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The high-dimensional space of human diseases built from diagnosis records and mapped to genetic loci. | LitMetric

AI Article Synopsis

  • Traditional research focuses on diseases as separate issues, overlooking how they interact in a complex health system.
  • By analyzing clinical records from over 151 million Americans, the study maps diseases in a high-dimensional space based on their similarities.
  • The researchers identify 116 genetic associations linked to health states and show how these associations help predict various health issues using clustering analysis.

Article Abstract

Human diseases are traditionally studied as singular, independent entities, limiting researchers' capacity to view human illnesses as dependent states in a complex, homeostatic system. Here, using time-stamped clinical records of over 151 million unique Americans, we construct a disease representation as points in a continuous, high-dimensional space, where diseases with similar etiology and manifestations lie near one another. We use the UK Biobank cohort, with half a million participants, to perform a genome-wide association study of newly defined human quantitative traits reflecting individuals' health states, corresponding to patient positions in our disease space. We discover 116 genetic associations involving 108 genetic loci and then use ten disease constellations resulting from clustering analysis of diseases in the embedding space, as well as 30 common diseases, to demonstrate that these genetic associations can be used to robustly predict various morbidities.

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Source
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC10766526PMC
http://dx.doi.org/10.1038/s43588-023-00453-yDOI Listing

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