Associations between physical activity and CVD-related metabolomic and proteomic biomarkersDepartment of Clinical Physiology, Karolinska University Hospital, Karolinska Institutet, Stockholm, Sweden; Department of Clinical Physiology, Sahlgrenska University Hospital, Sahlgrenska Academy, Gothenburg, Sweden.
Cardiovascular Medicine Unit, Department of Medicine, Center for Molecular Medicine, Karolinska Institute, Karolinska University Hospital, Stockholm, Sweden.
Department of Clinical Sciences in Malmö, Lund University, Malmö, Sweden.
CMIV, Centre of Medical Image Science and Visualization, Linköping University, Linköping, Sweden; Department of Clinical Physiology, Department of Health, Medicine and Caring Sciences, Linköping University, Linköping, Sweden.
Department of Clinical Sciences Lund,, Cardiology, Lund University, Skåne University Hospital, Lund, Sweden.
Department of Medical Sciences, Molecular Epidemiology, Uppsala University, Uppsala, Sweden.
Division of Cardiovascular Medicine, Department of Medicine, Karolinska Institutet, Stockholm, Sweden; Department of Cardiology, Danderyds Hospital, Stockholm, Sweden.
Department of Clinical Genetics and Genomics, Region Västra Götaland, Sahlgrenska University Hospital, Gothenburg, Sweden; Department of Molecular and Clinical Medicine, Institute of Medicine, Sahlgrenska Academy, University of Gothenburg, Gothenburg, Sweden.
Department of Clinical Sciences, Danderyd University Hospital, Karolinska Institutet, Stockholm, Sweden.
Department of Medical Sciences, Clinical Epidemiology, University of Uppsala, Uppsala, Sweden.
Department of Molecular and Clinical Medicine, Institute of Medicine, Sahlgrenska Academy, University of Gothenburg, Gothenburg, Sweden; Department of Radiology, Region Västra Götaland, Sahlgrenska University Hospital, Gothenburg, Sweden.
Department of Medical Sciences, Cardiology, Uppsala University, Uppsala, Sweden; Uppsala Clinical Research Center, Uppsala University, Uppsala, Sweden.
Department of Molecular and Clinical Medicine, Institute of Medicine, Sahlgrenska Academy, University of Gothenburg, Gothenburg, Sweden; Wallenberg Laboratory for Cardiovascular and Metabolic Research, Institute of Medicine, University of Gothenburg, Gothenburg, Sweden.
Department of Medical Sciences, Uppsala University, Uppsala, Sweden; The George Institute for Global Health, University of New South Wales, Sydney, Australia.
CMIV, Centre of Medical Image Science and Visualization, Linköping University, Linköping, Sweden; Department of Health, Medicine and Caring Sciences, Linköping University, Linköping, Sweden.
Show others and affiliations
2025 (English)In: PLOS ONE, E-ISSN 1932-6203, Vol. 20, no 6, article id e0325720
Article in journal (Refereed) Published
Abstract [en]
Aim: Habitual physical activity (PA) affects metabolism and homeostasis in various tissues and organs. However, detailed knowledge of associations between PA and cardiovascular disease (CVD) risk markers is limited. We sought to identify associations between accelerometer-assessed PA classes and 183 proteomic and 154 metabolomic CVD-related biomarkers.
Method: We utilized cross-sectional data from the main SCAPIS cohort (n = 4647, median age: 57.5 yrs, 50.5% female) as a discovery sample and the SCAPIS pilot cohort (n = 910, median age: 57.5 yrs, 50.3% female) as a validation sample. PA was assessed via hip-worn accelerometers, while plasma concentrations of proteomic biomarkers were measured using Olink CVD II and III panels. Metabolomic markers were assessed using the Nightingale NMR platform. We evaluated associations between four PA classes (moderate-to-vigorous PA [MVPA], low-intensity PA [LIPA], sedentary [SED], and prolonged SED [prolSED]) and biomarkers, controlling for potential confounders and applying a false discovery rate of 5% using multiple linear regressions.
Results: A total of eighty-five metabolomic markers and forty-three proteomic markers were validated and found to be significantly associated with one or more PA classes. LIPA and SED markers demonstrated significant mirroring or opposing relations to biomarkers, while prolSED mainly shared relations with SED. Notably, HDL species were predominantly negatively associated with SED, whereas LDL species were positively associated with SED and negatively associated with MVPA. Among the proteomic markers, eighteen were uniquely associated with MVPA (among those Interleukin – 6 [IL6] and Growth/differentiation factor 15 [GDF15] both negatively related), seven with SED (among those Metalloproteinase inhibitor 4 [TIMP4] and Tumor necrosis factor receptor 2 [TNFR2], both positively related), and eight were related to both SED/prolSED (among those Lipoprotein lipase [LPL] negatively related to SED and leptin [LEP] positively related to SED) and MVPA (with LPL positively related to MVPA and LEP negatively related to MVPA).
Conclusion: Our findings suggest the existence of specific associations between PA classes and metabolomic and cardiovascular protein biomarkers in a middle-aged population. Beyond validation of previous results, we identified new associations. This multitude of connections between PA and CVD-related markers may help elucidate the previously observed relationship between PA and CVD. The identified cross-sectional associations could inform the design of future experimental studies, serving as important outcome measures.
Place, publisher, year, edition, pages
Public Library of Science (PLoS), 2025. Vol. 20, no 6, article id e0325720
National Category
Epidemiology Public Health, Global Health and Social Medicine
Identifiers
URN: urn:nbn:se:umu:diva-240918DOI: 10.1371/journal.pone.0325720ISI: 001509994800045PubMedID: 40498722Scopus ID: 2-s2.0-105007909310OAI: oai:DiVA.org:umu-240918DiVA, id: diva2:1980365
2025-07-022025-07-022025-07-02Bibliographically approved