INTRODUCTION
Chronic obstructive pulmonary disease (COPD) is a common chronic respiratory disorder often complicated by multiple comorbidities, with depression being one of the most prevalent. The long-term physical burden of COPD, including symptoms such as dyspnea, significantly impairs patients’ quality of life, thereby increasing their vulnerability to depressive symptoms. This comorbidity exacerbates both the physical and psychological burdens on patients, adversely impacting disease management and recovery[1]. Current research on psychological interventions and holistic treatments for COPD patients with comorbid depression is continuously evolving, which is critical for alleviating their psychological distress and improving quality of life and treatment outcomes.
Yang et al[2] published a significant study examining the associations among heart rate variability (HRV) indices, depressive symptoms, and lung function in COPD patients. The research included 120 COPD patients hospitalized at The First Hospital of Zhangjiakou between January 2018 and January 2024, along with 60 healthy volunteers as a control group. Depressive symptoms were evaluated using the Beck Depression Inventory, and the COPD cohort was stratified into a depressed subgroup (n = 43) and a non-depressed subgroup (n = 77). HRV indices were measured via 24-hour Holter electrocardiography, while lung function was assessed using a spirometry analyzer. Results showed a 35.8% depression prevalence in COPD patients, significantly higher than the 5.0% rate in controls. HRV indices were notably lower in COPD patients compared with controls, with the depressed subgroup demonstrating significantly reduced HRV relative to the non-depressed subgroup. Additionally, COPD patients exhibited poorer lung function than controls, and depressed patients showed more severe impairment than their non-depressed counterparts. Pearson correlation analysis revealed that HRV indices correlated negatively with Beck Depression Inventory scores and positively with lung function parameters. This study underscores the importance of monitoring both psychological and physiological health in COPD, prompting clinicians to prioritize screening for depressive symptoms and tracking HRV changes to optimize disease management and improve patient quality of life.
These findings inform clinical practice by highlighting HRV, a critical marker of autonomic nervous system function, as significantly associated with depressive symptoms in COPD, where depression further exacerbates disease progression. However, existing research on the triad of HRV, depressive symptoms, and lung function in COPD has limitations. Most studies, including Yang et al’s work[2], employ cross-sectional designs, which hinder full characterization of HRV dynamics across disease stages and exploration of the bidirectional mechanisms linking depression, HRV, and pulmonary function. This paper aims to address these gaps by discussing key considerations in detail: (1) Potential mechanistic pathways connecting HRV, depressive symptoms, and lung function; (2) The impact of genetic and environmental factors on HRV and the risk of COPD-depression comorbidity; and (3) The longitudinal role of HRV in predicting lung function decline and enabling real-time disease monitoring. These insights aim to provide comprehensive theoretical and practical guidance for the integrated care of COPD patients.
INFLAMMATORY-AUTONOMIC-OXIDATIVE STRESS PATHWAY
Yang et al’s study[2] highlights the significant associations among HRV, depressive symptoms, and lung function in COPD, proposing potential mechanisms involving autonomic nervous system dysfunction, inflammatory processes, and social behavioral changes. However, the study did not deeply explore the complex interactions between these pathways, nor did it clarify their specificity for COPD with comorbid depression. Previous research indicates that oxidative stress acts as a critical mediator in the pathogenesis of both COPD and depression, with intricate crosstalk among oxidative stress, autonomic dysfunction, and inflammation[3-5]. It is noteworthy that although there are overlapping mechanisms with other chronic diseases such as cardiovascular disease (CVD), the interactions of these pathways in COPD with comorbid depression may exhibit distinct characteristics due to unique pathological contexts (e.g., chronic hypoxia, airway inflammation)[6]. Elucidating these connections is essential for understanding the mechanisms of COPD-depression comorbidity and developing precision targeted interventions.
