Circulating microRNAs predict future fatal myocardial infarction in healthy individuals - The HUNT study

Journal of Molecular and Cellular Cardiology

Volumn 97, Issue , 2016, Pages 162-168

  Bye, Anja ^a,b   Røsjø, Helge ^c,d   Nauman, Javaid ^a,b   Silva, Gustavo J J ^a   Follestad, Turid ^a   Omland, Torbjørn ^c,d   Wisløff, Ulrik ^a,e  

^a NORWEGIAN UNIVERSITY OF SCIENCE AND TECHNOLOGY   (Norway)

^b TRONDHEIM UNIVERSITY HOSPITAL   (Norway)

^c AKERSHUS UNIVERSITY HOSPITAL   (Norway)

^d UNIVERSITY OF OSLO   (Norway)

^e UNIVERSITY OF QUEENSLAND   (Australia)

Author keywords

Atherosclerosis; Biomarkers; Risk prediction

Indexed keywords

MICRORNA; MICRORNA 106-5P; MICRORNA 144-3P; MICRORNA 26-5P; MICRORNA 424-5P; MICRORNA 660-5P; MICRORNA LET-7G-5P; UNCLASSIFIED DRUG; BIOLOGICAL MARKER;

ACUTE HEART INFARCTION; ADULT; AGED; ARTICLE; CASE CONTROL STUDY; CONTROLLED STUDY; FEMALE; FRAMINGHAM RISK SCORE; HUMAN; MAJOR CLINICAL STUDY; MALE; PREDICTION; PRIORITY JOURNAL; PROSPECTIVE STUDY; RECEIVER OPERATING CHARACTERISTIC; RISK ASSESSMENT; SENSITIVITY AND SPECIFICITY; SEX DIFFERENCE; BLOOD; GENETICS; MIDDLE AGED; MORTALITY; MYOCARDIAL INFARCTION; ODDS RATIO; PROGNOSIS;

AGED; BIOMARKERS; CASE-CONTROL STUDIES; FEMALE; HUMANS; MALE; MICRORNAS; MIDDLE AGED; MYOCARDIAL INFARCTION; ODDS RATIO; PROGNOSIS; RISK ASSESSMENT; ROC CURVE;

EID: 84971425347     PISSN: 00222828     EISSN: 10958584     Source Type: Journal    
DOI: 10.1016/j.yjmcc.2016.05.009     Document Type: Article

References (74)

