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从个体化人群健康的基因组学到表型组学的转变。

The transition from genomics to phenomics in personalized population health.

机构信息

Phenome Health, Seattle, WA, USA.

Center for Phenomic Health, The Buck Institute for Research on Aging, Novato, CA, USA.

出版信息

Nat Rev Genet. 2024 Apr;25(4):286-302. doi: 10.1038/s41576-023-00674-x. Epub 2023 Dec 13.

DOI:10.1038/s41576-023-00674-x
PMID:38093095
Abstract

Modern health care faces several serious challenges, including an ageing population and its inherent burden of chronic diseases, rising costs and marginal quality metrics. By assessing and optimizing the health trajectory of each individual using a data-driven personalized approach that reflects their genetics, behaviour and environment, we can start to address these challenges. This assessment includes longitudinal phenome measures, such as the blood proteome and metabolome, gut microbiome composition and function, and lifestyle and behaviour through wearables and questionnaires. Here, we review ongoing large-scale genomics and longitudinal phenomics efforts and the powerful insights they provide into wellness. We describe our vision for the transformation of the current health care from disease-oriented to data-driven, wellness-oriented and personalized population health.

摘要

现代医疗保健面临着一些严峻的挑战,包括人口老龄化及其固有的慢性疾病负担、成本上升和边际质量指标。通过使用数据驱动的个性化方法评估和优化每个人的健康轨迹,该方法反映了他们的遗传、行为和环境,我们可以开始应对这些挑战。这种评估包括纵向表型测量,如血液蛋白质组和代谢组、肠道微生物组组成和功能,以及通过可穿戴设备和问卷调查的生活方式和行为。在这里,我们回顾了正在进行的大规模基因组学和纵向表型组学研究,以及它们为健康带来的强大洞察力。我们描述了我们对将当前的医疗保健从以疾病为导向转变为以数据为导向、以健康为导向和个性化的人群健康的愿景。

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本文引用的文献

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ExplaiNAble BioLogical Age (ENABL Age): an artificial intelligence framework for interpretable biological age.可解释生物学年龄(ENABL Age):一种用于可解释生物学年龄的人工智能框架。
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Large-scale plasma proteomics comparisons through genetics and disease associations.通过遗传学和疾病关联进行大规模血浆蛋白质组学比较。
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