Observational studies suggest that electrocardiogram (ECG) indices might be influenced by obesity and other anthropometric measures, though it is difficult to infer causal relationships based on observational data due to risk of residual confounding. We utilized mendelian randomization (MR) to explore causal relevance of multiple anthropometric measures on P-wave duration (PWD), PR interval, QRS duration, and corrected QT interval (QTc).
Methods and findings
Uncorrelated (r2 < 0.001) genome-wide significant (p < 5 × 10−8) single nucleotide polymorphisms (SNPs) were extracted from genome-wide association studies (GWAS) on body mass index (BMI, n = 806,834), waist:hip ratio adjusted for BMI (aWHR, n = 697,734), height (n = 709,594), weight (n = 360,116), fat mass (n = 354,224), and fat-free mass (n = 354,808). Genetic association estimates for the outcomes were extracted from GWAS on PR interval and QRS duration (n = 180,574), PWD (n = 44,456), and QTc (n = 84,630). Data source GWAS studies were performed between 2018 and 2022 in predominantly European ancestry individuals. Inverse-variance weighted MR was used for primary analysis; weighted median MR and MR-Egger were used as sensitivity analyses. Higher genetically predicted BMI was associated with longer PWD (β 5.58; 95%CI [3.66,7.50]; p = < 0.001), as was higher fat mass (β 6.62; 95%CI [4.63,8.62]; p < 0.001), fat-free mass (β 9.16; 95%CI [6.85,11.47]; p < 0.001) height (β 4.23; 95%CI [3.16, 5.31]; p < 0.001), and weight (β 8.08; 95%CI [6.19,9.96]; p < 0.001). Finally, genetically predicted BMI was associated with longer QTc (β 3.53; 95%CI [2.63,4.43]; p < 0.001), driven by both fat mass (β 3.65; 95%CI [2.73,4.57]; p < 0.001) and fat-free mass (β 2.08; 95%CI [0.85,3.31]; p = 0.001). Additionally, genetically predicted height (β 0.98; 95%CI [0.46,1.50]; p < 0.001), weight (β 3.45; 95%CI [2.54,4.36]; p < 0.001), and aWHR (β 1.92; 95%CI [0.87,2.97]; p = < 0.001) were all associated with longer QTc. The key limitation is that due to insufficient power, we were not able to explore whether a single anthropometric measure is the primary driver of the associations observed.
Why was the study done?
- Previous studies have shown that a higher body mass index (BMI) is associated with changes in electrocardiogram (ECG) measurements.
- However, it is unclear from available research whether this is driven by fat mass or by lean mass because BMI does not differentiate between these.
- Additionally, most available data come from observational studies, which are liable to confounding and reverse causation. This limitation can be overcome by the use of mendelian randomization (MR)
What did the researchers do and find?
- In this MR study, the authors explored the association between higher genetically predicted BMI, fat mass, fat free mass, weight, height, and waist:hip ratio and ECG measures of atrial and ventricular conduction.
- The authors found that higher genetically predicted BMI was associated with many ECG indices that have been linked to arrhythmic risk and that this was driven by fat mass, fat free mass, and height.
What do the findings mean?
- In this study, lean body mass traits appear to correlate with proarrhythmic electrophysiological remodeling.
- These data may help to risk stratify individuals who would benefit from targeted weight management interventions.
- The main limitations are inclusion of data from only predominantly European ancestry populations, and the inability to establish whether a single body size measure might be the most important in driving the associations.
