Research Article

Genetic risk factors for drug-induced long QT syndrome: findings from a large real-world case–control study

https://orcid.org/0000-0003-4307-7158

Department of Clinical Pharmacy, University of Michigan College of Pharmacy, Ann Arbor, MI, USA

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Alessandra M Campos-Staffico

https://orcid.org/0000-0003-4172-7524

Department of Clinical Pharmacy, University of Michigan College of Pharmacy, Ann Arbor, MI, USA

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Choudhary Anwar A Chahal

https://orcid.org/0000-0001-7825-8827

Center for Inherited Cardiovascular Diseases, WellSpan Health, Lancaster, PA, USA

Department of Cardiovascular Medicine, Mayo Clinic, Rochester, MN, USA

Department of Cardiology, Barts Heart Centre, London, UK

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Isabella Volkers

Department of Clinical Pharmacy, University of Michigan College of Pharmacy, Ann Arbor, MI, USA

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Juliet P Jacoby

Department of Clinical Pharmacy, University of Michigan College of Pharmacy, Ann Arbor, MI, USA

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Omer Berenfeld

https://orcid.org/0000-0001-9532-7871

Center for Arrhythmia Research, Departments of Internal Medicine – Cardiology, Biomedical Engineering, & Applied Physics, University of Michigan, Ann Arbor, MI, USA

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Jasmine A Luzum

*Author for correspondence: Tel.: +1 734 615 4851;

E-mail Address: jluzum@med.umich.edu

https://orcid.org/0000-0003-3639-717X

Department of Clinical Pharmacy, University of Michigan College of Pharmacy, Ann Arbor, MI, USA

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Published Online:20 Mar 2024https://doi.org/10.2217/pgs-2023-0229

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Abstract

Aim: Drug-induced long QT syndrome (diLQTS), an adverse effect of many drugs, can lead to sudden cardiac death. Candidate genetic variants in cardiac ion channels have been associated with diLQTS, but several limitations of previous studies hamper clinical utility. Materials & methods: Thus, the purpose of this study was to assess the associations of KCNE1-D85N, KCNE2-I57T and SCN5A-G615E with diLQTS in a large observational case–control study (6,083 self-reported white patients treated with 27 different high-risk QT-prolonging medications; 12.0% with diLQTS). Results:KCNE1-D85N significantly associated with diLQTS (adjusted odds ratio: 2.24 [95% CI: 1.35–3.58]; p = 0.001). Given low minor allele frequencies, the study had insufficient power to analyze KCNE2-I57T and SCN5A-G615E. Conclusion:KCNE1-D85N is a risk factor for diLQTS that should be considered in future clinical practice guidelines.

Plain language summary

Some medications can lead to a condition called drug-induced long QT syndrome (diLQTS), which can be a serious abnormal heart rhythm in some patients. In our research, we explored three specific changes in DNA related to the electrical function of the heart (KCNE1-D85N, KCNE2-I57T, SCN5A-G615E) and their link to diLQTS. Our study revealed a connection between KCNE1-D85N and diLQTS. This study emphasized the importance of including KCNE1-D85N in the medical guidelines to help identify patients at risk of diLQTS. We were unable to identify the connection of KCNE2-I57T and SCN5A-G615E with diLQTS, due to a low number of carriers in the study.

Keywords:

