Metabolic research from the standpoint of personalized medicine
- Authors: Paltsev M.A.1, Zolnikova O.Y.2
-
Affiliations:
- Lomonosov Moscow State University
- I.M. Sechenov First Moscow State Medical University (Sechenov University)
- Issue: Vol 21, No 1 (2023)
- Pages: 3-7
- Section: Reviews
- URL: https://journals.eco-vector.com/1728-2918/article/view/303665
- DOI: https://doi.org/10.29296/24999490-2023-01-01
- ID: 303665
Cite item
Abstract
One of the most important tasks of modern medicine is to recognize diseases at the preclinical stage, as well as to assess their development risks and their possible prevention. This dictates the need to use new, modern technologies aimed at the early detection of biomarkers and the identification of new therapeutic targets.
The purpose to summarize the available data on metabolomic studies used in medicine.
Material and methods. The analysis of the main foreign and domestic sources in the PubMed/Medline, RSCI/elibrary databases over the past 5 years was carried out.
Results. Metabolomics is a rapidly developing research method used in biomedicine to illustrate in detail the pathological mechanisms that occur and to develop new disease biomarkers. Analytical approaches used to study the metabolome are not inferior to genetic studies in their specificity and sensitivity. Along with this, the ability to simultaneously quantify several thousand metabolites in samples makes metabolomics a method oriented towards personalized medicine.
Full Text
About the authors
Mikhail A. Paltsev
Lomonosov Moscow State University
Author for correspondence.
Email: mpaltzev@gmail.com
ORCID iD: 0000-0002-5737-5706
Director. Center for Immunology and Molecular Biomedicine. Faculty of Biology. Moscow State University. Professor, Doctor of Medical Sciences.
Russian Federation, Leninskie gory, 1, Moscow, 119991Oxana Yuryevna Zolnikova
I.M. Sechenov First Moscow State Medical University (Sechenov University)
Email: ks.med@mail.ru
ORCID iD: 0000-0002-6701-789X
MD, Professor at the Department of Internal Medicine Propaedeutics, Sechenov First Moscow State Medical University (Sechenov University)
Russian Federation, Trubetskaya st., 8/2, Moscow, 119991References
- Zhou J., Zhong L. Applications of liquid chromatography-mass spectrometry based metabolomics in predictive and personalized medicine. Front. Mol. Biosci. 2022; 9: 1049016. doi: 10.3389/fmolb.2022.1049016
- Sheikhy A, Fallahzadeh A, Aghaei Meybodi HR, Hasanzad M, Tajdini M, Hosseini K. Personalized medicine in cardiovascular disease: review of literature. J. Diabetes Metab Disord. 2021; 20 (2): 1793–805. doi: 10.1007/s40200-021-00840-0
- Masoodi M., Gastaldelli A., Hyötyläinen T., Arretxe E., Alonso C., Gaggini M., Brosnan J., Anstee Q.M., Millet O., Ortiz P., Mato J.M., Dufour J.F., Orešič M. Metabolomics and lipidomics in NAFLD: biomarkers and non-invasive diagnostic tests. Nat Rev Gastroenterol Hepatol. 2021; 18 (12): 835–56. doi: 10.1038/s41575-021-00502-9
- Guiot J., Vaidyanathan A., Deprez L., Zerka F., Danthine D., Frix A.N., Lambin P., Bottari F., Tsoutzidis N., Miraglio B., Walsh S., Vos W., Hustinx R., Ferreira M., Lovinfosse P., Leijenaar R.T.H. A review in radiomics: Making personalized medicine a reality via routine imaging. Med Res Rev. 2022; 42 (1): 426–40. doi: 10.1002/med.21846.
- Zygulska A.L., Pierzchalski P. Novel Diagnostic Biomarkers in Colorectal Cancer. Int J. Mol. Sci. 2022; 23 (2): 852. doi: 10.3390/ijms23020852.
- Vellekoop H., Versteegh M., Huygens S., Corro Ramos I., Szilberhorn L., Zelei T., Nagy B., Tsiachristas A., Koleva-Kolarova R., Wordsworth S., Rutten-van Mölken M. HEcoPerMed consortium. The Net Benefit of Personalized Medicine: A Systematic Literature Review and Regression Analysis. Value Health. 2022; 25 (8): 1428–38. doi: 10.1016/j.jval.2022.01.006
- Li R., Li L., Xu Y., Yang J. Machine learning meets omics: applications and perspectives. Brief Bioinform. 2022; 23 (1): bbab460. doi: 10.1093/bib/bbab460.
- Braig Z.V. Personalized medicine: From diagnostic to adaptive. Biomed J. 2022; 45 (1): 132–42. doi: 10.1016/j.bj.2019.05.004
- Hassan M., Awan F.M., Naz A., deAndrés-Galiana E.J., Alvarez O., Cernea A., Fernández-Brillet L., Fernández-Martinez J.L., Kloczkowski A. Innovations in Genomics and Big Data Analytics for Personalized Medicine and Health Care: A Review. Int. J. Mol. Sci. 2022; 23 (9): 4645. doi: 10.3390/ijms23094645.
