以色列魏兹曼科学研究所Amos Tanay团队发现，个性化的实验室测试模型可量化健康个体的疾病潜能。这一研究成果于2021年8月23日在线发表在国际学术期刊《自然—医学》上。

研究人员报告了对280万成年人在18年内92种不同实验室测试的21亿次实验室测量结果进行了建模。在对131种慢性病和5223个药物测试对进行无监督过滤后，研究人员对健康个体的实验室测试分布进行了虚拟调查。在92项测试中，仅年龄和性别就能解释其中89项测试的正态测试方差不到10%。

基于患者病史的个性化模型对17项测试的方差进行了60%的解释，对一半的测试进行了36%以上的解释。这可以对未来检测水平异常和随后出现的疾病的风险进行系统分层。正常范围内实验室检测的多变量模型可以很容易地实现，并作为定量评估病人的基础。

据了解，标准化的实验室测试是病人评估、鉴别诊断和治疗的核心。然而，这些数据的解释却缺乏定量和个性化的指标。

附：英文原文

Title: Personalized lab test models to quantify disease potentials in healthy individuals

Author: Cohen, Netta Mendelson, Schwartzman, Omer, Jaschek, Ram, Lifshitz, Aviezer, Hoichman, Michael, Balicer, Ran, Shlush, Liran I., Barbash, Gabi, Tanay, Amos

Issue&Volume: 2021-08-23

Abstract: Standardized lab tests are central for patient evaluation, differential diagnosis and treatment. Interpretation of these data is nevertheless lacking quantitative and personalized metrics. Here we report on the modeling of 2.1 billion lab measurements of 92 different lab tests from 2.8 million adults over a span of 18 years. Following unsupervised filtering of 131 chronic conditions and 5,223 drug–test pairs we performed a virtual survey of lab tests distributions in healthy individuals. Age and sex alone explain less than 10% of the within-normal test variance in 89 out of 92 tests. Personalized models based on patients’ history explain 60% of the variance for 17 tests and over 36% for half of the tests. This allows for systematic stratification of the risk for future abnormal test levels and subsequent emerging disease. Multivariate modeling of within-normal lab tests can be readily implemented as a basis for quantitative patient evaluation.

DOI: 10.1038/s41591-021-01468-6

Source: https://www.nature.com/articles/s41591-021-01468-6