Skip to main content

Thank you for visiting nature.com. You are using a browser version with limited support for CSS. To obtain the best experience, we recommend you use a more up to date browser (or turn off compatibility mode in Internet Explorer). In the meantime, to ensure continued support, we are displaying the site without styles and JavaScript.

Advertisement

Scientific Reports
  • View all journals
  • Search
  • My Account Login
  • Content Explore content
  • About the journal
  • Publish with us
  • Sign up for alerts
  • RSS feed
  1. nature
  2. scientific reports
  3. articles
  4. article
Changes in the proteomic profile of athletes’ plasma associated with exercise intensity
Download PDF
Download PDF
  • Article
  • Open access
  • Published: 19 March 2026

Changes in the proteomic profile of athletes’ plasma associated with exercise intensity

  • Kristina A. Malsagova  ORCID: orcid.org/0000-0001-9404-16601,
  • Tatiana V. Butkova  ORCID: orcid.org/0000-0001-5111-38631,
  • Kirill S. Nikolsky  ORCID: orcid.org/0000-0003-3571-513X1,
  • Denis V. Petrovskiy  ORCID: orcid.org/0000-0002-7281-75251,
  • Arthur T. Kopylov  ORCID: orcid.org/0000-0002-7199-372X1,
  • Valeriya I. Nakhod  ORCID: orcid.org/0000-0003-2322-49661,
  • Liudmila I. Kulikova  ORCID: orcid.org/0000-0002-7293-15041,
  • Vladimir R. Rudnev  ORCID: orcid.org/0000-0001-9618-346X1,
  • Ksenia A. Yurku  ORCID: orcid.org/0000-0002-1973-16932,
  • Evgenii I. Balakin  ORCID: orcid.org/0000-0001-5545-135X2,
  • Anastasiia S. Bukreeva  ORCID: orcid.org/0009-0006-1073-53951,
  • Alexander A. Izotov  ORCID: orcid.org/0000-0001-9367-97851,
  • Vasiliy I. Pustovoyt  ORCID: orcid.org/0000-0003-3396-58132 &
  • …
  • Anna L. Kaysheva  ORCID: orcid.org/0000-0003-4472-20161 

Scientific Reports , Article number:  (2026) Cite this article

  • 684 Accesses

  • Metrics details

We are providing an unedited version of this manuscript to give early access to its findings. Before final publication, the manuscript will undergo further editing. Please note there may be errors present which affect the content, and all legal disclaimers apply.

Subjects

  • Biochemistry
  • Biological techniques
  • Biomarkers
  • Computational biology and bioinformatics

Abstract

High physical activity often results in complex and ambiguous proteomic changes. This study aimed to analyze possible changes in the composition and content of proteins in plasma samples of elite athletes from sports with various activity levels and intensity. We performed proteomic profiling of blood plasma from 93 elite athletes with over eight years of continuous professional experience. High-resolution tandem mass spectra obtained in the data-independent acquisition mode were analyzed using the PLGS algorithm against a library of known protein sequences, and the PowerNovo algorithm for de novo protein sequencing. Combining the two protein identification strategies improved the completeness of the analysis and expanded the number of identified proteins. We identified changes in levels of circulating proteins that distinguish the high-intensity group from other groups by proteins involved in immune response, lipid transport and metabolism, and oxygen and iron transport. Changes in protein levels in biological samples of professional athletes may be associated with the training load intensity and type of sport.

Similar content being viewed by others

Human plasma proteomic profiles indicative of cardiorespiratory fitness

Article 27 May 2021

Sportomics method to assess acute phase proteins in Olympic level athletes using dried blood spots and multiplex assay

Article Open access 18 November 2022

A proteomics perspective on 2 years of high-intensity training in horses: a pilot study

Article Open access 10 October 2024

Data availability

The datasets generated and analysed during the current study are available in the MassIVE repository, full member of the ProteomeXchange consortium – https://massive.ucsd.edu/ProteoSAFe/QueryPXD?id=PXD067642.