Citation: Ardissino M, Patel KHK, Rayes B, Reddy RK, Mellor GJ, Ng FS (2023) Multiple anthropometric measures and proarrhythmic 12-lead ECG indices: A mendelian randomization study. PLoS Med 20(8): e1004275. https://doi.org/10.1371/journal.pmed.1004275
Received: November 12, 2022; Accepted: July 10, 2023; Published: August 8, 2023
Copyright: © 2023 Ardissino et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
Data Availability: All data used in this study is available to download freely at cited publications and links, as follows: Body mass index: https://portals.broadinstitute.org/collaboration/giant/images/1/14/Bmi.giant-ukbb.meta-analysis.combined.23May2018.HapMap2_only.txt.gz Fat mass: https://broad-ukb-sumstats-us-east-1.s3.amazonaws.com/round2/additive-tsvs/23100_irnt.gwas.imputed_v3.both_sexes.tsv.bgz Fat free mass: https://broad-ukb-sumstats-us-east-1.s3.amazonaws.com/round2/additive-tsvs/23101_irnt.gwas.imputed_v3.both_sexes.tsv.bgz Height: https://portals.broadinstitute.org/collaboration/giant/images/6/63/Meta-analysis_Wood_et_al%2BUKBiobank_2018.txt.gz Weight: https://broad-ukb-sumstats-us-east-1.s3.amazonaws.com/round2/additive-tsvs/23098_irnt.gwas.imputed_v3.both_sexes.tsv.bgz Waist hip ratio: https://portals.broadinstitute.org/collaboration/giant/images/6/6e/Whradjbmi.giant-ukbb.meta-analysis.combined.23May2018.HapMap2_only.txt.gz P-wave duration: http://ftp.ebi.ac.uk/pub/databases/gwas/summary_statistics/GCST004001-GCST005000/GCST004826/Pwave_duration_GWAS_ALL_maf0.01.txt.gz PR interval: http://ftp.ebi.ac.uk/pub/databases/gwas/summary_statistics/GCST008001-GCST009000/GCST008042/ QRS duration: http://ftp.ebi.ac.uk/pub/databases/gwas/summary_statistics/GCST008001-GCST009000/GCST008054/ QTc interval: https://personal.broadinstitute.org/ryank/Nauffal_2022_QT_GWAS_SAIGE.zip Statistical code is available on GitHub at: https://github.com/mardissino/bmi-ecg-mr.git.
Funding: This study was supported by the British Heart Foundation (RG/F/22/110078 for FSN, RE/18/4/34215 for FSN), the National Institute for Health Research Imperial Biomedical Research Centre funding for FSN, and the National Institute for Health Research Academic Clinical Fellowship for MA. All other authors report no relevant funding. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.
Competing interests: The authors have declared that no competing interests exist.
Abbreviations: AF, atrial fibrillation; aWHR, adjusted waist:hip ratio; BMI, body mass index; ECG, electrocardiogram; GWAS, genome-wide association study; IVW, inverse-variance weighted; MR, mendelian randomization; PWD, P-wave duration; QTc, corrected QT; SCD, sudden cardiac death; SNP, single nucleotide polymorphism; 95%CI, 95% confidence interval
Obesity is a growing global health problem associated with high rates of cardiometabolic morbidity [1–3] and is an independent risk factor for atrial  and ventricular arrhythmias . Previous observational studies have identified an association between increasing adiposity and abnormalities on the 12-lead electrocardiogram (ECG) , such as longer P-wave duration (PWD) and increased P-wave dispersion (signifying delayed atrial conduction) [7,8] and increased QT dispersion and prolongation of corrected QT interval (QTc) (markers of abnormal ventricular activation and repolarization) [9–11]. These ECG changes are of clinical importance, as prior mendelian randomization (MR) studies have demonstrated their causal relevance on multiple adverse outcomes such as atrial fibrillation (AF)  and sudden cardiac death (SCD) .
However, body mass index (BMI) is not only determined by adiposity. Additional anthropometric measures, such as fat mass, fat-free mass, and height, are important determinants of BMI. Although it is thought that the association between BMI and electrophysiological remodeling is strongly mediated by adipose tissue, the other determinants of BMI above may also play an important role, and the potential influence of these parameters on ECG indices remains relatively less explored. Additionally, beyond overall adiposity, its distribution might play an important role in determining arrhythmic risk. This is clinically relevant given that visceral fat is thought to be more detrimental to health than subcutaneous adiposity . From the current well-established association of BMI with proarrhythmic ECG changes, it is therefore impossible to establish whether it is truly only increasing adiposity that influences electrophysiological remodeling, and beyond this, it is difficult to establish whether this association is truly causal and independent of other concurrent cardiometabolic risk factors. For example, higher BMI is associated with multiple morbidities such as hypertension and ischemic heart disease, which also induce adverse cardiac remodeling . In turn, these associations may also be at least partly mediated by an intermediate risk factor, such as type 2 diabetes. In the setting of observational data, it is difficult to accurately characterize these complex causal relationships.
