腔隙性脑梗死患者血浆同型半胱氨酸和血脂水平检测的意义

目的探讨腔隙性脑梗死(LS)与血浆同型半胱氨酸(Hcy)和血脂水平的关系。方法采用全自动生化分析仪测定99例LS患者和242例对照血浆中Hcy、总胆固醇(TC)、三酰甘油(TG)、高密度脂蛋白胆固醇(HDL-C)、低密度脂蛋白胆固醇(LDL-C)、载脂蛋白A1(ApoA1),载脂蛋白B(ApoB)和Lp(a)水平并进行主成分分析(PCA)和Logistic回归分析。结果 LS患者吸烟、高血压、糖尿病的比例,血浆Hcy、TC、TG、ApoB和Lp(a)水平均显著高于对照组(P0.05),ApoA1水平显著低于对照组(P0.05)。PCA模型表明,LS患者血浆Hcy和血脂代谢明显紊乱且与对照组有分离趋势。Logistic回归模型能准确预测82.8%的LS患者和95.9%的对照,并提示高血压和血浆Hcy的比值比(OR)分别为(8.45,P0.05)和(2.47,P0.05)。结论高血压和血浆Hcy水平升高均是LS的独立危险因素。基于血浆Hcy和血脂水平的PCA和Logistic回归模型有助于LS的辅助诊断和预测分析。

Significance of plasma homocysteine and lipids level detection in patients with lacunar cerebral infarction

Objective To study the relationship between the lacunar cerebral infarction (LS) and plasma homocysteine (Hcy) and lipids levels .Methods The plasma Hcy ,total cholesterin(TC) ,triglyceride(TG) ,high density lipoprotein cholesterol(HDL‐C) ,low density lipoprotein cholesterol(LDL‐C) ,apolipoprotein A1(ApoA1) ,apolipoprotein B(ApoB) and Lp(a) levels in 99 pa‐tients with LS and 242 controls were measured by the automatic biochemical analyzer .Then the principle component analysis (PCA) and Logistic regression analysis were performed .Results The proportions of smoking ,hypertension and diabetes and the plasma Hcy ,TC ,TG ,ApoB and Lp(a) levels in the LS patients were significantly higher than those in the control group (P <0 .05) ,and the plasma ApoA1 level was significantly lower than that in the control group (P< 0 .05) .The PCA model revealed that there were obvious disorders of plasma Hcy level and blood lipids metabolism in the patients with LS and a separation tendency from the control group .The Logistic regression model could accurately predict 82 .8% of LS patients and 95 .9% of controls ,and revealed that the odds ratio (OR) of hypertension and plasma Hcy level was 8 .45(P< 0 .05) and 2 .47(P< 0 .05) respectively .Con‐clusion Hypertension and the increase of plasma Hcy level are the independent risk factors of LS .The regression models of PCA and Logistic based on level of plasma Hcy and lipids are helpful for the assisted diagnosis and prediction of LS .