Relating QRS voltages to left ventricular mass and body composition in elite endurance athletes.

Ruben De Bosscher; Jonathan Moeyersons; Christophe Dausin; Mathias Claeys; Kristel Janssens; Piet Claus; Kaatje Goetschalckx; Jan Bogaert; Caroline M Van De Heyning; Bernard Paelinck; Prashanthan Sanders; Jonathan Kalman; Sabine Van Huffel; Carolina Varon; André La Gerche; Hein Heidbuchel; Guido Claessen; Rik Willems;
Abstract
Electrocardiogram (ECG) QRS voltages correlate poorly with left ventricular mass (LVM). Body composition explains some of the QRS voltage variability. The relation between QRS voltages, LVM and body composition in endurance athletes is unknown.Elite endurance athletes from the Pro@Heart trial were evaluated with 12-lead ECG for Cornell and Sokolow-Lyon voltage and product. Cardiac magnetic resonance imaging assessed LVM. Dual energy x-ray absorptiometry assessed fat mass (FM) and lean mass of the trunk and whole body (LBM). The determinants of QRS voltages and LVM were identified by multivariable linear regression. Models combining ECG, demographics, DEXA and exercise capacity to predict LVM were developed.In 122 athletes (19 years, 71.3% male) LVM was a determinant of the Sokolow-Lyon voltage and product (β = 0.334 and 0.477, p < 0.001) but not of the Cornell criteria. FM of the trunk (β = - 0.186 and - 0.180, p < 0.05) negatively influenced the Cornell voltage and product but not the Sokolow-Lyon criteria. DEXA marginally improved the prediction of LVM by ECG (r = 0.773 vs 0.510, p < 0.001; RMSE = 18.9 ± 13.8 vs 25.5 ± 18.7 g, p > 0.05) with LBM as the strongest predictor (β = 0.664, p < 0.001). DEXA did not improve the prediction of LVM by ECG and demographics combined and LVM was best predicted by including VOmax (r = 0.845, RMSE = 15.9 ± 11.6 g).2LVM correlates poorly with QRS voltages with adipose tissue as a minor determinant in elite endurance athletes. LBM is the strongest single predictor of LVM but only marginally improves LVM prediction beyond ECG variables. In endurance athletes, LVM is best predicted by combining ECG, demographics and VOmax.2
Journal
ISSN 1439-6327
Published 14 Nov 2022
Volume
Issue
Pages
DOI 10.1007/s00421-022-05080-5
Type Journal Article
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