What does this research mean for the field?

Plasma proteomic risk models improve risk prediction for ischemic heart disease beyond conventional risk factors and polygenic scores in both Chinese and European populations. Novelty: ClaimNovelty.INCREMENTAL. Consensus alignment: ConsensusAlignment.NEUTRAL.

What question did this study set out to answer?

Assess the utility of plasma proteomics for predicting ischemic heart disease risk compared with and added to conventional risk factors and polygenic scores in Chinese and European populations.

November 1, 2024European Journal of EpidemiologyOpen Access

Risk prediction of ischemic heart disease using plasma proteomics, conventional risk factors and polygenic scores in Chinese and European adults

Q: What is the clinical evidence from this study?

Study design: Cohort. Population: Ischemic heart disease (n=41187). Intervention: Plasma proteomics-based prediction models vs. Conventional risk factors and polygenic scores. Primary outcome: Discrimination of ischemic heart disease (C-statistics) (C-statistic 0.855, 95% CI 0.841-0.868).

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Key result

Plasma proteomic models outperform conventional risk factors for ischemic heart disease prediction with 0.855 C-statistic.

C-statistic 0.855
95% CI 0.841-0.868
n=41,187

Why the study?

Does plasma proteomics improve risk prediction of ischemic heart disease compared to conventional risk factors and polygenic scores in Chinese and European adults?

Key Points

Assess the utility of plasma proteomics for predicting ischemic heart disease risk compared with and added to conventional risk factors and polygenic scores in Chinese and European populations.
Conducted a nested case-cohort study measuring 2,923 plasma proteins via the Olink Explore panel in ~4,000 Chinese adults (1,976 incident IHD cases and 2,001 sub-cohort controls).
Developed risk models using conventional statistics and machine learning (Boruta), comparing discrimination metrics against conventional risk factors and polygenic scores in Chinese adults and 37,187 Europeans.
Proteomic models alone demonstrated superior discrimination for IHD compared to conventional risk factors or polygenic scores (C-statistic 0.855 [95% CI 0.841–0.868] vs. 0.845 [95% CI 0.829–0.860] vs. 0.553 [95% CI 0.528–0.578]).
Adding 446 disease-associated proteins to conventional risk factors elevated the C-statistic to 0.868 (95% CI 0.854–0.882) and net reclassification index by 86.9%, with a 30-protein subset capturing approximately 90% of the improvement.

Study Design

Type

Cohort (n=41,187)

Multicenter

Yes

Structured PICO

Does plasma proteomics improve risk prediction of ischemic heart disease compared to conventional risk factors and polygenic scores in Chinese and European adults?

Population

41,187 Chinese and European adults evaluated in a nested case-cohort study to assess the utility of plasma proteomics for predicting incident ischemic heart disease.

Exposure

Plasma proteomics-based prediction models (using 2923 proteins measured by Olink Explore panel)

Comparator

Conventional risk factors and polygenic scores (PS)

Outcome

Risk prediction and discrimination of incident ischemic heart disease (IHD) assessed by C-statistics and net reclassification index (NRI)surrogate

Main Result

Effect estimate: C-statistic 0.855 (95% CI 0.841-0.868)

Absolute Event Rate: 0.855% vs 0.845%

Discussion

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Implication

Plasma proteomics significantly improves the risk prediction of ischemic heart disease beyond conventional risk factors and polygenic scores, potentially enhancing precision medicine for primary prevention.

Cite This Study

Mazidi et al. (2024) conducted a cohort in Ischemic heart disease (n=41,187). Plasma proteomics-based prediction models vs. Conventional risk factors and polygenic scores was evaluated on Discrimination of ischemic heart disease (C-statistics) (C-statistic 0.855, 95% CI 0.841-0.868). Plasma proteomic risk models yielded higher C-statistics for ischemic heart disease prediction than conventional risk factors or polygenic scores (0.855 vs 0.845 and 0.553, respectively).

synapsesocial.com/papers/6a7de3b9629c956ef7ac8929 https://doi.org/10.1007/s10654-024-01168-8

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Risk prediction of ischemic heart disease using plasma proteomics, conventional risk factors and polygenic scores in Chinese and European adults

Key Points

Study Design

Structured PICO

Main Result

Discussion

Implication

Cite This Study

Risk prediction of ischemic heart disease using plasma proteomics, conventional risk factors and polygenic scores in Chinese and European adults

Key Points

Study Design

Structured PICO

Main Result

Discussion

Implication

Cite This Study

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