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Screening multiple biomarkers for associations with major coronary events

Session Poster Session 1

Speaker Maciej Olszowka

Congress : ESC Congress 2018

  • Topic : preventive cardiology
  • Sub-topic : Prevention - Cardiovascular Risk Assessment: Biomarkers
  • Session type : Poster Session
  • FP Number : P625

Authors : M Olszowka (Uppsala,SE), L Wallentin (Uppsala,SE), N Eriksson (Uppsala,SE), E Hagstrom (Uppsala,SE), C Held (Uppsala,SE), R Stewart (Auckland,NZ), H White (Auckland,NZ), A Siegbahn (Uppsala,SE)

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Authors:
M. Olszowka1 , L. Wallentin1 , N. Eriksson2 , E. Hagstrom1 , C. Held1 , R. Stewart3 , H. White3 , A. Siegbahn4 , 1Uppsala University, Department of Medical Sciences, Cardiology - Uppsala - Sweden , 2Uppsala Clinical Research Center - Uppsala - Sweden , 3The University of Auckland - Auckland - New Zealand , 4Uppsala University, Department of Medical Sciences, Clinical Chemistry - Uppsala - Sweden ,

On behalf: the STABILITY Investigators

Citation:
European Heart Journal ( 2018 ) 39 ( Supplement ), 81

Background: Multiple biomarkers are associated with the risk of new coronary events but rarely simultaneously evaluated in a large patient population.

Methods: Based on the 3–5 years follow-up of patients with stable coronary heart disease in the STABILITY trial, 1,144 cases and 2,654 randomly selected non-cases concerning major coronary event (MCE; composite of coronary death, myocardial infarction, and urgent revascularization) were identified. Plasma levels of 155 biomarkers were measured with conventional ELISA assays and proximity extension assay panels. Associations with outcomes were evaluated by Random Survival Forest and Cox regression analyses.

Results: There were significant (p<0.0001) differences in the levels of 68 biomarkers between cases and non-cases. Using Random Survival Forest, 31 biomarkers contributed to the prediction of MCE. When also using Cox regression, including adjustment for clinical characteristics and applying multiplicity correction, 26 biomarkers were independently associated with MCE. The most important were N-terminal prohormone of brain natriuretic peptide (NTproBNP) and high sensitivity troponin T (cTnT-hs). When adjusting also for these, five additional biomarkers remained associated with MCE: lipoprotein associated phospholipase A2 (Lp-PLA2), growth differentiation factor 15 (GDF-15), interleukin 6 (IL-6), stem cell factor (SCF), and matrix metalloproteinase 12 (MMP-12).

Conclusions: In patients with stable coronary heart disease, the levels of 68 biomarkers differed between patients with and without MCE. After adjustment for clinical characteristics 26 biomarkers were still associated with the risk of MCE, most notably NTproBNP and cTnT-hs. When adjusting also for these, five additional biomarkers (Lp-PLA2, GDF-15, IL-6, SCF, and MMP-12) remained associated with MCE.

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