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      Metabolomic Profiling of Long‐Term Weight Change: Role of Oxidative Stress and Urate Levels in Weight Gain

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          Abstract

          Objective

          To investigate the association between long‐term weight change and blood metabolites.

          Methods

          Change in BMI over 8.6 ± 3.79 years was assessed in 3,176 females from the TwinsUK cohort (age range: 18.3‐79.6, baseline BMI: 25.11 ± 4.35) measured for 280 metabolites at follow‐up. Statistically significant metabolites (adjusting for covariates) were included in a multivariable least absolute shrinkage and selection operator (LASSO) model. Findings were replicated in the Cooperative Health Research in the Region of Augsburg (KORA) study ( n = 1,760; age range: 25‐70, baseline BMI: 27.72 ± 4.53). The study examined whether the metabolites identified could prospectively predict weight change in KORA and in the Prostate, Lung, Colorectal, and Ovarian Cancer (PLCO) study ( n = 471; age range: 55‐74, baseline BMI: 27.24 ± 5.37).

          Results

          Thirty metabolites were significantly associated with change in BMI per year in TwinsUK using Bonferroni correction. Four were independently associated with weight change in the multivariable LASSO model and replicated in KORA: namely, urate (meta‐analysis β [95% CI] = 0.05 [0.040 to 0.063]; P = 1.37 × 10 −19), gamma‐glutamyl valine (β [95% CI] = 0.06 [0.046 to 0.070]; P = 1.23 × 10 −20), butyrylcarnitine (β [95% CI] = 0.04 [0.028 to 0.051]; P = 6.72 × 10 −12), and 3‐phenylpropionate (β [95% CI] = −0.03 [−0.041 to −0.019]; P = 9.8 × 10 −8), all involved in oxidative stress. Higher levels of urate at baseline were associated with weight gain in KORA and PLCO.

          Conclusions

          Metabolites linked to higher oxidative stress are associated with increased long‐term weight gain.

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          Most cited references26

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          Redox environment of the cell as viewed through the redox state of the glutathione disulfide/glutathione couple.

          Redox state is a term used widely in the research field of free radicals and oxidative stress. Unfortunately, it is used as a general term referring to relative changes that are not well defined or quantitated. In this review we provide a definition for the redox environment of biological fluids, cell organelles, cells, or tissue. We illustrate how the reduction potential of various redox couples can be estimated with the Nernst equation and show how pH and the concentrations of the species comprising different redox couples influence the reduction potential. We discuss how the redox state of the glutathione disulfide-glutathione couple (GSSG/2GSH) can serve as an important indicator of redox environment. There are many redox couples in a cell that work together to maintain the redox environment; the GSSG/2GSH couple is the most abundant redox couple in a cell. Changes of the half-cell reduction potential (E(hc)) of the GSSG/2GSH couple appear to correlate with the biological status of the cell: proliferation E(hc) approximately -240 mV; differentiation E(hc) approximately -200 mV; or apoptosis E(hc) approximately -170 mV. These estimates can be used to more fully understand the redox biochemistry that results from oxidative stress. These are the first steps toward a new quantitative biology, which hopefully will provide a rationale and understanding of the cellular mechanisms associated with cell growth and development, signaling, and reductive or oxidative stress.
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            Increased levels of plasma acylcarnitines in obesity and type 2 diabetes and identification of a marker of glucolipotoxicity.

            Dysregulation of fatty acid oxidation (FAO) is recognized as important in the pathophysiology of obesity and insulin resistance (IR). However, demonstrating FAO defects in vivo in humans has entailed complex and invasive methodologies. Recently, the identification of genetic blocks in FAO has been vastly simplified by using tandem mass spectrometry (MS/MS) of dried bloodspots to specify acylcarnitine (AcylCN) alterations characteristic for each disorder. This technology has recently been applied to examine FAO alterations in human and animal models of obesity and type 2 diabetes mellitus (T2DM). This study focused on characterizing AcylCN profiles in human plasma from individuals with obesity and T2DM during fasting and insulin-stimulated conditions. Following an overnight fast, plasma was obtained from lean (n = 12), obese nondiabetic (n = 14), and T2DM (n = 10) participants and analyzed for AcylCN using MS/MS. Plasma samples were also obtained at the end of a 4-h insulin-stimulated euglycemic clamp. In obesity and T2DM, long-chain AcylCNs were similarly significantly increased in the fasted state; free-CN levels were also elevated. Additionally, T2DM subjects of comparable BMI had increased short- and medium-chain AcylCNs, both saturated and hydroxy, as well as increased C(4)-dicarboxylcarnitine (C(4)DC-CN) that correlated with an index of poor glycemic control (HbA(1c); r = 0.74; P < 0.0001). Insulin infusion reduced all species of plasma AcylCN but this reduction was blunted in T2DM. Plasma long-chain AcylCN species are increased in obesity and T2DM, suggesting that more fatty acids can enter mitochondria. In T2DM, many shorter species accumulate, suggesting that they have a generalized complex oxidation defect.
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              Gut microbiome diversity and high-fibre intake are related to lower long-term weight gain

