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      Liver Fibrosis Biomarkers Accurately Exclude Advanced Fibrosis and Are Associated with Higher Cardiovascular Risk Scores in Patients with NAFLD or Viral Chronic Liver Disease

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          Abstract

          Liver fibrosis predicts liver-related and cardiovascular outcomes in chronic liver disease patients. We compared the diagnostic performance of various liver fibrosis biomarkers for identifying histological significant/advanced fibrosis. Additionally, the correlations of such liver fibrosis biomarkers with cardiovascular risk (CVR) scores were evaluated. 173 patients with viral hepatitis (157 HCV and 16 HBV) and 107 with a non-alcoholic fatty liver disease (NAFLD) were consecutively enrolled. Various liver fibrosis biomarkers: aspartate aminotransferase (AST) to alanine aminotransferase (ALT) ratio (ARR), AST to Platelet Ratio Index (APRI), Fibrosis-4 (FiB-4), Forns index, NAFLD fibrosis score (NFS), BARD (body mass index (BMI), AAR, Diabetes) score, and Hepamet fibrosis score (HFS), were used to identify significant/advanced fibrosis. CVR was assessed by using the SCORE, the Progetto CUORE, or the Framingham risk scoring systems. Liver fibrosis biomarkers performed better in predicting advanced rather than significant liver fibrosis in all patients, regardless of chronic liver disease aetiology. Forns index and HFS performed best in predicting advanced fibrosis in patients with viral chronic liver disease and NAFLD. Lower cut-offs of these liver fibrosis biomarkers had high negative predictive values for advanced fibrosis overall, as well as in patients with NAFLD or viral chronic liver disease. FIB-4, Forns index, NFS, and HFS were positively correlated with SCORE and Framingham risk scores. In conclusion, liver fibrosis biomarkers accurately exclude advanced fibrosis and positively correlate with CVR scores in patients with chronic liver disease.

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          Global epidemiology of nonalcoholic fatty liver disease-Meta-analytic assessment of prevalence, incidence, and outcomes.

          Nonalcoholic fatty liver disease (NAFLD) is a major cause of liver disease worldwide. We estimated the global prevalence, incidence, progression, and outcomes of NAFLD and nonalcoholic steatohepatitis (NASH). PubMed/MEDLINE were searched from 1989 to 2015 for terms involving epidemiology and progression of NAFLD. Exclusions included selected groups (studies that exclusively enrolled morbidly obese or diabetics or pediatric) and no data on alcohol consumption or other liver diseases. Incidence of hepatocellular carcinoma (HCC), cirrhosis, overall mortality, and liver-related mortality were determined. NASH required histological diagnosis. All studies were reviewed by three independent investigators. Analysis was stratified by region, diagnostic technique, biopsy indication, and study population. We used random-effects models to provide point estimates (95% confidence interval [CI]) of prevalence, incidence, mortality and incidence rate ratios, and metaregression with subgroup analysis to account for heterogeneity. Of 729 studies, 86 were included with a sample size of 8,515,431 from 22 countries. Global prevalence of NAFLD is 25.24% (95% CI: 22.10-28.65) with highest prevalence in the Middle East and South America and lowest in Africa. Metabolic comorbidities associated with NAFLD included obesity (51.34%; 95% CI: 41.38-61.20), type 2 diabetes (22.51%; 95% CI: 17.92-27.89), hyperlipidemia (69.16%; 95% CI: 49.91-83.46%), hypertension (39.34%; 95% CI: 33.15-45.88), and metabolic syndrome (42.54%; 95% CI: 30.06-56.05). Fibrosis progression proportion, and mean annual rate of progression in NASH were 40.76% (95% CI: 34.69-47.13) and 0.09 (95% CI: 0.06-0.12). HCC incidence among NAFLD patients was 0.44 per 1,000 person-years (range, 0.29-0.66). Liver-specific mortality and overall mortality among NAFLD and NASH were 0.77 per 1,000 (range, 0.33-1.77) and 11.77 per 1,000 person-years (range, 7.10-19.53) and 15.44 per 1,000 (range, 11.72-20.34) and 25.56 per 1,000 person-years (range, 6.29-103.80). Incidence risk ratios for liver-specific and overall mortality for NAFLD were 1.94 (range, 1.28-2.92) and 1.05 (range, 0.70-1.56).
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              General cardiovascular risk profile for use in primary care: the Framingham Heart Study.

