Novel and Emerging Biomarkers with Risk Predictive Utility for Atherosclerotic Cardiovascular Disease

Novel and Emerging Risk Factors (K Nasir, Section Editor)
Part of the following topical collections:
  1. Topical Collection on Novel and Emerging Risk Factors


Purpose of Review

Since the release of the American Heart Association and American College of Cardiology’s 2013 pooled cohort equations and the European Cardiology Society’s 2016 SCORE, numerous studies have better characterized the predictive ability of emerging and novel biomarkers for atherosclerotic cardiovascular disease (ASCVD). Here, we review these emerging ASCVD biomarkers, with a focus on those that have been assessed using risk discrimination and reclassification performance indices in large population studies.

Recent Findings

These biomarkers include genetic risk scores (GRS) based on a growing number of risk alleles, inflammatory and thrombotic markers, lipid components and functional measures, protein metabolites, microRNAs, and a variety of subclinical atherosclerosis imaging measures. While most of these markers have demonstrated some degree of association with and predictive utility for ASCVD, only coronary artery calcium (CAC) has demonstrated consistent risk prediction improvement across multiple population and risk profiles.


Although CAC has garnered evidence to merit inclusion in modern risk prediction algorithms, large population studies and high-throughput genetic and protein technologies have shown promise for the risk prediction utility of several emerging biomarkers that may warrant consideration in future multimodality ASCVD risk prediction algorithms.


ASCVD Risk prediction Biomarkers Atherosclerosis Coronary artery calcium Lipoproteins 


Compliance with Ethical Standards

Conflict of Interest

Dr. Shah declares no conflicts of interests.

Dr. Rohatgi declares consulting fees with Merck, CSL Limited, HDL Diagnostics, and Cleveland Heartlabs, as well as receiving grants from NIH/NHLBI (K08HL118131) and AHA (15CVGPSD27030013).

Human and Animal Rights and Informed Consent

This article does not contain any studies with human or animal subjects performed by any of the authors.


Papers of particular interest, published recently, have been highlighted as: • Of importance

