Marcelino Bermudez-Lopez1, Carles Forne2,3, Nuria Amigo4, Milica Bozic1, David Arroyo1,5, Teresa Bretones6, Nuria Alonso7,8, Serafi Cambray1, Maria Dolores Del Pino9, Didac Mauricio1,8,10, Jose Luis Gorriz11, Elvira Fernandez1, Jose Manuel Valdivielso1. 1. a Vascular & Renal Translational Research Group , IRBLleida, Spain and Spanish Research Network for Renal Diseases (RedInRen. ISCIII) , Lleida , Spain. 2. b Biostatistics Unit , IRBLleida , Lleida , Spain. 3. c Department of Basic Medical Sciences , University of Lleida , Lleida , Spain. 4. d Biosfer Teslab SL , Reus , Spain. 5. e Servicio de nefrología , Hospital Universitario Severo Ochoa , Leganés , Spain. 6. f Department of Cardiology , Hospital Universitario Puerta del Mar , Cádiz , Spain. 7. g Endocrinology and Nutrition Department , Hospital Universitari Germans Trias i Pujol , Badalona , Spain. 8. h Center for Biomedical Research on Diabetes and Associated Metabolic Diseases (CIBERDEM) , Barcelona , Spain. 9. i Department of Nephrology , Centro Hospitalario Torrecardenas , Almeria , Spain. 10. j Endocrinology and Nutrition Department , Hospital de la Santa Creu i Sant Pau , Barcelona , Spain. 11. k Hospital Clínico Universitario Valencia , Universitat de Valencia, INCLIVA , Lleida , Spain.
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% CI), .789 (.711, .853) vs .687 (.611, .755). Conclusions: non-diabetic CKD patients have a hidden proatherogenic lipoprotein profile.
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% CI), .789 (.711, .853) vs .687 (.611, .755). Conclusions: non-diabetic CKD patients have a hidden proatherogenic lipoprotein profile.
Authors: Vicente Pallares-Carratalá; José Manuel Valdivielso; José Luis Gorriz; David Arroyo; Luis D'Marco; Roser Torra; Patricia Tomás; María Jesús Puchades; Nayara Panizo; Jonay Pantoja; Marco Montomoli; José Luis Llisterri Journal: BMC Nephrol Date: 2021-03-25 Impact factor: 2.388
Authors: Borja Quiroga; Patricia Muñoz Ramos; Ana Sánchez Horrillo; Alberto Ortiz; José Manuel Valdivieso; Juan Jesús Carrero Journal: Clin Kidney J Date: 2022-03-10