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.
Lv et al. (Tue,) studied this question.