1
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Bagdade JD, McCurdy CE. Conventional HDL Subclass Measurements Mask Thyroid Hormone-dependent Remodeling Activity Sites in Hypothyroid Individuals. J Endocr Soc 2024; 8:bvae018. [PMID: 38379854 PMCID: PMC10877315 DOI: 10.1210/jendso/bvae018] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 10/03/2023] [Indexed: 02/22/2024] Open
Abstract
Context Earlier nuclear magnetic resonance spectroscopy (NMR) studies of plasma lipoproteins estimated by size as small, medium, and large particles, demonstrated hypothyroidism was associated with increases in very low-density lipoprotein (VLDL), low-density lipoprotein (LDL), and intermediate-density lipoprotein (IDL) subclass particle number but variable changes in the high-density lipoprotein (HDL) subclasses. These disparate changes in HDL might be explained by reduced activity of the thyroid hormone-dependent remodeling proteins whose subclass specificity may be obscured when the 5 HDL subclasses identified by NMR are combined by size. Objective This work aimed to determine whether directional changes in particle number of individually measured HDL subclasses correlate with reduced activity of their thyroid hormone-dependent remodeling proteins in hypothyroid individuals. Methods VLDL, LDL, IDL, and HDL subclasses were measured by NMR in 13 thyroidectomized individuals 1 month following thyroid hormone withdrawal and 3 months after replacement. Changes in particle numbers in each subclass were compared when expressed individually and by size. Results Following thyroid hormone withdrawal, plasma lipids and VLDL, LDL, and IDL subclass particle number increased. HDL particle number nearly doubled in very small HDL-1 (P = .04), declined in small HDL-2 (P = .02), and increased 2-fold in HDL-5 (P = .0009). Conclusion The increment in HDL-1 and decline in HDL-2 subclasses is consistent with their precursor-product relationship and reduced lecithin cholesterol acyltransferase activity while the almost 2-fold increase in large HDL-5 is indicative of diminished action of hepatic lipase, phospholipid transfer protein, and endothelial lipase. These findings are inapparent when the 5 subclasses are expressed conventionally by size. This linking of specific HDL subclasses with HDL remodeling protein function provides new details about the specificity of their interactions.
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Affiliation(s)
- John D Bagdade
- Department of Human Physiology, University of Oregon, Eugene, OR 97403, USA
| | - Carrie E McCurdy
- Department of Human Physiology, University of Oregon, Eugene, OR 97403, USA
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2
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Lucero D, Wolska A, Aligabi Z, Turecamo S, Remaley AT. Lipoprotein Assessment in the twenty-first Century. Endocrinol Metab Clin North Am 2022; 51:459-481. [PMID: 35963624 PMCID: PMC9382697 DOI: 10.1016/j.ecl.2022.02.009] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/21/2022]
Abstract
Based on decades of both basic science and epidemiologic research, there is overwhelming evidence for the causal relationship between high levels of cholesterol, especially low-density lipoprotein cholesterol and cardiovascular disease. Risk evaluation and monitoring the response to lipid-lowering therapies are heavily dependent on the accurate assessment of plasma lipoproteins in the clinical laboratory. This article provides an update of lipoprotein metabolism as it relates to atherosclerosis and how diagnostic measures of lipids and lipoproteins can serve as markers of cardiovascular risk, with a focus on recent advances in cardiovascular risk marker testing.
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Affiliation(s)
- Diego Lucero
- Lipoprotein Metabolism Laboratory, Translational Vascular Medicine Branch, National Heart, Lung, and Blood Institute, National Institutes of Health, 9000 Rockville Pike, Building 10, Room 5D09, Bethesda, MD 20892, USA.
