Lin Jui-Chu, Liu Yi-Lien, Hsiao Wesley Wei-Wen, Fan Chien-Te
Graduate Institute of Applied Science and Technology, National Taiwan University of Science and Technology, Taipei, Taiwan, ROC.
College of Liberal Arts and Social Sciences, National Taiwan University of Science and Technology, Taipei, Taiwan, ROC.
Comput Struct Biotechnol J. 2024 Nov 5;24:690-698. doi: 10.1016/j.csbj.2024.10.049. eCollection 2024 Dec.
Precision health extends beyond the scope of precision medicine and involves a broader range of activities, including the prediction, prevention, treatment, and management of diseases. Tailored to specific populations, precision health offers personalized treatment and preventive measures considering genetics, lifestyle behaviors, social determinants of health, and environmental factors. Precision medicine focuses on the personalized treatment of diseases, whereas precision health aims to promote health and prevent diseases using tools such as big data and advanced analytics to predict health risks and prevent diseases at the population level. Biobanks play a crucial role in achieving precision health because they provide well-characterized biological samples and related data for disease prediction, diagnosis, and treatment. Challenges in integrating different biobanks include data format consistency, privacy concerns, and legal constraints. Standardized methodologies and digitalization can mitigate these challenges. The integration of biobanks can facilitate comprehensive analyses across multiple datasets to achieve various research goals. This study proposes strategies to address these challenges, including the development of a dynamic consent mechanism for population-based biobanks using digitalization and blockchain technology. This study recommends the following: 1) integrating population-based biobanks, 2) introducing dynamic consent tools for human biobanks, and 3) using large human biobanks with dynamic consent for research on diverse diseases. These recommendations can increase the utility of biobanks in realizing precision health. A case study implemented at Taoyuan Tiansheng Hospital demonstrated the effectiveness of these recommendations for achieving precision health and enhancing the value of biobanks. Through a comprehensive examination of precision health and biobanks, this study provides valuable insights for researchers, healthcare professionals, and policymakers in the precision healthcare sector.
精准健康超越了精准医学的范畴,涉及更广泛的活动,包括疾病的预测、预防、治疗和管理。精准健康是针对特定人群量身定制的,它会考虑遗传学、生活方式行为、健康的社会决定因素和环境因素,提供个性化的治疗和预防措施。精准医学侧重于疾病的个性化治疗,而精准健康旨在利用大数据和先进分析等工具促进健康和预防疾病,在人群层面预测健康风险并预防疾病。生物样本库在实现精准健康方面发挥着关键作用,因为它们为疾病预测、诊断和治疗提供了特征明确的生物样本和相关数据。整合不同生物样本库面临的挑战包括数据格式一致性、隐私问题和法律限制。标准化方法和数字化可以缓解这些挑战。生物样本库的整合有助于对多个数据集进行全面分析,以实现各种研究目标。本研究提出了应对这些挑战的策略,包括利用数字化和区块链技术为基于人群的生物样本库开发动态同意机制。本研究建议如下:1)整合基于人群的生物样本库;2)为人类生物样本库引入动态同意工具;3)使用具有动态同意功能的大型人类生物样本库进行多种疾病的研究。这些建议可以提高生物样本库在实现精准健康方面的效用。在桃园天晟医院实施的一个案例研究证明了这些建议对于实现精准健康和提升生物样本库价值的有效性。通过对精准健康和生物样本库的全面审视,本研究为精准医疗领域的研究人员、医疗保健专业人员和政策制定者提供了有价值的见解。