Associations of cardiometabolic risk factors with heart rate variability in 6- to 8-year-old children: The PANIC Study

Heart rate variability to track autonomic nervous system health in young children: Effects of physical activity and cardiometabolic risk factors

Evidence for a key role of dysregulated autonomic nervous system (ANS) activity in maladaptive stress response/recovery and non-communicable disease development is extensive. Monitoring ANS activity via regular heart rate variability (HRV) measurement is growing in popularity in adult populations given that low HRV has been associated with ANS dysregulation, poor stress response/reactivity, increased cardiometabolic disease risk and early mortality. Although cardiometabolic disease may originate in early life, regular HRV measurement for assessing ANS activity in childhood populations, especially those consisting of children < 6 years of age, remains largely unpractised. A greater understanding of ANS activity modifiers in early life may improve analysis and interpretation of HRV measurements, thereby optimising its usefulness. Taking into consideration that HRV and ANS activity can be improved via daily engagement in physical activity (PA), this review will discuss the ANS and HRV, ANS activity modifiers, cardiometabolic disease risk factors and PA as they relate to childhood/adolescent populations (≤ 18 years old).

Short-Term Heart Rate Variability in Metabolic Syndrome: A Systematic Review and Meta-Analysis

BACKGROUND

Our aim was to determine the differences in short-term heart rate variability (HRV) between patients with metabolic syndrome (MS) and healthy controls.

METHODS

We searched electronic databases for primary works with short-term HRV recordings (≤30 min) that made comparisons between individuals with MS versus healthy controls. This systematic review and meta-analysis (MA) was performed according to PRISMA guidelines and registered at PROSPERO (CRD42022358975).

RESULTS

Twenty-eight articles were included in the qualitative synthesis and nineteen met the criteria for the MA. Patients with MS showed decreased SDNN (-0.36 [-0.44, -0.28], p < 0.001), rMSSD (-7.59 [-9.98, -5.19], p < 0.001), HF (-0.36 [-0.51, -0.20], p < 0.00001) and LF (-0.24 [-0.38, -0.1], p = 0.001). In subsequent subanalyses, we found a decrease in SDNN (-0.99 (-1.45, -0.52], p < 0.001), rMSSD (-10.18 [-16.85, -3.52], p < 0.01) and HF (-1.04 [-1.97, -0.1] p < 0.05) in women. In men, only LF showed a significant lower value (-0.26 [-0.5, -0.02], p < 0.05). We could not perform MA for non-linear variables.

CONCLUSIONS

Patients with MS showed changes in time-domain analyses, with lower values in SDNN and rMSSD. Regarding frequency-domain analyses, MS patients showed a decrease in HF and LF When sex was used as a grouping variable, the MA was only possible in one of both sexes (men or women) in rMSSD and LF/HF. Lastly, when data for both men and women were available, subanalyses showed a different behavior compared to mixed analyses for SDNN, HF and LF, which might point towards a different impact of MS in men and women.

Heart Rate Variability and Cardiorespiratory Fitness in Youth Populations: The Impact of Heart Rate on Data Analysis

The positive association between heart rate variability and cardiorespiratory fitness in youth populations is unclear. In this regard, several methodological aspects related to heart rate variability analysis could partially explain the disagreement between studies. To the best of the authors’ knowledge, the influence of the heart rate on data analysis is unclear. In the present short communication, we discuss the impact of the heart rate on the associations between heart rate variability and cardiorespiratory fitness in youth. In addition, we proposed some aspects that should be considered for statistical analyses when the relationship between heart rate variability and cardiorespiratory fitness is studied. Finally, we should acknowledge that these recommendations may be applicable for other health-related outcomes different than cardiorespiratory fitness (e.g., inflammatory markers, cognition, cardiovascular disease status).

Comprehensive cardiac evaluation to maximal exercise in a contemporary population of prepubertal children

BACKGROUND

Heart rate (HR) is a biomarker used to measure physiological function, health status and cardiovascular autonomic function. The purpose of this study was to determine sex- and age-specific reference values for cardiac autonomic function at rest, during maximal exercise and the recovery phase in prepubertal children.

METHODS

Five hundred and twelve healthy children 7-11 years of age performed a Léger test. A heart RR-interval monitor recorded the heart data and a specific software analysed the cardiac autonomic response through HR and HR variability (HRV). It analysed HR before the test (resting HR, RHR), during the test (HR_peak) and HR recovery (HRR) in the first minute (HRR1) and the fifth minute (HRR5). The values are mean ± SD.

