Identifying molecular pathways of type 2 diabetes using proteomics, metabolic, and anthropometric profiles in UK and Chinese adults

BackgroundProteogenomic analyses in biobanks provide opportunities to improve understanding of aetiology and drug discovery for type 2 diabetes (T2D). MethodsWe identified proteins (Olink Explore) associated with glycaemic traits and/or T2D with observational designs in UK Biobank (UKB-EUR, n =33,301). The Bayesian non-negative matrix factorisation (bNMF) was applied to cluster T2D-associated proteins incorporating their phenotypic associations with 43 metabolic/anthropometric traits. For clusters leading proteins (top 10% by ranking), two-steps colocalization and bidirectional Mendelian randomization were used to investigate three-way (i.e., protein-metabolic/anthropometric traits-T2D) relationships. We performed equivalent genetic analyses in China Kadoorie Biobank (CKB-EAS, n=2,029) to investigate shared/distinct findings. Results1,793 proteins were observationally associated with glycaemic traits and/or T2D in UKB-EUR, which were classified by bNMF into five clusters (Adiposity, Reduced-adiposity, Lipids, Liver, Kidney) where 906 proteins were cluster-leading. We triangulated observational and genetic evidence identifying five (B4GAT1, DNER, ENO3, HOMX2, OMG), one (ENTR1) and three (RTBDN, TSPAN8, NCR3LG1) proteins potentially affecting T2D in UKB-EUR, CKB-EAS, and both, respectively. In UKB-EUR, six (CD34, FGFBP3, GALNT10, KHK, MENT, MXRA8) were affected by T2D and five (GSTA1, GSTA3, MEGF9, NCAN, SHBG) were bidirectionally associated with T2D. The genetic analyses also revealed potential pathways in T2D aetiology (e.g., effects of RTBDN and TSPAN8 on T2D via BMI and SHBG respectively). ConclusionThis study identified multiple candidate proteins involved in the development of T2D that may make useful biomarkers for monitoring disease onset and progression in the future. These findings may inform molecular sub-phenotyping of T2D and more personalised T2D management.