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To explore the heterogeneity of disease-specific anxiety profiles among patients with chronic obstructive pulmonary disease (COPD) using latent profile analysis (LPA), and to identify the associations between distinct anxiety subtypes and inhaler medication adherence in patients with COPD.
Adherence to inhaled medication among patients with COPD continues to be suboptimal. Anxiety, a common comorbidity, may exacerbate this issue. However, the specific relationship between anxiety and adherence to inhaled medications remains unclear.
A prospective cohort study was conducted following the STROBE Checklist.
A prospective observational study employed the Anxiety Inventory for Respiratory Disease (AIR) to assess disease-specific anxiety in patients with COPD. Inhaler medication adherence was evaluated using the Test of Adherence to Inhalers (TAI) 6 months after initiating treatment. Latent Profile Analysis (LPA) was performed to identify distinct anxiety subtypes. Multiple linear regression analysis was conducted to examine the associations between identified anxiety subtypes and adherence dimensions, adjusting for sociodemographic and clinical variables.
Among 298 COPD patients, the overall AIR score was 5 (IQR: 2–11). Using LPA, three distinct anxiety subtypes were identified: Low Anxiety—Irritable Subtype (57.05%), Moderate Anxiety—Tense Subtype (26.85%) and High Anxiety—Anticipatory Subtype (16.10%). Through multiple linear regression analysis, the High Anxiety—Anticipatory Subtype was significantly associated with lower inhaler medication adherence among COPD patients.
This study revealed three latent profiles of disease-specific anxiety among COPD patients. The High Anxiety–Anticipatory Subtype was associated with a lower inhaler medication adherence in individuals with COPD after initiating treatment.
Identifying the relationship between disease-specific anxiety and inhaler medication adherence in patients with COPD after initiating treatment underscores the need for healthcare providers to assess anxiety during patient visits and prioritise patients with high anticipatory anxiety. When high anxiety adversely affects inhaler medication adherence, targeted interventions should be developed to improve adherence and prognosis.
No patient or public contribution.
Hypertrophic scar (HS) is a chronic inflammatory skin disorder characterized by excessive deposition of extracellular matrix, and the mechanisms underlying their formation remain poorly understood. We analysed scRNA-seq data from samples of normal skin and HS. Using the hdWGCNA method, key gene modules of fibroblasts in HS were identified. Non-negative matrix factorization was employed to perform subtype analysis of HS patients using these gene modules. Multiple machine learning algorithms were applied to screen and validate accurate gene signatures for identifying and predicting HS, and a convolutional neural network (CNN) based on deep learning was established and validated. Quantitative reverse transcription-polymerase chain reaction and western blotting were performed to measure mRNA and protein expression. Immunofluorescence was used for gene localization analysis, and biological features were assessed through CCK8 and wound healing assay. Single-cell sequencing revealed distinct subpopulations of fibroblasts in HS. HdWGCNA identified key gene characteristics of this population, and pseudotime analysis was conducted to investigate gene variation during fibroblast differentiation. By employing various machine learning algorithms, the gene range was narrowed down to three key genes. A CNN was trained using the expression of these key genes and immune cell infiltration, enabling diagnosis and prediction of HS. Functional experiments demonstrated that THBS2 is associated with fibroblast proliferation and migration in HS and affects the formation and development of HS through the TGFβ1/P-Smad2/3 pathway. Our study identifies unique fibroblast subpopulations closely associated with HS and provides biomarkers for the diagnosis and treatment of HS.
FreshRSS 