MR is a genetic epidemiological method that can provide estimates of the effects of risk factors on an outcome using a framework that is less liable to influence by reverse causation and confounding . MR leverages the random process of allele assortment and conception, which leads to an effective “randomization” of individuals to high or low genetic risk of a risk factor. This genetic liability can then be used as a proxy for the exposure itself, in an instrumental variable analysis framework. Since the initial genetic risk allocation is almost entirely random, similar to randomization in a clinical trial, it is not influenced by confounding or reverse causation. Thus, the MR framework can be used to investigate the causal relevance of the exposure on an outcome under a set of key assumptions.
The aim of this study was to utilize MR to explore the association of anthropometric metrics and on PWD, PR interval, QRS duration, and QTc interval, to elucidate the effect of body size and composition on proarrhythmic electrophysiological remodeling. Uniquely, in addition to the commonly used metric of BMI, we sought to investigate the specific effects of its determinants of height, fat mass, and fat-free mass and to explore the influence of adiposity distribution through exploring BMI-adjusted waist:hip ratio (aWHR). At present, despite the extensive observational evidence linking higher BMI to changes in ECG measures, no study, observational or MR based, has explored this.
Ethics and data access
This study used publicly available genome-wide association summary data. All included studies had gained ethical approval and participant consent according to individual protocols available at the referenced publications. The study is reported on the basis of the Strengthening the Reporting of Observational Studies in Epidemiology using Mendelian Randomization (STROBE-MR) Guidelines (S1 STROBE Checklist) . All analyses were carried out on R version 4.1.2  using the TwoSampleMR  and Mendelianrandomization packages . No protocol was preregistered.
Instrumental variable selection
Genome-wide significant (p < 5 × 10−8) instrumental variables for BMI (in kg/m2) , aWHR (in cm:cm) , and height (in inverse normal transformed and standardized cm)  were extracted from summary statistics of published genome-wide association studies (GWAS), respectively, including 806,834 participants, 697,734 participants, and 709,594 participants. Genome-wide significant (p < 5 × 10−8) instrumental variables for weight (in inverse normal rank transformed kg, n = 360,116), whole-body fat mass (in inverse normal rank transformed kg, n = 354,244), and whole-body fat-free mass (in inverse normal rank transformed kg, n = 354,808) were extracted from Neale lab UK Biobank GWAS summary statistics (http://www.nealelab.is/uk-biobank/). Whole-body fat mass and fat-free mass were measured using impedance, which were measured as outlined in the protocol and original paper .
Genetic association estimates for the outcomes were extracted from GWAS of outcomes, including PR interval and QRS duration (ms, n = 180,574 participants) , PWD (n = 44,456 participants) , and QTc (n = 84,630) . The ECG measures in the original GWAS studies were derived from 12-lead ECG at rest taken in the supine position. Extraction of ECG measures, such as P-wave indices, was performed using a variety of software algorithms that are described in detail in the individual GWAS papers [23–25].
All GWAS studies for both exposures and outcomes were performed within the scope of large, multicenter population-based studies. Further details on study cohorts are available at the respective publications and are provided in Table 1.
Harmonization and clumping
Gene-exposure association estimates for each exposure were harmonized with gene-outcome association estimates for corresponding instrumental SNPs in the outcome data. An attempt was made to infer positive strand alleles during harmonization. Where this could not be inferred, or if SNPs were palindromic or ambiguous, the SNP was excluded. Only SNPs with available gene-exposure and gene-outcome association estimates were included; if there were no matching SNPs for an instrumental variable in the outcome data, proxies were not sought. After harmonization, SNPs were clumped to retain only uncorrelated variants (pairwise linkage disequilibrium r2 < 0.001). Instrument strength was quantified using F-statistics.
Inverse-variance weighted (IVW) MR with multiplicative random effects  was used as the primary analysis method for all models, to estimate the association between each genetically predicted anthropometric trait and ECG phenotype . Results are presented as beta coefficients (β) with respective 95% confidence intervals (95%CI), which can be interpreted as the expected change in ECG interval, in ms, per 1-unit change in the exposure. The units for each exposure are reported in Table 1.
