Evaluation of multiple-flows exhaled nitric oxide and its clinical significance in severe asthmatic patients treated with biologics: a prospective real-life study

Abstract

Background

Specific biomarkers, such as eosinophilia in peripheral blood or fractional exhaled nitric oxide (FeNO), can guide us in the choice of biologic therapy, allowing a more personalized approach. Although there are multiple evidences in the literature about the role of FeNO as a predictor of response to different biologic treatments, there are no data on the relationship between FeNO changes and clinical response to the four biologic drugs currently in use.

Objective

To evaluate and to compare the expression of multiple-flows FeNO parameters in a cohort of patients with severe asthma (SA) before and during the treatment with biologics to evaluate the performance of these biomarkers in predicting the achievement of clinical remission.

Methods

We prospectively enrolled 50 patients with severe asthma eligible for biologic therapy. Patients underwent clinical and functional monitoring at baseline (T0) and after 1, 6, and 12 months of treatment (T1, T6, T12), including multiple flows FeNO assessment.

Results

A statistically significant reduction of FeNO50 values and J’awNO was observed only in benralizumab and dupilumab subgroups. Among biomarkers, the reduction of FeNO 50 values at T1 was associated with a higher probability of achieving clinical remission at T12 (p = 0.003), which was also confirmed by ROC curve analysis (AUC 0.758, p = 0.002; sensitivity 60% and specificity 74% for a reduction of 16 ppb).

Conclusion

These data confirm the potential of this biomarker in predicting clinical response to biologic treatment in patients with severe asthma in order to guide clinical decisions and evaluate a shift to other biologic therapy.

Keywords:

• Nitric oxide
• biomarker
• severe asthma
• biologic
• omalizumab
• benralizumab
• mepolizumab
• dupilumab

Author contributions

Conceptualization, Tommaso Pianigiani, Simona Luzzi, Akter Dilroba, Martina Meocci, Elisa Salvadori, Lorenzo Alderighi, Laura Bergantini, Miriana D’Alessandro, Piersante Sestini, Elena Bargagli and Paolo Cameli; Data curation, Tommaso Pianigiani, Simona Luzzi, Akter Dilroba, Martina Meocci, Elisa Salvadori, Lorenzo Alderighi, Laura Bergantini, Piersante Sestini, Elena Bargagli and Paolo Cameli; Formal analysis, Tommaso Pianigiani, Simona Luzzi, Akter Dilroba, Martina Meocci, Elisa Salvadori, Laura Bergantini, Miriana D’Alessandro, Piersante Sestini, Elena Bargagli and Paolo Cameli; Investigation, Simona Luzzi, Akter Dilroba, Martina Meocci, Elisa Salvadori, Lorenzo Alderighi, Laura Bergantini, Miriana D’Alessandro, Elena Bargagli and Paolo Cameli; Methodology, Tommaso Pianigiani; Resources, Tommaso Pianigiani, Simona Luzzi, Akter Dilroba, Lorenzo Alderighi, Miriana D’Alessandro, Piersante Sestini, Elena Bargagli and Paolo Cameli; Software, Tommaso Pianigiani, Elena Bargagli and Paolo Cameli; Validation, Tommaso Pianigiani, Simona Luzzi, Akter Dilroba, Martina Meocci, Elisa Salvadori, Lorenzo Alderighi, Laura Bergantini, Miriana D’Alessandro, Piersante Sestini, Elena Bargagli and Paolo Cameli; Writing – original draft, Tommaso Pianigiani, Simona Luzzi, Akter Dilroba, Martina Meocci, Elisa Salvadori, Laura Bergantini, Miriana D’Alessandro, Piersante Sestini, Elena Bargagli and Paolo Cameli; Writing – review & editing, Tommaso Pianigiani, Elena Bargagli and Paolo Cameli.

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Disclosure statement

P.C. received speaking fees by Astrazeneca and GlaxoSmithKline companies; the other authors declared no conflict of interests for this paper.

Additional information

Funding

This research received no external funding.