Large-scale Metabolomic Profiling Identifies Novel Biomarkers for Incident Coronary Heart Disease

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Non-targeted metabolomic profiling of large population-based studies has become feasible only in the past 1–2 years and this hypothesis-free exploration of the metabolome holds a great potential to fuel the discovery of novel biomarkers for coronary heart disease (CHD). Such biomarkers are not only important for risk stratification and treatment decisions, but can also improve understanding of cardiovascular disease pathophysiology to identify new drug targets. In this study, we investigated the metabolic profiles of more than 3,600 individuals from three population-based studies, and discovered four metabolites that are consistently associated with incident CHD. We integrate genetic and metabolomic analysis to delineate the underlying biological mechanisms and evaluate potential causal effects of the novel biomarkers. Specifically, we found one metabolite to be strongly associated with single nucleotides polymorphisms previously reported for association with CHD, and consistent with a potential causal role in CHD development, as suggested by Mendelian randomization analysis.

Vyšlo v časopise: Large-scale Metabolomic Profiling Identifies Novel Biomarkers for Incident Coronary Heart Disease. PLoS Genet 10(12): e32767. doi:10.1371/journal.pgen.1004801
Kategorie: Research Article
prolekare.web.journal.doi_sk: https://doi.org/10.1371/journal.pgen.1004801

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1. SuhreK, ShinSY, PetersenAK, MohneyRP, MeredithD, et al. (2011) Human metabolic individuality in biomedical and pharmaceutical research. Nature 477: 54–60.

2. MagnussonM, LewisGD, EricsonU, Orho-MelanderM, HedbladB, et al. (2013) A diabetes-predictive amino acid score and future cardiovascular disease. Eur Heart J 34: 1982–1989.

3. ShahSH, BainJR, MuehlbauerMJ, StevensRD, CrosslinDR, et al. (2010) Association of a peripheral blood metabolic profile with coronary artery disease and risk of subsequent cardiovascular events. Circ Cardiovasc Genet 3: 207–214.

4. BoyanovskyBB, WebbNR (2009) Biology of secretory phospholipase A2. Cardiovasc Drugs Ther 23: 61–72.

5. WilsonPW, D'AgostinoRB, LevyD, BelangerAM, SilbershatzH, et al. (1998) Prediction of coronary heart disease using risk factor categories. Circulation 97: 1837–1847.

6. LemaitreRN, TanakaT, TangW, ManichaikulA, FoyM, et al. (2011) Genetic loci associated with plasma phospholipid n-3 fatty acids: a meta-analysis of genome-wide association studies from the CHARGE Consortium. PLoS Genet 7: e1002193.

7. SchunkertH, KonigIR, KathiresanS, ReillyMP, AssimesTL, et al. (2011) Large-scale association analysis identifies 13 new susceptibility loci for coronary artery disease. Nat Genet 43: 333–338.

8. DemirkanA, van DuijnCM, UgocsaiP, IsaacsA, PramstallerPP, et al. (2012) Genome-wide association study identifies novel loci associated with circulating phospho- and sphingolipid concentrations. PLoS Genet 8: e1002490.

9. CARDIoGRAMplusC4D Consortium (2013) DeloukasP, KanoniS, WillenborgC, FarrallM, et al. (2013) Large-scale association analysis identifies new risk loci for coronary artery disease. Nat Genet 45: 25–33.

10. MillerM, StoneNJ, BallantyneC, BittnerV, CriquiMH, et al. (2011) Triglycerides and cardiovascular disease: a scientific statement from the American Heart Association. Circulation 123: 2292–2333.

11. DoR, WillerCJ, SchmidtEM, SenguptaS, GaoC, et al. (2013) Common variants associated with plasma triglycerides and risk for coronary artery disease. Nat Genet 45: 1345–1352.

12. RozenbergO, ShihDM, AviramM (2003) Human serum paraoxonase 1 decreases macrophage cholesterol biosynthesis: possible role for its phospholipase-A2-like activity and lysophosphatidylcholine formation. Arterioscler Thromb Vasc Biol 23: 461–467.

13. GausterM, RechbergerG, SovicA, HorlG, SteyrerE, et al. (2005) Endothelial lipase releases saturated and unsaturated fatty acids of high density lipoprotein phosphatidylcholine. J Lipid Res 46: 1517–1525.

14. SekasG, PattonGM, LincolnEC, RobinsSJ (1985) Origin of plasma lysophosphatidylcholine: evidence for direct hepatic secretion in the rat. J Lab Clin Med 105: 190–194.

