Skip to main content

Advertisement

Log in

Should We Use Genetic Scores in the Determination of Treatment Strategies to Control Dyslipidemias?

  • Lipid Abnormalities and Cardiovascular Prevention (G. De Backer, Section Editor)
  • Published:
Current Cardiology Reports Aims and scope Submit manuscript

Abstract

Purpose of Review

Conventional risk stratification algorithms that rely on age, clustered phenotypic traits, and biomarkers under-recognize the sizeable subgroup of individuals at high polygenic risk for atherosclerotic cardiovascular disease (ASCVD). This review provides perspective on the promising role of genetic testing in cardiovascular prevention through the lens of lipid metabolism.

Recent Findings

Recent advances in cardiovascular genetics identified a number of common and rare variants affecting ASCVD risk. This genetic susceptibility can be assessed by polygenic risk scores (PRS) which quantify risk conferred by the cumulative impact of common variants. This results in a normally distributed spectrum of risk for coronary artery disease that is present at birth and amplifies the effects of modifiable risk factors including lipids.

Summary

Polygenic risk is a significant determinant of ASCVD risk that is below the discrimination level of conventional guideline-based clinical frameworks. Genetic risk scores thus hold potential to refine phenotypic screening in cardiovascular prevention, identify subsets of the population that might derive particular benefit from early lifestyle and pharmaceutical interventions, and guide treatment eligibility. This might pave the way to personalized prevention aimed at reducing the unacceptable global burden of ASCVD.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1

Similar content being viewed by others

References

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

  1. Ferrucci L, Fabbri E. Inflammageing: chronic inflammation in ageing, cardiovascular disease, and frailty. Nat Rev Cardiol. 2018;15(9):505–22. https://doi.org/10.1038/s41569-018-0064-2.

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  2. Lechner K, von Schacky C, McKenzie AL, Worm N, Nixdorff U, Lechner B, et al. Lifestyle factors and high-risk atherosclerosis: pathways and mechanisms beyond traditional risk factors. Eur J Prev Cardiol. 2019;27(4):394–406. https://doi.org/10.1177/2047487319869400.

    Article  PubMed  PubMed Central  Google Scholar 

  3. Melzer D, Pilling LC, Ferrucci L. The genetics of human ageing. Nat Rev Genet. 2020;21(2):88–101. https://doi.org/10.1038/s41576-019-0183-6.

    Article  PubMed  CAS  Google Scholar 

  4. Otsuka F, Kramer MC, Woudstra P, Yahagi K, Ladich E, Finn AV, et al. Natural progression of atherosclerosis from pathologic intimal thickening to late fibroatheroma in human coronary arteries: a pathology study. Atherosclerosis. 2015;241(2):772–82. https://doi.org/10.1016/j.atherosclerosis.2015.05.011.

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  5. Lechner K, McKenzie AL, Kränkel N, Von Schacky C, Worm N, Nixdorff U, et al. High-risk atherosclerosis and metabolic phenotype: the roles of ectopic adiposity, atherogenic dyslipidemia, and inflammation. Metab Syndr Relat Disord. 2020;18(4):176–85. https://doi.org/10.1089/met.2019.0115.

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  6. Tomaniak M, Katagiri Y, Modolo R, Silva RD, Khamis RY, Bourantas CV, et al. Vulnerable plaques and patients: state-of-the-art. Eur Heart J. 2020. https://doi.org/10.1093/eurheartj/ehaa227.

  7. Emdin CA, Khera AV, Natarajan P, Klarin D, Zekavat SM, Hsiao AJ, et al. Genetic association of waist-to-hip ratio with cardiometabolic traits, type 2 diabetes, and coronary heart disease. Jama. 2017;317(6):626–34. https://doi.org/10.1001/jama.2016.21042.

    Article  PubMed  PubMed Central  Google Scholar 

  8. Schunkert H, von Scheidt M, Kessler T, Stiller B, Zeng L, Vilne B. Genetics of coronary artery disease in the light of genome-wide association studies. Clin Res Cardiol : official journal of the German Cardiac Society. 2018;107(Suppl 2):2–9. https://doi.org/10.1007/s00392-018-1324-1.

