COPD: Journal of Chronic Obstructive Pulmonary Disease最新文献

The involvement of Group 3 innate lymphoid cells (ILC3s) and dendritic cells (DCs) in chronic lung inflammation has been increasingly regarded as the key to understand the inflammatory mechanisms of smoke-related chronic obstructive pulmonary disease (COPD). However, the mechanism underlying the engagement of both remains unclear. Our study aimed to explore NCR^-ILC3 differentiation in the lungs of mice exposed to cigarette smoke (CS) and to further investigate whether DCs activated by CS exposure contribute to the differentiation of ILCs into NCR^-ILC3s. The study involved both in vivo and in vitro experiments. In the former, the frequencies of lung NCR^-ILC3s and NKp46^-IL-17A⁺ ILCs and the expression of DCs, CD40, CD86, IL-23, and IL-1β quantified by flow cytometry were compared between CS-exposed mice and air-exposed mice. In the latter, NKp46^-IL-17A⁺ ILC frequencies quantified by flow cytometry were compared after two cocultures, one involving lung CD45⁺Lin^-CD127⁺ ILCs sorted from air-exposed mice and DCs sifted by CD11c magnetic beads from CS-exposed mice and another including identical CD45⁺Lin^-CD127⁺ ILCs and DCs from air-exposed mice. The results indicated significant increases in the frequencies of NCR^-ILC3s and NKp46^-IL-17A⁺ ILCs; in the expression of DCs, CD40, CD86, IL-23, and IL-1β in CS-exposed mice; and in the frequency of NKp46^-IL-17A⁺ ILCs after the coculture with DCs from CS-exposed mice. In conclusion, CS exposure increases the frequency of lung ILCs and NCR^-ILC3s. CS-induced DC activation enhances the differentiation of ILCs into NCR^-ILC3s, which likely acts as a mediating step in the involvement of NCR-ILC3s in chronic lung inflammation.

Background: Patients with chronic obstructive pulmonary disease (COPD) often do not seek care until they experience an exacerbation. Improving self-management for these patients may increase health-related quality of life and reduce hospitalizations. Patients are willing to use wearable technology for real-time data reporting and perceive mobile technology as potentially helpful in COPD management, but there are many barriers to the uptake of these technologies.

Objective: We aimed to understand patients' experiences using a wearable and mobile app and identify areas for improvement.

Methods: We conducted semi-structured interviews as part of a larger prospective cohort study wherein patients used a wearable and app for 6 months. We asked which features patients found accessible, acceptable and useful.

Results: We completed 26 interviews. We summarized our research findings into four main themes: (1) information, support and reassurance, (2) barriers to adoption, (3) impact on communication with health care providers, and (4) opportunities for improvement. Most patients found the feedback received through the app to be reassuring and useful. Some patients experienced technical difficulties with the app and found the wearable to be uncomfortable.

Conclusions: Patients found a wearable device and mobile application to be acceptable and useful for the management of COPD. We identified barriers to adoption and opportunities for improvement to the design of our app. Further research is needed to understand what people with COPD and their healthcare providers want and will use in a mobile app and wearable for COPD management.

Observational studies that have reported an association between aspirin use in chronic obstructive pulmonary disease (COPD) with reductions in mortality and COPD exacerbations were shown to be affected by time-related biases. We assessed this association using a prevalent new-user study design that avoids these biases. We used the United Kingdom's Clinical Practice Research Datalink (CPRD) to form a cohort of patients with COPD. Aspirin initiators were matched on time and propensity score with nonusers during 2002-2018. The outcomes were all-cause mortality and COPD exacerbation within a one-year follow-up. Hazard ratios (HR) and 95% confidence interval (CI) of each outcome associated with aspirin use compared to nonuse were estimated using an as-treated approach. The study cohort included 10,287 initiators of aspirin and 10,287 matched nonusers. The cumulative incidence of all-cause mortality at one year was 11.5% for aspirin users and 9.2% for nonusers. The HR of all-cause mortality associated with aspirin initiation was 1.22 (95% CI: 1.08-1.37), while for severe exacerbation it was 1.21 (95% CI 1.08-1.37), compared with nonuse. The HR of a first moderate or severe exacerbation with aspirin use was 0.90 (95% CI 0.85-0.95). These estimates did not vary by platelet count. This large population-based study, designed to emulate a trial, found aspirin use in patients with COPD associated with a higher risk of all-cause mortality and severe exacerbation, but a lower risk of moderate or severe exacerbation. Further research is warranted to assess this reduction in moderate or severe exacerbations, particularly in patients with cardiovascular risk factors.

