Xie Ruijie, Seum Teresa, Sha Sha, Trares Kira, Holleczek Bernd, Brenner Hermann, Schöttker Ben
Division of Clinical Epidemiology and Aging Research, German Cancer Research Center, Im Neuenheimer Feld 581, 69120, Heidelberg, Germany.
Faculty of Medicine, University of Heidelberg, 69115, Heidelberg, Germany.
Cardiovasc Diabetol. 2025 Jan 13;24(1):18. doi: 10.1186/s12933-025-02581-3.
Existing cardiovascular risk prediction models still have room for improvement in patients with type 2 diabetes who represent a high-risk population. This study evaluated whether adding metabolomic biomarkers could enhance the 10-year prediction of major adverse cardiovascular events (MACE) in these patients.
Data from 10,257 to 1,039 patients with type 2 diabetes from the UK Biobank (UKB) and the German ESTHER cohort, respectively, were used for model derivation, internal and external validation. A total of 249 metabolites were measured with nuclear magnetic resonance (NMR) spectroscopy. Sex-specific LASSO regression with bootstrapping identified significant metabolites. The enhanced model's predictive performance was evaluated using Harrell's C-index.
Seven metabolomic biomarkers were selected by LASSO regression for enhanced MACE risk prediction (three for both sexes, three male- and one female-specific metabolite(s)). Especially albumin and the omega-3-fatty-acids-to-total-fatty-acids-percentage among males and lactate among females improved the C-index. In internal validation with 30% of the UKB, adding the selected metabolites to the SCORE2-Diabetes model increased the C-index statistically significantly (P = 0.037) from 0.660 to 0.678 in the total sample. In external validation with ESTHER, the C-index increase was higher (+ 0.043) and remained statistically significant (P = 0.011).
Incorporating seven metabolomic biomarkers in the SCORE2-Diabetes model enhanced its ability to predict MACE in patients with type 2 diabetes. Given the latest cost reduction and standardization efforts, NMR metabolomics has the potential for translation into the clinical routine.
现有的心血管疾病风险预测模型在2型糖尿病患者（这是一个高危人群）中仍有改进空间。本研究评估了添加代谢组学生物标志物是否能增强对这些患者主要不良心血管事件（MACE）的10年预测能力。
分别来自英国生物银行（UKB）的10257例和德国埃丝特队列的1039例2型糖尿病患者的数据用于模型推导、内部和外部验证。用核磁共振（NMR）光谱法测量了总共249种代谢物。采用带自助法的性别特异性套索回归确定显著的代谢物。使用哈雷尔C指数评估增强模型的预测性能。
通过套索回归选择了7种代谢组学生物标志物以增强MACE风险预测（3种为两性共有，3种为男性特异性，1种为女性特异性代谢物）。特别是白蛋白以及男性中的ω-3脂肪酸与总脂肪酸百分比和女性中的乳酸提高了C指数。在对UKB 30%的数据进行内部验证时，将选定的代谢物添加到SCORE2糖尿病模型中，总样本的C指数从0.660显著提高到0.678（P = 0.037）。在对埃丝特队列进行外部验证时，C指数的增加更高（+0.043）且仍具有统计学显著性（P = 0.011）。
将7种代谢组学生物标志物纳入SCORE2糖尿病模型可增强其对2型糖尿病患者MACE的预测能力。鉴于最新的成本降低和标准化努力，NMR代谢组学有转化为临床常规应用的潜力。
