Association between metabolic syndrome and autonomic nervous system activity in middle-aged menopausal women in South Korea: a cross-sectional study
Year: 2025
Type: article
Abstract: Purpose: This study aimed to investigate the association between metabolic syndrome and autonomic nervous system activity in middle-aged postmenopausal women. In addition, it explored the clinical applicability of heart rate variability (HRV) as a physiological marker for the early diagnosis and management of metabolic disorders.Methods: This study was conducted as a secondary data analysis using health examination data. The dataset included 276 postmenopausal women aged 50 to 65 years. Autonomic nervous system activity was evaluated using HRV indices, including standard deviation of the normal-to-normal interval (SDNN), total power (TP), low frequency (LF), and high frequency (HF). Statistical analyses were performed using the t-test, chi-square test, and Pearson correlation analysis with SPSS version 26.0.Results: The prevalence of metabolic syndrome among the participants was 21.4%. The LF index was significantly lower in the metabolic syndrome group compared with the normal group (t = 2.74, p = .007). Additionally, fasting blood glucose showed significant negative correlations with SDNN (r = -.16, p = .010), TP (r = -.21, p < .001), LF (r = -.17, p = .004), and HF (r = -.18, p = .003).Conclusion: A reduction in HRV, particularly in LF, may serve as an indicator of metabolic syndrome in postmenopausal women. Because autonomic dysfunction can potentially be improved through nursing interventions, the use of HRV-based monitoring and personalized nursing strategies should be considered in clinical practice to help prevent the onset of metabolic syndrome in postmenopausal women.
DOI: 10.7586/jkbns.25.051
Authors: Myung-hwa Kim, So-Eun Choi
Institutions: Wonkwang University, Mokpo National University
Language: English
Subfield: Cardiology and Cardiovascular Medicine
Field: Medicine
Domain: Health Sciences
Open Access status: diamond
OpenAlex ID: W4416099786
