RESUMEN
Background: Atherosclerotic carotid intima-media thickness (IMT) may be associated with alterations in the sensitivity of carotid baroreceptors. The aim of this study was to investigate the association between carotid IMT and the autonomic modulation of heart rate variability (HRV). Methods: A total of 101 subjects were enrolled in this prospective observational study. The carotid IMT was determined by duplex ultrasonography. The cardiac autonomic function was determined through HRV measures during the Deep Breathing Test. Linear regression models, adjusted for demographics, comorbidities, body mass index, waist-hip-ratio, and left ventricular ejection fraction were used to evaluate the association between HRV parameters and carotid IMT. Results: Participants had a mean age of 60.4 ± 13.4 years and an estimated 10-year atherosclerotic cardiovascular disease (ASCVD) risk score (using the Pooled Cohort Equations) of 16.4 ± 17. The mean carotid media thickness was highest (0.90 ± 0.19 mm) in the first quartile of the standard deviation of all RR intervals (SDNN) (19.7 ± 5.1 ms) and progressively declined in each subsequent quartile to 0.82 ± 0.21 mm, 0.81 ± 0.16 mm, and 0.68 ± 0.19 in quartiles 2 (36.5 ± 5.9 ms), 3 (57.7 ± 6.2 ms) and 4 (100.9 ± 22.2 ms), respectively. In multivariable adjusted models, there was a statistical significant association between SDNN and carotid IMT (OR -0.002; 95%CI -0.003 to -0.001, p = 0.005). The same significant association was found between carotid IMT and other measures of HRV, including coefficient of variation of RR intervals (CV) and dispersion of points along the line of identity (SD2). Conclusions: In a cohort of individuals at increased cardiovascular risk, carotid IMT as a marker of subclinical atherosclerosis was associated with alterations of HRV indicating an impaired cardiac autonomic control, independently of other cardiovascular risk factors.
RESUMEN
Heart rate and blood pressure are the most important vital signs in diagnosing disease. Both heart rate and blood pressure are characterized by a high degree of short term variability from moment to moment, medium term over the normal day and night as well as in the very long term over months to years. The study of new mathematical algorithms to evaluate the variability of these cardiovascular parameters has a high potential in the development of new methods for early detection of cardiovascular disease, to establish differential diagnosis with possible therapeutic consequences. The autonomic nervous system is a major player in the general adaptive reaction to stress and disease. The quantitative prediction of the autonomic interactions in multiple control loops pathways of cardiovascular system is directly applicable to clinical situations. Exploration of new multimodal analytical techniques for the variability of cardiovascular system may detect new approaches for deterministic parameter identification. A multimodal analysis of cardiovascular signals can be studied by evaluating their amplitudes, phases, time domain patterns, and sensitivity to imposed stimuli, i.e., drugs blocking the autonomic system. The causal effects, gains, and dynamic relationships may be studied through dynamical fuzzy logic models, such as the discrete-time model and discrete-event model. We expect an increase in accuracy of modeling and a better estimation of the heart rate and blood pressure time series, which could be of benefit for intelligent patient monitoring. We foresee that identifying quantitative mathematical biomarkers for autonomic nervous system will allow individual therapy adjustments to aim at the most favorable sympathetic-parasympathetic balance.