Proteome-Wide Association and LLM-Based Prioritization of Type 2 Diabetes Protein Targets (LAUNCH-T2D)
Integrative Proteome-Wide Association Study and Large Language Model-Based Prioritization of Causal Protein Targets of Type 2 Diabetes
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.
Studienübersicht
Status
Bedingungen
Detaillierte Beschreibung
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.
The 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.
Large 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.
Studientyp
Einschreibung (Geschätzt)
Teilnahmekriterien
Zulassungskriterien
Studienberechtigtes Alter
Akzeptiert gesunde Freiwillige
Studienberechtigte Geschlechter
Probenahmeverfahren
Studienpopulation
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.
Beschreibung
Inclusion Criteria:
- 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.
Exclusion Criteria:
- 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.
This 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.
Studienplan
Wie ist die Studie aufgebaut?
Designdetails
- Beobachtungsmodelle: OTHER
- Zeitperspektiven: RETROSPECTIVE
Kohorten und Interventionen
Gruppe / Kohorte |
|---|
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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. |
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European 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. |
Was misst die Studie?
Primäre Ergebnismessungen
Ergebnis Maßnahme |
Maßnahmenbeschreibung |
Zeitfenster |
|---|---|---|
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Performance of Population-Specific Protein Prediction Models
Zeitfenster: Up to 12 months
|
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
|
Sekundäre Ergebnismessungen
Ergebnis Maßnahme |
Maßnahmenbeschreibung |
Zeitfenster |
|---|---|---|
|
Genetically Predicted Protein Associations With Type 2 Diabetes Risk
Zeitfenster: Up to 12 months
|
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. |
Up to 12 months
|
|
Prioritized Protein Targets With Citation-Grounded Functional Evidence
Zeitfenster: Up to 12 months
|
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. |
Up to 12 months
|
Mitarbeiter und Ermittler
Studienaufzeichnungsdaten
Haupttermine studieren
Studienbeginn (Geschätzt)
Primärer Abschluss (Geschätzt)
Studienabschluss (Geschätzt)
Studienanmeldedaten
Zuerst eingereicht
Zuerst eingereicht, das die QC-Kriterien erfüllt hat
Zuerst gepostet (Tatsächlich)
Studienaufzeichnungsaktualisierungen
Letztes Update gepostet (Tatsächlich)
Letztes eingereichtes Update, das die QC-Kriterien erfüllt
Zuletzt verifiziert
Mehr Informationen
Begriffe im Zusammenhang mit dieser Studie
Zusätzliche relevante MeSH-Bedingungen
Andere Studien-ID-Nummern
- LSUHSC-LAUNCHED-T2D-01
- U24DK132740 (US NIH Stipendium/Vertrag)
Plan für individuelle Teilnehmerdaten (IPD)
Planen Sie, individuelle Teilnehmerdaten (IPD) zu teilen?
Beschreibung des IPD-Plans
Individual participant data will not be shared because this study uses controlled-access data from external sources, including the UK Biobank and the Multi-Ethnic Study of Atherosclerosis, that are subject to source-specific data use agreements and participant privacy protections. The study team does not have authority to redistribute these data. To support transparency and reproducibility, analytic code, study documentation, and aggregate, non-identifiable results will be shared when permitted by applicable agreements.
Arzneimittel- und Geräteinformationen, Studienunterlagen
Studiert ein von der US-amerikanischen FDA reguliertes Arzneimittelprodukt
Studiert ein von der US-amerikanischen FDA reguliertes Geräteprodukt
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