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.
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.
Authors: Jian Liu; Christopher T Sempos; Richard P Donahue; Joan Dorn; Maurizio Trevisan; Scott M Grundy Journal: Am J Cardiol Date: 2006-09-29 Impact factor: 2.778
Authors: Jelena Vekic; Aleksandra Zeljkovic; Arrigo F G Cicero; Andrej Janez; Anca Pantea Stoian; Alper Sonmez; Manfredi Rizzo Journal: Medicina (Kaunas) Date: 2022-02-16 Impact factor: 2.430
Authors: Arcangelo Iannuzzi; Paolo Rubba; Marco Gentile; Vania Mallardo; Ilenia Calcaterra; Alessandro Bresciani; Giuseppe Covetti; Gianluigi Cuomo; Pasquale Merone; Anna Di Lorenzo; Roberta Alfieri; Emilio Aliberti; Francesco Giallauria; Matteo Nicola Dario Di Minno; Gabriella Iannuzzo Journal: Biomedicines Date: 2021-05-06