Abstract

Asthma is a complex, chronic respiratory disease influenced by both genetic and environmental factors, and it 
carries significant public health and economic burdens. Emerging spatial omics technologies, including spatial 
transcriptomics, proteomics, and metabolomics, enable high-resolution molecular characterization of tissues. At 
the same time, environmental exposure datasets capture temporally and spatially resolved risk factors such as air 
pollution, urban vegetation, and land-use patterns. Integrating these heterogeneous datasets can improve 
predictive models for asthma risk, enhance the interpretability of biological and environmental interactions, and 
inform precision public health interventions. Challenges remain in model interpretability, bias, equity, real-world 
validation, and implementation, particularly in ensuring fairness across diverse populations and maintaining data 
privacy. Approaches to data fusion, bias detection, and stakeholder engagement are critical to facilitate ethical and 
effective deployment. This review highlights current methodologies, practical considerations, and prospective 
deployment scenarios for integrating spatial omics with environmental exposure data to advance asthma risk 
prediction, with a focus on equity, reproducibility, and translational impact.