    Article  Google Scholar 

  9. •• Zeng L, Talukdar HA, Koplev S, Giannarelli C, Ivert T, Gan LM, et al. Contribution of gene regulatory networks to heritability of coronary artery disease. J Am Coll Cardiol. 2019;73(23):2946–57. https://doi.org/10.1016/j.jacc.2019.03.520.

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  10. Khera Amit V, Chaffin M, Zekavat Seyedeh M, Collins Ryan L, Roselli C, Natarajan P, et al. Whole-genome sequencing to characterize monogenic and polygenic contributions in patients hospitalized with early-onset myocardial infarction. Circulation. 2019;139(13):1593–602. https://doi.org/10.1161/CIRCULATIONAHA.118.035658.

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  11. Khera AV, Kathiresan S. Genetics of coronary artery disease: discovery, biology and clinical translation. Nat Rev Genet. 2017;18(6):331–44. https://doi.org/10.1038/nrg.2016.160.

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  12. Erdmann J, Kessler T, Munoz Venegas L, Schunkert H. A decade of genome-wide association studies for coronary artery disease: the challenges ahead. Cardiovasc Res. 2018;114(9):1241–57. https://doi.org/10.1093/cvr/cvy084.

    Article  PubMed  CAS  Google Scholar 

  13. Kessler T, Vilne B, Schunkert H. The impact of genome-wide association studies on the pathophysiology and therapy of cardiovascular disease. EMBO Mol Med. 2016;8(7):688–701. https://doi.org/10.15252/emmm.201506174.

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  14. Boren J, Chapman MJ, Krauss RM, Packard CJ, Bentzon JF, Binder CJ, et al. Low-density lipoproteins cause atherosclerotic cardiovascular disease: pathophysiological, genetic, and therapeutic insights: a consensus statement from the European Atherosclerosis Society Consensus Panel. Eur Heart J. 2020;41:2313–30. https://doi.org/10.1093/eurheartj/ehz962.

    Article  PubMed  PubMed Central  Google Scholar 

  15. Ference BA, Ginsberg HN, Graham I, Ray KK, Packard CJ, Bruckert E, et al. Low-density lipoproteins cause atherosclerotic cardiovascular disease. 1. Evidence from genetic, epidemiologic, and clinical studies. A consensus statement from the European Atherosclerosis Society Consensus Panel. Eur Heart J. 2017;38(32):2459–72. https://doi.org/10.1093/eurheartj/ehx144.

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  16. Kasikara C, Doran AC, Cai B, Tabas I. The role of non-resolving inflammation in atherosclerosis. J Clin Invest. 2018;128(7):2713–23. https://doi.org/10.1172/JCI97950.

    Article  PubMed  PubMed Central  Google Scholar 

  17. Zewinger S, Reiser J, Jankowski V, Alansary D, Hahm E, Triem S, et al. Apolipoprotein C3 induces inflammation and organ damage by alternative inflammasome activation. Nat Immunol. 2020;21(1):30–41. https://doi.org/10.1038/s41590-019-0548-1.

    Article  PubMed  CAS  Google Scholar 

  18. Linsel-Nitschke P, Götz A, Erdmann J, Braenne I, Braund P, Hengstenberg C, et al. Lifelong reduction of LDL-cholesterol related to a common variant in the LDL-receptor gene decreases the risk of coronary artery disease--a Mendelian randomisation study. PLoS One. 2008;3(8):e2986. https://doi.org/10.1371/journal.pone.0002986.

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  19. Mach F, Baigent C, Catapano AL, Koskinas KC, Casula M, Badimon L, et al. 2019 ESC/EAS guidelines for the management of dyslipidaemias: lipid modification to reduce cardiovascular risk: the task force for the management of dyslipidaemias of the European Society of Cardiology (ESC) and European Atherosclerosis Society (EAS). Eur Heart J. 2019;41(1):111–88. https://doi.org/10.1093/eurheartj/ehz455.

    Article  Google Scholar 

  20. Mega JL, Stitziel NO, Smith JG, Chasman DI, Caulfield M, 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 (London, England). 2015;385(9984):2264–71. https://doi.org/10.1016/s0140-6736(14)61730-x.