References

1. Roden DM. Drug-induced prolongation of the QT interval. N. Engl. J. Med. 350(10), 1013–1022 (2004).
Medline CASGoogle Scholar
2. Drew BJ, Ackerman MJ, Funk M et al. Prevention of torsade de pointes in hospital settings: a scientific statement from the American Heart Association and the American College of Cardiology Foundation. J. Am. Coll. Cardiol. 55(9), 934–947 (2010).
MedlineGoogle Scholar
3. Desai M, Li L, Desta Z, Malik M, Flockhart D. Variability of heart rate correction methods for the QT interval. Br. J. Clin. Pharmacol. 55(6), 511–517 (2003).
Medline CASGoogle Scholar
4. Luo S, Michler K, Johnston P, Macfarlane PW. A comparison of commonly used QT correction formulae: the effect of heart rate on the QTc of normal ECGs. J. Electrocardiol. (37 Suppl.), 81–90 (2004).
MedlineGoogle Scholar
5. Patel C, Yan GX, Antzelevitch C. Short QT syndrome: from bench to bedside. Circ. Arrhythm Electrophysiol. 3(4), 401–408 (2010).
MedlineGoogle Scholar
6. Goldenberg I, Horr S, Moss AJ et al. Risk for life-threatening cardiac events in patients with genotype-confirmed long-QT syndrome and normal-range corrected QT intervals. J. Am. Coll. Cardiol. 57(1), 51–59 (2011).
MedlineGoogle Scholar
7. Adler A, Novelli V, Amin AS et al. An international, multicentered, evidence-based reappraisal of genes reported to cause congenital long QT syndrome. Circulation 141(6), 418–428 (2020).
Medline CASGoogle Scholar
8. Woosley RL, Heise CW, Gallo T, Woosley D, Romero KA. QTdrugs List,AZCERT, Inc. 1457 E. Desert Garden Dr., Tucson, AZ 85718 (2022). www.CredibleMeds.org
Google Scholar
9. Roden DM. A current understanding of drug-induced QT prolongation and its implications for anticancer therapy. Cardiovasc. Res. 115(5), 895–903 (2019).
Medline CASGoogle Scholar
10. Del Camino D, Holmgren M, Liu Y, Yellen G. Blocker protection in the pore of a voltage-gated K+ channel and its structural implications. Nature 403(6767), 321–325 (2000).
MedlineGoogle Scholar
11. Mitcheson JS, Chen J, Lin M, Culberson C, Sanguinetti MC. A structural basis for drug-induced long QT syndrome. Proc. Natl Acad. Sci. USA 97(22), 12329–12333 (2000).
Medline CASGoogle Scholar
12. Wang W, Mackinnon R. Cryo-EM structure of the open human ether-à-go-go-related K(+) channel hERG. Cell 169(3), 422–430; e410 (2017).
Medline CASGoogle Scholar
13. Pickham D, Helfenbein E, Shinn JA et al. High prevalence of corrected QT interval prolongation in acutely ill patients is associated with mortality: results of the QT in Practice (QTIP) Study. Crit. Care Med. 40(2), 394–399 (2012).
MedlineGoogle Scholar
14. Khatib R, Sabir FRN, Omari C, Pepper C, Tayebjee MH. Managing drug-induced QT prolongation in clinical practice. Postgrad Med. J. 97(1149), 452–458 (2021).
MedlineGoogle Scholar
15. Strauss DG, Vicente J, Johannesen L et al. Common genetic variant risk score is associated with drug-induced QT prolongation and torsade de pointes risk: a pilot study. Circulation 135(14), 1300–1310 (2017).
MedlineGoogle Scholar
16. Niemeijer MN, Van Den Berg ME, Eijgelsheim M, Rijnbeek PR, Stricker BH. Pharmacogenetics of drug-induced QT interval prolongation: an update. Drug Saf. 38(10), 855–867 (2015).
Medline CASGoogle Scholar
17. Lopez-Medina AI, Chahal CaA, Luzum JA. The genetics of drug-induced QT prolongation: evaluating the evidence for pharmacodynamic variants. Pharmacogenomics 23(9), 543–557 (2022).
CASGoogle Scholar
18. Kannankeril PJ, Roden DM, Norris KJ, Whalen SP, George AL Jr, Murray KT. Genetic susceptibility to acquired long QT syndrome: pharmacologic challenge in first-degree relatives. Heart Rhythm 2(2), 134–140 (2005).
MedlineGoogle Scholar
19. Itoh H, Crotti L, Aiba T et al. The genetics underlying acquired long QT syndrome: impact for genetic screening. Eur. Heart J. 37(18), 1456–1464 (2016).
