Sotalol

Following the analytic methods used in the pilot study (Peters et al. 2000 (link)), we assessed the association of arrhythmias with air pollution using time-series methods. We merged the patient-specific record of days on study and ICD-detected ventricular arrhythmias with the daily mean air pollution and weather measurements. The association of arrhythmic episode-days with air pollution was analyzed by logistic regression using generalized estimating equations (Diggle 1988 (link); Zeger et al. 1988 (link)) with random effects for patients, a linear trend, sine and cosine terms with periods of one, one-half, one-third, and one-quarter year, quadratic functions of minimum temperature and mean humidity, indicators for day of the week, and an indicator for a previous arrhythmia within 3 days.
We considered mean air pollution concentrations on the same day and lags of 1, 2, and 3 days. The lag structure of the data was estimated by evaluating each lag day (0 to 3) separately and jointly in an unconstrained distributed lag model (Pope and Schwartz 1996 (link)). We have found consistently elevated (although not statistically significant) risk estimates associated with air pollution concentrations on the day of (lag 0) and the day before (lag 1) the arrhythmia (Dockery et al., in press ). Therefore, in this article we report only the effects of 2-day running mean air pollution concentrations.
We explored potential modification of the air pollution associations in multivariate logistic regression including interactions between air pollution and indicators of patient characteristics. Patients were stratified by reported ejection fraction before implantation (≤35% vs. > 35%), prior myocardial infarction, and the diagnosis of coronary artery disease before implantation (not sufficient numbers of patients for specific analyses of other cardiac diagnoses). We assessed modification of the air pollution associations by usual prescribed medications (reported at more than half of clinical follow-ups) grouped as beta-blockers, digoxin, and other antiarrhythmics (amiodarone, sotalol, mexilitine, and quinidine). The strongest predictor of a ventricular arrhythmia was an arrhythmia in the previous 3 days. Therefore, in addition to controlling for prior arrhythmias, we assessed the modification of the air pollution association by a prior ventricular arrhythmia.
We present odds ratios (ORs) and 95% confidence intervals (CIs) based on an interquartile range (25th percentile–75th percentile) increase in each air pollution concentration. p-Values are reported for the effects of air pollution and for the interactions of air pollution with posited effect modifiers. We characterize p-values < 0.05 as statistically significant, and p-values < 0.10 as marginally significant. For air pollutants and subgroups of events with statistically significant associations, we examined the risk of arrhythmias versus quintiles of air pollution concentration.

Regression analyses of resting HR, SDNN, and RMSSD were adjusted for gender, age, gender-age interaction, body mass index (BMI), BMI*BMI, the first 30 principal components, and genotyping chip (Affymetrix UK Biobank Axiom or Affymetrix UK BiLEVE Axiom array). To fully account for aerobic exercise capacity in HR increase and HR recovery, the model also included exercise duration, exercise program (30% or 50% maximum load), maximum workload achieved, and the interaction between exercise program and maximum workload achieved.
Participants were excluded if they stopped exercising earlier than planned, experienced chest-pain or other discomfort, were at medium-to-high cardiovascular risk⁴⁶ at the time of the test, or terminated exercise for unknown reasons. In a post-hoc analysis, the population was stratified by participants that reported taking sotalol medication, beta-blockers, anti-depressants, atropine, glycosides or other anti-cholinergic drugs, or were previously diagnosed with myocardial infarction, supraventricular tachycardia, bundle branch block, heart failure, cardiomyopathy, or previously had a pacemaker or ICD implant. In a post-hoc sensitivity analysis, the differences in beta estimates in participants with and without cardiovascular disease or HR-altering medication were assessed using a Chow test.
In total, 58,818 participants were included in the GWAS. The genome-wide association study and heritability analyses were performed using BOLT-LMM^{53 (link)} and BOLT-REML^{54 (link)}, respectively. A conjugate gradient-based iterative framework for fast mixed-model computations was employed to accurately account for population structure and relatedness; additive effects were assumed. The BOLT software was used with 509,255 genotyped SNPs that were extracted from the final imputation set (to ensure a 100% call rate per SNP). After pruning (R² > 0.5, using plink ‘--indep-pairwise 50 5 0.5), LD scores also used by BOLT, were estimated from 2,000 randomly selected UK Biobank participants (who were selected after sample exclusions based on relatedness, missingness, and heterozygosity). To control for relatedness among participants in linear logistic, or cox regression analyses, we used cluster-robust standard errors with genetic family IDs as clusters. A family ID was given to individuals belonging together based on 3^rd-degree or closer as indicated by the kinship matrix, which was provided by UK Biobank (kinship coefficient > 0.0442). All statistical analyses other than the genome-wide analysis were carried out using R v3.3.2 or STATA/SE release 13.
Since the current study is by far the largest population-based study of electrocardiographic exercise tests, independent cohorts that matched this study in size and availability of variables (specific HR response variables and genetics) were unavailable for replication purposes. Therefore, a conservative genome-wide significant threshold of p< 8.3 × 10⁻⁹ was adopted to account for six independent traits, in accordance with similar multi-phenotype studies of this scale^{55 (link)–59 (link)}.
Variants were considered to be independent if the pairwise LD (R²) was less than 0.01. A locus was defined as the highest associated independent SNP +/− 1MB. The strongest associated variant within a locus was assigned the sentinel SNP. If there was evidence for multiple independent SNPs in one locus based on LD, it was confirmed by using linear regression and adjusting for the sentinel SNP.

