Objective:To evaluate the impact of deep learning image reconstruction (DLIR) on quantitatively assessing emphysema, air trapping and small airway dysfunction in chronic obstructive pulmonary disease (COPD) using low-dose inspiratory-expiratory chest CT. Methods:Sixty-nine COPD patients underwent low-dose inspiratory-expiratory chest CT scans and pulmonary function tests (PFT) were prospectively enrolled. The CT images were reconstructed using 50% adaptive statistical iterative reconstruction (ASiR-V), DLIR-high (DLIR-H), medium (DLIR-M), and low (DLIR-L) strengths. The volumes and its percentages (relative to whole lung) characterizing emphysema, air trapping and small airway dysfunction were quantified on the inspiratory-expiratory CT scans. Results:The total dose-length product was 128.99 ± 39.00 mGy·cm. For all patients, emphysema parameters were lowest for DLIR-H and highest for ASiR-V; small airway dysfunction parameters were highest with DLIR-H and lowest with ASiR-V; air trapping parameters were lowest with ASiR-V; highest with DLIR-M. Emphysema parameters demonstrated moderate negative correlations with FEV1/FVC (r = -0.570 to -0.649, all p < 0.001). Air trapping and small airway dysfunction parameters showed weak negative correlations with MEF25%, MEF50%, and MEF75% (r = -0.320 to -0.381, all p < 0.001). When differentiating GOLD I-II from III-IV, all parameters showed AUC values ranging from 0.69 to 0.76, without statistically differences among reconstructions (DeLong's test, p > 0.05), while the optimal thresholds varied across reconstructions. Conclusion:In low-dose inspiratory-expiratory chest CT, DLIR may alter the lung function-related CT parameters compared to ASiR-V, but does not affect their correlations with PFTs or their efficacies in GOLD grading.
Introduction:Ground-based walking training is an aerobic exercise used in supervised pulmonary rehabilitation (PR). Physical activity (PA) interventions typically promote step counts, but it is unclear whether community-based walking intensity can be targeted as aerobic exercise. This randomized controlled trial evaluated a web-based, pedometer-mediated PA intervention designed to increase walking amount and intensity. Material and Methods:Participants with COPD who had never enrolled in PR were randomized 1:1 to control or intervention. The intervention included individualized step-count goals, iterative feedback, educational content, and an online community forum. The Fitbit Inspire Heart Rate objectively monitored daily step counts. Participants were instructed to achieve step-count goals with as many steps of moderate-intensity as possible guided by a modified Borg rating of 4-5 for dyspnea. The primary outcome was change in PA measured as average daily step count at 12 weeks. Aerobic intensity was assessed by the Rapid Assessment of PA Questionnaire which uses self-reported moderate or vigorous PA to categorize responders as underactive or active. Linear mixed-effects models (PROC MIXED, SAS v9.4), adjusting for group, time, group*time, FEV1%predicted, enrollment season, and study modality (eg, in-person, virtual, hybrid), assessed between-group change. Results:Participants (57 intervention, 52 control) were 97% male, mean age 73±7 years, and baseline FEV1 73±23% predicted. Baseline daily steps were 4,222±1,929 (intervention) and 4,851±2,637 (control). Intervention participants increased average daily steps by 1,410 steps/day more than controls (p=0.005). The intervention group showed greater transitions from underactive to active intensity (between-group: p=0.025), with 20 (41%) moving to active status (within-group: p=0.001). Conclusion:Technology-mediated community-based walking increased PA amount and intensity. These findings support further evaluation of this intervention as a potential option for ground-based walking training with objective measurement of exercise intensity.
Chronic obstructive pulmonary disease (COPD) is a prevalent and devastating condition responsible for substantial morbidity and mortality in the United States (US). Although the burden of cardiopulmonary (CP) comorbidity in COPD is increasingly known, CP risk remains frequently under-recognized and suboptimally managed in routine clinical practice. Definitions of CP risk in COPD vary across the literature and clinical settings. CP risk may be broadly defined as the risk of serious respiratory and/or cardiovascular events in patients with COPD, including COPD exacerbations, myocardial infarction, stroke, heart failure decompensation, arrhythmia, and death. While current clinical guidelines recognize comorbidity, there remain no clear protocols for screening, risk assessment, or a coordinated multidisciplinary approach to address the CP risk in patients with COPD. To address these gaps, a multidisciplinary expert panel developed consensus-based guidance through an iterative process incorporating an expert-led narrative review supported by targeted desk searches alongside multiple rounds of expert input. This paper identifies critical gaps in the management of CP risk among patients with COPD in the US and outlines actionable steps towards improvement of care. Actionable recommendations include encouraging earlier screening and risk assessment to identify patients at risk of exacerbations and improving patient access to multidisciplinary teams, prompting earlier initiation or optimization of evidence-based treatment. Furthermore, implementation of fully coordinated treatment strategies through primary care settings and increasing the use of preventative strategies in the clinic such as spirometry, vaccination, and smoking cessation are recommended. By shifting to proactive, multidisciplinary care, healthcare practitioners can better address the complex needs of patients with COPD to reduce their CP risk and improve individual and population-level outcomes.
