Heart rate variability associated with depression and lung decline in chronic obstructive pulmonary disease: Mechanisms and interventions

Abstract

In this article, we comment on the study by Yang et al, which demonstrated significant cross-sectional associations between heart rate variability (HRV) indices, depressive symptoms, and lung function in patients with chronic obstructive pulmonary disease (COPD). Building on these findings, we further explore the underlying mechanisms, particularly inflammatory-autonomic-oxidative stress pathways, as key causal mediators. Moreover, analyzing genetic polymorphisms alongside environmental factors may uncover susceptibility pathways explaining interindividual differences in HRV and comorbidity risk. Additionally, longitudinal studies tracking HRV trajectories could identify thresholds predictive of accelerated lung function decline or cardiovascular events, informing personalized prevention strategies. Integrating longitudinal HRV data with multi-omics biomarkers and machine learning models could enable real-time prediction of depression relapses or COPD exacerbations, facilitating proactive interventions such as personalized biofeedback training or precision anti-inflammatory therapies. By synthesizing these perspectives, this integrative approach promises to advance precision medicine for COPD patients, particularly those with comorbid depression, by addressing both mechanistic insights and clinical translation.

Keywords: Chronic obstructive pulmonary disease; Heart rate variability; Depression; Lung function; Underlying mechanisms; Dynamic process; Machine learning

Core Tip: The comorbidity of chronic obstructive pulmonary disease and depression severely impacts patients’ health. This editorial explores associations and clinical implications among heart rate variability (HRV), depression, and lung function in chronic obstructive pulmonary disease patients, emphasizing the inflammatory-autonomic-oxidative stress pathway as the core mechanism. It reveals the effects of genetic polymorphisms, environmental factors, and their interactions on HRV differences and comorbidity risk, and further highlights HRV’s longitudinal predictive value in lung function decline and real-time monitoring potential. We propose a comprehensive framework integrating HRV monitoring, multi-omics biomarkers, and machine learning for early intervention and improved patients’ quality of life.