To provide a scoping review of studies on factors affecting smoking cessation in patients with chronic obstructive pulmonary disease (COPD), so as to provide a basis for healthcare professionals to intervene early in the process of cessation of smoking in patients with COPD, and to formulate personalized interventions for smoking cessation. Arksey and O'Malley's scoping review methodology as a framework, searched databases including CNKI, Wanfang Data, VIP, China Biomedical Database, PubMed, Web of Science, Embase, ProQuest, CINAHL, and Cochrane Library to collect literature on factors influencing smoking cessation among COPD patients. The literature was screened, data extracted, and summarized accordingly. A total of 28 papers were included. The socio-demographic related factors affecting smoking cessation in patients with COPD were age, educational level, residence, marital status, occupational status, economic status, race, and sex; tobacco related factors included smoking index, smoking duration (years), cumulative smoking (packs/year), smoking intensity (packs/day), and tobacco addiction; disease related factors included mMRC score, GOLD level, severity of airflow restrictions, symptom, activity limitation due to lung problems, history of deterioration in outpatient care, receipt of COPD medication, receipt of lung CT, receipt of pulmonary function tests, receipt of surgery, and comorbid comorbidities; psychologically related factors included mental health status, quit smoking health beliefs, smoking cessation self-efficacy, motivation to quit smoking, stress, and adverse emotions; environmental/Interpersonal network related factors-included environmental impacts, social support, family support, tobacco control policies, and satisfaction with cessation care; and behavior related factors included alcohol consumption, coffee consumption, eating, physical activity, and have a hobby. Healthcare professionals should avoid critical education of COPD patients in the process of smoking cessation management, pay attention to the adverse effects of medication side effects on patients, emphasize the improvement of patients' health beliefs and self-efficacy in smoking cessation, and help patients to establish a correct cognition of smoking cessation.

Identifying patients with rare diseases like alpha-1 antitrypsin deficiency (AATD) is challenging. Machine-learning models may be trained to identify patients with rare diseases using large-scale, real-world databases, whereas electronic medical records have low numbers of confirmed cases and have limited use in training such models. We applied a machine-learning model to a large US claims database to identify undiagnosed symptomatic patients with AATD. Using deidentified data from the Komodo US claims database (April 26, 2016-January 31, 2023), a model was trained to identify symptomatic patients with high probability of AATD. Eighty claims records for high-probability candidates identified by the model were independently reviewed and validated by 2 clinical experts. The experts independently indicated that of the 80 high-probability candidate patients, 65 (81%) and 62 (78%) patients, respectively, should be tested for AATD. Feedback from this validation step informed model optimization. The optimized model was applied to claims data to identify symptomatic patients with probable AATD. Eleven and 14 "features" of the claims data were informative in distinguishing patients with AATD from patients with COPD without AATD and from unspecified chronic liver diseases. Moreover, patients with diagnosed AATD and COPD without AATD had unique cadences of similar medical events in their diagnostic journeys. Our work shows that a machine-learning model trained on a large US claims database can accurately identify symptomatic patients with AATD and provides useful insights into the diagnostic journey of patients with AATD.

Purpose: Chronic Obstructive Pulmonary Disease (COPD) is regarded as an accelerated aging disease. Aging-related genes in COPD are still poorly understood.

Method: Data set GSE76925 was obtained from the Gene Expression Omnibus (GEO) database. The "limma" package identified the differentially expressed genes. The weighted gene co-expression network analysis (WGCNA) constructes co-expression modules and detect COPD-related modules. The least absolute shrinkage and selection operator (LASSO) and the support vector machine recursive feature elimination (SVM-RFE) algorithms were chosen to identify the hub genes and the diagnostic ability. Three external datasets were used to identify differences in the expression of hub genes. Real-time reverse transcription polymerase chain reaction (RT-qPCR) was used to verify the expression of hub genes.

Result: We identified 15 differentially expressed genes associated with aging (ARDEGs). The SVM-RFE and LASSO algorithms pinpointed four potential diagnostic biomarkers. Analysis of external datasets confirmed significant differences in PIK3R1 expression. RT-qPCR results indicated decreased expression of hub genes. The ROC curve demonstrated that PIK3R1 exhibited strong diagnostic capability for COPD.

Conclusion: We identified 15 differentially expressed genes associated with aging. Among them, PIK3R1 showed differences in external data sets and RT-qPCR results. Therefore, PIK3R1 may play an essential role in regulating aging involved in COPD.