RESULTS

Collectively, 91.2% of girls and 92.3% of boys were within the recommended ranges regarding RHR. The average HR_peak was 199 ± 10.83 b.p.m. and 96.8% of girls and 95.3% of boys were within the minimum threshold value recommended (180 b.p.m.). Boys showed lower values of RHR than girls (p < 0.001) and larger values of HRR 1 and HRR5 (p < 0.001).

CONCLUSIONS

This study comprehensively provides a reference set of data for the most important HR variables that can be obtained during exercise testing in prepubertal children regarding age and sex and in a field setting.

IMPACT

This is the first study to provide reference values of autonomic cardiac function at rest, during maximal exercise and during the recovery period in prepubertal children aged 7-11 years. Despite the early age of participants, cardiorespiratory fitness, RHR and HRR are different according to sex. Aerobic performance and HR_peak have a negative correlation with body mass index and cardiometabolic risk.

Determinants of Cognitive Performance in Children and Adolescents: A Populational Longitudinal Study

We evaluated the determinants of cognitive performance in children and adolescents. This is a longitudinal study, secondary analysis of the Physical Activity and Nutrition in Children (PANIC) study. We assessed 502 children (51.6% girls) at middle childhood (range: 6.6 to 9.0 years), at late childhood, 437 children (51.0% girls, range: 8.8 to 11.2 years), and in 277 adolescents (54.5% girls, range: 15.0 to 17.4 years). Raven's progressive matrices tests estimated the participants' cognitive performance (outcome variable) at all time points. In total, we evaluated 29 factors from various dimensions (prenatal, neonatal, child fitness, lifestyle and anthropometrics). None of the neonatal and anthropometric parameters were associated with cognitive performance. Preeclampsia (prenatal) and listening to music, writing, arts and craft and watching TV (lifestyle) were negatively associated with cognitive performance. Shuttle run and box and block tests (fitness), and playing music, reading and time at the computer (lifestyle) were positive determinants of cognitive performance in children and adolescents. Fitness and lifestyle factors during childhood and adolescence diminished the importance of prenatal factors on cognitive performance and lifestyle factors were especially relevant in regard to cognitive performance. Reading was positively associated with cognitive performance, regardless of age and time dedicated, and should be promoted.

Relationship between heart rate variability and body mass index: A cross-sectional study of preschool children

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Heart rate variability and BMI are inversely related in preschool children.

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One unit increase in BMI resulted in a reduction in RMSSD(ln) of 0.06%

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Age, sex and physical activity levels did not influence this relationship.

Reduced heart rate variability (HRV) is associated with overweight and obesity in adults. However, little is known about this relationship in early childhood. We investigated the relationship between resting vagally-mediated HRV and body mass index (BMI) in Australian preschool children. Children were recruited from 13 non-government early learning centres located in Queensland and New South Wales, Australia. From this population-based sample, data from 146 healthy children (58 females) between 3 and 5 years of age (mean age 4.35 ± 0.44 years) were analysed. BMI was calculated from child body weight and height. Physical activity was recorded using an Actigraph wGT3x accelerometer worn at the waist of participants over 3 consecutive days. A Polar H10 chest strap measured seated, resting RR intervals for the calculation of HRV with the root mean square of successive differences (RMSSD) reflecting vagally-mediated activity. The relationship between HRV and BMI was analysed using a linear mixed model adjusted for age, sex and physical activity. Analysis revealed that RMSSD (ln) demonstrated a significant inverse relationship with BMI (β = -0.06; 95% CI = -0.12 – −0.01; p = 0.032), and the model accounted for 23% of the variance in RMSSD (ln). Notably, a one unit increase in BMI resulted in a reduction in RMSDD (ln) of 0.06. This investigation demonstrated evidence for a significant inverse linear relationship between vagally-mediated HRV and BMI in 3 – 5-year-old Australian children, similar to that of adults. Furthermore, this relationship was independent of age, sex and physical activity levels. Results may indicate that the cardiometabolic health of preschool children is, in part, influenced by the relationship between vagally-mediated HRV and weight status.

