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Relationships of Measured and Genetically Determined Height With the Cardiac Conduction System in Healthy Adults

Originally publishedhttps://doi.org/10.1161/CIRCEP.116.004735Circulation: Arrhythmia and Electrophysiology. 2017;10:e004735

    Abstract

    Background—

    Increasing height is an independent risk factor for atrial fibrillation, but the underlying mechanisms are unknown. We hypothesized that height-related differences in electric conduction could be potential mediators of this relationship.

    Methods and Results—

    We enrolled 2149 adults aged 25 to 41 years from the general population. Height was directly measured, and a resting 12-lead ECG obtained under standardized conditions. Multivariable linear regression models were used to evaluate the association between measured height and ECG parameters. Mendelian randomization analyses were then performed using 655 independent height-associated genetic variants previously identified in the GIANT consortium. Median age was 37 years, and median height was 1.71 m. Median PR interval, QRS duration, and QTc interval were 156, 88, and 402 ms, respectively. After multivariable adjustment, β-coefficients (95% confidence intervals) per 10 cm increase in measured height were 4.17 (2.65–5.69; P<0.0001) for PR interval and 2.06 (1.54–2.58; P<0.0001) for QRS duration. Height was not associated with QTc interval or the Sokolow–Lyon index. An increase of 10 cm in genetically determined height was associated with increases of 4.33 ms (0.76–7.96; P=0.02) in PR interval and 2.57 ms (1.33–3.83; P<0.0001) in QRS duration but was not related to QTc interval or Sokolow–Lyon index.

    Conclusions—

    In this large population-based study, we found significant associations of measured and genetically determined height with PR interval and QRS duration. Our findings suggest that adult height is a marker of altered cardiac conduction and that these relationships may be causal.

    Introduction

    WHAT IS KNOWN

    • Adult height is an independent risk factor for AF.

    • SNPs that increase height are associated with an increased risk of incident AF.

    WHAT THE STUDY ADDS

    • We found significant associations of measured and genetically determined height with PR interval and QRS duration. These results suggest that adult height is a marker of altered cardiac conduction and that these relationships may be causal.

    • Our findings may improve the pathophysiological understanding of the relationship between body size and AF development.

    Atrial fibrillation (AF) is the most prevalent cardiac arrhythmia worldwide and significantly associated with cardiovascular events, congestive heart failure, and mortality.1,2 The increasing AF prevalence over time further underscores its public health importance.3,4

    Previous studies have consistently shown an increased AF risk among taller individuals, an association that remained significant after multivariable adjustment for potential confounders.5,6 Rosenberg et al7 showed in a candidate gene analysis based on height-associated single-nucleotide polymorphisms (SNPs) that genetic predictors of height were significantly associated with AF occurrence, suggesting that this relationship may be causal. However, the mechanisms for this association are currently unclear. It is possible that taller individuals have larger left atria (LA), which may predispose them to AF development.8,9 It is also possible that adult height is associated with an increased burden of AF-related triggers. For example, we have previously shown that adult height is strongly associated with the frequency of premature atrial contractions,10 which by itself is a strong predictor of incident AF.11 Finally, several parts of the cardiac conduction system, in particular PR interval, are also associated with incident AF.12,13 Thus, adult height may be associated with changes in the cardiac conduction system that may predispose taller individuals to AF development, but little data are currently available to support this hypothesis.7

    To gain additional mechanistic insights into the relationship between height and AF, our primary objective was to evaluate the observational association between height and several indices of cardiac conduction on the 12-lead ECG in a large population-based study of healthy adults. To test the causality of the observed associations, we performed Mendelian randomization analyses using previously published height-associated genetic variants.

    Methods

    Study Participants

    Between 2010 and 2013, all inhabitants of the Principality of Liechtenstein aged 25 to 41 years were invited to participate in the prospective population-based GAPP study (Genetic and Phenotypic Determinants of Blood Pressure and other Cardiovascular Risk Factors ).14 Main exclusion criteria were a body mass index >35 kg/m2, current intake of antidiabetic drugs, established cardiovascular disease, renal failure, or any other severe illness. Of the 2170 included participants, we excluded 11 (0.5%) with incomplete ECG data, 9 (0.4%) with complete bundle branch block, and 1 (0.04%) because of undetectable P waves, leaving 2149 (99.0%) participants for the present analyses. Our study was approved by the local ethics committee, and informed written consent was obtained from each participant.

