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      Obesity-Related Changes in Human Plasma Lipidome Determined by the Lipidyzer Platform

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          Abstract

          Obesity is an increasing public health concern both in the developed and developing countries. Previous studies have demonstrated that considerable alterations in lipid metabolism and consequently marked changes in lipid profile are associated with the onset and progression of obesity-related complications. To characterize the full spectrum of obesity-induced changes in lipid metabolism, direct infusion tandem mass spectrometry analysis is the most promising approach. To better understand which of the many lipid species are the most strongly associated with obesity, the aim of our work was to measure and profile plasma lipids in normal ( n = 57), overweight ( n = 31), and obese ( n = 48) individuals randomly selected from samples of Hungarian general and Roma populations by using the targeted quantitative lipidomics platform, the Lipidyzer. Principal component and stepwise regression analyses were used to identify the most significant clusters and species of lipids by increasing body mass index (BMI). From the 18 clusters identified four key lipid species (PE P-16:0/20:3, TG 20:4_33:1, TG 22:6_36:4, TG 18:3_33:0) showed a strong significant positive and three others (Hex-Cer 18:1;O2/22:0, LPC 18:2, PC 18:1_18:1) significant negative association with BMI. Compared to individual lipid species alone, the lipid species ratio (LSR) we introduced showed an extremely strong, at least 9 orders of magnitude stronger, association with BMI. The LSR can be used as a sensitive and predictive indicator to monitor obesity-related alterations in human plasma and control the effectiveness of treatment of obesity associated non-communicable diseases.

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          2019 ESC/EAS Guidelines for the management of dyslipidaemias: lipid modification to reduce cardiovascular risk

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            Youden Index and optimal cut-point estimated from observations affected by a lower limit of detection.

            The receiver operating characteristic (ROC) curve is used to evaluate a biomarker's ability for classifying disease status. The Youden Index (J), the maximum potential effectiveness of a biomarker, is a common summary measure of the ROC curve. In biomarker development, levels may be unquantifiable below a limit of detection (LOD) and missing from the overall dataset. Disregarding these observations may negatively bias the ROC curve and thus J. Several correction methods have been suggested for mean estimation and testing; however, little has been written about the ROC curve or its summary measures. We adapt non-parametric (empirical) and semi-parametric (ROC-GLM [generalized linear model]) methods and propose parametric methods (maximum likelihood (ML)) to estimate J and the optimal cut-point (c *) for a biomarker affected by a LOD. We develop unbiased estimators of J and c * via ML for normally and gamma distributed biomarkers. Alpha level confidence intervals are proposed using delta and bootstrap methods for the ML, semi-parametric, and non-parametric approaches respectively. Simulation studies are conducted over a range of distributional scenarios and sample sizes evaluating estimators' bias, root-mean square error, and coverage probability; the average bias was less than one percent for ML and GLM methods across scenarios and decreases with increased sample size. An example using polychlorinated biphenyl levels to classify women with and without endometriosis illustrates the potential benefits of these methods. We address the limitations and usefulness of each method in order to give researchers guidance in constructing appropriate estimates of biomarkers' true discriminating capabilities. Copyright 2008 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim
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              Dyslipidemia in Obesity: Mechanisms and Potential Targets

              Obesity has become a major worldwide health problem. In every single country in the world, the incidence of obesity is rising continuously and therefore, the associated morbidity, mortality and both medical and economical costs are expected to increase as well. The majority of these complications are related to co-morbid conditions that include coronary artery disease, hypertension, type 2 diabetes mellitus, respiratory disorders and dyslipidemia. Obesity increases cardiovascular risk through risk factors such as increased fasting plasma triglycerides, high LDL cholesterol, low HDL cholesterol, elevated blood glucose and insulin levels and high blood pressure. Novel lipid dependent, metabolic risk factors associated to obesity are the presence of the small dense LDL phenotype, postprandial hyperlipidemia with accumulation of atherogenic remnants and hepatic overproduction of apoB containing lipoproteins. All these lipid abnormalities are typical features of the metabolic syndrome and may be associated to a pro-inflammatory gradient which in part may originate in the adipose tissue itself and directly affect the endothelium. An important link between obesity, the metabolic syndrome and dyslipidemia, seems to be the development of insulin resistance in peripheral tissues leading to an enhanced hepatic flux of fatty acids from dietary sources, intravascular lipolysis and from adipose tissue resistant to the antilipolytic effects of insulin. The current review will focus on these aspects of lipid metabolism in obesity and potential interventions to treat the obesity related dyslipidemia.
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                Author and article information

                Contributors
                Role: Academic Editor
                Journal
                Biomolecules
                Biomolecules
                biomolecules
                Biomolecules
                MDPI
                2218-273X
                21 February 2021
                February 2021
                : 11
                : 2
                : 326
                Affiliations
                [1 ]MTA-DE Public Health Research Group, University of Debrecen, 4032 Debrecen, Hungary; piko.peter@ 123456med.unideb.hu
                [2 ]Department of Public Health and Epidemiology, Faculty of Medicine, University of Debrecen, 4032 Debrecen, Hungary; pal.laszlo@ 123456med.unideb.hu (L.P.); szucs.sandor@ 123456med.unideb.hu (S.S.); sandor.janos@ 123456med.unideb.hu (J.S.)
                [3 ]Department of Health Methodology and Public Health, Faculty of Health, University of Debrecen, 4400 Nyíregyháza, Hungary; kosa.zsigmond@ 123456foh.unideb.hu
                Author notes
                [* ]Correspondence: adany.roza@ 123456med.unideb.hu ; Tel.: +36-52-512-765 (ext. 77408)
                [†]

                These authors contributed equally to this work.

                Author information
                https://orcid.org/0000-0001-5539-907X
                https://orcid.org/0000-0003-4672-4066
                https://orcid.org/0000-0002-9679-6669
                Article
                biomolecules-11-00326
                10.3390/biom11020326
                7924880
                33669967
                2db0f140-e5f4-410e-8d57-79bc69930987
                © 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
                : 14 January 2021
                : 18 February 2021
                Categories
                Article

                obesity,body mass index (bmi), lipidomic analysis,lipidyzer platform,exploratory principal component analysis,stepwise regression analysis,lipid species ratio

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