Background:Chronic obstructive pulmonary disease (COPD) often coexists with lung cancer, worsening clinical outcomes. This study aimed to develop machine learning models for early COPD screening in lung cancer patients using clinical variables and a novel systemic coagulation-inflammation index (SCI). Methods:We retrospectively enrolled 1016 patients, extracting demographic, smoking, vital, and laboratory data. After feature selection with Boruta and least absolute shrinkage and selection operator (LASSO), six models-logistic regression, decision tree (DT), multilayer perceptron (MLP), support vector machine (SVM), gradient boosting decision tree (GBDT), and extreme gradient boosting (XGBoost)-were trained on 70% of the data and tested on 30%. Model performance was assessed with area under the curve (AUC), accuracy, sensitivity, specificity, precision, F1 score, and calibration, while SHapley Additive exPlanations (SHAP) and contour plots helped interpret the best model and explore predictor interactions. Results:Among the 1,016 patients, 182 (17.9%) had concomitant COPD. Boruta and LASSO identified 8 key predictors: age, historical smoking index (HSI), SCI, eosinophils (EOS), bicarbonate (HCO3 -), sex, lymphocytes (LYM), and hemoglobin (HGB). The GBDT model showed the best performance, with an area under the curve (AUC) of 0.74 (95% CI: 0.69-0.80) and moderate sensitivity (0.68) and specificity (0.66). SHAP analysis revealed age, HSI, and SCI as major risk factors, with contour plots indicating a synergistic effect between SCI, age, and HSI on COPD risk. Conclusion:A GBDT-based model built from routine clinical variables and SCI showed moderate discrimination for identifying patients with lung cancer at high risk of concomitant COPD.
Background:Chronic obstructive pulmonary disease (COPD) has high morbidity and mortality, and integrated stress response (ISR) participates in its progression. Traditional COPD diagnosis relies mainly on GOLD criteria, while ISR-based auxiliary molecular markers are still lacking. This study aimed to comprehensively analyze and identify ISR-related biomarkers in COPD, and to uncover their underlying molecular mechanisms. Methods:COPD data were obtained from the GEO database. Biomarkers were identified and validated through a comprehensive approach, including differential expression analysis, machine learning algorithms, expression level verification, and receiver operating characteristic (ROC) curve analysis. Moreover, a nomogram was applied to construct an ISR-related predictive model for COPD. Enrichment analysis, immune infiltration analysis, molecular regulatory network, and drug prediction were performed to further confirm the regulatory roles of biomarkers. Furthermore, the expression patterns of biomarkers in core cells were investigated using dataset GSE173896. Furthermore, Western blotting was employed to detect the protein expression levels of biomarkers in clinical samples. Results:Overall, 2 biomarkers (MYC and BAG3) were identified as significantly associated with ISR in COPD. A predictive nomogram constructed based on MYC and BAG3 demonstrated excellent diagnostic performance, with an area under the curve (AUC) value of 0.799. The biomarkers were significantly enriched in the MAPK signaling pathway and cell cycle. Notably, biomarkers were positively correlated with effector memory CD4 T cells and eosinophils. Subsequently, 36 microRNAs (miRNAs) (like hsa-miR-4699-3p), 260 transcription factors (TFs) (like SNAI2, ZBTB43, and ZNF395) and 9 drugs (like staurosporine and N-acetyl-L-cysteine) were associated with the biomarkers. Moreover, significant changes in MYC and BAG3 expression were observed during T cell differentiation. Clinically, MYC and BAG3 were significantly upregulated in COPD tissues, with protein levels increased by 58.4% and 252.2% respectively. Conclusion:This study identified and preliminarily verified ISR-associated MYC and BAG3, offering novel molecular references to complement conventional COPD clinical assessment.
Background:Advanced chronic obstructive pulmonary disease (COPD) is associated with multimorbidity and substantial healthcare use. However, sex differences in documented multimorbidity and healthcare use near the end of life remain insufficiently understood. Methods:This retrospective population-based cohort study used routinely collected administrative and clinical data from Clalit Health Services, Israel. The study included deceased patients with advanced COPD during 2016-2020 and 2022-2023. The year 2021 was excluded a priori as the period designated in the study protocol to minimize distortion in healthcare utilization, and mortality patterns; 2020 remained part of the prespecified observation window. The cohort comprised 2261 patients aged ≥55 years who met an operational definition requiring documented COPD, FEV1/FVC <0.70, FEV1 <50% predicted, and at least two opioid prescriptions or dispensations during the final year of life. The opioid criterion was not considered a stand-alone diagnostic criterion or evidence of refractory dyspnea. Women and men were compared regarding demographic characteristics, multimorbidity, selected healthcare-use indicators, coded palliative care encounters, and time from first documented COPD diagnosis to death. Results:The cohort included 547 women and 1714 men. Women were older and more frequently had dementia and rheumatic/connective tissue disease. Men more frequently had myocardial infarction, renal disease, peripheral vascular disease, and diabetes. Men had more documented procedures, although the effect size was small. The mean Charlson Comorbidity Index differed by 0.44 points, of uncertain clinical importance. Coded palliative care encounters were rare and available only for small subsamples. Median time from first documented COPD diagnosis to death was 33 months among women and 29 months among men, without a statistically significant difference (log-rank p=0.328). Conclusion:Sex differences were observed primarily in documented multimorbidity. Healthcare-use and palliative-care findings were partial and descriptive and should not be interpreted as complete end-of-life trajectories or causal effects of sex.