    Assessment of Study Variables

    On the basis of standardized questionnaires, we collected personal, medical, lifestyle, and nutritional variables at study entry.14 Height was measured without shoes using a standardized device (SECA 202). Physical activity was defined as minutes of physical activity per week and assessed using the International Physical Activity Questionnaire.15 Smoking was self-reported and classified as current, former, or never. Sitting conventional blood pressure (BP) was measured 3× after 5 minutes of rest, and the mean of the second and third measurements was used to define conventional BP. Hypertension was defined as systolic BP ≥140 mm Hg, diastolic BP ≥90 mm Hg, or the use of antihypertensive drugs. Highest educational level achieved was categorized into high school, college, or university degree. Bioelectrical impedance was used to measure body composition (BIA ego fit, 2010, Germany). Prediabetes was defined as glycated hemoglobin A1c between 5.7% and 6.4%.

    Resting 12-Lead ECG

    Standardized resting 12-lead ECGs were obtained with a validated device (AT 104; Schiller AG, Switzerland). ECG amplitudes and intervals were automatically determined using a dedicated software (SEMA 200; Schiller AG) and randomly reviewed for accuracy by trained study staff. PR interval was measured from the onset of the P wave until the beginning of the QRS complex. QRS duration was defined as the time between Q-wave onset and S-wave offset. The QRS onset and the T-wave offset were used to determine QT duration, which was corrected for heart rate using the Bazett formula. The Sokolow–Lyon index was calculated as the sum of the higher S amplitude in V1 or V2 and the higher R wave in V5 or V6.

    Blood Sampling

    Fasting plasma levels of glucose, creatinine, high-sensitivity C-reactive protein, triglycerides, high-density lipoprotein cholesterol, and low-density lipoprotein cholesterol were analyzed on a Roche Cobas 6000 analyzer (F. Hoffmann, La Roche, Switzerland).14 Glycated hemoglobin A1c was analyzed with high-performance liquid chromatography (Bio-Rad D-10, Bio-Rad Laboratories AG, Switzerland). The estimated glomerular filtration rate was calculated with the creatinine-based chronic kidney disease epidemiology collaboration formula.16

    Genetic Analyses

    Genotyping was performed in 2164 individuals from the GAPP cohort using the HumanCoreExome-12 BeadChip versions 1.0 (538 448 SNPs, 1650 participants) and 1.1 (542 585 SNPs, 514 participants; Illumina Inc, San Diego, CA). There was an important overlap between the 2 versions (535 478 SNPs were common). This array (HumanCoreExome) is designed to provide results for a combination of genome-wide tag SNPs (≈240 000 markers) and exonic variants (≈300 000 markers) representing diverse populations and common conditions such as type 2 diabetes mellitus, cancer, metabolic, and psychiatric disorders. Quality control procedures were performed using PLINK software version 1.07.17 Individuals with missing call rate >5%, outlier heterozygosity rate, sex discrepancies, or non-White ethnicity (based on principal component analysis and self-reporting) were removed (n=234). Among the remaining 1930 individuals, 1910 had available ECG data. Markers with missing call rate >5%, deviating from Hardy–Weinberg equilibrium (P<1×106), or with minor allele frequency <0.01 were excluded. Imputation was performed using IMPUTE version 2.3.1 with a reference panel based on 1000 Genomes Project Phase I integrated variant set v3 (June 2014 release) in NCBI Build 37 (hg19) coordinates.18 After imputation, SNPs with minor allele frequency <1% or with imputation certainty (info score) <0.3 were excluded.

    Height-Associated Variants

    We selected all the SNPs associated with height at a genome-wide significance level in the latest meta-analysis from the GIANT consortium (n=697).19 To insure independence of the selected variants, we pruned for linkage disequilibrium at a stringent threshold of r2 <0.1 using the 1000 Genomes data (Europeans). SNPs were selectively prioritized based on the significance of the association with height. A total of 658 SNPs remained after the pruning process. Among those, 655 could be retrieved from the GAPP imputed data (Table I in the Data Supplement and Figure IA and IB in the Data Supplement).