| | - Anna Wolska
- Heart Disease Phenomics Laboratory, Epidemiology and Community Health Branch, National Heart, Lung, and Blood Institute. National Institutes of Health, 9000 Rockville Pike, Building 10, Room 5N323, Bethesda, MD 20892, USA
| | - Zahra Aligabi
- Lipoprotein Metabolism Laboratory, Translational Vascular Medicine Branch, National Heart, Lung, and Blood Institute, National Institutes of Health, 9000 Rockville Pike, Building 10, Room 5D09, Bethesda, MD 20892, USA
| | - Sarah Turecamo
- Heart Disease Phenomics Laboratory, Epidemiology and Community Health Branch, National Heart, Lung, and Blood Institute. National Institutes of Health, 9000 Rockville Pike, Building 10, Room 5N323, Bethesda, MD 20892, USA
| | - Alan T Remaley
- Lipoprotein Metabolism Laboratory, Translational Vascular Medicine Branch, National Heart, Lung, and Blood Institute, National Institutes of Health, 9000 Rockville Pike, Building 10, Room 5D09, Bethesda, MD 20892, USA
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3
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Wishart DS, Cheng LL, Copié V, Edison AS, Eghbalnia HR, Hoch JC, Gouveia GJ, Pathmasiri W, Powers R, Schock TB, Sumner LW, Uchimiya M. NMR and Metabolomics-A Roadmap for the Future. Metabolites 2022; 12:678. [PMID: 35893244 PMCID: PMC9394421 DOI: 10.3390/metabo12080678] [Citation(s) in RCA: 43] [Impact Index Per Article: 21.5] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/29/2022] [Revised: 07/21/2022] [Accepted: 07/21/2022] [Indexed: 12/03/2022] Open
Abstract
Metabolomics investigates global metabolic alterations associated with chemical, biological, physiological, or pathological processes. These metabolic changes are measured with various analytical platforms including liquid chromatography-mass spectrometry (LC-MS), gas chromatography-mass spectrometry (GC-MS) and nuclear magnetic resonance spectroscopy (NMR). While LC-MS methods are becoming increasingly popular in the field of metabolomics (accounting for more than 70% of published metabolomics studies to date), there are considerable benefits and advantages to NMR-based methods for metabolomic studies. In fact, according to PubMed, more than 926 papers on NMR-based metabolomics were published in 2021-the most ever published in a given year. This suggests that NMR-based metabolomics continues to grow and has plenty to offer to the scientific community. This perspective outlines the growing applications of NMR in metabolomics, highlights several recent advances in NMR technologies for metabolomics, and provides a roadmap for future advancements.
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Affiliation(s)
- David S. Wishart
- Departments of Biological Sciences and Computing Science, University of Alberta, Edmonton, AB T6G 2E9, Canada
| | - Leo L. Cheng
- Department of Pathology, Department of Radiology, Massachusetts General Hospital, Harvard Medical School, Boston, MA 02114, USA;
| | - Valérie Copié
- Department of Chemistry and Biochemistry, Montana State University, Bozeman, MT 59715, USA;
| | - Arthur S. Edison
- Complex Carbohydrate Research Center, University of Georgia, Athens, GA 30602, USA; (A.S.E.); (G.J.G.); (M.U.)
- Department of Biochemistry & Molecular Biology, University of Georgia, Athens, GA 30602-0001, USA
| | - Hamid R. Eghbalnia
- Department of Molecular Biology and Biophysics, UConn Health, Farmington, CT 06030-3305, USA; (H.R.E.); (J.C.H.)
| | - Jeffrey C. Hoch
- Department of Molecular Biology and Biophysics, UConn Health, Farmington, CT 06030-3305, USA; (H.R.E.); (J.C.H.)
| | - Goncalo J. Gouveia
- Complex Carbohydrate Research Center, University of Georgia, Athens, GA 30602, USA; (A.S.E.); (G.J.G.); (M.U.)
- Department of Biochemistry & Molecular Biology, University of Georgia, Athens, GA 30602-0001, USA
| | - Wimal Pathmasiri
- Nutrition Research Institute, Department of Nutrition, School of Public Health, University of North Carolina at Chapel Hill, Chapel Hill, NC 27599, USA;
| | - Robert Powers
- Department of Chemistry, University of Nebraska-Lincoln, Lincoln, NE 68588-0304, USA
- Nebraska Center for Integrated Biomolecular Communication, University of Nebraska-Lincoln, Lincoln, NE 68588-0304, USA
| | - Tracey B. Schock
- National Institute of Standards and Technology (NIST), Chemical Sciences Division, Charleston, SC 29412, USA;
| | - Lloyd W. Sumner
- Interdisciplinary Plant Group, MU Metabolomics Center, Bond Life Sciences Center, Department of Biochemistry, University of Missouri, Columbia, MO 65211, USA
| | - Mario Uchimiya
- Complex Carbohydrate Research Center, University of Georgia, Athens, GA 30602, USA; (A.S.E.); (G.J.G.); (M.U.)