Background:Anemia is common in chronic obstructive pulmonary disease (COPD) and may be influenced by air pollution-related metal accumulation. However, the biological links among air pollution, intracellular metals, immune gene expression, and erythrocyte alterations remain unclear. Methods:We examined associations between air pollutants, intracellular metals, gene expression, and erythrocyte indices in sixty-one COPD patients and ten healthy controls. Annual exposures to particulate matter with aerodynamic diameter ≤10 µm (PM10), ≤2.5 µm (PM2.5), nitrogen dioxide (NO2), and nitrogen oxides (NOx) were estimated using land use regression. Metals were quantified by inductively coupled plasma mass spectrometry (ICP-MS) and single-cell ICP-MS. Gene expression in peripheral blood mononuclear cells (PBMCs) was profiled by RNA sequencing. Results:In COPD, PM2.5 was associated with higher red blood cell (RBC) count (β = 0.0314×106 cells/µL; 95% confidence interval (CI): 0.0066 ~ 0.0561) and ln(red cell distribution width) (β = 0.0051; 95% CI: 0.0003 ~ 0.0098). PM2.5 was also associated with higher ln(chromium (Cr)) in PBMCs (β = 0.0632; 95% CI: 0.0124 ~ 0.1139), ln(zinc (Zn)) in RBCs (β = 0.1338; 95% CI: 0.0388 ~ 0.2288), and ln(cadmium (Cd)) in RBCs (β = 0.1297; 95% CI: 0.0125 ~ 0.2469). RBC Cr, Cd, lead, and vanadium were positively associated with hemoglobin, hematocrit, and mean corpuscular volume. In PBMCs, Cr, Cd, Zn, and single-cell iron were associated with ALPL, CXCR1, and PI3 expression. Anemic COPD patients showed upregulation of IFI27 and TRAV38-1 and downregulation of DEFA1, PI3, CXCR1, and ALPL. Conclusion:Intracellular metals, particularly Cr and Cd, were associated with immune-related gene expression profiles and anemia-related erythrocyte indices in COPD. These findings suggest potential biological associations among air pollution exposure, intracellular metal burden, immune dysregulation, and hematologic alterations in COPD.
Purpose:We compared salivary metabolomic signatures from patients with stable COPD across a range of severity of airflow obstruction with healthy controls. Patients and Methods:In this exploratory study, 47 people with COPD and 48 age-matched, healthy controls, provided saliva that was assessed by flow infusion electrospray mass spectrometry (FIE-MS). Spectra were interrogated using an open-source library DIMEpy package. Results:Four potential biomarkers identified the presence of COPD with a sensitivity of 73% and specificity of 72%. Six metabolites predicted the level of airflow obstruction, FEV1% in the COPD cohort (P < 0.001, R2 > 0.3, AUC > 0.7), whilst a range of multivariate approaches targeted six metabolites linked to COPD stage of severity (P < 0.001, AUC > 0.7). Identification of the metabolites suggested changes in pterin biosynthesis, lipid processing, nucleotide metabolism and melatonin in COPD patients. Conclusion:This proof-of-concept study shows metabolic fingerprinting of saliva samples is feasible and can differentiate patients with COPD from people (including smokers) without COPD and correlates with COPD severity as defined by level of airflow obstruction. Metabolic fingerprinting also offers insights into the metabolic pathways involved in COPD aetiology.
Background:This study systematically depicts the global research landscape, knowledge base, and thematic evolution of chronic obstructive pulmonary disease combined with obstructive sleep apnea overlap syndrome (COPD-OSA overlap syndrome), providing a reference for subsequent research and clinical translation. Methods:Relevant literature published between 2000 and 2025 was retrieved from the Web of Science Core Collection, Scopus, and PubMed databases. Records from Web of Science and Scopus were merged and deduplicated to construct the primary bibliometric dataset, while PubMed was analyzed separately as a supplementary source for cross-database comparison using keywords and MeSH terms. Bibliometrix, VOSviewer, and CiteSpace were used to analyze research output, collaboration networks, core sources, co-citation structure, and research hotspots. A latent Dirichlet allocation (LDA) model was applied to identify latent topics and their temporal trends. Results:The primary dataset included 1239 publications, with an average annual growth rate of 20.62%; publication volume increased markedly after 2017. The United States, China, and Italy ranked highest in publication count, yet the overall international co-authorship rate was only 6.3%. The field's knowledge base centers on nocturnal hypoxemia, cardiovascular outcomes, acute exacerbations, and positive airway pressure therapy. The LDA model identified 10 latent topics, with two showing significant upward trends (COVID-19 with OSA; acute cardiovascular events and adipose tissue) and one showing a downward trend (anthropometry and lung function parameters). The 274 PubMed-included articles complemented cross-database comparisons and indicated growing attention to cognitive function, oxygen saturation, and digital screening. Conclusion:Research on COPD-OSA overlap syndrome is shifting from describing disease coexistence toward complex multimorbidity, risk stratification, and individualized management. However, international collaboration and high-level clinical evidence remain insufficient. Future work should standardize case definitions, establish multicenter longitudinal cohorts, and conduct phenotype-based treatment studies.
Purpose:Experimental studies suggest that lithium may modulate emphysema-related tissue injury through activation of the WNT/β-catenin signaling pathway. However, clinical data examining the relationship between lithium exposure and emphysema burden remain limited. This study aimed to investigate the association between long-term lithium use and CT-derived emphysema burden in smokers with bipolar disorder. Patients and Methods:This retrospective observational study included 67 smokers with bipolar disorder who had received lithium treatment for at least 5 years and 71 matched controls with similar demographic and smoking characteristics. Quantitative emphysema measurements were obtained from thoracic computed tomography using low attenuation area percentage (LAA%). Multivariable linear regression analyses were performed adjusting for age, sex, BMI, and smoking exposure. Results:Right, left, and total lung LAA% values were lower in the lithium-treated group than in controls (7.53% vs 10.11%, p=0.009; 7.73% vs 10.02%, p=0.019; and 7.64% vs 10.07%, p=0.011, respectively). In multivariable analysis, lithium treatment remained independently associated with lower total LAA% after adjustment for age, sex, body mass index, and smoking exposure (B = -2.636, standardized β = -0.236, 95% CI -4.537 to -0.736; p = 0.007). Weak inverse correlations between serum lithium concentrations and emphysema measurements were observed in unadjusted analyses but were no longer significant after multivariable adjustment. Conclusion:Long-term lithium exposure was associated with lower CT-derived emphysema burden in smokers with bipolar disorder. Given the observational design and potential influence of inspiratory effort during CT acquisition, these findings should be considered hypothesis-generating rather than evidence of a protective effect. Prospective longitudinal and mechanistic studies are warranted to clarify the relationship between lithium exposure and smoking-related parenchymal lung injury.