    Statistical Analysis

    Baseline characteristics were stratified by sex-specific quartiles of measured height. Continuous variables were presented as medians with first and third quartiles and compared using Kruskal–Wallis tests. χ2 tests were used to compare categorical variables.

    All continuous ECG variables were normally distributed. Multivariable linear regression models were constructed to compare β-coefficients and 95% confidence intervals (CIs) of PR interval, QTc interval, RR interval, QRS duration, S-wave amplitude in V1 and V2, R-wave amplitude in V5 and V6, and Sokolow–Lyon index across quartiles of measured height. All models were adjusted for a prespecified list of covariates known to be associated with height, cardiovascular diseases, or the cardiac conduction system and included sex, age, body fat mass, systolic and diastolic BP, glycated hemoglobin A1c, estimated glomerular filtration rate, education level, high-density lipoprotein cholesterol, low-density lipoprotein cholesterol, triglycerides, physical activity, and smoking status. Linear trends across height quartiles were calculated using quartile-specific medians. Subsequently, all ECG parameters significantly associated with height were added to the same multivariable model. Collinearity between ECG parameters was evaluated by Spearman correlations, and all correlations assessed were found to be <0.5. Potential subgroup effects for the relationship of PR interval or QRS duration with height were assessed for age, sex, weight, fat mass, smoking status, prediabetes, hypertension, and education.

    Association of the individual height-related genetic variants with ECG parameters was determined using univariable linear mixed modeling implemented in GEMMA.20 Models were adjusted for age and sex, and a genetic relatedness matrix (generated using centered genotypes, after pruning for linkage disequilibrium at a threshold of 0.8) was used to account for population stratification and relatedness because the study was performed in a small country and therefore led to the inclusion of related individuals. For individual variants, the significance threshold for association with ECG parameter was set at 7.6×105 (0.05/655).

    For the Mendelian randomization analysis, we used (1) the effect of the selected SNPs on height from the GIANT consortium and (2) the effect of the SNPs on ECG parameters in the GAPP study. Mendelian randomization associations were performed using the Wald method by regressing genetic effect estimates for ECG parameters (dependent variables) on genetic effect estimates of height.2123 To determine significance, a bootstrap method was used under the null hypothesis that the ratio of genetic effect estimates for ECG parameters on height is 0.24 Predicted effects on ECG parameters were sampled from a normal distribution with mean and SDs as determined in GAPP. A 2-tailed P value was calculated using a z test from 100 000 random simulations (Methods in the Data Supplement). Regression coefficients obtained from the simulations were used to estimate 95% CIs.

    To determine the variance in height explained by the selected variants and examine the impact of measured height and AF risk factors on the association between genetically determined height and ECG parameters, we used the individual data from the GAPP study to calculate a weighted genetic height score21 (Methods in the Data Supplement).

    All statistical analyses were performed using SAS version 9.4 (SAS Institute Inc) or R (version 3.0.1). A P value <0.05 was prespecified to indicate statistical significance unless defined otherwise.

    Results

    Baseline characteristics across sex-specific quartiles of measured height are presented in Table 1. The median age was 37 years; median height was 1.71 m; and 53.7% of the participants were female. Taller participants had a higher education level, higher body fat mass, and a lower estimated glomerular filtration rate (all P<0.01). The distribution across height quartiles was nonlinear for age (P=0.02).

