Fukushima-Nomura Ayano, Kawasaki Hiroshi, Amagai Masayuki
Department of Dermatology, Keio University School of Medicine, Tokyo, Japan.
Department of Dermatology, Keio University School of Medicine, Tokyo, Japan.
Allergol Int. 2025 Oct;74(4):514-524. doi: 10.1016/j.alit.2025.08.007. Epub 2025 Sep 19.
Allergic diseases are characterized by heterogeneity driven by complex interactions between genetic, environmental, and immunological factors. Conventional classifications based solely on clinical phenotypes often fails to capture the underlying molecular diversity, thereby limiting therapeutic precision and patient outcomes. Integrative omics-encompassing genomics, transcriptomics, proteomics, metabolomics, and microbiomics-has emerged as a powerful approach to redefine disease mechanisms and advance precision medicine. By integrating high-dimensional molecular data with clinical phenotyping, omics approaches enable the identification of disease endotypes, biomarker discovery, and patient stratification. This review highlights recent developments in clinical-omics integration, with a focus on atopic dermatitis (AD) as a prototypical allergic disease. Drawing from our studies, we illustrate how tissue-level transcriptomic profiling, combined with unbiased computational analysis, can uncover immunological heterogeneity and treatment-response patterns in AD. Additional examples in asthma and food allergy demonstrate how integrated multi-omics can uncover gene-environment interactions and elucidate mechanisms behind disease severity and health disparities. We also address practical and ethical challenges in data harmonization, privacy, and interoperability, and underscore the critical role of computational methods and infrastructure development in enabling clinically meaningful interpretation. Importantly, successful translation of multi-omics data into clinical practice requires iterative, interdisciplinary collaboration between clinicians, data scientists, and basic researchers. By bridging molecular complexity and clinical heterogeneity, integrative omics is reshaping the landscape of allergy research. As technologies evolve, this framework will be crucial for developing predictive models and personalized therapeutic strategies, ultimately bringing us closer to individualized, data-driven care in allergic diseases.
过敏性疾病的特征是由遗传、环境和免疫因素之间的复杂相互作用驱动的异质性。仅基于临床表型的传统分类往往无法捕捉潜在的分子多样性，从而限制了治疗的精准性和患者的治疗效果。整合组学——包括基因组学、转录组学、蛋白质组学、代谢组学和微生物组学——已成为一种强大的方法，用于重新定义疾病机制并推进精准医学。通过将高维分子数据与临床表型相结合，组学方法能够识别疾病内型、发现生物标志物并对患者进行分层。本综述重点介绍了临床-组学整合的最新进展，以特应性皮炎（AD）作为典型的过敏性疾病为例。借鉴我们的研究，我们阐述了组织水平的转录组分析如何与无偏计算分析相结合，揭示AD中的免疫异质性和治疗反应模式。哮喘和食物过敏的其他例子表明，整合多组学如何揭示基因-环境相互作用，并阐明疾病严重程度和健康差异背后的机制。我们还讨论了数据协调、隐私和互操作性方面的实际和伦理挑战，并强调了计算方法和基础设施开发在实现具有临床意义的解释方面的关键作用。重要的是，将多组学数据成功转化为临床实践需要临床医生、数据科学家和基础研究人员之间进行迭代的跨学科合作。通过弥合分子复杂性和临床异质性，整合组学正在重塑过敏研究的格局。随着技术的发展，这个框架对于开发预测模型和个性化治疗策略至关重要，最终使我们更接近过敏性疾病的个体化、数据驱动的治疗。