The use of BBs was identified according to the self-reported prescription medications questionnaire (https://wwwn.cdc.gov/nchs/nhanes/continuousnhanes/questionnaires.aspx?BeginYear=2005). Non-selective BBs included propranolol, carvedilol, nadolol, sotalol, pindolol, labetalol, penbutolol and timolol. Selective BBs included nebivolol, metoprolol, atenolol, bisoprolol, acebutolol, and betaxolol. The duration of the use of BBs was also obtained from the questionnaire, which was divided into four quartiles as follows: First quartile, ≤2 years; second quartile, 2-4 years; third quartile, 4-6 years; fourth quartile, >6 years). The long-term use of BBs in participants was defined as a BB treatment duration of >6 years.

The chromatographic separation was carried out in an HPLC system Agilent 1260 infinity (Agilent, Santa Clara, CA, USA) consisting of a degasser, a pump, an autosampler, and the column with a thermostat. The mass spectrometer was calibrated according to standard ABSciex procedure using PPG Positive solution (2 × 10⁻⁷ M) from an ABSciex calibration kit. The solution was injected at 5 µL/min. The list of the monitored masses included m/z 59.050, 175.133, 500.380, 616.464, and 906.673. The tuning was performed until the mass error becomes less than 0.1 and peak width—between 0.6–0.8. The procedure was repeated for both quadrupole 1 and 3 at scan rates of 10, 200, 1000, and 2000 Da/s.
A Waters SPHERISORB column (Waters, Milford, Massachusetts, USA) 2.1 × 150 mm × 5 µm without a guard column was used for sotalol measurement. Ultrapure H₂O with the ammonium acetate (10 mmol/L) was used as a mobile phase A. Undiluted acetonitrile (100%) was used as a mobile phase B. The measurements were performed in isocratic mode during 5 min at a constant phase ratio A/B 40/60. The flow was 800 µL/min. The injection volume was 5 µL. The column temperature was 40 °C. The system dead time was less than 30 s under these conditions and the retention time was 2.3 min and 2.7 min for sotalol and IS, respectively.
A Poroshel 120 EC-C18 column (Agilent, Santa Clara, CA, USA) 2.1 × 150 mm × 2.7 µm was used for the digoxin analysis with a guard column Zorbax (Agilent, Santa Clara, CA, USA) 2.1 × 5 mm × 1.9 µm. Ultrapure H₂O with formic 0.1% acid and the ammonium acetate (10 mmol/L) was used as a mobile phase A. An ACN:H₂O = 9:1 mixture with 0.1% formic acid and ammonium acetate (10 mmol/L) was used as a mobile phase B. The measurements were performed during 7 min: the first 1.5 min the ratio A/B was 9:1, after it initially increased to 5/95 and maintained a constant during 3 min then instantly returned to the initial parameters and maintained a constant during 2.5 min. The flow rate was 500 µL/min. The injection volume was 25 µL. The column temperature was 25 °C. The system dead time was less than 30 s and the retention time was 3.3 min and 3.6 min for digoxin and IS, respectively. Table 3 illustrated the multiple reaction monitoring (MRM)-selected transitions of sotalol, digoxin, and its internal standards.