    Table 1. Baseline Characteristics According to Quartiles of Measured Height

    n=2149Quartile 1, n=545Quartile 2, n=573Quartile 3, n=513Quartile 4, n=518P Value*
    Age, y37 (32–40)37 (31–40)37 (32–40)36 (30–40)0.05
    Female sex, %292 (53.6)326 (56.9)265 (51.7)270 (52.1)0.29
    Highest education level, %<0.0001
     High school80 (15.0)46 (8.1)29 (5.7)18 (3.5)
     College295 (55.4)328 (58.1)264 (52.0)261 (50.9)
     University degree158 (29.6)191 (33.8)215 (42.3)234 (45.6)
    Smoking status, %0.48
     Never284 (52.3)314 (54.9)277 (54.0)295 (57.0)
     Past122 (22.5)139 (24.3)126 (24.6)117 (22.6)
     Current137 (25.2)119 (20.8)110 (21.4)106 (20.5)
    Physical activity, min/wk180 (40; 360)150 (60–360)150 (60–315)180 (60–360)0.20
    Fat mass, %25.4 (20.1–30.3)25.8 (20.3–30.4)24.5 (20.3–29.1)24.9 (20.4–28.5)0.03
    Systolic BP, mm Hg120 (110–128)119 (111–127)120 (111–128)120 (112–130)0.15
    Diastolic BP, mm Hg79 (72–84)78 (73–84)78 (73–84)79 (73–85)0.49
    eGFR, mL min−1 1.73 m−2114 (107–120)112 (103–119)111 (103–117)111 (101–117)<0.0001
    HbA1c, %5.4 (5.2–5.7)5.4 (5.2–5.7)5.4 (5.2–5.6)5.4 (5.1–5.6)0.06
    LDL-C, mmol/L2.9 (2.3–3.6)2.9 (2.4–3.4)2.9 (2.3–3.5)2.8 (2.3–3.4)0.65
    HDL-C, mmol/L1.5 (1.2–1.8)1.5 (1.2–1.8)1.5 (1.2–1.8)1.5 (1.2–1.8)0.29
    Triglycerides, mmol/L0.9 (0.6–1.3)0.8 (0.6–1.2)0.8 (0.6–1.1)0.9 (0.6–1.2)0.21

    Data are medians with first and third quartiles or numbers (percentages). BP indicates blood pressure; eGFR, estimated glomerular filtration rate; HbA1c, glycated hemoglobin A1c; HDL-C, high-density lipoprotein cholesterol; and LDL-C, low-density lipoprotein cholesterol.

    *P values were based on Kruskal−Wallis tests or χ2 tests, as appropriate.

    Relationship of Measured Height With ECG Parameters

    The distribution of ECG parameters across quartiles of measured height is shown in Table 2. Median PR interval, QRS duration, QTc interval, and Sokolow–Lyon index were 156 ms, 402 ms, 88 ms, and 2.54 mV, respectively. Taller individuals had significantly longer PR intervals, QRS duration, and RR intervals compared with shorter participants (all P<0.005; Table 2).

    Table 2. ECG Indices According to Quartiles of Measured Height

    n=2149Quartile 1, n=545Quartile 2, n=573Quartile 3, n=513Quartile 4, n=518P Value*
    PR interval, ms154 (138 to 166)156 (140 to 170)156 (144 to 172)160 (144 to 174)<0.0001
    QRS duration, ms88 (82 to 94)88 (82 to 94)90 (84 to 96)90 (84 to 98)<0.0001
    QTc interval, ms401 (387 to 416)403 (389 to 417)402 (386 to 416)401 (388 to 415)0.53
    RR interval, ms961 (884 to 1063)968 (880 to 1061)983 (891 to 1084)993 (904 to 1081)0.005
    S-wave amplitude in V1, mV−0.82 (−1.03 to −0.64)−0.84 (−1.04 to −0.66)−0.84 (−1.03 to −0.64)−0.81 (−1.02 to −0.63)0.59
    R-wave amplitude in V6, mV1.01 (0.81 to 1.27)1.05 (0.87 to 1.26)1.05 (0.83 to 1.27)1.05 (0.85 to 1.27)0.35
    SLI, mV2.5 (2.1 to 3.1)2.6 (2.1 to 3.0)2.6 (2.1 to 3.1)2.6 (2.1 to 3.2)0.13

    Data are medians with first and third quartiles. SLI indicates Sokolow–Lyon index.

    *P values were based on Kruskal–Wallis tests.

    After multivariable adjustment for potential confounders (Table 3, multivariable model a), a 10 cm increase in measured height remained associated with a 4.17 ms (95% CI, 2.65–5.69) longer PR interval, a 2.06 ms (95% CI, 1.54–2.58) longer QRS duration (both P<0.0001), and a 10.85 ms (95% CI, 1.30–20.40) longer RR interval (P=0.03). No significant associations were found for the other ECG parameters assessed (Table 3). In multivariable models that further controlled simultaneously for all significant ECG indices, PR interval and QRS duration remained significantly associated with measured height, whereas RR interval became nonsignificant (Table 3, multivariable model b). Subgroup analyses for PR interval and QRS duration showed generally consistent results, as shown in Tables II and III in the Data Supplement.

