Comparison of eosinophil biomarkers related to blood eosinophil cutoffs in adult asthma

Biologics in practice: A unique opportunity for allergist/immunologist expertise

Molecular biomarkers that identify the phenotype of blood eosinophilia were evaluated in adult asthmatics, and their relationship with clinically significant asthma outcomes was assessed. Patients were clustered based on their molecular fingerprint. At inclusion, 64 patients were evaluated for phenotypic traits, sputum and blood eosinophilia, exhaled NO, serum cytokines and chemokines, total serum IgE, lung function (LF), and airway hyper-responsiveness (AHR). Within-patient changes were evaluated in 44 patients 6 weeks later. Lung function, asthma control, and monocyte chemotactic protein-1 (MCP-1) were identified as the most important distinguisher and blood eosinophilia as second most important identifier in principal component analysis. A robust relationship was observed between blood eosinophilia and IL-5, IL-13, and eosinophil-derived neurotoxin (EDN), which stayed consistent after 6 weeks. Serum IL-5 and IL-13 were the two best, followed by EDN as separators of high vs low blood eosinophilia. Periostin did not identify blood or sputum eosinophilia, even after stratification for total IgE, and did not correlate with IL-5, IL-13, eotaxin, or EDN. IL-5 and IL-13 showed strong correlations with AHR and monocyte chemoattractant protein (MCP)-1 with asthma severity and fast LF decline. The presence of high or low expression of MCP-1, eotaxin, and IL-8 identified two separate blood eosinophilia patient clusters linked to asthma severity. Serum IL-5 and IL-13 are reliable biomarkers for the blood eosinophilia asthma phenotype. High or low expression of MCP-1, eotaxin, and IL-8 discriminates between eosinophilic asthma severity clusters.

While studies have demonstrated the impact of asthma symptoms on quality of life, very few studies have investigated the relationship between detailed asthma symptoms, as reported by the patient, and lung function and inflammation. A cross-sectional study was conducted on treated (ICS/LABA) adult (> 18 years) asthma patients recruited from the Liege University Hospital Asthma Clinic (Belgium) between 2018 and 2023 (n = 505). The intensity of asthma symptoms (dyspnea, wheezing, chest tightness, cough, and airway secretion) was measured using five-point Likert scales (5 expressing the greatest intensity). Multiple linear regression models including all independent variables were carried out to evaluate whether lung function and inflammatory parameters were independently associated with distinct symptoms. Cough associated with female gender (p < 0.05), smoking (p < 0.01), low FeNO (p < 0.05) and FEV1% pred. (p < 0.05), and high blood and sputum eosinophils (p < 0.05 for both). Airway secretion associated with smoking (p < 0.05). Chest tightness associated with young age (p < 0.001), female gender (p < 0.05) and low FEV1% pred. (p < 0.01). Dyspnea associated with female gender (p < 0.001), high BMI (p < 0.05), low FEV1% pred. (p < 0.0001) and high FEV1/FVC % (p < 0.01). Wheezing associated with young age (p < 0.01), high BMI (p < 0.05), smoking (p < 0.01), low FEV1% pred. (p < 0.0001) and high FEV1/FVC % (p < 0.05). Different respiratory symptoms are associated with distinct demographic, functional and inflammatory features paving the way for personalized therapeutic interventions.

BackgroundTo date, many researches have investigated the correlation of folate and asthma occurrence. Nevertheless, few studies have discussed whether folate status is correlated with dis-ease severity, control or progression of asthma. So, we explored the correlation of serum folate and blood eosinophil counts in asthmatic adults to gain the role of folate in the control, progression, and treatment of asthma.MethodsData were obtained from the 2011–2018 NHANES, in which serum folate, blood eosinophils, and other covariates were measured among 2332 asthmatic adults. The regression model, XGBoost algorithm model, and generalized linear model were used to explore the potential correlation. Moreover, we conducted stratified analyses to determine certain populations.ResultsAmong three models, the multivariate regression analysis demonstrated serum folate levels were negatively correlated with blood eosinophil counts among asthmatic adults with statistical significance. And we observed that blood eosinophil counts decreased by 0.20 (-0.34, -0.06)/uL for each additional unit of serum folate (nmol/L) after adjusting for confounders. Moreover, we used the XGBoost Algorithm model to identify the relative significance of chosen variables correlated with blood eosinophil counts and observed the linear relationship between serum folate levels and blood eosinophil counts by constructing the generalized linear model.ConclusionsOur study indicated that serum folate levels were inversely associated with blood eosinophil counts in asthmatic adult populations of America, which indicated serum folate might be correlated with the immune status of asthmatic adults in some way. We suggested that serum folate might affect the control, development, and treatment of asthma. Finally, we hope more people will recognize the role of folate in asthma.