    Article  CAS  Google Scholar 

  21. Yusuf S, Hawken S, Ôunpuu S, Dans T, Avezum A, Lanas F, et al. Effect of potentially modifiable risk factors associated with myocardial infarction in 52 countries (the INTERHEART study): case-control study. Lancet. 2004;364(9438):937–52. https://doi.org/10.1016/S0140-6736(04)17018-9.

    Article  PubMed  Google Scholar 

  22. Damask A, Steg PG, Schwartz GG, Szarek M, Hagström E, Badimon L, et al. Patients with high genome-wide polygenic risk scores for coronary artery disease may receive greater clinical benefit from alirocumab treatment in the ODYSSEY OUTCOMES trial. Circulation. 2020;141(8):624–36. https://doi.org/10.1161/circulationaha.119.044434.

    Article  PubMed  Google Scholar 

  23. Marston NA, Kamanu FK, Nordio F, Gurmu Y, Roselli C, Sever PS, et al. Predicting benefit from evolocumab therapy in patients with atherosclerotic disease using a genetic risk score: results from the FOURIER trial. Circulation. 2020;141(8):616–23. https://doi.org/10.1161/circulationaha.119.043805.

    Article  PubMed  Google Scholar 

  24. Sniderman Allan D, Pencina M, Thanassoulis G. ApoB. Circ Res. 2019;124(10):1425–7. https://doi.org/10.1161/CIRCRESAHA.119.315019.

    Article  PubMed  CAS  Google Scholar 

  25. Ference BA, Bhatt DL, Catapano AL, Packard CJ, Graham I, Kaptoge S, et al. Association of genetic variants related to combined exposure to lower low-density lipoproteins and lower systolic blood pressure with lifetime risk of cardiovascular disease. Jama. 2019;322:1381. https://doi.org/10.1001/jama.2019.14120.

    Article  PubMed Central  CAS  PubMed  Google Scholar 

  26. Sniderman Allan D, Thanassoulis G, Wilkins John T, Furberg Curt D, Pencina M. Sick individuals and sick populations by Geoffrey Rose: cardiovascular prevention updated. J Am Heart Assoc. 2018;7(19):e010049. https://doi.org/10.1161/JAHA.118.010049.

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  27. Aragam KG, Dobbyn A, Judy R, Chaffin M, Chaudhary K, Hindy G, et al. Limitations of contemporary guidelines for managing patients at high genetic risk of coronary artery disease. J Am Coll Cardiol. 2020;75(22):2769–80. https://doi.org/10.1016/j.jacc.2020.04.027.

    Article  PubMed  CAS  PubMed Central  Google Scholar 

  28. Erdmann J, Grosshennig A, Braund PS, König IR, Hengstenberg C, Hall AS, et al. New susceptibility locus for coronary artery disease on chromosome 3q22.3. Nat Genet. 2009;41(3):280–2. https://doi.org/10.1038/ng.307.

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  29. Samani NJ, Erdmann J, Hall AS, Hengstenberg C, Mangino M, Mayer B, et al. Genomewide association analysis of coronary artery disease. N Engl J Med. 2007;357(5):443–53. https://doi.org/10.1056/NEJMoa072366.

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  30. Schunkert H, König IR, Kathiresan S, Reilly MP, Assimes TL, Holm H, et al. Large-scale association analysis identifies 13 new susceptibility loci for coronary artery disease. Nat Genet. 2011;43(4):333–8. https://doi.org/10.1038/ng.784.

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  31. Deloukas P, Kanoni S, Willenborg C, Farrall M, Assimes TL, Thompson JR, et al. Large-scale association analysis identifies new risk loci for coronary artery disease. Nat Genet. 2013;45(1):25–33. https://doi.org/10.1038/ng.2480.

    Article  PubMed  CAS  Google Scholar 

  32. Nikpay M, Goel A, Won HH, Hall LM, Willenborg C, Kanoni S, et al. A comprehensive 1,000 Genomes-based genome-wide association meta-analysis of coronary artery disease. Nat Genet. 2015;47(10):1121–30. https://doi.org/10.1038/ng.3396.