First, oxidative stress is a pivotal factor in the development of COPD and depression. In addition to intrinsic inflammatory responses, environmental factors such as exposure to cigarette smoke, exposure to air pollution, and occupational exposures lead to excessive production of reactive oxygen species (ROS) and an imbalance in the antioxidant defense system, triggering oxidative stress[3]. Oxidative stress induces DNA damage, lipid peroxidation, and disruption of mitochondrial function. Specifically, lipid peroxidation generates substances involved in secondary metabolic interactions that damage lung tissues, trigger cellular responses, and ultimately lead to decreased lung function. Studies have shown that levels of oxidative stress markers such as malondialdehyde (MDA) are significantly elevated in COPD patients and are correlated with disease severity[4]. Additionally, oxidative stress may contribute to depression through mechanisms such as neuronal damage, disruption of neurotransmitter metabolism, and impairment of neuroplasticity. Compared with healthy individuals, patients with depression exhibit higher blood levels of oxidative stress biomarkers such as 8-hydroxy-2’-deoxyguanosine (8-OHdG) and F2-isoprostanes, reflecting increased cellular and molecular damage[7]. Smokers with depression, a subgroup with high COPD risk, present further alterations in levels of oxidative stress biomarkers, including elevated levels of nitrogen oxides and advanced oxidation protein products, and reduced antioxidant capacity (total radical-trapping antioxidant parameter)[8]. In contrast, CVDs (such as atherosclerosis) are characterized by vascular oxidative stress, which promotes endothelial dysfunction and plaque instability[9]. While both CVD and COPD involve systemic oxidative stress, the unique pulmonary ROS burden in patients with COPD may amplify trans-organ effects (e.g., dissemination of systemic inflammation to the brain, thereby triggering depression). For example, COPD patients exhibit elevated circulating MDA levels, correlating with both a decline in pulmonary function and depressive symptoms[4]; this association is less pronounced in patients with isolated CVD.
Second, COPD patients often exhibit autonomic nervous system dysfunction, which is characterized primarily by sympathetic nerve activation driven by factors such as chronic hypoxia, inflammatory stimulation, and airway obstruction[6]. HRV analysis, a key tool for assessing autonomic balance, reveals reduced high-frequency (HF) power (indicating parasympathetic impairment) and increased low-frequency (LF)/HF ratios (indicating sympathetic dominance) in COPD patients. These HRV alterations are closely associated with COPD severity, exercise intolerance, and cardiovascular complications[6]. However, unlike those with CVD, HRV changes in COPD patients are more strongly correlated with factors such as ventilatory dysfunction and the inflammatory response than with cardiac pathology[6]. In contrast, autonomic nervous system imbalance in patients with CVD (such as heart failure) may stem from cardiac injury or baroreflex dysfunction[10]. Depression patients also experience autonomic dysfunction, which is characterized by sympathetic overactivity and parasympathetic inhibition. Studies regarding individuals with a history of major depressive episodes have revealed a significantly reduced incidence of respiratory sinus arrhythmia, indicating a disruption in the balance between the sympathetic and parasympathetic nervous systems[11]. Another study revealed that in depressed individuals, HRV indices, including the triangular index, HF power, LF power, and root mean square of successive differences (RMSSD), were significantly lower. These reductions are correlated with depressive symptom severity, suggesting impaired autonomic regulation[12]. The convergence of autonomic imbalance in COPD and depression creates a bidirectional relationship: Sympathetic activation exacerbates airway dysfunction and inflammation in COPD, while depressive states further disrupt autonomic control, thereby exacerbating both respiratory and psychological symptoms.
Third, inflammation is a core pathogenic mechanism in COPD, persisting throughout the disease course. Chronic airway and lung inflammation leads to airway obstruction, lung tissue damage, and ultimately decreased lung function. The infiltration of inflammatory cells (e.g., macrophages, neutrophils, and lymphocytes) and the release of inflammatory mediators such as interleukin (IL)-6, tumor necrosis factor-α (TNF-α), and C-reactive protein (CRP) play key roles in COPD progression[13]. These mediators not only induce local inflammation but also enter the bloodstream, causing systemic inflammation[6]. Compared with healthy individuals, COPD patients have elevated serum levels of the inflammatory markers high-sensitivity CRP and IL-6, with IL-6 Levels negatively correlated with percentage of normal-to-normal intervals with a difference greater than 50 milliseconds and positively correlated with the LF/HF ratio (reflecting the sympathetic-parasympathetic nervous system balance)[14]. Inflammation also plays a pivotal role in the pathogenesis of depression, with multiple studies showing that patients with depression have significantly higher blood levels of inflammatory markers such as IL-6, TNF-α, and CRP than healthy controls[11,12]. In COPD patients with comorbid depression, inflammation may serve as a critical link exacerbating both conditions. Elevated inflammation levels are correlated with the severity of depressive symptoms in COPD patients, and increased IL-6 Levels are an important predictor of depressive symptoms[13]. IL-6 Levels are greater in COPD patients, patients with depression, and COPD patients with comorbid depression than in healthy controls[15].
