| Title: | Integrated Analysis of Behavioral, Environmental, Socioeconomic, Genetic, and Microbiome Predictors of Respiratory Disease Onset, Progression, and Multimorbidity. |
| Lead Institution: | Shandong University of Traditional Chinese Medicine |
| Principal investigator: |
Application 1263482
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About
Respiratory diseases constitute a leading cause of global morbidity and mortality, arising from complex interactions among genetic, environmental, behavioral, and socioeconomic determinants. These conditions exhibit intricate pathophysiological connections with multiple organ systems, contributing to multimorbidity through mechanisms involving genetic susceptibility and dysbiosis. Large-scale prospective cohorts (e.g., UK Biobank) and multi-omic technologies (GWAS, scRNA-seq, TWAS) enable systematic investigation of these complex interactions, while machine learning facilitates high-dimensional risk screening and causal inference. Research Questions Which behavioral, environmental, socioeconomic, and multi-omic determinants predict respiratory disease onset and clinical trajectory? How do these multidimensional determinants interact to modulate disease progression, severity, and systemic complications? What mechanisms underlie pathophysiological cross-talk between respiratory diseases and distant organ systems in multimorbidity? How do modifiable risk factors and genetic susceptibility alleles conjointly determine disease co-occurrence patterns? Research Objectives To delineate multidimensional determinants predicting respiratory disease susceptibility, onset, and progression. To decipher molecular mechanisms through integrative genomic, transcriptomic, and microbiome analyses. To characterize systemic networks linking respiratory diseases with multimorbidity patterns across cardiovascular, metabolic, and immune systems. To establish causal inference regarding exposure-disease relationships through Mendelian randomization. To identify pleiotropic genetic variants and shared biological pathways for therapeutic target discovery. To construct clinically applicable prediction models integrating multi-omic profiles for individualized risk stratification.1 Publication
| Pub ID | Title | Author(s) | Year | Journal |
|---|---|---|---|---|
| 20866 | Association between the C-reactive protein-albumin-lymphocyte index and incident depression in patients with chronic obstructive pulmonary disease: a prospective cohort study from the UK biobank | Yushan Shi (+2) | 2026 | Frontiers in Medicine |
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