    Table 3. Multivariable Linear Regression Analyses for the Relationship Between Measured Height and ECG Indices

    Continuous, n=2149P ValueQuartile 1, n=545Quartile 2, n=573Quartile 3, n=513Quartile 4, n=518P Trend
    PR interval, ms
     Sex and age adjusted4.09 (2.65 to 5.53)<0.0001Reference2.39 (−0.15 to 4.93)4.78 (2.16 to 7.39)7.02 (4.40 to 9.63)<0.0001
     Multivariable model a*4.17 (2.65 to 5.69)<0.0001Reference2.23 (−0.40 to 4.85)4.82 (2.11 to 7.53)7.16 (4.44 to 9.88)<0.0001
     Multivariable model b4.17 (2.62 to 5.71)<0.0001Reference2.23 (−0.39 to 4.86)4.78 (2.06 to 7.50)7.14 (4.39 to 9.89)<0.0001
    QRS duration, ms
     Sex and age-adjusted model2.10 (1.61 to 2.59)<0.0001Reference0.73 (−0.14 to 1.60)1.77 (0.88 to 2.67)3.22 (2.33 to 4.12)<0.0001
     Multivariable model a*2.06 (1.54 to 2.58)<0.0001Reference0.82 (−0.08 to 1.71)1.77 (0.84 to 2.69)3.12 (2.19 to 4.05)<0.0001
     Multivariable model b2.02 (1.51 to 2.54)<0.0001Reference0.75 (−0.13 to 1.64)1.73 (0.81 to 2.64)3.07 (2.15 to 4.00)<0.0001
    SLI, mV
     Sex and age adjusted0.03 (−0.01 to 0.07)0.14Reference0.04 (−0.03 to 0.12)0.02 (−0.05 to 0.10)0.05 (−0.03 to 0.12)0.33
     Multivariable model a*0.01 (−0.04 to 0.05)0.85Reference0.03 (−0.04 to 0.11)−0.001 (−0.08 to 0.08)0.01 (−0.08 to 0.08)0.84
     Multivariable model b†−0.02 (−0.07 to 0.02)0.28Reference0.02 (−0.05 to 0.09)−0.03 (−0.10 to 0.05)−0.04 (−0.12 to 0.04)0.18
    RR interval, ms
     Sex and age adjusted20.24 (10.51 to 29.97)<0.0001Reference7.67 (−9.53 to 24.86)21.69 (4.01 to 39.36)29.06 (11.41 to 46.72)0.0004
     Multivariable model a*10.85 (1.30 to 20.40)0.03Reference4.02 (−12.43 to 20.47) 12.00 (−4.98 to 29.00)16.55 (−0.51 to 33.62)0.04
     Multivariable model b5.93 (−3.73 to 15.60)0.23Reference1.25 (−15.05 to 17.55)7.41 (−9.52 to 24.33)8.83 (−8.34 to 26.01)0.24
    QTc interval, ms
     Sex and age adjusted0.02 (−1.20 to 1.24)0.98Reference.0.45 (−1.71 to 2.60)0.30 (−1.92 to 2.51)−0.04 (−2.25 to 2.18)0.94
     Multivariable model a*0.19 (−1.07 to 1.46)0.76Reference0.15 (−2.03 to 2.33)0.48 (−1.78 to 2.73)0.21 (−2.06 to 2.47)0.82
    S-wave amplitude in V1, mV
     Sex and age adjusted0.01 (−0.01 to 0.03)0.24Reference−0.02 (−0.05 to 0.02)0.01 (−0.02 to 0.05)0.01 (−0.02 to 0.05)0.22
     Multivariable model a*0.02 (−0.003 to 0.04)0.09Reference−0.02 (−0.05 to 0.02)0.02 (−0.02 to 0.05)0.03 (−0.01 to 0.07)0.08
    S-wave amplitude in V2, mV
     Sex and age adjusted−0.02 (−0.05 to 0.02)0.31Reference0.03 (−0.02 to 0.09)0.03 (−0.03 to 0.09)−0.01 (−0.07 to 0.05)0.66
     Multivariable model a*−0.003 (−0.03 to 0.04)0.85Reference0.05 (−0.01 to 0.10)0.05 (−0.01 to 0.11)0.02 (−0.04 to 0.08)0.47
    R-wave amplitude in V5, mV
     Sex and age adjusted0.03 (0.01 to 0.06)0.02Reference0.06 (0.01 to 0.10)0.06 (0.01 to 0.11)0.05 (0.001 to 0.10)0.05
     Multivariable model a*0.02 (−0.01 to 0.05)0.09Reference0.05 (0.01 to 0.10)0.05 (0.004 to 0.10)0.04 (−0.01 to 0.08)0.18
    R-wave amplitude in V6, mV
     Sex and age adjusted0.01 (−0.01 to 0.03)0.32Reference0.02 (−0.02 to 0.06)0.01 (−0.03 to 0.04)0.01 (−0.03 to 0.05)0.78
     Multivariable model a*0.01 (−0.01 to 0.03)0.54Reference0.02 (−0.01 to 0.06)0.001 (−0.04 to 0.04)−0.01 (−0.04 to 0.04)0.89