Abbreviations

MaxO2:

Maximal oxygen uptake

PLGS:

Protein Lynx Global Server

MS:

Mass spectrometry

CROSSL:

C-Terminal telopeptides of type I collagen

A2M:

Alpha-2-macroglobulin

APOH:

Apolipoprotein H (Beta-2-glycoprotein I)

ApoD:

Apolipoprotein D

ALT:

Alanine aminotransferase

References

  1. Malsagova, K. A. et al. Molecular Portrait of an Athlete. Diagnostics (Basel). 11, 1095. https://doi.org/10.3390/diagnostics11061095 (2021).

    Google Scholar 

  2. Militello, R. et al. Modulation of Plasma Proteomic Profile by Regular Training in Male and Female Basketball Players: A Preliminary Study. Front. Physiol. 13, 813447. https://doi.org/10.3389/fphys.2022.813447 (2022).

    Google Scholar 

  3. Mi, M. Y. et al. Plasma Proteomic Kinetics in Response to Acute Exercise. Mol. Cell. Proteom. 22, 100601. https://doi.org/10.1016/j.mcpro.2023.100601 (2023).

    Google Scholar 

  4. He, Y. et al. Plasma metabolomics dataset of race-walking athletes illuminating systemic metabolic reaction of exercise. Sci. Data. 12, 448. https://doi.org/10.1038/s41597-025-04751-0 (2025).

    Google Scholar 

  5. Muniz-Santos, R., Magno-França, A., Jurisica, I. & Cameron, L. C. From Microcosm to Macrocosm: The -Omics, Multiomics, and Sportomics Approaches in Exercise and Sports. OMICS 27, 499–518. https://doi.org/10.1089/omi.2023.0169 (2023).

    Google Scholar 

  6. Owen, A. L. et al. Socceromics: The Integration of Omics Technologies in Soccer to Enhance Performance and Health (A Comprehensive, 2024). Critical Review of the Literature.

  7. Khoramipour, K. et al. From Multi-omics To Personalized Training: The Rise of Enduromics and Resistomics. Sports Med. - Open. 11, 52. https://doi.org/10.1186/s40798-025-00855-4 (2025).

    Google Scholar 

  8. Mourtakos, S. et al. The effect of prolonged intense physical exercise of special forces volunteers on their plasma protein denaturation profile examined by differential scanning calorimetry. J. Therm. Biol. 96, 102860. https://doi.org/10.1016/j.jtherbio.2021.102860 (2021).

    Google Scholar 

  9. Robbins, J. M. et al. Plasma proteomic changes in response to exercise training are associated with cardiorespiratory fitness adaptations. JCI Insight. 8, e165867. https://doi.org/10.1172/jci.insight.165867 (2023).

    Google Scholar 

  10. Gunzer, W., Konrad, M. & Pail, E. Exercise-induced immunodepression in endurance athletes and nutritional intervention with carbohydrate, protein and fat-what is possible, what is not? Nutrients 4, 1187–1212. https://doi.org/10.3390/nu4091187 (2012).

    Google Scholar 

  11. Ganeshan, K. & Chawla, A. Metabolic Regulation of Immune Responses. Annu. Rev. Immunol. 32, 609–634. https://doi.org/10.1146/annurev-immunol-032713-120236 (2014).

    Google Scholar 

  12. Schild, M. et al. Effects of Acute Endurance Exercise on Plasma Protein Profiles of Endurance-Trained and Untrained Individuals over Time. Mediators Inflamm. 2016, 4851935. https://doi.org/10.1155/2016/4851935 (2016).

    Google Scholar 

  13. Athanasiou, N., Bogdanis, G. C. & Mastorakos, G. Endocrine responses of the stress system to different types of exercise. Rev. Endocr. Metab. Disord. https://doi.org/10.1007/s11154-022-09758-1 (2022).

    Google Scholar 

  14. White, M. Y. & Van Eyk, J. E. Cardiovascular proteomics: past, present, and future. Mol. Diagn. Ther. 11, 83–95. https://doi.org/10.1007/BF03256227 (2007).

    Google Scholar 

  15. Sharma, P., Cosme, J. & Gramolini, A. O. Recent advances in cardiovascular proteomics. J. Proteom. 81, 3–14. https://doi.org/10.1016/j.jprot.2012.10.026 (2013).

