[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"health-study-detail:100657109":3},{"organization":4,"outcomesModule":7,"designInfo":21,"detailedDescription":23,"studyPopulation":24,"armGroups":25,"interventions":20,"overallOfficials":20,"centralContacts":20,"locations":20,"responsibleParty":31,"collaborators":33,"id":37,"slug":38,"hasResults":39,"nctId":40,"briefTitle":41,"officialTitle":42,"acronym":43,"eligibilityCriteria":44,"healthyVolunteers":45,"sex":46,"minAge":47,"maxAge":48,"enrollmentInfo":49,"targetDuration":20,"studyType":52,"phases":20,"briefSummary":53,"conditions":54,"keywords":20,"overallStatus":56,"whyStopped":20,"lastUpdateSubmitDate":57,"lastUpdatePostDateStruct":58,"startDateStruct":61,"completionDateStruct":63,"leadSponsor":65,"locationsCount":20},{"fullName":5,"class":6},"Louisiana State University Health Sciences Center in New Orleans","OTHER",{"primaryOutcomes":8,"secondaryOutcomes":13,"otherOutcomes":20},[9],{"measure":10,"description":11,"timeFrame":12},"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.","Up to 12 months",[14,17],{"measure":15,"description":16,"timeFrame":12},"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.",{"measure":18,"description":19,"timeFrame":12},"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.",null,{"allocation":20,"interventionModel":20,"interventionModelDescription":20,"primaryPurpose":20,"observationalModel":6,"timePerspective":22,"maskingInfo":20},"RETROSPECTIVE","Type 2 diabetes is a major cause of illness and health disparities. Genetic association studies can identify regions of the genome associated with disease risk, but they do not always identify the proteins or biological mechanisms that contribute to disease development. This project will conduct a retrospective secondary analysis of existing, controlled-access genetic, proteomic, and phenotype data, including data accessed through the United Kingdom Biobank (UK Biobank) and other previously collected datasets. No new participants will be recruited, enrolled, contacted, treated, or followed as part of this study.\n\nThe study will use proteome-wide association methods to evaluate whether genetically predicted circulating protein levels are associated with type 2 diabetes risk. Analyses will consider evidence across African American and European American datasets when available, with attention to population-specific and shared signals. Statistical genetic evidence will be integrated with relevant biological and clinical information to prioritize protein targets that may have a causal role in type 2 diabetes.\n\nLarge language model-based methods will be used as a structured evidence-synthesis tool to organize and summarize publicly available information relevant to prioritized proteins, including biological function, disease relevance, and potential therapeutic tractability. All computational results will be reviewed by the research team. The project will generate reproducible analytic workflows, a ranked list of candidate protein targets, and hypotheses for future experimental validation. Findings are intended for research use and will not be used to make clinical decisions for individual patients.","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\u002FEuropean 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.",[26,29],{"label":27,"type":20,"description":28,"interventionNames":20},"African American Participants","Retrospective analysis of existing genetic and OLINK proteomic data to develop and validate population-specific protein prediction models and evaluate genetically predicted proteins associated with type 2 diabetes risk.",{"label":30,"type":20,"description":28,"interventionNames":20},"European American Participants",{"type":32,"investigatorFullName":20,"investigatorTitle":20,"investigatorAffiliation":20,"oldNameTitle":20,"oldOrganization":20},"SPONSOR",[34],{"name":35,"class":36},"National Institute of Diabetes and Digestive and Kidney Diseases (NIDDK)","NIH","100657109","proteome-wide-association-and-llm-based-prioritization-of-type-2-diabetes-protein-targets-100657109",false,"NCT07832656","Proteome-Wide Association and LLM-Based Prioritization of Type 2 Diabetes Protein Targets","Integrative Proteome-Wide Association Study and Large Language Model-Based Prioritization of Causal Protein Targets of Type 2 Diabetes","LAUNCH-T2D","Inclusion Criteria:\n\n* Participants enrolled in the source studies who were aged 40 to 84 years at enrollment.\n* Availability of genetic data and plasma proteomic data for population-specific protein prediction modeling.\n* Participants with or without type 2 diabetes, depending on the source dataset and analytic objective.\n\nExclusion Criteria:\n\n* Participants younger than 40 years or older than 84 years at enrollment in the source studies.\n* Participants lacking genetic data or plasma proteomic data required for protein prediction modeling.\n\nThis study will conduct a retrospective secondary analysis of existing, controlled-access datasets. No new participants will be recruited or contacted. Type 2 diabetes genome-wide association summary statistics will also be used for association analyses; the participant-level eligibility criteria above apply to individual-level source-study records used for protein prediction modeling.",true,"ALL","40 Years","84 Years",{"count":50,"type":51},31994,"ESTIMATED","OBSERVATIONAL","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.",[55],"Type 2 Diabetes Mellitus (DM)","NOT_YET_RECRUITING","2026-09-23",{"date":59,"type":60},"2026-09-28","ACTUAL",{"date":62,"type":51},"2026-09-15",{"date":64,"type":51},"2027-08-02",{"name":5,"class":6}]