Research Aims:
This study aims to advance the treatment of chronic metabolic diseases by generating evidence for pharmacological strategies and developing personalized therapies. By leveraging genetic data, we will predict drug effects, validate these findings through cohort studies using real-world data, and synthesize robust evidence. Additionally, by exploring genetic factors that influence individual drug responses, we seek to establish personalized therapeutic strategies tailored to genetic profiles.
Scientific Rationale:
Drugs often influence multiple pathways, affecting various clinical outcomes. Genetically mimicked drug target variations can provide insights into their effects on chronic metabolic disease prognosis. Large-scale real-world data enables transparent, reproducible evidence generation. Understanding pharmacogenetic factors behind individual drug response variability supports personalized treatment strategies.
Methods:
We will conduct three main types of analyses:
1. Genetic data from the UK Biobank will support Genetic Risk Score (GRS) to estimate the impact of drug targets on disease prognosis.
2. Cohort Studies: We will create prospective and retrospective cohorts to compare drug users and non-users using UK Biobank and NHS-linked data. Korean real-world data will validate findings, and meta-analysis will synthesize results.
3. Pharmacogenetic Analysis: A cohort of drug users will be analyzed to identify genetic variations (e.g., star alleles). Differences in clinical outcomes will be evaluated based on these variations, identifying genetic factors that contribute to variability in drug response.
Public Health Impact:
This research combines genetic epidemiology and real-world data to predict therapeutic effects, validate drug effectiveness, and explore genetic factors influencing drug responses. These findings will support personalized treatment strategies, improving care and outcomes for chronic metabolic disease patients.