{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,19]],"date-time":"2026-03-19T22:19:05Z","timestamp":1773958745385,"version":"3.50.1"},"reference-count":35,"publisher":"MDPI AG","issue":"1","license":[{"start":{"date-parts":[[2024,1,16]],"date-time":"2024-01-16T00:00:00Z","timestamp":1705363200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"the Russian Federation Fundamental Research Program for the long-term period of 2021\u20132030","award":["122092200056-9"],"award-info":[{"award-number":["122092200056-9"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Data"],"abstract":"<jats:p>High exercise loading causes intricate and ambiguous proteomic and metabolic changes. This study aims to describe the dataset on protein and metabolite contents in plasma samples collected from highly trained athletes across different sports disciplines. The proteomic and metabolomic analyses of the plasma samples of highly trained athletes engaged in sports disciplines of different intensities were carried out using HPLC-MS\/MS. The results are reported as two datasets (proteomic data in a derived mgf-file and metabolomic data in processed format), each containing the findings obtained by analyzing 93 mass spectra. Variations in the protein and metabolite contents of the biological samples are observed, depending on the intensity of training load for different sports disciplines. Mass spectrometric proteomic and metabolomic studies can be used for classifying different athlete phenotypes according to the intensity of sports discipline and for the assessment of the efficiency of the recovery period.<\/jats:p>","DOI":"10.3390\/data9010015","type":"journal-article","created":{"date-parts":[[2024,1,16]],"date-time":"2024-01-16T04:03:30Z","timestamp":1705377810000},"page":"15","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":4,"title":["Proteomic and Metabolomic Analyses of the Blood Samples of Highly Trained Athletes"],"prefix":"10.3390","volume":"9","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-9404-1660","authenticated-orcid":false,"given":"Kristina A.","family":"Malsagova","sequence":"first","affiliation":[{"name":"Institute of Biomedical Chemistry, 119121 Moscow, Russia"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-7199-372X","authenticated-orcid":false,"given":"Arthur T.","family":"Kopylov","sequence":"additional","affiliation":[{"name":"Institute of Biomedical Chemistry, 119121 Moscow, Russia"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-3396-5813","authenticated-orcid":false,"given":"Vasiliy I.","family":"Pustovoyt","sequence":"additional","affiliation":[{"name":"State Research Center\u2014Burnasyan Federal Medical Biophysical Center of Federal Medical Biological Agency, 119435 Moscow, Russia"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-5545-135X","authenticated-orcid":false,"given":"Evgenii I.","family":"Balakin","sequence":"additional","affiliation":[{"name":"State Research Center\u2014Burnasyan Federal Medical Biophysical Center of Federal Medical Biological Agency, 119435 Moscow, Russia"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Ksenia A.","family":"Yurku","sequence":"additional","affiliation":[{"name":"State Research Center\u2014Burnasyan Federal Medical Biophysical Center of Federal Medical Biological Agency, 119435 Moscow, Russia"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-9113-9440","authenticated-orcid":false,"given":"Alexander A.","family":"Stepanov","sequence":"additional","affiliation":[{"name":"Institute of Biomedical Chemistry, 119121 Moscow, Russia"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Liudmila I.","family":"Kulikova","sequence":"additional","affiliation":[{"name":"Institute of Biomedical Chemistry, 119121 