(1) Background: Heavy metals are widely used and dispersed in the environment and people’s daily routines. Many studies have reported an association between heavy metal exposure and asthma. Blood eosinophils play a crucial role in the occurrence, progression, and treatment of asthma. However, there have thus far been few studies that aimed to explore the effects of heavy metal exposure on blood eosinophil counts in adults with asthma. Our study aims to discuss the association between metal exposure and blood eosinophil counts among asthmatic adults. (2) Methods: A total of 2026 asthmatic individuals were involved in our research from NHANES with metal exposure, blood eosinophils, and other covariates among the American population. A regression model, the XGBoost algorithm, and a generalized linear model (GAM) were used to explore the potential correlation. Furthermore, we conducted a stratified analysis to determine high-risk populations. (3) Results: The multivariate regression analysis indicated that concentrations of blood Pb (log per 1 mg/L; coefficient β, 25.39; p = 0.010) were positively associated with blood eosinophil counts. However, the associations between blood cadmium, mercury, selenium, manganese, and blood eosinophil counts were not statistically significant. We used stratified analysis to determine the high-risk group regarding Pb exposure. Pb was identified as the most vital variable influencing blood eosinophils through the XGBoost algorithm. We also used GAM to observe the linear relationship between the blood Pb concentrations and blood eosinophil counts. (4) Conclusions: The study demonstrated that blood Pb was positively correlated with blood eosinophil counts among asthmatic adults. We suggested that long-time Pb exposure as a risk factor might be correlated with the immune system disorder of asthmatic adults and affect the development, exacerbation, and treatment of asthma.

The "type 2 (T2)-high" phenotype has a relatively well-defined pathophysiology compared with "T2-low" asthma. T2-low asthma has been mostly studied among severe asthma cases. To estimate the proportion and assess the clinical features of asthma with low T2 markers in a general asthma population, including all severities. Participants with current asthma from the population-based adult West Sweden Asthma Study were included. High T2 markers were defined as the blood eosinophil count ≥0.15 × 109/L or fractional exhaled nitric oxide ≥20 parts per billion or asthma being allergen driven. If none of these were present, the case was considered as asthma with low T2 markers. In total, 896 participants were included, of whom 14.3% had low T2 markers. The low T2 marker group had female predominance and had later asthma-onset age compared with the high T2 marker group. Participants with low T2 markers had better spirometry results in terms of airflow obstruction parameters but had more asthma-related emergency visits in the past 12 months than those with asthma with high T2 markers. In a general adult asthma population, 1 in 7 individuals have low T2 markers according to the criteria used in this study. Importantly, these individuals had better lung function than individuals with high T2 markers but a significantly increased risk of asthma exacerbations. Thus, having asthma with low T2 markers is an independent risk factor for exacerbations, which should be considered when assessing patients with asthma clinically.

Rethinking the Pathogenesis of Asthma

Many studies in children had identified risk factors associated with partly or uncontrolled asthma. In adults, factors related to asthma control were not well defined. To find the factors related to partly or uncontrolled asthma in adults. Asthmatic adults who had severity at least in the mild persistent level, were recruited. The asthma control levels were classified as controlled, partly controlled and uncontrolled according to the Global Initiative for Asthma guideline. Risk factors were compared between the patients with controlled and partly controlled/ uncontrolled asthma groups. Two hundred (75.5 % females) were included. The mean age was 38.4 ± 11.93 years old. There were 100 patients (50 %) in the controlled and 100 patients (50 %) in partly controlled and uncontrolled asthma group. For asthma severity, 35 patients (88.2 %) were in mild persistent, 14 patients (10 %) in moderate persistent and 97 patients (1.8 %) in severe persistent groups. The absence of stress yielded an OR = 0.41 (95 % CI = 0.23-0.75), p = 0.03. While active smoking, OR = 4.23 (95 % CI = 1.35-13.2), p = 0.007. Patients who have no contact with stuffed animals or rugs have OR = 0.4 (IC 95 % = 0.2-0.79). It was important the result of the patients that if they correctly use their inhalers OR = 0.36 (IC 95 % = 0.13-0.97), p = 0.031. Current and active smoking and atopic dermatitis are risk factors. As a protective factor to the normal levels of eosinophils in blood, correct use of inhalers, FEV1 > 80 %, the absence of stress and the absence of contact with carpets and stuffed animals.

European Respiratory Society guidelines for the diagnosis of asthma in adults.