  1. 1.
    Goff DC, Lloyd-Jones DM, Bennett G, Coady S, D’Agostino RB, Gibbons R, et al. 2013 ACC/AHA guideline on the assessment of cardiovascular risk: a report of the American College of Cardiology/American Heart Association task force on practice guidelines. J Am Coll Cardiol. 2014;63(25):2935–59. Scholar
  2. 2.
    Piepoli MF, Hoes AW, Agewall S, Albus C, Brotons C, Catapano AL, et al. 2016 European guidelines on cardiovascular disease prevention in clinical practice. Eur Heart J. [Internet]. 2016 [cited 2017 Jul 11];37:2315–81. Available from:
  3. 3.
    Hlatky MA, Greenland P, Arnett DK, Ballantyne CM, Criqui MH, Elkind MSV, et al. Criteria for evaluation of novel markers of cardiovascular risk: a scientific statement from the American Heart Association. Circulation. 2009;119(17):2408–16. Scholar
  4. 4.
    Pencina MJ, D’Agostino RB, Steyerberg EW. Extensions of net reclassification improvement calculations to measure usefulness of new biomarkers. Stat Med. [Internet]. England; 2011;30:11–21. Available from:
  5. 5.
    Wassertheil-Smoller S, McGinn A, Allison M, Ca T, Curb D, Eaton C, et al. Improvement in stroke risk prediction: role of C-reactive protein and lipoprotein-associated phospholipase A2 in the women’s health initiative. Int J Stroke [Internet]. 2014 [cited 2017 Jul 13];9:902–9. Available from:
  6. 6.
    Tikkanen E, Havulinna AS, Palotie A, Salomaa V, Ripatti S. Genetic risk prediction and a 2-stage risk screening strategy for coronary heart disease. Arterioscler Thromb Vasc Biol. [Internet]. 2013 [cited 2017 Jul 11];33:2261–6. Available from:
  7. 7.
    Mega JL, Stitziel NO, Smith JG, Chasman DI, Caulfield MJ, Devlin JJ, et al. Genetic risk, coronary heart disease events, and the clinical benefit of statin therapy: an analysis of primary and secondary prevention trials. Lancet [Internet]. 2015 [cited 2017 Jul 11];385:2264–71. Available from:
  8. 8.
    Tada H, Melander O, Louie JZ, Catanese JJ, Rowland CM, Devlin JJ, et al. Risk prediction by genetic risk scores for coronary heart disease is independent of self-reported family history. Eur Heart J. 2016;37(6):561–7. Scholar
  9. 9.
    Krarup NT, Borglykke A, Allin KH, Sandholt CH, Justesen JM, Andersson EA, et al. A genetic risk score of 45 coronary artery disease risk variants associates with increased risk of myocardial infarction in 6041 Danish individuals. Atherosclerosis [Internet]. 2015 [cited 2017 Jul 15];240:305–10. Available from:
  10. 10.
    Morris RW, Cooper JA, Shah T, Wong A, Drenos F, Engmann J, et al. Marginal role for 53 common genetic variants in cardiovascular disease prediction. Heart [Internet]. 2016 [cited 2017 Jul 19];102:1640–7. Available from:
  11. 11.
    Joseph PG, Pare G, Asma S, Engert JC, Yusuf S, Anand SS. Impact of a genetic risk score on myocardial infarction risk across different ethnic populations. Can J Cardiol. [Internet]. 2016 [cited 2017 Jul 11];32:1440–6. Available from:
  12. 12.
    Khera A V., Emdin CA, Drake I, Natarajan P, Bick AG, Cook NR, et al. Genetic Risk. Adherence to a healthy lifestyle, and coronary disease. N Engl J Med. [Internet]. Massachusetts Medical Society; 2016 [cited 2017 Jul 11];375:NEJMoa1605086. Available from:
  13. 13.
    • Natarajan P, Young R, Stitziel NO, Padmanabhan S, Baber U, Mehran R, et al. Polygenic risk score identifies subgroup with higher burden of atherosclerosis and greater relative benefit from statin therapy in the primary prevention setting. Circulation [Internet]. 2017;135:2091–101. Available from: Natarajan et al. demonstrate via a large genetic risk score that genetic risk for ASCVD may be mediated by atherosclerosis, and that a person’s overall genetic predisposition for ASCVD may modify the effectiveness of statin therapy.
  14. 14.
    Isaacs A, Willems SM, Bos D, Dehghan A, Hofman A, Ikram MA, et al. Risk scores of common genetic variants for lipid levels influence atherosclerosis and incident coronary heart disease. Arterioscler Thromb Vasc Biol. [Internet]. 2013 [cited 2017 Jul 13];33:2233–9. Available from:
  15. 15.
    Ganna A, Magnusson PKE, Pedersen NL, De Faire U, Reilly M, Ärnlöv J, et al. Multilocus genetic risk scores for coronary heart disease prediction. Arterioscler Thromb Vasc Biol. [Internet]. 2013 [cited 2017 Jul 11];33:2267–72. Available from:
  16. 16.
    Tragante V, Doevendans PAFM, Nathoe HM, Van Der Graaf Y, Spiering W, Algra A, et al. The impact of susceptibility loci for coronary artery disease on other vascular domains and recurrence risk. Eur Heart J. 2013;34(37):2896–904. Scholar
  17. 17.
    Hemerich D, van der Laan SW, Tragante V, den Ruijter HM, de Borst GJ, Pasterkamp G, et al. Impact of carotid atherosclerosis loci on cardiovascular events. Atherosclerosis [Internet]. 2015 [cited 2017 Jul 15];243:466–8. Available from:
  18. 18.
    Ridker PM. From C-reactive protein to interleukin-6 to interleukin-1: moving upstream to identify novel targets for atheroprotection. Circ Res. [Internet]. 2016 [cited 2017 Jul 13];118:145–56. Available from:
  19. 19.
    Collaboration TERF. C-reactive protein, fibrinogen, and cardiovascular disease prediction. N Engl J Med. [Internet]. Massachusetts Medical Society ; 2012 [cited 2017 Jul 18];367:1310–20. Available from:
  20. 20.
    Van Wijk DF, Boekholdt SM, Wareham NJ, Ahmadi-Abhari S, Kastelein JJPP, Stroes ESGG, et al. C-Reactive protein, fatal and nonfatal coronary artery disease, stroke, and peripheral artery disease in the prospective EPIC-Norfolk Cohort Study. Arterioscler. Thromb Vasc Biol. [Internet]. 2013 [cited 2017 Jul 13];33:2888–94. Available from:
  21. 21.
    Yeboah J, Young R, McClelland RL, Delaney JC, Polonsky TS, Dawood FZ, et al. Utility of nontraditional risk markers in atherosclerotic cardiovascular disease risk assessment. J Am Coll Cardiol. [Internet]. 2016 [cited 2017 Jul 13];67:139–47. Available from:
  22. 22.
    DeFilippis AP, Young R, Carrubba CJ, McEvoy JW, Budoff MJ, Blumenthal RS, et al. An analysis of calibration and discrimination among multiple cardiovascular risk scores in a modern multiethnic cohort. Ann Intern Med. [Internet]. American College of Physicians; 2015 [cited 2017 Jul 24];162:266. Available from:
  23. 23.
    Shoamanesh A, Preis SR, Beiser AS, Kase CS, Wolf PA, Vasan RS, et al. Circulating biomarkers and incident ischemic stroke in the Framingham Offspring Study. Neurology [Internet]. 2016 [cited 2017 Jul 13];87:1206–11. Available from:
  24. 24.
    Jiménez MC, Rexrode KM, Glynn RJ, Ridker PM, Gaziano JM, Sesso HD. Association between high-sensitivity c-reactive protein and total stroke by hypertensive status among men. J Am Heart Assoc. [Internet]. 2015 [cited 2017 Jul 13];4:e002073. Available from:
  25. 25.
    Kormi I, Nieminen MT, Havulinna AS, Zeller T, Blankenberg S, Tervahartiala T, et al. Matrix metalloproteinase-8 and tissue inhibitor of matrix metalloproteinase-1 predict incident cardiovascular disease events and all-cause mortality in a population-based cohort. Eur J Prev Cardiol. [Internet]. 2017;63:204748731770658. Available from:
  26. 26.
    Durda P, Sabourin J, Lange EM, Nalls MA, Mychaleckyj JC, Jenny NS, et al. Plasma levels of soluble interleukin-2 receptor α. arterioscler. Thromb Vasc Biol. [Internet]. 2015 [cited 2017 Jul 15];35. Available from:
  27. 27.
    Piepoli MF, Hoes AW, Agewall S, Albus C, Brotons C, Catapano AL, et al. 2016 European guidelines on cardiovascular disease prevention in clinical practice. Eur Heart J. 2016;37(29):2315–81. Scholar
  28. 28.
    van Iperen EPA, Sivapalaratnam S, Holmes M V, Hovingh GK, Zwinderman AH, Asselbergs FW. Genetic analysis of emerging risk factors in coronary artery disease. Atherosclerosis [Internet]. 2016 [cited 2017 Jul 11];254:35–41. Available from:
  29. 29.
