2026 年第 6 期 第 21 卷

不同危险因素在动脉粥样硬化性心血管疾病风险预测中的价值分析

Value analysis of different risk factors in predicting the risk of atherosclerotic cardiovascular disease

作者：罗丹1郑煜博2杨伟1孟恬宇1郑文玲1任延平1

英文作者：Luo Dan1 Zheng Yubo2 Yang Wei1 Meng Tianyu1 Zheng Wenling1 Ren Yanping1

单位：1西安交通大学第一附属医院老年医学科，西安710061；2陕西省渭南市临渭区交斜中心卫生院内科，渭南714000

英文单位：1Department of Geriatrics The First Affiliated Hospital of Xi′an Jiaotong University Xi′an 710061 China; 2Department of Internal Medicine Jiaoxie Central Health Center Linwei District Weinan City Shaanxi Province Weinan 714000 China

关键词：动脉粥样硬化性心血管疾病；焦虑抑郁；全血细胞相关炎症指数

英文关键词：Atheroscleroticcardiovasculardisease;Anxietyanddepression;Completebloodcell-relatedinflammatoryindex

• 摘要：

• 目的 探讨健康管理中不同危险因素在动脉粥样硬化性心血管疾病（ASCVD）风险预测中的价值。方法 选取陕西省西安市某单位2023年2月1日—4月30日年度健康体检的1 679名职工的资料进行回顾性分析。收集相关资料，将纳入人群根据中国成年人ASCVD总体风险评估流程分为低危、中危、高危、极高危、超高危，其中低危、中危定义为非高危，高危、极高危、超高危定义为高危。比较高危人群和非高危人群一般资料，通过多因素Logistic回归方法 分析情绪因素（焦虑/抑郁状态）、炎症指标［全身免疫炎症指数（SII）、全身炎症反应指数（SIRI）和全身炎症聚集指数（AISI）］与ASCVD高危风险之间的关系，采用限制性立方样条模型分析炎症指标与ASCVD高危风险之间的潜在剂量-反应关联，采用受试者工作特征曲线和De-long检验评估炎症指标对ASCVD高危风险的诊断预测价值。结果 非高危人群（1 419名）和高危人群（260名）在性别、年龄、SII、SIRI、AISI、焦虑评分、抑郁评分等多个方面差异均有统计学意义（均P<0.05）。多因素Logistic回归分析显示，完全调整相关变量后，焦虑、抑郁评分与ASCVD高危风险无关（均P>0.05），SII、SIRI、AISI与ASCVD高危风险有关（均P<0.05），采用限制性立方样条模型分析结果显示SII（P总体<0.001，P非线性=0.074）与高危风险之间存在线性相关，SIRI（P总体<0.001，P非线性=0.024）、AISI（P总体<0.001，P非线性=0.006）与高危风险之间存在非线性相关。 SII、SIRI、AISI对ASCVD高危风险均有预测价值（均P<0.05），曲线下面积分别为0.562、0.630、0.611。结论 健康体检数据回顾性研究结果提示情绪因素(一定程度上)、全血细胞相关炎症指数对ASCVD高危风险具有预测价值。

• Objective To investigate the value of different risk factors in health management for predicting the risk of atherosclerotic cardiovascular disease (ASCVD). Methods Data of 1 679 employees who underwent annual physical examination in a unit in Xi′an City, Shaanxi Province from February 1 to April 30, 2023 were retrospectively analyzed. Relevant data were collected, and the included population was divided into low-risk, intermediate-risk, high-risk, very high-risk and ultra-high-risk groups according to the overall risk assessment process of ASCVD in Chinese adults. Low-risk and intermediate-risk were defined as non-high-risk, while high-risk, very high-risk and ultra-high-risk were defined as high-risk. General data were compared between high-risk and non-high-risk populations. Multivariate Logistic regression was used to analyze the relationship between emotional factors (anxiety/depression status), inflammatory indicators ［systemic immune-inflammation index (SII), systemic inflammatory response index (SIRI) and systemic inflammation aggregation index (AISI)］ and high ASCVD risk. Restricted cubic spline model was used to analyze the potential dose-response association between inflammatory indicators and high ASCVD risk. Receiver operating characteristic curve and De-long test were used to evaluate the diagnostic and predictive value of inflammatory indicators for high ASCVD risk. Results There were statistically significant differences between non-high-risk population (1 419 cases) and high-risk population (260 cases) in gender, age, SII, SIRI, AISI, anxiety score, depression score and other aspects (all P<0.05). Multivariate Logistic regression analysis showed that after fully adjusting relevant variables, anxiety and depression scores were not associated with high ASCVD risk (all P>0.05), while SII, SIRI and AISI were associated with high ASCVD risk (all P<0.05). Restricted cubic spline model analysis showed a linear correlation between SII (Poverall<0.001, Pnon-linear=0.074) and high risk, and nonlinear correlations between SIRI (Poverall<0.001, Pnon-linear=0.024), AISI (Poverall<0.001, Pnon-linear=0.006) and high risk. SII, SIRI and AISI all had predictive value for high ASCVD risk (all P<0.05), with areas under the curve of 0.562, 0.630 and 0.611, respectively. Conclusion The retrospective study of physical examination data suggests that emotional factors (to some extent) and complete blood cell-related inflammatory indexes have predictive value for high ASCVD risk.