Risk prediction of ischemic heart disease using plasma proteomics, conventional risk factors and polygenic scores in Chinese and European adults
<p><strong>Background:</strong> Plasma proteomics could enhance risk prediction for multiple diseases beyond conventional risk factors or polygenic scores (PS). Objectives: To assess utility of proteomics for risk prediction of ischemic heart disease (IHD) compared with conventiona...
| Main Authors: | , , , , , , , , , , , , , , , , , , , , , , , , , , |
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| Other Authors: | |
| Format: | Journal article |
| Language: | English |
| Published: |
Springer
2024
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| Summary: | <p><strong>Background:</strong> Plasma proteomics could enhance risk prediction for multiple diseases beyond conventional risk factors or polygenic scores (PS). Objectives: 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.</p>
<p><strong>Methods:</strong> 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 subcohort 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.</p>
<p><strong>Results:</strong> Overall, 446 proteins were associated with IHD (false discovery rate <0.05) in Chinese after adjustment for conventional cardiovascular disease risk factors. Proteomic risk models alone yielded higher C-statistics for IHD than conventional risk factors or PS (0.855 [95%CI 0.841-0.868] vs. 0.845 [0.829-0.860] vs 0.553 [0.528-0.578], respectively). Addition of 446 proteins to PS improved C-statistics to 0.857 (0.843-0.871) and NRI by 109.1%; and addition to conventional risk factors improved C-statistics to 0.868 (0.854-0.882) and NRI by 86.9%. Boruta analysis identified 30 proteins accounting for ~90% of improvement in NRI for IHD conferred by all 2923 proteins. Similar proteomic panels yielded comparable improvements in risk prediction of IHD in Europeans.</p>
<p><strong>Conclusions:</strong> Plasma proteomics improved risk prediction of IHD beyond conventional risk factors and PS and could enhance precision medicine approaches for primary prevention of IHD.</p> |
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