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4
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Development of internal standard for lipoprotein subclass analysis using dual detection gel-permeation high-performance liquid chromatography system. Biosci Rep 2022; 42:231316. [PMID: 35583205 PMCID: PMC9160529 DOI: 10.1042/bsr20220291] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/03/2022] [Revised: 04/26/2022] [Accepted: 05/18/2022] [Indexed: 11/20/2022] Open
Abstract
The LipoSEARCH® System is an innovative lipoprotein class analysis method based on gel-permeation high-performance liquid chromatography (HPLC). This system uses a gel permeation column to separate the major lipoprotein subclasses (chylomicron, very low-density lipoprotein, low-density lipoprotein, and high-density lipoprotein) in serum according to particle size and splits them into two pathways to measure total cholesterol (TC; esterified + unesterified cholesterol) and triglyceride (TG) concentrations simultaneously to obtain chromatograms for each. These chromatograms were analyzed based on the results of the calibration serum by fitting Gaussian curves to profile the 20 lipoprotein subclasses defined in detail. An important assumption of this HPLC system is its simultaneous detection of two pathways to guarantee the accuracy of each analysis. Therefore, in the present study, we investigated the development of an internal standard that can guarantee the simultaneous detection of this system by adding a pigment to the serum. We focused on quinone pigments with absorption at 550 nm, which is the wavelength used for the enzymatic assay of TC and TG concentrations in the system. As a result, we succeeded in producing overlapping pigment peaks that appeared after the analytical chromatograms in two pathways. It is also suggested that the pigment solution as an internal standard is stable in freezing storage and has little effect on the analysis. The developed internal standard is expected to contribute to the accuracy assurance of lipoprotein analysis by this dual-detection HPLC system.
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5
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Rojo-López MI, Castelblanco E, Real J, Hernández M, Falguera M, Amigó N, Julve J, Alonso N, Franch-Nadal J, Granado-Casas M, Mauricio D. Advanced Quantitative Lipoprotein Characteristics Do Not Relate to Healthy Dietary Patterns in Adults from a Mediterranean Area. Nutrients 2021; 13:4369. [PMID: 34959921 PMCID: PMC8706087 DOI: 10.3390/nu13124369] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/27/2021] [Revised: 12/01/2021] [Accepted: 12/03/2021] [Indexed: 11/16/2022] Open
Abstract
We aimed to assess the potential relationship between dietary patterns (i.e., Mediterranean diet and healthy eating) and the advanced lipoprotein profile (ALP) in a representative cohort of the Mediterranean population. Thus, ALP data from 1142 participants, including 222 with type 1 (19.4%) and 252 type 2 diabetes (22.1%), and 668 subjects without diabetes were used to study cross-sectional associations between quantitative characteristics of lipoproteins and adherence to the Mediterranean diet. The alternate Mediterranean diet score (aMED) and the alternate healthy eating index (aHEI) were calculated. The ALP was determined by nuclear magnetic resonance (NMR) spectrometry. Bivariable and multivariable analyses were performed. Participants in the third tertile of the aMED showed higher levels of low-density lipoprotein triglycerides (LDL-TG) (mean (SD) 17.5 (5.0); p = 0.037), large high-density lipoprotein particles (HDL-P) (0.3 (0.1); p = 0.037), and medium low-density lipoprotein particles (LDL-P) (434.0 (143.0); p = 0.037). In comparison with participants in the second and first tertiles of the aHEI, participants in the third tertile had higher levels of LDL-TG (17.7 (5.0); p = 0.010), and large HDL-P (0.3 (0.1); p = 0.002), IDL-C (11.8 (5.0); p = 0.001), intermediate-density lipoprotein triglycerides (IDL-TG) (13.2 (4.2); p < 0.001), LDL-TG (17.7(5.0); p = 0.010), high-density lipoprotein triglycerides (HDL-TG) (14.5 (4.4); p = 0.029,) large HDL-P (0.3 (0.1); p = 0.002) and very-low-density lipoprotein particles (VLDL-P) size (42.1 (0.2); p = 0.011). The adjusted-multivariable analysis for potential confounding variables did not show any association between the lipoproteins and dietary patterns (i.e., aMED and aHEI). In conclusion, none of the quantitative characteristics of lipoproteins were concomitantly associated with the extent of adherence to the Mediterranean diet measured using the aMED or aHEI scores in the studied population. Our findings also revealed that people with the highest adherence were older, had a higher body mass index (BMI) and more frequently had dyslipidemia, hypertension, or diabetes than those with the lowest adherence to the Mediterranean diet (MDiet). Thus, further research may be needed to assess the potential role of the dietary pattern on the ALP.
