RESUMEN
OBJECTIVE: We analyzed the impact of different inhalant allergens on T-lymphocyte subsets in patients diagnosed with bronchial asthma. METHODS: The study included 57 bronchial asthma patients and 22 healthy controls. Asthma patients were categorized into dust mite, animal hair, pollen, and mold groups. Flow cytometry was used to measure the cells in the case group and control group. These T-lymphocyte subset markers were evaluated among patients with bronchial asthma caused by different allergens as well as between the case group and control group. RESULTS: Peripheral blood CD4+ T-cells, CD8+ T-cells, CD4/CD8 ratio, and Th17/Treg ratios were all higher in the case group than in the control group (p < 0.05). Peripheral blood T-lymphocyte subsets were compared among the four groups, and it was found that there were statistical differences in the Th17/Treg ratio among the four groups (p < 0.05). There were no significant differences observed among the four groups in terms of CD3+ cells, CD4+ cells, CD8+ cells, Th1 cells, Th2 cells, Th17 cells, Treg cells, Th9 cells, and Th22 cells. Further pairwise comparison was made, and the results suggested that the peripheral blood Th17/Treg ratio in the pollen mixed group was lower than that in the dust mite mixed group, animal hair mixed group, and mold mixed group (p < 0.05). CONCLUSION: Patients with bronchial asthma show varied T-lymphocyte subset responses to different inhalant allergens. Elevated CD4+ T cells and Th17 cells in peripheral blood could indicate asthma risk. However, small sample size may introduce bias to these findings.
RESUMEN
Background: The frequent exacerbator phenotype of acute exacerbations of chronic obstructive pulmonary disease (AECOPD) is characterized by experiencing at least two exacerbations per year, leading to a significant economic burden on healthcare systems worldwide. Although several biomarkers have been shown to be effective in assessing AECOPD severity in recent years, there is a lack of studies on markers to predict the frequent exacerbator phenotype of AECOPD. The current study aimed to develop a new predictive model for the frequent exacerbator phenotype of AECOPD based on rapid, inexpensive, and easily obtained routine markers. Methods: This was a single-center, retrospective study that enrolled a total of 2,236 AECOPD patients. The participants were divided into two groups based on the frequency of exacerbations: infrequent group (n=1,827) and frequent group (n=409). They underwent a complete blood count, as well as blood biochemistry, blood lipid and coagulation testing, and general characteristics were also recorded. Univariate analysis and binary multivariate logistic regression analyses were used to explore independent risk factors for the frequent exacerbator phenotype of AECOPD, which could be used as components of a new predictive model. The receiver operator characteristic (ROC) curve was used to assess the predictive value of the new model, which consisted of all significant risk factors predicting the primary outcome. The nomogram risk prediction model was established using R software. Results: Age, gender, length of stay (LOS), neutrophils, monocytes, eosinophils, direct bilirubin (DBil), gamma-glutamyl transferase (GGT), and the glucose-to-lymphocyte ratio (GLR) were independent risk factors for the frequent exacerbator phenotype of AECOPD. The area under the curve (AUC) of the new predictive model was 0.681 [95% confidence interval (CI): 0.653-0.708], and the sensitivity was 63.6% (95% CI: 58.9-68.2%) and the specificity was 65.0% (95% CI: 60.3-69.6%). Conclusions: A new predictive model based on demographic characteristics and blood parameters can be used to predict the frequency of acute exacerbations in the management of chronic obstructive pulmonary disease (COPD).
