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      Is endocan a novel potential biomarker of liver steatosis and fibrosis? Translated title: Da li je endokan novi potencijalni biomarker za steatozu i fibrozu jetre?

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          Abstract

          Background

          Studies that evaluated endocan levels in nonalcoholic fatty liver disease (NAFLD) and liver fibrosis are scarce. We aimed to explore endocan levels in relation to different stages of liver diseases, such as NAFLD, as determined with fatty liver index (FLI) and liver fibrosis, as assessed with BARD score.

          Methods

          A total of 147 participants with FLI≥60 were compared with 64 participants with FLI <30. An FLI score was calculated using waist circumference, body mass index, gamma-glutamyl transferase and triglycerides. Patients with FLI≥60 were further divided into those with no/mild fibrosis (BARD score 0-1 point; n=23) and advanced fibrosis (BARD score 2-4 points; n=124). BARD score was calculated as follows: diabetes mellitus (1 point) + body mass index≥28 kg/m2 (1 point) + aspartate amino transferase/alanine aminotransferase ratio≥0.8 (2 points).

          Results

          Endocan was independent predictor for FLI and BARD score, both in univariate [OR=1.255 (95% CI= 1.104-1.426), P=0.001; OR=1.208 (95% CI=1.029-1.419), P=0.021, respectively] and multivariate binary logistic regression analysis [OR=1.287 (95% CI=1.055-1.570), P=0.013; OR=1.226 (95% CI=1.022-1.470), P=0.028, respectively]. Endocan as a single predictor showed poor discriminatory capability for steatosis/fibrosis [AUC=0.648; (95% CI=0.568-0.727), P=0.002; AUC= 0.667 (95% CI=0.555-0.778), P=0.013, respectively], whereas in a Model, endocan showed an excellent clinical accuracy [AUC=0.930; (95% CI=0.886-0.975), P<0.001, AUC=0.840 (95% CI=0.763-0.918), P<0.001, respectively].

          Conclusions

          Endocan independently correlated with both FLI and BARD score. However, when tested in models (with other biomarkers), endocan showed better discriminatory ability for liver steatosis/fibrosis, instead of its usage as a single biomarker.

          Translated abstract

          Uvod

          Nema mnogo studija koje su ispitivale vrednosti endokana kod obolelih od nealkoholne steatoze i fibroze jetre. Na cilj je bio da se ispita nivo endokana u različitim stadijumima oboljenja jetre, kao to su nealkoholna steatoza jetre, predstavljena indeksom masne jetre (FLI) i fibroza jetre, predstavljena BARD skorom.

          Metode

          Ukupno 147 učesnika sa FLI≥60 poređeno je sa 64 učesnika sa FLI <30. FLI skor je izračunat koristeći vrednosti obim struka, indeksa telesne mase, aktivnosti gama-glutamil transferaze i vrednosti triglicerida. Ispitanici sa FLI≥60 su dalje podeljeni u 2 grupe: bez fibroze/blaga fibroza (BARD skor 0-1 poen; n=23) i uznapredovala fibroza (BARD skor 2-4 poena; n=124). BARD skor je računat na sledeći način: šećerna bolest (1 poen) + indeks telesne mase≥28 kg/m2 (1 poen) + odnos aspartat aminotransferaza/alanin aminotransferaza≥0,8 (2 poena).

          Rezultati

          Endokan je nezavisan prediktor FLI i BARD skora, kako u univarijantnoj [OR=1,255 (95% CI=1,104-1,426), P=0,001; odnosno OR=1,208 (95% CI=1,029-1,419), P=0,021], tako i u multivarijantnoj binarnoj logističkoj regresionoj analizi [OR=1.287 (95% CI=1,055-1,570), P=0,013; odnosno OR=1,226 (95% CI=1,022-1,470), P=0,028]. Endokan kao samostalan prediktor pokazao je slabu diskriminatornu moć za steatozu/fibrozu jetre [AUC=0,648; (95% CI=0,568-0,727), P=0,002; odnosno AUC=0,667 (95% CI=0,555-0,778), P=0,013], ali je u Modelu pokazao odličnu kliničku tačnost [AUC=0,930; (95% CI=0,886-0,975), P<0,001; odnosno AUC=0,840 (95% CI=0,763-0,918), P<0,001].

          Zaključak

          Endokan je nezavisno povezan kako sa FLI, tako i sa BARD skorom. Ipak, u modelu (sa drugim biomarkerima), endokan je pokazao bolju diskriminatornu sposobnost za steatozu/fibrozu jetre.