Background:The systemic immune-inflammation index (SII) is derived from routine blood counts, but its incremental value for disease-specific early readmission after acute exacerbation of chronic obstructive pulmonary disease (AECOPD) remains uncertain. We evaluated the association between admission SII and 90-day AECOPD-related unplanned readmission and its value beyond available clinical variables. Methods:This single-center retrospective development cohort included 207 unique patients hospitalized for AECOPD from January 2024 to January 2025; only the first eligible hospitalization per patient was analyzed. Readmissions were ascertained from electronic records and structured telephone follow-up completed on June 30, 2025. SII was calculated from the first blood count obtained within 24 hours of arrival. Logistic regression, receiver operating characteristic analysis, 1000-resample bootstrap internal validation, calibration assessment and decision curve analysis were performed. Bootstrap validation assessed optimism in discrimination, calibration and net benefit. Results:Seventy-eight patients (37.7%) experienced AECOPD-related unplanned readmission within 90 days. Each 500-unit increase in SII was associated with higher adjusted odds of readmission (odds ratio 1.329, 95% confidence interval 1.153-1.530; p < 0.001). Apparent AUC was 0.809 (95% confidence interval 0.742-0.869) for the integrated model versus 0.752 (0.678-0.821) for the limited clinical model. The absolute increase was 0.057 (bootstrap 95% confidence interval 0.014-0.101), and adding SII improved model fit (likelihood-ratio p < 0.001). Optimism-corrected AUC, calibration intercept and slope were 0.785, -0.003 and 0.874. Otherwise identical NLR and PLR models had corrected AUCs of 0.775 and 0.765; pairwise differences were inconclusive. Conclusion:Higher admission SII was associated with 90-day AECOPD-related unplanned readmission and improved discrimination beyond a limited clinical model. These exploratory single-center findings require external validation, recalibration and prospective impact assessment before clinical implementation.
Objective:This study aimed to construct a biopsychosocial network model of dyspnea-related fear in older adults with acute exacerbation of chronic obstructive pulmonary disease (AECOPD), identify central and bridge nodes within the psychological system, and explore structural associations linking fear symptoms to emotional distress and functional impairment. Methods:A total of 317 hospitalized older adults with AECOPD were enrolled. Eight variables were assessed, including fear of dyspnea (Fear-D), fear of activity (Fear-A), anxiety (Anx), depression (Dep), psychological resilience (Psy-R), self-efficacy (Self-E), social support (Social-S), and activities of daily living (ADL). Gaussian Graphical Models (GGM) were estimated using EBICglasso. Centrality indices, bridge strength, node predictability (R2), and non-parametric bootstrap procedures were applied to evaluate network stability and accuracy. Results:Anxiety demonstrated the highest expected influence (z = 1.34), functioning as the most central node. Fear-D and Fear-A formed a cohesive dyad (edge weight = 0.51), while Anx and Dep exhibited the strongest edge (0.76). Protective factors (Self-E and Psy-R) showed negative associations with fear and anxiety nodes and significant bridge strength across communities. ADL was structurally embedded between psychological and emotional domains. Network stability analysis confirmed high robustness (CS coefficient > 0.70). The topology reflects a "indicating a structured pattern of fear-emotion co-occurrence and resource-related associations" configuration. Social support (Social-S) demonstrated near-zero centrality, suggesting minimal structural integration within the network under acute hospitalization conditions. Conclusion:Dyspnea-related fear in older AECOPD patients operates within a structured pattern of emotional and resource co-occurrence rather than a simple linear fear-disability pathway. Anxiety serves as the most central node, whereas self-efficacy functions as a cross-domain bridge node. Targeting high-centrality emotional nodes and strengthening resource bridges may optimize precision psychological intervention in AECOPD.