    Data are β-coefficients (95% confidence intervals) and are given per 10 cm increase of height in the continuous analyses. BP indicates blood pressure; and SLC, Sokolow–Lyon index.

    *n=2061; adjusted for sex, age, body fat mass, systolic BP, diastolic BP, glycated hemoglobin A1c, estimated glomerular filtration rate, education level, high-density lipoprotein, low-density protein, triglycerides, physical activity (min at wk), and smoking status (current or past).

    n=2061; additionally adjusted for QRS duration, SLI, PR interval, and RR interval as appropriate.

    Relationship of Genetically Determined Height With ECG Parameters

    In Mendelian randomization analyses using the 655 height-associated variants, there was a significant relationship of genetically determined height with PR interval and QRS duration (Figure IA and IB in the Data Supplement). A 10 cm increase in predicted height was associated with a 4.33 ms (95% CI, 0.76–7.96; P=0.019) increase in PR interval and a 2.57 ms (95% CI, 1.33–3.83; P<0.0001) increase in QRS duration.

    The weighted genetic height score (Figure II in the Data Supplement) was significantly associated with measured height after adjusting for age and sex (P<0.0001), explaining 20% of the residual variance in measured height. Multivariable-adjusted models for the relationship of the genetic risk score with either PR interval or QRS duration were very similar to the Mendelian randomization results and are provided in Table 4. These associations were completely attenuated when adding measured height to the models (Table 4). Associations of PR interval and QRS duration across quartiles of genetically determined height showed a similar pattern and are presented in Table IV in the Data Supplement and Figure III in the Data Supplement.

    Table 4. Relationship Between Genetic Risk Score and ECG Parameters

    Effect (95% CI), n=1910P Value
    PR interval, ms
     Multivariate model a*4.21 (0.88 to 7.55)0.013
     Multivariable model b4.25 (0.80 to 7.70)0.016
     Multivariable model c1.01 (−2.78 to 4.79)0.60
    QRS duration, ms
     Multivariate model a*2.33 (1.18 to 3.48)<0.0001
     Multivariable model b2.35 (1.17 to 3.53)<0.0001
     Multivariable model c0.63 (−0.66 to 1.92)0.34
    SLI, mV
     Multivariate model a*0.04 (−0.04 to 0.13)0.31
     Multivariable model b0.01 (−0.08 to 0.10)0.81
     Multivariable model c0.03 (−0.06 to 0.13)0.50
    RR interval, ms
     Multivariate model a*6.13 (−16.43 to 28.68)0.60
     Multivariable model b−12.01 (−33.52 to 9.51)0.27
     Multivariable model c−1.91 (−25.57 to 21.76)0.11
    QTc interval, ms
     Multivariate model a*1.85 (−0.95 to 4.66)0.20
     Multivariable model b1.50 (−1.01 to 4.01)0.24
     Multivariable model c1.16 (−1.60 to 3.92)0.41

    Effect is given per 10 cm increase of height. BP indicates blood pressure; and SLI, Sokolow–Lyon index.

    *Adjusted for sex and age.

    Adjusted for sex, age, body fat mass, systolic BP, diastolic BP, glycated hemoglobin A1c, estimated glomerular filtration rate, education level, high-density lipoprotein, low-density protein, triglycerides, physical activity (min at wk), smoking status (current or past), PR interval, QRS duration, SLI, and RR interval as appropriate.