    Holmes M V., Exeter HJ, Folkersen L, Nelson CP, Guardiola M, Cooper JA, et al. Novel genetic approach to investigate the role of plasma secretory phospholipase A2 (sPLA2)-V isoenzyme in coronary heart disease: modified Mendelian randomization analysis using PLA2G5 expression levels. Circ Cardiovasc Genet. [Internet]. 2014 [cited 2017 Jul 15];7:144–50. Available from:
  30. 30.
    Guardiola M, Exeter HJ, Perret C, Folkersen L, Van’T Hooft F, Eriksson P, et al. PLA2G10 gene variants, sPLA2 activity, and coronary heart disease risk. Circ Cardiovasc Genet. 2015;8(2):356–62. Scholar
  31. 31.
    Ueshima H, Kadowaki T, Hisamatsu T, Fujiyoshi A, Miura K, Ohkubo T, et al. Lipoprotein-associated phospholipase A2 is related to risk of subclinical atherosclerosis but is not supported by Mendelian randomization analysis in a general Japanese population. Atherosclerosis [Internet]. 2016 [cited 2017 Jul 13];246:141–7. Available from:
  32. 32.
    Gregson JM, Freitag DF, Surendran P, Stitziel NO, Chowdhury R, Burgess S, et al. Genetic invalidation of Lp-PLA2 as a therapeutic target: Large-scale study of five functional Lp-PLA2-lowering alleles. Eur J Prev Cardiol. [Internet]. SAGE PublicationsSage UK: London, England; 2016 [cited 2017 Jul 15];24:492–504. Available from:
  33. 33.
    Polfus LM, Gibbs RA, Boerwinkle E. Coronary heart disease and genetic variants with low phospholipase A 2 activity. N Engl J Med. [Internet]. Massachusetts Medical Society; 2015 [cited 2017 Jul 15];372:295–6. Available from:
  34. 34.
    Di Castelnuovo A, Agnoli C, de Curtis A, Giurdanella MC, Sieri S, Mattiello A, et al. Elevated levels of D-dimers increase the risk of ischaemic and haemorrhagic stroke: findings from the EPICOR Study. Thromb Haemost. 2014;112(5):941–6. Scholar
  35. 35.
    Willeit P, Thompson A, Aspelund T, Rumley A, Eiriksdottir G, Lowe G, et al. Hemostatic factors and risk of coronary heart disease in general populations: new prospective study and updated meta-analyses. PLoS One Public Libr Sci. 2013;8(2):e55175. Scholar
  36. 36.
    Danziger J, Young RL, Shea MK, Tracy RP, Ix JH, Jenny NS, et al. Vitamin K-dependent protein activity and incident ischemic cardiovascular disease: the multi-ethnic study of atherosclerosis. Arterioscler Thromb Vasc Biol. [Internet]. 2016 [cited 2017 Jul 13];36:1037–42. Available from:
  37. 37.
    Steffen BT, Guan W, Remaley AT, Paramsothy P, Heckbert SR, McClelland RL, et al. Use of lipoprotein particle measures for assessing coronary heart disease risk post-American Heart Association/American College of Cardiology guidelines: the multi-ethnic study of atherosclerosis. Arterioscler Thromb Vasc Biol. [Internet]. 2015 [cited 2017 Jul 19];35:448–54. Available from:
  38. 38.
    Gianfagna F, Veronesi G, Guasti L, Chambless LE, Brambilla P, Corrao G, et al. Do apolipoproteins improve coronary risk prediction in subjects with metabolic syndrome? Insights from the North Italian Brianza cohort study. Atherosclerosis [Internet]. 2014 [cited 2017 Jul 13];236:175–81. Available from:
  39. 39.
    Pencina MJ, D’Agostino RB, Zdrojewski T, Williams K, Thanassoulis G, Furberg CD, et al. Apolipoprotein B improves risk assessment of future coronary heart disease in the Framingham Heart Study beyond LDL-C and non-HDL-C Eur J Prev Cardiol. [Internet]. 2015 [cited 2017 Jul 13];22:1321–7. Available from:
  40. 40.
    MacKey RH, Greenland P, Goff DC, Lloyd-Jones D, Sibley CT, Mora S. High-density lipoprotein cholesterol and particle concentrations, carotid atherosclerosis, and coronary events: MESA (Multi-Ethnic Study of Atherosclerosis). J Am Coll Cardiol. [Internet]. Elsevier Inc.; 2012;60:508–16. Available from:
  41. 41.
    Chandra A, Neeland IJ, Das SR, Khera A, Turer AT, Ayers CR, et al. Relation of Black race between high density lipoprotein cholesterol content, high density lipoprotein particles and coronary events (from the Dallas Heart Study). Am J Cardiol. [Internet]. Elsevier Inc.; 2015;115:890–4. Available from:
  42. 42.
