Improvement of Sympathovagal Balance by Regular Exercise May Counteract the Ageing Process. A Study by the Analysis of QT Variability

Abstract

QT interval (QT) variability analysis provides pathophysiological and prognostic information utilized in cardiac and non-cardiac diseases, complementary to those obtained from the analysis of heart period (HP) variability. An increased QT variability has been associated to a higher risk for cardiac events and poorest prognosis. Autonomic cardiovascular adaptation to internal and external challenges, such those occurring in athletes exposed to high levels of physical stress and in ageing could also be deepen by analyzing QT variability, searching for early prognostic signatures. The aim of the study was to analyze the QT variability and cardiac control complexity in a group of middle-aged half-marathon runners at baseline (B) and at a 10-year follow-up (FU). We found that the overall QT variability decreased at FU, despite the inescapable increase in age (52.3 ± 8.0 years at FU). This change was accompanied by an increase of the HP variability complexity without changes of the QT variability complexity. Of notice, over the years, the group of athletes maintained their regular physical activity by switching to a moderate intensity rather than strenuous. In conclusion, regular and moderate exercise over the years was beneficial for this group of athletes, as reflected by the decreased overall QT variability that is known to be associated to lower cardiovascular risk. The concomitant enhanced cardiac control complexity also suggests a trend opposite to what usually occurs with ageing, resulting in a more flexible cardiac control, typical of younger people.

Keywords: QT interval variability; ageing; athletes; autonomic nervous system; complexity; half-marathon; heart rate variability; physical exercise.

FIGURE 1

Results of the HP variability analysis in a group of amateur half-marathon runners at baseline (B, black bars) and at a 10-year follow-up (FU, white bars) in resting condition (REST) and during active standing (STAND). The bar graphs show the HP interval mean (μ_HP, panel (A)), HP variance (σ² _HP, panel (B)) and the power of HP variability in the high frequency band (HF_HP expressed in absolute and normalized units, panels (C,D), respectively). Data are presented as mean ± standard deviation. # indicates p < 0.05 FU vs B, while *p < 0.05 REST vs STAND.

FIGURE 2

Results of the QT variability analysis in a group of amateur half-marathon runners at baseline (B, black bars) and at a 10-year follow-up (FU, white bars) in resting condition (REST) and during active standing (STAND). The bar graphs show the QT interval mean (μ_QT, panel (A)), QT interval variance (σ² _QT, panel (B)) and the absolute power of QT variability in the low frequency band (LF_QT, panel (C)). Data are presented as mean ± standard deviation. # indicates p < 0.05 FU vs B, while *p < 0.05 REST vs STAND.

FIGURE 3

Results of the complexity analysis of the HP series in a group of amateur half-marathon runners at baseline (B, black bars) and at a 10-year follow-up (FU, white bars) in resting condition (REST) and during active standing (STAND). The bar graphs show the complexity index (CI_HP, panel (A)) and the normalized complexity index (NCI_HP, panel (B)). Data are presented as mean ± standard deviation. # indicates p < 0.05 FU vs B, while *p < 0.05 REST vs STAND.

FIGURE 4

Results of the complexity analysis of the QT series in a group of amateur half-marathon runners at baseline (B, white bars) and at a 10-year follow-up (FU, black bars) in resting condition (REST) and during active standing (STAND). The bar graphs show the complexity index (CI_QT, panel (A)) and the normalized complexity index (NCI_QT, panel (B)). Data are presented as mean ± standard deviation.