There are 3 core assumptions of the IVW MR approach that, if not met, can lead to unreliable results. These include the following:
- That instrumental variables predict the exposure;
- That instrumental variables are not associated with confounders of the association between the exposure and outcome; and
- That instrumental variables are only associated with the outcome through the exposure.
The first assumption was tested by quantification of instrument strength using F-statistics. In instances where instrumental SNPs influence the outcome through additional biological pathways that are parallel to, but do not act through the exposure, these assumptions are violated in a phenomenon called horizontal pleiotropy. Sensitivity analysis using weighted median MR  and MR-Egger can be used to explore this phenomenon. The weighted median method has been shown to provide consistent estimates even in situations where up to half of the instrumental SNPs are invalid or horizontally pleiotropic . Additionally, the MR-Egger method can be used to more formally test for the presence of directional pleiotropy through the addition and testing of an intercept term, though the method relies on the weaker assumption that the instrument strength is independent of direct effects (InSIDE assumption) .
Statistical significance was considered at an alpha value of 0.0021 after Bonferroni adjustment for testing of 24 hypotheses (6 exposures on 4 outcomes, 0.05/24).
Higher genetically predicted BMI was associated with longer PWD (β 5.58; 95%CI [3.66,7.50]; p < 0.001), as was higher genetically predicted fat mass (β 6.62; 95%CI [4.63,8.62]; p < 0.001) and fat-free mass (β 9.16; 95%CI [6.85,11.47]; p < 0.001). Genetically predicted height (β 4.23; 95%CI [3.16, 5.31]; p < 0.001) and weight (β 8.08; 95%CI [6.19,9.96]; p < 0.001) were also associated with longer PWD. However, genetically predicted aWHR was not associated with differences in PWD (β −1.24; 95%CI [−3.73,1.25]; p = 0.330). The results are displayed in Fig 1 and Table 2.
aWHR, adjusted waist:hip ratio; BMI, body mass index; CI, confidence interval; MR, mendelian randomization; ms, milliseconds; PWD, P-wave duration.
As displayed in Table 3, the results remained consistent on sensitivity analyses using weighted median MR. MR-Egger estimates also remained consistent, and intercept test did not identify evidence of directional pleiotropy in the association with BMI (p = 0.534), height (p = 0.107), fat mass (p = 0.657), fat-free mass (p = 0.326), weight (p = 0.292), and aWHR (p = 0.748). Instrumental variable F-statistics were >10 in all cases, as reported in Table 4.
Higher genetically predicted BMI was associated with longer PR interval (β 2.29; 95%CI [0.78,3.79]; p = 0.003), but this was no longer significant after accounting for multiple testing (threshold p-value = 0.0021). No association was observed between higher genetically predicted fat mass (β −0.97; 95%CI [−2.61,0.67]; p = 0.248), fat-free mass (β 0.66; 95%CI [−1.29,2.60]; p = 0.509), height (β −0.52; 95%CI [−1.41,0.38]; p = 0.258), weight (β 0.42; 95%CI [−1.11,1.94]; p = 0.594), or aWHR (β −0.24; 95%CI [−2.00,1.52]; p = 0.791) and PR interval. The results are displayed in Fig 1 and Table 2.
As displayed in Table 3, the results remained consistent on sensitivity analyses using weighted median MR and MR-Egger. MR-Egger intercept test did not identify evidence of directional pleiotropy in the association with BMI (p = 0.498), height (p = 0.451), fat mass (p = 0.631), fat-free mass (p = 0.913), weight (p = 0.689), and aWHR (p = 0.078). F-statistics for all instruments were >10, as reported in Table 4.
Higher genetically predicted BMI was not associated differences in QRS interval (β 0.06; 95%CI [−0.62,0.74]; p = 0.856) and neither were genetically predicted fat mass (β −0.20; 95%CI [−0.96,0.55]; p = 0.599), fat-free mass (β −0.01; 95%CI [−1.35,1.33]; p = 0.989), height (β −0.59; 95%CI [−1.52,0.34]; p = 0.211), weight (β −0.24; 95%CI [−1.08,0.59]; p = 0.567), and aWHR (β 0.69; 95%CI [−1.09,2.48]; p = 0.445). The results are displayed in Fig 2 and Table 2.
aWHR, adjusted waist:hip ratio; BMI, body mass index; CI, confidence interval; MR, mendelian randomization; ms, milliseconds.
