Journal of Atherosclerosis and Thrombosis
Online ISSN : 1880-3873
Print ISSN : 1340-3478
ISSN-L : 1340-3478
Association of Small Dense Low-Density Lipoprotein Cholesterol with Stroke Risk, Severity and Prognosis
Peiyang ZhouJincheng LiuLingyun WangWenmin FengZhihua CaoPu WangGuangzhi LiuChenglin SunYan ShenLijun WangJiahuan XuPeng MengZiwei LiWang-yang XuXifa Lan
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JOURNALS FREE ACCESS Advance online publication

Article ID: 53132


Aim: To investigate the association of small dense low-density lipoprotein cholesterol (sdLDL-C) and acute ischemic stroke (AIS) in terms of risk, severity, and outcomes. Prediction models were established to screen high-risk patients and predict prognosis of AIS patients.

Methods: We enrolled in this study 355 AIS patients and 171 non-AIS controls. AIS was subtyped according to TOAST criteria, and the severity and outcomes of AIS were measured. Blood glucose and lipid profiles including total cholesterol, triglyceride, and lipoproteins were measured in all patients using automatic measure. Lipoprotein subfractions were detected by the Lipoprint LDL system.

Results: As compared with the non-AIS control group, the AIS group had higher sdLDL-C levels. Pearson correlation analysis revealed that the sdLDL-C level and risk of AIS, especially non-cardioembolic stroke, were positively correlated. The area under the curve of sdLDL-C for AIS risk was 0.665, better than that of other lipids. Additionally, the sdLDL-C level was significantly correlated with AIS severity and bad outcomes. A logistic regression model for assessing the probability of AIS occurrence and a prognostic prediction model were established based on sdLDL-C and other variables.

Conclusions: Elevated levels of sdLDL-C were associated with a higher prevalence of AIS, especially in non-cardioembolic stroke subtypes. After adjustment for other risk factors, sdLDL-C was found to be an independent risk factor for AIS. Also, sdLDL-C level was strongly associated with AIS severity and poor functional outcomes. Logistic regression models for AIS risk and prognosis prediction were established to help clinicians provide better prevention for high-risk subjects and monitor their prognosis.

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