Cardiovascular Risk Factors and Heart Rate Variability: Impact of the Level of the Threshold-Based Artefact Correction U
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IMAGE & SIGNAL PROCESSING
Cardiovascular Risk Factors and Heart Rate Variability: Impact of the Level of the Threshold-Based Artefact Correction Used to Process the Heart Rate Variability Signal Abel Plaza-Florido 1
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J. M.A. Alcantara 1 & Francisco J. Amaro-Gahete 1,2 & Jerzy Sacha 3,4 & Francisco B. Ortega 1
Received: 7 August 2020 / Accepted: 5 November 2020 # Springer Science+Business Media, LLC, part of Springer Nature 2020
Abstract The associations between cardiovascular disease (CVD) risk factors and heart rate variability (HRV) have shown some inconsistencies. To examine the impact of the different Kubios threshold-based artefact correction levels on the associations between different CVD risk factors and a heart rate variability (HRV) score in three independent human cohorts. A total of 107 children with overweight/obesity, 132 young adults, and 73 middle-aged adults were included in the present study. Waist circumference and the HRV score were negatively associated using the medium and the strong Kubios filters in children (β = −0.22 and − 0.24, P = 0.03 and 0.02 respectively) and the very strong Kubios filter in middle-aged adults (β = −0.39, P = 0.01). HDL-C was positively associated with the HRV score across Kubios filters (β ranged from 0.21 to 0.31, all P ≤ 0.04), while triglycerides were negatively associated with the HRV score using the very strong Kubios filter in young adults (β = −0.22, P = 0.02). Glucose metabolism markers (glucose, insulin, and HOMA index) were inversely associated with the HRV score across Kubios filters in young adults (β ranged from −0.29 to −0.22; all P ≤ 0.03). Importantly, most of these associations disappeared after including HR as a covariate, especially in children and young adults. It should be mandatory to report the Kubios filter used and to include the HR (as a confounder factor) to allow the comparability of the results across different studies. Keywords Kubios software . Parasympathetic . Autonomic nervous system . Metabolism . Metabolic syndrome
Introduction
This article is part of the Topical Collection on Image & Signal Processing * Abel Plaza-Florido [email protected] 1
PROFITH “PROmoting FITness and Health Through Physical Activity” Research Group, Sport and Health University Research Institute (iMUDS), Department of Physical and Sports Education, Faculty of Sport Sciences, University of Granada, Carretera de Alfacar, s/n CP, 18071 Granada, Spain
2
EFFECTS-262 Research Group, Department of Physiology, School of Medicine, University of Granada, 18071 Granada, Spain
3
Faculty of Physical Education and Physiotherapy, Opole University of Technology, Opole, Poland
4
Department of Cardiology, University Hospital in Opole, University of Opole, Opole, Poland
Cardiovascular diseases (CVD) are the leading cause of mortality [1]. Therefore, the identification of non-invasive biomarkers for detecting the development of CVD is a matter of interest [2]. Usually, individual CVD risk factors are clustered and commonly called “metabolic syndrome” [3]. Cardiac
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