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      Untargeted metabolomics reveals plasma metabolites predictive of ectopic fat in pancreas and liver as assessed by magnetic resonance imaging: the TOFI_Asia study

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          Abstract

          Background

          Excess visceral obesity and ectopic organ fat is associated with increased risk of cardiometabolic disease. However, circulating markers for early detection of ectopic fat, particularly pancreas and liver, are lacking.

          Methods

          Lipid storage in pancreas, liver, abdominal subcutaneous adipose tissue (SAT) and visceral adipose tissue (VAT) from 68 healthy or pre-diabetic Caucasian and Chinese women enroled in the TOFI_Asia study was assessed by magnetic resonance imaging/spectroscopy (MRI/S). Plasma metabolites were measured with untargeted liquid chromatography–mass spectroscopy (LC–MS). Multivariate partial least squares (PLS) regression identified metabolites predictive of VAT/SAT and ectopic fat; univariate linear regression adjusting for potential covariates identified individual metabolites associated with VAT/SAT and ectopic fat; linear regression adjusted for ethnicity identified clinical and anthropometric correlates for each fat depot.

          Results

          PLS identified 56, 64 and 31 metabolites which jointly predicted pancreatic fat (R2Y = 0.81, Q2 = 0.69), liver fat (RY2 = 0.8, Q2 = 0.66) and VAT/SAT ((R2Y = 0.7, Q2 = 0.62)) respectively. Among the PLS-identified metabolites, none of them remained significantly associated with pancreatic fat after adjusting for all covariates. Dihydrosphingomyelin (dhSM(d36:0)), 3 phosphatidylethanolamines, 5 diacylglycerols (DG) and 40 triacylglycerols (TG) were associated with liver fat independent of covariates. Three DGs and 12 TGs were associated with VAT/SAT independent of covariates. Notably, comparison with clinical correlates showed better predictivity of ectopic fat by these PLS-identified plasma metabolite markers.

          Conclusions

          Untargeted metabolomics identified candidate markers of visceral and ectopic fat that improved fat level prediction over clinical markers. Several plasma metabolites were associated with level of liver fat and VAT/SAT ratio independent of age, total and visceral adiposity, whereas pancreatic fat deposition was only associated with increased sulfolithocholic acid independent of adiposity-related parameters, but not age.

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          Most cited references60

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

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            NIH Image to ImageJ: 25 years of image analysis

            For the past twenty five years the NIH family of imaging software, NIH Image and ImageJ have been pioneers as open tools for scientific image analysis. We discuss the origins, challenges and solutions of these two programs, and how their history can serve to advise and inform other software projects.
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              Ror2 signaling regulates Golgi structure and transport through IFT20 for tumor invasiveness

              Signaling through the Ror2 receptor tyrosine kinase promotes invadopodia formation for tumor invasion. Here, we identify intraflagellar transport 20 (IFT20) as a new target of this signaling in tumors that lack primary cilia, and find that IFT20 mediates the ability of Ror2 signaling to induce the invasiveness of these tumors. We also find that IFT20 regulates the nucleation of Golgi-derived microtubules by affecting the GM130-AKAP450 complex, which promotes Golgi ribbon formation in achieving polarized secretion for cell migration and invasion. Furthermore, IFT20 promotes the efficiency of transport through the Golgi complex. These findings shed new insights into how Ror2 signaling promotes tumor invasiveness, and also advance the understanding of how Golgi structure and transport can be regulated.
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                Author and article information

                Contributors
                karl.fraser@agresearch.co.nz
                Journal
                Int J Obes (Lond)
                Int J Obes (Lond)
                International Journal of Obesity (2005)
                Nature Publishing Group UK (London )
                0307-0565
                1476-5497
                16 May 2021
                16 May 2021
                2021
                : 45
                : 8
                : 1844-1854
                Affiliations
                [1 ]GRID grid.417738.e, ISNI 0000 0001 2110 5328, Food Nutrition & Health, Food and Bio-based Products, , AgResearch Limited, ; Palmerston North, New Zealand
                [2 ]GRID grid.148374.d, ISNI 0000 0001 0696 9806, School of Health Sciences, , Massey University, ; Palmerston North, New Zealand
                [3 ]High-Value Nutrition National Science Challenge, Auckland, New Zealand
                [4 ]GRID grid.148374.d, ISNI 0000 0001 0696 9806, Riddet Institute, , Massey University, ; Palmerston North, New Zealand
                [5 ]GRID grid.9654.e, ISNI 0000 0004 0372 3343, Human Nutrition Unit, School of Biological Sciences, , University of Auckland, ; Auckland, New Zealand
                [6 ]GRID grid.9654.e, ISNI 0000 0004 0372 3343, Department of Surgery, , University of Auckland, ; Auckland, New Zealand
                [7 ]GRID grid.9654.e, ISNI 0000 0004 0372 3343, Department of Medicine, , University of Auckland, ; Auckland, New Zealand
                [8 ]GRID grid.9654.e, ISNI 0000 0004 0372 3343, School of Biological Sciences University of Auckland, ; Auckland, New Zealand
                [9 ]GRID grid.5379.8, ISNI 0000000121662407, Centre for Advanced Discovery and Experimental Therapeutics, School of Medical Sciences, , University of Manchester, ; Manchester, UK
                [10 ]GRID grid.5399.6, ISNI 0000 0001 2176 4817, Aix-Marseille University, INSERM, INRAe, C2VN, BioMeT, ; Marseille, France
                [11 ]GRID grid.1006.7, ISNI 0000 0001 0462 7212, Translational and Clinical Research Institute, Faculty of Medical Sciences, , Newcastle University, ; Newcastle upon Tyne, UK
                Author information
                http://orcid.org/0000-0003-1896-0221
                http://orcid.org/0000-0003-2737-0151
                http://orcid.org/0000-0002-0043-2423
                http://orcid.org/0000-0002-2870-0012
                http://orcid.org/0000-0002-2214-8378
                Article
                854
                10.1038/s41366-021-00854-x
                8310794
                33994541
                9b4cfc72-8119-4c3a-bcd9-155828217ada
                © The Author(s) 2021

                Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons license, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons license and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this license, visit http://creativecommons.org/licenses/by/4.0/.

                History
                : 10 June 2020
                : 10 April 2021
                : 30 April 2021
                Categories
                Article
                Custom metadata
                © Springer Nature Limited 2021

                Nutrition & Dietetics
                obesity,lipidomics,risk factors
                Nutrition & Dietetics
                obesity, lipidomics, risk factors

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