    Google Scholar 

  16. Hadžović - Džuvo, A. et al. Oxidative stress status in elite athletes engaged in different sport disciplines. Bosn J. Basic. Med. Sci. 14, 56–62 (2014).

    Google Scholar 

  17. Varamenti, E., Tod, D. & Pullinger, S. A. Redox Homeostasis and Inflammation Responses to Training in Adolescent Athletes: a Systematic Review and Meta-analysis. Sports Med. Open. 6, 34. https://doi.org/10.1186/s40798-020-00262-x (2020).

    Google Scholar 

  18. Anderson, N. L. & Anderson, N. G. The human plasma proteome: history, character, and diagnostic prospects. Mol. Cell. Proteom. 1, 845–867. https://doi.org/10.1074/mcp.r200007-mcp200 (2002).

    Google Scholar 

  19. Mitchell, J. H., Haskell, W., Snell, P. & Van Camp, S. P. Task Force 8: Classification of sports. JACC 45, 1364–1367. https://doi.org/10.1016/j.jacc.2005.02.015 (2005).

    Google Scholar 

  20. Deutsch, E. W. et al. The ProteomeXchange consortium at 10 years: 2023 update. Nucleic Acids Res. 51, D1539–D1548. https://doi.org/10.1093/nar/gkac1040 (2023).

    Google Scholar 

  21. Malsagova, K. A. et al. Proteomic and Metabolomic Analyses of the Blood Samples of Highly Trained Athletes. Data 9, 15. https://doi.org/10.3390/data9010015 (2024).

    Google Scholar 

  22. Petrovskiy, D. V. et al. PowerNovo: de novo peptide sequencing via tandem mass spectrometry using an ensemble of transformer and BERT models. Sci. Rep. 14, 15000. https://doi.org/10.1038/s41598-024-65861-0 (2024).

    Google Scholar 

  23. Uhlén, M. et al. Proteomics. Tissue-based map of the human proteome. Science 347, 1260419. https://doi.org/10.1126/science.1260419 (2015).

    Google Scholar 

  24. Mann, H. B. & Whitney, D. R. On a Test of Whether one of Two Random Variables is Stochastically Larger than the Other. Ann. Math. Stat. 18, 50–60. https://doi.org/10.1214/aoms/1177730491 (1947).

    Google Scholar 

  25. mannwhitneyu —. SciPy v1.16.2 Manual. https://docs.scipy.org/doc/scipy/reference/generated/scipy.stats.mannwhitneyu.html. Accessed 17 Dec 2025.

  26. Benjamini, Y. & Hochberg, Y. Controlling the False Discovery Rate: A Practical and Powerful Approach to Multiple Testing. J. Royal Stat. Soc. Ser. B (Methodological). 57, 289–300 (1995).

    Google Scholar 

  27. scipy.stats. false_discovery_control — SciPy v1.13.0 Manual. https://docs.scipy.org/doc/scipy/reference/generated/scipy.stats.false_discovery_control.html. Accessed 19 Apr 2024.

  28. Diagnostics (ed) cobas® 6000 analyzer series. In:. https://diagnostics.roche.com/global/en/products/systems/cobas-6000-analyzer-series-sys-65.html. Accessed 21 Jan 2026.

  29. Szklarczyk, D. et al. The STRING database in 2023: protein-protein association networks and functional enrichment analyses for any sequenced genome of interest. Nucleic Acids Res. 51, D638–D646. https://doi.org/10.1093/nar/gkac1000 (2023).

    Google Scholar 

  30. Jin, L. et al. Identification of Plasma Biomarkers from Rheumatoid Arthritis Patients Using an Optimized Sequential Window Acquisition of All THeoretical Mass Spectra (SWATH) Proteomics Workflow. Proteomes 11, 32. https://doi.org/10.3390/proteomes11040032 (2023).

    Google Scholar 

  31. Liu, Y. et al. Quantitative variability of 342 plasma proteins in a human twin population. Mol. Syst. Biol. 11, 786. https://doi.org/10.15252/msb.20145728 (2015).

    Google Scholar 

  32. Petrovsky, D. V. et al. Tracking Health, Performance and Recovery in Athletes Using Machine Learning. Sports 10, 160. https://doi.org/10.3390/sports10100160 (2022).