Background:Medication adherence is defined as the extent to which patients take medications as prescribed by their healthcare providers, encompassing three critical dimensions: initiation (starting the prescribed therapy), implementation (taking doses correctly as scheduled), and persistence (continuing treatment for the recommended duration). Long-term management of chronic obstructive pulmonary disease (COPD) relies heavily on inhalation therapy; however, real-world adherence remains suboptimal, with reported medication possession ratios (MPR) typically ranging from 40% to 60% in observational studies. Poor adherence is associated with increased exacerbation frequency, accelerated lung function decline, and substantial healthcare costs. Purpose:Based on the Capability-Opportunity-Motivation-Behaviour (COM-B) model, this narrative review aims to summarize and conceptually organize existing literature on measurement methods, epidemiological status, influencing factors, and intervention strategies for inhalation medication adherence in COPD patients, with particular emphasis on adherence differences between single-inhaler triple therapy (SITT) and multiple-inhaler triple therapy (MITT). Methods:A narrative review methodology was employed, utilizing purposing literature search combined with citation back-tracking. Observational studies and randomized controlled trials from PubMed, CNKI, and other databases (up to December 2024) were included, with influencing factors categorized according to COM-B dimensions. Quantitative estimates reported in this review are derived from individual observational studies with heterogeneous designs and populations, and should be interpreted as illustrative ranges rather than pooled or definitive values. Quantitative estimates are derived from heterogeneous observational studies and should be interpreted as illustrative rather than pooled values. The non-systematic, purposing nature of this review limits comprehensive generalizability. Results:Across the heterogeneous observational studies included in this review, MPR ≥80% was achieved by approximately 30% to 50% of patients in reported cohorts, while inhalation technique error rates were reported as illustrative ranges of 30% to 50%. The COM-B framework suggests that adherence deficits arise from the interplay of capability limitations (operational skill deficits), opportunity constraints (with the MITT associated with an illustrative range of approximately 12% to 14% lower MPR compared with SITT in reported real-world studies), and motivational barriers (with depression and anxiety associated with approximately 20% to 25% reductions in adherence in individual cohorts). In observational data, higher adherence has been associated with a reported 51% reduction in exacerbation-related hospitalization risk and an attenuation of forced expiratory volume in 1 second (FEV1) decline by approximately 21 mL/year according to individual observational estimates. SITT was associated with improved treatment persistence compared with MITT in available real-world studies (18-month persistence rates ranging from approximately 2.3% for MITT to 16.5% for SITT). Promising intervention approaches described in the literature include artificial intelligence (AI)-based visual error-correction technology, device simplification strategies, and digital therapeutics, though evidence for some of these strategies remains preliminary. Conclusion:Inhalation medication adherence in COPD represents a multidimensional behavioral syndrome. This review proposes that transitioning from traditional "one-size-fits-all" education models to precision stratified interventions based on the COM-B framework may enable more individualized chronic disease management, though further empirical validation of phenotype-stratified interventions is warranted. This phenotype-stratified model represents a conceptual framework advanced by the authors to stimulate future research, rather than an empirically validated clinical tool.
Chi-Thien Dinh,1,2 Jer-Hwa Chang,3,4 Tzu-Tao Chen,5– 7 Ching-Huang Lai,8 Kuan-Yuan Chen,4,5,7 Po-Hao Feng,5,7 Kang-Yun Lee,4– 7 Chien-Hua Tseng,5,7 Huan Minh Tran,9 Tzu-Hsuen Yuan,10 Shu-Chuan Ho,3 Sheng-Ming Wu,5,7,11 Hsiao-Chi Chuang3– 5,121International Ph.D. Program in Medicine, College of Medicine, Taipei Medical University, Taipei, Taiwan; 2Department of Internal Medicine, Faculty of Medicine, Can Tho University of Medicine and Pharmacy, Can Tho, Vietnam; 3School of Respiratory Therapy, College of Medicine, Taipei Medical University, Taipei, Taiwan; 4Department of Internal Medicine, Wan Fang Hospital, Taipei Medical University, Taipei, Taiwan; 5Department of Internal Medicine, Shuang Ho Hospital, Taipei Medical University, New Taipei, Taiwan; 6Graduate Institute of Clinical Medicine, College of Medicine, Taipei Medical University, Taipei, Taiwan; 7Department of Internal Medicine, School of Medicine, College of Medicine, Taipei Medical University, Taipei, Taiwan; 8School of Public Health, National Defense Medical Center, Taipei, Taiwan; 9Faculty of Public Health, Da Nang University of Medical Technology and Pharmacy, Da Nang, Vietnam; 10Department of Health and Welfare, College of City Management, University of Taipei, Taipei, Taiwan; 11Department of Medical Research, Wan Fang Hospital, Taipei Medical University, Taipei, Taiwan; 12Cell Physiology and Molecular Image Research Center, Wan Fang Hospital, Taipei Medical University, Taipei, TaiwanCorrespondence: Hsiao-Chi Chuang, Inhalation Toxicology Research Laboratory (ITRL), School of Respiratory Therapy, College of Medicine, Taipei Medical University, 250 Wuxing Street, Taipei, 11031, Taiwan, Tel +886-2-27361661 ext. 27483, Fax +886-2-27391143, Email chuanghc@tmu.edu.twBackground: Anemia is common in chronic obstructive pulmonary disease (COPD) and may be influenced by air pollution-related metal accumulation. However, the biological links among air pollution, intracellular metals, immune gene expression, and erythrocyte alterations remain unclear.Methods: We examined associations between air pollutants, intracellular metals, gene expression, and erythrocyte indices in sixty-one COPD patients and ten healthy controls. Annual