- Luengo O., Galvan-Blasco P., Cardona V. Molecular diagnosis contribution for personalized medicine. Curr Opin Allergy Clin. Immunol. 2022; 22 (3): 175–80. doi: 10.1097/ACI.0000000000000822.
- Crosby D., Bhatia S., Brindle K.M., Coussens L.M., Dive C., Emberton M. Early detection of cancer. Science 2022; 375 (6586): eaay9040. doi: 10.1126/science.aay9040
- Yuan Y., Zhao Z., Xue L., Wang G., Song H., Pang R. Identification of diagnostic markers and lipid dysregulation in oesophageal squamous cell carcinoma through lipidomic analysis and machine learning. Br. J. Cancer. 2021; 125 (3): 351–7. doi: 10.1038/s41416-021-01395-w
- Wang G., Yao H., Gong Y., Lu Z., Pang R., Li Y. Metabolic detection and systems analyses of pancreatic ductal adenocarcinoma through machine learning, lipidomics, and multi-omics. Sci. Adv. 2021; 7 (52): eabh2724. doi: 10.1126/sciadv.abh2724
- Wang G., Qiu M., Xing X., Zhou J., Yao H., Li M. Lung cancer scRNA-seq and lipidomics reveal aberrant lipid metabolism for early-stage diagnosis. Sci. Transl. Med. 2022; 14 (630): eabk2756. doi: 10.1126/scitranslmed.abk2756.
- Zhou J., Ji N., Wang G., Zhang Y., Song H., Yuan Y. Metabolic detection of malignant brain gliomas through plasma lipidomic analysis and support vector machine-based machine learning. EBioMedicine. 2022; 81: 104097. doi: 10.1016/j.ebiom.2022.104097
- Chen F., Dai X., Zhou C. C., Li K.X., Zhang Y.J., Lou X.Y. Integrated analysis of the faecal metagenome and serum metabolome reveals the role of gut microbiome-associated metabolites in the detection of colorectal cancer and adenoma. Gut. 2022; 71 (7): 1315–25. doi: 10.1136/gutjnl-2020-323476
- Talmor-Barkan Y., Bar N., Shaul A.A., Shahaf N., Godneva A., Bussi Y. Metabolomic and microbiome profiling reveals personalized risk factors for coronary artery disease. Nat. Med. 2022; 28 (2): 295–302. doi: 10.1038/s41591-022-01686-6
- Murthy V.L., Reis J.P., Pico A.R., Kitchen R., Lima J.A., Lloyd-Jones D. Comprehensive metabolic phenotyping refines cardiovascular risk in young adults. Circulation. 2020; 142 (22): 2110–27. doi: 10.1161/circulationaha.120.047689
- Bar N., Korem T., Weissbrod O., Zeevi D, Rothschildm D.A reference map of potential determinants for the human serum metabolome. Nature. 2020; 588 (7836): 135–40. doi: 10.1038/s41586-020-2896-2
- Chen Z.Z., Gerszten R.E. Metabolomics and Proteomics in Type 2 Diabetes. Circ Res. 2020; 126 (11): 1613–27. doi: 10.1161/CIRCRESAHA.120.315898
- Xiao Y., Ma D., Yang Y.S., Yang F., Ding J.H., Gong Y. Comprehensive metabolomics expands precision medicine for triple-negative breast cancer. Cell Res. 2022; 32 (5): 477–90. doi: 10.1038/s41422-022-00614-0
- Thomas I., Dickens A.M., Posti J.P., Czeiter E., Duberg D., Sinioja T. Serum metabolome associated with severity of acute traumatic brain injury. Nat. Commun. 2022; 13 (1): 2545. doi: 10.1038/s41467-022-30227-5
- Bajaj J.S., Garcia-Tsao G., Reddy K.R., O’Leary J.G., Vargas H.E., Lai J.C. Admission urinary and serum metabolites predict renal outcomes in hospitalized patients with cirrhosis. Hepatology. 2021; 74 (5): 2699–713. doi: 10.1002/hep.31907
- Liu J., Geng W., Sun H., Liu C., Huang F., Cao J. Integrative metabolomic characterisation identifies altered portal vein serum metabolome contributing to human hepatocellular carcinoma. Gut. 2022; 71 (6): 1203–13. doi: 10.1136/gutjnl-2021-325189
- Hasegawa K., Stewart C.J., Celedón J.C., Mansbach J.M., Tierney C., Camargo C.A. Serum 25-hydroxyvitamin D, metabolome, and bronchiolitis severity among infants-A multicenter cohort study. Pediatr Allergy Immunol. 2018; 29 (4): 441–5. doi: 10.1111/pai.12880
- Liang L., Rasmussen M.H., Piening B., Shen X., Chen S., Röst H. Metabolic dynamics and prediction of gestational age and time to delivery in pregnant women. Cell. 2020; 181 (7): 1680–92. doi: 10.1016/j.cell.2020.05.002
- Ma C., Tian B., Wang J., Yang G., Pan C., Lu J. Metabolic characteristics of acute necrotizing pancreatitis and chronic pancreatitis. Mol. Med. Rep. 2012; 6 (1): 57–62. doi: 10.3892/mmr.2012.881