    Khera A V., Demler O V., Adelman SJ, Collins HL, Glynn RJ, Ridker PM, et al. Cholesterol efflux capacity, high-density lipoprotein particle number, and incident cardiovascular eventsclinical perspective. Circulation [Internet]. 2017 [cited 2017 Jul 15];135:2494–504. Available from:
  43. 43.
    Reina SA, Llabre MM, Allison MA, Wilkins JT, Mendez AJ, Arnan MK, et al. HDL cholesterol and stroke risk: the Multi-Ethnic Study of Atherosclerosis. Atherosclerosis [Internet]. Elsevier Ltd; 2015 [cited 2017 Jul 15];243:314–9. Available from:
  44. 44.
    Hoogeveen RC, Gaubatz JW, Sun W, Dodge RC, Crosby JR, Jiang J, et al. Small dense low-density lipoprotein-cholesterol concentrations predict risk for coronary heart disease: the Atherosclerosis Risk in Communities (ARIC) Study. Arterioscler Thromb Vasc Biol. [Internet]. 2014 [cited 2017 Jul 13];34:1069–77. Available from:
  45. 45.
    Tehrani DM, Zhao Y, Blaha MJ, Mora S, Mackey RH, Michos ED, et al. Discordance of low-density lipoprotein and high-density lipoprotein cholesterol particle versus cholesterol concentration for the prediction of cardiovascular disease in patients with metabolic syndrome and diabetes mellitus (from the Multi-Ethnic Study). Am J Cardiol. [Internet]. 2016 [cited 2017 Jul 13];117:1921–7. Available from:
  46. 46.
    Rohatgi A, Khera A, Berry JD, Givens EG, Ayers CR, Wedin KE, et al. HDL cholesterol efflux capacity and incident cardiovascular events. N Engl J Med. 2014;371(25):2383–93. Scholar
  47. 47.
    Mody P, Joshi PH, Khera A, Ayers CR, Rohatgi A. Beyond coronary calcification, family history, and c-reactive protein: cholesterol efflux capacity and cardiovascular risk prediction. J Am Coll Cardiol. [Internet]. 2016 [cited 2017 Jul 15];67:2480–7. Available from:
  48. 48.
    Kamstrup PR, Tybjærg-Hansen A, Nordestgaard BG. Extreme lipoprotein(a) levels and improved cardiovascular risk prediction. J Am Coll Cardiol. 2013;61(11):1146–56. Scholar
  49. 49.
    Waldeyer C, Makarova N, Zeller T, Schnabel RB, Brunner FJ, Jørgensen T, et al. Lipoprotein(a) and the risk of cardiovascular disease in the European population: results from the BiomarCaRE consortium. Eur Heart J. [Internet]. 2017;[Epub ahead of print]. Available from:
  50. 50.
    Willeit P, Kiechl S, Kronenberg F, Witztum JL, Santer P, Mayr M, et al.. Discrimination and net reclassification of cardiovascular risk with lipoprotein(a): prospective 15-year outcomes in the Bruneck Study. J Am Coll Cardiol. [Internet]. 2014 [cited 2017 Jul 19];64:851–60. Available from:
  51. 51.
    Schwartz GG, Ballantyne CM, Barter PJ, Kallend D, Leiter LA, Leitersdorf E, McMurray JJV, Nicholls SJ, Olsson AG, Shah PK, Tardif JC, Kittelson J. Association of lipoprotein(a) with risk of recurrent ischemic events following acute coronary syndrome. JAMA Cardiol [Internet] 2017;80220:1–5. Available from:
  52. 52.
    Stegemann C, Pechlaner R, Willeit P, Langley SR, Mangino M, Mayr U, et al. Lipidomics profiling and risk of cardiovascular disease in the prospective population-based Bruneck Study. Circulation [Internet]. 2014 [cited 2017 Jul 15];129:1821–31. Available from:
  53. 53.
    Würtz P, Havulinna AS, Soininen P, Tynkkynen T, Prieto-Merino D, Tillin T, et al. Metabolite profiling and cardiovascular event risk: a prospective study of 3 population-based cohorts. Circulation [Internet]. 2015 [cited 2017 Jul 11];131:774–85. Available from:
  54. 54.
    Ganna A, Salihovic S, Sundström J, Broeckling CD, Hedman ÅK, Magnusson PKE, et al. Large-scale metabolomic profiling identifies novel biomarkers for incident coronary heart disease. Gibson G, editor. PLoS Genet. [Internet]. Public Library of Science; 2014 [cited 2017 Jul 13];10:e1004801. Available from:
  55. 55.
    Yin X, Subramanian S, Hwang S-JJ, O’Donnell CJ, Fox CS, Courchesne P, et al. Protein biomarkers of new-onset cardiovascular disease. Arterioscler. Thromb Vasc Biol. [Internet]. 2014 [cited 2017 Jul 13];34:939–45. Available from:
  56. 56.