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  33. Nelson CP, Goel A, Butterworth AS, Kanoni S, Webb TR, Marouli E, et al. Association analyses based on false discovery rate implicate new loci for coronary artery disease. Nat Genet. 2017;49(9):1385–91. https://doi.org/10.1038/ng.3913.

    Article  PubMed  CAS  Google Scholar 

  34. van der Harst P, Verweij N. Identification of 64 novel genetic loci provides an expanded view on the genetic architecture of coronary artery disease. Circ Res. 2018;122(3):433–43. https://doi.org/10.1161/CIRCRESAHA.117.312086.

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  35. Tragante V, Hemerich D, Alshabeeb M, Brænne I, Lempiäinen H, Patel Riyaz S, et al. Druggability of coronary artery disease risk loci. Circulation: Genomic and Precision Medicine. 2018;11(8):e001977. https://doi.org/10.1161/CIRCGEN.117.001977.

    Article  CAS  Google Scholar 

  36. Lempiäinen H, Brænne I, Michoel T, Tragante V, Vilne B, Webb TR, et al. Network analysis of coronary artery disease risk genes elucidates disease mechanisms and druggable targets. Sci Rep. 2018;8(1):3434. https://doi.org/10.1038/s41598-018-20721-6.

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  37. •• Hall KT, Kessler T, Buring JE, Passow D, Sesso HD, Zee RYL, et al. Genetic variation at the coronary artery disease risk locus GUCY1A3 modifies cardiovascular disease prevention effects of aspirin. Eur Heart J. 2019;40(41):3385–92. https://doi.org/10.1093/eurheartj/ehz384Findings from this study suggest that in the setting of primary prevention of cardiovascular diseases, efficacy of aspirin is influenced by a common allele in guanylate cyclase GUCY1A3. While aspirin reduced CVD risk in individuals homozygous for the GUCY1A3 risk (G) allele, it increased risk in heterozygote individuals, calling for personalized prevention.

    Article  PubMed  CAS  PubMed Central  Google Scholar 

  38. Kessler T, Wolf B, Eriksson N, Kofink D, Mahmoodi BK, Rai H, et al. Association of the coronary artery disease risk gene GUCY1A3 with ischaemic events after coronary intervention. Cardiovasc Res. 2019;115(10):1512–8. https://doi.org/10.1093/cvr/cvz015.

    Article  PubMed  CAS  Google Scholar 

  39. Khera AV, Won H-H, Peloso GM, Lawson KS, Bartz TM, Deng X, et al. Diagnostic yield and clinical utility of sequencing familial hypercholesterolemia genes in patients with severe hypercholesterolemia. J Am Coll Cardiol. 2016;67(22):2578–89. https://doi.org/10.1016/j.jacc.2016.03.520.

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  40. Stitziel NO, Stirrups KE, Masca NG, Erdmann J, Ferrario PG, König IR, et al. Coding variation in ANGPTL4, LPL, and SVEP1 and the risk of coronary disease. N Engl J Med. 2016;374(12):1134–44. https://doi.org/10.1056/NEJMoa1507652.

    Article  PubMed  CAS  Google Scholar 

  41. Abul-Husn NS, Manickam K, Jones LK, Wright EA, Hartzel DN, Gonzaga-Jauregui C, et al. Genetic identification of familial hypercholesterolemia within a single U.S. health care system. Science (New York, NY). 2016;354(6319):aaf7000. https://doi.org/10.1126/science.aaf7000.

    Article  CAS  Google Scholar 

  42. Besseling J, Huijgen R, Martin SS, Hutten BA, Kastelein JJ, Hovingh GK. Clinical phenotype in relation to the distance-to-index-patient in familial hypercholesterolemia. Atherosclerosis. 2016;246:1–6. https://doi.org/10.1016/j.atherosclerosis.2015.12.033.

    Article  PubMed  CAS  Google Scholar 

  43. Berberich AJ, Hegele RA. The role of genetic testing in dyslipidaemia. Pathology. 2019;51(2):184–92. https://doi.org/10.1016/j.pathol.2018.10.014.

