Plasma proteomic profiles reveal proteins and characteristic patterns associated with hypertension: A prospective...

Longitudinal proteomic studies on hypertension remain scarce. We evaluated the epidemiological associations of circulating proteins with hypertension on the basis of the UK Biobank (UKB) and public GWAS-summary data in this study.

This study included 22 788 participants without hypertension on baseline and 2910 plasma proteins from the UKB. The competing risk regression and Mendelian randomization analysis were used to examine the association between plasma proteins and hypertension. Machine learning was used to identify proteins with strong predictive power for hypertension, and WGCNA was employed to characterize their patterns.

A total of 462 proteins exhibited significant associations with hypertension: 328 proteins of them had significant associations with hypertension, SBP, and DBP. Both the Fine-Gray competing risk regression model and the Mendelian randomization analysis revealed eight proteins (ADM, ELN, NCAN, PRSS8, ZBTB17, FURIN, TINAGL1, and WARS) with consistent directional effects. We identified potential 13 drugs targeting using the DGIdb database. Further, we created a predictive model using the 10 proteins and traditional risk factors by random forest and light gradient boosting machine model. The ROC curve had an AUC of 0.751 (95% confidence interval: 0.727-0.773). Immunity and disordered lipid metabolism-related protein modules identified by WGCNA were associated with an increased risk of hypertension.

The 462 proteins are significantly associated with hypertension. The eight proteins have causal associations with hypertension. The characteristic patterns of immunity and disordered lipid metabolism are capable of contributing to the pathogenesis of hypertension.