Finally, oxidative stress, autonomic nervous dysfunction, and inflammation interact in intricate ways, collectively contributing to the development and exacerbation of COPD and depression. Although interactions among these three factors are observed in multiple chronic diseases, they exhibit unique mechanistic roles in COPD with comorbid depression.
Oxidative stress and autonomic nervous dysfunction
Oxidative stress and autonomic nervous system dysfunction exhibit a bidirectional relationship. Oxidative stress disrupts neuronal function and autonomic nervous system balance, enhancing sympathetic excitability while weakening parasympathetic regulation. Studies have shown that plasma MDA levels in COPD patients are significantly correlated with HRV: Increased MDA reduces parasympathetic activity indices (such as RMSSD, HF) and increases sympathetic activity indices (such as LF, LF/HF ratio)[4]. In turn, autonomic dysfunction exacerbates oxidative stress: Sympathetic excitation promotes the release of catecholamines, which generate ROS during metabolism, further amplifying the oxidative stress response[16]. This bidirectional interaction differs from its effects in other chronic diseases. For instance, in CVDs, oxidative stress may indirectly induce autonomic nervous system dysfunction by damaging myocardial cells or nerve endings. A study has found that reduced antioxidant enzyme activity in the right ventricle correlates negatively with the LF/HF ratio, suggesting that diminished oxidative defense capacity may exacerbate sympathetic activation[16]. In COPD patients, the focus is on the mutual reinforcement between nervous system imbalance and oxidative stress, with its targets of action and specific molecular mechanisms differing from those in other chronic diseases, highlighting the uniqueness of this pathway in COPD pathogenesis and progression.
Oxidative stress and inflammation
Under normal physiological conditions, the body employs intrinsic negative feedback mechanisms to maintain balance between oxidative stress and inflammation, such as upregulating antioxidant compounds or anti-inflammatory cytokines. However, in diseases like depression, this balance is disrupted, establishing a positive feedback loop between oxidative stress and inflammation. On one hand, oxidative stress triggers an excess of oxygen free radicals that surpass the body’s antioxidant capacity, activating multiple inflammation-related signaling pathways. For example, ROS can activate the NOD-like receptor family pyrin domain containing 3 inflammasome, increasing production of the pro-inflammatory cytokine IL-1β. On the other hand, the inflammatory response reciprocally induces oxidative stress: Pro-inflammatory cytokines activate indoleamine 2,3-dioxygenase, leading to aberrant tryptophan metabolism, production of neurotoxic metabolites, and subsequent ROS generation. This interactive process disrupts physiological homeostasis, potentially driving disease progression and exacerbation[5]. Studies in COPD patients show that oxidative stress activates inflammatory cells like alveolar macrophages and neutrophils, prompting release of large quantities of inflammatory mediators that perpetuate inflammatory responses, which in turn further induce oxidative stress[3]. In depression patients, the inflammatory response is closely intertwined with oxidative stress: Blood levels of inflammatory markers (e.g., IL-6) and oxidative stress indices (e.g., 8-OHdG) are higher than in healthy individuals, indicating concurrent activation of both pathways. In untreated major depressive disorder (MDD) patients, the concentration of the oxidative stress marker F2-isoprostanes is positively correlated with the pro-inflammatory cytokine IL-6, negatively correlated with the anti-inflammatory cytokine IL-10, and positively correlated with the IL-6/IL-10 ratio, underscoring the close association between oxidative stress and inflammation[5]. In CVDs such as experimental pulmonary hypertension, early oxidative stress damages myocardial cells and vascular endothelium, activating inflammatory signaling pathways and triggering an inflammatory response (e.g., mild inflammatory cell infiltration in the right ventricle at week 1). Subsequently, pro-inflammatory mediators released during the inflammatory response further promote ROS generation and inhibit antioxidant enzymes like superoxide dismutase, forming a vicious cycle between oxidative stress and inflammation[16]. In contrast, the interplay between inflammation and oxidative stress in COPD primarily involves the activation of immune cells such as alveolar macrophages and neutrophils and the release of inflammatory mediators[3]. This differs from the core cell types involved in CVDs, highlighting the distinctiveness of this pathway in the pathological process of COPD.
