Li Shuying, Ma Yue, Li Jiaying, Feng Xian, Xiong Fang, Han Miaomiao, Li Huabin, Lou Hongfei
Department of Otolaryngology, Eye & ENT Hospital, Fudan University, Shanghai, China.
Clin Transl Allergy. 2025 Sep;15(9):e70099. doi: 10.1002/clt2.70099.
The efficacy of subcutaneous immunotherapy (SCIT) in allergic rhinitis (AR) patients varies. Component-resolved diagnostics (CRD) may serve as a useful tool to predict therapeutic responses.
Forty-three house dust mite (HDM)-sensitized AR patients undergoing SCIT were enrolled. Clinical data and serum samples were collected at baseline (V1), 15 weeks (V2), and 1 year (V3). The levels of specific immunoglobulin E (sIgE) and sIgG4 to nine HDM components were measured, and a predictive model was established. An independent prospective cohort of 23 patients was used for validation.
The most prevalent sensitizing components were Dermatophagoides pteronyssinus (Der p) 1, Dermatophagoides farinae (Der f) 1, Der p 2, Der f 2, and Der p 23. The responders had a significantly higher Combined Symptom and Medication Score (CSMS) at baseline than did the nonresponders. At V2, the responders showed a greater increase in serum levels of Der f 1 sIgE, higher levels of Der p 23 sIgG4, and greater incremental changes in Der p 23 sIgG4 levels. A composite model based on V1 CSMS, ∆ Der f 1 sIgE, and ∆ Der p 23 sIgG4 achieved an area under the receiver operating characteristic curve (AUC) of 0.896 (cutoff: 0.455), with 83.3% sensitivity and 84.0% specificity. In the validation cohort, the model showed a 75.0% positive predictive value, 86.7% negative predictive value, and 82.6% overall accuracy.
A composite biomarker model based on HDM component responses enabled early prediction of SCIT efficacy, supporting more personalized treatment strategies for AR management.
皮下免疫疗法（SCIT）在过敏性鼻炎（AR）患者中的疗效存在差异。组分分辨诊断（CRD）可能是预测治疗反应的有用工具。
纳入43例接受SCIT的屋尘螨（HDM）致敏的AR患者。在基线（V1）、15周（V2）和1年（V3）时收集临床数据和血清样本。检测针对9种HDM组分的特异性免疫球蛋白E（sIgE）和sIgG4水平，并建立预测模型。使用23例患者的独立前瞻性队列进行验证。
最常见的致敏组分是粉尘螨（Der p）1、屋尘螨（Der f）1、Der p 2、Der f 2和Der p 23。应答者在基线时的综合症状和药物评分（CSMS）显著高于无应答者。在V2时，应答者血清中Der f 1 sIgE水平升高幅度更大，Der p 23 sIgG4水平更高，Der p 23 sIgG4水平的增量变化更大。基于V1 CSMS、ΔDer f 1 sIgE和ΔDer p 23 sIgG4的复合模型在受试者工作特征曲线（AUC）下的面积为0.896（临界值：0.455），敏感性为83.3%，特异性为84.0%。在验证队列中，该模型的阳性预测值为75.0%，阴性预测值为86.7%，总体准确率为82.6%。
基于HDM组分反应的复合生物标志物模型能够早期预测SCIT疗效，支持更个性化的AR管理治疗策略。
