This study will utilize a multi-step, integrative approach combining epidemiological, genomic, and bioinformatic methods to identify biomarkers and therapeutic targets for chronic kidney disease (CKD).
1. Cohort Selection and Phenotype Definition:
Using the UK Biobank dataset, we will identify individuals with and without CKD based on clinical measures such as estimated glomerular filtration rate (eGFR), albumin-to-creatinine ratio (ACR), and ICD-coded diagnoses. Longitudinal data will be used to define disease progression.
2. Genomic and Multi-Omics Analysis:
Genome-wide association studies (GWAS) will be conducted to identify single nucleotide polymorphisms (SNPs) associated with CKD onset and progression. In addition, polygenic risk scores (PRS) will be constructed to evaluate genetic susceptibility. Where available, transcriptomic and proteomic data will be integrated to identify functional variants and expression-based biomarkers.
3. Epidemiological and Environmental Analysis:
Demographic, lifestyle, and clinical variables (e.g., age, BMI, smoking status, comorbidities) will be analyzed to assess their interactions with genetic risk factors. Multivariate regression and machine learning models will be used to evaluate predictive potential.
4. Biomarker and Therapeutic Target Discovery:
Candidate biomarkers will be validated using independent subsets of the UK Biobank and external datasets if available. Pathway analysis and drug-gene interaction databases (e.g., DrugBank, LINCS) will be employed to explore potential therapeutic targets.
5. Predictive Model Development:
Integrated models combining genetic, clinical, and biomarker data will be developed using machine learning techniques (e.g., random forest, logistic regression, neural networks) to predict CKD risk and progression.
This methodologically comprehensive approach aims to advance early diagnosis and targeted treatment strategies for CKD through precision medicine.
