Faculty of Biology, Medicine and Health, University of Manchester, Manchester, UK.
National Heart and Lung Institute, Imperial College London, London, UK.
Clin Exp Allergy. 2024 May;54(5):339-349. doi: 10.1111/cea.14468. Epub 2024 Mar 12.
Previous studies which applied machine learning on multiplex component-resolved diagnostics arrays identified clusters of allergen components which are biologically plausible and reflect the sources of allergenic proteins and their structural homogeneity. Sensitization to different clusters is associated with different clinical outcomes.
To investigate whether within different allergen component sensitization clusters, the internal within-cluster sensitization structure, including the number of c-sIgE responses and their distinct patterns, alters the risk of clinical expression of symptoms.
In a previous analysis in a population-based birth cohort, by clustering component-specific (c-s)IgEs, we derived allergen component clusters from infancy to adolescence. In the current analysis, we defined each subject's within-cluster sensitization structure which captured the total number of c-sIgE responses in each cluster and intra-cluster sensitization patterns. Associations between within-cluster sensitization patterns and clinical outcomes (asthma and rhinitis) in early-school age and adolescence were examined using logistic regression and binomial generalized additive models.
Intra-cluster sensitization patterns revealed specific associations with asthma and rhinitis (both contemporaneously and longitudinally) that were previously unseen using binary sensitization to clusters. A more detailed description of the subjects' within-cluster c-sIgE responses in terms of the number of positive c-sIgEs and unique sensitization patterns added new information relevant to allergic diseases, both for diagnostic and prognostic purposes. For example, the increase in the number of within-cluster positive c-sIgEs at age 5 years was correlated with the increase in prevalence of asthma at ages 5 and 16 years, with the correlations being stronger in the prediction context (e.g. for the largest 'Broad' component cluster, contemporaneous: r = .28, p = .012; r = .22, p = .043; longitudinal: r = .36, p = .004; r = .27, p = .04).
Among sensitized individuals, a more detailed description of within-cluster c-sIgE responses in terms of the number of positive c-sIgE responses and distinct sensitization patterns, adds potentially important information relevant to allergic diseases.
先前应用机器学习对多重成分分辨诊断阵列进行的研究确定了过敏原成分簇,这些成分簇在生物学上是合理的,反映了过敏原蛋白的来源及其结构的均一性。对不同簇的致敏与不同的临床结果相关。
研究在不同的过敏原成分致敏簇内,内部的簇内致敏结构,包括 c-sIgE 反应的数量及其不同的模式,是否改变了症状临床表达的风险。
在之前的基于人群的出生队列的分析中,通过对特定成分的 c-sIgE 进行聚类,我们从婴儿期到青春期衍生出了过敏原成分簇。在当前的分析中,我们定义了每个受试者的簇内致敏结构,该结构捕获了每个簇中的总 c-sIgE 反应数量和簇内致敏模式。使用逻辑回归和二项广义加性模型检查簇内致敏模式与早期和青春期临床结局(哮喘和鼻炎)之间的关联。
簇内致敏模式与哮喘和鼻炎(同时和纵向)有特定的关联,这是以前使用对簇的二元致敏无法看到的。用阳性 c-sIgE 的数量和独特的致敏模式更详细地描述受试者在簇内的 c-sIgE 反应,为诊断和预后目的提供了与过敏疾病相关的新信息。例如,5 岁时簇内阳性 c-sIgE 的数量增加与 5 岁和 16 岁时哮喘的患病率增加相关,在预测方面相关性更强(例如,对于最大的“广泛”成分簇,同时:r =.28,p =.012;r =.22,p =.043;纵向:r =.36,p =.004;r =.27,p =.04)。
在致敏个体中,更详细地描述簇内 c-sIgE 反应的数量和独特的致敏模式,可能增加了与过敏疾病相关的重要信息。