Context-dependent molecular responses to heterogeneous metabolic disease traits
Year: 2026
Type: preprint
Abstract: ABSTRACT Metabolic diseases such as type 2 diabetes (T2D) arise through complex interactions between physiological, molecular, and environmental processes. However, molecular studies often examine traits such as age, sex, adiposity, and glycaemic status independently, overlooking how their combined effects shape molecular phenotypes and relationships across molecular layers. Here, integrating transcriptomic, proteomic, metabolomic, and genetic data from 3,027 individuals in the IMI DIRECT cohort, we investigated the joint effects of age, sex, BMI, and HbA1c on molecular phenotypes and cross-omic relationships. We identified widespread molecular associations alongside extensive context-dependent effects, demonstrating that the effect of one trait frequently depended on the state of another, with sex-dependent age effects particularly prominent. These interactions were also evident in cross-omic relationships, indicating that physiological and metabolic context shapes not only molecular abundance but also the coordination between transcripts, proteins, and metabolites. Probabilistic causal inference further identified directionally structured relationships across molecular layers, with age-associated effects more frequently supporting dependent models consistent with inter-omic mediation. Together, our findings demonstrate that metabolic disease-relevant traits jointly shape molecular phenotypes and cross-omic network organisation. This context dependence highlights the importance of accounting for physiological state when interpreting molecular mechanisms and biomarkers, and provides a framework for investigating dynamic molecular signatures that may inform future approaches to disease stratification and precision medicine.
Source: medRxiv
Authors: Theodora-Dafni Michalettou, Sapna Sharma, Natalie N. Atabaki, Juan J. Fernandez-Tajes, Mun-gwan Hong, Jonathan Adam, Jerzy Adamski, Søren Brunak, Federico De Masi, Emmanouil T. Dermitzakis, Ian M. Forgie, Paul W. Franks, Tim Frayling, Giuseppe N. Giordano, Mark Haid, Torben Hansen, Tue H. Hansen, Peter P. Harms, Andrew T. Hattersley, Ulrik Plesner Jacobsen, Angus G. Jones, Robert W. Koivula, Tarja Kokkola, Markku Laakso, Anubha Mahajan, Andrea Mari, Mark I McCarthy, Timothy J. McDonald, Imre Pavo, Oluf Pedersen, Anna Ramisch, Hartmut Ruetten, Femke Rutters, Jochen M. Schwenk, Leen ’t Hart, Henrik Vestergaard, Mark Walker, Ewan R. Pearson, Andrew A. Brown, Ana Viñuela
Institutions: Newcastle University, Helmholtz Munich, Technical University of Munich, University of Copenhagen, Lund University, Centre for Human Genetics, University of Oxford, Science for Life Laboratory, KTH Royal Institute of Technology, German Center for Diabetes Research, University of Ljubljana, National University of Singapore, Novo Nordisk Foundation, Technical University of Denmark, University of Geneva, SIB Swiss Institute of Bioinformatics, Institute of Genetics and Genomics in Geneva, University of Dundee, Ninewells Hospital, Zealand University Hospital Køge, Amsterdam Public Health, Amsterdam UMC Location Vrije Universiteit Amsterdam, University of Exeter, Oxford Centre for Diabetes, Endocrinology and Metabolism, University of Eastern Finland, Genentech, National Research Council, Royal Devon & Exeter NHS Foundation Trust, Gentofte Hospital, Novo Nordisk Foundation Center for Basic Metabolic Research, Sanofi (Germany), Amsterdam University Medical Centers, Leiden University Medical Center, Steno Diabetes Center Copenhagen
Language: English
Subfield: Molecular Biology
Domain: Life Sciences
Open Access status: green
OpenAlex ID: W7164025581