    Google Scholar 

  33. Bassini, A. et al. Sportomics method to assess acute phase proteins in Olympic level athletes using dried blood spots and multiplex assay. Sci. Rep. 12, 19824. https://doi.org/10.1038/s41598-022-23300-y (2022).

    Google Scholar 

  34. Lambert, M. I. General Adaptations to Exercise: Acute Versus Chronic and Strength Versus Endurance Training. In: (eds Vaamonde, D., du Plessis, S. S. & Agarwal, A.) Exercise and Human Reproduction: Induced Fertility Disorders and Possible Therapies. Springer, New York, NY, 93–100 (2016).

    Google Scholar 

  35. Vann, C. G. et al. Effects of High-Volume Versus High-Load Resistance Training on Skeletal Muscle Growth and Molecular Adaptations. Front. Physiol. 13 https://doi.org/10.3389/fphys.2022.857555 (2022).

  36. Sanchis-Gomar, F. et al. The Acquisition of Cardiovascular Adaptation to Aerobic Exercise: When Does It Begin and How Does It Evolve Depending on Intrinsic and Extrinsic Factors? Can. J. Cardiol. 41, 386–397. https://doi.org/10.1016/j.cjca.2024.12.023 (2025).

    Google Scholar 

  37. Oliveira, A. N. & Hood, D. A. Exercise is mitochondrial medicine for muscle. Sports Med. Health Sci. 1, 11–18. https://doi.org/10.1016/j.smhs.2019.08.008 (2019).

    Google Scholar 

  38. Alghannam, A. F., Ghaith, M. M. & Alhussain, M. H. Regulation of Energy Substrate Metabolism in Endurance Exercise. Int. J. Environ. Res. Public. Health. 18, 4963. https://doi.org/10.3390/ijerph18094963 (2021).

    Google Scholar 

  39. Zanini, G. et al. Mitochondrial DNA and Exercise: Implications for Health and Injuries in Sports. Cells 10, 2575. https://doi.org/10.3390/cells10102575 (2021).

    Google Scholar 

  40. Hackney, A. C. & Walz, E. A. Hormonal adaptation and the stress of exercise training: the role of glucocorticoids. Trends Sport Sci. 20, 165–171 (2013).

    Google Scholar 

  41. Crescioli, C. Vitamin D, exercise, and immune health in athletes: A narrative review. Front. Immunol. 13, 954994. https://doi.org/10.3389/fimmu.2022.954994 (2022).

    Google Scholar 

  42. Qiu, Y. et al. Exercise sustains the hallmarks of health. J. Sport Health Sci. 12, 8–35. https://doi.org/10.1016/j.jshs.2022.10.003 (2023).

    Google Scholar 

  43. Badau, D. & Badau, A. Identifying the Incidence of Exercise Dependence Attitudes, Levels of Body Perception, and Preferences for Use of Fitness Technology Monitoring. Int. J. Environ. Res. Public. Health. 15, 2614. https://doi.org/10.3390/ijerph15122614 (2018).

    Google Scholar 

  44. Sabiston, C. M., Pila, E., Vani, M. & Thogersen-Ntoumani, C. Body image, physical activity, and sport: A scoping review. Psychol. Sport Exerc. 42, 48–57. https://doi.org/10.1016/j.psychsport.2018.12.010 (2019).

    Google Scholar 

  45. Shteynberg, D., Nesvizhskii, A. I., Moritz, R. L. & Deutsch, E. W. Combining Results of Multiple Search Engines in Proteomics *. Mol. Cell. Proteom. 12, 2383–2393. https://doi.org/10.1074/mcp.R113.027797 (2013).

    Google Scholar 

  46. Peptide identification in shotgun proteomics using tandem mass spectrometry: Comparison of search engine algorithms | Journal of Analytical Chemistry | Springer Nature Link. https://link.springer.com/article/10.1134/S1061934815140075. Accessed 18 Feb 2026.

  47. Audain, E. et al. In-depth analysis of protein inference algorithms using multiple search engines and well-defined metrics. J. Proteom. 150, 170–182. https://doi.org/10.1016/j.jprot.2016.08.002 (2017).