Autonomic nervous dysfunction and inflammation
Autonomic nervous dysfunction and inflammation exhibit a bidirectional association. Sympathetic nervous system excitation induces autonomic dysfunction, elevating circulating catecholamines, promoting inflammatory cell infiltration, exacerbating inflammatory responses, and interfering with immune regulation, thereby impeding inflammation control and accelerating disease progression. Conversely, inflammation disrupts neurotransmitter metabolism and transmission, impairs neuronal function, and destabilizes cardiac neuroregulatory mechanisms, leading to an escalation of autonomic nervous system imbalance, enhanced sympathetic activity, and weakened parasympathetic activity that further aggravate autonomic dysfunction[17]. Studies in COPD patients reveal significantly higher standard deviation of normal-to-normal intervals (SDNN), heart rate, and serum inflammatory markers high-sensitivity CRP and IL-6 compared with healthy individuals; further analysis shows that the parasympathetic activity index percentage of normal-to-normal intervals with a difference greater than 50 milliseconds correlates negatively with serum IL-6 Levels, while the LF/HF ratio (sympathetic/parasympathetic balance) correlates positively with IL-6, indicating a close relationship between autonomic function and inflammation levels, with enhanced sympathetic activity associated with systemic inflammation[14]. In depression patients, 24-hour HRV (triangular index) and daytime HRV (triangular index, HF, LF, RMSSD) correlate negatively with IL-6, indicating an association between autonomic dysfunction (manifested by reduced HRV) and heightened inflammatory response[12]. In CVDs, the association between autonomic dysfunction and inflammation primarily manifests in cardiac rhythm regulation (e.g., LF/HF ratio imbalance) and ventricular remodeling (e.g., increased right ventricular pressure load). For example, in pulmonary hypertension, sympathetic activation (elevated LF) and parasympathetic inhibition (decreased HF) exacerbate inflammatory infiltration and oxidative damage in the late stage of the disease (week 3), with the LF/HF ratio positively correlated with the degree of right ventricular hypertrophy[16]. In COPD patients with comorbid depression, however, the interaction between the autonomic nervous system and inflammation involves wider-ranging physiological system regulation and immune responses, with both its action patterns and scope of involvement being unique and differing from the mechanisms in CVDs.
In summary, inflammation, autonomic nervous system dysfunction, and oxidative stress form a complex network with distinct regulatory features in the pathological progression of COPD with comorbid depression. Oxidative stress triggers autonomic dysfunction by damaging neurons, which in turn release catecholamines to exacerbate oxidative stress and induce inflammation. Simultaneously, inflammatory responses further amplify oxidative stress and disrupt neural regulation through pro-inflammatory cytokines, ultimately creating a multi-step vicious cycle. Deepening the exploration of this mechanism not only provides new cognitive dimensions for unraveling the pathogenesis of COPD with comorbid depression but also holds promise for informing the development of more precision targeted clinical prevention and treatment strategies. For instance, interdisciplinary interventions against this complex comorbidity may be achieved by regulating the pulmonary oxidative stress-inflammation axis or improving autonomic nerve function.
