International Journal of Chronic Obstructive Pulmonary Disease最新文献

Introduction: Accelerated lung aging is observed in chronic obstructive pulmonary disease (COPD), this study delved into the precise mechanisms through which azithromycin mitigated lung aging associated with COPD.

Methods: Employing network pharmacology, we predicted potential pathways through which azithromycin might affect COPD development. We collected lung tissues from non-smoking individuals, smokers with normal lung function, and smokers with COPD. COPD models were created by exposing mice to cigarette smoke (CS) for 24 weeks and stimulating human bronchial epithelial cells (BEAS-2B) with 0.2% cigarette smoke extract (CSE) for 24 hours. Azithromycin was then given to CS-exposed emphysema mice. BEAS-2B cells were pre-treated with azithromycin before being exposed to CSE and JY-2, a Forkhead box O3 (FOXO3A) inhibitor. Senescence-associated secretory phenotype (SASP) cytokines (interleukin-6, interleukin-8) in mouse BALF were quantified using ELISA. Markers associated with cellular aging (β-galactosidase activity, p53, and p21), FOXO3A, and Cyclin D1 (CCND1) were assessed via qPCR, Western blot, immunohistochemistry, and β-galactosidase staining.

Results: COPD patients who smoked showed increased pulmonary expression of CCND1, p53, and p21, with decreased FOXO3A in comparison with other groups. Similarly, CS-exposed mouse lung tissue exhibited reduced FOXO3A and elevated p53, p21, and CCND1, along with higher SASP secretion in BALF. Azithromycin treatment lowered SASP secretion and decreased CCND1, p53, and p21 expression, while increasing FOXO3A. In BEAS-2B cells, CSE and JY-2 stimulation raised senescence markers and CCND1 while lowering FOXO3A. Azithromycin preconditioning reduced p53, p21, and CCND1 expression and increased FOXO3A.

Conclusion: Azithromycin demonstrated anti-aging properties and modulated lung senescence in COPD via the FOXO3A/CCND1 pathway, presenting fresh insights for the treatment of COPD.

Objective: Chronic obstructive pulmonary disease (COPD) is the third leading cause of death worldwide, which has driven efficient and economical management strategies to become a research focus. This study conducted a bibliometric analysis of drug treatment and comprehensive rehabilitation strategies for COPD, providing a scientific basis for future COPD management and offering valuable information and guidance to clinicians, researchers, and policymakers to optimize clinical treatment plans for COPD.

Materials and methods: Using the statistical analysis software CiteSpace6.4. R1, 11,417 articles exported from the Web of Science Core Collection and Scopus from January 1, 2005, to December 31, 2024, were quantitatively analyzed in terms of annual publication volume, countries, institutions, and keywords, and presented in the form of graphs.

Results: After systematic screening, 3668 articles were included. The annual number of published papers in this field is increasing, but little cooperation has been observed between countries and institutions. In terms of contribution, the United States and the United Kingdom are in leading positions. At the institutional level, Sapienza University of Rome in Italy published the largest number of papers. The high-frequency keywords were pulmonary rehabilitation, quality of life, and bronchodilating agents. Keyword emergent analysis results showed that targeted drugs and intelligent healthcare are new research hotspots.

Conclusion: The management of COPD has achieved a fundamental transformation from being "drug-centered" to "patient-centered". Research hotspots are mainly focused on bronchodilators and pulmonary rehabilitation, whereas targeted drugs and intelligent healthcare represent future directions of individualization and precision. In future, drugs, rehabilitation, and intelligent technologies should be integrated to build a full-process management model, and key empirical support should be provided through strengthened international collaboration.

Purpose:  This study aims to evaluate potential associations between the weight-adjusted waist index (WWI) and all-cause and cardiovascular mortality in patients with chronic obstructive pulmonary disease (COPD).

Methods: Our investigation analyzed data from the National Health and Nutrition Examination Survey (NHANES, 1999-2018). From an initial cohort of 101,316 participants, we incorporated 1,396 qualified individuals with COPD. To investigate the relationship between WWI and mortality, we employed multiple analytical methods, including multivariate Cox proportional hazards regression, Kaplan-Meier survival analysis, subgroup stratification and restricted cubic spline (RCS) modeling. Additionally, the prognostic utility of WWI in predicting mortality risk was further assessed through time-dependent receiver operating characteristic (ROC) curve analysis.

Results: In fully adjusted models, the highest WWI tertile (T3) revealed higher risks for both all-cause mortality (HR: 1.82, 95% CI: 1.19-2.77, p=0.006) and CVD mortality (HR: 2.79, 95% CI: 1.48-5.26, p=0.002) compared to the lowest tertile (T1). RCS analyses revealed a strong and statistically significant linear association between WWI and mortality risk. These findings suggest that WWI may be a meaningful predictor of adverse outcomes in COPD.

