Improvement of myocardial infarction risk prediction via inflammation-associated metabolite biomarkers

• Published in: Heart (British Cardiac Society) Vol. 103; no. 16; pp. 1278 - 1285
• Main 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
• Format: Journal Article
• Language: English
• Published: England BMJ Publishing Group LTD 01.08.2017
• Subjects: Adult; Age; Aged; Biomarkers; Biomarkers - metabolism; Cardiology; Cardiovascular disease; Diabetes; Disease Progression; Fasting; Female; Genomes; Germany - epidemiology; Health risk assessment; Heart attacks; Humans; Incidence; Inflammation - metabolism; Male; Mass Spectrometry; Medical research; Metabolites; Middle Aged; Myocardial Infarction - diagnosis; Myocardial Infarction - epidemiology; Myocardial Infarction - metabolism; Nitric oxide; Physiology; Population; Predictive Value of Tests; Prospective Studies; Risk Assessment - methods; Studies; Surveys and Questionnaires; Germany; Myocardial infarction; inflammation; metabolomics; biomarkers
• ISSN: 1355-6037; 1468-201X; 1468-201X
• DOI: 10.1136/heartjnl-2016-310789

ObjectiveThe 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 resultsUsing 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.ConclusionsWe 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.