1. Mortality G.B.D., Causes of Death C. Global, regional, and national age-sex specific all-cause and cause-specific mortality for 240 causes of death, 1990-2013: a systematic analysis for the Global Burden of Disease Study 2013. Lancet 2015, 385:117-171.
2. Organisation WH Obesity in Europe 2005.
3. Organization WH Obesity Report 2000.
4. Wang Y., Beydoun M.A. The obesity epidemic in the United States-gender, age, socioeconomic, racial/ethnic, and geographic characteristics: a systematic review and meta-regression analysis. Epidemiol. Rev. 2007, 29:6-28.
5. Stene L.C., Midthjell K., Jenum A.K., Skeie S., Birkeland K.I., Lund E., et al. Prevalence of diabetes mellitus in Norway. Tidsskr. Nor. Laegeforen. 2004, 124:1511-1514.
6. Kariuki J.K., Stuart-Shor E.M., Leveille S.G., Hayman L.L. Evaluation of the performance of existing non-laboratory based cardiovascular risk assessment algorithms. BMC Cardiovasc. Disord. 2013, 13:123.
7. Petretta M., Cuocolo A. Prediction models for risk classification in cardiovascular disease. Eur. J. Nucl. Med. Mol. Imaging 2012, 39:1959-1969.
8. D'Agostino R.B., Vasan R.S., Pencina M.J., Wolf P.A., Cobain M., Massaro J.M., et al. General cardiovascular risk profile for use in primary care: the Framingham Heart Study. Circulation 2008, 117:743-753.
9. Conroy R.M., Pyorala K., Fitzgerald A.P., Sans S., Menotti A., De Backer G., et al. Estimation of ten-year risk of fatal cardiovascular disease in Europe: the SCORE project. Eur. Heart J. 2003, 24:987-1003.
10. Hippisley-Cox J., Coupland C., Vinogradova Y., Robson J., May M., Brindle P. Derivation and validation of QRISK, a new cardiovascular disease risk score for the United Kingdom: prospective open cohort study. BMJ 2007, 335:136.
11. Selmer R., Lindman A.S., Tverdal A., Pedersen J.I., Njolstad I., Veierod M.B. Model for estimation of cardiovascular risk in Norway. Tidsskr. Nor. Laegeforen. 2008, 128:286-290.
12. Khot U.N., Khot M.B., Bajzer C.T., Sapp S.K., Ohman E.M., Brener S.J., et al. Prevalence of conventional risk factors in patients with coronary heart disease. J. Am. Med. Assoc. 2003, 290:898-904.
13. Cooney M.T., Dudina A., De Bacquer D., Fitzgerald A., Conroy R., Sans S., et al. How much does HDL cholesterol add to risk estimation? A report from the SCORE Investigators. Eur. J. Cardiovasc. Prev. Rehabil. 2009, 16:304-314.
14. Cook N.R., Buring J.E., Ridker P.M. The effect of including C-reactive protein in cardiovascular risk prediction models for women. Ann. Intern. Med. 2006, 145:21-29.
15. Wang T.J., Gona P., Larson M.G., Tofler G.H., Levy D., Newton-Cheh C., et al. Multiple biomarkers for the prediction of first major cardiovascular events and death. N. Engl. J. Med. 2006, 355:2631-2639.
16. Kavousi M., Elias-Smale S., Rutten J.H., Leening M.J., Vliegenthart R., Verwoert G.C., et al. Evaluation of newer risk markers for coronary heart disease risk classification: a cohort study. Ann. Intern. Med. 2012, 156:438-444.
17. Di Angelantonio E., Butterworth A.S. Clinical utility of genetic variants for cardiovascular risk prediction: a futile exercise or insufficient data?. Circ. Cardiovasc. Genet. 2012, 5:387-390.
18. Morrison A.C., Bare L.A., Chambless L.E., Ellis S.G., Malloy M., Kane J.P., et al. Prediction of coronary heart disease risk using a genetic risk score: the Atherosclerosis Risk in Communities Study. Am. J. Epidemiol. 2007, 166:28-35.
19. Ridker P.M., Buring J.E., Rifai N., Cook N.R. Development and validation of improved algorithms for the assessment of global cardiovascular risk in women: the Reynolds Risk Score. J. Am. Med. Assoc. 2007, 297:611-619.
