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      Lipidomic profiling reveals distinct differences in plasma lipid composition in healthy, prediabetic, and type 2 diabetic individuals

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          Abstract

          The relationship between dyslipidemia and type 2 diabetes mellitus (T2D) has been extensively reported, but the global lipid profiles, especially in the East Asia population, associated with the development of T2D remain to be characterized. Liquid chromatography coupled to tandem mass spectrometry was applied to detect the global lipidome in the fasting plasma of 293 Chinese individuals, including 114 T2D patients, 81 prediabetic subjects, and 98 individuals with normal glucose tolerance (NGT). Both qualitative and quantitative analyses revealed a gradual change in plasma lipid features with T2D patients exhibiting characteristics close to those of prediabetic individuals, whereas they differed significantly from individuals with NGT. We constructed and validated a random forest classifier with 28 lipidomic features that effectively discriminated T2D from NGT or prediabetes. Most of the selected features significantly correlated with diabetic clinical indices. Hydroxybutyrylcarnitine was positively correlated with fasting plasma glucose, 2-hour postprandial glucose, glycated hemoglobin, and insulin resistance index (HOMA-IR). Lysophosphatidylcholines such as lysophosphatidylcholine (18:0), lysophosphatidylcholine (18:1), and lysophosphatidylcholine (18:2) were all negatively correlated with HOMA-IR. The altered plasma lipidome in Chinese T2D and prediabetic subjects suggests that lipid features may play a role in the pathogenesis of T2D and that such features may provide a basis for evaluating risk and monitoring disease development.

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          Most cited references31

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          Type 2 diabetes in East Asians: similarities and differences with populations in Europe and the United States

          There is an epidemic of diabetes in Asia. Type 2 diabetes develops in East Asian patients at a lower mean body mass index (BMI) compared with those of European descent. At any given BMI, East Asians have a greater amount of body fat and a tendency to visceral adiposity. In Asian patients, diabetes develops at a younger age and is characterized by early β cell dysfunction in the setting of insulin resistance, with many requiring early insulin treatment. The increasing proportion of young-onset and childhood type 2 diabetes is posing a particular threat, with these patients being at increased risk of developing diabetic complications. East Asian patients with type 2 diabetes have a higher risk of developing renal complications than Europeans and, with regard to cardiovascular complications, a predisposition for developing strokes. In addition to cardiovascular–renal disease, cancer is emerging as the other main cause of mortality. While more research is needed to explain these interethnic differences, urgent and concerted actions are needed to raise awareness, facilitate early diagnosis, and encourage preventive strategies to combat these growing disease burdens.
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            Global metabolic profiling procedures for urine using UPLC-MS.

            The production of 'global' metabolite profiles involves measuring low molecular-weight metabolites (<1 kDa) in complex biofluids/tissues to study perturbations in response to physiological challenges, toxic insults or disease processes. Information-rich analytical platforms, such as mass spectrometry (MS), are needed. Here we describe the application of ultra-performance liquid chromatography-MS (UPLC-MS) to urinary metabolite profiling, including sample preparation, stability/storage and the selection of chromatographic conditions that balance metabolome coverage, chromatographic resolution and throughput. We discuss quality control and metabolite identification, as well as provide details of multivariate data analysis approaches for analyzing such MS data. Using this protocol, the analysis of a sample set in 96-well plate format, would take ca. 30 h, including 1 h for system setup, 1-2 h for sample preparation, 24 h for UPLC-MS analysis and 1-2 h for initial data processing. The use of UPLC-MS for metabolic profiling in this way is not faster than the conventional HPLC-based methods but, because of improved chromatographic performance, provides superior metabolome coverage.
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              metaX: a flexible and comprehensive software for processing metabolomics data

