Identifying molecular mediators of macro- and microvascular complications in type 2 diabetes by integrating large-scale genetic, proteomic, and metabolomic data

• Applicant: Kamil Demircan
• Subject Area: Endocrinology, Diabetology, Metabolism
• Term: since 2024
• Project identifier: Deutsche Forschungsgemeinschaft (DFG) - Project number 547107463

The effects of type 2 diabetes (T2D) extend beyond elevated glucose levels, fostering a wide-ranging end organ damage on vital organs, particularly causing macro- and microvascular complications. Recent large-scale genetic studies have enhanced our understanding of disease onset, however, molecular mediators of its complications that eventually lead to premature death, are poorly understood. Moreover, identifying patients with high risk for complications, who could benefit from intensified therapy, remains challenging. In this proposal, we aim to 1) leverage large-scale genetic datasets with electronic health record (EHR) linkage to identify molecular mediators of high priority complications in patients with manifest T2D. Additionally, we would like to 2) integrate clinically accessible and omics data to construct prognostic models for T2D patients. For the first aim, we will conduct genome-wide association studies for T2D complications using genetic data sourced from four ready to analyse multi-ancestry datasets accessible at the host institute comprising over one million individuals. Consequently, integrating techniques established in the group of Prof. Langenberg, e.g., functional annotations, gene assignment pipelines, and molecular quantitative trait loci , the identified loci will be mapped to the likely effector genes. These genes will then be investigated in pathway analyses and overlayed with druggable targets, providing insights into underlying mechanisms and drug repurposing opportunities using open resource databases. We also propose an additional/alternative strategy to identify blood biomarkers causally involved in the T2D-complication relationship, i.e. conducting proteome- and metabolome-wide Mendelian randomization studies. For this strategy, genetic instruments from the largest current database via pre-publication access from Prof. Langenberg will be used. Extending my previous expertise on prognosis, we will build prognostic models using readily available clinical data in these cohorts, which can be used to monitor future complications. Complementing this with metabolome and proteome data, we will investigate the potentially added prognostic value by further omic layers. Finally, we will evaluate the prognostic models in important subgroups.

• DFG Programme: WBP Fellowship
• International Connection: United Kingdom
• Host: Professorin Claudia Langenberg, Ph.D.