MedlineGoogle Scholar
20. Magavern EF, Kaski JC, Turner RM et al. The role of pharmacogenomics in contemporary cardiovascular therapy: a position statement from the European Society of Cardiology Working Group on Cardiovascular Pharmacotherapy. Eur. Heart J. Cardiovasc. Pharmacother. 8(1), 85–99 (2022).
MedlineGoogle Scholar
21. Zawistowski M, Fritsche LG, Pandit A et al. The Michigan Genomics Initiative: a biobank linking genotypes and electronic clinical records in Michigan Medicine patients. Cell Genom. 3(2), 100257 (2023).
Medline CASGoogle Scholar
22. Office of Research. University of Michigan. https://research.medicine.umich.edu/our-units/data-office-clinical-translational-research/self-serve-data-tools (Accessed 28 Jun 2021).
Google Scholar
23. Hanauer DA, Mei Q, Law J, Khanna R, Zheng K. Supporting information retrieval from electronic health records: a report of University of Michigan's nine-year experience in developing and using the Electronic Medical Record Search Engine (EMERSE). J. Biomed. Inform 55, 290–300 (2015).
MedlineGoogle Scholar
24. Wasey JO. Package “icd” version 3.3. Comorbidities from ICD-9 and ICD-10 codes, manipulation and validation (2018). www.rdocumentation.org/packages/icd/versions/3.3 (Accessed 8 February 2023).
Google Scholar
25. Woosley RL, Black K, Heise CW, Romero K. CredibleMeds.org: what does it offer? Trends Cardiovasc. Med. 28(2), 94–99 (2018).
MedlineGoogle Scholar
26. Micromedex^® (electronic version). Merative, Ann Arbor, Michigan, USA. www.micromedexsolutions.com/ (Accessed 22 August 2022).
Google Scholar
27. Sorajja D, Bhakta MD, Scott LR, Altemose GT, Srivathsan K. Utilization of electrocardiographic P-wave duration for AV interval optimization in dual-chamber pacemakers. Indian Pacing Electrophysiol. J. 10(9), 383–392 (2010).
MedlineGoogle Scholar
28. Chiladakis J, Kalogeropoulos A, Zagkli F, Koutsogiannis N, Chouchoulis K, Alexopoulos D. Predicting torsade de pointes in acquired long QT syndrome: optimal identification of critical QT interval prolongation. Cardiology 122(1), 3–11 (2012).
MedlineGoogle Scholar
29. Fridericia LS. Die Systolendauer im Elektrokardiogramm bei normalen Menschen und bei Herzkranken. Acta Med. Scandin. 53(1), 469–486 (1920).
Google Scholar
30. Sagie A, Larson MG, Goldberg RJ, Bengtson JR, Levy D. An improved method for adjusting the QT interval for heart rate (the Framingham Heart Study). Am. J. Cardiol. 70(7), 797–801 (1992).
Medline CASGoogle Scholar
31. Lester RM, Paglialunga S, Johnson IA. QT assessment in early drug development: the long and the short of it. Int. J. Mol. Sci. 20(6), 1324 (2019).
Medline CASGoogle Scholar
32. Zajac GJM, Fritsche LG, Weinstock JS et al. Estimation of DNA contamination and its sources in genotyped samples. Genet. Epidemiol. 43(8), 980–995 (2019).
MedlineGoogle Scholar
33. Michigan Imputation Services. https://imputationserver.sph.umich.edu/index.html#! (Accessed 28 June 2022).
Google Scholar
34. Mccarthy S, Das S, Kretzschmar W et al. A reference panel of 64,976 haplotypes for genotype imputation. Nat. Genet. 48(10), 1279–1283 (2016).
Medline CASGoogle Scholar
35. Manichaikul A, Mychaleckyj JC, Rich SS, Daly K, Sale M, Chen WM. Robust relationship inference in genome-wide association studies. Bioinformatics 26(22), 2867–2873 (2010).
Medline CASGoogle Scholar
36. D'agostino RB Jr. Propensity score methods for bias reduction in the comparison of a treatment to a non-randomized control group. Stat. Med. 17(19), 2265–2281 (1998).
MedlineGoogle Scholar
37. Tisdale JE, Jaynes HA, Kingery JR et al. Development and validation of a risk score to predict QT interval prolongation in hospitalized patients. Circ. Cardiovasc Qual Outcomes 6(4), 479–487 (2013).
