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Trends in Metabolomics Research:A Scientometric Analysis (1992–2017)


Affiliations
1 Department of Pharmacy, Beijing Tiantan Hospital Affiliated to Capital Medical University, Dongcheng District, Beijing Tiantan Xili the 6th, Beijing 100050, China
 

The aim of this study is to identify thematic trends, landmark articles, influential scientists and journals of metabolomics by exploring the scientific outputs in this field. This work was based on 66,721 bibliographic records retrieved from the Web of Science Core Collection database during 1992–2017. The results show that the USA was the leading country, and the Chinese Academy of Sciences had the largest number of publications. The Proceedings of the National Academy of Sciences of the United States of America was the most influential journal, meanwhile PLOS ONE had the most number of publications. Nicholson was identified as the most prominent scientist with the most number of articles and the highest co-citation counts. Metabolic syndromes and related diseases, disease biomarkers, novel pathways, as well as system biology association studies in metabolomics research, might be closely observed in the coming years.

Keywords

CiteSpace, Metabolomics, Scientometrics, Visualization Analysis.
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  • Fukusaki, E. and Kobayashi, A., Plant metabolomics: potential for practical operation. J. Biosci. Bioeng., 2005, 100, 347–354.
  • Goodacre, R. et al., Metabolomics by numbers: acquiring and understanding global metabolite data. Trends Biotechnol., 2004, 22, 245–252.
  • Wishart, D. S., Proteomics and the human metabolome project. Expert Rev. Proteomics, 2014, 4, 333–335.
  • Ryan, D. and Robards, K., Metabolomics: the greatest omics of them all? Anal. Chem., 2006, 78, 7954–7958.
  • Putri, S. P. et al., Current metabolomics: practical applications. J. Biosci. Bioeng., 2013, 115, 579–589.
  • Horning, E. C. and Horning, M. G., Human metabolic profiles obtained by gc and gc/ms. J. Chromatogr. Sci., 1971, 9, 129–140.
  • Nicholson, J. K. et al., ‘Metabonomics’: understanding the metabolic responses of living systems to pathophysiological stimuli via multivariate statistical analysis of biological NMR spectroscopic data. Xenobiotica, 1999, 29, 1181–1189.
  • Fiehn, O., Metabolomics – the Link Between Genotypes and Phenotypes, Springer Netherlands, 2002.
  • Chen, C., Citespace ii: Detecting and visualizing emerging trends and transient patterns in scientific literature. J. Chin. Soc. Sci. Tech. Inf., 2006, 57, 359–377.
  • Balaram, P., Scientometrics: a dismal science. Curr. Sci., 2008, 95, 431–432.
  • Wu, Y. and Duan, Z., Visualization analysis of author collaborations in schizophrenia research. BMC Psychiatry, 2015, 15, 1–8.
  • Qian, G., Scientometric sorting by importance for literatures on life cycle assessments and somerelated methodological discussions. Int. J. Life Cycle Ass., 2014, 19, 1462–1467.
  • Chen, C. et al., Emerging trends and new developments in regenerative medicine: a scientometricupdate (2000–2014). Expert Opin. Biol. Ther., 2014, 14, 1295–1317.
  • Coen, M. et al., NMR-based metabolic profiling and metabonomic approaches to problems inmolecular toxicology. Chem. Res. Toxicol., 2008, 21, 9–27.
  • Bino, R. J. et al., Potential of metabolomics as a functional genomics tool. Trends Plant Sci., 2004, 9, 418–425.
  • Weckwerth, W., Metabolomics in systems biology. Annu. Rev. Plant Biol., 2003, 54, 669–689.
  • Group, B. D. W., Biomarkers and surrogate endpoints: Preferred definitions and conceptual framework. Clin. Pharmacol. Ther., 2001, 69, 89–95.
  • Feng, F. et al., Visualization and quantitative study in bibliographic databases: A case in the field of university–industry cooperation. J. Informetr., 2015, 9, 118–134.
  • Chen, C. et al., Orphan drugs and rare diseases: A scientometric review (2000–2014). Expert Opin. Orphan Drugs, 2014, 2, 709–724.
  • Pollare, T. et al., A comparison of the effects of hydrochlorothiazide and captopril on glucose and lipid metabolism in patients with hypertension. New. Engl. J. Med., 1989, 321, 868–873.
  • Considine, R. V. et al., Serum immunoreactive-leptin concentrations in normal-weight and obesehumans. New Engl. J. Med., 1996, 334, 292–295.
  • Fiehn, O. et al., Metabolite profiling for plant functional genomics. Nat. Biotechnol., 2000, 18, 1157–1161.
  • Soga, T. et al., Quantitative metabolome analysis using capillary electrophoresis mass spectrometry. J. Proteome Res., 2003, 2, 488–494.
  • Hirai, M. Y. et al., Integration of transcriptomics and metabolomics for understanding of globalresponses to nutritional stress in arabidopsis thaliana. Proc. Natl. Acad. Sci. USA, 2004, 101, 10205–10210.
  • Wang, T. J. et al., Metabolite profiles and the risk of developing diabetes. Nat. Med., 2011, 17, U448–U483.
  • Sreekumar, A. et al., Metabolomic profiles delineate potential role for sarcosine in prostate cancerprogression. Nature, 2009, 457, 910–914.
  • Holmes, E. et al., Human metabolic phenotype diversity and its association with diet and bloodpressure. Nature, 2008, 453, 396–400.
  • Chomczynski, P. and Sacchi, N., Single-step method of RNA isolation by acid guanidiniumthiocyanate-phenol-chloroform extraction. Anal. Biochem., 1987, 162, 156–159.
  • Despres, J. P. et al., Hyperinsulinemia as an independent risk factor for ischemic heart disease. New. Engl. J. Med., 1996, 334, 952–957.
  • Group, C. C. T., The effect of intensive treatment of diabetes on the development and progression of long-term complications in insulin-dependent diabetes mellitus. New Engl. J. Med., 1993, 329, 977–986.
  • Group, U. K. P. D. S., Intensive blood-glucose control with sulphonylureas or insulin compared with conventional treatment and risk of complications in patients with type 2 diabetes (ukpds 33). Lancet, 1998, 352, 837–853.
  • Roessner, U. et al., Metabolic profiling allows comprehensive phenotyping of genetically or environmentally modified plant systems. The Plant Cell, 2001, 13, 11–29.
  • Brindle, J. T. et al., Rapid and noninvasive diagnosis of the presence and severity of coronary heart disease using h-1-nmr-based metabonomics. Nat. Med., 2002, 8, 1439–1444.
  • Cloarec, O. et al., Statistical total correlation spectroscopy: an exploratory approach for latent biomarker identification from metabolic 1 h NMR data sets. Anal. Chem., 2005, 77, 1282–1289.
  • Kopka, J. et al., Gmd@csb.Db: The golm metabolome database. Bioinformatics, 2005, 21, 1635–1638.
  • Clayton, T. A. et al., Pharmaco-metabonomic phenotyping and personalized drug treatment. Nature, 2006, 440, 1073–1077.
  • Wishart, D. S. et al., Hmdb: The human metabolome database. Nucleic Acids Res., 2007, 35, D521–D526.
  • Subramanian, A. et al., Gene set enrichment analysis: A knowledge-based approach for interpretinggenome-wide expression profiles. Proc. Natl. Acad. Sci., 2005, 102, 15545–15550.
  • Smith, C. A. et al., Xcms: Processing mass spectrometry data for metabolite profiling using nonlinear peak alignment, matching, and identification. Anal. Chem., 2006, 78, 779–787.
  • Wishart, D. S. et al., Hmdb: a knowledgebase for the human metabolome. Nucleic Acids Res., 2009, 37, D603–D610.
  • Wikoff, W. R. et al., Metabolomics analysis reveals large effects of gut microflora on mammalian blood metabolites. Proc. Natl. Acad. Sci. USA, 2009, 106, 3698–3703.
  • Mortazavi, A. et al., Mapping and quantifying mammalian transcriptomes by RNA-seq. Nat. Meth., 2008, 5, 621–628.
  • Wishart, D. S. et al., Hmdb 3.0 – the human metabolome database in 2013. Nucleic Acids Res., 2013, 41, D801–D807.
  • Grabherr, M. G. et al., Full-length transcriptome assembly from RNA-seq data without a reference genome. Nat. Biotechnol., 2011, 29, 644–652.
  • Kanehisa, M. et al., Data, information, knowledge and principle: Back to metabolism in kegg. Nucleic Acids Res., 2014, 42, D199–D205.

