数字医学时代罕见病研究发展新趋势
收稿日期: 2024-01-03
网络出版日期: 2024-02-02
基金资助
国家重点研发计划(2022YFC2705200)
New trends in the diagnosis and treatment of rare diseases in the digital medical era
Received date: 2024-01-03
Online published: 2024-02-02
基因组DNA高通量测序技术大幅提升了罕见病的诊断效率，但目前仍有部分患者未能确诊。近年来转录组、蛋白质组、代谢组和脂质组、表观遗传组等多种检测技术逐步应用于临床，使得基于多组学手段综合诊断罕见病患者成为可能。另一方面，伴随确诊病例的增加，如何有效整合患者临床资料、搭建罕见病数据库以满足高质量研究型患者队列建设需求已成为世界各国政府愈来愈重视的课题。更重要的是，整合了多组学信息大数据模型的开发可促进人工智能和机器学习在罕见病研究中的应用，这将助力罕见病患者的临床评估和精准分型等，并有效提升基因治疗等创新诊疗技术的研发效率。罕见病研究已迈入数字医学时代，这也是更高水平地满足患者精准诊断和个体化治疗的现实需要。
王剑 , 李牛 . 数字医学时代罕见病研究发展新趋势[J]. 临床儿科杂志, 2024 , 42(2) : 96 -101 . DOI: 10.12372/jcp.2024.23e1260
The high-throughput sequencing technology of genomic DNA has greatly improved the diagnostic efficiency of rare diseases, but currently there are still some patients who have not been diagnosed. In recent years, various detection techniques such as transcriptome, proteomics, metabolomics and lipidomics, and epigenetics have gradually been applied in clinical practice, making it possible to comprehensively diagnose rare disease patients based on these multiomics methods. On the other hand, with the increase of confirmed cases, how to effectively integrate patient clinical data and build rare disease databases to meet the construction needs of high-quality research-oriented patient cohorts has become an increasingly important issue for governments around the world. More importantly, the development of big data models that integrate multiomics information can promote the application of artificial intelligence and machine learning in rare disease research. This will contribute to the clinical evaluation and precise classification of rare disease patients, and effectively improve the research and development efficiency of innovative diagnostic and therapeutic technologies such as gene therapy. Rare disease research has entered the era of digital medicine, which is also a practical need to meet the precise diagnosis and personalized treatment of patients at a higher level.
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