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          Most cited references40

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          EASL-EASD-EASO Clinical Practice Guidelines for the management of non-alcoholic fatty liver disease.

          (2016)
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            Measuring the accuracy of diagnostic systems.

            J Swets (1988)
            Diagnostic systems of several kinds are used to distinguish between two classes of events, essentially "signals" and "noise". For them, analysis in terms of the "relative operating characteristic" of signal detection theory provides a precise and valid measure of diagnostic accuracy. It is the only measure available that is uninfluenced by decision biases and prior probabilities, and it places the performances of diverse systems on a common, easily interpreted scale. Representative values of this measure are reported here for systems in medical imaging, materials testing, weather forecasting, information retrieval, polygraph lie detection, and aptitude testing. Though the measure itself is sound, the values obtained from tests of diagnostic systems often require qualification because the test data on which they are based are of unsure quality. A common set of problems in testing is faced in all fields. How well these problems are handled, or can be handled in a given field, determines the degree of confidence that can be placed in a measured value of accuracy. Some fields fare much better than others.
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              The Fatty Liver Index: a simple and accurate predictor of hepatic steatosis in the general population

              Background Fatty liver (FL) is the most frequent liver disease in Western countries. We used data from the Dionysos Nutrition & Liver Study to develop a simple algorithm for the prediction of FL in the general population. Methods 216 subjects with and 280 without suspected liver disease were studied. FL was diagnosed by ultrasonography and alcohol intake was assessed using a 7-day diary. Bootstrapped stepwise logistic regression was used to identify potential predictors of FL among 13 variables of interest [gender, age, ethanol intake, alanine transaminase, aspartate transaminase, gamma-glutamyl-transferase (GGT), body mass index (BMI), waist circumference, sum of 4 skinfolds, glucose, insulin, triglycerides, and cholesterol]. Potential predictors were entered into stepwise logistic regression models with the aim of obtaining the most simple and accurate algorithm for the prediction of FL. Results An algorithm based on BMI, waist circumference, triglycerides and GGT had an accuracy of 0.84 (95%CI 0.81–0.87) in detecting FL. We used this algorithm to develop the "fatty liver index" (FLI), which varies between 0 and 100. A FLI < 30 (negative likelihood ratio = 0.2) rules out and a FLI ≥ 60 (positive likelihood ratio = 4.3) rules in fatty liver. Conclusion FLI is simple to obtain and may help physicians select subjects for liver ultrasonography and intensified lifestyle counseling, and researchers to select patients for epidemiologic studies. Validation of FLI in external populations is needed before it can be employed for these purposes.
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                Author and article information

                Contributors
                Journal
                J Med Biochem
                J Med Biochem
                JOMB
                Journal of Medical Biochemistry
                Society of Medical Biochemists of Serbia, Belgrade
                1452-8258
                1452-8266
                02 September 2020
                02 September 2020
                02 September 2020
                : 39
                : 3
                : 363-371 (pp. 363-371)
                Affiliations
                [1 ] orgnameUniversity of Montenegro, Faculty of Medicine, Primary Health Care Center, Podgorica, Montenegro
                [2 ] orgnameUniversity Magna Graecia, Department of Health Sciences, Catanzaro, Italy
                [3 ] orgnameClinical Center of Montenegro, Podgorica, Montenegro
                [4 ] orgnameUniversity of Belgrade, Faculty of Pharmacy, Department for Medical Biochemistry, Belgrade
                Author notes

                Correspondence to: Aleksandra Klisic, MD PhD, Center for Laboratory Diagnostics, Primary Health Care Center, University of Montenegro – Faculty of Medicine, Podgorica, Montenegro aleksandranklisic@ 123456gmail.com

                Article
                jomb-39-3-2003363K
                10.2478/jomb-2019-0042
                7682781
                33269025
                eadc1f30-af10-432c-a6af-c83f55e8dfb2
                2020 Aleksandra Klisić, Nebojša Kavarić, Ludovico Abenavoli, Verica Stanišić, Vesna Spasojević-Kalimanovska, Jelena Kotur-Stevuljević, Ana Ninić, published by CEON/CEES

                This work is licensed under the Creative Commons Attribution 4.0 License.

                History
                : 09 September 2019
                : 28 June 2019
                Funding
                Funded by: This work was financially supported in part by a grant from the Ministry of Science, Montenegro and the Ministry of Education, Science and Technological Development, Republic of Serbia (project number 175035);
                Categories
                Original Paper

                endocan,inflammation,liver steatosis,liver fibrosis,cardiovascular disease,endokan,inflamacija,steatoza jetre,fibroza jetre,kardiovaskularne bolesti

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