Moscow, Russia"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Vladimir R.","family":"Rudnev","sequence":"additional","affiliation":[{"name":"Institute of Biomedical Chemistry, 119121 Moscow, Russia"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-4472-2016","authenticated-orcid":false,"given":"Anna L.","family":"Kaysheva","sequence":"additional","affiliation":[{"name":"Institute of Biomedical Chemistry, 119121 Moscow, Russia"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2024,1,16]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","unstructured":"Malsagova, K.A., Butkova, T.V., Kopylov, A.T., Izotov, A.A., Rudnev, V.R., Klyuchnikov, M.S., Stepanov, A.A., and Kaysheva, A.L. (2021). Molecular Portrait of an Athlete. Diagnostics, 11.","DOI":"10.3390\/diagnostics11061095"},{"key":"ref_2","first-page":"11011","article-title":"Sportomics: Metabolomics Applied to Sports. The New Revolution?","volume":"23","author":"Bongiovanni","year":"2019","journal-title":"Eur. Rev. Med. Pharmacol. Sci."},{"key":"ref_3","doi-asserted-by":"crossref","unstructured":"Nieman, D.C., Gillitt, N.D., Knab, A.M., Shanely, R.A., Pappan, K.L., Jin, F., and Lila, M.A. (2013). Influence of a Polyphenol-Enriched Protein Powder on Exercise-Induced Inflammation and Oxidative Stress in Athletes: A Randomized Trial Using a Metabolomics Approach. PLoS ONE, 8.","DOI":"10.1371\/journal.pone.0072215"},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"6405","DOI":"10.1021\/pr100684t","article-title":"1H NMR-Based Metabonomic Investigation of the Effect of Two Different Exercise Sessions on the Metabolic Fingerprint of Human Urine","volume":"9","author":"Pechlivanis","year":"2010","journal-title":"J. Proteome Res."},{"key":"ref_5","doi-asserted-by":"crossref","unstructured":"Nieman, D.C., Gillitt, N.D., Henson, D.A., Sha, W., Shanely, R.A., Knab, A.M., Cialdella-Kam, L., and Jin, F. (2012). Bananas as an Energy Source during Exercise: A Metabolomics Approach. PLoS ONE, 7.","DOI":"10.1371\/journal.pone.0037479"},{"key":"ref_6","first-page":"324","article-title":"Sportomics in Professional Soccer Players: Metabolomics Results during Preseason","volume":"61","author":"Pintus","year":"2021","journal-title":"J. Sports Med. Phys. Fit."},{"key":"ref_7","doi-asserted-by":"crossref","unstructured":"Schader, J.F., Haid, M., Cecil, A., Schoenfeld, J., Halle, M., Pfeufer, A., Prehn, C., Adamski, J., Nieman, D.C., and Scherr, J. (2020). Metabolite Shifts Induced by Marathon Race Competition Differ between Athletes Based on Level of Fitness and Performance: A Substudy of the Enzy-MagIC Study. Metabolites, 10.","DOI":"10.3390\/metabo10030087"},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"613","DOI":"10.1093\/cvr\/cvaa051","article-title":"Effect of Chronic Exercise in Healthy Young Male Adults: A Metabolomic Analysis","volume":"117","author":"Koay","year":"2021","journal-title":"Cardiovasc. Res."},{"key":"ref_9","doi-asserted-by":"crossref","unstructured":"Cao, B., Liu, S., Yang, L., and Chi, A. (2020). Changes of Differential Urinary Metabolites after High-Intensive Training in Teenage Football Players. BioMed Res. Int., 2020.","DOI":"10.1155\/2020\/2073803"},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"4610","DOI":"10.1021\/acs.jproteome.5b00470","article-title":"Monitoring the Response of the Human Urinary Metabolome to Brief Maximal Exercise by a Combination of RP-UPLC-MS and 1H NMR Spectroscopy","volume":"14","author":"Pechlivanis","year":"2015","journal-title":"J. Proteome Res."},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"33ra37","DOI":"10.1126\/scitranslmed.3001006","article-title":"Metabolic Signatures of Exercise in Human Plasma","volume":"2","author":"Lewis","year":"2010","journal-title":"Sci. Transl. Med."