exposures to particulate matter with aerodynamic diameter ≤ 10 μm (PM10), ≤ 2.5 μm (PM2.5), nitrogen dioxide (NO2), and nitrogen oxides (NOx) were estimated using land use regression. Metals were quantified by inductively coupled plasma mass spectrometry (ICP-MS) and single-cell ICP-MS. Gene expression in peripheral blood mononuclear cells (PBMCs) was profiled by RNA sequencing.Results: In COPD, PM2.5 was associated with higher red blood cell (RBC) count (β = 0.0314× 106 cells/μL; 95% confidence interval (CI): 0.0066 ~ 0.0561) and ln(red cell distribution width) (β = 0.0051; 95% CI: 0.0003 ~ 0.0098). PM2.5 was also associated with higher ln(chromium (Cr)) in PBMCs (β = 0.0632; 95% CI: 0.0124 ~ 0.1139), ln(zinc (Zn)) in RBCs (β = 0.1338; 95% CI: 0.0388 ~ 0.2288), and ln(cadmium (Cd)) in RBCs (β = 0.1297; 95% CI: 0.0125 ~ 0.2469). RBC Cr, Cd, lead, and vanadium were positively associated with hemoglobin, hematocrit, and mean corpuscular volume. In PBMCs, Cr, Cd, Zn, and single-cell iron were associated with ALPL, CXCR1, and PI3 expression. Anemic COPD patients showed upregulation of IFI27 and TRAV38-1 and downregulation of DEFA1, PI3, CXCR1, and ALPL.Conclusion: Intracellular metals, particularly Cr and Cd, were associated with immune-related gene expression profiles and anemia-related erythrocyte indices in COPD. These findings suggest potential biological associations among air pollution exposure, intracellular metal burden, immune dysregulation, and hematologic alterations in COPD.Keywords: air pollution, erythrocyte indices, mass spectrometry, peripheral blood mononuclear cells, trace elements
Anat Romem,1,2 Ayal Romem3,41Henrietta Szold Hadassah-Hebrew University School of Nursing, Faculty of Medicine, Hadassah and the Hebrew University, Jerusalem, Israel; 2Department of Complex Care, Herzog Medical Center, Jerusalem, Israel; 3Department of Pulmonology, Meir Medical Center, Kfar Saba, Israel; 4Gray Faculty of Medical and Health Sciences, Tel Aviv University, Tel Aviv, IsraelCorrespondence: Anat Romem, Henrietta Szold Hadassah-Hebrew University School of Nursing, Faculty of Medicine, Hadassah and the Hebrew University, Jerusalem, Israel, Email anat.romem@mail.huji.ac.ilBackground: Advanced chronic obstructive pulmonary disease (COPD) is associated with multimorbidity and substantial healthcare use. However, sex differences in documented multimorbidity and healthcare use near the end of life remain insufficiently understood.Methods: This retrospective population-based cohort study used routinely collected administrative and clinical data from Clalit Health Services, Israel. The study included deceased patients with advanced COPD during 2016– 2020 and 2022– 2023. The year 2021 was excluded a priori as the period designated in the study protocol to minimize distortion in healthcare utilization, and mortality patterns; 2020 remained part of the prespecified observation window. The cohort comprised 2261 patients aged ≥ 55 years who met an operational definition requiring documented COPD, FEV1/FVC < 0.70, FEV1 < 50% predicted, and at least two opioid prescriptions or dispensations during the final year of life. The opioid criterion was not considered a stand-alone diagnostic criterion or evidence of refractory dyspnea. Women and men were compared regarding demographic characteristics, multimorbidity, selected healthcare-use indicators, coded palliative care encounters, and time from first documented COPD diagnosis to death.Results: The cohort included 547 women and 1714 men. Women were older and more frequently had dementia and rheumatic/connective tissue disease. Men more frequently had myocardial infarction, renal disease, peripheral vascular disease, and diabetes. Men had more documented procedures, although the effect size was small. The mean Charlson Comorbidity Index differed by 0.44 points, of uncertain clinical importance. Coded palliative care encounters were rare and available only for small subsamples. Median time from first documented COPD diagnosis to death was 33 months among women and 29 months among men, without a statistically significant difference (log-rank p=0.328).Conclusion: Sex differences were observed primarily in documented multimorbidity. Healthcare-use and palliative-care findings were partial and descriptive and should not be interpreted as complete end-of-life trajectories or causal effects of sex.Keywords: chronic obstructive pulmonary disease, sex differences, multimorbidity, palliative care, end of life, retrospective cohort
Qianfei Liu,1,2,* Ling Hou,3,* Huiling Li,1 Yin Li,1 Quanfang Chen11Department of Respiratory, The First Affiliated Hospital of Guangxi Medical University, Nanning, Guangxi, People’s Republic of China; 2Department of Pulmonary and Critical Care Medicine, The Central Hospital of Enshi Tujia and Miao Autonomous Prefecture, Enshi, Hubei, People’s Republic of China; 3Department of Emergency, The First Affiliated Hospital of Guangxi Medical University, Nanning, Guangxi, People’s Republic of China*These authors contributed equally to this workCorrespondence: Quanfang Chen, Department of Respiratory, The First Affiliated Hospital of Guangxi Medical University, No. 6, Shuangyong Road, Nanning, Guangxi, 530000, People’s Republic of China, Email chenquanfang@stu.gxmu.edu.cnBackground: Chronic obstructive pulmonary disease (COPD) often coexists with lung cancer, worsening clinical outcomes. This study aimed to develop machine learning models for early COPD screening in lung cancer patients using clinical variables and a novel systemic coagulation–inflammation index (SCI).Methods: We retrospectively enrolled 1016 patients, extracting demographic, smoking, vital, and laboratory data. After feature selection with Boruta and least absolute shrinkage and selection operator (LASSO), six models—logistic regression, decision tree (DT), multilayer perceptron (MLP), support vector machine (SVM), gradient boosting decision tree (GBDT), and extreme gradient boosting (XGBoost)—were trained on 70% of the data and tested on 30%. Model performance was assessed with area under the curve (AUC), accuracy, sensitivity, specificity, precision, F1 score, and calibration, while SHapley Additive