GENETIC POLYMORPHISMS ALONGSIDE ENVIRONMENTAL FACTORS
Genetic and environmental factors play pivotal roles in explaining interindividual variability in HRV and the susceptibility pathways for COPD-depression comorbidity. First, both genetic and environmental influences shape individual HRV profiles. A study has shown that genetic factors account for 47% to 64% of the total variance in different HRV variables. Specifically, the heritability of the time-domain index RMSSD is 64% in males and 52% in females, while the heritability of LF/HF is 57% and 64% in males and females, respectively. In terms of environmental factors, nonshared environments (such as individual unique life experiences) contribute to 36% to 53% of HRV variance, but no significant role of shared environmental factors (such as family environment) has been identified[9].
Genetic polymorphisms impact HRV by influencing gene expression and function. The rs1048101 (Arg347Cys) polymorphism in the ADRA1A gene on chromosome 8, a non-synonymous mutation, is associated with reduced LF power, a lower LF/HF ratio, and increased HF power among Cys allele carriers. These changes suggest a shift toward parasympathetic dominance. The Gly16Arg (rs1042713) polymorphism in the ADRB2 gene also correlates with HRV: Arg allele carriers exhibit reduced LF power, increased HF power, and elevated time-domain indices (SDNN, RMSSD), indicating altered cardiac autonomic balance[18]. Exogenous environmental factors, such as climate, noise, occupational stressors, toxic exposures, and medications, modulate HRV through effects on the autonomic nervous system. High-noise conditions elevate LF/HF ratios, while heat exposure activates sympathetic pathways, decreasing HRV. Occupational hazards and medication use further impact HRV, though effects vary due to individual differences in adaptability, metabolism, and environmental sensitivity[19,20]. A study investigated the impact of temperature on HRV, revealing that in healthy adults, the LF/HF ratio fluctuates between 0.5 and 1 under moderate temperatures of 25-27 °C. Both high temperature (30 °C) and low temperature (22 °C) increase the LF/HF ratio, with high temperature having a greater effect on LF/HF than low temperature[21]. Noise exposure shows a short-term concurrent association with HRV: Each 1 dB(A) increase in noise is associated with a 0.97% increase in SDNN and a 1.16% increase in the LF/HF ratio. The impact on HRV is significantly stronger when noise is below 65 dB(A), where the association strength for SDNN and the LF/HF ratio is nearly six times that at noise levels above 65 dB(A)[22]. Additionally, a short-term increase in ambient particulate matter (PM2.5) concentration is linked to a decrease in SDNN. When PM2.5 concentration increases by one interquartile range (7.8 μg/m³), SDNN decreases by approximately 1%-2% 5-6 hours after exposure[23].
Second, genetic and environmental factors are critical for the pathogenesis of COPD-depression comorbidity. A study that genotyped five tag single-nucleotide polymorphisms of the SLC6A4 gene in COPD patients and controls found that the association between the rs2020936 Locus and COPD risk was partially mediated by tobacco consumption[24]. Additionally, the rs3794808 Locus correlated with Hospital Anxiety and Depression Scale depression scores, highlighting SLC6A4 variants as modifiers of COPD pathogenesis and depressive symptoms[24]. Social environmental factors also significantly influence comorbidity risk: Low educational level and urban residence independently predict higher anxiety/depression risk in COPD patients. Limited health literacy and social stigma among less-educated patients, combined with urban stressors like air pollution and interpersonal strain, exacerbate dyspnea, disrupt sleep, and heighten emotional vulnerability[1]. A study found that compared to cities with better air quality (air quality index: 49.4 ± 8.9), COPD patients in cities with heavier air pollution (air quality index: 113.1 ± 14.2) had significantly lower forced expiratory volume in the first second[25]. Air pollutants were also associated with higher rates of COPD acute exacerbations, hospitalizations, and reduced quality of life[25]. Additionally, COPD patients with higher social support scores exhibited a significantly lower incidence of depressive symptoms. The beneficial effects of social support on respiratory symptoms and functional status in COPD patients were primarily mediated through the alleviation of depressive symptoms, with depression accounting for more than 85% of the mediating effect[26].
