Context-dependent molecular responses to heterogeneous metabolic disease traits

Metabolic diseases such as type 2 diabetes (T2D) arise through complex interactions between physiological, molecular, and environmental processes. Clinical traits including age, sex, adiposity, and glycaemic status are strongly associated with disease risk and progression, yet most molecular studies examine these factors independently and assume relatively static molecular regulation. Consequently, how physiological state dynamically reshapes molecular organisation across omics layers remains poorly understood. Here, we integrated transcriptomic, proteomic, metabolomic, and genetic data from 3,027 individuals in the IMI DIRECT cohort to characterise the joint molecular effects of age, sex, body mass index (BMI), and glycated haemoglobin (HbA1c). We identified widespread associations between these traits and molecular phenotypes. However, interaction analyses revealed a more complex context-dependent regulation, showing that the molecular effect of one trait frequently depends on the state of another, with sex-specific effects of age being more prominent. We also investigated relationships between different types of molecular phenotypes and how these relationships are modulated by metabolic disease relevant traits, demonstrating that cross-omic molecular coordination is itself dynamically remodelled by physiological and metabolic state. Probabilistic causal inference identified a directionally structured network of age-associated molecules, revealing pathways through which age effects propagate across omics layers, showcased in the example of the mTOR signalling pathway. Integration of this directed network with genetic colocalisation analyses also identified a sub-network relevant for T2D. Collectively, our findings demonstrate that metabolic disease relevant traits not only independently influence molecular phenotype abundance but also jointly reshape the directional organisation of cross-omic molecular networks. These results support a model in which metabolic disease susceptibility emerges through dynamic rewiring of interconnected molecular systems and provide a framework for context-dependent biomarker discovery, disease stratification, and precision metabolic medicine.