    Zampetaki A, Willeit P, Tilling L, Drozdov I, Prokopi M, Renard JM, et al. Prospective study on circulating microRNAs and risk of myocardial infarction. J Am Coll Cardiol [Internet]. Elsevier Inc. 2012;60:290–9. Scholar
  57. 57.
    Bye A, Røsjø H, Nauman J, Silva GJJ, Follestad T, Omland T, et al. Circulating microRNAs predict future fatal myocardial infarction in healthy individuals—the HUNT Study. J Mol Cell Cardiol. [Internet]. 2016 [cited 2017 Jul 15];97:162–8. Available from:
  58. 58.
    Kavousi M, Desai CS, Ayers C, Blumenthal RS, Budoff MJ, Mahabadi A-A, et al. Prevalence and prognostic implications of coronary artery calcification in low-risk women: a meta-analysis. Jama [Internet]. European Heart Network and European Society of Cardiology, Brussels, Belgium; 2016 [cited 2017 Jul 11];316:2126–34. Available from:
  59. 59.
    Geisel MH, Bauer M, Hennig F, Hoffmann B, Lehmann N, Möhlenkamp S, et al. Comparison of coronary artery calcification, carotid intima-media thickness and ankle-brachial index for predicting 10-year incident cardiovascular events in the general population. Eur Heart J [Internet]. 2017;38(23):1815–22. Scholar
  60. 60.
    • Blaha MJ, Cainzos-Achirica M, Greenland P, McEvoy JW, Blankstein R, Budoff MJ, et al. Role of coronary artery calcium score of zero and other negative risk markers for cardiovascular disease: the Multi-Ethnic Study of Atherosclerosis (MESA). Circulation. 2016;133:849–58. Blaha et al. demonstrate the superior downward risk reclassification conferred by the absence of CAC compared to other negative risk markers in MESA. CrossRefPubMedPubMedCentralGoogle Scholar
  61. 61.
    Mehta A, Blaha MJ, Miller J, Joshi PH. Coronary artery calcium scoring: a valuable aid in shared decision making among non-traditional risk markers. Curr Cardiovasc Imaging Rep. [Internet]. 2017;10:33. Available from:
  62. 62.
    Baldassarre D, Hamsten A, Veglia F, de Faire U, Humphries SE, Smit AJ, et al. Measurements of carotid intima-media thickness and of interadventitia common carotid diameter improve prediction of cardiovascular events. J Am Coll Cardiol. [Internet]. 2012;60:1489 LP-1499. Available from:
  63. 63.
    Polak JF, Szklo M, Kronmal RA, Burke GL, Shea S, Zavodni AEH, et al. The value of carotid artery plaque and intima-media thickness for incident cardiovascular disease: the Multi-Ethnic Study of Atherosclerosis. J Am Heart Assoc. [Internet]. 2013 [cited 2017 Jul 19];2. Available from:
  64. 64.
    Gepner AD, Young R, Delaney JA, Tattersall MC, Blaha MJ, Post WS, et al. Comparison of coronary artery calcium presence, carotid plaque presence, and carotid intima-media thickness for cardiovascular disease prediction in the Multi-Ethnic Study of Atherosclerosis. Circ Cardiovasc Imaging. 2015;8(1):e002262. Scholar
  65. 65.
    Gardin JM, Bartz TM, Polak JF, O’Leary DH, Wong ND. What do carotid intima-media thickness and plaque add to the prediction of stroke and cardiovascular disease risk in older adults? The Cardiovascular Health Study. J Am Soc Echocardiogr. 2014;27(9):998–1005. Scholar
  66. 66.
    • De Lemos JA, Ayers CR, Levine B, de Filippi CR, Wang TJ, Hundley WG, et al. Multimodality strategy for cardiovascular risk assessment: performance in 2 population-based cohorts. Circulation [Internet]. 2017 [cited 2017 Jul 19];135:2119–32. Available from: De Lemos et al. recently demonstrated a multimodality risk assessment score with significant and substantial improvements over the PCE in risk discrimination and reclassification of global CVD, ASCVD, CHD, HF, all-cause mortality, and CV mortality.

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© Springer Science+Business Media, LLC, part of Springer Nature 2018

Authors and Affiliations

  1. 1.The University of Texas Southwestern Medical Center and the Donald W. Reynolds Cardiovascular Clinical Research CenterDallasUSA
  2. 2.Division of Cardiology, Department of Internal MedicineUniversity of Texas Southwestern Medical CenterDallasUSA

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