Association between Metabolic Syndrome Components and Cardiac Autonomic Modulation among Children and Adolescents: A Systematic Review and Meta-Analysis

Simple Summary

The clustering of metabolic syndrome (MetS) risk factors is becoming more prevalent in young people (up to the age of 19 years) leading to the development of type 2 diabetes (T2D) and cardiovascular diseases in early adulthood. The impact of MetS risk factors on cardiac autonomic modulation (CAM) or vice versa have been noted to track from childhood to pre-adolescence and adolescence. Understating associations in this age group may help improve the clinical outcomes of the MetS, even when MetS symptoms are not visible. Potential damage from each individual MetS component and the ability to predict early cardiac damage or upcoming cardiovascular events is very important. Therefore, the present systematic review and meta-analysis investigated the associations between CAM and MetS risk factors individually to verify which MetS risk components were significantly correlated with which heart rate variability (HRV) indices before or at the onset of the MetS among young people. The purpose of this review was to outline the importance of potentially screening HRV indices in young people even with only one MetS risk factor, as a pre-indicator for early cardiovascular risk stratification. Cross-sectional studies that examined the relationship of MetS risk factors with HRV indices were searched using four databases including PubMed, the Cochrane clinical trials library, Medline and the Web of Science. Correlation coefficients with 95% confidence intervals (95% CI), and random effects meta-analyses of the association between MetS risk factors with HRV indices were performed. Our results propose that lipid profiles including high density lipoprotein (HDL) and triglycerides (TGs), waist circumference (WC) and blood pressure (BP) are associated with CAM in young people up to the age of 19 years. The use of HRV indices to predict future MetS risk, and relationships with individual risk factors including HDL, BP, WC and TGs, were established. Furthermore, arterial pressure, respiration, stress and physical activity must be taken into consideration for future studies along with CAM related to young people (up to the age of 19 years), and it is recommended to explore further the associations reported here, as CAM is not the only determinant of neurovisceral regulation.

Abstract

Background: the clustering of metabolic syndrome (MetS) risk factors is becoming more prevalent in children, leading to the development of type 2 diabetes (T2D) and cardiovascular diseases in early adulthood. The impact of MetS risk factors on cardiac autonomic modulation (CAM) or vice versa has been noted to track from childhood to pre-adolescence and adolescence. Understating associations in this age group may help to improve the clinical outcomes of the MetS, even when MetS symptoms are not visible. Potential damage from each individual MetS component and the ability to predict early cardiac damage or upcoming cardiovascular events is very important. Therefore, the present systematic review and meta-analysis investigated the associations between CAM and MetS risk factors individually to verify which of the MetS risk components were significantly correlated with heart rate variability (HRV) indices before or at the onset of the MetS among young people. The purpose of this review was to outline the importance of potentially screening HRV indices in young people even with only one MetS risk factor, as a pre-indicator for early cardiovascular risk stratification. Methods: cross-sectional studies that examined the relationship of MetS risk factors with HRV indices were searched using four databases including PubMed, the Cochrane clinical trials library, Medline and the Web of Science. Correlation coefficients with 95% confidence intervals (95% CI), and random effects meta-analyses of the association between MetS risk factors with HRV indices were performed. Results: out of 14 cross-sectional studies and one case-control study, 8 studies (10 data sets) provided association data for the meta-analysis. Our results indicated significant positive correlations for systolic blood pressure (SBP) (correlation coefficient 0.13 (95%CI: 0.06; 0.19), I² = 47.26%) and diastolic blood pressure (DBP) (correlation coefficient 0.09 (95%CI: −0.01; 0.18), I² = 0%) with a Low Frequency/High Frequency ratio (LF/HF). Significant negative correlations for waist circumference (WC) (correlation coefficient −0.12 (95%CI: −0.19; −0.04), I² = 51.50%), Triglycerides (TGs) (correlation coefficient −0.09 (95%CI: −0.15; −0.02), I² = 0%) and ≥2 MetS risk factors (correlation coefficient −0.10 (95%CI: −0.16; −0.03), I² = 0%); with high frequency (HF) were revealed. Significant positive correlations for high density lipoprotein (HDL) (correlation coefficient 0.08 (95%CI: 0.05; 0.11), I² = 0%) and significant negative correlations of ≥2 MetS risk (correlation coefficient −0.04 (95%CI: −0.12; 0.03), I² = 0.0%) with low frequency (LF) were revealed. Significant negative correlations for TGs (correlation coefficient −0.09 (95%CI: −0.23; 0.05), I² = 2.01%) with a mean square root of the sum of differences between mean time between two successive intervals (rMSSD) and significant positive correlation of HDL (correlation coefficient 0.09 (95%CI: −0.01; 0.19), I² = 0.33%) with standard deviation of the time between two successive intervals (SDNN) were also revealed. An Egger’s test indicated that there was no obvious publication bias for any of the above relationships except for TGs and rMSSD. The significance level stipulated for the meta-analysis was p < 0.05. Conclusions: lipid profiles (HDL and TGs), WC and BP were associated with CAM in young people up to the age of 19 years. The use of HRV indices to predict future MetS risk, and relationships with individual risk factors including HDL, BP, WC and TGs, were established. Future studies related to young people (up to the age of 19 years) are recommended to explore the associations reported here further.

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

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.