Research on the association between non-traditional lipid parameters and intracranial-extracranial artery stenosis in stroke patients

Objectives: Study the association between non-traditional lipid parameters and intracranial and extracranial arterial stenosis in stroke patients. Materials and Methods: Exploring the predictive ability of non-traditional lipid parameters for cerebral arterial stenosis (CAS) using different statistical methods. Results: Compared to controls, CAS patients exhibited significantly lower levels of triglycerides, atherogenic coefficient, Castelli’s Risk Index I, and remnant cholesterol (RC) ( P < 0.001), while total cholesterol, high-density lipoprotein cholesterol (HDL-C), low-density lipoprotein cholesterol (LDL-C), and nonHDL-C levels were elevated ( P < 0.001). Significant subgroup differences were observed for non-HDL-C and RC within the CAS group ( P < 0.001), whereas conventional lipid parameters did not show such variations. Strong correlations were identified among various lipid markers ( P < 0.001). Logistic regression analysis confirmed a significant association between lipid parameters and CAS. A restricted cubic spline model demonstrated a non-linear relationship between RC levels and CAS risk. Receiver operating characteristic curve analysis revealed that non-HDL-C had the highest predictive accuracy (area under the curve = 0.655, 95% confidence interval: 0.606–0.703), indicating its strong potential as a biomarker for CAS. Conclusion: Non-traditional lipid parameters, particularly non-HDL-C and RC, exhibit significant differences across intracranial, extracranial, and combined intracranial-extracranial arterial stenosis. Moreover, non-HDL-C shows the strongest predictive value for CAS risk, suggesting its potential role in routine clinical assessments. Incorporating non-traditional lipid parameters into standard lipid profiling may enhance early detection and risk stratification of CAS.