Stratifying autonomic nervous system regulation patterns in healthy men: A machine learning approach

Heart rate variability (HRV) is a critical non-invasive marker of autonomic nervous system regulation and plays an essential role in cardiovascular health. Individual differences in autonomic function necessitate the development of personalized health strategies. This study aimed to develop and validate a method that integrates principal component analysis (PCA) and K-means clustering to identify distinct patterns of autonomic regulation in healthy men using HRV data. A total of 80 young, healthy men (22.0 ± 2.8 years old, 65.2 ± 6.9 kg, and 171.0 ± 6.5 cm) were recruited, and their HRV data were analyzed using time-domain and frequency-domain parameters. PCA was applied to reduce the dimensionality of the HRV data, while K-means clustering was employed to identify distinct autonomic profiles. Silhouette index values were 0.397 for one cluster, 0.481 for two clusters, and 0.556 for three clusters, indicating that the three-cluster solution provided the best fit. Three statistically distinct and physiologically meaningful clusters were identified. Cluster 3 (n = 19) demonstrated significantly higher HRV parameters than cluster 1 (n = 33) and cluster 2 (n = 28) (p = 0.001). Post hoc analysis further confirms that cluster 1 differed significantly from both cluster 2 and cluster 3 (p = 0.001). Based on HRV characteristics, the clusters were characterized as “high vagal tone,” “intermediate vagal tone,” and “low vagal tone.” The “high vagal tone” cluster exhibited the strongest parasympathetic activity, while the “low vagal tone” cluster showed evidence of sympathetic predominance. This study demonstrates a robust approach for stratifying autonomic profiles, highlighting the potential of machine learning in advancing personalized cardiovascular health assessment.