Personal data for 108 individuals were collected during a 9-month period, including whole genome sequences; clinical tests, metabolomes, proteomes, and microbiomes at three time points; and daily activity tracking. Using all of these data, we generated a correlation network that revealed communities of related analytes associated with physiology and disease. Connectivity within analyte communities enabled the identification of known and candidate biomarkers (e.g., gamma-glutamyltyrosine was densely interconnected with clinical analytes for cardiometabolic disease). We calculated polygenic scores from genome-wide association studies (GWAS) for 127 traits and diseases, and used these to discover molecular correlates of polygenic risk (e.g., genetic risk for inflammatory bowel disease was negatively correlated with plasma cystine). Finally, behavioral coaching informed by personal data helped participants to improve clinical biomarkers. Our results show that measurement of personal data clouds over time can improve our understanding of health and disease, including early transitions to disease states.

中文翻译：

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使用个人，密集，动态数据云对108个人进行的健康研究

在9个月内收集了108个人的个人数据，包括整个基因组序列；在三个时间点进行临床测试，代谢组，蛋白质组和微生物组；以及日常活动跟踪。使用所有这些数据，我们生成了一个相关网络，该网络揭示了与生理和疾病相关的相关分析物的群落。分析物群落之间的连通性使得能够鉴定已知的和候选的生物标记物（例如，γ-谷氨酰酪氨酸与心脏代谢疾病的临床分析物紧密地相互连接）。我们从全基因组关联研究（GWAS）中计算了127个性状和疾病的多基因评分，并用它们来发现多基因风险的分子相关性（例如，炎症性肠病的遗传风险与血浆胱氨酸呈负相关）。最后，个人数据提供的行为指导可帮助参与者改善临床生物标志物。我们的结果表明，随着时间的推移对个人数据云进行测量可以增进我们对健康和疾病的理解，包括从早期过渡到疾病状态。