Improvement of myocardial infarction risk prediction via inflammation-associated metabolite biomarkers. Issue 16 (2nd March 2017)

• Record Type: Journal Article
• Authors: Ward-Caviness, Cavin K; Xu, Tao; Aspelund, Thor; Thorand, Barbara; Montrone, Corinna; Meisinger, Christa; Dunger-Kaltenbach, Irmtraud; Zierer, Astrid; Yu, Zhonghao; Helgadottir, Inga R; Harris, Tamara B; Launer, Lenore J; Ganna, Andrea; Lind, Lars; Eiriksdottir, Gudny; Waldenberger, Melanie; Prehn, Cornelia; Suhre, Karsten; Illig, Thomas; Adamski, Jerzy; Ruepp, Andreas; Koenig, Wolfgang; Gudnason, Vilmundur; Emilsson, Valur; Wang-Sattler, Rui; Peters, Annette
• Is Part Of: Heart. Volume 103:Issue 16(2017)
• Page Start: 1278
• Page End: 1285
• Publication Date: 2017-03-02
• Subjects: Myocardial infarction -- biomarkers -- metabolomics -- inflammation; Heart -- Diseases -- Treatment -- Periodicals; Cardiology -- Periodicals; 616.12
• DOI: 10.1136/heartjnl-2016-310789
• Languages: English
• ISSNs: 1355-6037

Abstract

Abstract : Objective: The comprehensive assaying of low-molecular-weight compounds, for example, metabolomics, provides a unique tool to uncover novel biomarkers and understand pathways underlying myocardial infarction (MI). We used a targeted metabolomics approach to identify biomarkers for MI and evaluate their involvement in the pathogenesis of MI. Methods and results: Using three independent, prospective cohorts (KORA S4, KORA S2 and AGES-REFINE), totalling 2257 participants without a history of MI at baseline, we identified metabolites associated with incident MI (266 cases). We also investigated the association between the metabolites and high-sensitivity C reactive protein (hsCRP) to understand the relation between these metabolites and systemic inflammation. Out of 140 metabolites, 16 were nominally associated (p<0.05) with incident MI in KORA S4. Three metabolites, arginine and two lysophosphatidylcholines (LPC 17:0 and LPC 18:2), were selected as biomarkers via a backward stepwise selection procedure in the KORA S4 and were significant (p<0.0003) in a meta-analysis comprising all three studies including KORA S2 and AGES-REFINE. Furthermore, these three metabolites increased the predictive value of the Framingham risk score, increasing the area under the receiver operating characteristic score in KORA S4 (from 0.70 to 0.78, p=0.001) and AGES-REFINE study (from 0.70 to 0.76, p=0.02), but was not observed in KORA S2. The metabolite biomarkers attenuated the association between hsCRP and MI, indicating a potential link to systemic inflammatory processes. Conclusions: We identified three metabolite biomarkers, which in combination increase the predictive value of the Framingham risk score. The attenuation of the hsCRP–MI association by these three metabolites indicates a potential link to systemic inflammation.