Abstract
INTRODUCTION: Metabolic syndrome (MetS) is a clinical condition characterized by multiple risk 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. METHODS: This study evaluated nonlinear characteristics of heart rate variability (HRV) series, including sample entropy (SampEn), multifractal spectrum parameters, and detrended fluctuation analysis (DFA). A total of 278 participants were classified into three groups: no metabolic alterations, one or two alterations, and MetS (defined as three or more alterations based on ATP III criteria). HRV data were recorded at three time points: rest, exercise, and recovery. RESULTS: Participants with MetS showed significantly lower SampEn and DFA values at rest compared to those without alterations, indicating reduced signal complexity. Moreover, a decrease in SampEn was observed in individuals with one or two metabolic alterations, suggesting that autonomic dysfunction may begin in the early stages of metabolic risk. DISCUSSION: These findings support the integration of nonlinear HRV analysis with traditional methods to improve the early detection and management of metabolic syndrome. The progressive reduction in heart rate signal complexity may serve as a sensitive marker of early autonomic dysfunction in metabolic deterioration.