Trajectory and genetic correlation mapping of cross-disease risks following incident type 2 inflammatory diseases

Abstract Background Type 2 inflammatory diseases (T2IDs) such as atopic dermatitis (AD), asthma, food allergy, eosinophilic esophagitis (EoE), chronic sinusitis (CS), and others, are immune-mediated conditions that frequently co-occur across organ systems and contribute to substantial disease burden. While the atopic march has long served as a conceptual model for T2ID progression, it remains unclear whether this linear sequence captures the broader comorbidity structure of T2IDs. Methods We systematically analyzed comorbidity patterns across nine T2IDs using two complementary methods: Large-scale real-world data (RWD) from over 129 million electronic health records, genome-wide and local genetic correlation, including three novel (asthma, chronic sinusitis, allergic rhinitis) metanalyses of genome-wide association studies. Results Retrospective matched cohort analyses reveal a highly interconnected disease network in which CS emerged as a central node, increasing the risk of multiple other T2IDs across diverse patient populations. AD, food allergy, and vasomotor and allergic rhinitis also show strong cross-disease effects. Subgroup analyses uncover notable age-, sex-, and race-specific disparities. Genetic correlation analyses support shared heritability among many disease pairs, complemented by the temporal ordering of associations present in the RWD. Local genetic correlation position asthma as the central node of the shared polygenic architecture, converging on canonical type 2 inflammatory loci. Conclusions Together, our results highlight that T2ID progression extends beyond the atopic march and is shaped by a complex and heterogeneous network of interactions. This work provides a data-driven framework to guide risk stratification, inform early preventive strategies, and prioritize upstream disease targets within the type 2 inflammatory spectrum.