Search

Aim: The mechanisms governing the organism's response to exercise are complex and difficult to study. Spectral analysis of heart rate variability (HRV) could represent a convenient methodology for studying humans' autonomic nervous system (ANS). However, difficulties in interpreting the multitude of correlated HRV-derived indices, mainly when computed over different time segments, may represent a barrier to its usage. This preliminary investigation addressed to elite athletes proposes a novel method describing the cardiac autonomic response to exercise based on multilevel exploratory factor analysis (MEFA), which reduces the multitude of HRV-derived indices to fewer uncorrelated ANS indicators capable of accounting for their interrelationships and overcoming the above difficulties. Methods: The study involved 30 Italian Olympic athletes, divided into 15 cyclists (prevalent high-intensity endurance training) and 15 shooters (prevalent technical training with low-intensity endurance component). All athletes underwent a complete test of a dynamic protocol, constituted by a rest-stand test followed by a stepwise bicycle stress test subdivided into a single bout of progressive endurance (from aerobic to anaerobic) exercise and recovery. Then, by spectral analysis, values of 12 ANS proxies were computed at each time segment (9 epochs in all) of the complete test. Results: We obtained two global ANS indicators (amplitude and frequency), expressing the athletes' overall autonomic response to the complete test, and three dynamic ANS indicators (amplitude, signal self-similarity, and oscillatory), describing the principal dynamics over time of the variability of RR interval (RRV). Globally, cyclists have significantly higher amplitude levels (median ± MAD: cyclists 69.9 ± 20.5; shooters 37.2 ± 19.4) and lower frequency levels (median ± MAD: cyclists 37.4 ± 14.8; shooters 78.2 ± 10.2) than shooters, i.e., a parasympathetic predominance compared to shooters. Regarding the RRV dynamics, the signal self-similarity and oscillatory indicators have the strongest sensitivity in detecting the rest-stand change; the amplitude indicator is highly effective in detecting the athletes' autonomic changes in the exercise fraction; the amplitude and oscillatory indicators present significant differences between cyclists and shooters in specific test epochs. Conclusion: This MEFA application permits a more straightforward representation of the complexity characterizing ANS modulation during exercise, simplifying the interpretation of the HRV-derived indices and facilitating the possible real-life use of this non-invasive methodology. [ABSTRACT FROM AUTHOR]

Athletic performance is determined by many factors, such as cardiorespiratory fitness, muscular strength and psychological features, which all interact simultaneously. The large Italian National Olympic Committee database of Olympic athletes offers a unique healthy population to verify the strength of the interplay among a number of major elements of training, including autonomic nervous system (ANS) modulation, biochemical indicators and body composition, in a system medicine approach. This observational, retrospective study involved 583 individuals. As part of the yearly precompetitive examination, cardiac autonomic (heart rate variability), psychological, physical (cycloergometer stress test), biochemical and body composition (BOD POD) evaluations were performed. In subsequent analysis, we first considered the relationship between body composition and single individual variables in a simple correlation matrix, including a multitude of variables; then, Exploratory Factor Analysis (EFA) restricted the information to six latent domains, each combining congruent information in relation to body composition. Finally, we employed a multiple quantile regression model to evaluate possible relationships between ANSIs (index capable of synthetizing ANS regulation) and the latent domains indicated by EFA reflecting body composition. We observed a clear relationship between ANS and body mass composition parameters, as indicated by both bivariate correlations and the quantile regression result of ANSIs versus the latent domain aggregating mainly body composition data expressed in % (p = 0.002). In conclusion, these results suggest that specific training may elicit parallel adaptation of ANS control and body composition. The analysis of Olympic athletes' data allowed us to obtain a better understanding of the complex, multidimensional factors involved in determining sport performance. The latter appears to be determined by the simultaneous interaction not only of cardiorespiratory fitness, muscular strength and psychological features, but also of ANS cardiovascular modulation and body composition. [ABSTRACT FROM AUTHOR]