Conclusion: Our study demonstrates that higher WWI significantly predicts increased mortality risk in COPD patients, highlighting its prognostic value and suggesting potential utility for risk stratification in clinical practice.

Background: Remote technology is recommended for exercise management in chronic obstructive pulmonary disease (COPD) patients to enhance health and exercise function. Despite numerous studies, the optimal combination of remote technologies and auxiliary interventions remains unclear.

Objective: Evaluate which remote exercise interventions and their additional behavioral support measures are most effective in improving exercise function, exercise behavior, and healthy quality of life in COPD patients.

Methods: A systematic review using a network meta-analysis (NMA) of randomized controlled trials (RCTs). We searched PubMed, the Cochrane Central Register of Controlled Trials, Web of Science, EMBASE, CINAHL, and Medline from their inception to December 31, 2024. RCTs evaluating remote exercise interventions were included. The NMA was performed using STATA software.

Results: Twenty-nine RCTs were included (3,234 participants). Remote device & Exercise was superior in improving 6-minute walking distance (6MWD, SMD 0.51, 95% CI 0.10-0.93, SUCRA 81.8%) compared to usual care, traditional face-to-face rehabilitation, and other remote exercise intervention types; Online self-management & Exercise was superior in facilitating daily activity time (SMD 0.48, 95% CI 0.08-0.88, SUCRA 90.1%); Application & Exercise significantly improved healthy quality of life (Negative scale, SMD -0.67, 95%CI-1.05--0.29, SUCRA 81.1%); increased behavioral aids had an integrative effect with the remote exercise intervention, Motivation & Feedback + Health education significantly improved exercise function and quality of life; Motivational interviewing + Goal setting + Activity monitor/Pedometer + Health education significantly promoted exercise behavior. On average, the quality of evidence ranged from low to very low.

Conclusion: This review found evidence that remote exercise series of interventions is superior in improving outcomes such as exercise function, promoting exercise behavior and enhancing healthy quality of life in patients with COPD. Additional behavioral aids had an integrative effect on outcome improvement.

Purpose: There is an association between low socioeconomic status and chronic obstructive pulmonary disease (COPD), but it is not known whether this impacts on drug prescription for COPD treatment. The aim of the study was to explore whether drug prescription differs between COPD patients with low and high socioeconomic status.

Patients and methods: Data from patients with incident and prevalent COPD (age>40 years), without an asthma diagnosis, visiting primary care in 2021-2022 were extracted from The Swedish National Airway Register (SNAR) and linked to Swedish National Health registries. Socioeconomic status was assessed by educational level and annual income. Statistical analyses were conducted by means of chi square tests, together with post-hoc test.

Results: In total, 38692 patients (mean age 73.6 years, 55.5% women, FEV₁ 59.5% of predicted value) were included. Subdivision into GOLD group A, B and E was possible in 29128 patients. Triple therapy (long-acting antimuscarinic antagonists, LAMA + long-acting beta-2-agonist, LABA + inhaled steroids, ICS), was more often prescribed in the low education group (observed/expected ratio 1.09; p<0.0001) and low income group (ratio 1.05; p<0.05) and less often in the high education group (ratio 0.87; p<0.0001) and high income group (ratio 0.88; p<0.0001). Monotherapy (LAMA) was more often prescribed in patients with high income (ratio 1.09; p<0.0001) and less often in patients with low income (ratio 0.93; p=0.0004). Differences between high and low education and income were driven by differences in group B.

Conclusion: Prescription patterns were associated with small, statistically significant differences, between COPD patients with low and high socioeconomic status. Triple therapy was more often prescribed to patients with low socioeconomic status, and monotherapy with LAMA more often to patients with high. The reason for this is not clear but may be caused by differences in exacerbation rate between the socioeconomic groups.

Purpose: This two-sample Mendelian randomization (MR) analysis and study based on the National Health and Nutrition Examination Survey (NHANES) aimed to evaluate the effects of diabetes mellitus and glycemic traits on chronic obstructive pulmonary disease (COPD) and pulmonary function traits.

Patients and methods: Utilizing a two-sample MR analysis and NHANES data (2007-2012), this study investigated the associations of diabetes and glycemic traits with COPD and pulmonary function traits. Exposures included type 1 (T1DM) and type 2 diabetes (T2DM), fasting glucose (FGlu), fasting insulin (FIns), glycated hemoglobin (HbA1c), and 2-hour glucose (2hGlu). Outcomes included COPD, forced expiratory volume in 1 second (FEV1), forced vital capacity (FVC), the FEV1/FVC ratio, and peak expiratory flow (PEF). The MR analysis employed inverse variance weighted (IVW) and weighted median methods. Multivariate logistic regression and linear regression were used to evaluate the associations, adjusting for age, gender, race, body mass index (BMI), and blood cholesterol in the NHANES database.