              Background Non-targeted metabolomics based on mass spectrometry enables high-throughput profiling of the metabolites in a biological sample. The large amount of data generated from mass spectrometry requires intensive computational processing for annotation of mass spectra and identification of metabolites. Computational analysis tools that are fully integrated with multiple functions and are easily operated by users who lack extensive knowledge in programing are needed in this research field. Results We herein developed an R package, metaX, that is capable of end-to-end metabolomics data analysis through a set of interchangeable modules. Specifically, metaX provides several functions, such as peak picking and annotation, data quality assessment, missing value imputation, data normalization, univariate and multivariate statistics, power analysis and sample size estimation, receiver operating characteristic analysis, biomarker selection, pathway annotation, correlation network analysis, and metabolite identification. In addition, metaX offers a web-based interface (http://metax.genomics.cn) for data quality assessment and normalization method evaluation, and it generates an HTML-based report with a visualized interface. The metaX utilities were demonstrated with a published metabolomics dataset on a large scale. The software is available for operation as either a web-based graphical user interface (GUI) or in the form of command line functions. The package and the example reports are available at http://metax.genomics.cn/. Conclusions The pipeline of metaX is platform-independent and is easy to use for analysis of metabolomics data generated from mass spectrometry. Electronic supplementary material The online version of this article (doi:10.1186/s12859-017-1579-y) contains supplementary material, which is available to authorized users.
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                Author and article information

                Journal
                Gigascience
                Gigascience
                gigascience
                GigaScience
                Oxford University Press
                2047-217X
                July 2017
                15 May 2017
                15 May 2017
                : 6
                : 7
                : 1-12
                Affiliations
                [1 ]BGI-Shenzhen, Shenzhen 518083, China
                [2 ]China National GeneBank-Shenzhen, BGI-Shenzhen, Shenzhen 518083, China
                [3 ]Shenzhen Key Laboratory of Human Commensal Microorganisms and Health Research, BGI-Shenzhen, Shenzhen 518083, China
                [4 ]Shenzhen Engineering Laboratory of Detection and Intervention of Human Intestinal Microbiome, BGI-Shenzhen, Shenzhen 518083, China
                [5 ]Suzhou Center for Disease Prevention and Control, Suzhou 215007, China
                [6 ]Proteomics Division, BGI-Shenzhen, Shenzhen 518083, China
                [7 ]BGI Education Center, University of Chinese Academy of Sciences, Beijing, China
                [8 ]School of Bioscience and Bioengineering, South China University of Technology, Guangzhou, China
                [9 ]James D. Watson Institute of Genome Sciences, Hangzhou 310058, China
                [10 ]Laboratory of Genomics and Molecular Biomedicine, Department of Biology, University of Copenhagen, 2100 Copenhagen Ø, Denmark
                [11 ]National Institute of Nutrition and Seafood Research (NIFES), 5817 Bergen, Norway
                [12 ]CAS Key Laboratory of Genome Sciences and Information, Beijing Institute of Genomics, Chinese Academy of Sciences, Beijing, China
                Author notes
                [* ]Correspondence address. Siqi Liu, BGI-Shenzhen, Shenzhen 518083, China; Tel: +86-13910021096, Fax: +86-755-36307162; E-mail: siqiliu@ 123456genomics.cn ; Junhua Li, BGI-Shenzhen, Shenzhen 518083, China; Tel: +86-13929566296, Fax: +86-755-36307162; E-mail: lijunhua@ 123456genomics.cn ; Chuanming Ni, Suzhou Center for Disease Prevention and Control, Suzhou 215007, China; Tel: +86-18962168899, Fax: +86-512-68262371. E-mail: nicm2008@ 123456126.com
                []Equal contribution
                Article
                gix036
                10.1093/gigascience/gix036
                5502363
                28505362
                bfff08c3-3b92-454a-8b90-66007ac45f91
                © The Authors 2017. Published by Oxford University Press.

                This is an Open Access article distributed under the terms of the Creative Commons Attribution License ( http://creativecommons.org/licenses/by/4.0/), which permits unrestricted reuse, distribution, and reproduction in any medium, provided the original work is properly cited.

                History
                : 09 May 2017
                : 07 October 2016
                : 04 March 2017
                Page count
                Pages: 12
                Categories
                Research

                lipidomics,type 2 diabetes,prediabetes,plasma
                lipidomics, type 2 diabetes, prediabetes, plasma

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