    Article  PubMed  CAS  Google Scholar 

  44. Brown MS, Goldstein JL. Expression of the familial hypercholesterolemia gene in heterozygotes: mechanism for a dominant disorder in man. Science (New York, NY). 1974;185(4145):61–3. https://doi.org/10.1126/science.185.4145.61.

    Article  CAS  Google Scholar 

  45. Tolleshaug H, Hobgood KK, Brown MS, Goldstein JL. The LDL receptor locus in familial hypercholesterolemia: multiple mutations disrupt transport and processing of a membrane receptor. Cell. 1983;32(3):941–51. https://doi.org/10.1016/0092-8674(83)90079-x.

    Article  PubMed  CAS  Google Scholar 

  46. Do R, Stitziel NO, Won HH, Jørgensen AB, Duga S, Angelica Merlini P, et al. Exome sequencing identifies rare LDLR and APOA5 alleles conferring risk for myocardial infarction. Nature. 2015;518(7537):102–6. https://doi.org/10.1038/nature13917.

    Article  PubMed  CAS  Google Scholar 

  47. Clarke R, Peden JF, Hopewell JC, Kyriakou T, Goel A, Heath SC, et al. Genetic variants associated with Lp(a) lipoprotein level and coronary disease. N Engl J Med. 2009;361(26):2518–28. https://doi.org/10.1056/NEJMoa0902604.

    Article  PubMed  CAS  Google Scholar 

  48. Abifadel M, Varret M, Rabès JP, Allard D, Ouguerram K, Devillers M, et al. Mutations in PCSK9 cause autosomal dominant hypercholesterolemia. Nat Genet. 2003;34(2):154–6. https://doi.org/10.1038/ng1161.

    Article  PubMed  CAS  Google Scholar 

  49. Beheshti SO, Madsen CM, Varbo A, Nordestgaard BG. Worldwide prevalence of familial hypercholesterolemia. J Am Coll Cardiol. 2020;75(20):2553–66. https://doi.org/10.1016/j.jacc.2020.03.057.

    Article  PubMed  CAS  Google Scholar 

  50. Kastelein JJP, Reeskamp LF, Hovingh GK. Familial hypercholesterolemia. J Am Coll Cardiol. 2020;75(20):2567–9. https://doi.org/10.1016/j.jacc.2020.03.058.

    Article  PubMed  Google Scholar 

  51. Brænne I, Kleinecke M, Reiz B, Graf E, Strom T, Wieland T, et al. Systematic analysis of variants related to familial hypercholesterolemia in families with premature myocardial infarction. Eur J Hum Genet. 2016;24(2):191–7. https://doi.org/10.1038/ejhg.2015.100.

    Article  PubMed  CAS  Google Scholar 

  52. Khera AV, Kathiresan S. Is coronary atherosclerosis one disease or many? Setting realistic expectations for precision medicine. Circulation. 2017;135(11):1005–7. https://doi.org/10.1161/circulationaha.116.026479.

    Article  PubMed  PubMed Central  Google Scholar 

  53. Broeckel U, Hengstenberg C, Mayer B, Holmer S, Martin LJ, Comuzzie AG, et al. A comprehensive linkage analysis for myocardial infarction and its related risk factors. Nat Genet. 2002;30(2):210–4. https://doi.org/10.1038/ng827.

    Article  PubMed  CAS  Google Scholar 

  54. Ripatti S, Tikkanen E, Orho-Melander M, Havulinna AS, Silander K, Sharma A, et al. A multilocus genetic risk score for coronary heart disease: case-control and prospective cohort analyses. Lancet (London, England). 2010;376(9750):1393–400. https://doi.org/10.1016/s0140-6736(10)61267-6.

    Article  Google Scholar 

  55. •• Khera AV, Chaffin M, Aragam KG, Haas ME, Roselli C, Choi SH, et al. Genome-wide polygenic scores for common diseases identify individuals with risk equivalent to monogenic mutations. Nat Genet. 2018;50(9):1219–24. https://doi.org/10.1038/s41588-018-0183-z Findings from this study show that polygenic risk scores (PRS) identify approximately 10–20 times as many patients at comparable risk for CAD as rare monogenic mutations.

