Biomarkers of Type 2 Airway Inflammation as Predictors of Loss of Asthma Control During Step-Down Therapy for Well-Controlled Disease: The Long-Acting Beta-Agonist Step-Down Study (LASST)

Utility of eosinophil peroxidase as a biomarker of eosinophilic inflammation in asthma

BACKGROUND

The relative utility of eosinophil peroxidase (EPX) and blood and sputum eosinophil counts as disease biomarkers in asthma is uncertain.

OBJECTIVE

We sought to determine the utility of EPX as a biomarker of systemic and airway eosinophilic inflammation in asthma.

METHODS

EPX protein was measured by immunoassay in serum and sputum in 110 healthy controls to establish a normal reference range and in repeated samples of serum and sputum collected during 3 years of observation in 480 participants in the Severe Asthma Research Program 3.

RESULTS

Over 3 years, EPX levels in patients with asthma were higher than normal in 27% to 31% of serum samples and 36% to 53% of sputum samples. Eosinophils and EPX correlated better in blood than in sputum (rs values of 0.74 and 0.43, respectively), and high sputum EPX levels occurred in 27% of participants with blood eosinophil counts less than 150 cells/μL and 42% of participants with blood eosinophil counts between 150 and 299 cells/μL. Patients with persistently high sputum EPX values for 3 years were characterized by severe airflow obstruction, frequent exacerbations, and high mucus plug scores. In 59 patients with asthma who started mepolizumab during observation, serum EPX levels normalized in 96% but sputum EPX normalized in only 49%. Lung function remained abnormal even when sputum EPX normalized.

CONCLUSIONS

Serum EPX is a valid protein biomarker of systemic eosinophilic inflammation in asthma, and sputum EPX levels are a more sensitive biomarker of airway eosinophilic inflammation than sputum eosinophil counts. Eosinophil measures in blood frequently miss airway eosinophilic inflammation, and mepolizumab frequently fails to normalize airway eosinophilic inflammation even though it invariably normalizes systemic eosinophilic inflammation.

Promising treatment biomarkers in asthma

Asthma is a highly heterogenous disease which researchers over time have attempted to classify into different phenotypes and endotypes to improve diagnosis, prognosis and treatment. Earlier classifications based on reaction to environmental allergens, age, sex and lung function have evolved, and today, the use of precision medicine guided by biomarkers offers new perspectives on asthma management. Identifying biomarkers that may reveal the underlying pathophysiology of the disease will help to select the patients who will benefit most from specific treatments. This review explores the classification of asthma phenotypes and focuses on the most recent advances in using biomarkers to guide treatment.

Validation of a new portable system containing both FeNO analysis and spirometry measurement

Introduction

Pulmonary function tests and FeNO measurements are widely used for the diagnosis and management of respiratory diseases. They are used to evaluate airway limitation and respiratory inflammation. Standard spirometers and nitric oxide (NO) analyzers are widely used in hospitals. However, their high price has made some hospitals in underdeveloped areas unable to afford or purchase these devices. The development of a new portable system (SUNVOU TM2125) combining FeNO measurement and spirometry provides additional possibilities for optimizing the diagnosis and management of respiratory diseases. However, its accuracy needs further validation.

Methods

The FeNO analysis component of SUNVOU TM2125 was compared with that of a widely used NO analyzer (NIOX VERO). The spirometry component of the TM2125 was compared with a standard spirometer (Jaeger MasterScreen) for pulmonary parameters such as FEV1, FVC, FEV1/FVC, and PEF. Pearson correlation and Bland-Altman plots were used to evaluate the agreement between the devices.

Results

FeNO values measured using TM2125 were higher than those measured using VERO, with a mean difference of 1.8 ppb. There was a strong correlation between FeNO values measured using the two devices (r = 0.988, p < 0.001). Bland-Altman plots showed a high degree of agreement between the two devices, with 93.3% of values within the 95% confidence interval range. The spirometric parameters (FEV1, FVC, FEV1/FVC, and PEF) measured using the TM2125 were lower than those measured using the MasterScreen. Good correlations were observed between the values measured using the TM2125 and MasterScreen (r > 0.9). Based on the Bland-Altman plots, there was a high degree of agreement between the devices.

