Risk prediction of ischemic heart disease using plasma proteomics, conventional risk factors and polygenic scores in Chinese and European adults.
Mazidi M., Wright N., Yao P., Kartsonaki C., Millwood IY., Fry H., Said S., Pozarickij A., Pei P., Chen Y., Wang B., Avery D., Du H., Schmidt DV., Yang L., Lv J., Yu C., Sun D., Chen J., Hill M., Peto R., Collins R., Bennett DA., Walters RG., Li L., Clarke R., Chen Z., China Kadoorie Biobank Collaborative Group None.
Plasma proteomics could enhance risk prediction for multiple diseases beyond conventional risk factors or polygenic scores (PS). To assess utility of proteomics for risk prediction of ischemic heart disease (IHD) compared with conventional risk factors and PS in Chinese and European populations. A nested case-cohort study measured plasma levels of 2923 proteins using Olink Explore panel in ~ 4000 Chinese adults (1976 incident IHD cases and 2001 sub-cohort controls). We used conventional and machine learning (Boruta) methods to develop proteomics-based prediction models of IHD, with discrimination assessed using area under the curve (AUC), C-statistics and net reclassification index (NRI). These were compared with conventional risk factors and PS in Chinese and in 37,187 Europeans. Overall, 446 proteins were associated with IHD (false discovery rate