Proteomic Analyses in Diverse Populations Improved Risk Prediction and Identified New Drug Targets for Type 2 Diabetes
Authors' conclusion
Proteomic analyses in Chinese adults identified novel associations of multiple proteins with T2D with strong genetic evidence supporting their causal relevance and potential as novel drug targets for prevention and treatment of T2D.
Study context
- Population
- Participants from China Kadoorie Biobank without prior diabetes at baseline92 incident T2D cases
- Participants
- ∼2,000 participants
- Outcome
- Associations of individual proteins with incident T2D
Methodological detailsplasma proteomics (Olink Explore), randomly selected participants, two-sample Mendelian randomization, colocalization analyses
- Plasma proteomics (Olink Explore)
- Randomly selected participants
- Two-sample Mendelian randomization
- Colocalization analyses
Main finding
As reported in the abstract
Proteins associated with incident T2D
- Proteins significantly associated (false discovery rate <0.05) with incident T2D risk, incl. IGFBP1, GHR and amylase
- 33
Observational data: associations, not effects.
Further findings
Risk prediction: adding 33 proteins to conventional model
- AUC, conventional model
- 0.77 (0.73-0.82)
- AUC, conventional model + 33 proteins
- 0.88 (0.85-0.91)
- Net reclassification index (NRI) improvement
- 38%
- Predicted risks well calibrated with observed risks
Mendelian randomization (MR) causal relevance for T2D
- MR supported causal relevance of ENTR1, LPL and PON3 for T2D; ENTR1 and LPL replicated in Europeans with different genetic instruments
Colocalization with T2D
- Strong evidence of shared genetic variants of LPL and PON3 with T2D
- pH4 > 0.6
Original publication
Proteomic Analyses in Diverse Populations Improved Risk Prediction and Identified New Drug Targets for Type 2 Diabetes. Diabetes Care. Published 16 April 2024. doi:10.
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