A similarity graph-theory algorithm detected sex differences in rapid heart rate fluctuations, showing males had lower interbeat interval variability compared to females (OR 2.78).
Cross-Sectional (n=269)
Yes
Does a similarity graph theory algorithm better capture sex differences in rapid interbeat interval variability compared to conventional HRV metrics in young healthy adults?
Nonlinear methods sensitive to rapid spontaneous IBI changes can complement traditional short-term HRV metrics for assessing sex differences in autonomic heart regulation in young adults.
Effect estimate: OR 2.78 (95% CI 1.32-5.86)
Abstract Background Autonomic control of the heart is an important indicator of self-regulation and overall mental and physical health. The vagus nerve plays a central role in this regulation, and resting-state heart rate variability (HRV), reflecting fluctuations in inter-beat intervals (IBIs), is the primary noninvasive marker of vagal activity. As males and females differ in aspects of self-regulation, HRV may help elucidate underlying neurobiological differences. However, sex differences in commonly used HRV metrics, such as natural log transformed root mean square of successive RR interval differences (lnRMSSD) and high-frequency HRV (lnHF-HRV) derived from 5-minute recordings, appear smaller in young adults than in other age groups. These metrics capture vagally mediated activity as averaged linear measures and may therefore overlook rapid, spontaneous IBI fluctuations. In the present study, we tested whether a similarity graph theory algorithm could better capture sex differences in nonlinear, rapid IBI variability within 2–5-seconds time windows. Methods Electrocardiogram (ECG) recordings of 269 young, healthy adults between 18 and 30 years old (M = 21.5, SD = 3.0) were pooled from three different studies. Males accounted for 52.4% of participants, indicating a comparable distribution between sexes. Similarity graph–theory metrics were computed to quantify nonlinear, rapid interbeat interval (IBI) variability using sliding windows of 2–5 s and ≥12 s. In addition, conventional linear and nonlinear heart rate variability metrics, including lnRMSSD and lnHF-HRV, were calculated. Logistic regression models were used to assess the predictive value of graph-theory and HRV metrics for sex, both separately and in combined models for comparison. All models were adjusted for age, body mass index, mean heart rate, and respiratory rate. Results Males showed higher graph-metric values, indicating lower IBI variability compared with females (odds ratio 2.78; 95% CI 1.32–5.86). Neither lnRMSSD nor lnHF-HRV distinguished sexes alone; however, lnRMSSD became predictive when combined with the graph metric (odds ratio 1.73; 95% CI 1.06–2.81), although this effect was attenuated after controlling for mean heart rate. Conclusions These findings suggest that nonlinear methods sensitive to rapid spontaneous IBI changes can complement traditional short-term HRV metrics for assessing sex differences in autonomic heart regulation.
Sørensen et al. (Sat,) conducted a cross-sectional in Healthy (n=269). Similarity graph theory algorithm vs. Conventional HRV metrics (lnRMSSD and lnHF-HRV) was evaluated on Prediction of sex based on rapid interbeat interval variability (OR 2.78, 95% CI 1.32-5.86). A similarity graph-theory algorithm detected sex differences in rapid heart rate fluctuations, showing males had lower interbeat interval variability compared to females (OR 2.78).