读文献

A longitudinal big data approach for precision health.

纵向健康大数据助力精准医学

影响因子：32.621

期刊年卷：Nat. Med. 2019 May; 25(5)

作者:  Schüssler-Fiorenza Rose SM等 （斯坦福大学医学院遗传学部门）

首页

Precision health relies on the ability to assess disease risk at an individual level, detect early preclinical conditions and initiate Preventive strategies.

精准医疗的实现依赖于个体水平上的疾病风险评估、能够检测出早期临床前状况并制定预防策略。

Recent technological advances in omics and wearable monitoring enable deep molecular and physiological profiling and may provide important tools for precision health. We explored the ability of deep longitudinal profiling to make Health-related discoveries, identify clinically relevant molecular pathways and affect behavior in a prospective longitudinal Cohort (n = 109) enriched for risk of type 2 diabetes mellitus.

最新的组学和可穿戴设备监测方面的技术进步使得深层次分子和生理分析成为可能，并可为精准医疗提供重要工具。在本次研究中，我们通过探索个体深度纵向分析谱，研究2型糖尿病的风险前瞻性纵向队列（n = 109）中与临床相关的分子通路和表型变化，并发现与健康相关标志物。

The cohort underwent integrative personalized omics profiling from samples collected quarterly for up to 8 years(median, 2.8 years) using clinical measures and emerging technologies including genome, immunome, transcriptome, proteome, metabolome, microbiome and wearable monitoring.

通过临床和新兴技术，按季度采集队列样本并进行测量，时间长达8年（中位数为2.8年），并经过综合的个体化组学分析，包括基因组，免疫组，转录组，蛋白组，代谢组，微生物组和可穿戴设备进行监测。

We discovered more than 67 clinically actionable health discoveries and identified multiple molecular pathways associated with metabolic, cardiovascular and oncologic pathophysiology. We developed prediction models for insulin resistance by using omics measurements,illustrating their potential to replace burdensome tests.

我们找到了超过67个临床可行的健康发现，并确定了与代谢、心血管和肿瘤病理生理学相关的多种分子途径。我们通过组学测量建立了胰岛素抵抗的预测模型，揭示了其取代以往繁琐检测的潜力。

Finally, study participation led the majority of participants to implement diet and exercise changes. Altogether, we conclude that deep longitudinal profiling can lead to actionable health discoveries and provide relevant information for precision health.

最后，参与此次研究使得大多数受试者改变了饮食和锻炼习惯。总而言之，我们得出结论：深度纵向分析研究可以促成可落地的健康相关发现，并为精准医疗提供更多信息。

图1

图1 | 实验设计和数据收集。关于深入纵向表型剖析确定健康风险和状态的概述。数据类别分为：标准（蓝色），增强（紫色）和显露（红色）测试。

PBMCs，外周血单核细胞; HbA1C，糖化血红蛋白; OGTT，口服葡萄糖耐量试验; SSPG，稳态血浆葡萄糖; CBC，全血细胞计数; hsCRP，高灵敏度C反应蛋白; CVD，心血管疾病。