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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.