Proteomic Profiling Captures Residual Cardiovascular Risk Beyond the PREVENT Model in Individuals With Cardiovascular–Kidney–Metabolic Syndrome Stages 2–3

医学 疾病 内科学 弗雷明翰风险评分 病态的 人口 肿瘤科 生物信息学 肾脏疾病 预测值 风险评估 GDF15型 生物标志物 剩余风险 试验预测值 预测建模 仿形(计算机编程)
作者
Xuefei Han,Jiacheng Ding,Jingqian Li,Yiyin Gao,Yunqian Li
出处
期刊:Diabetes, Obesity and Metabolism [Wiley]
标识
DOI:10.1111/dom.70933
摘要

BACKGROUND: Cardiovascular-kidney-metabolic (CKM) syndrome reflects complex pathobiological interactions among metabolic disorders, kidney injury, and cardiovascular disease (CVD). Stages 2 and 3 represent critical phases of disease progression characterised by high pathological heterogeneity. This study aimed to develop a CVD protein risk score (PRS) for this population and evaluate its incremental predictive value over the PREVENT model. METHODS: This study included 24 017 participants with CKM Stages 2-3 from the UK Biobank. Using 2923 plasma proteins measured via the Olink platform, a PRS was developed in a training set (n = 19 218) using the LASSO method. In the validation set (n = 4799), the incremental predictive performance of this score over the PREVENT model was assessed using Harrell's C-statistic, net reclassification improvement (NRI) and integrated discrimination improvement (IDI). RESULTS: A risk score comprising 63 proteins was constructed, primarily reflecting inflammation, kidney injury and matrix remodelling. Key proteins included growth differentiation factor 15 (GDF15), hepatitis A virus cellular receptor 1 (HAVCR1), matrix metallopeptidase 12 (MMP12) and NT-proBNP. In the validation set, after adjusting for PREVENT risk factors, individuals in the high PRS group had a 2.56-fold higher risk of CVD compared to those in the low score group (HR: 2.56, 95% CI: 1.96-3.37). Integrating the score into the PREVENT model improved the C-statistic by 0.034 (0.672-0.706) and achieved a 10-year NRI of 15.8% (95% CI: 9.5%-20.9%) and an IDI of 2.2% (95% CI: 1.3%-3.3%). CONCLUSION: Combining the PREVENT model with the PRS developed in this study enhances the prediction of future CVD events in the CKM Stages 2-3 population. This approach facilitates the capture of residual risk and supports precision risk stratification and management for this high-risk group.

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