Xiao Erya, Yu Ronghui, Cai Xinyuan, Jiang Lang, Li Junhong, Ma Cong, Liu Yuankang, Liu Le, Su Guanghao, Wang Xiaodong
Children's Hospital of Soochow University, Suzhou, 215000, China.
Center of Clinical Laboratory and Translational Medicine, Suzhou Dushu Lake Hospital, The Fourth Affiliated Hospital of Soochow University, Suzhou, 215028, China.
Lipids Health Dis. 2025 Feb 11;24(1):45. doi: 10.1186/s12944-025-02445-5.
The prevalence of prediabetes among adults in the U.S. is three times higher than that of diabetes, highlighting a greater disease burden. Both diabetes and prediabetes have been demonstrated to be associated with an increased risk of cardiovascular disease (CVD). However, research has primarily focused on diabetes, with limited attention to CVD risk prediction in prediabetes. Emerging 13 metabolic health-related indicators have been proposed to optimize the predictive effect on CVD risk in patients with prediabetes. This study aimed to compare the predictive efficacy of these biomarkers and further develop a nomogram to improve predictive performance of the CVD risk in patients with prediabetes.
All eligible participants in the National Health and Nutrition Examination Survey (NHANES) 1999-2020 were enrolled in this study and randomly assigned to the development and validation cohorts in a ratio of 7:3. In the development cohort, the efficacy of 13 indicators used to predict the CVD risk was assessed by receiver operative characteristic (ROC) curves. Independent risk predictors identified by multivariate logistic regression were used to construct a nomogram, and internal and external validation were further implemented.
The ROC curve demonstrated that the triglyceride-glucose (TyG) index was an effective predictor of CVD risk [area under the curve (AUC) = 0.694] and exhibited the best predictive performance among the 13 metabolic health-related indices. Based on independent risk factors identified by multivariate logistic regression, the CVD risk nomogram [including age, gender, hypertension, TyG, stress hyperglycemia ratio (SHR), and neutrophil-to-lymphocyte ratio (NLR)] was successfully constructed and validated with good performance (AUCs/C-indexes > 0.70 for all).
This study developed a reliable nomogram for predicting CVD risk in patients with prediabetes. The model demonstrated robust performance and offered a simple yet individualized approach for predicting the CVD risk in patients with prediabetes.
美国成年人中糖尿病前期的患病率是糖尿病的三倍，凸显了更大的疾病负担。糖尿病和糖尿病前期均已被证明与心血管疾病（CVD）风险增加相关。然而，研究主要集中在糖尿病，对糖尿病前期的CVD风险预测关注有限。已提出13种与代谢健康相关的新兴指标，以优化对糖尿病前期患者CVD风险的预测效果。本研究旨在比较这些生物标志物的预测效能，并进一步开发一种列线图，以提高糖尿病前期患者CVD风险的预测性能。
纳入1999 - 2020年美国国家健康与营养检查调查（NHANES）中所有符合条件的参与者，并按7:3的比例随机分配至开发队列和验证队列。在开发队列中，通过受试者工作特征（ROC）曲线评估用于预测CVD风险的13项指标的效能。通过多变量逻辑回归确定的独立风险预测因子用于构建列线图，并进一步进行内部和外部验证。
ROC曲线表明，甘油三酯 - 葡萄糖（TyG）指数是CVD风险的有效预测指标[曲线下面积（AUC） = 0.694]，并且在13种与代谢健康相关的指标中表现出最佳的预测性能。基于多变量逻辑回归确定的独立危险因素，成功构建并验证了CVD风险列线图[包括年龄、性别、高血压、TyG、应激性高血糖比率（SHR）和中性粒细胞与淋巴细胞比率（NLR）]，其性能良好（所有AUC/C指数> 0.70）。
本研究开发了一种可靠的列线图，用于预测糖尿病前期患者的CVD风险。该模型表现出强大的性能，并为预测糖尿病前期患者的CVD风险提供了一种简单且个性化的方法。