exPlanations (SHAP) and contour plots helped interpret the best model and explore predictor interactions.Results: Among the 1,016 patients, 182 (17.9%) had concomitant COPD. Boruta and LASSO identified 8 key predictors: age, historical smoking index (HSI), SCI, eosinophils (EOS), bicarbonate (HCO3−), sex, lymphocytes (LYM), and hemoglobin (HGB). The GBDT model showed the best performance, with an area under the curve (AUC) of 0.74 (95% CI: 0.69– 0.80) and moderate sensitivity (0.68) and specificity (0.66). SHAP analysis revealed age, HSI, and SCI as major risk factors, with contour plots indicating a synergistic effect between SCI, age, and HSI on COPD risk.Conclusion: A GBDT-based model built from routine clinical variables and SCI showed moderate discrimination for identifying patients with lung cancer at high risk of concomitant COPD.Keywords: machine learning, chronic obstructive pulmonary disease, lung cancer, systemic coagulation-inflammation index, gradient boosting decision tree, screening model
Xueping Liu,1,* Lu Wang,1,* Ting Tang,2 Hang Qian,1 Binfeng He,3 Zhi Xu,1 Guansong Wang,1 Mingzhou Zhang11Department of Respiratory and Critical Care Medicine, Xinqiao Hospital, Third Military Medical University, Chongqing, People’s Republic of China; 2Department of Respiratory Disease, Chongqing Public Health Emergency Medical Center, Chongqing, People’s Republic of China; 3Department of General Practice, Xinqiao Hospital, Third Military Medical University, Chongqing, People’s Republic of China*These authors contributed equally to this workCorrespondence: Guansong Wang; Mingzhou Zhang, Email wanggs@tmmu.edu.cn; mingzhou06@tmmu.edu.cnBackground: Chronic obstructive pulmonary disease (COPD) has high morbidity and mortality, and integrated stress response (ISR) participates in its progression. Traditional COPD diagnosis relies mainly on GOLD criteria, while ISR-based auxiliary molecular markers are still lacking. This study aimed to comprehensively analyze and identify ISR-related biomarkers in COPD, and to uncover their underlying molecular mechanisms.Methods: COPD data were obtained from the GEO database. Biomarkers were identified and validated through a comprehensive approach, including differential expression analysis, machine learning algorithms, expression level verification, and receiver operating characteristic (ROC) curve analysis. Moreover, a nomogram was applied to construct an ISR-related predictive model for COPD. Enrichment analysis, immune infiltration analysis, molecular regulatory network, and drug prediction were performed to further confirm the regulatory roles of biomarkers. Furthermore, the expression patterns of biomarkers in core cells were investigated using dataset GSE173896. Furthermore, Western blotting was employed to detect the protein expression levels of biomarkers in clinical samples.Results: Overall, 2 biomarkers (MYC and BAG3) were identified as significantly associated with ISR in COPD. A predictive nomogram constructed based on MYC and BAG3 demonstrated excellent diagnostic performance, with an area under the curve (AUC) value of 0.799. The biomarkers were significantly enriched in the MAPK signaling pathway and cell cycle. Notably, biomarkers were positively correlated with effector memory CD4 T cells and eosinophils. Subsequently, 36 microRNAs (miRNAs) (like hsa-miR-4699-3p), 260 transcription factors (TFs) (like SNAI2, ZBTB43, and ZNF395) and 9 drugs (like staurosporine and N-acetyl-L-cysteine) were associated with the biomarkers. Moreover, significant changes in MYC and BAG3 expression were observed during T cell differentiation. Clinically, MYC and BAG3 were significantly upregulated in COPD tissues, with protein levels increased by 58.4% and 252.2% respectively.Conclusion: This study identified and preliminarily verified ISR-associated MYC and BAG3, offering novel molecular references to complement conventional COPD clinical assessment.Keywords: chronic obstructive pulmonary disease, integrated stress response, machine learning, single-cell RNA sequencing, biomarkers
Liwei Xue,1,2,* Qiong Lin,3,* Xiongxin Ye,1,2,* Xiaoyong Zhang,1 Borong Tang,4 Xiaojuan Lin,4 Wanyi Zheng,1,2 Yunjing Xue,1 Yuanfen Liu11Department of Radiology, Fujian Medical University Union Hospital, Fuzhou, People’s Republic of China; 2The Graduate School of Fujian Medical University, Fuzhou, People’s Republic of China; 3Department of Respiratory and Critical Care Medicine, Fujian Medical University Union Hospital, Fuzhou, People’s Republic of China; 4School of Medical Imaging, Fujian Medical University, Fuzhou, People’s Republic of China*These authors contributed equally to this workCorrespondence: Yunjing Xue; Yuanfen Liu, Department of Radiology, Fujian Medical University Union Hospital, Fuzhou, People’s Republic of China, Email xueyunjing@126.com; 5692054@qq.comObjective: To evaluate the impact of deep learning image reconstruction (DLIR) on quantitatively assessing emphysema, air trapping and small airway dysfunction in chronic obstructive pulmonary disease (COPD) using low-dose inspiratory–expiratory chest CT.Methods: Sixty-nine COPD patients underwent low-dose inspiratory-expiratory chest CT scans and pulmonary function tests (PFT) were prospectively enrolled. The CT images were reconstructed using 50% adaptive statistical iterative reconstruction (ASiR-V), DLIR-high (DLIR-H), medium (DLIR-M), and low (DLIR-L) strengths. The volumes and its percentages (relative to whole lung) characterizing emphysema, air trapping and small airway dysfunction were quantified on the inspiratory-expiratory CT scans.Results: The total dose-length product was 128.99 ± 39.00 mGy·cm. For all patients, emphysema parameters were lowest for DLIR-H and highest for ASiR-V; small airway dysfunction parameters were highest with DLIR-H and lowest with ASiR-V; air trapping parameters were lowest with ASiR-V; highest with DLIR-M. Emphysema parameters demonstrated moderate negative correlations with FEV1/FVC (r = – 0.570 to – 0.649, all p < 0.001). Air trapping and small airway dysfunction parameters showed weak negative correlations with MEF25%, MEF50%, and MEF75% (r = – 0.320 to – 0.381, all p < 0.001). When differentiating GOLD I–II from III–IV, all parameters showed AUC values ranging from 0.69 to 0.76, without statistically differences among reconstructions (DeLong’s test, p > 0.05), while the optimal thresholds varied across reconstructions.Conclusion: In low-dose inspiratory–expiratory chest CT, DLIR may alter the lung function-related CT parameters compared to ASiR-V, but does not affect their correlations with PFTs or their efficacies in GOLD grading.Keywords: chronic obstructive pulmonary disease, chest computed tomography, deep learning image reconstruction, low dose, GOLD grading
