Evidence mapPaperPMID 41801539Full record

ArticleSports medicine - open2026

Characterizing Human Oxidative, Anabolic and Glycolytic Metabolism in Athletes with Extreme Physiologies.

Daniela Schranner, Henning Wackerhage, Patrick Weinisch, Jürgen Schlegel, Stephanie Bremer, Johannes Scherr, Werner Römisch-Margl, Annett Riermeier, Otto Zelger, Fabian Stöcker and 6 more

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Article in Sports medicine - open, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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0citing papers in PubMed
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1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

0 citing papers in PubMed.

No citing paper in PubMed yet.

4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

16 authors.

Daniela SchrannerInstitute of Computational Biology, Helmholtz Zentrum München - German Research Center for Environmental Health, Neuherberg, Germany.ORCID http://orcid.org/0000-0003-2316-318X
Henning WackerhageProfessorship of Exercise Biology, TUM School of Medicine and Health, Technical University of Munich, Munich, Germany.
Patrick WeinischInstitute of Computational Biology, Helmholtz Zentrum München - German Research Center for Environmental Health, Neuherberg, Germany.
Jürgen SchlegelProfessorship of Exercise Biology, TUM School of Medicine and Health, Technical University of Munich, Munich, Germany.
Stephanie BremerProfessorship of Exercise Biology, TUM School of Medicine and Health, Technical University of Munich, Munich, Germany.
Johannes ScherrUniversity Center for Prevention and Sports Medicine, University Hospital Balgrist, Zurich, Switzerland.
Werner Römisch-MarglInstitute of Computational Biology, Helmholtz Zentrum München - German Research Center for Environmental Health, Neuherberg, Germany.
Annett RiermeierProfessorship of Exercise Biology, TUM School of Medicine and Health, Technical University of Munich, Munich, Germany.
Otto ZelgerDepartment of Occupational Health, TUM University Hospital Rechts der Isar, Technical University of Munich, Munich, Germany.
Fabian StöckerPrevention Center, TUM School of Medicine and Health, Technical University of Munich, Munich, Germany.
Anna ArtatiMetabolomics and Proteomics Core, Helmholtz Zentrum München - German Research Center for Environmental Health, Neuherberg, Germany.
Michael WittingMetabolomics and Proteomics Core, Helmholtz Zentrum München - German Research Center for Environmental Health, Neuherberg, Germany.
Jan KrumsiekInstitute for Computational Biomedicine, Englander Institute for Precision Medicine, Department of Physiology and Biophysics, Weill Cornell Medicine, New York, NY, USA.
Martin HalleDepartment for Preventive Sports Medicine and Sports Cardiology, TUM School of Medicine and Health, TUM University Hospital Rechts der Isar, Technical University of Munich, Munich, Germany.
Martin SchönfelderProfessorship of Exercise Biology, TUM School of Medicine and Health, Technical University of Munich, Munich, Germany.
Gabi KastenmüllerInstitute of Computational Biology, Helmholtz Zentrum München - German Research Center for Environmental Health, Neuherberg, Germany. g.kastenmueller@helmholtz-muenchen.de.ORCID http://orcid.org/0000-0002-2368-7322

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundRegular physical activity is known to benefit health but the long-term effects of specific exercise training on human metabolism remain incompletely described. In this study, we comprehensively characterized the blood metabolomes of male athletes with distinct exercise-adapted metabolic profiles, comparing endurance athletes (n = 11), sprinters (n = 8), and natural body builders (n = 9) as models for highly oxidative, glycolytic, and anabolic metabolism, respectively.

methodsSerum samples of these athletes and a control group of male untrained individuals (n = 7) were collected both at rest and after maximum exercise. Using untargeted metabolomics profiling and weighted correlation network analysis, we examined associations of metabolites and metabolite modules with athlete groups and their characteristic traits (e.g., cardiovascular fitness or muscularity).

resultsOur analyses revealed distinct metabolic signatures for the different groups: a highly anabolic metabolism was characterized by lower levels of sulfated steroids; a highly oxidative metabolism by higher levels of phospholipids; and a highly glycolytic metabolism by lower levels of sphingomyelins. In response to maximum exercise, 130 metabolites changed across all groups (e.g., N-lactoyl amino acids, acylcholines, energy metabolites), while 57 metabolites showed differences in magnitude or direction of change between groups (e.g., fatty acid oxidative products, cortisol).

conclusionOur findings demonstrate that exercise-induced adaptations in metabolism distinctly shape the human serum metabolome and influence the metabolic response to exercise. These insights are relevant for diseases driven by dysfunctional metabolism, such as impaired fat oxidation and dysregulated glycolysis (e.g., diabetes, dementia) and muscle wasting (e.g., sarcopenia), where our specialized populations may serve as useful models.

Identifiers

PMID41801539
PMCPMC12972380

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the Socratic graph.