在 POISED 试验中,基线表位特异性 IgE 图谱可预测 OIT 一年后的持续无反应或高阈值。

IF 11.4 1区 医学 Q1 ALLERGY Journal of Allergy and Clinical Immunology Pub Date : 2024-11-04 DOI:10.1016/j.jaci.2024.10.017
Maria Suprun, Ashley Sang Eun Lee, Robert Getts, Simon Peck, Sayantani B Sindher, Kari C Nadeau, R Sharon Chinthrajah, Stephen J Galli, Hugh Sampson
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引用次数: 0

摘要

背景:POISED 试验的结果表明,停止花生口服免疫疗法会增加对花生恢复临床反应性的风险:我们试图确定获得持续无应答(SU)或持续高阈值(SHT)的患者与获得短暂脱敏(TD)的患者是否具有不同的基线序列表位特异性(es-)IgE谱:POISED试验(NCT02103270)中的受试者被随机分配到花生(95人)或安慰剂(25人)中,为期24个月。然后,OIT 脱敏的受试者被分配到不服用花生(PN-0,n=51)或服用 300 毫克(PN-300,n=30),为期 12 个月。PN-0和PN-300中的受试者通过4000毫克花生口服挑战后,分别测定SU和SHT。对花生、Ara h 1-3 蛋白质和 64 个过敏原表位的特异性 IgE 和 IgG4 水平进行了测定。我们利用基线数据开发了机器学习 glmnet 模型,并进行了引导模拟,以预测 SU/SHT:结果:80 名受试者(84%)对花生脱敏。其中,13%(n=8)和 37%(n=13)的受试者在 PN-0 和 PN-300 中达到 SU/SHT。在 2 年的 OIT 期间,观察到表位和蛋白质特异性 IgE 水平下降,IgG4 水平上升。与 TD 组相比,花生-0 而非花生-300 的基线 SU 患者的 es-IgE 和蛋白-SIgE 水平较低。一个包含 12 项基线 es-IgE 和年龄的机器学习模型可以预测 SU/SHT,准确率为 94%,AUC 为 0.97,灵敏度为 1.00,特异度为 0.91:与 TD 患者相比,获得 SU/SHT 的患者具有不同的基线蛋白和表位特异性 IgE 特征。这些特征可能有助于识别更有可能达到 SU/SHT 的患者。
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Baseline Epitope-Specific IgE Profiles are Predictive of Sustained Unresponsiveness or High Threshold One-Year Post OIT in the POISED Trial.

Background: Results from the POISED trial suggest that discontinuation of peanut oral immunotherapy can increase the risk of regaining clinical reactivity to peanut.

Objective: We sought to determine whether those who achieved sustained unresponsiveness (SU) or sustained high threshold (SHT) have different baseline sequential epitope-specific (es-) IgE profiles than those who achieved transient desensitization (TD).

Methods: Subjects in the POISED trial (NCT02103270) were randomized to peanut (n=95) or placebo (n=25) for 24 months. OIT-desensitized subjects were then assigned to no peanut (PN-0, n=51) or 300mg (PN-300, n=30) for 12 months. SU and SHT were determined by those in PN-0 and PN-300, respectively, passing 4000mg peanut oral challenge. Specific IgE and IgG4 levels to peanut, Ara h 1-3 proteins and 64 allergenic epitopes were measured. We developed machine learning glmnet models with bootstrap simulations using baseline data to predict SU/SHT.

Results: Eighty (84%) subjects were desensitized to peanut. Of those, 13% (n=8) and 37% (n=13) achieved SU/SHT in PN-0 and PN-300. Decreases in epitope-and protein-specific IgE levels and increases in IgG4 levels were observed during 2 years of OIT. At baseline, patients with SU in Peanut-0 but not Peanut-300 had lower es-IgE and protein-sIgE levels compared to the TD group. A machine-learning model with 12 baseline es-IgEs and age could predict SU/SHT with an accuracy of 94%, AUC 0.97, Sensitivity 1.00, Specificity 0.91.

Conclusions: Patients who achieved SU/SHT have different baseline protein-and epitope-specific IgE profiles than those with TD. These profiles may help identify patients with an increased likelihood of achieving SU/SHT.

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来源期刊
CiteScore
25.90
自引率
7.70%
发文量
1302
审稿时长
38 days
期刊介绍: The Journal of Allergy and Clinical Immunology is a prestigious publication that features groundbreaking research in the fields of Allergy, Asthma, and Immunology. This influential journal publishes high-impact research papers that explore various topics, including asthma, food allergy, allergic rhinitis, atopic dermatitis, primary immune deficiencies, occupational and environmental allergy, and other allergic and immunologic diseases. The articles not only report on clinical trials and mechanistic studies but also provide insights into novel therapies, underlying mechanisms, and important discoveries that contribute to our understanding of these diseases. By sharing this valuable information, the journal aims to enhance the diagnosis and management of patients in the future.
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