Diagnosing Asthma with and without Aerosol-Generating Procedures

<b>Background:</b> Associations between severe asthma and high body mass index (BMI), blood eosinophils (eos), neutrophils (neu) and fractional exhaled nitric oxide (FeNO) are rarely described in individuals with adult onset asthma. <b>Aim:</b> To study if high BMI, FeNO and blood eosinophils and neutrophils associate to severe asthma, in individuals with adult onset asthma. <b>Methods:</b> Postal surveys were performed in population samples from Northern Sweden in 2016. Participants were invited in 2019-2020 to follow-ups with structured interviews, spirometry, FeNO-test, skin prick test and blood sampling. N=251 with asthma onset ≥15 years of age participated. BMI was grouped as underweight (&lt;18.5), normal weight (18.5-24.9), overweight (25.0-29.9), obesity (30.0-34.9) and severe obesity (≥35). Severe asthma (SA) was defined as daily use of GINA step 4-5 treatment, uncontrolled SA was defined by report of severe exacerbations or asthma control test score &lt;20 and non-severe asthma as not using GINA step 4-5 treatment. <b>Results:</b> In total 66.1% were women, mean age was 62.7y, mean FeNO 18.4ppb, mean eos 0.21x109/L, mean neu 3.78x109/L, 40.6% were overweight, 25.9% obese and 10.8% severely obese. N=47 had SA whereof 23 had uncontrolled SA. There were no differences in smoking, allergic sensitization, FeNO or eos between SA and non-severe asthma. In SA, 36% had FEV1&lt;80% of predicted, vs 21% in non-severe asthma. Neutrophil levels in blood were higher in both SA and uncontrolled SA than in non-severe asthma. Severe obesity was more common in uncontrolled SA (26.1%) than in non-severe asthma (9.3%). <b>Conclusion:</b> Higher blood neutrophil levels and severe obesity were more common in severe than in non-severe adult onset asthma.

Background: In adults, asthma is typically classified as Th2-high/Th2-low based on Th2-inflammation markers like eosinophils. In children, it is mostly characterized by atopy. Aim: We assess whether Th2 asthma phenotypes based on eosinophils and if atopy can be defined in children (as in adults). Methods: In the ALL-Age-Asthma-Cohort (ALLIANCE), blood eosinophils and specific IgE (sIgE) were assessed in 149 wheezers (7mos–6yrs), 194 asthmatic children (6-18yrs) and 210 asthmatic adults. We defined atopy as any sIgE≥0.7 IU/mL. Eosinophil cutoffs were determined as the 90th percentile of the absolute eosinophil counts in age-matched healthy controls. Phenotypes were defined using low/high eosinophils and atopy. Results: The prevalence of phenotypes (Th2-low, Eos-only, Atopy-only, Th2-high) was 49.0%, 15.4%, 18.8%, 16.8% in wheezers; 19.1%, 1.5%, 42.3%, 37.1% in asthmatic children and 17.1%, 18.6%, 33.8%, 30.5% in adults. In adults, exacerbations were mostly in the Eos-only phenotype (64.1%) compared to 35.9% and 26.8% in Th2-high and Atopy-only phenotypes (p=0.001). In contrast, in asthmatic children, wheezing episodes were most frequent in the Th2-high phenotype (50.7%) compared to 34.1% in the Atopy-only phenotype (p=0.017). Those with Eos-only were too few for analysis. In wheezers, phenotypes based on eosinophils and atopy had no association with wheezing. However, both atopic groups with/without high eosinophils had the highest risk of asthma at 6yrs (78.9% and 72.7%) compared to the only eosinophilic group (41.7%, p=0.002). Conclusion: It is unclear if the Th2-phenotype as is described in adults exists in children. Eosinophils possibly play a different role in children.

Blood inflammatory phenotypes were associated with distinct clinical expressions of asthma in adults from a large population-based cohort.

BackgroundSome patients with asthma demonstrate normal spirometry and remain undiagnosed without further testing.ObjectiveTo determine clinical predictors of asthma in symptomatic adults with normal spirometry, and to generate a tool to help clinicians decide who should undergo bronchial challenge testing (BCT).MethodsUsing random-digit dialling and population-based case-finding, we recruited adults from the community with respiratory symptoms and no previous history of diagnosed lung disease. Participants with normal pre- and post-bronchodilator spirometry subsequently underwent BCT. Asthma was diagnosed in those with symptoms and a methacholine provocative concentration (PC20) of < 8 mg/ml. Sputum and blood eosinophils, and exhaled nitric oxide were measured. Univariate analyses identified potentially predictive variables, which were then used to construct a multivariable logistic regression model to predict asthma. Model sensitivity, specificity, and area under the receiver operating curve (AUC) were calculated.ResultsOf 132 symptomatic individuals with normal spirometry, 34 (26%) had asthma. Of those ultimately diagnosed with asthma, 33 (97%) answered ‘yes’ to a question asking whether they experienced cough, chest tightness or wheezing provoked by exercise or cold air. Other univariate predictors of asthma included female sex, pre-bronchodilator FEV1 percentage predicted, and percent positive change in FEV1 post bronchodilator. A multivariable model containing these predictive variables yielded an AUC of 0.82 (95% CI: 0.72–0.91), a sensitivity of 82%, and a specificity of 66%. The model was used to construct a nomogram to advise clinicians which patients should be prioritized for BCT.ConclusionsFour readily available patient characteristics demonstrated a high sensitivity and AUC for predicting undiagnosed asthma in symptomatic adults with normal pre- and post-bronchodilator spirometry. These characteristics can potentially help clinicians to decide which individuals with normal spirometry should be investigated with bronchial challenge testing. However, further prospective validation of our decision tool is required.

Biomarkers to identify sputum eosinophilia in different adult asthma phenotypes.