Personalized lab test models to quantify disease potentials in healthy individuals

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

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

14 Citations (Scopus)

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.

Original languageEnglish
Pages (from-to)1582-1591
Number of pages10
JournalNature Medicine
Volume27
Issue number9
DOIs
Publication statusPublished - Sept 2021

Funding

Publisher Copyright: © 2021, The Author(s), under exclusive licence to Springer Nature America, Inc.

All Science Journal Classification (ASJC) codes

  • General Biochemistry,Genetics and Molecular Biology

Fingerprint

Dive into the research topics of 'Personalized lab test models to quantify disease potentials in healthy individuals'. Together they form a unique fingerprint.

Cite this