In the context of functional determinants of cardiovascular risk, a simple excess in body weight, as indexed by a rise in body mass index (BMI), plays a significant, well-recognized causal role. Conversely, BMI reductions toward normal result in an improvement of risk. Obesity is associated with impaired cardiac autonomic regulation (CAR), through either vagal or sympathetic mechanisms, which could favor the tendency to foster hypertension. Here we study the changing properties of the relationship between increasing grades of BMI and CAR in a population of 756 healthy subjects (age 35.9 ± 12.41 years, 37.4% males, 21.6% overweight, and 16% obese). Evaluation of CAR is based on autoregressive spectral analysis of short-term RR interval and systolic arterial pressure variability, from which a multitude of indices, treated overall as autonomic nervous system (ANS) proxies, is derived. Inspection of the study hypothesis that elevated BMI conditions associate significantly with alterations of CAR, independently of age and gender, is carried out using a mix of statistical transformations, exploratory factor analysis, non-parametric testing procedures, and graphical tools particularly well suited to address alterations of CAR as a disturbed process. In particular, to remove the effects of the inter-individual variability, deriving from components like age, gender or ethnicity, and to reduce the number of ANS proxies, we set up six age-and-gender-adjusted CAR indicators, corresponding to four ANS latent domains (oscillatory, amplitude, pressure, and pulse), cardiac baroreflex regulation, and autonomic nervous system index (ANSI). An impairment of the CAR indicators is overall evident in the overweight group and more marked in the obesity group. Empirical evidence is strong (9/9 concordant non-parametric test results) for pressure domain, almost strong (8/9) for ANSI, medium-strong for baroreflex (6/9) and pulse (7/9), weak for oscillatory (2/9) and amplitude (1/9) domains. In addition, the distribution of the CAR indicators corresponding to pressure, pulse, baroreflex, and ANSI is skewed toward the unfavorable abscissa extremity, particularly in the obese group. The significant association of increased BMI with progressive impairments of CAR regarding specifically the pressure domain and the overall ANS performance might underscore the strong hypertensive tendency observed in obesity. [ABSTRACT FROM AUTHOR]

Interests about the fine underpinnings of cardiovascular beat-by-beat variability have historical roots. Over the last decades, various aspects of the relationships between arterial pressure and heart period were taken as a proxy of the baroreflex in physiology and medicine, stimulating the interest of investigators in several interconnected scientific fields, in particular, bioengineering, neurophysiology, and clinical medicine. Studies of the overall system facilitated the emergence of a simplified negative (vagal) feedback model of the baroreflex and overshadowed the simultaneous interaction with excitatory, sympathetic positive-feedback mechanisms that would, however, better suit the model of a "paired antagonistic (parasympathetic/sympathetic) innervation of the internal organs." From the bioengineering side, the simplicity of obtaining the series of subsequent RR intervals stimulated the analysis of beat-by-beat variations, providing a multitude of heart rate variability (HRV) indices considered as proxies of the underlying sympatho-vagal balance, and participating to the management of several important clinical conditions, such as hypertension. In this context, advanced statistical methods, used in an integrated manner and controlling for age and gender biases, might help shed new light on the relationship between cardiac baroreflex, assessed by the frequency domain index α, and the HRV indices with the varying of systolic arterial pressure (SAP) levels. The focus is also on a novel unitary Autonomic Nervous System Index (ANSI) built as a synthesis of HRV considering its three most informative proxies [RR, RR variance, and the rest-stand difference in the normalized power of low-frequency (LF) variability component]. Data from a relatively large set of healthy subjects (n = 1154) with a broad range of SAP [from normal (n Nt = 778) to elevated (n Ht = 232)] show that, e.g., α and ANSI significantly correlate overall (r = 0.523, p < 0.001), and that this correlation is lower in hypertensives (r = 0.444, p < 0.001) and higher in pre-hypertensives (r = 0.618, p < 0.001) than in normotensives (r = 0.5, p < 0.001). That suggests the existence of curvilinear "umbrella" patterns that might better describe the effects of the SAP states on the relationships between baroreflex and HRV. By a mix of robust, non-parametric and resampling statistical techniques, we give empirical support to this study hypothesis and show that the pre-hypertensive group results at the apex/bottom in most of the studied trends. [ABSTRACT FROM AUTHOR]