Finally, the interaction between genetic and environmental factors affects individual HRV and the risk of COPD-depression comorbidity. A study found that individuals with higher polygenic scores including the BDNF Val66Met polymorphism exhibited significantly enhanced HRV reactivity (i.e., a greater reduction in HRV) under high environmental stress (such as a composite index of adverse life events and low socioeconomic status), whereas HRV responses were closer to normal levels in low-stress environments[27]. Another study found that individuals with the GSTM1 null genotype showed more pronounced HRV reduction upon PM2.5 exposure, suggesting that loss of detoxification gene function amplifies autonomic nerve damage by environmental pollutants[28]. Additionally, the development of COPD and depression results from long-term gene-environment interactions. Multiple genetic variants are associated with COPD risk, and most are common variants with small individual effects that act together. Environmental factors like tobacco smoke, air pollution, and respiratory infections are closely linked to COPD development. For example, maternal smoking during pregnancy can disrupt fetal lung development and increase the child’s future COPD risk. Long-term exposure to air pollution also impairs lung function. These gene-environment interactions occur throughout a person’s life cycle, influencing COPD progression[29]. Depression is closely associated with the 5-hydroxytryptamine (5-HT) system, with genetic variation in the 5-HT transporter gene-linked polymorphic region (5HTTLPR) being one of the key influencing factors. Different alleles of 5HTTLPR (particularly the short allele) may affect an individual’s biological sensitivity to environmental stress by regulating 5-HTergic neurotransmission efficiency. Individuals carrying the short allele have a significantly higher risk of developing depression after experiencing major stressors (such as bereavement, romantic loss, or childhood abuse) compared to those with the long allele[30]. In COPD patients, the physical and psychological stress of long-term illness can be seen as a persistent stressor. If patients also carry genetic variants associated with depression, such as the 5HTTLPR short allele, gene-environment interactions may make them more prone to depressive symptoms when coping with the stress of COPD, thereby increasing the risk of COPD with comorbid depression.
LONGITUDINAL ROLE OF HRV IN LUNG FUNCTION DECLINE AND REAL-TIME DISEASE PREDICTION
Current research exploring the relationship between HRV, depressive symptoms, and lung function in COPD is primarily cross-sectional, which, while uncovering associations, struggles to establish causal relationships or track dynamic changes over time. Long-term monitoring of HRV fluctuations and analysis of how HRV trends correlate with the rate of lung function decline may enable more precise assessment of disease progression in COPD patients, prediction of future adverse events (such as exacerbations or hospitalizations), and facilitation of early interventional strategies.
Reduced HRV has emerged as a potential indicator of early-stage lung function abnormalities and increased risk of clinical events. A longitudinal study examining cardiovascular autonomic function indices, including HRV and orthostatic hypotension, in relation to COPD hospitalization risk was conducted. The study found that higher HRV parameters (such as longer mean R-R interval, greater LF power, and a higher LF/HF ratio) were associated with a lower risk of COPD-related hospitalizations. Conversely, poorer autonomic function markers like orthostatic hypotension and significant postural blood pressure changes were linked to a higher risk of hospitalization, with more pronounced effects observed in individuals without preexisting airflow obstruction[31]. This suggests that HRV plays a critical role in the pathogenesis of COPD, particularly in its early stages, and diminished HRV may serve as a predictive marker for future COPD-related adverse events. Another investigation reported that the SDNN exhibited a negative correlation with indicators of COPD severity and functional impairment, such as the St. George’s Respiratory Questionnaire score and the BODE index (a composite measure used to evaluate COPD prognosis), highlighting HRV’s ability to reflect disease burden and prognosis[32]. Additionally, a more rapid decline in lung function has been associated with an elevated risk of CVD, particularly among individuals under 60 years of age, underscoring the importance of integrating HRV monitoring with pulmonary function assessments[33].
These findings emphasize the need for longitudinal research to characterize HRV trajectories, determine clinical thresholds for risk stratification, and develop personalized prevention strategies. For example, further research could build on these correlations to investigate the rate of lung function decline and changes in hospitalization risk among COPD patients at different HRV levels, with the goal of identifying critical thresholds to guide personalized prevention strategies. The integration of longitudinal HRV data with multi-omics biomarkers (such as genetic and proteomic profiles) and machine learning models holds promise for real-time prediction of depression relapse or COPD exacerbation, enabling proactive management and early therapeutic intervention.