    Google Scholar 

  48. Muth, T. & Renard, B. Y. Evaluating de novo sequencing in proteomics: already an accurate alternative to database-driven peptide identification? Brief. Bioinform. 19, 954–970. https://doi.org/10.1093/bib/bbx033 (2018).

    Google Scholar 

  49. Highly Robust de Novo. Full-Length Protein Sequencing | Analytical Chemistry. https://pubs.acs.org/doi/10.1021/acs.analchem.1c03718. Accessed 20 Feb 2026.

  50. Mass spectrometry-based. protein identification by integrating de novo sequencing with database searching | BMC Bioinformatics | Springer Nature Link. https://link.springer.com/article/10.1186/1471-2105-14-S2-S24. Accessed 20 Feb 2026.

  51. NovoBoard:, A. Comprehensive Framework for Evaluating the False Discovery Rate and Accuracy of De Novo Peptide Sequencing - Molecular & Cellular Proteomics. https://www.mcponline.org/article/S1535-9476(24)00139-7/fulltext. Accessed 20 Feb 2026.

  52. Comprehensive evaluation of peptide de. novo sequencing tools for monoclonal antibody assembly | Briefings in Bioinformatics | Oxford Academic. https://academic.oup.com/bib/article/24/1/bbac542/6955273. Accessed 20 Feb 2026.

  53. Muranjan, M., Nussenzweig, V. & Tomlinson, S. Characterization of the Human Serum Trypanosome Toxin, Haptoglobin-related Protein*. J. Biol. Chem. 273, 3884–3887. https://doi.org/10.1074/jbc.273.7.3884 (1998).

    Google Scholar 

  54. Nielsen, M. J. et al. Haptoglobin-related protein is a high-affinity hemoglobin-binding plasma protein. Blood 108, 2846–2849. https://doi.org/10.1182/blood-2006-05-022327 (2006).

    Google Scholar 

  55. Miller, G. D. et al. Assessing serum albumin concentration following exercise-induced fluid shifts in the context of the athlete biological passport. Drug. Test. Anal. 11, 782–791. https://doi.org/10.1002/dta.2571 (2019).

    Google Scholar 

  56. Moreillon, B. et al. Prediction of plasma volume and total hemoglobin mass with machine learning. Physiol. Rep. 11, e15834. https://doi.org/10.14814/phy2.15834 (2023).

    Google Scholar 

  57. McDonnell, T. et al. The role of beta-2-glycoprotein I in health and disease associating structure with function: More than just APS. Blood Rev. 39, 100610. https://doi.org/10.1016/j.blre.2019.100610 (2020).

    Google Scholar 

  58. Ruiu, G. et al. Influence of APOH protein polymorphism on apoH levels in normal and diabetic subjects. Clin. Genet. 52, 167–172. https://doi.org/10.1111/j.1399-0004.1997.tb02538.x (1997).

    Google Scholar 

  59. Lackner, K. J. & Müller-Calleja, N. Antiphospholipid Antibodies: Their Origin and Development. Antibodies (Basel). 5, 15. https://doi.org/10.3390/antib5020015 (2016).

    Google Scholar 

  60. Schwarzenbacher, R. et al. Crystal structure of human beta2-glycoprotein I: implications for phospholipid binding and the antiphospholipid syndrome. EMBO J. 18, 6228–6239. https://doi.org/10.1093/emboj/18.22.6228 (1999).

    Google Scholar 

  61. Bouillon, R., Schuit, F., Antonio, L. & Rastinejad, F. Vitamin D Binding Protein: A Historic Overview. Front. Endocrinol. (Lausanne). 10, 910. https://doi.org/10.3389/fendo.2019.00910 (2020).

    Google Scholar 

  62. Gc globulin (vitamin D-binding protein) levels: an inhibition ELISA assay for determination of the total concentration of Gc globulin in plasma and serum: Scandinavian Journal of Clinical and Laboratory Investigation: Vol 64, No 2. https://www.tandfonline.com/doi/abs/10.1080/00365510410001149. Accessed 10 Dec 2025.

  63. Moir, H. J. et al. Editorial: Exercise-induced oxidative stress and the role of antioxidants in sport and exercise. Front. Sports Act. Living. 5, 1269826. https://doi.org/10.3389/fspor.2023.1269826 (2023).

