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

• Published in: Nature communications 2023-03, Vol.14 (1), p.1662-15, Article 1662
• Main Authors: Sadegh, Sepideh, Skelton, James, Anastasi, Elisa, Maier, Andreas, Adamowicz, Klaudia, Möller, Anna, Kriege, Nils M., Kronberg, Jaanika, Haller, Toomas, Kacprowski, Tim, Wipat, Anil, Baumbach, Jan, Blumenthal, David B.
• Format: Article
• Language: English
• Subjects: 631/114/2164; 631/114/2401; 631/553/117; 692/308/575; 692/4017; Annotations; Data analysis; Disease; Genotype & phenotype; Humanities and Social Sciences; multidisciplinary; Phenotypes; Questions; Science; Science (multidisciplinary)
• ISSN: 2041-1723; 2041-1723
• DOI: 10.1038/s41467-023-37349-4

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. Large-scale disease-association data are widely used for pathomechanism mining, even if disease definitions used for annotation are mostly phenotype-based. Here, the authors show that this bias can lead to a blurred view on disease mechanisms, highlighting the need for close-up studies based on molecular data for well-characterized patient cohorts.