Type 2 diabetes is a common condition in which the body has difficulty controlling blood sugar. This study will use existing genetic, protein, and health data from prior research studies to identify blood proteins that may play a role in type 2 diabetes. The study will not recruit participants, provide treatment, or collect new samples. Researchers will use computer-based analyses to identify and prioritize protein targets for future laboratory and clinical research. The goal is to support the development of better approaches for understanding, preventing, and treating type 2 diabetes.
Participants enrolled in the source studies who were aged 40 to 84 years at enrollment.
Availability of genetic data and plasma proteomic data for population-specific protein prediction modeling.
Participants with or without type 2 diabetes, depending on the source dataset and analytic objective.
Participants younger than 40 years or older than 84 years at enrollment in the source studies.
Participants lacking genetic data or plasma proteomic data required for protein prediction modeling.
Existing adult participants from the UK Biobank and Multi-Ethnic Study of Atherosclerosis with available genetic and OLINK proteomic data, including African American and European American/European ancestry participants. Type 2 diabetes GWAS summary statistics from the Million Veteran Program will be integrated for association analyses. No new participants will be recruited or contacted.
Other
Retrospective
Not listed
Performance of Population-Specific Protein Prediction Models
Cross-validated and external validation coefficients of determination (R-squared values) for prediction models of 2,943 plasma proteins based on single-nucleotide polymorphisms located near the genes encoding the corresponding proteins (cis variants). Models with reproducible performance (R-squared greater than 0.01) will be retained.
Genetically Predicted Protein Associations With Type 2 Diabetes Risk
Number and effect estimates of proteins associated with type 2 diabetes risk after integration of validated population-specific protein prediction models with type 2 diabetes genome-wide association summary statistics. Statistical significance will be assessed using false discovery rate less than 0.05.
Prioritized Protein Targets With Citation-Grounded Functional Evidence
Number of proteins identified through proteome-wide association analyses that are assigned a structured, citation-grounded functional evidence profile and prioritization score using retrieval-augmented large language model-assisted annotation and expert review.
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Louisiana State University Health Sciences Center in New Orleans
Lead sponsor
National Institute of Diabetes and Digestive and Kidney Diseases (NIDDK)
Collaborator
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