In spite of the large body of evidence suggesting Heart Rate Variability (HRV) alone or combined with blood pressure variability (providing an estimate of baroreflex gain) as a useful technique to assess the autonomic regulation of the cardiovascular system, there is still an ongoing debate about methodology, interpretation, and clinical applications. In the present investigation, we hypothesize that non-parametric and multivariate exploratory statistical manipulation of HRV data could provide a novel informational tool useful to differentiate normal controls from clinical groups, such as athletes, or subjects affected by obesity, hypertension, or stress. With a data-driven protocol in 1,352 ambulant subjects, we compute HRV and baroreflex indices from short-term data series as proxies of autonomic (ANS) regulation. We apply a three-step statistical procedure, by first removing age and gender effects. Subsequently, by factor analysis, we extract four ANS latent domains that detain the large majority of information (86.94%), subdivided in oscillatory (40.84%), amplitude (18.04%), pressure (16.48%), and pulse domains (11.58%). Finally, we test the overall capacity to differentiate clinical groups vs. control. To give more practical value and improve readability, statistical results concerning individual discriminant ANS proxies and ANS differentiation profiles are displayed through peculiar graphical tools, i.e., significance diagram and ANS differentiation map, respectively. This approach, which simultaneously uses all available information about the system, shows what domains make up the difference in ANS discrimination. e.g., athletes differ from controls in all domains, but with a graded strength: maximal in the (normalized) oscillatory and in the pulse domains, slightly less in the pressure domain and minimal in the amplitude domain. The application of multiple (non-parametric and exploratory) statistical and graphical tools to ANS proxies defines differentiation profiles that could provide a better understanding of autonomic differences between clinical groups and controls. ANS differentiation map permits to rapidly and simply synthesize the possible difference between clinical groups and controls, evidencing the ANS latent domains that have at least a medium strength of discrimination, while the significance diagram permits to identify the single ANS proxies inside each ANS latent domain that resulted in significant comparisons according to statistical tests. [ABSTRACT FROM AUTHOR]

Background This study addresses whether a unitary cardiac autonomic nervous system index ( ANSI), obtained combining multiple metrics from heart rate variability ( HRV) into a radar plot could provide an easy appreciation of autonomic performance in a clinical setting. Materials and Methods Data are standardized using percentile ranking of autonomic proxies from a relatively large reference population ( n = 1593, age 39 ± 13 years). Autonomic indices are obtained from autoregressive spectral analysis of ( ECG derived) HRV at rest and during standing up. A reduced ANSI (using RR, RR variance and rest-stand difference of LFnu) is then constructed as a radar plot, quantified according to its combined area and tested against different risk subgroups. Results With growing risk profile, there is a marked reduction of the rank value of ANSI, quantified individually by the radar plot area. The practical usefulness of the approach was tested in small groups of additional subjects putatively characterized by elevated or poor autonomic performance. Data show that elite endurance athletes are characterized by elevated values of ANSI (80·6 ± 14·9, P < 0·001) while subjects with either Type 1 or Type 2 diabetes show lower values ( DM1 = 37·0 ± 18·9 and DM2 = 26·8 ± 23·3, P = 0·002), and patients with coronary artery disease ( CAD) represent a nadir (17 ± 20, P < 0·001). Conclusions This observational study shows the feasibility of testing simpler metrics of cardiac autonomic regulation based on a multivariate unitary index in a preventive setting. This simple approach might foster a wider application of HRV in the clinical arena, and permit an easier appreciation of autonomic performance. [ABSTRACT FROM AUTHOR]