Data_ALL

Metabolic syndrome (MetS) is a clinical condition characterized by multiple risk Q8
factors that significantly increase the likelihood of developing cardiovascular
diseases and type 2 diabetes. Traditional markers, such as body mass index (BMI)
and waist circumference, often fail to detect early metabolic dysfunctions. This
study evaluates the potential of nonlinear characteristics of heart rate variability
series, including sample entropy (SampEn), multifractal spectrum parameters
and detrended fluctuation analysis (DFA), to identify autonomic dysfunction
associated with MetS. A total of 278 participants were classified into three
groups: no metabolic alterations, one or two alterations, and MetS defined by
three or more alterations based on the ATP III criteria. This study was carried out
in three moments rest, exercise, and recovery, then heart rate variability series
were analyzed and compared. Participants with MetS showed significantly lower
sample entropy and DFA values at rest compared to those without alterations,
indicating a reduction of the complexity of the signal. Additionally, a decrease
in sample entropy was observed in individuals with one or two metabolic
alterations, suggesting that autonomic dysfunction begins at early stages of
metabolic risk. These findings support the integration of nonlinear analysis with
traditional methods to enhance early detection and management of MetS.