20. Ridker P.M., Paynter N.P., Rifai N., Gaziano J.M., Cook N.R. C-reactive protein and parental history improve global cardiovascular risk prediction: the Reynolds Risk Score for men. Circulation 2008, 118:2243-2251. (4p following 51).
21. Cook N.R., Paynter N.P., Eaton C.B., Manson J.E., Martin L.W., Robinson J.G., et al. Comparison of the Framingham and Reynolds Risk scores for global cardiovascular risk prediction in the multiethnic Women's Health Initiative. Circulation 2012, 125:1748-1756. (S1-11).
22. Greenland P., Alpert J.S., Beller G.A., Benjamin E.J., Budoff M.J., Fayad Z.A., et al. 2010 ACCF/AHA guideline for assessment of cardiovascular risk in asymptomatic adults: executive summary: a report of the American College of Cardiology Foundation/American Heart Association Task Force on Practice Guidelines. Circulation 2010, 122:2748-2764.
23. Cortez M.A., Calin G.A. MicroRNA identification in plasma and serum: a new tool to diagnose and monitor diseases. Expert. Opin. Biol. Ther. 2009, 9:703-711.
24. Bartel D.P. MicroRNAs: genomics, biogenesis, mechanism, and function. Cell 2004, 116:281-297.
25. Thum T., Catalucci D., Bauersachs J. MicroRNAs: novel regulators in cardiac development and disease. Cardiovasc. Res. 2008, 79:562-570.
26. Care A., Catalucci D., Felicetti F., Bonci D., Addario A., Gallo P., et al. MicroRNA-133 controls cardiac hypertrophy. Nat. Med. 2007, 13:613-618.
27. Latronico M.V., Catalucci D., Condorelli G. Emerging role of microRNAs in cardiovascular biology. Circ. Res. 2007, 101:1225-1236.
28. Romaine S.P., Tomaszewski M., Condorelli G., Samani N.J. MicroRNAs in cardiovascular disease: an introduction for clinicians. Heart 2015, 101:921-928.
29. Ai J., Zhang R., Li Y., Pu J., Lu Y., Jiao J., et al. Circulating microRNA-1 as a potential novel biomarker for acute myocardial infarction. Biochem. Biophys. Res. Commun. 2010, 391:73-77.
30. Tijsen A.J., Creemers E.E., Moerland P.D., de Windt L.J., van der Wal A.C., Kok W.E., et al. MiR423-5p as a circulating biomarker for heart failure. Circ. Res. 2010, 106:1035-1039.
31. Wang G.K., Zhu J.Q., Zhang J.T., Li Q., Li Y., He J., et al. Circulating microRNA: a novel potential biomarker for early diagnosis of acute myocardial infarction in humans. Eur. Heart J. 2010, 31:659-666.
32. Ji X., Takahashi R., Hiura Y., Hirokawa G., Fukushima Y., Iwai N. Plasma miR-208 as a biomarker of myocardial injury. Clin. Chem. 2009, 55:1944-1949.
33. Corsten M.F., Dennert R., Jochems S., Kuznetsova T., Devaux Y., Hofstra L., et al. Circulating MicroRNA-208b and MicroRNA-499 reflect myocardial damage in cardiovascular disease. Circ. Cardiovasc. Genet. 2010, 3:499-506.
34. Zampetaki A., Willeit P., Tilling L., Drozdov I., Prokopi M., Renard J.M., et al. Prospective study on circulating MicroRNAs and risk of myocardial infarction. J. Am. Coll. Cardiol. 2012, 60:290-299.
35. Krokstad S., Langhammer A., Hveem K., Holmen T.L., Midthjell K., Stene T.R., et al. Cohort profile: the HUNT study, Norway. Int. J. Epidemiol. 2013, 42:968-977.
36. Bye A., Rosjo H., Aspenes S.T., Condorelli G., Omland T., Wisloff U. Circulating microRNAs and aerobic fitness-the HUNT-Study. PLoS One 2013, 8.
37. Rosjo H., Dahl M.B., Bye A., Andreassen J., Jorgensen M., Wisloff U., et al. Prognostic value of circulating microRNA-210 levels in patients with moderate to severe aortic stenosis. PLoS One 2014, 9.
38. Robin X., Turck N., Hainard A., Tiberti N., Lisacek F., Sanchez J.C., et al. pROC: an open-source package for R and S+ to analyze and compare ROC curves. BMC Bioinf. 2011, 12:77.