    Coefficients were additionally adjusted for measured height.

    No individual genetic variant was significantly associated with PR interval or QRS duration after correction for multiple testing (Table I in the Data Supplement). The strongest association with PR interval was seen for a variant in the SHOX2 gene (P=0.0055).

    Discussion

    In this large population-based study of young and healthy adults, we found that height is strongly associated with QRS duration and PR interval, but not with QTc duration or indirect markers of cardiac hypertrophy. These data add to our knowledge on the cardiovascular effects of adult height and may at least in part explain the relationship between height and incident AF because PR interval has been consistently associated with new-onset AF in previous studies.5,12,25 Importantly, Mendelian randomization analyses confirmed our findings on measured height and thereby suggest a causal relationship between height and cardiac depolarization, as explained in detail in Figure IV in the Data Supplement. Taken together, the previously shown relationship between height and incident AF5,26 could be explained by genetically determined alterations in body size, which may have an impact on the cardiac conduction system. More studies are needed to assess whether taller people are also at risk for other types of arrhythmias related to the prolongation of the PR interval or QRS duration.25

    Height is a strong determinant of LA size,9 which by itself is a key risk factor for incident AF and other cardiovascular events.27,28 LA size and left ventricular hypertrophy are correlated in adolescents, suggesting a symmetrical increase in cardiac chamber size and volumes in healthy adults.29 In addition, PR interval has been directly related to body mass across different mammalian species.30 From a mechanistic perspective, LA enlargement may induce mechanical stretch, slowing of the atrial conduction, and an increased susceptibility for atrial ectopy, all of which favor the development of AF.31,32 However, it is important to note that the improved risk discrimination by height in the Framingham model for incident AF was only minimally attenuated after adjusting for LA size and left ventricular mass.5 In another study, the strength of the relationship between adult height and new-onset AF was similar whether or not individuals had LA enlargement at the time of AF diagnosis.33 Thus, these studies suggest that the increased risk of AF in taller individuals is not predominantly mediated by increased cardiac dimensions. Accordingly, none of the available ventricular amplitude markers on the ECG was associated with height in our study, although it has to be acknowledged that cardiac dimensions are poorly reflected on the surface ECG and that imaging data are not available in our study.

    Among the 655 selected variants, the strongest association with PR interval was observed for a variant located in SHOX2. Although this association was not statistically significant after adjustment for multiple testing, this gene is nevertheless interesting from a pathophysiological perspective. In mice models, this gene has been identified as a mediator of pacemaker function in the sinoatrial node and pulmonary vein myocardium.34,35 Consequently, an increased expression of the SHOX2 gene may alter depolarization and increase ectopy activity, which may then increase the risk of AF.35,36 In line with these experimental data, we have shown that adult height is independently related to the frequency of premature atrial contractions.10

    A major strength of our population-based study is the large number of well-characterized healthy participants without established cardiovascular disease and the availability of genetic data in all participants. There are some potential limitations which also need to be taken into account. First, the generalizability of our findings to other study populations remains unclear because mainly white adults were enrolled. Second, although the genetic approach makes confounding very unlikely, we cannot exclude that the relationship between genetically determined height and ECG parameters is mediated through various behaviors adopted by individuals as a consequence of their height. Third, cardiac imaging was not available in our study, and imaging determined cardiac dimensions could be informative to enhance our understanding of the associations between cardiac conduction parameters and height in the general population.

    In conclusion, in this large population-based study of young and healthy adults, we found significant associations of measured and genetically determined height with PR interval and QRS duration, but not with repolarization or indirect markers of hypertrophy. Our findings, therefore, suggest that adult height may be a marker of altered cardiac conduction and that these relationships may be causal. These changes may in part explain the increased AF risk among taller individuals.

    Footnotes

    Guest Editor for this article was Gerhard Hindricks, MD.

    *Drs Kofler and Thériault contributed equally to this work.

    The Data Supplement is available at http://circep.ahajournals.org/lookup/suppl/doi:10.1161/CIRCEP.116.004735/-/DC1.

    Correspondence to David Conen, MD, MPH, Department of Medicine, University Hospital Basel, Petersgraben 4, 4031 Basel, Switzerland. E-mail

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