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An in-depth analysis shows a hidden atherogenic lipoprotein profile in non-diabetic chronic kidney disease patients

机译：深入的分析显示了非糖尿病慢性肾病患者的隐藏致动脂蛋白概况

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ABSTRACT Background: Chronic kidney disease (CKD) is an independent risk factor for atherosclerotic disease. We hypothesized that CKD promotes a proatherogenic lipid profile modifying lipoprotein composition and particle number. Methods: Cross-sectional study in 395 non-diabetic individuals (209 CKD patients and 186 controls) without statin therapy. Conventional lipid determinations were combined with advanced lipoprotein profiling by nuclear magnetic resonance, and their discrimination ability was assessed by machine learning. Results: CKD patients showed an increase of very-low-density (VLDL) particles and a reduction of LDL particle size. Cholesterol and triglyceride content of VLDLs and intermediate-density (IDL) particles increased. However, low-density (LDL) and high-density (HDL) lipoproteins gained triglycerides and lost cholesterol. Total-Cholesterol, HDL-Cholesterol, LDL-Cholesterol, non-HDL-Cholesterol and Proprotein convertase subtilisin-kexin type (PCSK9) were negatively associated with CKD stages, whereas triglycerides, lipoprotein(a), remnant cholesterol, and the PCSK9/LDL-Cholesterol ratio were positively associated. PCSK9 was positively associated with total-Cholesterol, LDL-Cholesterol, LDL-triglycerides, LDL particle number, IDL-Cholesterol, and remnant cholesterol. Machine learning analysis by random forest revealed that new parameters have a higher discrimination ability to classify patients into the CKD group, compared to traditional parameters alone: area under the ROC curve (95% Cl), .789 (.711, .853) vs .687 (.611,755).Conclusions: non-diabetic CKD patients have a hidden proatherogenic lipoprotein profile.
机译：摘要背景：慢性肾病（CKD）是动脉粥样硬化疾病的独立危险因素。我们假设CKD促进了改性脂蛋白组合物和颗粒数的亲素脂质曲线。方法：395名非糖尿病个体（209例CKD患者和186名对照）的横截面研究，没有他汀类药物治疗。将常规的脂质测定与通过核磁共振的晚期脂蛋素分析结合，并且通过机器学习评估了它们的辨别能力。结果：CKD患者显示出非常低密度（VLDL）颗粒的增加和LDL粒度的降低。胆固醇和甘油三酯含量的VLDL和中间密度（IDL）颗粒增加。然而，低密度（LDL）和高密度（HDL）脂蛋白获得甘油三酯并丧失胆固醇。总胆固醇，HDL-胆固醇，LDL-胆固醇，非HDL-胆固醇和先前转化酶枯草杆菌蛋白-Kexin型（PCSK9）与CKD阶段负相关，而甘油三酯，脂蛋白（A），残余胆固醇和PCSK9 / LDL是负相关的。 - 富含硫醇比率正相关。 PCSK9与总胆固醇，LDL-胆固醇，LDL-甘油三酯，LDL颗粒数，碘胆固醇和残余胆固醇呈正相关。随机森林的机器学习分析显示，与单独的传统参数相比，新参数将患者分类为CKD组的歧视能力较高：ROC曲线下的面积（95％CL），.789（.711，。853）vs .687（.611,755）。结论：非糖尿病CKD患者具有隐藏的亲素脂蛋白谱。

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