Integrated Bioinformatics and Pharmacogenomic Profiling of a Gene Panel in Diabetes Mellitus Treatment Response
Discuss this preprint
Start a discussion What are Sciety discussions?Listed in
This article is not in any list yet, why not save it to one of your lists.Abstract
Background
Pharmacogenomic variability significantly influences diabetes mellitus (DM) treatment outcomes, yet systematic integration of multi-gene panels combining bioinformatics-driven discovery with cross-database validation remains limited across diverse populations.
Objective
To develop and validate a comprehensive 20-gene pharmacogenomic panel for predicting drug metabolism variability and treatment response in DM through integrated bioinformatics approaches.
Methods
Systematic literature mining identified candidate genes through PubMed searches (2015-2025). Multi-criteria decision analysis prioritized genes across insulin secretion, insulin sensitivity, glucose metabolism, and drug metabolism pathways. Analyses included Gene Ontology enrichment, KEGG pathway mapping, STRING protein-protein interaction networks, variant annotation (dbSNP/ClinVar/PharmGKB), pathogenicity prediction (CADD/PolyPhen-2/SIFT), GTEx tissue-specific expression profiling, and DrugBank drug-gene interaction mapping. Cross-database validation assessed concordance across PharmGKB, DrugBank, GWAS Catalog, and PhKB.
Results
The panel encompassed 20 genes distributed across 14 chromosomes. Network analysis revealed 87 edges with clustering coefficient 0.653, identifying 5 hub genes. Variant annotation catalogued 3,847 polymorphisms, including 247 pathogenic/likely pathogenic variants. Population analyses demonstrated 3.8-fold inter-ethnic allele frequency variations. PharmGKB integration identified 127 gene-drug pairs (23 Level 1A associations). Cross-database concordance achieved 87.3% (PharmGKB-DrugBank), 82.6% (GWAS Catalog), and 79.4% (PhKB). DrugBank identified 89 antidiabetic drug-gene interactions. Novel associations from recent publications demonstrated statistical significance in cohorts exceeding 2,000 patients.
Conclusions
This integrated framework provides validated foundations for precision diabetes therapeutics. Prospective clinical validation remains essential to translate computational discoveries into actionable decision-support tools optimizing therapeutic outcomes.