Results: MR analyses (IVW results) indicated significant causal relationships between T1DM and COPD, and FEV1/FVC ratio (OR = 1.023, 95% CI = 1.012 to 1.034, P < 0.0001; beta = -0.0075, 95% CI = -0.0122 to -0.0028, P = 0.0018, respectively). T2DM also exhibited significant causal associations with FVC and FEV1/FVC ratio (beta = -0.0330, 95% CI = -0.0448 to -0.0212, P < 0.0001; beta = 0.0172, 95% CI = 0.0065 to 0.0279, P = 0.0016). 2hGlu showed a significant causal relationship with FEV1/FVC ratio (beta = 0.0472, 95% CI = 0.0089 to 0.0856, P = 0.0159). A total of 389 participants were enrolled in this study (unweighted), with a weighted sample size of 6324845, based on the NHANES database. Multivariate logistic regression revealed no statistically significant association between diabetes, glycemic traits, and COPD. Multivariate linear regression indicated that a 2.7-fold increase in HbA1c levels was negatively correlated with declines in FEV1 (42.56%), FVC (34.92%), and PEF (37.77%).

Conclusion: This study demonstrated the impact of diabetes and glycemic traits on COPD and lung function traits, highlighting important clinical implications.

Introduction: The Dyspnea-12 is a brief patient reported tool assessing physical and emotional components of breathlessness. However, the reliability and effectiveness of the Chinese version of Dyspnea-12 (D-12-C) needs to be verified.

Purpose: This study aimed to assess the reliability and validity of D-12-C in patients with chronic obstructive pulmonary disease (COPD) and investigate its associations with clinical outcomes of COPD.

Patients and methods: Patients from three centers completed baseline assessments (pulmonary function test, the COPD Assessment Test (CAT), the Modified Medical Research Council Dyspnea Scale (mMRC), the Hospital Anxiety and Depression Scale (HADS), St George's Respiratory Questionnaire (SGRQ), Borg dyspnea scale, 6-minute walking distance (6MWD) and the history of exacerbations. The internal consistency, construct validity, convergent validity and reliability of the D-12-C were evaluated. The binary multivariate logistic regression analysis was performed to explore the influencing factors for hospitalization during follow-up.

Results: A total of 279 patients were recruited. Exploratory factor analysis divided the D-12-C into physical and affective dimensions. A high level of internal consistency was manifested by the Cronbach's alpha values of the D-12-C with total scores, physical, and affective dimensions of 0.94, 0.96, and 0.92, respectively. The score of D-12-C increased as the GOLD or mMRC grade rose, and patients suffering from anxiety or depression had more severe dyspnea. The score of D-12-C was significantly correlated with CAT, mMRC, HADS, SGRQ and hospitalization during follow-up. Additionally, baseline D-12-C was an independent predictor of hospitalization during the one year follow-up (OR=1.086, 95% CI: 1.035-1.139, p < 0.001).

Conclusion: The D-12-C demonstrated good internal consistency and validity in COPD. The score of D-12-C correlates with the clinical parameters of disease severity and predicts severe exacerbations of hospitalization, supporting its use in risk stratification and management planning to prevent adverse outcomes.

Background: Previously, we reported that applying artificial intelligence and natural language processing to electronic health record (EHR) data can identify patients at risk of chronic obstructive pulmonary disease (COPD) exacerbations, based on clinical attributes identified in COPDGene.

Purpose: Building on these data and using real-world data, we established a predictive model for identifying patients at risk of COPD exacerbations within 24 months of their initial COPD diagnosis.

Methods: Structured and unstructured data were obtained from Epic EHR data. Summary statistics for independent variables, including age, gastroesophageal reflux disease, coronary artery disease, congestive heart failure, cor pulmonale, asthma, dyspnea, smoking status, number of comorbidities, and blood eosinophil counts, were calculated. Bivariate associations with COPD exacerbations were calculated using odds ratios and 95% confidence intervals. A multivariable prediction model using the flexible machine-learning approach, Bayesian Additive Regression Trees (BART), was then developed. Model performance was assessed using receiver operating characteristic (ROC) curves and area under the ROC curve (AUC).

Results: Of the 3007 patients with COPD as a primary diagnosis, 886 had a COPD exacerbation within 24 months. In the bivariate logistic regression analyses, strong associations (odds ratio >1.5; P <0.05) existed between COPD exacerbation and cor pulmonale, moderate and severe dyspnea, and number of comorbidities (≥4 vs 0). In the BART model, the predictors that were selected most for the branching-tree analyses were eosinophil count, pack years, and moderate dyspnea (in order of most selected). The AUC derived from our BART model was 0.69.

Conclusion: Eosinophil count and dyspnea were identified as important predictors of exacerbations. Our data suggest that active monitoring of eosinophil counts and selected patient-reported experiences of dyspnea may identify patients at risk of exacerbations, enabling clinicians to tailor therapies to improve health outcomes among patients with COPD.