Evaluation of Large-Scale Plasma Proteomics for Prediction of Heart Failure in Individuals with A Full Range of Glucose Metabolism Profiles.

IF 8.4 2区 医学 Q1 CARDIAC & CARDIOVASCULAR SYSTEMS
Yujie Zhang, Rui Hou, Wen Sun, Jin Guo, Zhiguo Chen, Haibin Li, Changwei Li, Lijuan Wu, Jianguang Ji, Deqiang Zheng
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Abstract

Aims: Individuals with abnormal glucose metabolism are at a significantly higher risk of developing heart failure (HF). However, strategies for early identification of HF in this high-risk population remain inadequate. This study aimed to identify plasma protein biomarkers associated with HF development and construct predictive models to identify at-risk individuals.

Methods: We analyzed HF development in abnormal glucose metabolism population using data from 6,517 participants in discovery cohort and 2,783 in validation cohort, all from the UK Biobank, with no prior history of HF. Proteomic profiling was performed, and Lasso-Cox regression was used to identify protein associations, followed by Cox regression to develop predictive models. The model incorporated four proteins (NTproBNP, LTBP2, REN, GDF15) and clinical factors to create a protein-panel-clinical-factors (PPCF) model. For comparison, the model's performance was also evaluated in individuals with normal glucose metabolism.

Results: Over a median follow-up of 13.90 years, 555 incident HF cases were recorded in discovery cohort. The PPCF model achieved an AUC of 0.823 (95% CI: 0.785 - 0.860) in validation cohort, improving predictive performance by 0.05 (P < 0.001) compared to clinical factors-only model. In general population of 23,107 individuals, PPCF model obtained an AUC of 0.807 (95% CI: 0.786 - 0.829). Both protein panel model and PPCF model demonstrated superior net benefits over clinical factors model in abnormal glucose metabolism population.

Conclusion: This study identified plasma protein biomarkers linked to HF development in abnormal glucose metabolism population and established the predictive models. These findings support early identification in high-risk populations.

大规模血浆蛋白质组学在预测具有全范围糖代谢谱的个体心力衰竭中的价值。
目的:糖代谢异常的个体发生心力衰竭(HF)的风险显著增高。然而,在这一高危人群中早期识别心衰的策略仍然不足。本研究旨在确定与HF发展相关的血浆蛋白生物标志物,并构建预测模型来识别高危人群。方法:我们分析了糖代谢异常人群中HF的发展情况,使用了来自英国生物银行(UK Biobank)的6,517名参与者和2,783名验证队列的数据,这些参与者之前没有HF病史。进行蛋白质组学分析,使用Lasso-Cox回归确定蛋白质关联,然后使用Cox回归建立预测模型。该模型将4种蛋白(NTproBNP、LTBP2、REN、GDF15)和临床因子结合,建立蛋白-面板-临床因子(PPCF)模型。为了进行比较,还在葡萄糖代谢正常的个体中评估了该模型的性能。结果:在13.90年的中位随访中,发现队列中记录了555例心衰事件。PPCF模型在验证队列中的AUC为0.823 (95% CI: 0.785 - 0.860),与仅考虑临床因素的模型相比,预测性能提高了0.05 (P < 0.001)。在23,107个体的一般人群中,PPCF模型的AUC为0.807 (95% CI: 0.786 ~ 0.829)。在糖代谢异常人群中,蛋白组模型和PPCF模型均表现出优于临床因素模型的净效益。结论:本研究确定了与糖代谢异常人群HF发生相关的血浆蛋白生物标志物,并建立了预测模型。这些发现支持在高危人群中进行早期识别。
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来源期刊
European journal of preventive cardiology
European journal of preventive cardiology CARDIAC & CARDIOVASCULAR SYSTEMS-
CiteScore
12.50
自引率
12.00%
发文量
601
审稿时长
3-8 weeks
期刊介绍: European Journal of Preventive Cardiology (EJPC) is an official journal of the European Society of Cardiology (ESC) and the European Association of Preventive Cardiology (EAPC). The journal covers a wide range of scientific, clinical, and public health disciplines related to cardiovascular disease prevention, risk factor management, cardiovascular rehabilitation, population science and public health, and exercise physiology. The categories covered by the journal include classical risk factors and treatment, lifestyle risk factors, non-modifiable cardiovascular risk factors, cardiovascular conditions, concomitant pathological conditions, sport cardiology, diagnostic tests, care settings, epidemiology, pharmacology and pharmacotherapy, machine learning, and artificial intelligence.
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