Respiratory health outcomes are determined by a complex interplay between host defense mechanisms and environmental exposures. Current risk stratification relies heavily on basic functional metrics, such as spirometry, which often fail to capture the early molecular and structural heterogeneity that precedes overt clinical disease. Consequently, there is a critical need to elucidate the upstream biological drivers that distinguish individuals who maintain respiratory health from those who experience rapid functional decline. The primary objective of this project is to investigate multi-omics patterns of respiratory susceptibility that may predict adverse trajectories independent of traditional risk factors.
To achieve this, we will employ a data-driven approach to integrate genomic architectures with systemic proteomic and metabolomic profiles. A key aspect of our strategy involves anchoring these molecular features to organ-level structural phenotypes derived from medical imaging. This linkage aims to clarify how systemic inflammation translates into tissue-specific remodeling. Furthermore, we will incorporate lifestyle factors and environmental exposures as essential covariates. This comprehensive modeling is designed to distinguish intrinsic biological susceptibility from extrinsic damage, ensuring the specificity of the identified associations.
The clinical validity of these integrated profiles will be assessed by associating them with longitudinal health records, focusing on acute inflammatory events and decompensation episodes as key phenotypic endpoints. By bridging the gap between systemic biomarkers and structural lung changes, this research aims to provide mechanistic insights that could facilitate precision prevention strategies for at-risk populations.
