Serum neuropilin-1 level may predict airway remodeling based on age and smoking status in asthma

Abstract

Background: Airway remodeling in asthma is induced by the production of vascular endothelial growth factor, and this process is mediated by neuropilin-1 (NRP-1).

Objective: To investigate the association between the serum level of NRP-1 and clinical characteristics in asthma.

Methods: We measured the serum level of NRP-1 and performed subgroup analysis of NRP-1 according to the clinical characteristics of 421 asthma patients registered in the Cohort for Reality and Evolution of Adult Asthma in Korea.

Results: The serum level of NRP-1 was significantly higher in the group under 65 years of age than in the group over 65 years of age (1628.36 ± 589.51 vs 1416.75 ± 671.42; P < 0.001). Current smokers and ex-smokers had a significantly higher serum NRP-1 level compared with that of never smokers (1647.16 ± 572.34 vs 1528.5 ± 579.37; P = 0.041). Medium- and high-inhaled corticosteroid users had a higher serum NRP-1 level compared with that of low-inhaled corticosteroid users (1589.87 ± 591.7 vs 1436.12 ± 419.21; P = 0.049). The serum NRP-1 level was significantly higher in the group with a forced vital capacity (FVC) of 70% or higher than in the group with an FVC of 70% or lower. In univariate analysis, age was a factor influencing serum NRP-1 with a negative correlation (coefficient=-260.90; P < 0.001). Ex-smokers and current smokers were factors influencing serum NRP-1 with a positive correlation (coefficient = 118.66; P = 0.0412). An FVC of 70% or higher was a factor affecting serum NRP-1 with a positive correlation (coefficient = 173.86; P = 0.0224). Multivariable analysis revealed that age (coefficient = -233.53; P < 0.001) and smoking history (coefficient = -122.4; P = 0.0358) were significant variables affecting serum NRP-1.

Conclusion: Serum NRP-1 level was high in asthma patients with a younger age and smoking history, suggesting its potential as a clinical marker for predicting early-stage airway remodeling based on the age and smoking status.