As displayed in Table 3, the results remained consistent on sensitivity analyses using weighted median MR. MR-Egger estimates also remained consistent, and intercept test did not identify evidence of directional pleiotropy in the association with BMI (p = 0.802), height (p = 0.263), fat mass (p = 0.537), fat-free mass (p = 0.927), weight (p = 0.987), and aWHR (p = 0.076). F-statistics for all instruments were >10, as reported in Table 4.
Corrected QT interval
Higher genetically predicted BMI was associated with longer QTc (β 3.53; 95%CI [2.63,4.43]; p < 0.001), driven by both genetically predicted fat mass (β 3.65; 95%CI [2.73,4.57]; p < 0.001) and fat-free mass (β 2.08; 95%CI [0.85,3.31]; p = 0.001). Additionally, genetically predicted height (β 0.98; 95%CI [0.46,1.50]; p < 0.001), weight (β 3.45; 95%CI [2.54,4.36]; p < 0.001), and aWHR (β 1.92; 95%CI [0.87,2.97]; p < 0.001) were all associated with longer QTc. The results are displayed in Fig 2 and Table 2.
As displayed in Table 3, the results remained consistent on sensitivity analyses using weighted median MR. MR-Egger estimates also remained consistent, and intercept test did not identify evidence of directional pleiotropy in the association with BMI (p = 0.709), height (p = 0.088), fat mass (p = 0.704); fat-free mass (p = 0.939), weight (p = 0.305), and aWHR (p = 0.575). F-statistics for all instruments were >10, as reported in Table 4.
In this study, we used MR to investigate the causal relevance of multiple anthropometric traits, relating to adiposity and lean body composition, and 12-lead ECG indices that are associated with atrial and ventricular arrhythmias. The main findings may be summarized in 4 key points. First, the results support a causal nature of the association between adiposity and prolonged PWD but do not support a causal role of adiposity on PR interval. Second, they reveal an association between higher lean mass and height with PWD but not PR interval. Third, higher adiposity was associated with a prolonged QTc interval but was not associated with a longer QRS interval. Finally, we demonstrate an association between lean mass and height with prolonged QTc, which has not been previously reported.
The causal inferences that can be made on the basis of MR study results is reliant on meeting a number of instrumental variant assumptions. These state that the instruments must predict the exposure, that they must be associated with the outcome only through the exposure, and that no confounders or common causes of the exposure, in this case the genetic variants, and the outcome exist. In order to address the first assumption, we formally quantified the F-statistics and detected no evidence of weak instruments that might bias the results. To evaluate the second assumption, sensitivity analyses were carried out. These only identified evidence of horizontal pleiotropy for the association of BMI with PR interval, suggesting that this association might be biased, and, therefore, this result should not be taken to support causal relevance. Importantly, however, it is known that MR-Egger performs less well in settings where there is significant overlap between the GWAS studies used to source gene-exposure and gene-outcome association estimates. Since this is the case in the present study, it cannot be excluded that some pleiotropy might exist for other associations . To further investigate, we also performed weighted median MR analyses to corroborate findings from MR-Egger, and this provides further reassurance that directional pleiotropy is unlikely to have occurred undetected. The final assumption cannot formally be tested but can be mitigated by the use of GWAS data from similar ancestries. For this reason, we only utilized data from studies in European ancestry individuals. However, for some data sources, specifically that of PR interval and QRS duration, there was a proportion of non-European ancestry individuals included in the study due to lack of availability of data in European ancestry individuals only. Though only a minority of participants were non-European ancestry in these studies, it should be considered that some bias might arise from this.
The results of this study support a causal nature of the association between BMI and slower atrial conduction. Prior observational studies have outlined an association between adiposity traits, including weight, BMI, and fat mass, on atrial depolarization, slower atrial conduction, most commonly describing longer PWD [8,11,30–33] in individuals with higher BMI. The results of our study suggests that weight control is likely to play an important role in reduction of atrial arrhythmic risk in individuals with obesity and optimization of rhythm control strategies in AF. Promising evidence has established a degree of reversibility of these atrial ECG changes following weight loss [8,34–37]. Combined with prior observational evidence linking longer PWD with greater AF risk and higher recurrence rates after catheter ablation [38–46], our results provide further evidence to support the central role of weight reduction to reduce both the risk of onset and recurrence of atrial arrhythmias.