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  56. Evans DM, Visscher PM, Wray NR. Harnessing the information contained within genome-wide association studies to improve individual prediction of complex disease risk. Hum Mol Genet. 2009;18(18):3525–31. https://doi.org/10.1093/hmg/ddp295.

    Article  PubMed  CAS  Google Scholar 

  57. Stitziel NO, Won HH, Morrison AC, Peloso GM, Do R, Lange LA, et al. Inactivating mutations in NPC1L1 and protection from coronary heart disease. N Engl J Med. 2014;371(22):2072–82. https://doi.org/10.1056/NEJMoa1405386.

    Article  PubMed  CAS  Google Scholar 

  58. Cohen JC, Boerwinkle E, Mosley TH, Hobbs HH. Sequence variations in PCSK9, low LDL, and protection against coronary heart disease. N Engl J Med. 2006;354(12):1264–72. https://doi.org/10.1056/NEJMoa054013.

    Article  PubMed  CAS  Google Scholar 

  59. Crosby J, Peloso GM, Auer PL, Crosslin DR, Stitziel NO, Lange LA, et al. Loss-of-function mutations in APOC3, triglycerides, and coronary disease. N Engl J Med. 2014;371(1):22–31. https://doi.org/10.1056/NEJMoa1307095.

    Article  PubMed  CAS  Google Scholar 

  60. Musunuru K, Kathiresan S. Cardiovascular endocrinology: is ANGPTL3 the next PCSK9? Nat Rev Endocrinol. 2017;13(9):503–4. https://doi.org/10.1038/nrendo.2017.88.

    Article  PubMed  CAS  Google Scholar 

  61. Sabatine MS, Giugliano RP, Keech AC, Honarpour N, Wiviott SD, Murphy SA, et al. Evolocumab and clinical outcomes in patients with cardiovascular disease. N Engl J Med. 2017;376(18):1713–22. https://doi.org/10.1056/NEJMoa1615664.

    Article  PubMed  CAS  Google Scholar 

  62. Schwartz GG, Steg PG, Szarek M, Bhatt DL, Bittner VA, Diaz R, et al. Alirocumab and cardiovascular outcomes after acute coronary syndrome. N Engl J Med. 2018;379(22):2097–107. https://doi.org/10.1056/NEJMoa1801174.

    Article  PubMed  CAS  Google Scholar 

  63. Cannon CP, Blazing MA, Giugliano RP, McCagg A, White JA, Theroux P, et al. Ezetimibe added to statin therapy after acute coronary syndromes. N Engl J Med. 2015;372(25):2387–97. https://doi.org/10.1056/NEJMoa1410489.

    Article  PubMed  CAS  Google Scholar 

  64. Dewey FE, Gusarova V, O’Dushlaine C, Gottesman O, Trejos J, Hunt C, et al. Inactivating variants in ANGPTL4 and risk of coronary artery disease. N Engl J Med. 2016;374(12):1123–33. https://doi.org/10.1056/NEJMoa1510926.

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  65. Graham MJ, Lee RG, Brandt TA, Tai L-J, Fu W, Peralta R, et al. Cardiovascular and metabolic effects of ANGPTL3 antisense oligonucleotides. N Engl J Med. 2017;377(3):222–32. https://doi.org/10.1056/NEJMoa1701329.

    Article  PubMed  CAS  Google Scholar 

  66. Gaudet D, Alexander VJ, Baker BF, Brisson D, Tremblay K, Singleton W, et al. Antisense inhibition of apolipoprotein C-III in patients with hypertriglyceridemia. N Engl J Med. 2015;373(5):438–47. https://doi.org/10.1056/NEJMoa1400283.

    Article  PubMed  CAS  Google Scholar 

  67. •• Khera AV, 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. 2016;375(24):2349–58. https://doi.org/10.1056/NEJMoa1605086Findings from this study demonstrate that among participants at high genetic risk for CAD, a healthy lifestyle (defined as no current smoking, no obesity, regular physical activity, and a healthy diet) can offset about 50% of the inherited CAD risk.