In the realm of depression research, one study utilized machine learning algorithms to combine HRV and serum proteomics data for diagnosing MDD, achieving an 80.1% classification accuracy. This accuracy was significantly higher than using either data type in isolation. Key discriminatory indices included apolipoprotein B, haptoglobin, RMSSD, and sample entropy, demonstrating the utility of multimodal data in depression risk assessment[34]. Another study developed MDD BranchNet, a deep learning model designed to detect MDD using electrocardiogram signals, validating the impact of signal segment length and prediction thresholds on model performance. For example, a 20-second segment length with a 70% prediction threshold minimized the misclassification rate of healthy subjects, providing a more reliable framework for clinical application[35].
In COPD detection, a study that employed 1-30 days of HRV, heart rate, and respiratory features for binary logistic regression classification achieved an accuracy of 0.958, with classification performance improving as the monitoring duration increased. This result indicates that long-term HRV data more accurately reflect individual health status and are more valuable for COPD diagnosis. Analysis of individual features revealed that HRV-derived metrics (such as HF) and respiratory parameters (such as respiratory rate) were critical for COPD diagnosis, with the area under the curve (AUC) for the 7-day average respiratory rate feature reaching 0.955, establishing these features as important predictors[36]. Another study introduced the RE presentation learning for Genetic discovery on Low-dimensional Embeddings framework to integrate spirogram (lung function) and photoplethysmography signals, extracting and reducing features to construct polygenic risk scores for disease risk prediction. Polygenic risk scores incorporating spirometry-derived features and photoplethysmography features outperformed traditional models based solely on clinical data, enabling more effective risk stratification for asthma and COPD[37].
Collectively, these findings highlight HRV as a dynamic biomarker with significant potential for monitoring COPD progression, predicting depressive comorbidity, and informing precision medicine approaches that leverage longitudinal physiological data alongside advanced analytical techniques. However, it is important to note that existing studies lack standardization in data collection methods and artificial intelligence (AI) model selection, leading to insufficient reproducibility and generalizability of results. This issue may cause models to fail to adapt to the data formats of different populations or medical institutions in clinical settings. Additionally, inadequate interdisciplinary collaboration may lead to demand mismatches, while cross-institutional data sharing still requires resolving standardization and ethical approval issues. Long-term data collection also faces practical challenges such as variability in patient compliance and device battery life limitations.
A study on the application of machine learning in clinical research discussed its current status, potential, application scenarios, and challenges[38]. It found that clinical data contain sensitive information such as electronic health records and genomic data, posing risks of privacy leakage and lacking interoperability during cross-institutional sharing. Existing public datasets struggle to cover all disease types and patient populations, resulting in poor generalizability of models during cross-scenario applications. Therefore, the development of machine learning models requires collaboration between clinical experts and data scientists; otherwise, models may deviate from clinical reality. Approaches such as federated learning and cloud technology can be adopted to enable data localization during model training, balancing privacy and research needs. Furthermore, unified model evaluation standards (such as receiver-operating characteristic curves and calibration metrics) should be established. Relevant institutions need to develop regulatory frameworks for machine learning, with a particular focus on iterative updates and bias monitoring in high-risk scenarios[38]. These measures can enhance model reproducibility and build trust among users (e.g., clinicians) in machine learning tools. By using AI as an auxiliary tool to provide actionable insights for physicians while preserving their final decision-making authority, human-AI collaboration models can optimize clinical disease monitoring and diagnostic processes.
Moreover, in clinical practice, long-term monitoring may face challenges related to patient compliance. Some patients (e.g., elderly individuals or those unfamiliar with technology) may experience stress or discomfort from wearing devices, leading to low compliance or dropouts during data collection, which degrades data quality or completeness. To address this, future improvements could focus on device design (e.g., simplified operation, lightweight and comfortable design, enhanced anti-interference capabilities) and user education (e.g., building technology trust) to improve the suitability of wearable health devices for long-term clinical monitoring[39,40].