39. Folsom A.R., Chambless L.E., Ballantyne C.M., Coresh J., Heiss G., Wu K.K., et al. An assessment of incremental coronary risk prediction using C-reactive protein and other novel risk markers: the atherosclerosis risk in communities study. Arch. Intern. Med. 2006, 166:1368-1373.
40. Zethelius B., Johnston N., Venge P. Troponin I as a predictor of coronary heart disease and mortality in 70-year-old men: a community-based cohort study. Circulation 2006, 113:1071-1078.
41. Essebag V., Genest J., Suissa S., Pilote L. The nested case-control study in cardiology. Am. Heart J. 2003, 146:581-590.
42. Sayed A.S., Xia K., Li F., Deng X., Salma U., Li T., et al. The diagnostic value of circulating microRNAs for middle-aged (40-60-year-old) coronary artery disease patients. Clinics 2015, 70:257-263.
43. Munoz-Pacheco P., Ortega-Hernandez A., Miana M., Cachofeiro V., Fernandez-Cruz A., Gomez-Garre D. Ezetimibe inhibits PMA-induced monocyte/macrophage differentiation by altering microRNA expression: a novel anti-atherosclerotic mechanism. Pharmacol. Res. 2012, 66:536-543.
44. Li T., Cao H., Zhuang J., Wan J., Guan M., Yu B., et al. Identification of miR-130a, miR-27b and miR-210 as serum biomarkers for atherosclerosis obliterans. Clin. Chim. Acta 2011, 412:66-70.
45. Zhu D., Pan C., Li L., Bian Z., Lv Z., Shi L., et al. MicroRNA-17/20a/106a modulate macrophage inflammatory responses through targeting signal-regulatory protein alpha. J. Allergy Clin. Immunol. 2013, 132:426-436.
46. Hu Y.W., Hu Y.R., Zhao J.Y., Li S.F., Ma X., Wu S.G., et al. An agomir of miR-144-3p accelerates plaque formation through impairing reverse cholesterol transport and promoting pro-inflammatory cytokine production. PLoS One 2014, 9.
47. Raitoharju E., Lyytikainen L.P., Levula M., Oksala N., Mennander A., Tarkka M., et al. miR-21, miR-210, miR-34a, and miR-146a/b are up-regulated in human atherosclerotic plaques in the Tampere Vascular Study. Atherosclerosis 2011, 219:211-217.
48. Zhang X., Wang X., Zhu H., Zhu C., Wang Y., Pu W.T., et al. Synergistic effects of the GATA-4-mediated miR-144/451 cluster in protection against simulated ischemia/reperfusion-induced cardiomyocyte death. J. Mol. Cell. Cardiol. 2010, 49:841-850.
49. Ovchinnikova E.S., Schmitter D., Vegter E.L., Ter Maaten J.M., Valente M.A., Liu L.C., et al. Signature of circulating microRNAs in patients with acute heart failure. Eur. J. Heart Fail. 2015.
50. Hackl M., Brunner S., Fortschegger K., Schreiner C., Micutkova L., Muck C., et al. miR-17, miR-19b, miR-20a, and miR-106a are down-regulated in human aging. Aging Cell 2010, 9:291-296.
51. Merlet E., Atassi F., Motiani R.K., Mougenot N., Jacquet A., Nadaud S., et al. miR-424/322 regulates vascular smooth muscle cell phenotype and neointimal formation in the rat. Cardiovasc. Res. 2013, 98:458-468.
52. Ghosh G., Subramanian I.V., Adhikari N., Zhang X., Joshi H.P., Basi D., et al. Hypoxia-induced microRNA-424 expression in human endothelial cells regulates HIF-alpha isoforms and promotes angiogenesis. J. Clin. Invest. 2010, 120:4141-4154.
53. de Gonzalo-Calvo D., Davalos A., Montero A., Garcia-Gonzalez A., Tyshkovska I., Gonzalez-Medina A., et al. Circulating inflammatory miRNA signature in response to different doses of aerobic exercise. J. Appl. Physiol. 2015, 119:124-134.
54. Sharma S., Eghbali M. Influence of sex differences on microRNA gene regulation in disease. Biol. Sex Differ. 2014, 5:3.
55. Feng J., Sun G., Yan J., Noltner K., Li W., Buzin C.H., et al. Evidence for X-chromosomal schizophrenia associated with microRNA alterations. PLoS One 2009, 4.
56. Hewagama A., Gorelik G., Patel D., Liyanarachchi P., McCune W.J., Somers E., et al. Overexpression of X-linked genes in T cells from women with lupus. J. Autoimmun. 2013, 41:60-71.
