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      Family history and obesity in youth, their effect on acylcarnitine/aminoacids metabolomics and non-alcoholic fatty liver disease (NAFLD). Structural equation modeling approach

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          Abstract

          Background

          Structural equation modeling (SEM) can help understanding complex functional relationships among obesity, non-alcoholic fatty liver disease (NAFLD), family history of obesity, targeted metabolomics and pro-inflammatory markers. We tested two hypotheses: 1) If obesity precedes an excess of free fatty acids that increase oxidative stress and mitochondrial dysfunction, there would be an increase of serum acylcarnitines, amino acids and cytokines in obese subjects. Acylcarnitines would be related to non-alcoholic fatty disease that will induce insulin resistance. 2) If a positive family history of obesity and type 2 diabetes are the major determinants of the metabolomic profile, there would be higher concentration of amino acids and acylcarnitines in patients with this background that will induce obesity and NAFLD which in turn will induce insulin resistance.

          Methods/Results

          137 normoglycemic subjects, mean age (SD) of 30.61 (8.6) years divided in three groups: BMI<25 with absence of NAFLD (G1), n = 82; BMI>30 with absence of NAFLD (G2), n = 24; and BMI>30 with NAFLD (G3), n = 31. Family history of obesity (any) was present in 53%. Both models were adjusted in SEM. Family history of obesity predicted obesity but could not predict acylcarnitines and amino acid concentrations (effect size <0.2), but did predict obesity phenotype.

          Conclusion

          Family history of obesity is the major predictor of obesity, and the metabolic abnormalities on amino acids, acylcarnitines, inflammation, insulin resistance, and NAFLD.

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          Most cited references28

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          Controlling the False Discovery Rate: A Practical and Powerful Approach to Multiple Testing

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            Principles and practice in reporting structural equation analyses.

            Principles for reporting analyses using structural equation modeling are reviewed, with the goal of supplying readers with complete and accurate information. It is recommended that every report give a detailed justification of the model used, along with plausible alternatives and an account of identifiability. Nonnormality and missing data problems should also be addressed. A complete set of parameters and their standard errors is desirable, and it will often be convenient to supply the correlation matrix and discrepancies, as well as goodness-of-fit indices, so that readers can exercise independent critical judgment. A survey of fairly representative studies compares recent practice with the principles of reporting recommended here.
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              Metabolomics--the link between genotypes and phenotypes.

              Metabolites are the end products of cellular regulatory processes, and their levels can be regarded as the ultimate response of biological systems to genetic or environmental changes. In parallel to the terms 'transcriptome' and proteome', the set of metabolites synthesized by a biological system constitute its 'metabolome'. Yet, unlike other functional genomics approaches, the unbiased simultaneous identification and quantification of plant metabolomes has been largely neglected. Until recently, most analyses were restricted to profiling selected classes of compounds, or to fingerprinting metabolic changes without sufficient analytical resolution to determine metabolite levels and identities individually. As a prerequisite for metabolomic analysis, careful consideration of the methods employed for tissue extraction, sample preparation, data acquisition, and data mining must be taken. In this review, the differences among metabolite target analysis, metabolite profiling, and metabolic fingerprinting are clarified, and terms are defined. Current approaches are examined, and potential applications are summarized with a special emphasis on data mining and mathematical modelling of metabolism.
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                Author and article information

                Contributors
                Role: ConceptualizationRole: Data curationRole: Formal analysisRole: InvestigationRole: MethodologyRole: Project administrationRole: SoftwareRole: Writing – original draftRole: Writing – review & editing
                Role: ConceptualizationRole: Funding acquisitionRole: InvestigationRole: MethodologyRole: VisualizationRole: Writing – original draft
                Role: Investigation
                Role: Methodology
                Role: Investigation
                Role: Investigation
                Role: Investigation
                Role: Investigation
                Role: ConceptualizationRole: Writing – original draft
                Role: Funding acquisitionRole: Investigation
                Role: ConceptualizationRole: Data curationRole: Formal analysisRole: Funding acquisitionRole: InvestigationRole: MethodologyRole: SupervisionRole: VisualizationRole: Writing – original draft
                Role: Editor
                Journal
                PLoS One
                PLoS ONE
                plos
                plosone
                PLoS ONE
                Public Library of Science (San Francisco, CA USA )
                1932-6203
                21 February 2018
                2018
                : 13
                : 2
                : e0193138
                Affiliations
                [1 ] Research Divison, Hospital General de México, Mexico City, Mexico
                [2 ] Vinculation Unit Faculty of Medicine UNAM, Instituto Nacional de Medicina Genomica (INMEGEN), Mexico City, Mexico
                [3 ] Harvard Medical School, Boston, Massachusetts, United States of America
                [4 ] Inborn errors of Metabolism Unit, Instituto Nacional de Pediatría, Mexico City, Mexico
                [5 ] Internal Medicine Department, Hospital General de México, Mexico City, Mexico
                [6 ] South Texas Diabetes and Obesity Institute, University of Texas Rio Grande Valley, Edinburg, TX, United States of America
                [7 ] Research department, Universidad Mexico Americana del Norte, Reynosa, Tamaulipas, Mexico
                Medical University of Vienna, AUSTRIA
                Author notes

                Competing Interests: The authors have declared that no competing interests exist.

                Author information
                http://orcid.org/0000-0002-2572-4043
                http://orcid.org/0000-0002-0966-8766
                Article
                PONE-D-17-23955
                10.1371/journal.pone.0193138
                5821462
                29466466
                6cc8c6eb-31fd-4a4b-824e-9e5755c43b42
                © 2018 Romero-Ibarguengoitia et al

                This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.

                History
                : 23 June 2017
                : 5 February 2018
                Page count
                Figures: 4, Tables: 2, Pages: 17
                Funding
                This study was supported by internal funds of Instituto Nacional de Medicina Genomica and Hospital General de Mexico. The funder had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.
                Categories
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                Biology and Life Sciences
                Physiology
                Physiological Parameters
                Body Weight
                Obesity
                Medicine and Health Sciences
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                Physiological Parameters
                Body Weight
                Obesity
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