人工智能整合多组学方法在重度哮喘中的当前认识与未来方向
2026/06/01
摘要
尽管在治疗策略方面取得了重大进展，但重度哮喘仍然是一种具有挑战性的异质性疾病。一部分患者继续经历控制不良、频繁加重和高疾病负担。多组学技术的出现，包括基因组学、转录组学、蛋白质组学和代谢组学，为理解哮喘的分子基础开辟了新的途径。当与人工智能（AI）相结合时，这些方法有可能通过识别关键生物标志物、改善疾病内型和实现个性化治疗策略来改变和加强重度哮喘的管理。本综述探讨了人工智能集成多组学方法在哮喘研究中的作用，强调了人工智能驱动的模型如何分析大量数据集，以揭示传统方法经常遗漏的模式。这些见解可以提高诊断精度，预测治疗反应并指导新型靶向治疗的发展。重点领域包括与哮喘严重程度相关的遗传位点，揭示细胞异质性的单细胞RNA测序，以及区分哮喘表型的蛋白质组学谱。通过这篇综述，我们旨在让读者清楚地了解重度哮喘研究的现状，重点介绍通过人工智能集成多组学方法取得的突破。此外，我们希望通过解决将这些发现转化为临床实践的挑战来指导该领域的未来方向。通过加深对这些技术潜力的理解，我们希望激发进一步的创新，为精准医学改变重度哮喘管理铺平道路，最终为患者带来更个性化和更有效的治疗选择。
Current understanding and future directions in severe asthma through artificial intelligence-integrated multi-omic approaches
Sundhas Rafeeq Valappil, Mohammed Uddin, Saba Al Heialy
Abstract
Severe asthma remains a challenging, heterogeneous condition, despite significant advances in therapeutic strategies. A subset of patients continues to experience poor control, frequent exacerbations and a high burden of disease. The advent of multi-omic technologies, including genomics, transcriptomics, proteomics and metabolomics, has opened new avenues for understanding the molecular underpinnings of asthma. When combined with artificial intelligence (AI), these approaches hold the potential to transform and augment the management of severe asthma by identifying key biomarkers, refining disease endotypes and enabling personalised treatment strategies. This review explores the role of AI-integrated multi-omic approaches in asthma research, highlighting how AI-driven models can analyse vast datasets to uncover patterns often missed by traditional methods. These insights can improve diagnostic precision, predict therapeutic responses and guide the development of novel, targeted therapies. Key areas of focus include genetic loci associated with asthma severity, single-cell RNA sequencing to uncover cellular heterogeneity, and proteomic profiles that differentiate asthma phenotypes. Through this review, we aim to provide readers with a clear understanding of the current landscape in severe asthma research, highlighting the breakthroughs achieved through AI-integrated multi-omic approaches. Additionally, we aspire to guide the future direction of the field by addressing the challenges that remain in translating these discoveries into clinical practice. By fostering a deeper understanding of the potential of these technologies, we hope to inspire further innovations that will pave the way for precision medicine to transform severe asthma management, ultimately leading to more personalised and effective treatment options for patients.
尽管在治疗策略方面取得了重大进展，但重度哮喘仍然是一种具有挑战性的异质性疾病。一部分患者继续经历控制不良、频繁加重和高疾病负担。多组学技术的出现，包括基因组学、转录组学、蛋白质组学和代谢组学，为理解哮喘的分子基础开辟了新的途径。当与人工智能（AI）相结合时，这些方法有可能通过识别关键生物标志物、改善疾病内型和实现个性化治疗策略来改变和加强重度哮喘的管理。本综述探讨了人工智能集成多组学方法在哮喘研究中的作用，强调了人工智能驱动的模型如何分析大量数据集，以揭示传统方法经常遗漏的模式。这些见解可以提高诊断精度，预测治疗反应并指导新型靶向治疗的发展。重点领域包括与哮喘严重程度相关的遗传位点，揭示细胞异质性的单细胞RNA测序，以及区分哮喘表型的蛋白质组学谱。通过这篇综述，我们旨在让读者清楚地了解重度哮喘研究的现状，重点介绍通过人工智能集成多组学方法取得的突破。此外，我们希望通过解决将这些发现转化为临床实践的挑战来指导该领域的未来方向。通过加深对这些技术潜力的理解，我们希望激发进一步的创新，为精准医学改变重度哮喘管理铺平道路，最终为患者带来更个性化和更有效的治疗选择。
（北京朝阳医院呼吸与危重症医学科 顾宪民 摘译 中日友好医院呼吸与危重症医学科 林江涛 审校）
（J Pediatr Health Care. Jan-Feb 2017;31(1):37-45. doi: 10.1016/j.pedhc.2016.01.005.Eur Respir Rev. 2026 May 27;35(180):250040. doi: 10.1183/16000617.0040-2025. Print 2026 Apr.）
（J Pediatr Health Care. Jan-Feb 2017;31(1):37-45. doi: 10.1016/j.pedhc.2016.01.005.Eur Respir Rev. 2026 May 27;35(180):250040. doi: 10.1183/16000617.0040-2025. Print 2026 Apr.）
Current understanding and future directions in severe asthma through artificial intelligence-integrated multi-omic approaches
Sundhas Rafeeq Valappil, Mohammed Uddin, Saba Al Heialy
Abstract
Severe asthma remains a challenging, heterogeneous condition, despite significant advances in therapeutic strategies. A subset of patients continues to experience poor control, frequent exacerbations and a high burden of disease. The advent of multi-omic technologies, including genomics, transcriptomics, proteomics and metabolomics, has opened new avenues for understanding the molecular underpinnings of asthma. When combined with artificial intelligence (AI), these approaches hold the potential to transform and augment the management of severe asthma by identifying key biomarkers, refining disease endotypes and enabling personalised treatment strategies. This review explores the role of AI-integrated multi-omic approaches in asthma research, highlighting how AI-driven models can analyse vast datasets to uncover patterns often missed by traditional methods. These insights can improve diagnostic precision, predict therapeutic responses and guide the development of novel, targeted therapies. Key areas of focus include genetic loci associated with asthma severity, single-cell RNA sequencing to uncover cellular heterogeneity, and proteomic profiles that differentiate asthma phenotypes. Through this review, we aim to provide readers with a clear understanding of the current landscape in severe asthma research, highlighting the breakthroughs achieved through AI-integrated multi-omic approaches. Additionally, we aspire to guide the future direction of the field by addressing the challenges that remain in translating these discoveries into clinical practice. By fostering a deeper understanding of the potential of these technologies, we hope to inspire further innovations that will pave the way for precision medicine to transform severe asthma management, ultimately leading to more personalised and effective treatment options for patients.
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