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Sökning: id:"swepub:oai:DiVA.org:kth-344590" > Plasma proteomics f...

Plasma proteomics for prediction of subclinical coronary artery calcifications in primary prevention

Royer, Patrick (författare)
aDepartment of Molecular and Clinical Medicine, Institute of Medicine, Sahlgrenska Academy, Gothenburg University, Gothenburg, Sweden.; bRegion Västra Götaland, Sahlgrenska University Hospital, Department of Clinical Physiology, Gothenburg, Sweden.; cDepartment of Critical Care, University Hospital of Martinique, Fort-de-France, France
Björnson, Elias (författare)
aDepartment of Molecular and Clinical Medicine, Institute of Medicine, Sahlgrenska Academy, Gothenburg University, Gothenburg, Sweden.
Adiels, Martin (författare)
aDepartment of Molecular and Clinical Medicine, Institute of Medicine, Sahlgrenska Academy, Gothenburg University, Gothenburg, Sweden.; dSchool of Public Health and Community Medicine, Institute of Medicine, University of Gothenburg, Gothenburg, Sweden
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Alvez, Maria Bueno (författare)
KTH,Proteinvetenskap
Fagerberg, Linn (författare)
KTH,Proteinvetenskap,Science for Life Laboratory, SciLifeLab
Bäckhed, Fredrik (författare)
aDepartment of Molecular and Clinical Medicine, Institute of Medicine, Sahlgrenska Academy, Gothenburg University, Gothenburg, Sweden.; bRegion Västra Götaland, Sahlgrenska University Hospital, Department of Clinical Physiology, Gothenburg, Sweden.
Uhlén, Mathias (författare)
KTH,Systembiologi,Science for Life Laboratory, SciLifeLab,Department of Neuroscience, Karolinska Institutet, Stockholm, Sweden, Stockholm
Gummesson, Anders (författare)
aDepartment of Molecular and Clinical Medicine, Institute of Medicine, Sahlgrenska Academy, Gothenburg University, Gothenburg, Sweden.; gRegion Västra Götaland, Sahlgrenska University Hospital, Department of Clinical Genetics and Genomics, Gothenburg, Sweden
Bergström, Göran (författare)
aDepartment of Molecular and Clinical Medicine, Institute of Medicine, Sahlgrenska Academy, Gothenburg University, Gothenburg, Sweden.; bRegion Västra Götaland, Sahlgrenska University Hospital, Department of Clinical Physiology, Gothenburg, Sweden.
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aDepartment of Molecular and Clinical Medicine, Institute of Medicine, Sahlgrenska Academy, Gothenburg University, Gothenburg, Sweden; bRegion Västra Götaland, Sahlgrenska University Hospital, Department of Clinical Physiology, Gothenburg, Sweden.; cDepartment of Critical Care, University Hospital of Martinique, Fort-de-France, France aDepartment of Molecular and Clinical Medicine, Institute of Medicine, Sahlgrenska Academy, Gothenburg University, Gothenburg, Sweden. (creator_code:org_t)
Elsevier BV, 2024
2024
Engelska.
Ingår i: American Heart Journal. - : Elsevier BV. - 0002-8703 .- 1097-6744. ; 271, s. 55-67
Relaterad länk:
https://doi.org/10.1...
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https://urn.kb.se/re...
https://doi.org/10.1...
http://kipublication...
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  • Tidskriftsartikel (refereegranskat)
Abstract Ämnesord
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  • Background and aims: Recent developments in high-throughput proteomic technologies enable the discovery of novel biomarkers of coronary atherosclerosis. The aims of this study were to test if plasma protein subsets could detect coronary artery calcifications (CAC) in asymptomatic individuals and if they add predictive value beyond traditional risk factors. Methods: Using proximity extension assays, 1,342 plasma proteins were measured in 1,827 individuals from the Impaired Glucose Tolerance and Microbiota (IGTM) study and 883 individuals from the Swedish Cardiopulmonary BioImage Study (SCAPIS) aged 50-64 years without history of ischaemic heart disease and with CAC assessed by computed tomography. After data-driven feature selection, extreme gradient boosting machine learning models were trained on the IGTM cohort to predict the presence of CAC using combinations of proteins and traditional risk factors. The trained models were validated in SCAPIS. Results: The best plasma protein subset (44 proteins) predicted CAC with an area under the curve (AUC) of 0.691 in the validation cohort. However, this was not better than prediction by traditional risk factors alone (AUC = 0.710, P = .17). Adding proteins to traditional risk factors did not improve the predictions (AUC = 0.705, P = .6). Most of these 44 proteins were highly correlated with traditional risk factors. Conclusions: A plasma protein subset that could predict the presence of subclinical CAC was identified but it did not outperform nor improve a model based on traditional risk factors. Thus, support for this targeted proteomics platform to predict subclinical CAC beyond traditional risk factors was not found.

Ämnesord

MEDICIN OCH HÄLSOVETENSKAP  -- Klinisk medicin -- Kardiologi (hsv//swe)
MEDICAL AND HEALTH SCIENCES  -- Clinical Medicine -- Cardiac and Cardiovascular Systems (hsv//eng)

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