MedlineGoogle Scholar
38. Sesti F, Abbott GW, Wei J et al. A common polymorphism associated with antibiotic-induced cardiac arrhythmia. Proc. Natl Acad. Sci. USA 97(19), 10613–10618 (2000).
Medline CASGoogle Scholar
39. Yang P, Kanki H, Drolet B et al. Allelic variants in long-QT disease genes in patients with drug-associated torsades de pointes. Circulation 105(16), 1943–1948 (2002).
Medline CASGoogle Scholar
40. Itoh H, Sakaguchi T, Ding WG et al. Latent genetic backgrounds and molecular pathogenesis in drug-induced long-QT syndrome. Circ. Arrhythm Electrophysiol. 2(5), 511–523 (2009).
Medline CASGoogle Scholar
41. Westenskow P, Splawski I, Timothy KW, Keating MT, Sanguinetti MC. Compound mutations: a common cause of severe long-QT syndrome. Circulation 109(15), 1834–1841 (2004).
MedlineGoogle Scholar
42. Sakata S, Kurata Y, Li P et al. Instability of KCNE1-D85N that causes long QT syndrome: stabilization by verapamil. Pacing Clin. Electrophysiol. 37(7), 853–863 (2014).
MedlineGoogle Scholar
43. Roden DM. Taking the “idio” out of “idiosyncratic”: predicting torsades de pointes. Pacing Clin. Electrophysiol. 21(5), 1029–1034 (1998).
Medline CASGoogle Scholar
44. Paulussen AD, Gilissen RA, Armstrong M et al. Genetic variations of KCNQ1, KCNH2, SCN5A, KCNE1, and KCNE2 in drug-induced long QT syndrome patients. J. Mol. Med. (Berl) 82(3), 182–188 (2004).
Medline CASGoogle Scholar
45. Kaab S, Crawford DC, Sinner MF et al. A large candidate gene survey identifies the KCNE1-D85N polymorphism as a possible modulator of drug-induced torsades de pointes. Circ. Cardiovasc Genet 5(1), 91–99 (2012).
MedlineGoogle Scholar
46. Martinez-Matilla M, Blanco-Verea A, Santori M et al. Genetic susceptibility in pharmacodynamic and pharmacokinetic pathways underlying drug-induced arrhythmia and sudden unexplained deaths. Forensic Sci. Int. Genet 42, 203–212 (2019).
Medline CASGoogle Scholar
47. Weeke P, Mosley JD, Hanna D et al. Exome sequencing implicates an increased burden of rare potassium channel variants in the risk of drug-induced long QT interval syndrome. J. Am. Coll. Cardiol. 63(14), 1430–1437 (2014).
Medline CASGoogle Scholar
48. Corponi F, Fabbri C, Boriani G et al. Corrected QT Interval Prolongation in Psychopharmacological Treatment and Its Modulation by Genetic Variation. Neuropsychobiology 77(2), 67–72 (2019).
MedlineGoogle Scholar
49. Spellmann I, Reinhard MA, Veverka D et al. QTc prolongation in short-term treatment of schizophrenia patients: effects of different antipsychotics and genetic factors. Eur. Arch. Psychiatry Clin. Neurosci. 268(4), 383–390 (2018).
MedlineGoogle Scholar
50. Behr ER, Ritchie MD, Tanaka T et al. Genome wide analysis of drug-induced torsades de pointes: lack of common variants with large effect sizes. PLOS ONE 8(11), e78511 (2013).
Medline CASGoogle Scholar
51. Avery CL, Sitlani CM, Arking DE et al. Drug-gene interactions and the search for missing heritability: a cross-sectional pharmacogenomics study of the QT interval. Pharmacogenomics J. 14(1), 6–13 (2014).
Medline CASGoogle Scholar
52. Chevalier P, Rodriguez C, Bontemps L et al. Non-invasive testing of acquired long QT syndrome: evidence for multiple arrhythmogenic substrates. Cardiovasc. Res. 50(2), 386–398 (2001).
Medline CASGoogle Scholar
53. Makita N, Horie M, Nakamura T et al. Drug-induced long-QT syndrome associated with a subclinical SCN5A mutation. Circulation 106(10), 1269–1274 (2002).
MedlineGoogle Scholar
54. Aberg K, Adkins DE, Liu Y et al. Genome-wide association study of antipsychotic-induced QTc interval prolongation. Pharmacogenomics J. 12(2), 165–172 (2012).
Medline CASGoogle Scholar
55. Zerdazi EH, Vorspan F, Marees AT et al. QT length during methadone maintenance treatment: gene x dose interaction. Fundam. Clin. Pharmacol. 33(1), 96–106 (2019).