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  68. Collins R, Reith C, Emberson J, Armitage J, Baigent C, Blackwell L, et al. Interpretation of the evidence for the efficacy and safety of statin therapy. Lancet (London, England). 2016;388(10059):2532–61. https://doi.org/10.1016/s0140-6736(16)31357-5.

    Article  CAS  Google Scholar 

  69. Sever PS, Dahlöf B, Poulter NR, Wedel H, Beevers G, Caulfield M, et al. Prevention of coronary and stroke events with atorvastatin in hypertensive patients who have average or lower-than-average cholesterol concentrations, in the Anglo-Scandinavian Cardiac Outcomes Trial--Lipid Lowering Arm (ASCOT-LLA): a multicentre randomised controlled trial. Lancet (London, England). 2003;361(9364):1149–58. https://doi.org/10.1016/s0140-6736(03)12948-0.

    Article  CAS  Google Scholar 

  70. Ridker PM, Danielson E, Fonseca FA, Genest J, Gotto AM Jr, Kastelein JJ, et al. Rosuvastatin to prevent vascular events in men and women with elevated C-reactive protein. N Engl J Med. 2008;359(21):2195–207. https://doi.org/10.1056/NEJMoa0807646.

    Article  PubMed  CAS  Google Scholar 

  71. Natarajan P, Young R, Stitziel Nathan O, 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. 2017;135(22):2091–101. https://doi.org/10.1161/CIRCULATIONAHA.116.024436.

    Article  PubMed  PubMed Central  Google Scholar 

  72. Gola D, Erdmann J, Müller-Myhsok B, Schunkert H, König IR. Polygenic risk scores outperform machine learning methods in predicting coronary artery disease status. Genet Epidemiol. 2020;44(2):125–38. https://doi.org/10.1002/gepi.22279.

    Article  PubMed  Google Scholar 

Download references

Funding

Open Access funding provided by Projekt DEAL. The authors acknowledge the support of the Bavarian State Ministry of Health and Care within the framework of DigiMed Bayern (grant No: DMB-1805-0001), the German Federal Ministry of Education and Research (BMBF) within the framework of ERA-NET on Cardiovascular Disease (Druggable-MI-genes: 01KL1802), within the scheme of target validation (BlockCAD: 16GW0198K), within the framework of the e:Med research and funding concept (AbCD-Net: 01ZX1706C), and within the British Heart Foundation (BHF)/German Centre of Cardiovascular Research (DZHK) collaboration. Further support was granted by the German Research Foundation (DFG) as part of the Sonderforschungsbereich SFB 1123 (B02) and the Sonderforschungsbereich SFB TRR 267 (B05) as well as the Corona-Foundation (Junior Research Group Translational Cardiovascular Genomics).

Author information

Authors and Affiliations

Authors

Contributions

K.L. did the literature search and drafted the manuscript. T.K. and H.S. critically revised and edited the manuscript. All authors approved the final version of the manuscript.

Corresponding author

Correspondence to Heribert Schunkert.

Ethics declarations

Conflict of Interest

Katharina Lechner has received speaker’s honoraria from Goerlich Pharma, Novo Nordisk, Sanofi, and Amgen.

Thorsten Kessler declares no conflict of interest.

Heribert Schunkert has received an institutional grant and honorarium from AstraZeneca; a travel grant from Vifor Pharma; and honoraria from MSD Sharpe & Dohme, Sanofi-Aventis, Brahms, Boehringer-Ingelheim, Novartis, Amgen, Synlab, Daiichi-Sankyo, Servier, Bristol-Myers Squibb, Medtronic, Pfizer, and Bayer Vital.

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.

Additional information

Publisher’s Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Topical Collection on Lipid Abnormalities and Cardiovascular Prevention

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Lechner, K., Kessler, T. & Schunkert, H. Should We Use Genetic Scores in the Determination of Treatment Strategies to Control Dyslipidemias?. Curr Cardiol Rep 22, 146 (2020). https://doi.org/10.1007/s11886-020-01408-9

Download citation

  • Published:

  • DOI: https://doi.org/10.1007/s11886-020-01408-9

Keywords

Navigation