    Google Scholar 

  64. (PDF). Role and Diagnostic Significance of Apolipoprotein D in Selected Neurodegenerative Disorders. ResearchGate. (2025). https://doi.org/10.3390/diagnostics14242814

  65. Curry, M. D., McConathy, W. J. & Alaupovic, P. Quantitative determination of human apolipoprotein D by electroimmunoassay and radial immunodiffusion. Biochim. Biophys. Acta. 491, 232–241. https://doi.org/10.1016/0005-2795(77)90059-9 (1977).

    Google Scholar 

  66. Fyfe-Desmarais, G., Desmarais, F., Rassart, É. & Mounier, C. Apolipoprotein D in Oxidative Stress and Inflammation. Antioxidants 12, 1027. https://doi.org/10.3390/antiox12051027 (2023).

    Google Scholar 

  67. Ingenbleek, Y. & Bernstein, L. H. Plasma Transthyretin as a Biomarker of Lean Body Mass and Catabolic States. Adv. Nutr. 6, 572–580. https://doi.org/10.3945/an.115.008508 (2015).

    Google Scholar 

  68. Ceciliani, F., Giordano, A. & Spagnolo, V. The systemic reaction during inflammation: the acute-phase proteins. Protein Pept. Lett. 9, 211–223. https://doi.org/10.2174/0929866023408779 (2002).

    Google Scholar 

  69. Spengler, C. M., Roos, M., Laube, S. M. & Boutellier, U. Decreased exercise blood lactate concentrations after respiratory endurance training in humans. Eur. J. Appl. Physiol. Occup. Physiol. 79, 299–305. https://doi.org/10.1007/s004210050511 (1999).

    Google Scholar 

  70. Baird, M. F., Graham, S. M., Baker, J. S. & Bickerstaff, G. F. Creatine-kinase- and exercise-related muscle damage implications for muscle performance and recovery. J. Nutr. Metab. 2012, 960363. https://doi.org/10.1155/2012/960363 (2012).

    Google Scholar 

Download references

Funding

The work was performed within the framework of the Program for Basic Research in the Russian Federation for a long-term period (2021–2030) (125020701771-5).

Author information

Authors and Affiliations

  1. Institute of Biomedical Chemistry, Moscow, 119121, Russian Federation

    Kristina A. Malsagova, Tatiana V. Butkova, Kirill S. Nikolsky, Denis V. Petrovskiy, Arthur T. Kopylov, Valeriya I. Nakhod, Liudmila I. Kulikova, Vladimir R. Rudnev, Anastasiia S. Bukreeva, Alexander A. Izotov & Anna L. Kaysheva

  2. State Research Center-Burnasyan Federal Medical Biophysical Center of Federal Medical Biological Agency, Moscow, 123098, Russian Federation