Medline CASGoogle Scholar
56. Roberts JD, Krahn AD, Ackerman MJ et al. Loss-of-Function KCNE2 Variants: True Monogenic Culprits of Long-QT Syndrome or Proarrhythmic Variants Requiring Secondary Provocation? Circ. Arrhythm Electrophysiol. 10(8), e005282 (2017).
Medline CASGoogle Scholar
57. Schmitt N, Grunnet M, Olesen SP. Cardiac potassium channel subtypes: new roles in repolarization and arrhythmia. Physiol. Rev. 94(2), 609–653 (2014).
Medline CASGoogle Scholar
58. Itoh H, Crotti L, Aiba T et al. The genetics underlying acquired long QT syndrome: impact for genetic screening. Eur. Heart J. 37(18), 1456–1464 (2016).
MedlineGoogle Scholar
59. Mccrossan ZA, Roepke TK, Lewis A, Panaghie G, Abbott GW. Regulation of the Kv2.1 potassium channel by MinK and MiRP1. J. Membr. Biol. 228(1), 1–14 (2009).
Medline CASGoogle Scholar
60. Abbott GW, Sesti F, Splawski I et al. MiRP1 forms IKr potassium channels with HERG and is associated with cardiac arrhythmia. Cell 97(2), 175–187 (1999).
Medline CASGoogle Scholar
61. Tinel N, Diochot S, Borsotto M, Lazdunski M, Barhanin J. KCNE2 confers background current characteristics to the cardiac KCNQ1 potassium channel. EMBO J. 19(23), 6326–6330 (2000).
Medline CASGoogle Scholar
62. Sanchez O, Campuzano O, Fernández-Falgueras A et al. Natural and undetermined sudden death: value of post-mortem genetic investigation. PLOS ONE 11(12), e0167358 (2016).
MedlineGoogle Scholar
63. Aronsen JM, Swift F, Sejersted OM. Cardiac sodium transport and excitation-contraction coupling. J. Mol. Cell. Cardiol. 61, 11–19 (2013).
Medline CASGoogle Scholar
64. Albert CM, Nam EG, Rimm EB et al. Cardiac sodium channel gene variants and sudden cardiac death in women. Circulation 117(1), 16–23 (2008).
Medline CASGoogle Scholar
65. Ramirez AH, Shaffer CM, Delaney JT et al. Novel rare variants in congenital cardiac arrhythmia genes are frequent in drug-induced torsades de pointes. Pharmacogenomics J. 13(4), 325–329 (2013).
Medline CASGoogle Scholar
66. Napolitano C, Schwartz PJ, Brown AM et al. Evidence for a cardiac ion channel mutation underlying drug-induced QT prolongation and life-threatening arrhythmias. J. Cardiovasc Electrophysiol. 11(6), 691–696 (2000).
Medline CASGoogle Scholar
67. Splawski I, Timothy KW, Tateyama M et al. Variant of SCN5A sodium channel implicated in risk of cardiac arrhythmia. Science 297(5585), 1333–1336 (2002).
Medline CASGoogle Scholar
68. Watanabe J, Fukui N, Suzuki Y et al. Effect of GWAS-identified genetic variants on maximum QT interval in patients with schizophrenia receiving antipsychotic agents: a 24-hour Holter ECG study. J. Clin. Psychopharmacol. 37(4), 452–455 (2017).
Medline CASGoogle Scholar
69. Ramsey LB, Bruun GH, Yang W et al. Rare versus common variants in pharmacogenetics: SLCO1B1 variation and methotrexate disposition. Genome Res. 22(1), 1–8 (2012).
Medline CASGoogle Scholar
70. Luzum JA, Petry N, Taylor AK, Van Driest SL, Dunnenberger HM, Cavallari LH. Moving pharmacogenetics into practice: it's all about the evidence! Clin. Pharmacol. Ther. 110(3), 649–661 (2021).
MedlineGoogle Scholar
71. Swen JJ, Van Der Wouden CH, Manson LE et al. A 12-gene pharmacogenetic panel to prevent adverse drug reactions: an open-label, multicentre, controlled, cluster-randomised crossover implementation study. Lancet 401(10374), 347–356 (2023).
Medline CASGoogle Scholar
72. Dahlberg P, Diamant UB, Gilljam T, Rydberg A, Bergfeldt L. QT correction using Bazett's formula remains preferable in long QT syndrome type 1 and 2. Ann. Noninv. Electrocardiol. 26(1), e12804 (2021).
MedlineGoogle Scholar
73. Vandenberk B, Vandael E, Robyns T et al. Which QT correction formulae to use for QT monitoring? J. Am. Heart Assoc. 5(6), e004252 (2016).
Google Scholar

