title: Impact of β(1)- and β(2)-adrenergic receptor gene single nucleotide polymorphisms on heart rate response to metoprolol prior to coronary computed tomographic angiography.
abstract: A slow, steady heart rate (HR) is necessary for optimal image quality during coronary computed tomographic angiography. Beta blockers are often used, but the goal HR is not achieved in some patients. The aim of this study was to examine the influence of single-nucleotide polymorphisms (SNPs) of the β(1) (codons 49 and 389) and β(2) (codons 16, 27, and 164) adrenergic receptor (AR) genes on HR response to metoprolol in 200 adults (mean age 56 ± 11 years) referred for coronary computed tomographic angiography (using a 64-slice scanner). Oral and intravenous (IV) metoprolol was given to achieve a goal HR of <60 beats/min. Overall, 37 patients (18.5%) did not reach the goal HR despite the administration of oral (181 ± 116 mg) and IV (4.2 ± 9.4 mg) metoprolol. Patients with the β(1)-AR Ser49Gly or Gly49Gly genotype (n = 49) more often failed to reach an optimal HR compared to those with the Ser49Ser genotype (n = 151) (29% vs 15%, p = 0.04), despite receiving higher doses of oral (210 ± 115 vs 172 ± 115 mg, p = 0.048) and IV (7 ± 13 vs 3 ± 8 mg, p = 0.02) metoprolol. Similarly, patients with the β(1)-AR Gly389Gly genotype (n = 11) more often failed to reach an optimal HR compared to those with the Arg389Arg and Arg389Gly genotypes (n = 189) (45% vs 17%, p = 0.02), despite receiving higher doses of IV (13 ± 15 vs 4 ± 9 mg, p = 0.002) but not oral (162 ± 105 vs 182 ± 117 mg, p = 0.50) metoprolol. Multivariate analysis identified β(1)-AR SNPs at codons 49 and 389 and β(2)-AR SNP at codon 27 as independent predictors of suboptimal HR response. In conclusion, these data indicate that the selected SNPs of β(1)-AR and β(2)-AR genes influence HR response to metoprolol in patients who undergo coronary computed tomographic angiography.
authors.affiliation: Department of Cardiology, Geisinger Medical Center, Danville, PA, USA.
authors.forename: Vlad
authors.initials: V
authors.lastname: Cotarlan
authors.forename: Alessandra
authors.initials: A
authors.lastname: Brofferio
authors.forename: Glenn S
authors.initials: GS
authors.lastname: Gerhard
authors.forename: Xin
authors.initials: X
authors.lastname: Chu
authors.forename: Jamshid
authors.initials: J
authors.lastname: Shirani
mesh_terms: D000319:Adrenergic beta-Antagonists / Q000008:administration & dosage; D000328:Adult; D000368:Aged; D000369:Aged, 80 and over; D017023:Coronary Angiography / Q000379:methods*; D003324:Coronary Artery Disease / Q000000981:diagnostic imaging / Q000188:drug therapy / Q000235:genetics*; D004247:DNA / Q000235:genetics; D005260:Female; D005838:Genotype; D006339:Heart Rate / Q000235:genetics*; D006801:Humans; D008297:Male; D008790:Metoprolol / Q000008:administration & dosage*; D008875:Middle Aged; D020641:Polymorphism, Single Nucleotide; D011446:Prospective Studies; D018342:Receptors, Adrenergic, beta-1 / Q000235:genetics* / Q000378:metabolism; D018343:Receptors, Adrenergic, beta-2 / Q000235:genetics* / Q000378:metabolism; D014057:Tomography, X-Ray Computed; D055815:Young Adult
chemical_list: D000319:Adrenergic beta-Antagonists; D018342:Receptors, Adrenergic, beta-1; D018343:Receptors, Adrenergic, beta-2; D004247:DNA; D008790:Metoprolol