Research Questions
Rheumatic and immunological diseases (e.g., rheumatoid arthritis, systemic lupus erythematosus) are complex disorders marked by immune dysregulation, chronic inflammation, and multi-organ involvement. These conditions often lead to high comorbidity, including cardiovascular complications in up to 40% of patients and neurological manifestations in 25-35%. Shared mechanisms like chronic inflammation and metabolic disturbances are hypothesized but not well understood. Patients face a 1.5-3 fold increased risk of premature cardiovascular mortality, highlighting a critical knowledge gap regarding the molecular drivers of comorbidity development.
Objective
Genetic Susceptibility: Integrate GWAS with environmental factors (e.g., smoking, vitamin D levels) to identify genetic loci for rheumatic diseases and their interactions.
Biomarker Discovery: Use metabolomics, proteomics, imaging, and epigenetics to find new biomarkers for these diseases.
Patient Subtyping: Apply machine learning to classify patients into subtypes based on clinical features and prognostic differences.
Comorbidity Networks: Analyze relationships between rheumatic diseases and cardiovascular/neurological conditions to identify shared biomarkers and develop integrated risk models.
Scientific Rationale
The inclusion of neurological and cardiovascular endpoints addresses a critical clinical gap – current management guidelines lack evidence-based strategies for comorbidity prevention in autoimmune populations. By establishing subtype-specific comorbidity profiles, we can inform targeted surveillance protocols – for instance, identifying RA subgroups requiring enhanced cardiovascular monitoring versus those at higher risk of CNS involvement. Developing personalized treatment plans for different subtypes can increase treatment effectiveness and reduce the risk of drug side effects.