57. Carrel L., Willard H.F. X-inactivation profile reveals extensive variability in X-linked gene expression in females. Nature 2005, 434:400-404.
58. Chen K.C., Hsieh I.C., Hsi E., Wang Y.S., Dai C.Y., Chou W.W., et al. Negative feedback regulation between microRNA let-7 g and the oxLDL receptor LOX-1. J. Cell Sci. 2011, 124:4115-4124.
59. Satoh M., Tabuchi T., Minami Y., Takahashi Y., Itoh T., Nakamura M. Expression of let-7i is associated with Toll-like receptor 4 signal in coronary artery disease: effect of statins on let-7i and Toll-like receptor 4 signal. Immunobiology 2012, 217:533-539.
60. Liao Y.C., Wang Y.S., Guo Y.C., Lin W.L., Chang M.H., Juo S.H. Let-7 g improves multiple endothelial functions through targeting transforming growth factor-beta and SIRT-1 signaling. J. Am. Coll. Cardiol. 2014, 63:1685-1694.
61. Sun D., Zhang J., Xie J., Wei W., Chen M., Zhao X. MiR-26 controls LXR-dependent cholesterol efflux by targeting ABCA1 and ARL7. FEBS Lett. 2012, 586:1472-1479.
62. Zhang N., Lei J., Lei H., Ruan X., Liu Q., Chen Y., et al. MicroRNA-101 overexpression by IL-6 and TNF-alpha inhibits cholesterol efflux by suppressing ATP-binding cassette transporter A1 expression. Exp. Cell Res. 2015, 336:33-42.
63. Rayner K.J., Moore K.J. MicroRNA control of high-density lipoprotein metabolism and function. Circ. Res. 2014, 114:183-192.
64. Goedeke L., Rotllan N., Canfran-Duque A., Aranda J.F., Ramirez C.M., Araldi E., et al. MicroRNA-148a regulates LDL receptor and ABCA1 expression to control circulating lipoprotein levels. Nat. Med. 2015, 21:1280-1289.
65. Economou E.K., Oikonomou E., Siasos G., Papageorgiou N., Tsalamandris S., Mourouzis K., et al. The role of microRNAs in coronary artery disease: from pathophysiology to diagnosis and treatment. Atherosclerosis 2015, 241:624-633.
66. Xiang M., Zeng Y., Yang R., Xu H., Chen Z., Zhong J., et al. U6 is not a suitable endogenous control for the quantification of circulating microRNAs. Biochem. Biophys. Res. Commun. 2014, 454:210-214.
67. Benz F., Roderburg C., Vargas Cardenas D., Vucur M., Gautheron J., Koch A., et al. U6 is unsuitable for normalization of serum miRNA levels in patients with sepsis or liver fibrosis. Exp. Mol. Med. 2013, 45.
68. Kok M.G., Halliani A., Moerland P.D., Meijers J.C., Creemers E.E., Pinto-Sietsma S.J. Normalization panels for the reliable quantification of circulating microRNAs by RT-qPCR. FASEB J. 2015.
69. Peltier H.J., Latham G.J. Normalization of microRNA expression levels in quantitative RT-PCR assays: identification of suitable reference RNA targets in normal and cancerous human solid tissues. RNA 2008, 14:844-852.
70. Sourvinou I.S., Markou A., Lianidou E.S. Quantification of circulating miRNAs in plasma: effect of preanalytical and analytical parameters on their isolation and stability. J. Mol. Diagn. 2013, 15:827-834.
71. Tang G., Shen X., Lv K., Wu Y., Bi J., Shen Q. Different normalization strategies might cause inconsistent variation in circulating microRNAs in patients with hepatocellular carcinoma. Med. Sci. Monit. 2015, 21:617-624.
72. Mestdagh P., Van Vlierberghe P., De Weer A., Muth D., Westermann F., Speleman F., et al. A novel and universal method for microRNA RT-qPCR data normalization. Genome Biol. 2009, 10:R64.
73. Kagias K., Podolska A., Pocock R. Reliable reference miRNAs for quantitative gene expression analysis of stress responses in Caenorhabditis elegans. BMC Genomics 2014, 15:222.
74. Chang K.H., Mestdagh P., Vandesompele J., Kerin M.J., Miller N. MicroRNA expression profiling to identify and validate reference genes for relative quantification in colorectal cancer. BMC Cancer 2010, 10:173.