CONCLUSION
This article explores the potential mechanisms underlying the associations among HRV, depressive symptoms, and lung function in COPD patients, identifying the inflammatory-autonomic-oxidative stress pathway as a central causal mediator driving the progression and exacerbation of both COPD and depression. Oxidative stress, autonomic nervous dysfunction, and inflammation interact synergistically, creating a self-reinforcing cycle that disrupts physiological homeostasis and amplifies disease severity. Genetic polymorphisms and environmental factors further contribute to interindividual differences in HRV and susceptibility to COPD-depression comorbidity: Genetic variations influence HRV-related gene expression and function, while environmental exposures modulate HRV via autonomic nervous system effects. The interactions between genetic polymorphisms and environmental factors shape individual HRV profiles and modulate the risk of comorbidity, emphasizing the necessity of integrated analysis to unravel disease pathophysiology and inform targeted preventive strategies. Longitudinal investigation of HRV trajectories is also pivotal. Reduced HRV may serve as an early biomarker of incipient lung function abnormalities, with long-term monitoring enabling precise assessment of disease progression, prediction of adverse events (e.g., exacerbations, hospitalizations), and timely intervention in COPD management. Integrating longitudinal HRV data with multi-omics biomarkers (e.g., genetic, proteomic profiles) and machine learning models holds promise for the real-time prediction of depression relapse or COPD exacerbation, facilitating proactive clinical responses to mitigate disease burden.
In clinical practice, the comprehensive assessment of HRV, inflammatory markers, and genetic/environmental risk factors should be integrated into all stages of COPD diagnosis, treatment, and long-term management. Through regular case discussions by multidisciplinary teams involving pulmonology, psychiatry, and nutrition, personalized plans encompassing pharmacotherapy, psychological interventions, and lifestyle adjustments can be developed. Specifically, at the initial stage of COPD diagnosis and treatment, Holter monitors or wearable devices (such as heart rate monitoring wristwatches) should be used to measure HRV time-domain and frequency-domain indices (e.g., SDNN, RMSSD, LF/HF ratio). Concurrently, serum inflammatory markers (e.g., IL-6, TNF-α), oxidative stress indicators (e.g., 8-OHdG), and genetic risk loci (e.g., BDNF gene polymorphisms) should be detected, while environmental exposure history (e.g., smoking, air pollution, psychological stressors) should be collected via standardized questionnaires. By integrating multidimensional data, individual disease risk profiles for COPD patients can be established. Through multidisciplinary collaboration, clinicians can develop precise, personalized strategies to predict disease trajectories, optimize treatment approaches, and improve patients’ quality of life. For example, pulmonologists can develop personalized medication plans for COPD patients, prioritizing drugs with minimal impact on autonomic nervous function. Psychiatrists can initiate cognitive behavioral therapy or pharmacotherapy for COPD patients with high depressive symptom scores, regularly assessing their psychological status. Clinical dietitians and rehabilitation therapists can design appropriate dietary plans and exercise programs based on patients’ individual genetic profiles to enhance autonomic function and emotional states. Additionally, integrating longitudinal HRV data with multi-omics biomarkers (e.g., proteomics, metabolomics) and machine learning models can construct real-time prediction models for disease exacerbation or depressive relapse. When embedded in electronic health record systems, these models provide dynamic support for clinical decision-making. For instance, when the model alerts of increased risk of acute exacerbation, clinicians can promptly conduct clinical examinations and adjust treatment plans. Future research should focus on deepening the understanding of mechanisms and further exploring non-invasive early intervention strategies based on HRV monitoring. Advanced analytical methods should be utilized to improve prediction models, while new interventions should be explored to address the complex interactions among HRV, inflammation, and neuropsychiatric dysfunction. Ultimately, these efforts will advance the prevention, management, and treatment outcomes of COPD with comorbid depression.
Provenance and peer review: Invited article; Externally peer reviewed.
Peer-review model: Single blind
Specialty type: Psychiatry
Country of origin: China
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P-Reviewer: Muner RD, PhD, Researcher, Pakistan; Zhu CR, China S-Editor: Bai Y L-Editor: A P-Editor: Yu HG
