A study of specific immunoglobulin G4 expression in allergic rhinitis and its value in assessing efficacy and in predicting prognosis of sublingual immunotherapy

Abstract

Allergic rhinitis (AR) is a widespread health issue with a rising global prevalence, and sublingual immunotherapy (SLIT) has shown efficacy in AR treatment. We examined specific immunoglobulin G4 (sIgG4) expression in AR and its role in evaluating SLIT efficacy and predicting patient prognosis. We compared total nasal symptom score (TNSS), total medication score (TMS), visual analogue scale (VAS) score, inflammatory cytokines, and immune function markers in AR patients before and after SLIT. SLIT reduced TNSS, TMS, VAS scores, IL-4, IL-17, eosinophilia percentage (EOS%), and specific immunoglobulin E (sIgE) levels, while increasing INF-γ, IL-10, and sIgG4. The sIgG4 level at pre-treatment and 12 months post-treatment was negatively correlated with TNSS, TMS, VAS score, IL-4, IL-17, EOS%, and sIgE, and positively correlated with IFN-γ and IL-10. Most patients showed symptomatic improvement. After 12 months, sIgG4 level demonstrated an area under the curve (AUC) of 0.867 for assessing SLIT as effective. Pre-treatment sIgG4 level showed an AUC of 0.869 for predicting SLIT as effective. Collectively, sIgG4 has strong potential assessing SLIT efficacy and prognosis in AR patients, with correlations to TNSS, TMS, VAS score, and IL-4, IL-10, IL-17, INF-γ, EOS% and sIgE levels.

Keywords: allergic rhinitis; specific immunoglobulin G4; sublingual immunotherapy; value of prognostic prediction; value of treatment efficacy assessment.

FIGURE 1

Comparisons of inflammatory cytokine levels in AR patients before and after treatment. Comparisons of serum IL‐4 (A), IL‐17 (B), INF‐γ (C), and IL‐10 (D) levels pre‐ and post‐treatment. Normally distributed measurement data were expressed as mean ± SD. Intergroup comparisons between different time points were analyzed by repeated measures ANOVA. **p <0.01, ***p <0.001.

FIGURE 2

Comparison of immune function level in AR patients pre‐ and post‐treatment. Comparisons of ESO% (A), sIgE level (B), and sIgG4 level (C). Normally distributed measurement data were expressed as mean ± SD, and intergroup comparisons between different time points were analyzed using repeated‐measures ANOVA. **p <0.01.

FIGURE 3

Correlation analysis of sIgG4 level with TNSS, TMS, and VAS scores. Spearman's correlation analysis was used to analyze the correlations of pre‐treatment sIgG4 level with TNS (A), TMS (B), and VAS scores (C), and the correlations of sIgG4 level with TNS (D), TMS (E), and VAS scores (F) after 12 months of treatment.

FIGURE 4

Correlation analysis of sIgG4 level with inflammatory factors and immune functions. Pearson's correlation coefficient was used to analyze the correlations of sIgG4 level with IL‐4, IL‐17, INF‐γ, IL‐10, ESO%, and sIgE before treatment (A–F) and after 12 months of treatment (G–L).

FIGURE 5

Analysis of the value of different indicators in assessing SLIT efficacy in AR patients after 12 months of treatment. (A–G) ROC curves to analyze the value of IL‐4 (A), IL‐17 (B), IL‐10 (C), INF‐γ (D), EOS (E), sIgE (F), and sIgG4 (G) levels for assessing effective efficacy in AR patients after 12 months of SLIT treatment, respectively; (H) ROC curves to analyze the value of sIgG4 levels in assessing markedly effective efficacy in AR patients.

FIGURE 6

Analysis of the value of sIgG4 level in predicting SLIT efficacy in AR patients. ROC _effective curve (A) and ROC_{markedly effective} curve (B).