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  • Trends in Metabolomics Research:A Scientometric Analysis (1992–2017)

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Authors

Shanshan Guo
Department of Pharmacy, Beijing Tiantan Hospital Affiliated to Capital Medical University, Dongcheng District, Beijing Tiantan Xili the 6th, Beijing 100050, China
Jingchen Tian
Department of Pharmacy, Beijing Tiantan Hospital Affiliated to Capital Medical University, Dongcheng District, Beijing Tiantan Xili the 6th, Beijing 100050, China
Bin Zhu
Department of Pharmacy, Beijing Tiantan Hospital Affiliated to Capital Medical University, Dongcheng District, Beijing Tiantan Xili the 6th, Beijing 100050, China
Shu Yang
Department of Pharmacy, Beijing Tiantan Hospital Affiliated to Capital Medical University, Dongcheng District, Beijing Tiantan Xili the 6th, Beijing 100050, China
Kefu Yu
Department of Pharmacy, Beijing Tiantan Hospital Affiliated to Capital Medical University, Dongcheng District, Beijing Tiantan Xili the 6th, Beijing 100050, China
Zhigang Zhao
Department of Pharmacy, Beijing Tiantan Hospital Affiliated to Capital Medical University, Dongcheng District, Beijing Tiantan Xili the 6th, Beijing 100050, China

Abstract


The aim of this study is to identify thematic trends, landmark articles, influential scientists and journals of metabolomics by exploring the scientific outputs in this field. This work was based on 66,721 bibliographic records retrieved from the Web of Science Core Collection database during 1992–2017. The results show that the USA was the leading country, and the Chinese Academy of Sciences had the largest number of publications. The Proceedings of the National Academy of Sciences of the United States of America was the most influential journal, meanwhile PLOS ONE had the most number of publications. Nicholson was identified as the most prominent scientist with the most number of articles and the highest co-citation counts. Metabolic syndromes and related diseases, disease biomarkers, novel pathways, as well as system biology association studies in metabolomics research, might be closely observed in the coming years.

Keywords


CiteSpace, Metabolomics, Scientometrics, Visualization Analysis.

References





DOI: https://doi.org/10.18520/cs%2Fv114%2Fi11%2F2248-2255