Cargando…
Multiple anthropometric measures and proarrhythmic 12-lead ECG indices: A mendelian randomization study
BACKGROUND: 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 (...
Autores principales: | , , , , , |
---|---|
Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Public Library of Science
2023
|
Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10443852/ https://www.ncbi.nlm.nih.gov/pubmed/37552661 http://dx.doi.org/10.1371/journal.pmed.1004275 |
_version_ | 1785093926763364352 |
---|---|
author | Ardissino, Maddalena Patel, Kiran Haresh Kumar Rayes, Bilal Reddy, Rohin K. Mellor, Greg J. Ng, Fu Siong |
author_facet | Ardissino, Maddalena Patel, Kiran Haresh Kumar Rayes, Bilal Reddy, Rohin K. Mellor, Greg J. Ng, Fu Siong |
author_sort | Ardissino, Maddalena |
collection | PubMed |
description | BACKGROUND: 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 (r(2) < 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. CONCLUSIONS: The results of this study support a causal role of BMI on multiple ECG indices that have previously been associated with atrial and ventricular arrhythmic risk. Importantly, the results identify a role of both fat mass, fat-free mass, and height in this association. |
format | Online Article Text |
id | pubmed-10443852 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2023 |
publisher | Public Library of Science |
record_format | MEDLINE/PubMed |
spelling | pubmed-104438522023-08-23 Multiple anthropometric measures and proarrhythmic 12-lead ECG indices: A mendelian randomization study Ardissino, Maddalena Patel, Kiran Haresh Kumar Rayes, Bilal Reddy, Rohin K. Mellor, Greg J. Ng, Fu Siong PLoS Med Research Article BACKGROUND: 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 (r(2) < 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. CONCLUSIONS: The results of this study support a causal role of BMI on multiple ECG indices that have previously been associated with atrial and ventricular arrhythmic risk. Importantly, the results identify a role of both fat mass, fat-free mass, and height in this association. Public Library of Science 2023-08-08 /pmc/articles/PMC10443852/ /pubmed/37552661 http://dx.doi.org/10.1371/journal.pmed.1004275 Text en © 2023 Ardissino et al https://creativecommons.org/licenses/by/4.0/This is an open access article distributed under the terms of the Creative Commons Attribution License (https://creativecommons.org/licenses/by/4.0/) , which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. |
spellingShingle | Research Article Ardissino, Maddalena Patel, Kiran Haresh Kumar Rayes, Bilal Reddy, Rohin K. Mellor, Greg J. Ng, Fu Siong Multiple anthropometric measures and proarrhythmic 12-lead ECG indices: A mendelian randomization study |
title | Multiple anthropometric measures and proarrhythmic 12-lead ECG indices: A mendelian randomization study |
title_full | Multiple anthropometric measures and proarrhythmic 12-lead ECG indices: A mendelian randomization study |
title_fullStr | Multiple anthropometric measures and proarrhythmic 12-lead ECG indices: A mendelian randomization study |
title_full_unstemmed | Multiple anthropometric measures and proarrhythmic 12-lead ECG indices: A mendelian randomization study |
title_short | Multiple anthropometric measures and proarrhythmic 12-lead ECG indices: A mendelian randomization study |
title_sort | multiple anthropometric measures and proarrhythmic 12-lead ecg indices: a mendelian randomization study |
topic | Research Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10443852/ https://www.ncbi.nlm.nih.gov/pubmed/37552661 http://dx.doi.org/10.1371/journal.pmed.1004275 |
work_keys_str_mv | AT ardissinomaddalena multipleanthropometricmeasuresandproarrhythmic12leadecgindicesamendelianrandomizationstudy AT patelkiranhareshkumar multipleanthropometricmeasuresandproarrhythmic12leadecgindicesamendelianrandomizationstudy AT rayesbilal multipleanthropometricmeasuresandproarrhythmic12leadecgindicesamendelianrandomizationstudy AT reddyrohink multipleanthropometricmeasuresandproarrhythmic12leadecgindicesamendelianrandomizationstudy AT mellorgregj multipleanthropometricmeasuresandproarrhythmic12leadecgindicesamendelianrandomizationstudy AT ngfusiong multipleanthropometricmeasuresandproarrhythmic12leadecgindicesamendelianrandomizationstudy |