              Separate multivariable risk algorithms are commonly used to assess risk of specific atherosclerotic cardiovascular disease (CVD) events, ie, coronary heart disease, cerebrovascular disease, peripheral vascular disease, and heart failure. The present report presents a single multivariable risk function that predicts risk of developing all CVD and of its constituents. We used Cox proportional-hazards regression to evaluate the risk of developing a first CVD event in 8491 Framingham study participants (mean age, 49 years; 4522 women) who attended a routine examination between 30 and 74 years of age and were free of CVD. Sex-specific multivariable risk functions ("general CVD" algorithms) were derived that incorporated age, total and high-density lipoprotein cholesterol, systolic blood pressure, treatment for hypertension, smoking, and diabetes status. We assessed the performance of the general CVD algorithms for predicting individual CVD events (coronary heart disease, stroke, peripheral artery disease, or heart failure). Over 12 years of follow-up, 1174 participants (456 women) developed a first CVD event. All traditional risk factors evaluated predicted CVD risk (multivariable-adjusted P<0.0001). The general CVD algorithm demonstrated good discrimination (C statistic, 0.763 [men] and 0.793 [women]) and calibration. Simple adjustments to the general CVD risk algorithms allowed estimation of the risks of each CVD component. Two simple risk scores are presented, 1 based on all traditional risk factors and the other based on non-laboratory-based predictors. A sex-specific multivariable risk factor algorithm can be conveniently used to assess general CVD risk and risk of individual CVD events (coronary, cerebrovascular, and peripheral arterial disease and heart failure). The estimated absolute CVD event rates can be used to quantify risk and to guide preventive care.
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                Author and article information

                Journal
                Diagnostics (Basel)
                Diagnostics (Basel)
                diagnostics
                Diagnostics
                MDPI
                2075-4418
                09 January 2021
                January 2021
                : 11
                : 1
                : 98
                Affiliations
                [1 ]Internal Medicine Unit, Pavullo Hospital, Azienda USL, 41026 Modena, Italy; a.marrazzo@ 123456ausl.mo.it
                [2 ]Section of Endocrinology, Diabetes and Metabolism, Department of Medicine, University and Azienda Ospedaliera Universitaria Integrata of Verona, 37126 Verona, Italy; alessandro.mantovani@ 123456univr.it (A.M.); giovanni.targher@ 123456univr.it (G.T.)
                [3 ]Department of Biomedical, Metabolic and Neural Sciences, University of Modena and Reggio Emilia, 41126 Modena, Italy; enrica.baldelli@ 123456unimore.it
                [4 ]Metabolic Medicine Unit, Ospedale Civile di Baggiovara, Azienda Ospedaliero-Universitaria, 41126 Modena, Italy; simonetta.lugari@ 123456libero.it (S.L.); maurantonio.mauro@ 123456aou.mo.it (M.M.); nascimbeni.fabio@ 123456aou.mo.it (F.N.)
                [5 ]Gastroenterology Unit, Ospedale Policlinico di Modena, Azienda Ospedaliero-Universitaria, 41126 Modena, Italy; romagnoli.dante@ 123456aou.mo.it
                [6 ]Metabolic Syndrome Unit, Ospedale Civile di Baggiovara, Azienda Ospedaliero-Universitaria, 41126 Modena, Italy; lonardo.amedeo@ 123456aou.mo.it
                Author notes
                [* ]Correspondence: s.ballestri@ 123456ausl.mo.it ; Tel.: +39-0536-29409
                Author information
                https://orcid.org/0000-0002-1424-7894
                https://orcid.org/0000-0002-7271-6329
                https://orcid.org/0000-0002-4325-3900
                https://orcid.org/0000-0001-9886-0698
                Article
                diagnostics-11-00098
                10.3390/diagnostics11010098
                7827076
                33435415
                dae4d256-552e-4147-9fc5-3f3fb07a1b9a
                © 2021 by the authors.

                Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license ( http://creativecommons.org/licenses/by/4.0/).

                History
                : 07 December 2020
                : 06 January 2021
                Categories
                Article

                accuracy,aminotransferase,liver biopsy,liver enzymes,major cardiovascular events,mortality,nash,fibrosis,non-invasive,sensitivity,specificity

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