Proteomic analyses in diverse populations improved risk prediction and identified new drug targets for type 2 diabetes

Objective: Integrated analyses of plasma proteomics and genetic data in prospective studies can help assess the causal relevance of proteins, improve risk prediction and discover novel protein drug targets for T2D.

Research Design and Methods: We measured plasma levels of 2923 proteins using OLINK Explore among ~2000 randomly selected participants from CKB without prior diabetes at baseline. Cox regression assessed associations of individual protein with incident T2D (n=92 cases). Proteomic-based risk models were developed with discrimination, calibration, reclassification assessed using AUC, calibration plots and NRI, respectively. Two-sample MR analyses using cis-pQTLs identified in GWAS of CKB and UKB for specific proteins were conducted to assess their causal relevance for T2D, along with colocalization analyses to examine shared causal variants between proteins and T2D.

Results: Overall 33 proteins were significantly associated (FDR<0.05) with risk of incident T2D, including IGFBP1, GHR and amylase. The addition of these 33 proteins to conventional risk prediction model improved AUC from 0.77 (0.73-0.82) to 0.88 (0.85-0.91) and NRI by 38%, with predicted risks well calibrated with observed risks. MR analyses provided support for the causal relevance for T2D of ENTR1, LPL and PON3, with replication of ENTR1 and LPL in Europeans using different genetic instruments. Moreover, colocalization analyses showed strong evidence (PH4>0.6) of shared genetic variants of LPL and PON3 with T2D.

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.