Importantly, the results suggest that adiposity traits predominantly associate with PWD, with no evidence association with PR interval. We therefore suggest that the potential influence of anthropometric traits is likely predominantly though electrophysiological and structural remodeling of the atria (e.g., fibrosis, dilatation) , resulting in slower atrial conduction, rather than through direct influence on the specialized conduction tissue, as the AV node is the major determinant of PR interval. Mechanistically, epicardial fat might play a key role, as it is known to influence cardiac electrophysiology by modulating ionic currents via paracrine mechanisms . Importantly, as previously noted, the GWAS for PR interval was in a multi-ancestry cohort, so the null findings should not be interpreted as conclusive evidence of lack of association and should be replicated in a European ancestry cohort when this becomes available.
In contrast to the results of our study, a prior MR investigation identified an inverse, rather than direct, association between PWD, PR interval, and AF risk . This is not intuitively in agreement with our results. Higher BMI has a well-established causal effect on AF. In this study, we also demonstrate that higher BMI causes slower atrial depolarization, with higher PWD and PR interval. Thus, we would expect that higher, rather than lower, PWD and PR interval might be associated with higher risk of AF. This discrepancy is most likely explained by the existence of a nonlinear association between atrial ECG indices and AF risk, whereby both individuals at the low and high end of the distribution are at increased risk of AF. However, given the current lack of evidence to demonstrate this, it is an important target for further research as duly highlighted by the authors in the original study.
Together with the findings on adiposity-related traits, our results support an additional role of both fat-free mass and height on increased PWD. Height and fat-free body mass are 2 closely correlated phenotypes . Although the association between these anthropometric traits and ECG indices of atrial conduction has not been described previously, studies have reported a stronger contribution of lean body mass on excess AF risk conferred by BMI [49–52] compared to fat mass. Similarly, height has been associated with greater risk of AF in observational and MR studies [53–61]. Although the mechanisms underlying these associations are unclear, a possible explanation relates to left atrial volume. Indeed, the association between left atrial volumes and AF risk is well established . Taller individuals have higher absolute left atrial volumes since cardiac chamber volume is a direct function of body size . It is possible that, despite the nonpathological nature of the increased volume conferred by height, it nevertheless contributes to a degree of electrophysiological dysfunction and arrhythmia predisposition , as suggested by the association with longer PWD described in the MR analyses this study. Overall, the mechanism behind this association remains an important unanswered question and a key target for further exploration.
There were no associations between any of the anthropomorphic traits studied and QRS duration. Since ventricular depolarization is facilitated by the His–Purkinje network, this suggests that the effects of body composition are more pronounced on ventricular myocardium than on specialized conduction tissue. The observational evidence of associations between adiposity and QRS duration is mixed, with some studies finding no association  and another reporting an association between increasing BMI and QRS duration that was independent of other covariates such as sex and age . Our study adds to this contrasting evidence by suggesting a lack of causal association between adiposity and QRS duration. However, it must be noted that the GWAS study for QRS interval was performed a multi-ancestry cohort; therefore, the null findings should not be interpreted as conclusive evidence of lack of association and should be replicated in a European ancestry cohort when this becomes available.
Several observational studies have described direct associations between BMI and QTc [9,11,66], with a degree of reversibility after significant weight loss . Another recent observational study using UK Biobank data also showed that QTc interval prolongs with increasing BMI, body fat, waist:hip ratio, as well as hip and waist circumference . The association between longer QTc and risk of SCD is well established and supported by MR evidence . In line with this, obesity has been associated with a higher risk of ventricular arrhythmias in prior observational studies  and is the most common ischemic cause of SCD  even after adjusting for age, sex, ethnicity, and cardiovascular risk factors . The results of the present study support that this previously described association might be of causal relevance. The results specifically demonstrate an association between BMI and QTc, and additionally of both total fat mass and aWHR with QTc, suggesting an important role of both the volume of adiposity and its distribution. Considering the established association between QTc and SCD, as well as the reversibility in ECG phenotypes demonstrated after significant weight loss, the results herein stress the importance of weight reduction on reducing risk of SCD in individuals with obesity. Though SCD remains a rare cause of mortality, targeted weight loss intervention in those at high-risk, such as those with prior myocardial infarction or reduced ejection fraction, is likely of important benefit and should be a key priority for randomized study.