15. ShamburekRD, ZechLA, CooperPS, VandenbroekJM, SchwartzCC (1996) Disappearance of two major phosphatidylcholines from plasma is predominantly via LCAT and hepatic lipase. Am J Physiol 271: E1073–1082.

16. SchmitzG, RuebsaamenK (2010) Metabolism and atherogenic disease association of lysophosphatidylcholine. Atherosclerosis 208: 10–18.

17. Wang-SattlerR, YuZ, HerderC, MessiasAC, FloegelA, et al. (2012) Novel biomarkers for pre-diabetes identified by metabolomics. Mol Syst Biol 8: 615.

18. FernandezC, SandinM, SampaioJL, AlmgrenP, NarkiewiczK, et al. (2013) Plasma lipid composition and risk of developing cardiovascular disease. PLoS One 8: e71846.

19. StegemannC, PechlanerR, WilleitP, LangleyS, ManginoM, et al. (2014) Lipidomics Profiling and Risk of Cardiovascular Disease in the Prospective Population-Based Bruneck Study. Circulation

20. TzoulakiI, EbbelsTM, ValdesA, ElliottP, IoannidisJP (2014) Design and analysis of metabolomics studies in epidemiologic research: a primer on -omic technologies. Am J Epidemiol 180: 129–139.

21. GannaA, FallT, LeeW, BroecklingCD, KumarJ, et al. (2014) A workflow for UPLC-MS non-targeted metabolomic profiling in large human population-based studies. bioRxiv

22. GannaA, LeeD, IngelssonE, PawitanY (2014) Rediscovery rate estimation for assessing the validation of significant findings in high-throughput studies. Brief Bioinform

23. MagnussonPK, AlmqvistC, RahmanI, GannaA, ViktorinA, et al. (2013) The Swedish Twin Registry: establishment of a biobank and other recent developments. Twin Res Hum Genet 16: 317–329.

24. GannaA, ReillyM, de FaireU, PedersenN, MagnussonP, et al. (2012) Risk prediction measures for case-cohort and nested case-control designs: an application to cardiovascular disease. Am J Epidemiol 175: 715–724.

25. BybergL, SiegbahnA, BerglundL, McKeigueP, RenelandR, et al. (1998) Plasminogen activator inhibitor-1 activity is independently related to both insulin sensitivity and serum triglycerides in 70-year-old men. Arterioscler Thromb Vasc Biol 18: 258–264.

26. LindL, ForsN, HallJ, MarttalaK, StenborgA (2005) A comparison of three different methods to evaluate endothelium-dependent vasodilation in the elderly: the Prospective Investigation of the Vasculature in Uppsala Seniors (PIVUS) study. Arterioscler Thromb Vasc Biol 25: 2368–2375.

27. BroecklingCD, HeubergerAL, PrenniJE (2013) Large scale non-targeted metabolomic profiling of serum by ultra performance liquid chromatography-mass spectrometry (UPLC-MS). J Vis Exp e50242.

28. LindL, ForsN, HallJ, MarttalaK, StenborgA (2006) A comparison of three different methods to determine arterial compliance in the elderly: the Prospective Investigation of the Vasculature in Uppsala Seniors (PIVUS) study. J Hypertens 24: 1075–1082.

29. LindL, SiegbahnA, HultheJ, ElmgrenA (2008) C-reactive protein and e-selectin levels are related to vasodilation in resistance, but not conductance arteries in the elderly: the prospective investigation of the Vasculature in Uppsala Seniors (PIVUS) study. Atherosclerosis 199: 129–137.

30. LindL, SiegbahnA, IngelssonE, SundstromJ, ArnlovJ (2011) A detailed cardiovascular characterization of obesity without the metabolic syndrome. Arterioscler Thromb Vasc Biol 31: e27–34.

31. SmithCA, WantEJ, O'MailleG, AbagyanR, SiuzdakG (2006) XCMS: processing mass spectrometry data for metabolite profiling using nonlinear peak alignment, matching, and identification. Anal Chem 78: 779–787.

32. SumnerLW, AmbergA, BarrettD, BealeMH, BegerR, et al. (2007) Proposed minimum reporting standards for chemical analysis Chemical Analysis Working Group (CAWG) Metabolomics Standards Initiative (MSI). Metabolomics 3: 211–221.

33. LeeningMJ, VedderMM, WittemanJC, PencinaMJ, SteyerbergEW (2014) Net reclassification improvement: computation, interpretation, and controversies: a literature review and clinician's guide. Ann Intern Med 160: 122–131.