              Background: Cross-sectional studies suggest that the microbes in the human gut have a role in obesity by influencing the human body’s ability to extract and store calories. The aim of this study was to assess if there is a correlation between change in body weight over time and gut microbiome composition. Methods: We analysed 16S ribosomal RNA gene sequence data derived from the faecal samples of 1632 healthy females from TwinsUK to investigate the association between gut microbiome measured cross-sectionally and longitudinal weight gain (adjusted for caloric intake and baseline body mass index). Dietary fibre intake was investigated as a possible modifier. Results: Less than half of the variation in long-term weight change was found to be heritable (h2=0.41 (0.31, 0.47)). Gut microbiota diversity was negatively associated with long-term weight gain, whereas it was positively correlated with fibre intake. Nine bacterial operational taxonomic units (OTUs) were significantly associated with weight gain after adjusting for covariates, family relatedness and multiple testing (false discovery rate <0.05). OTUs associated with lower long-term weight gain included those assigned to Ruminococcaceae (associated in mice with improved energy metabolism) and Lachnospiraceae. A Bacterioides species OTU was associated with increased risk of weight gain but this appears to be driven by its correlation with lower levels of diversity. Conclusions: High gut microbiome diversity, high-fibre intake and OTUs implicated in animal models of improved energy metabolism are all correlated with lower term weight gain in humans independently of calorie intake and other confounders.
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                Author and article information

                Contributors
                ana.valdes@nottingham.ac.uk
                Journal
                Obesity (Silver Spring)
                Obesity (Silver Spring)
                10.1002/(ISSN)1930-739X
                OBY
                Obesity (Silver Spring, Md.)
                John Wiley and Sons Inc. (Hoboken )
                1930-7381
                1930-739X
                31 July 2017
                September 2017
                : 25
                : 9 ( doiID: 10.1002/oby.v25.9 )
                : 1618-1624
                Affiliations
                [ 1 ] Department of Twin Research & Genetic Epidemiology King's College London London UK
                [ 2 ] Mitchell Cancer Institute University of South Alabama Mobile Alabama USA
                [ 3 ] Institute of Bioinformatics and Systems Biology, Helmholtz Zentrum München Neuherberg Germany
                [ 4 ] German Center for Diabetes Research Neuherberg Germany
                [ 5 ] Institute of Epidemiology II, Helmholtz Zentrum München Neuherberg Germany
                [ 6 ] Metabolon, Inc Durham North Carolina USA
                [ 7 ] Department of Statistics and Quantitative Methods University of Milano‐Bicocca Milan Italy
                [ 8 ] Division of Cancer Epidemiology and Genetics National Cancer Institute, National Institutes of Health Bethesda Maryland USA
                [ 9 ] School of Medicine, University of Nottingham Nottingham UK
                [ 10 ] National Institute for Health Research, Nottingham Biomedical Research Centre Nottingham UK
                Author notes
                [*] [* ]Correspondence: Ana M. Valdes ( ana.valdes@ 123456nottingham.ac.uk )
                Author information
                http://orcid.org/0000-0002-9315-9541
                http://orcid.org/0000-0003-1141-4471
                Article
                OBY21922
                10.1002/oby.21922
                5601206
                28758372
                b89b9a65-5c52-4ca2-8bcf-64e5dbc86704
                © 2017 The Authors. Obesity published by Wiley Periodicals, Inc. on behalf of The Obesity Society (TOS)

                This is an open access article under the terms of the Creative Commons Attribution License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited.

                History
                : 23 January 2017
                : 16 May 2017
                : 31 May 2017
                Page count
                Figures: 1, Tables: 2, Pages: 7, Words: 4695
                Funding
                Funded by: Medical Research Council Ancestry and Biological Informative Markers for Stratification of Hypertension
                Award ID: MR/M016560/1
                Funded by: Wellcome Trust
                Funded by: European Community's Seventh Framework Programme
                Award ID: FP7/2007‐2013
                Funded by: National Institute for Health Research (NIHR)
                Funded by: Clinical Research Facility at Guy's & St Thomas' National Health Service (NHS)
                Funded by: NIHR Nottingham Biomedical Research Centre
                Funded by: Helmholtz Zentrum München German Research Center for Environmental Health
                Funded by: German Federal Ministry of Education and Research
                Funded by: Munich Center of Health Sciences
                Categories
                Original Article
                Original Articles
                Epidemiology/Genetics
                Custom metadata
                2.0
                oby21922
                September 2017
                Converter:WILEY_ML3GV2_TO_NLMPMC version:5.1.9 mode:remove_FC converted:18.09.2017

                Medicine
                Medicine

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