Lv Yonghui, Li Qixian, Wang Xiuping, Tang Yong, Shi Yan, Qian Zhengting, Xin Heng, Wu He-Ming, Li Xiang
Department of Neurosurgery, Nanjing First Hospital, Nanjing Medical University, No.68 Changle Road, Nanjing, 210006, Jiangsu, People's Republic of China.
The Nursing Team of Qinhuai Medical Area of General Hospital of Eastern Theater Command, Nanjing, 210002, People's Republic of China.
Eur J Med Res. 2025 Aug 26;30(1):806. doi: 10.1186/s40001-025-03001-8.
Stroke is the second leading global cause of death/disability, with 85% being ischemic. DM increases Stroke prevalence and worsens outcomes. While the TyG Index shows inconsistent Stroke prediction in DM, chronic inflammation drives pathology. MAR, reflecting integrated inflammation status, has prognostic value but lacks evidence for association with Stroke prevalence across glycemic states (diabetes/prediabetes/non-diabetes). This NHANES study evaluates MAR-Stroke associations and compares predictive performance with TyG Index in DM.
This NHANES 2005-2018 analysis included 70,190 adults (≥ 20 years). After excluding participants with missing Stroke data or key variables, 15,679 were included in the analysis. We employed weighted multivariable logistic regression (stratified by diabetes/prediabetes/non-diabetes), RCS curves for nonlinearity, ROC analysis comparing MAR/ TyG Index discriminatory ability, and BMI mediation analysis. NHANES sampling weights and covariate adjustments for age, gender, race, education, and BMI were applied.
Based on a cross-sectional analysis of the 2005-2018 NHANES database (sample size n = 15,679; Stroke prevalence 2.9%), the findings indicate that: In the general population, each 1-unit increase in MAR was associated with a 13% higher prevalence of Stroke (adjusted OR = 1.13, 95% CI: 1.03,1.24). This association was stronger among diabetic patients (OR = 1.23, 95% CI: 1.08, 1.41) and exhibited a near-linear dose-response relationship (P for nonlinearity = 0.013).MAR showed superior potential biomarker for Stroke (AUC = 0.594) compared with TyG Index (AUC = 0.583) and LDL (AUC = 0.568). After multivariate adjustment, MAR achieved an AUC of 0.808 (95% CI: 0.793,0.823). Mediation analysis revealed that BMI mediated 15.6% of MAR's effect on Stroke prevalence (indirect effect β = 0.314, 95% CI: 0.136,0.508).
This NHANES analysis (N = 15,679) establishes elevated MAR as significantly associated with prevalent Stroke, outperforming the TyG Index especially in diabetics. Diabetic individuals in the highest MAR quartile exhibited 2.5-fold greater Stroke prevalence vs. the lowest quartile, with optimal prevalence discrimination at MAR > 0.152. BMI mediated 15.6% of this association, indicating modifiable pathways. Clinical translation requires prospective validation of the MAR threshold, confirmation of BMI-mediated mechanisms, and evidence for MAR-guided intervention efficacy. Provided sufficient longitudinal and interventional evidence is obtained, MAR shows promise as a dual-purpose tool for Stroke prevalence stratification and therapeutic monitoring along the inflammation-metabolism axis in high-risk populations, particularly diabetics.
中风是全球第二大致死/致残原因,85%为缺血性中风。糖尿病会增加中风患病率并使预后恶化。虽然TyG指数在糖尿病患者中对中风的预测结果不一致,但慢性炎症会推动病理发展。反映综合炎症状态的MAR具有预后价值,但缺乏关于其与不同血糖状态(糖尿病/糖尿病前期/非糖尿病)下中风患病率之间关联的证据。这项美国国家健康与营养检查调查(NHANES)研究评估了MAR与中风的关联,并在糖尿病患者中比较了其与TyG指数的预测性能。
这项对2005 - 2018年NHANES数据的分析纳入了70190名成年人(≥20岁)。在排除中风数据或关键变量缺失的参与者后,15679人被纳入分析。我们采用加权多变量逻辑回归(按糖尿病/糖尿病前期/非糖尿病分层)、用于分析非线性关系的限制立方样条(RCS)曲线、比较MAR/TyG指数鉴别能力的ROC分析以及BMI中介分析。应用了NHANES抽样权重以及对年龄、性别、种族、教育程度和BMI的协变量调整。
基于对2005 - 2018年NHANES数据库的横断面分析(样本量n = 15679;中风患病率2.9%),研究结果表明:在一般人群中,MAR每增加1个单位,中风患病率就会升高13%(调整后的OR = 1.13,95%CI:1.03,1.24)。这种关联在糖尿病患者中更强(OR = 1.23,95%CI:1.08,1.41),并且呈现出近似线性的剂量反应关系(非线性P值 = 0.013)。与TyG指数(AUC = 0.583)和低密度脂蛋白(LDL,AUC = 0.568)相比,MAR显示出更优的中风潜在生物标志物(AUC = 0.594)。经过多变量调整后,MAR的AUC为0.808(95%CI:0.793,0.823)。中介分析显示,BMI介导了MAR对中风患病率影响的15.6%(间接效应β = 0.314,95%CI:0.136,0.508)。
这项NHANES分析(N = 15679)确定MAR升高与中风患病率显著相关,尤其在糖尿病患者中表现优于TyG指数。MAR最高四分位数的糖尿病个体中风患病率是最低四分位数的2.5倍,当MAR > 0.152时患病率判别最佳。BMI介导了这种关联的15.6%,表明存在可改变的途径。临床转化需要对MAR阈值进行前瞻性验证,确认BMI介导的机制,以及MAR指导干预效果的证据。如果获得足够的纵向和干预性证据,MAR有望成为一种两用工具,用于在高危人群(尤其是糖尿病患者)中沿着炎症 - 代谢轴对中风患病率进行分层以及进行治疗监测。