Shen Min, Xie Qingya, Zhang Ruizhe, Yu Chunjing, Xiao Pingxi
Department of Endocrinology and Metabolism, The First Affiliated Hospital of Nanjing Medical University, Nanjing, Jiangsu, China.
Department of Cardiology, The Fourth Affiliated Hospital, Nanjing Medical University, Nanjing, China.
Front Pharmacol. 2023 Mar 24;14:1175021. doi: 10.3389/fphar.2023.1175021. eCollection 2023.
Patients with diabetes have a two-to four-fold increased incidence of cardiovascular diseases compared with non-diabetics. Currently, there is no recognized model to predict the occurrence and progression of CVDs in diabetics. This work aimed to develop a metabolic biomarker-assisted model, a combination of metabolic markers with clinical variables, for risk prediction of CVDs in diabetics. A total of 475 patients with diabetes were studied. Each patient underwent coronary angiography. Plasma samples were analyzed by liquid chromatography-quadrupole time-of-flight mass spectrometry. Ordinal logistic regression and random forest were used to screen metabolites. Receiver operating characteristic (ROC) curve, nomogram, and decision curve analysis (DCA) were employed to evaluate their prediction performances. Ordinal logistic regression screened out 34 differential metabolites (adjusted-false discovery rate < 0.05) from 2059 ion features by comparisons of diabetics with and without CVDs. Random forest identified methylglutarylcarnitine and lysoPC (18:0) as the metabolic markers (mean decrease gini >1.0) for non-significant CVDs (nos-CVDs) normal coronary artery (NCA), 1,3-Octadiene and 3-Octanone for acute coronary syndrome (ACS) nos-CVDs, and lysoPC (18:0) for acute coronary syndrome normal coronary artery. For risk prediction, the metabolic marker-assisted models provided areas under the curve of 0.962-0.979 by ROC (0.576-0.779 for the base models), and c-indices of 0.8477-0.9537 by nomogram analysis (0.1514-0.5196 for the base models). Decision curve analysis (DCA) showed that the models produced greater benefits throughout a wide range of risk probabilities compared with the base model. Metabolic biomarker-assisted model remarkably improved risk prediction of cardiovascular disease in diabetics (>90%).
与非糖尿病患者相比，糖尿病患者心血管疾病的发病率增加了两到四倍。目前，尚无公认的模型可用于预测糖尿病患者心血管疾病的发生和进展。这项工作旨在开发一种代谢生物标志物辅助模型，即代谢标志物与临床变量的组合，用于预测糖尿病患者心血管疾病的风险。共研究了475例糖尿病患者。每位患者均接受了冠状动脉造影。血浆样本通过液相色谱-四极杆飞行时间质谱进行分析。采用有序逻辑回归和随机森林筛选代谢物。采用受试者工作特征（ROC）曲线、列线图和决策曲线分析（DCA）评估其预测性能。通过比较有和没有心血管疾病的糖尿病患者，有序逻辑回归从2059个离子特征中筛选出34种差异代谢物（校正后错误发现率<0.05）。随机森林将甲基谷氨酰肉碱和溶血磷脂酰胆碱（18:0）确定为非显著性心血管疾病（无心血管疾病）、正常冠状动脉（NCA）的代谢标志物（平均基尼系数下降>1.0），将1,3-辛二烯和3-辛酮确定为急性冠状动脉综合征（ACS）、无心血管疾病的代谢标志物，将溶血磷脂酰胆碱（18:0）确定为急性冠状动脉综合征、正常冠状动脉的代谢标志物。对于风险预测，代谢标志物辅助模型通过ROC得到的曲线下面积为0.962-0.979（基础模型为0.576-0.779），通过列线图分析得到的c指数为0.8477-0.9537（基础模型为0.1514-0.5196）。决策曲线分析（DCA）表明，与基础模型相比该模型在广泛的风险概率范围内产生了更大的益处。代谢生物标志物辅助模型显著改善了糖尿病患者心血管疾病的风险预测（>90%）。
