In the frame of the work, data on the implementation of metabolomics tests in medicine have been systematized. Based on the obtained data, a set of protocols was proposed, the sequential realization of which makes it possible to conduct a blood metabolome analysis for medical purposes. Using this analysis and the number of blood samples from healthy volunteers, a prototype of a healthy person's metabolomic image has been developed; it allows visually and digitally to assess the compliance of the human blood metabolome with the norm. At the same time, 99% of the metabolic processes reflected in the blood plasma are estimated. If abnormalities are detected, the metabolomic image allows to get the value of these deviations of metabolic processes in digital terms.
Download PDF:
Keywords: metabolomics, precision medicine, laboratory diagnostics, blood metabolome, direct injection mass spectrometry, digital image of a person
Citation:
Trifonova O.P., Balashova E.E., Maslov D.L., Grigoriev A.I., Lisitsa A.V., Ponomarenko E.A., Archakov A.I. (2020) Blood metabolome analysis for creating a digital image of a healthy person. Biomeditsinskaya Khimiya, 66(3), 216-223.
Trifonova O.P. et al. Blood metabolome analysis for creating a digital image of a healthy person // Biomeditsinskaya Khimiya. - 2020. - V. 66. -N 3. - P. 216-223.
Trifonova O.P. et al., "Blood metabolome analysis for creating a digital image of a healthy person." Biomeditsinskaya Khimiya 66.3 (2020): 216-223.
Trifonova, O. P., Balashova, E. E., Maslov, D. L., Grigoriev, A. I., Lisitsa, A. V., Ponomarenko, E. A., Archakov, A. I. (2020). Blood metabolome analysis for creating a digital image of a healthy person. Biomeditsinskaya Khimiya, 66(3), 216-223.
References
Micheel C.M., Nass S.J., Omenn G.S., Policy H.S. (2012) Institute of Medicine Committee on the Review of Omics-Based Tests for Predicting Patient Outcomes in Clinical Trials. Evolution of translational omics: lessons learned and the path forward. Washington (DC): National Academies Press (US). CrossRef Scholar google search
McShane L.M., Cavenagh M.M., Lively T.G., Eberhard D.A., Bigbee W.L., Williams P.M., Mesirov J.P., Polley M.Y., Kim K.Y., Tricoli J.V., Taylor J.M., Shuman D.J., Simon R.M., Doroshow J.H., Conley B.A. (2013) Nature, 502(7471), 317-320. CrossRef Scholar google search
Moshkovskii S., Pyatnitsky M., Lokhov P., Baranova A. (2015) OMICS for tumor biomarker research. In: Biomarkers in Disease: Methods, Discoveries and Applications: Biomarkers in Cancer. Springer, Netherlands, pp. 3-30. CrossRef Scholar google search
Cobb J., Gall W., Adam K.P., Nakhle P., Button E., Hathorn J., Lawton K., Milburn M., Perichon R., Mitchell M., Natali A., Ferrannini E. (2013) J. Diabetes Sci. Technol., 7(1), 100-110. CrossRef Scholar google search
Nass S.J., Moses H.L. (2007) Cancer biomarkers: The promises and challenges of improving detection and treatment. National Academies Press. 250 p. CrossRef Scholar google search
Broadhurst D., Goodacre R., Reinke S.N., Kuligowski J., Wilson I.D., Lewis M.R., Dunn W.B. (2018) Metabolomics, 14(6), 72. CrossRef Scholar google search
Bernini P., Bertini I., Luchinat C., Nincheri P., Staderini S., Turano P. (2011) J. Biomol. NMR, 49(3-4), 231-243. CrossRef Scholar google search
Bronsema K.J., Bischoff R., van de Merbel N.C. (2012) J. Chromatogr. B Analyt. Technol. Biomed. Life Sci., 893-894, 1-14. CrossRef Scholar google search