Conclusion

The accuracy of FeNO and spirometry measurements using SUNVOU TM2125 was validated. This can help improve the diagnosis and monitoring of chronic respiratory diseases in underdeveloped countries.

A prediction model for asthma exacerbations after stopping asthma biologics

BACKGROUND

Little is known regarding the prediction of the risks of asthma exacerbation after stopping asthma biologics.

OBJECTIVE

To develop and validate a predictive model for the risk of asthma exacerbations after stopping asthma biologics using machine learning models.

METHODS

We identified 3057 people with asthma who stopped asthma biologics in the OptumLabs Database Warehouse and considered a wide range of demographic and clinical risk factors to predict subsequent outcomes. The primary outcome used to assess success after stopping was having no exacerbations in the 6 months after stopping the biologic. Elastic-net logistic regression (GLMnet), random forest, and gradient boosting machine models were used with 10-fold cross-validation within a development (80%) cohort and validation cohort (20%).

RESULTS

The mean age of the total cohort was 47.1 (SD, 17.1) years, 1859 (60.8%) were women, 2261 (74.0%) were White, and 1475 (48.3%) were in the Southern region of the United States. The elastic-net logistic regression model yielded an area under the curve (AUC) of 0.75 (95% confidence interval [CI], 0.71-0.78) in the development and an AUC of 0.72 in the validation cohort. The random forest model yielded an AUC of 0.75 (95% CI, 0.68-0.79) in the development cohort and an AUC of 0.72 in the validation cohort. The gradient boosting machine model yielded an AUC of 0.76 (95% CI, 0.72-0.80) in the development cohort and an AUC of 0.74 in the validation cohort.

CONCLUSION

Outcomes after stopping asthma biologics can be predicted with moderate accuracy using machine learning methods.

Critical evaluation of asthma biomarkers in clinical practice

The advent of personalized medicine has revolutionized the whole approach to the management of asthma, representing the essential basis for future developments. The cornerstones of personalized medicine are the highest precision in diagnosis, individualized prediction of disease evolution, and patient-tailored treatment. To this aim, enormous efforts have been established to discover biomarkers able to predict patients' phenotypes according to clinical, functional, and bio-humoral traits. Biomarkers are objectively measured characteristics used as indicators of biological or pathogenic processes or clinical responses to specific therapeutic interventions. The diagnosis of type-2 asthma, prediction of response to type-2 targeted treatments, and evaluation of the risk of exacerbation and lung function impairment have been associated with biomarkers detectable either in peripheral blood or in airway samples. The surrogate nature of serum biomarkers, set up to be less invasive than sputum analysis or bronchial biopsies, has shown several limits concerning their clinical applicability. Routinely used biomarkers, like peripheral eosinophilia, total IgE, or exhaled nitric oxide, result, even when combined, to be not completely satisfactory in segregating different type-2 asthma phenotypes, particularly in the context of severe asthma where the choice among different biologics is compelling. Moreover, the type-2 low fraction of patients is not only an orphan of biological treatments but is at risk of being misdiagnosed due to the low negative predictive value of type-2 high biomarkers. Sputum inflammatory cell analysis, considered the highest specific biomarker in discriminating eosinophilic inflammation in asthma, and therefore elected as the gold standard in clinical trials and research models, demonstrated many limits in clinical applicability. Many factors may influence the measure of these biomarkers, such as corticosteroid intake, comorbidities, and environmental exposures or habits. Not least, biomarkers variability over time is a confounding factor leading to wrong clinical choices. In this narrative review, we try to explore many aspects concerning the role of routinely used biomarkers in asthma, applying a critical view over the "state of the art" and contemporarily offering an overview of the most recent evidence in this field.

