Allergic diseases are highly complex with respect to pathogenesis, inflammation, and response to treatment. Current efforts for allergic disease diagnosis have focused on clinical evidence as a binary outcome. Although outcome status based on clinical phenotypes (observable characteristics) is convenient and inexpensive to measure in large studies, it does not adequately provide insight into the complex molecular determinants of allergic disease. Individuals with similar clinical diagnoses do not necessarily have similar disease etiologies, natural histories, or responses to treatment. This heterogeneity contributes to the ineffective response to treatment leading to an annual estimated cost of $350 billion in the USA alone. There has been a recent focus to deconvolute the clinical heterogeneity of allergic diseases into specific endotypes using molecular and omics approaches. Endotypes are a means to classify patients based on the underlying pathophysiological mechanisms involving distinct functions or treatment response. The advent of high-throughput molecular omics, immunophenotyping, and bioinformatics methods including machine learning algorithms is facilitating the development of endotype-based diagnosis. As we move to the next decade, we should truly start treating clinical endotypes not clinical phenotype. This review highlights current efforts taking place to improve allergic disease endotyping via molecular omics profiling, immunophenotyping, and machine learning approaches in the context of precision diagnostics in allergic diseases. Graphical Abstract.

中文翻译：

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将临床表型解析为过敏内型：分子和组学方法。

过敏性疾病在发病机制、炎症和对治疗的反应方面非常复杂。目前对过敏性疾病诊断的努力集中在临床证据作为二元结果。尽管基于临床表型（可观察特征）的结果状态在大型研究中测量起来既方便又便宜，但它并不能充分提供对过敏性疾病复杂分子决定因素的深入了解。具有相似临床诊断的个体不一定具有相似的疾病病因、自然史或对治疗的反应。这种异质性导致对治疗的无效反应，导致仅在美国每年的估计费用就高达 3500 亿美元。最近有一个重点是使用分子和组学方法将过敏性疾病的临床异质性分解为特定的内型。内型是一种基于涉及不同功能或治疗反应的潜在病理生理机制对患者进行分类的方法。高通量分子组学、免疫表型和包括机器学习算法在内的生物信息学方法的出现正在促进基于内型的诊断的发展。随着我们进入下一个十年，我们应该真正开始治疗临床内型而不是临床表型。本综述重点介绍了在过敏性疾病精准诊断的背景下，通过分子组学分析、免疫表型分析和机器学习方法改善过敏性疾病内分型的当前努力。图形概要。