代谢风险评分在2型糖尿病人群心血管结局预测中的应用

doi:10.6040/j.issn.1671-7554.0.2025.0119

摘要/Abstract

摘要： 目的识别2型糖尿病人群中分别与心肌梗死、心力衰竭和缺血性脑卒中相关的代谢物,进而构建代谢风险评分,并评价将代谢风险评分纳入传统临床模型是否能提高模型预测准确性。方法利用英国生物银行队列数据,应用多变量Cox比例风险回归模型筛选出分别与心肌梗死、心力衰竭和缺血性脑卒中相关的代谢物,并进行时间截断的敏感性分析。随后,基于LightGBM算法构建代谢风险评分,最后将评分纳入传统临床模型,进行模型评价。结果多变量Cox比例风险回归模型和时间截断的敏感性分析共发现了分别与心肌梗死、心力衰竭和缺血性脑卒中相关的119种、77种和12种代谢物,随后利用这些代谢物构建代谢风险评分。将构建的代谢风险评分纳入传统临床模型后,模型的预测性能显著提高,3种疾病的预测模型AUC提高到0.804、0.900和0.844,与仅利用传统临床模型相比,分别提高了0.145、0.198和0.188。此外,敏感性和特异性结果表明,这3个模型具有较高的预测准确性(敏感性:0.706、0.804、0.801;特异性:0.763、0.861、0.722),校准曲线和决策曲线同样显示模型具有较好的预测性能。结论将代谢风险评分纳入传统临床模型可以显著提高2型糖尿病人群心血管疾病的预测准确性。

关键词: 2型糖尿病, 心肌梗死, 心力衰竭, 缺血性脑卒中, LightGBM, 代谢风险评分, 预测模型

Abstract: Objective To identify metabolites associated with myocardial infarction, heart failure, and ischemic stroke respectively in a population with type 2 diabetes mellitus, thereby constructing a metabolomic risk score and evaluating whether incorporation of the metabolomic risk score into a traditional clinical model improves the models predictive accuracy. Methods Using data from the UK Biobank cohort, a multivariate Cox proportional risk regression model was applied to screen for metabolites associated with myocardial infarction, heart failure, and ischemic stroke, respectively, and a time-truncated sensitivity analysis was performed. Subsequently, metabolomic risk scores were constructed based on the LightGBM algorithm, and finally the scores were incorporated into traditional clinical models for model evaluation. Results Multivariate Cox proportional risk regression models and time-truncated sensitivity analyses identified a total of 119, 77, and 12 metabolites associated with myocardial infarction, heart failure, and ischemic stroke, respectively, and these metabolites were then used to construct metabolomic risk scores. Incorporation of the constructed metabolomic risk scores into traditional clinical models significantly improved model prediction performance, with model AUCs improving to 0.804, 0.900, and 0.844 for the three diseases respectively, and the improvements were 0.145, 0.198, and 0.188, compared to utilizing only traditional clinical models. In addition, the sensitivity and specificity results showed that the three models had high prediction accuracy(sensitivity: 0.706, 0.804, 0.801; specificity: 0.763, 0.861, 0.722), and the calibration curves and decision curves likewise showed that the models had good prediction performance. Conclusion Incorporation of metabolomic risk scores into traditional clinical models can significantly improve the predictive accuracy of cardiovascular disease in the type 2 diabetes mellitus population.

Key words: Type 2 diabetes mellitus, Myocardial infarction, Heart failure, Ischemic stroke, LightGBM, Metabolomic risk score, Predictive modeling

中图分类号:

R743.3

申路佳,逯天威,巩伟明,赵岩松,王淑康,袁中尚. 代谢风险评分在2型糖尿病人群心血管结局预测中的应用[J]. 山东大学学报 (医学版), 2025, 63(8): 69-78.

SHEN Lujia, LU Tianwei, GONG Weiming, ZHAO Yansong, WANG Shukang, YUAN Zhongshang. Application of metabolomic risk score in predicting cardiovascular outcomes in patients with type 2 diabetes mellitus[J]. Journal of Shandong University (Health Sciences), 2025, 63(8): 69-78.

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