A Pilot Randomized Trial of As-needed Budesonide-formoterol for Stepping Down Controller Treatment in Moderate Asthma with Complete Remission

Background

The use of low-dose inhaled corticosteroid-formoterol as reliever monotherapy has recently been recommended in the asthma treatment guidelines. However, the efficacy of this treatment strategy has not yet been determined during the stepping-down period in moderate asthma. This study aimed to evaluate the feasibility of reducing treatment to as-needed budesonide-formoterol (BFM) in moderate asthma with complete remission.

Methods

We randomly assigned 31 patients (8 males and 23 females with a mean age of 57.2 years) with complete remission of asthma by inhaled BFM (160/4.5 μg) twice daily to receive BFM (160/4.5 μg) as needed (16 patients), or budesonide (BUD) (200 μg) twice daily (15 patients). The study was an open-label study done for 48 weeks, with the primary outcome as the cumulative percentages of patients with treatment failure (asthma exacerbation or loss of asthma control or lack of satisfaction after using medications) in the two groups.

Results

Six patients (42%) using as-needed BFM had treatment failure, as compared with three patients (21.4%) using BUD maintenance (hazards ratio for as-needed BFM, 1.77; 95% confidential interval, 0.44–7.12; p=0.41). The changes in forced expiratory volume in 1 second were −211.3 mL with as-needed BFM versus −97.8 mL with BUD maintenance (difference, 113.5 mL; p=0.75) and the change in fractional exhaled nitric oxide was significantly higher in both groups, at 8.68 parts per billion (ppb) in the as-needed BFM group and 2.5 ppb. in the BUD maintenance group (difference, 6.18 ppb; p=0.049).

Conclusion

Compared with BUD maintenance, there were no significant differences in treatment failure rate in patients who received as-needed BFM during the stepping down period in moderate asthma. However, they showed reduced lung function and relapsed airway inflammation. The results are limited by imprecision, and further large RCTs are needed.

Step-up and step-down approaches in the treatment of asthma

Introduction: Significant intraindividual and temporal variability in symptom control is a feature of asthma that requires careful monitoring and the need to periodically review and adjust therapy. Both NHLBI/NAEPP and GINA offer helpful algorithms for a stepping approach to asthma.Areas covered: The problems arisen in applying the stepwise approach to the treatment of asthma proposed by NHLBI/NAEPP and GINA algorithms and their possible alternatives.Expert opinion: The current therapeutic stepping approach to asthma, which takes into account lung function, symptoms and quality of life, is certainly useful, but it does not consider the underlying mechanisms. Furthermore, patient's overestimation or underestimation of the severity of the disease and differences in the opinions on the level of asthma control required between patients and physicians and also between physicians in both primary care and specialist settings are common and may negatively affect asthma control and future risks. A reassessment of the conventional stepping approach to management of asthma is now needed. A pragmatic approach that sets therapeutic goals for each individual and associates them with the treatable traits of asthma which, when therapeutically targeted, will in many cases help to achieve the goals, seems more reasonable than the present stepping approach.

A snapshot of exhaled nitric oxide and asthma characteristics: experience from high to low income countries

Nitric oxide is a gas produced in the airways of asthmatic subjects and related to T2 inflammation. It can be measured as fractional nitric oxide (FeNO) in the exhaled air and used as a non-invasive, easy to evaluate, rapid marker. It is now widely used in many settings to determine airway inflammation. The aim of this narrative review is to report relationship between FeNO and the physiopathologic characteristics of asthmatic patients. Factors affecting FeNO levels have also been analysed as well as the impact of corticosteroid, target therapies and rehabilitation programs. Considering the availability of the test, spreading this methodology to low income countries has also been considered as a possibility for evaluating airway inflammation and monitoring adherence to inhaled corticosteroid therapy. PubMed data search has been performed restricted to English language papers. Research was limited to studies in adults unless studies in children were the only ones reported for a particular issue. This revision could be useful to summarize the role of FeNO in relation to asthma characteristics and help in the use of FeNO in different clinical settings particularly in low income countries.