    Ksenia A. Yurku, Evgenii I. Balakin & Vasiliy I. Pustovoyt

Authors
  1. Kristina A. Malsagova
    View author publications

    Search author on:PubMed Google Scholar

  2. Tatiana V. Butkova
    View author publications

    Search author on:PubMed Google Scholar

  3. Kirill S. Nikolsky
    View author publications

    Search author on:PubMed Google Scholar

  4. Denis V. Petrovskiy
    View author publications

    Search author on:PubMed Google Scholar

  5. Arthur T. Kopylov
    View author publications

    Search author on:PubMed Google Scholar

  6. Valeriya I. Nakhod
    View author publications

    Search author on:PubMed Google Scholar

  7. Liudmila I. Kulikova
    View author publications

    Search author on:PubMed Google Scholar

  8. Vladimir R. Rudnev
    View author publications

    Search author on:PubMed Google Scholar

  9. Ksenia A. Yurku
    View author publications

    Search author on:PubMed Google Scholar

  10. Evgenii I. Balakin
    View author publications

    Search author on:PubMed Google Scholar

  11. Anastasiia S. Bukreeva
    View author publications

    Search author on:PubMed Google Scholar

  12. Alexander A. Izotov
    View author publications

    Search author on:PubMed Google Scholar

  13. Vasiliy I. Pustovoyt
    View author publications

    Search author on:PubMed Google Scholar

  14. Anna L. Kaysheva
    View author publications

    Search author on:PubMed Google Scholar

Contributions

Conceptualization Denis V. Petrovskiy, Arthur T. Kopylov and Anna L. Kaysheva; methodology, Kristina A. Malsagova, Tatiana V. Butkova, and Ksenia A. Yurku; software, Kirill S. Nikolsky and Denis V. Petrovskiy; validation Kirill S. Nikolsky, Denis V. Petrovskiy and Arthur T. Kopylov; formal analysis, Valeriya I. Nakhod and Vladimir R. Rudnev; data curation, Alexander A. Izotov, Anastasiia S. Bukreeva. and Evgenii I. Balakin; writing – original draft preparation, Liudmila I. Kulikova and Kirill S. Nikolsky; writing—review and editing, Arthur T. Kopylov and Vasiliy I. Pustovoyt. All authors have read and agreed to the published version of the manuscript.

Corresponding author

Correspondence to Tatiana V. Butkova.

Ethics declarations

Competing interests

The authors declare no competing interests.

Ethical approval

The study was approved by the Board for Ethical Questions in A. I. Burnazyan State Research Center of the FMBA of Russia (Protocol No. 40 from 18.11.2020) according to the principles expressed in the Declaration of Helsinki.

Additional information

Publisher’s note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Supplementary Information

Below is the link to the electronic supplementary material.

Supplementary Material 1 (download XLSX )

Supplementary Material 2 (download DOCX )

Rights and permissions

Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/.

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Malsagova, K.A., Butkova, T.V., Nikolsky, K.S. et al. Changes in the proteomic profile of athletes’ plasma associated with exercise intensity. Sci Rep (2026). https://doi.org/10.1038/s41598-026-44729-5

Download citation

  • Received: 11 August 2025

  • Accepted: 13 March 2026

  • Published: 19 March 2026

  • DOI: https://doi.org/10.1038/s41598-026-44729-5

Share this article

Anyone you share the following link with will be able to read this content:

Sorry, a shareable link is not currently available for this article.

Provided by the Springer Nature SharedIt content-sharing initiative

Keywords

  • Sport
  • Circulating proteins
  • Proteome
  • De novo peptide sequencing
  • DIA-MS
Download PDF

Advertisement

Explore content

  • Research articles
  • News & Comment
  • Collections
  • Subjects
  • Follow us on Facebook
  • Follow us on X
  • Sign up for alerts
  • RSS feed

About the journal

  • About Scientific Reports
  • Contact
  • Journal policies
  • Guide to referees
  • Calls for Papers
  • Editor's Choice
  • Journal highlights
  • Open Access Fees and Funding

Publish with us

  • For authors
  • Language editing services
  • Open access funding
  • Submit manuscript

Search

Advanced search

Quick links

  • Explore articles by subject
  • Find a job
  • Guide to authors
  • Editorial policies

Scientific Reports (Sci Rep)

ISSN 2045-2322 (online)

nature.com footer links

About Nature Portfolio

  • About us
  • Press releases
  • Press office
  • Contact us

Discover content

  • Journals A-Z
  • Articles by subject
  • protocols.io
  • Nature Index

Publishing policies

  • Nature portfolio policies
  • Open access

Author & Researcher services

  • Reprints & permissions
  • Research data
  • Language editing
  • Scientific editing
  • Nature Masterclasses
  • Research Solutions

Libraries & institutions

  • Librarian service & tools
  • Librarian portal
  • Open research
  • Recommend to library

Advertising & partnerships

  • Advertising
  • Partnerships & Services
  • Media kits
  • Branded content

Professional development

  • Nature Awards
  • Nature Careers
  • Nature Conferences

Regional websites

  • Nature Africa
  • Nature China
  • Nature India
  • Nature Japan
  • Nature Middle East
  • Privacy Policy
  • Use of cookies
  • Legal notice
  • Accessibility statement
  • Terms & Conditions
  • Your US state privacy rights
Springer Nature

© 2026 Springer Nature Limited

Nature Briefing: Translational Research

Sign up for the Nature Briefing: Translational Research newsletter — top stories in biotechnology, drug discovery and pharma.

Get what matters in translational research, free to your inbox weekly. Sign up for Nature Briefing: Translational Research