Author information
1Clinical Medicine and Hepatology Unit, Department of Internal Medicine and Geriatrics, Campus Bio-Medico University, Rome, Italy; Department of Molecular and Clinical Medicine, Institute of Medicine, Sahlgrenska Academy, Wallenberg Laboratory, University of Gothenburg, Gothenburg, Sweden. Electronic address: f.tavaglione@unicampus.it.
2Department of Molecular and Clinical Medicine, Institute of Medicine, Sahlgrenska Academy, Wallenberg Laboratory, University of Gothenburg, Gothenburg, Sweden.
3Internal Medicine Unit, Department of Internal Medicine and Geriatrics, Campus Bio-Medico University, Rome, Italy; Clinical Lecturer of Internal Medicine, Saint Camillus International University of Health and Medical Sciences, Rome, Italy.
4Department of Medicine, University of Helsinki and Helsinki University Hospital, Helsinki, Finland; Minerva Foundation Institute for Medical Research, Helsinki, Finland.
5Department of Computer & Information Sciences, College of Science and Technology, Temple University, Philadelphia, Pennsylvania.
6Research Unit of Microscopic and Ultrastructural Anatomy, Department of Medicine, Campus Bio-Medico University, Rome, Italy; Predictive Molecular Diagnostic Unit, Department of Pathology, Campus Bio-Medico University Hospital, Rome, Italy.
7Predictive Molecular Diagnostic Unit, Department of Pathology, Campus Bio-Medico University Hospital, Rome, Italy; Research Unit of Pathology, Campus Bio-Medico University, Rome, Italy.
8Bariatric Surgery Unit, Campus Bio-Medico University, Rome, Italy.
9Department of Endocrinology and Diabetes, University Campus Bio-Medico, Rome, Italy.
10Translational Medicine, Department of Transfusion Medicine and Hematology, Fondazione IRCCS Ca' Granda Ospedale Maggiore Policlinico, Milano, Italy.
11Clinical Medicine and Hepatology Unit, Department of Internal Medicine and Geriatrics, Campus Bio-Medico University, Rome, Italy.
12Unit of Colon and Rectal Surgery, Department of General Surgery, Campus Bio-Medico University, Rome, Italy.
13Translational Medicine, Department of Transfusion Medicine and Hematology, Fondazione IRCCS Ca' Granda Ospedale Maggiore Policlinico, Milano, Italy; Department of Pathophysiology and Transplantation, Università degli Studi di Milano, Milano, Italy.
14Clinical Medicine and Hepatology Unit, Department of Internal Medicine and Geriatrics, Campus Bio-Medico University, Rome, Italy. Electronic address: u.vespasiani@policlinicocampus.it.
15Department of Molecular and Clinical Medicine, Institute of Medicine, Sahlgrenska Academy, Wallenberg Laboratory, University of Gothenburg, Gothenburg, Sweden; Department of Cardiology, Sahlgrenska University Hospital, Gothenburg, Sweden; Clinical Nutrition Unit, Department of Medical and Surgical Sciences, University Magna Graecia, Catanzaro, Italy. Electronic address: stefano.romeo@wlab.gu.se.
Abstract
Background & aims: Noninvasive assessment of histological features of nonalcoholic fatty liver disease (NAFLD) has been an intensive research area over the last decade. Herein, we aimed to develop a simple noninvasive score using routine laboratory tests to identify, among individuals at high risk for NAFLD, those with fibrotic nonalcoholic steatohepatitis (NASH) defined as NASH, NAFLD activity score ≥4, and fibrosis stage ≥2.
Methods: The derivation cohort included 264 morbidly obese individuals undergoing intraoperative liver biopsy in Rome, Italy. The best predictive model was developed and internally validated using a bootstrapping stepwise logistic regression analysis (2000 bootstrap samples). Performance was estimated by the area under the receiver operating characteristic curve (AUROC). External validation was assessed in 3 independent European cohorts (Finland, n = 370; Italy, n = 947; England, n = 5368) of individuals at high risk for NAFLD.
Results: The final predictive model, designated as Fibrotic NASH Index (FNI), combined aspartate aminotransferase, high-density lipoprotein cholesterol, and hemoglobin A1c. The performance of FNI for fibrotic NASH was satisfactory in both derivation and external validation cohorts (AUROC = 0.78 and AUROC = 0.80-0.95, respectively). In the derivation cohort, rule-out and rule-in cutoffs were 0.10 for sensitivity ≥0.89 (negative predictive value, 0.93) and 0.33 for specificity ≥0.90 (positive predictive value, 0.57), respectively. In the external validation cohorts, sensitivity ranged from 0.87 to 1 (negative predictive value, 0.99-1) and specificity from 0.73 to 0.94 (positive predictive value, 0.12-0.49) for rule-out and rule-in cutoff, respectively.
Conclusion: FNI is an accurate, simple, and affordable noninvasive score which can be used to screen for fibrotic NASH in individuals with dysmetabolism in primary health care.