부산대학교 도서관

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 (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. 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. In a Mendelian randomization study, Fu Siong Ng and colleagues investigate how different anthropometric measures influence proarrhythmic 12-lead ECG indices. Author summary: 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. [ABSTRACT FROM AUTHOR]