Emerging Complexity in the Biomarkers of Exacerbation-Prone Asthma

Peripheral blood eosinophilia in adult asthmatic patients and its association with the severity of asthma

BACKGROUND

Asthma is a diverse disease with various etiologic bases. Severe asthma can be associated with increased mortality, hospitalization, and decreased quality of life for asthma patients. High blood eosinophil counts were associated with severe asthma, but recent studies have failed to confirm this as a marker of severe asthma among adult asthma patients. As a result, the purpose of this study was to determine the association between the severity of asthma and high blood eosinophil count.

METHODOLOGY

A simple random sampling technique was used to select 291 asthmatic patients for an institution-based cross-sectional study. Socio-demographic, behavioral, and clinical characteristics were collected by using a pre-tested structured questionnaire. Four milliliters of venous blood were collected from asthmatic patients for complete blood count and peripheral morphology assessment. The eosinophil count was analyzed by the Unicel DxH 800 (Beckman Coulter, Ireland) analyzer. A statistical package for social science version 20 (SPSS) software was used to analyze the data. The non-parametric (Mann-Whitney U) test was used to compare the eosinophil count with different background variables. A binary logistic regression analysis was used to assess the factors associated with eosinophilia. A p-value less than 0.05 in multivariable logistic regression analysis was considered statistically significant.

RESULT

In this study, the overall magnitude of eosinophilia was 19.6% (95% CI = 14.8-24.1). Being admitted to the emergency department (AOR = 0.25; 95% CI: 0.09-0.69, p = 0.007) and being female (AOR = 0.49; 95% CI: 0.26-0.9, p = 0.025) were shown to have a statistically significant association with eosinophilia. Moreover, the absolute eosinophil count was significantly higher among asthmatic patients infected with intestinal parasitic infection (p < 0.045).

CONCLUSION

Being female and admission to the emergency department were negatively associated with eosinophilia. Lack of eosinophilia can be related to the low-T2 asthma phenotype. The absolute eosinophil counts were higher among intestinal parasite-infected patients. Therefore, different biomarkers will be considered for the proper diagnosis and management of adult asthma patients.

A machine learning approach to characterize patients with asthma exacerbation attending an acute care setting

BACKGROUND

One of the main problems in poorly controlled asthma is the access to the Emergency Department (ED). Using a machine learning (ML) approach, the aim of our study was to identify the main predictors of severe asthma exacerbations requiring hospital admission.

METHODS

Consecutive patients with asthma exacerbation were screened for inclusion within 48 hours of ED discharge. A k-means clustering algorithm was implemented to evaluate a potential distinction of different phenotypes. K-Nearest Neighbor (KNN) as instance-based algorithm and Random Forest (RF) as tree-based algorithm were implemented in order to classify patients, based on the presence of at least one additional access to the ED in the previous 12 months.

RESULTS

To train our model, we included 260 patients (31.5% males, mean age 47.6 years). Unsupervised ML identified two groups, based on eosinophil count. A total of 86 patients with eosinophiles ≥370 cells/µL were significantly older, had a longer disease duration, more restrictions to daily activities, and lower rate of treatment compared to 174 patients with eosinophiles <370 cells/μL. In addition, they reported lower values of predicted FEV₁ (64.8±12.3% vs. 83.9±17.3%) and FEV₁/FVC (71.3±9.3 vs. 78.5±6.8), with a higher amount of exacerbations/year. In supervised ML, KNN achieved the best performance in identifying frequent exacerbators (AUROC: 96.7%), confirming the importance of spirometry parameters and eosinophil count, along with the number of prior exacerbations and other clinical and demographic variables.

CONCLUSIONS

This study confirms the key prognostic value of eosinophiles in asthma, suggesting the usefulness of ML in defining biological pathways that can help plan personalized pharmacological and rehabilitation strategies.

Biomarkers of asthma

Biomarkers may be diagnostic of asthma, they may predict or reflect response to therapy or they may identify patients at risk of asthma exacerbation. A biomarker is most often measured in biologic fluids that are sampled using relatively non-invasive sampling techniques such as blood, sputum, urine or exhaled breath. Biomarkers should be stable, readily quantifiable and their measurement should be reproducible and not confounded by other host factors, or the presence of comorbidities. However, asthma comprises multiple molecular endotypes and single, sensitive, specific, biomarkers reflecting these endotypes may not exist. Combining biomarkers may improve their predictive capability in asthma. The most well-established endotypes are those described as Type2 and non-Type2 asthma. Clinical trials established the fraction of exhaled nitric oxide (FeNO) and blood eosinophil counts as key biomarkers of response to corticosteroid or targeted anti-inflammatory therapy in Type2 asthma. However, these biomarkers may have limited value in the management of asthma in real-life settings or routine clinical practise. Biomarkers for Type2 asthma are not well described or validated and more research is needed. Breathomics has provided evidence to propose a number of exhaled volatile organic compounds (VOCs) as surrogate biomarkers for airway inflammatory phenotypes, disease activity and adherence to therapy. Analysis of urinary eicosanoids has identified eicosanoids related to Type2 and non-Type2 inflammation. Future clinical trials will be important in determining how exhaled VOCs or urinary eicosanoid profiles can be used to direct precision treatments. Their future clinical use will also depend on developing simplified instrumentation for biomarker analysis at the point-of-care.

The Predictive Role of Biomarkers and Genetics in Childhood Asthma Exacerbations

Asthma exacerbations are associated with significant childhood morbidity and mortality. Recurrent asthma attacks contribute to progressive loss of lung function and can sometimes be fatal or near-fatal, even in mild asthma. Exacerbation prevention becomes a primary target in the management of all asthmatic patients. Our work reviews current advances on exacerbation predictive factors, focusing on the role of non-invasive biomarkers and genetics in order to identify subjects at higher risk of asthma attacks. Easy-to-perform tests are necessary in children; therefore, interest has increased on samples like exhaled breath condensate, urine and saliva. The variability of biomarker levels suggests the use of seriate measurements and composite markers. Genetic predisposition to childhood asthma onset has been largely investigated. Recent studies highlighted the influence of single nucleotide polymorphisms even on exacerbation susceptibility, through involvement of both intrinsic mechanisms and gene-environment interaction. The role of molecular and genetic aspects in exacerbation prediction supports an individual-shaped approach, in which follow-up planning and therapy optimization take into account not only the severity degree, but also the risk of recurrent exacerbations. Further efforts should be made to improve and validate the application of biomarkers and genomics in clinical settings.