Comparison of kit-based metabolomics with other methodologies in a large cohort, towards establishing reference values

Daisuke Saigusa, Eiji Hishinuma, Naomi Matsukawa, Masatomo Takahashi, Jin Inoue, Shu Tadaka, Ikuko N. Motoike, Atsushi Hozawa, Yoshihiro Izumi, Takeshi Bamba, Kengo Kinoshita, Kim Ekroos, Seizo Koshiba, Masayuki Yamamoto

    Research output: Contribution to journalArticlepeer-review

    7 Citations (Scopus)


    Metabolic profiling is an omics approach that can be used to observe phenotypic changes, making it particularly attractive for biomarker discovery. Although several candidate metabolites biomarkers for disease expression have been identified in recent clinical studies, the reference values of healthy subjects have not been established. In particular, the accuracy of concentrations measured by mass spectrometry (MS) is unclear. Therefore, comprehensive metabolic profiling in large-scale cohorts by MS to create a database with reference ranges is essential for evaluating the quality of the discovered biomarkers. In this study, we tested 8700 plasma samples by commercial kit-based metabolomics and separated them into two groups of 6159 and 2541 analyses based on the different ultra-high-performance tandem mass spectrometry (UHPLC-MS/MS) systems. We evaluated the quality of the quantified values of the detected metabolites from the reference materials in the group of 2541 compared with the quantified values from other platforms, such as nuclear magnetic resonance (NMR), supercritical fluid chromatography tandem mass spectrometry (SFC-MS/MS) and UHPLC-Fourier transform mass spectrometry (FTMS). The values of the amino acids were highly correlated with the NMR results, and lipid species such as phosphatidylcholines and ceramides showed good correlation, while the values of triglycerides and cholesterol esters correlated less to the lipidomics analyses performed using SFC-MS/MS and UHPLC-FTMS. The evaluation of the quantified values by MS-based techniques is essential for metabolic profiling in a large-scale cohort.

    Original languageEnglish
    Article number652
    Issue number10
    Publication statusPublished - Oct 2021

    All Science Journal Classification (ASJC) codes

    • Endocrinology, Diabetes and Metabolism
    • Biochemistry
    • Molecular Biology


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