Vol. 25, No. 3

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Received 7 December 2023

Accepted 23 February 2024

Published online 20 March 2024

Published in print February 2024

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Supplementary data

To view the supplementary data that accompany this paper please visit the journal website at: www.futuremedicine.com/doi/suppl/10.2217/pgs-2023-0229

Author contributions

AI Lopez-Medina: conception and design of the work, data analysis, interpretation of data for the work and drafting the work. AM Campos-Staffico: drafting the work, interpretation of data for the work and revising it critically for important intellectual content. CA A Chahal: interpretation of data for the work and revising it critically for important intellectual content. I Volkers: acquisition of data for the work and revising it critically for important intellectual content. JP Jacoby: acquisition of data for the work and revising it critically for important intellectual content. O Berenfeld: interpretation of data for the work and revising it critically for important intellectual content revising it critically for important intellectual content. JA Luzum: conception and design of the work, interpretation of data, drafting the work and revising it critically for important intellectual content.

Financial disclosure

This research was funded by the following sources: T32 TR004371/NIH CTSA/United States; K08 HL146990/HL/NHLBI NIH HHS/United States; F32 HL162231/NIHLBI/United States; W81XWH-18-2-0038/Department of Defense Grant/United States; R21-HL153694 NIH/United States; R21-EB032661 NIH/United States; and R01-HL156961 NIH/United States.

Competing interests disclosure

J. Luzum is a consultant for Ariel Precision Medicine. O. Berenfeld is a co-founder of Cor-Dx LLC. The authors have no other competing interests or relevant affiliations with any organization or entity with the subject matter or materials discussed in the manuscript apart from those disclosed.

Writing disclosure

No writing assistance was utilized in the production of this manuscript.

Ethical conduct of research

The authors state that they have obtained appropriate institutional review board approval with a waiver of informed consent and have followed the principles outlined in the Declaration of Helsinki for all human investigations.

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