ORIGINAL RESEARCH article
Front. Physiol.
Sec. Computational Physiology and Medicine
Volume 16 - 2025 | doi: 10.3389/fphys.2025.1597314
Utility of Nonlinear Analysis of Heart Rate Variability in Early Detection of Metabolic Syndrome
Provisionally accepted
José Alberto Zamora-Justo1
Myriam Campos-Aguilar2
María del Carmen Beas Jara3Pedro Galván Fernandez3
Alberto Ponciano-Gomez2Santiago Cristóbal Sigrist-Flores2Rafael Jimenez-Flores3
Alejandro Muñoz-Diosdado1*- 1Unidad Profesional Interdisciplinaria de Biotecnología del Instituto Politécnico Nacional,, Mexico, Mexico
- 2Laboratorio de Inmunología, Unidad de Morfología y Función (UMF), Facultad de Estudios Superiores Iztacala, Universidad Nacional Autónoma de México, Tlalnepantla, Mexico
- 3Laboratorio de Fisiología del Esfuerzo (GIN). UIICSE, Facultad de Estudios Superiores Iztacala, Universidad Nacional Autónoma de México, Tlalnepantla, Mexico
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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. 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.
Keywords: heart rate variability time series, nonlinear dynamic techniques, sample entropy, detrended fluctuation analysis, autonomic dysfunction
Received: 21 Mar 2025; Accepted: 06 Jun 2025.
Copyright: © 2025 Zamora-Justo, Campos-Aguilar, Jara, Fernandez, Ponciano-Gomez, Sigrist-Flores, Jimenez-Flores and Muñoz-Diosdado. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
* Correspondence: Alejandro Muñoz-Diosdado, Unidad Profesional Interdisciplinaria de Biotecnología del Instituto Politécnico Nacional,, Mexico, Mexico
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