Lacking mechanistic disease definitions and corresponding association data hamper progress in network medicine and beyond

Sepideh Sadegh, James Skelton, Elisa Anastasi, Andreas Maier, Klaudia Adamowicz, Anna Möller, Nils M. Kriege, Jaanika Kronberg, Toomas Haller, Tim Kacprowski, Anil Wipat, Jan Baumbach and David B. Blumenthal ()
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Sepideh Sadegh: Technical University of Munich
James Skelton: Newcastle University
Elisa Anastasi: Newcastle University
Andreas Maier: University of Hamburg
Klaudia Adamowicz: University of Hamburg
Anna Möller: Friedrich-Alexander-Universität Erlangen-Nürnberg
Nils M. Kriege: University of Vienna
Jaanika Kronberg: University of Tartu
Toomas Haller: University of Tartu
Tim Kacprowski: Peter L. Reichertz Institute for Medical Informatics of Technische Universität Braunschweig and Hannover Medical School
Anil Wipat: Newcastle University
Jan Baumbach: University of Hamburg
David B. Blumenthal: Friedrich-Alexander-Universität Erlangen-Nürnberg

Nature Communications, 2023, vol. 14, issue 1, 1-15

Abstract: Abstract A long-term objective of network medicine is to replace our current, mainly phenotype-based disease definitions by subtypes of health conditions corresponding to distinct pathomechanisms. For this, molecular and health data are modeled as networks and are mined for pathomechanisms. However, many such studies rely on large-scale disease association data where diseases are annotated using the very phenotype-based disease definitions the network medicine field aims to overcome. This raises the question to which extent the biases mechanistically inadequate disease annotations introduce in disease association data distort the results of studies which use such data for pathomechanism mining. We address this question using global- and local-scale analyses of networks constructed from disease association data of various types. Our results indicate that large-scale disease association data should be used with care for pathomechanism mining and that analyses of such data should be accompanied by close-up analyses of molecular data for well-characterized patient cohorts.

Date: 2023
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DOI: 10.1038/s41467-023-37349-4

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