Abstract
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.
Highlights
Non-communicable diseases have become the primary cause of health concern worldwide, accounting for about 63% of annual deaths (Arena et al, 2015)
The set consisting of the q autonomic nervous system (ANS) indicators, autonomic nervous system indicator (ANSI), and the αaPRT index obtained represented the actual set of variables to which we addressed statistical analyses for inspecting their relationship with the body mass index (BMI) categorization into the three groups NW, OW, and OB
The present study suggests, we believe for the first time, a method to assess the strength of the empirical evidence of the information link between obesity and cardiac autonomic regulation (CAR) as recapitulated by multiple synthetic statistical indicators extracted from 756 otherwise healthy subjects
Summary
Non-communicable diseases have become the primary cause of health concern worldwide, accounting for about 63% of annual deaths (Arena et al, 2015). In 2017, behavioral, environmental and occupational, and metabolic risks accounted for 34 million deaths (GBD 2017 Risk Factor Collaborators, 2018). In this context, risk factor ranks resulted: high systolic arterial pressure, smoking, high fasting plasma glucose, and high body mass index (BMI), which alone was responsible for 4.7 million deaths and 148 million DALYs (Disability-Adjusted Life Years). The rise in metabolic risk might lead to growing cardiovascular mortality, coincidentally calling for more successful risk reduction strategies (GBD 2019 Risk Factors Collaborators, 2020). It follows that our current delivery model is poorly constructed to manage chronic disease. It may be best to base our approach on patient-centered technologies (Milani et al, 2004; Lucini et al, 2020) and rely on simple lifestyle interventions such as healthy diet and physical exercise (Fock and Khoo, 2013)
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