},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"2","DOI":"10.1186\/s40798-017-0114-z","article-title":"A Pilot Study Comparing the Metabolic Profiles of Elite-Level Athletes from Different Sporting Disciplines","volume":"4","author":"Diboun","year":"2018","journal-title":"Sports Med.-Open"},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"236","DOI":"10.1016\/j.jchromb.2008.04.031","article-title":"Analytical Strategies for LC\u2013MS-Based Targeted Metabolomics","volume":"871","author":"Lu","year":"2008","journal-title":"J. Chromatogr. B"},{"key":"ref_14","doi-asserted-by":"crossref","unstructured":"Alghannam, A.F., Ghaith, M.M., and Alhussain, M.H. (2021). Regulation of Energy Substrate Metabolism in Endurance Exercise. Int. J. Environ. Res. Public. Health, 18.","DOI":"10.3390\/ijerph18094963"},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"253","DOI":"10.1111\/j.1365-2125.2007.03001.x","article-title":"Muscular Exercise Can Cause Highly Pathological Liver Function Tests in Healthy Men","volume":"65","author":"Pettersson","year":"2008","journal-title":"Br. J. Clin. Pharmacol."},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"623","DOI":"10.1249\/01.mss.0000210192.49210.fc","article-title":"Serum Creatine Kinase Levels and Renal Function Measures in Exertional Muscle Damage","volume":"38","author":"Clarkson","year":"2006","journal-title":"Med. Sci. Sports Exerc."},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"254","DOI":"10.1007\/s42978-022-00186-w","article-title":"Inflammatory Biomarkers in Elite Cross-Country Skiers after a Competition Season: A Case\u2013Control Study","volume":"5","author":"Luoto","year":"2023","journal-title":"J. Sci. Sport Exerc."},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"e15291","DOI":"10.14814\/phy2.15291","article-title":"Effects of Training on Plasmatic Cortisol and Testosterone in Football Female Referees","volume":"10","author":"Muscella","year":"2022","journal-title":"Physiol. Rep."},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"55","DOI":"10.1006\/hbeh.1998.1496","article-title":"Testosterone, Cortisol, and Mood in a Sports Team Competition","volume":"35","author":"Salvador","year":"1999","journal-title":"Horm. Behav."},{"key":"ref_20","doi-asserted-by":"crossref","unstructured":"Forte, P., Branquinho, L., and Ferraz, R. (2022). The Relationships between Physical Activity, Exercise, and Sport on the Immune System. Int. J. Environ. Res. Public. Health, 19.","DOI":"10.3390\/ijerph19116777"},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"693","DOI":"10.1152\/japplphysiol.00008.2007","article-title":"Immune function in sport and exercise","volume":"103","author":"Gleeson","year":"2007","journal-title":"J. Appl. Physiol."},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"56","DOI":"10.17305\/bjbms.2014.2262","article-title":"Oxidative Stress Status in Elite Athletes Engaged in Different Sport Disciplines","volume":"14","author":"Valjevac","year":"2014","journal-title":"Bosn. J. Basic Med. Sci."},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"462","DOI":"10.1016\/j.intimp.2013.07.009","article-title":"Oxidative Stress and Antioxidant Status Response of Handball Athletes: Implications for Sport Training Monitoring","volume":"17","author":"Marin","year":"2013","journal-title":"Int. Immunopharmacol."},{"key":"ref_24","unstructured":"Havermale, L.A. (2017). Nutrition Knowledge of Collegiate Athletes in Endurance and Non-Endurance Sports, Southern Illinois University."