, and predicting weight loss before the operation remains challenging. We aimed to develop a model using machine learning to provide individual preoperative prediction of 5-year weight loss trajectories after surgery. Methods In this multinational retrospective observational study we enrolled adult participants (aged $\ge$18 years) from ten prospective cohorts (including ABOS [NCT01129297], BAREVAL [NCT02310178], the Swedish Obese Subjects study, and a large cohort from the Dutch Obesity Clinic [Nederlandse Obesitas Kliniek]) and two randomised trials (SleevePass [NCT00793143] and SM-BOSS [NCT00356213]) in Europe, the Americas, and Asia, with a 5 year followup after Roux-en-Y gastric bypass, sleeve gastrectomy, or gastric band. Patients with a previous history of bariatric surgery or large delays between scheduled and actual visits were excluded. The training cohort comprised patients from two centres in France (ABOS and BAREVAL). The primary outcome was BMI at 5 years. A model was developed using least absolute shrinkage and selection operator to select variables and the classification and regression trees algorithm to build interpretable regression trees. The performances of the model were assessed through the median absolute deviation (MAD) and root mean squared error (RMSE) of BMI. Findings10 231 patients from 12 centres in ten countries were included in the analysis, corresponding to 30 602 patient-years. Among participants in all 12 cohorts, 7701 (75$\bullet$3%) were female, 2530 (24$\bullet$7%) were male. Among 434 baseline attributes available in the training cohort, seven variables were selected: height, weight, intervention type, age, diabetes status, diabetes duration, and smoking status. At 5 years, across external testing cohorts the overall mean MAD BMI was 2$\bullet$8 kg/m${}^2$ (95% CI 2$\bullet$6-3$\bullet$0) and mean RMSE BMI was 4$\bullet$7 kg/m${}^2$ (4$\bullet$4-5$\bullet$0), and the mean difference between predicted and observed BMI was-0$\bullet$3 kg/m${}^2$ (SD 4$\bullet$7). This model is incorporated in an easy to use and interpretable web-based prediction tool to help inform clinical decision before surgery. InterpretationWe developed a machine learning-based model, which is internationally validated, for predicting individual 5-year weight loss trajectories after three common bariatric interventions.Comment: The Lancet Digital Health, 202