Proteomics assisted improvement prediction of risk for adverse cardiovascular events in individuals with metabolic syndrome.
6 affiliations
- 1Beijing Anzhen Hospital, Capital Medical University, Key Laboratory of Remodeling-Related Cardiovascular Disease, Ministry of Education, Beijing Collaborative Innovation Center for Cardiovascular Disorders, No. 2 Anzhen Road, Chaoyang District, Beijing, 100029, China.
- 2Beijing Institute of Heart, Lung and Blood Vessel Disease, No. 2 Anzhen Road, Chaoyang District, Beijing, 100029, China.
- 3Beijing Anzhen Hospital, Capital Medical University, Key Laboratory of Remodeling-Related Cardiovascular Disease, Ministry of Education, Beijing Collaborative Innovation Center for Cardiovascular Disorders, No. 2 Anzhen Road, Chaoyang District, Beijing, 100029, China. [email protected].
- 4Beijing Institute of Heart, Lung and Blood Vessel Disease, No. 2 Anzhen Road, Chaoyang District, Beijing, 100029, China. [email protected].
- 5Beijing Anzhen Hospital, Capital Medical University, Key Laboratory of Remodeling-Related Cardiovascular Disease, Ministry of Education, Beijing Collaborative Innovation Center for Cardiovascular Disorders, No. 2 Anzhen Road, Chaoyang District, Beijing, 100029, China. [email protected].
- 6Beijing Institute of Heart, Lung and Blood Vessel Disease, No. 2 Anzhen Road, Chaoyang District, Beijing, 100029, China. [email protected].
Abstract
Background
Traditional clinical models, designed for the general population, are insufficient for capturing the complex molecular pathology that drives major adverse cardiovascular events (MACEs) in individuals with metabolic syndrome (MetS). This study aimed to leverage large-scale proteomics to improve risk stratification in MetS and to investigate the potential causal effects of identified proteins using Mendelian Randomization (MR).Methods
We analyzed 2,911 plasma proteins in 12,308 MetS participants from the UK Biobank. A protein prediction model was constructed using the least absolute shrinkage and selection operator method to select MACE-associated proteins in the training set. The model's predictive performance was evaluated in a validation set and compared to the clinical PREVENT model. To investigate whether these identified proteins have a causal relationship with MACE, we utilized cis-pQTLs as genetic instruments to perform a two-sample MR analysis.Results
Over a median 14-year follow-up, 1,899 incident MACEs occurred. In the validation set, the protein model significantly outperformed the clinical PREVENT model (C-statistic: 0.730 vs. 0.673, P = 0.003). Crucially, among individuals classified as intermediate-risk by the PREVENT model, the protein score achieved a total net reclassification index of 0.44, driven by the correct reclassification of 64% of individuals who developed MACE. Time-dependent analysis revealed exceptional predictive accuracy for short-term heart failure risk (3-year AUC = 0.90), which was superior to long-term predictions. MR analysis against individual MACE components identified four proteins with causal evidence: genetically higher AGER (soluble RAGE) was associated with lower heart failure risk, and higher MMP-12 with lower stroke risk, whereas higher VAMP5 and NCAN were associated with increased coronary heart disease risk.Conclusion
A proteomics-derived model deconstructs the molecular heterogeneity of MACEs risk in individuals with MetS, significantly improving conventional models. Furthermore, integrating MR analysis distinguishes causal drivers from biomarkers, providing novel molecular insights to guide personalized prevention.
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BibTeX
@article{chen2026proteomics,
title = {{Proteomics assisted improvement prediction of risk for adverse cardiovascular events in individuals with metabolic syndrome.}},
author = {Yanlin Chen and Wei Pan and Haichu Wen and Yifan Wang and Jifang Zhang and Bokang Qiao and Lixin Jia and Jie Du},
journal = {BMC cardiovascular disorders},
year = {2026},
month = aug,
doi = {10.1186/s12872-026-06367-7},
url = {https://europepmc.org/article/MED/42972233},
note = {CC BY-NC-ND},
}
RIS
TY - JOUR
TI - Proteomics assisted improvement prediction of risk for adverse cardiovascular events in individuals with metabolic syndrome.
AU - Yanlin Chen
AU - Wei Pan
AU - Haichu Wen
AU - Yifan Wang
AU - Jifang Zhang
AU - Bokang Qiao
AU - Lixin Jia
AU - Jie Du
PY - 2026
DA - 2026/08/01
JO - BMC cardiovascular disorders
DO - 10.1186/s12872-026-06367-7
UR - https://europepmc.org/article/MED/42972233
AB - Background Traditional clinical models, designed for the general population, are insufficient for capturing the complex molecular pathology that drives major adverse cardiovascular events (MACEs) in individuals with metabolic syndrome (MetS). This study aimed to leverage large-scale proteomics to improve risk stratification in MetS and to investigate the potential causal effects of identified proteins using Mendelian Randomization (MR).Methods We analyzed 2,911 plasma proteins in 12,308 MetS participants from the UK Biobank. A protein prediction model was constructed using the least absolute shrinkage and selection operator method to select MACE-associated proteins in the training set. The model's predictive performance was evaluated in a validation set and compared to the clinical PREVENT model. To investigate whether these identified proteins have a causal relationship with MACE, we utilized cis-pQTLs as genetic instruments to perform a two-sample MR analysis.Results Over a median 14-year follow-up, 1,899 incident MACEs occurred. In the validation set, the protein model significantly outperformed the clinical PREVENT model (C-statistic: 0.730 vs. 0.673, P = 0.003). Crucially, among individuals classified as intermediate-risk by the PREVENT model, the protein score achieved a total net reclassification index of 0.44, driven by the correct reclassification of 64% of individuals who developed MACE. Time-dependent analysis revealed exceptional predictive accuracy for short-term heart failure risk (3-year AUC = 0.90), which was superior to long-term predictions. MR analysis against individual MACE components identified four proteins with causal evidence: genetically higher AGER (soluble RAGE) was associated with lower heart failure risk, and higher MMP-12 with lower stroke risk, whereas higher VAMP5 and NCAN were associated with increased coronary heart disease risk.Conclusion A proteomics-derived model deconstructs the molecular heterogeneity of MACEs risk in individuals with MetS, significantly improving conventional models. Furthermore, integrating MR analysis distinguishes causal drivers from biomarkers, providing novel molecular insights to guide personalized prevention.
SN - 1471-2261
ER -