},{"key":"ref_25","doi-asserted-by":"crossref","unstructured":"Stepanov, A.A., Malsagova, K.A., Kopylov, A.T., Rudnev, V.R., Karateev, D.E., Markelova, E.I., Luchikhina, E.L., Borisova, E.E., and Kaysheva, A.L. (2023). Determination of Heterogeneous Proteomic and Metabolomic Response in Anti-TNF and Anti-IL-6 Treatment of Patients with Rheumatoid Arthritis. Life, 13.","DOI":"10.3390\/life13020596"},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"4474","DOI":"10.1021\/acs.analchem.6b04310","article-title":"The Human Proteome Organization\u2013Proteomics Standards Initiative Quality Control Working Group: Making Quality Control More Accessible for Biological Mass Spectrometry","volume":"89","author":"Bittremieux","year":"2017","journal-title":"Anal. Chem."},{"key":"ref_27","unstructured":"Petrovskiy, D. (2023). Proteomic and Metabolomic Analyses of Blood Samples of Highly Trained Athletes. Figshare."},{"key":"ref_28","first-page":"9b00542","article-title":"Human Proteome Project Mass Spectrometry Data Interpretation Guidelines 3.0","volume":"2019","author":"Deutsch","year":"2019","journal-title":"J. Proteome Res."},{"key":"ref_29","doi-asserted-by":"crossref","unstructured":"Kopylov, A.T., Petrovsky, D.V., Stepanov, A.A., Rudnev, V.R., Malsagova, K.A., Butkova, T.V., Zakharova, N.V., Kostyuk, G.P., Kulikova, L.I., and Enikeev, D.V. (2021). Convolutional Neural Network in Proteomics and Metabolomics for Determination of Comorbidity between Cancer and Schizophrenia. J. Biomed. Inform., 122.","DOI":"10.1016\/j.jbi.2021.103890"},{"key":"ref_30","doi-asserted-by":"crossref","unstructured":"Petrovsky, D.V., Pustovoyt, V.I., Nikolsky, K.S., Malsagova, K.A., Kopylov, A.T., Stepanov, A.A., Rudnev, V.R., Balakin, E.I., and Kaysheva, A.L. (2022). Tracking Health, Performance and Recovery in Athletes Using Machine Learning. Sports, 10.","DOI":"10.3390\/sports10100160"},{"key":"ref_31","doi-asserted-by":"crossref","unstructured":"Petrovsky, D.V., Kopylov, A.T., Rudnev, V.R., Stepanov, A.A., Kulikova, L.I., Malsagova, K.A., and Kaysheva, A.L. (2021). Managing of Unassigned Mass Spectrometric Data by Neural Network for Cancer Phenotypes Classification. J. Pers. Med., 11.","DOI":"10.3390\/jpm11121288"},{"key":"ref_32","doi-asserted-by":"crossref","unstructured":"Ahsan, M.M., Luna, S.A., and Siddique, Z. (2022). Machine-Learning-Based Disease Diagnosis: A Comprehensive Review. Healthcare, 10.","DOI":"10.3390\/healthcare10030541"},{"key":"ref_33","doi-asserted-by":"crossref","first-page":"618976","DOI":"10.1155\/2014\/618976","article-title":"Screening for Prediabetes Using Machine Learning Models","volume":"2014","author":"Choi","year":"2014","journal-title":"Comput. Math. Methods Med."},{"key":"ref_34","doi-asserted-by":"crossref","first-page":"87","DOI":"10.1016\/j.surg.2010.03.023","article-title":"Novel Solutions for an Old Disease: Diagnosis of Acute Appendicitis with Random Forest, Support Vector Machines, and Artificial Neural Networks","volume":"149","author":"Hsieh","year":"2011","journal-title":"Surgery"},{"key":"ref_35","doi-asserted-by":"crossref","unstructured":"Balasubramanian, J.B., Boes, R.D., and Gopalakrishnan, V. (2020). A Novel Approach to Modeling Multifactorial Diseases Using Ensemble Bayesian Rule Classifiers. J. Biomed. Inform., 107.","DOI":"10.1016\/j.jbi.2020.103455"}],"container-title":["Data"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2306-5729\/9\/1\/15\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,10]],"date-time":"2025-10-10T13:47:32Z","timestamp":1760104052000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2306-5729\/9\/1\/15"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,1,16]]},"references-count":35,"journal-issue":{"issue":"1","published-online":{"date-parts":[[2024,1]]}},"alternative-id":["data9010015"],"URL":"https:\/\/doi.org\/10.3390\/data9010015","relation":{},"ISSN":["2306-5729"],"issn-type":[{"value":"2306-5729","type":"electronic"}],"subject":[],"published":{"date-parts":[[2024,1,16]]}}}