The association of lean body mass and height with ventricular repolarization is less well described than that with adiposity. To date, there are no published studies, observational or MR based, assessing the association of height and lean body mass with ventricular ECG parameters. However, observational evidence exists describing of greater burden of ventricular ectopics with increasing height [70,71]. Conversely, one prior study has described an inverse association between height and SCD . Pathophysiologically, lean body mass and height, which are closely correlated, are known to be major predictors of absolute left ventricular mass . Left ventricular mass and its diagnostic correlate of left ventricular hypertrophy are, in turn, known to be associated with longer QTc interval [74–77] and are predictors of SCD, including among young, healthy individuals and athletes [78–81]. Mechanistically, the associations between height and fat-free mass with longer QTc observed in this study may be mediated by what would be described as a “physiological” increase in left ventricular mass. Overall, the results raise the possibility that taller individuals with a greater lean body mass have a more proarrhythmic electroanatomic ventricular architecture.
There are a number of limitations to this study. First, the effect estimates presented cannot be used to infer the predicted change in ECG parameters per unit increase or decrement in the exposure phenotypes with the exceptions of BMI and aWHR. This is because the inverse normal rank transformation carried out on the exposure measures for analysis in the original GWAS studies mean that the unit changes in the exposure cannot be intuitively interpreted. Second, the outcome data sources for PR interval and QRS duration were from a GWAS on a population of mixed ancestry, while exposure data sets were on populations of European ancestry. Though both exposure and outcome estimates were adjusted for population structure, there can be residual bias in the MR estimates from population stratification. This can act in either direction (both away from the null or toward the null), and, therefore, the null results on the analyses on these outcomes should not be taken as unequivocal evidence of lack of an association. Third, we attempted to further explore the mechanistic role of left atrial size and ventricular mass in the associations demonstrated in this study, but there were insufficient instruments to carry out MR analysis in the largest available GWAS to date . Fourth, we attempted to perform multivariable analyses including multiple anthropometric traits that are known to be correlated (e.g., fat mass and fat-free mass) in an attempt to establish the key driving factors in the associations with PWD and QTc. Unfortunately, the majority of these analyses had weak instruments (conditional F-statistics <10) and therefore were unreliable. The results of this analysis are reported in S1 Table This remains an important target for future research once larger GWAS data becomes available. Fifth, the use of predominantly European ancestry specific data limits generalisability to other ancestries; the analysis should therefore be replicated using data from other ancestries once available. Sixth, in this study, there was a degree of sample overlap, as both the exposure and outcome data sets included UK Biobank participants. The potential bias that might stem from this is, however, limited, as sample overlap has been shown to exert a very limited influence on results in the setting of large biobanks even when complete sample overlap exists . Finally, the GWAS studies that were used to derive gene-outcome association data utilized a range of methods and software programs to extract the ECG indices. Within the scope of our study, we did not have access to individual-level data and therefore could not access the ECG tracings, and for this reason, we were unable to recalculate the ECG indices using different techniques such as signal averaging, which is known to be particularly useful in individuals with obesity.
Our results support causal associations between multiple anthropometric traits, both adiposity- and non-adiposity related, and abnormal ECG indices that have been previously causally related to risk of atrial and ventricular arrhythmias. The results stress the mechanistic importance of weight management in at-risk individuals to prevent or reverse proarrhythmic electrophysiological remodeling. The majority of associations related to ECG indices reflect electrophysiological function of cardiomyocytes rather than function of specialized conducting tissue. The results additionally identify an important role of fat-free mass and height, which have not been previously described as proarrhythmic factors except in the setting of AF risk. These are key targets for further investigation, as they might provide important biological insight and aid clinical risk stratification in high-risk individuals.
S1 STROBE Checklist. Strengthening the Reporting of Observational Studies in Epidemiology using Mendelian Randomization (STROBE-MR) checklist of recommended items to address in mendelian randomization (MR) studies.
The authors would like to acknowledge the participants and investigators of the GIANT, UK Biobank, ARIC, HCHS/SOL, MESA, and WHI studies.
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