美国SomaLogic公司Stephen A. Williams等人员发现血浆蛋白模式可作为健康综合指标。 2019年12月2日，《自然—医学》在线发表了这项成果。

研究人员发现，血浆蛋白表达模式显著编码多种不同的健康状态、未来的疾病风险和生活方式。研究人员针对11种不同的健康指标开发并验证了蛋白质表型模型：肝脂肪、肾脏滤过、体脂百分比、内脏脂肪量、瘦体重、心肺适应性、体育活动、饮酒、吸烟、糖尿病风险和原发性心血管事件风险。对分析进行了前瞻性计划、记录和大规模实施，以存档的样本和临床数据为基础，共对16894名参与者进行了约8500万次蛋白质测量。这一概念验证研究表明，蛋白质表达模式可以可靠地编码许多不同的健康问题，并且大规模蛋白质扫描加上机器学习技术可用于未来开发同时实现多种健康检测。研究人员认为，通过进一步验证并添加更多的蛋白质表型模型，该方法可以实现单一来源的、个性化的液体健康检查。

据了解，蛋白质是介导基因的功能并调节合并症、行为和药物治疗的效应分子。它们代表了个性化、系统化和数据驱动的诊断、预防、监测和治疗的巨大潜在资源。但是，使用血浆蛋白同时在多种健康状况下进行个性化健康评估的概念尚未得到测试。

附：英文原文

Title: Plasma protein patterns as comprehensive indicators of health

Author: Stephen A. Williams, Mika Kivimaki, Claudia Langenberg, Aroon D. Hingorani, J. P. Casas, Claude Bouchard, Christian Jonasson, Mark A. Sarzynski, Martin J. Shipley, Leigh Alexander, Jessica Ash, Tim Bauer, Jessica Chadwick, Gargi Datta, Robert Kirk DeLisle, Yolanda Hagar, Michael Hinterberg, Rachel Ostroff, Sophie Weiss, Peter Ganz, Nicholas J. Wareham

Issue&Volume: 2019-12-02

Abstract: Proteins are effector molecules that mediate the functions of genes1,2 and modulate comorbidities3,4,5,6,7,8,9,10, behaviors and drug treatments11. They represent an enormous potential resource for personalized, systemic and data-driven diagnosis, prevention, monitoring and treatment. However, the concept of using plasma proteins for individualized health assessment across many health conditions simultaneously has not been tested. Here, we show that plasma protein expression patterns strongly encode for multiple different health states, future disease risks and lifestyle behaviors. We developed and validated protein-phenotype models for 11different health indicators: liver fat, kidney filtration, percentage body fat, visceral fat mass, lean body mass, cardiopulmonary fitness, physical activity, alcohol consumption, cigarette smoking, diabetes risk and primary cardiovascular event risk. The analyses were prospectively planned, documented and executed at scale on archived samples and clinical data, with a total of ~85million protein measurements in 16,894participants. Our proof-of-concept study demonstrates that protein expression patterns reliably encode for many different health issues, and that large-scale protein scanning12,13,14,15,16 coupled with machine learning is viable for the development and future simultaneous delivery of multiple measures of health. We anticipate that, with further validation and the addition of more protein-phenotype models, this approach could enable a single-source, individualized so-called liquid health check.

DOI: 10.1038/s41591-019-0665-2

Source: https://www.nature.com/articles/s41591-019-0665-2