Stephen Brunton,1 Patrick A Cambier,2,* Nathaniel Marchetti,3,* Joel Solis4,*1US Primary Care Respiratory Group, Winnsboro, SC, USA; 2Interventional Cardiac Consultants, Tampa, FL, USA; 3Department of Thoracic Medicine and Surgery, Lewis Katz School of Medicine Temple University, Philadelphia, PA, USA; 4Valley Medical Arts Clinic, McAllen, TX, USA*These authors contributed equally to this workCorrespondence: Stephen Brunton, US Primary Care Respiratory Group, 608 Wateree Key Court, Winnsboro, SC, 29180, USA, Email ozdoc@aol.comAbstract: Chronic obstructive pulmonary disease (COPD) is a prevalent and devastating condition responsible for substantial morbidity and mortality in the United States (US). Although the burden of cardiopulmonary (CP) comorbidity in COPD is increasingly known, CP risk remains frequently under-recognized and suboptimally managed in routine clinical practice. Definitions of CP risk in COPD vary across the literature and clinical settings. CP risk may be broadly defined as the risk of serious respiratory and/or cardiovascular events in patients with COPD, including COPD exacerbations, myocardial infarction, stroke, heart failure decompensation, arrhythmia, and death. While current clinical guidelines recognize comorbidity, there remain no clear protocols for screening, risk assessment, or a coordinated multidisciplinary approach to address the CP risk in patients with COPD. To address these gaps, a multidisciplinary expert panel developed consensus-based guidance through an iterative process incorporating an expert-led narrative review supported by targeted desk searches alongside multiple rounds of expert input. This paper identifies critical gaps in the management of CP risk among patients with COPD in the US and outlines actionable steps towards improvement of care. Actionable recommendations include encouraging earlier screening and risk assessment to identify patients at risk of exacerbations and improving patient access to multidisciplinary teams, prompting earlier initiation or optimization of evidence-based treatment. Furthermore, implementation of fully coordinated treatment strategies through primary care settings and increasing the use of preventative strategies in the clinic such as spirometry, vaccination, and smoking cessation are recommended. By shifting to proactive, multidisciplinary care, healthcare practitioners can better address the complex needs of patients with COPD to reduce their CP risk and improve individual and population-level outcomes.Keywords: cardiovascular diseases, pulmonary disease, comorbidity, exacerbations
Fushi Dong,1,2 Yosuke Tanaka,1 Ken Okamura,1 Katsuyuki Higa,1 Shota Kaburaki,1 Yozo Sato,1 Toru Tanaka,1 Ayumi Shimizu,1,3 Susumu Takeuchi,1 Yoshiaki Kubota,4 Namiko Taniuchi,1 Koichiro Kamio,1 Kazuo Kasahara,1 Lina Wu,5 Masahiro Seike,1 Mitsunori Hino,1,3 Hiroshi Kimura1,61Department of Respiratory Medicine, Nippon Medical School Hospital, Tokyo, Japan; 2Department of Respiratory Medicine, the First Affiliated Hospital of Harbin Medical University, Harbin, People’s Republic of China; 3Respiratory Care Clinic, Nippon Medical School, Tokyo, Japan; 4Department of Cardiovascular Medicine, Nippon Medical School, Tokyo, Japan; 5Molecular Imaging Research Center, the Fourth Affiliated Hospital of Harbin Medical University, Harbin, People’s Republic of China; 6Respiratory Diseases Center, Fukujuji Hospital, Japan Anti-Tuberculosis Association, Tokyo, JapanCorrespondence: Yosuke Tanaka, Department of Respiratory Medicine, Nippon Medical School Hospital, Tokyo, Japan, Tel +81 3 3822 2131, Fax +81 3 5685 3075, Email yosuke-t@nms.ac.jpBackground: Pulmonary hypertension is a frequent complication of chronic obstructive pulmonary disease (COPD) and an independent determinant of prognosis. Increases in pulmonary vascular resistance often precede overt elevation of pulmonary arterial pressure, but early pulmonary vascular involvement remains difficult to detect noninvasively. Conventional echocardiographic assessment in COPD focuses mainly on right ventricular systolic indices, which may remain preserved despite increased pulmonary vascular load.Methods: We conducted a single-center retrospective observational study of 58 patients with stable COPD who underwent right heart catheterization and comprehensive transthoracic echocardiography during the same hospitalization. Right ventricular isovolumetric relaxation time (IRT), a Doppler-derived index of diastolic timing, was evaluated in relation to invasively measured pulmonary vascular resistance, dyspnea severity, exercise capacity, and pulmonary function.Results: A total of 58 patients were included. Patients meeting hemodynamic criteria for pulmonary hypertension exhibited lower diffusing capacity, more severe dyspnea, and reduced exercise capacity despite similar airflow limitation. Pulmonary vascular resistance was significantly higher, while cardiac index remained preserved. IRT was significantly prolonged in patients with pulmonary hypertension and showed a moderate correlation with pulmonary vascular resistance (ρ = 0.56, p < 0.001), six-minute walk distance (ρ = − 0.46, p < 0.001), and dyspnea severity (ρ = 0.36, p = 0.006). In contrast, conventional right ventricular systolic indices showed no significant associations with pulmonary vascular resistance.Conclusion: Echocardiographic assessment of IRT may provide a physiologically grounded, noninvasive parameter associated with early right ventricular diastolic response to pulmonary vascular load.Keywords: COPD, pulmonary hypertension, pulmonary vascular resistance, echocardiography, right ventricular function, exercise capacity
