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      Metabolomics: a tool for the diagnosis of GH deficiency and for monitoring GH replacement?

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          Abstract

          The diagnostic value of insulin-like growth factor 1 (IGF1) for GH deficiency (GHD) in adults is not optimal. Molecular profiling could be used for biomarker discovery. The aim of this pilot study was to compare the serum metabolome between GHD patients and healthy controls, and identification of potential markers for diagnosis and/or for individual GH dosing. A total of ten patients with GHD, median age of 55 years and BMI of 27 kg/m 2, were compared with ten healthy age- and gender-matched controls. The serum metabolic profiles were generated using gas chromatography-coupled mass spectroscopy on fasting samples taken in the morning from the controls and at baseline and during 6 months of GH replacement in the patients with GHD. The difference in low-molecular weight compounds (LMC) distinguished the healthy controls from GHD patients. Among 285 measured metabolites, 13 were identified as being most important in differentiating GHD patients from controls. Of these, 11 could not be structurally annotated but many were classified as lipids. The difference in the LMC pattern persisted despite normalisation of IGF1 following GH replacement. GH replacement increased the levels of specific fatty acid compounds and decreased the levels of certain amino acids. No metabolite changed in response to GH treatment, to the same extent as IGF1. The measurement of 285 metabolites resulted in a unique pattern in GHD, but changes in the metabolite patterns during GH treatment were limited. The utility of metabolomics to find new markers in GHD and GH replacement remains to be further elucidated.

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          Most cited references12

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          Extraction and GC/MS analysis of the human blood plasma metabolome.

          Analysis of the entire set of low molecular weight compounds (LMC), the metabolome, could provide deeper insights into mechanisms of disease and novel markers for diagnosis. In the investigation, we developed an extraction and derivatization protocol, using experimental design theory (design of experiment), for analyzing the human blood plasma metabolome by GC/MS. The protocol was optimized by evaluating the data for more than 500 resolved peaks using multivariate statistical tools including principal component analysis and partial least-squares projections to latent structures (PLS). The performance of five organic solvents (methanol, ethanol, acetonitrile, acetone, chloroform), singly and in combination, was investigated to optimize the LMC extraction. PLS analysis demonstrated that methanol extraction was particularly efficient and highly reproducible. The extraction and derivatization conditions were also optimized. Quantitative data for 32 endogenous compounds showed good precision and linearity. In addition, the determined amounts of eight selected compounds agreed well with analyses by independent methods in accredited laboratories, and most of the compounds could be detected at absolute levels of approximately 0.1 pmol injected, corresponding to plasma concentrations between 0.1 and 1 microM. The results suggest that the method could be usefully integrated into metabolomic studies for various purposes, e.g., for identifying biological markers related to diseases.
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            High-throughput data analysis for detecting and identifying differences between samples in GC/MS-based metabolomic analyses.

            In metabolomics, the objective is to identify differences in metabolite profiles between samples. A widely used tool in metabolomics investigations is gas chromatography-mass spectrometry (GC/MS). More than 400 compounds can be detected in a single analysis, if overlapping GC/MS peaks are deconvoluted. However, the deconvolution process is time-consuming and difficult to automate, and additional processing is needed in order to compare samples. Therefore, there is a need to improve and automate the data processing strategy for data generated in GC/MS-based metabolomics; if not, the processing step will be a major bottleneck for high-throughput analyses. Here we describe a new semiautomated strategy using a hierarchical multivariate curve resolution approach that processes all samples simultaneously. The presented strategy generates (after appropriate treatment, e.g., multivariate analysis) tables of all the detected metabolites that differ in relative concentrations between samples. The processing of 70 samples took similar time to that of the GC/TOFMS analyses of the samples. The strategy has been validated using two different sets of samples: a complex mixture of standard compounds and Arabidopsis samples.
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              Metabolomics: moving to the clinic.

              Assessment of a biological system by means of global and non-targeted metabolite profiling--metabolomics or metabonomics--provides the investigator with molecular information that is close to the phenotype in question in the sense that metabolites are an ultimate product of gene, mRNA, and protein activity. Over the last few years, there has been a rapidly growing number of metabolomics applications aimed at finding biomarkers which could assist diagnosis, provide therapy guidance, and evaluate response to therapy for particular diseases. Also, within the fields of drug discovery, drug toxicology, and personalized pharmacology, metabolomics is emerging as a powerful tool. This review seeks to update the reader on analytical strategies, biomarker findings, and implications of metabolomics for the clinic. Particular attention is paid to recent biomarkers found related to neurological, cardiovascular, and cancer diseases. Moreover, the impact of metabolomics in the drug discovery and development process is examined.

                Author and article information

                Journal
                Endocr Connect
                Endocr Connect
                EC
                Endocrine Connections
                Bioscientifica Ltd (Bristol )
                2049-3614
                29 October 2014
                01 December 2014
                : 3
                : 4
                : 200-206
                Affiliations
                [1 ]Department of Endocrinology, Metabolism and Diabetology , Karolinska University Hospital , 171 76 Stockholm, Sweden
                [2 ]Department of Molecular Medicine and Surgery , Karolinska Institute , Stockholm, Sweden
                Author notes
                Correspondence should be addressed to C Höybye Email: charlotte.hoybye@ 123456karolinska.se
                Article
                EC140098
                10.1530/EC-14-0098
                4212684
                25312907
                73c1a994-c577-4aca-b434-6659ee339976
                © 2014 The authors

                This work is licensed under a Creative Commons Attribution 3.0 Unported License

                History
                : 29 September 2014
                : 13 October 2014
                Categories
                Research

                metabolomics,ghd in adults,gh treatment
                metabolomics, ghd in adults, gh treatment

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