The geometry of clinical labs and wellness states from deeply phenotyped humans

Anat Zimmer, Yael Korem, Noa Rappaport, Tomasz Wilmanski, Priyanka Baloni, Kathleen Jade, Max Robinson, Andrew T. Magis, Jennifer Lovejoy, Sean M. Gibbons, Leroy Hood, Nathan D. Price

Research output: Contribution to journalArticlepeer-review

Abstract

Longitudinal multi-omics measurements are highly valuable in studying heterogeneity in health and disease phenotypes. For thousands of people, we have collected longitudinal multi-omics data. To analyze, interpret and visualize this extremely high-dimensional data, we use the Pareto Task Inference (ParTI) method. We find that the clinical labs data fall within a tetrahedron. We then use all other data types to characterize the four archetypes. We find that the tetrahedron comprises three wellness states, defining a wellness triangular plane, and one aberrant health state that captures aspects of commonality in movement away from wellness. We reveal the tradeoffs that shape the data and their hierarchy, and use longitudinal data to observe individual trajectories. We then demonstrate how the movement on the tetrahedron can be used for detecting unexpected trajectories, which might indicate transitions from health to disease and reveal abnormal conditions, even when all individual blood measurements are in the norm.

Original languageEnglish (US)
Article number3578
JournalNature communications
Volume12
Issue number1
DOIs
StatePublished - Dec 2021
Externally publishedYes

ASJC Scopus subject areas

  • Chemistry(all)
  • Biochemistry, Genetics and Molecular Biology(all)
  • Physics and Astronomy(all)

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