Background: Clinical studies have shown that the onset and exacerbation of chronic obstructive pulmonary disease (COPD) are related to obesity and dietary behaviours, but the genetic relationship between them is not clear.Aims: To investigate the relationship between the genetic determinants of obesity, dietary habits (alcohol consumption, intake of sweets, salt intake) and COPD.Methods: Exposure and outcome datasets were obtained from the IEU Open GWAS project. The exposure dataset includes dietary habits (Salt added to food, Sweets intake, Alcohol consumption), obesity level (represented by body mass index (BMI) and body fat percentage (BFP) data sets.). The outcome dataset includes COPD and acute COPD admissions. The collected data were imported into the RStudio software and conducted Mendelian randomisation analysis. Additionally, heterogeneity and horizontal pleiotropy tests were conducted on the data to ensure the veracity of the results.Results: The results showed that BMI was positively correlated with the risk of acute COPD admission (OR = 1.74, 95% CI 1.39-2.18) and COPD (OR = 1.81, 95%CI 1.41-2.33). In addition, BFP was also a risk factor for COPD (OR = 1.98, 95% CI 1.42-2.77) and acute exacerbation of COPD admission (OR = 1.99, 95%CI 1.43-2.77). The increase of salt, sugar and alcohol consumption will not increase the risk of COPD and the risk of hospitalisation due to COPD.Conclusion: Therefore, we should strengthen the guidance of diet and living habits of obese patients. For patients with heavier weight and higher body fat rate, they should be instructed to lose weight and fat to prevent the occurrence of COPD. For obese patients with COPD, more attention should be paid to prevent the occurrence of acute exacerbation of COPD in advance.

Chronic obstructive pulmonary disease (COPD) is a common lung disease that negatively affects health-related quality of life (QoL). Utility values, which measure QoL by weighting health states with societal preferences, are required for the cost-utility models that drive economic evaluations and policy decisions. Moayeri et al. published a systematic review and meta-analysis of utilities (EQ-5D) in COPD in June 2016. The current study investigated changes in mean utilities in more recent studies thereafter, exploring heterogeneity in utilities across diverse clinical and study characteristics. Systematic searches of databases, such as MEDLINE and Embase were undertaken from 1 July 2015 until 20 May 2024. A random-effects meta-analysis of utilities (EQ-5D) was performed which addressed inter-study heterogeneity and subgroup analyses. The pooled general mean (95% CI) utility value was 0.761 (0.726-0.795) from 43 studies, whereas Moayeri et al. reported 0.673 (0.653-0.693) from 32 studies. This improvement in mean utilities could be due to increased awareness, early detection, and better medical interventions over the past decade, but demonstrates that a general utility value should be approached with caution given significant heterogeneity. Four meta-regressions were performed on each subgroup: region, method of elicitation, reported comorbidities, and disease stage; of which, method of elicitation, disease stage, and region were found to be significant moderators of utilities. It is, therefore, important to use meta-analysed utilities for cost-utility analyses that reflect the context and patient population of the model. Moreover, these results provide additional evidence for the precision and sensitivity of EQ-5D-5L over EQ-5D-3L.

Background: There is limited data on the reasons for escalation or de-escalation of COPD inhaled therapies in routine clinical practice, especially after the follow-up pharmacological treatment guidance on the 2019 GOLD report and the 2020 ERS guideline on ICS withdrawal.

Methods: The STEPINCOPD study was a 12-week, two-visit, prospective observational study that aimed to describe the reasons for change of inhaled therapies, in accordance with GOLD recommendations 2021. Only patients that had a recent change in their inhaled medication were enrolled. Moreover, we investigated associations between physicians' and patients' characteristics and adherence to GOLD recommendations.

Results: 1429 patients were enrolled from 146 centers (138 private practice and 8 hospitals) throughout Greece. At enrollment, the most frequent reasons for treatment change were lack of clinical (78.9%) or spirometric (49.5%) response to previous treatment, change in CAT score (45.1%), and mMRC score (28.2%). At the follow-up visit, most common reasons were lack of clinical response to previous treatment (71.4%), COPD exacerbations (59.5%), changes in CAT score (52.4%), lack of spirometric response (42.9%) and lower respiratory tract infections (31%). We observed high adherence to the GOLD 2021 recommendations (81.6% at enrollment and 92.9% at follow-up). Physicians' age and consideration of GOLD recommendations for prescription choice, as well as patients' CAT score were significant predictors of adherence to GOLD.

Conclusion: The STEPINCOPD study highlights the reasons for inhaled treatment change in Greek physicians with high adherence to GOLD recommendations and provides insights for future research that may inform the development of decision support tools.