Icahn School of Medicine at Mount Sinai, New York, New York, USA.
AllerGenis LLC, Hatfield, Pennsylvania, USA.
Int Arch Allergy Immunol. 2023;184(3):273-278. doi: 10.1159/000526364. Epub 2022 Dec 9.
Currently, there is no laboratory test that can accurately identify children at risk of developing peanut allergy. Utilizing a subset of children randomized to the peanut avoidance arm of the LEAP trial, we monitored the development of epitope-specific (ses-)IgE and ses-IgG4 from 4-11 months to 5 years of age.
The aim of the study was to evaluate the prognostic ability of epitope-specific antibodies to predict the result of an oral food challenge (OFC) at 5 years.
A Bead-Based Epitope Assay was used to quantitate IgE and IgG4 to 64 sequential (linear) epitopes from Ara h 1-3 proteins at 4-11 months, 1 and 2.5 years of age in 74 subjects (38 of them with a positive OFC at 5 years). Specific IgE (sIgE) to peanut and component proteins was measured using ImmunoCAP. Machine learning methods were used to identify the earliest time point to predict 5-year outcome, developing prognostic algorithms based only on 4-11 month samples, 1-year or 2.5-year, and a combination of them. Data from 74 children were iteratively split 3:1 into training and validation sets, and machine learning models were developed to predict the 5-year outcome. A test set (n = 90) from an independent cohort was used for final evaluation.
Elastic-Net algorithm combining ses-IgE and IgE to Ara h 1, 2, 3, and 9 proteins could predict the 5-year peanut allergy status of LEAP participants with an average validation accuracy of 64% at baseline. Samples taken at 1 year accurately predicted a 5-year OFC outcome with 83% accuracy. This performance remained consistent when evaluated on an independent CoFAR2 cohort with an accuracy of 78% for the 1-year model.
IgE antibody profiles at 1 year of age are predictive of peanut OFC at 5 years in children avoiding peanuts. If further confirmed, this model may enable early identification of infants who may benefit from early immunotherapeutic interventions.
目前,尚无实验室检测手段可以准确识别出有发生花生过敏风险的儿童。利用 LEAP 试验中随机分配至花生回避组的部分儿童,我们从 4-11 月龄至 5 岁时监测了表位特异性(ses)IgE 和 ses-IgG4 的发展情况。
本研究旨在评估表位特异性抗体预测 5 岁时口服食物激发(OFC)结果的预后能力。
采用基于珠粒的表位分析方法,在 74 例受试者(其中 38 例在 5 岁时 OFC 阳性)中,于 4-11 个月、1 岁和 2.5 岁时检测 Ara h 1-3 蛋白 64 个连续(线性)表位的 IgE 和 IgG4。使用 ImmunoCAP 检测花生和成分蛋白的特异性 IgE(sIgE)。采用机器学习方法确定最早的时间点来预测 5 年的结果,基于仅 4-11 个月样本、1 年或 2.5 年的样本以及它们的组合建立预后算法。将 74 例儿童的数据以 3:1 的比例迭代分割为训练集和验证集,开发机器学习模型来预测 5 年的结果。使用来自独立队列的 90 例测试集(n = 90)进行最终评估。
结合 ses-IgE 和 IgE 与 Ara h 1、2、3 和 9 蛋白的弹性网络算法可预测 LEAP 参与者的 5 年花生过敏状态,基线平均验证准确性为 64%。1 岁时的样本可准确预测 5 年 OFC 结果,准确率为 83%。当在 CoFAR2 独立队列中进行评估时,1 岁模型的准确率为 78%,其性能保持一致。
1 岁时 IgE 抗体谱可预测儿童回避花生时的花生 OFC。如果得到进一步证实,该模型可能可以早期识别出可能受益于早期免疫治疗干预的婴儿。