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Affiliation(s)
- Marina Idalia Rojo-López
- Department of Endocrinology and Nutrition, Hospital de la Santa Creu i Sant Pau & Institut d’Investigació Biomèdica Sant Pau (IIB Sant Pau), 08041 Barcelona, Spain; (M.I.R.-L.); (J.J.)
| | - Esmeralda Castelblanco
- Department of Internal Medicine, Endocrinology, Metabolism and Lipid Research Division, Washington University School of Medicine, St Louis, MO 63110, USA;
| | - Jordi Real
- Center for Biomedical Research on Diabetes and Associated Metabolic Diseases (CIBERDEM), Instituto de Salud Carlos III, 28029 Madrid, Spain; (J.R.); (N.A.); (N.A.); (J.F.-N.)
- DAP-Cat Group, Unitat de Suport a la Recerca Barcelona, Institut Universitari d’Investigació en Atenció Primària Jordi Gol (IDIAP Jordi Gol), 08041 Barcelona, Spain
| | - Marta Hernández
- Department of Endocrinology & Nutrition, University Hospital Arnau de Vilanova, 25198 Lleida, Spain;
- Lleida Institute for Biomedical Research Dr. Pifarré Foundation IRBLleida, University of Lleida, 25198 Lleida, Spain;
| | - Mireia Falguera
- Lleida Institute for Biomedical Research Dr. Pifarré Foundation IRBLleida, University of Lleida, 25198 Lleida, Spain;
- Primary Health Care Centre Cervera, Gerència d’Atenció Primaria, Institut Català de la Salut, 25200 Lleida, Spain
| | - Núria Amigó
- Center for Biomedical Research on Diabetes and Associated Metabolic Diseases (CIBERDEM), Instituto de Salud Carlos III, 28029 Madrid, Spain; (J.R.); (N.A.); (N.A.); (J.F.-N.)
- Department of Basic Medical Sciences, Universitat RoviraiVirgili, IISPV, 43007 Tarragona, Spain
- Biosfer Teslab, SL., 43204 Reus, Spain
| | - Josep Julve
- Department of Endocrinology and Nutrition, Hospital de la Santa Creu i Sant Pau & Institut d’Investigació Biomèdica Sant Pau (IIB Sant Pau), 08041 Barcelona, Spain; (M.I.R.-L.); (J.J.)
- Center for Biomedical Research on Diabetes and Associated Metabolic Diseases (CIBERDEM), Instituto de Salud Carlos III, 28029 Madrid, Spain; (J.R.); (N.A.); (N.A.); (J.F.-N.)
- Department of Biochemistry and Molecular Biology, Universitat Autònoma de Barcelona, 08041 Barcelona, Spain
| | - Núria Alonso
- Center for Biomedical Research on Diabetes and Associated Metabolic Diseases (CIBERDEM), Instituto de Salud Carlos III, 28029 Madrid, Spain; (J.R.); (N.A.); (N.A.); (J.F.-N.)
- Endocrinology and Nutrition Department, Hospital Universitari Germans Trias i Pujol, 08916 Badalona, Spain
- Department of Medicine, Universitat Autònoma de Barcelona, 08041 Barcelona, Spain
| | - Josep Franch-Nadal
- Center for Biomedical Research on Diabetes and Associated Metabolic Diseases (CIBERDEM), Instituto de Salud Carlos III, 28029 Madrid, Spain; (J.R.); (N.A.); (N.A.); (J.F.-N.)
- DAP-Cat Group, Unitat de Suport a la Recerca Barcelona, Institut Universitari d’Investigació en Atenció Primària Jordi Gol (IDIAP Jordi Gol), 08041 Barcelona, Spain
- Primary Health Care Centre Raval Sud, Gerència d’Atenció Primaria Barcelona, InstitutCatalà de la Salut, 08001 Barcelona, Spain
| | - Minerva Granado-Casas
- Department of Endocrinology and Nutrition, Hospital de la Santa Creu i Sant Pau & Institut d’Investigació Biomèdica Sant Pau (IIB Sant Pau), 08041 Barcelona, Spain; (M.I.R.-L.); (J.J.)
- Center for Biomedical Research on Diabetes and Associated Metabolic Diseases (CIBERDEM), Instituto de Salud Carlos III, 28029 Madrid, Spain; (J.R.); (N.A.); (N.A.); (J.F.-N.)
- DAP-Cat Group, Unitat de Suport a la Recerca Barcelona, Institut Universitari d’Investigació en Atenció Primària Jordi Gol (IDIAP Jordi Gol), 08041 Barcelona, Spain
- Lleida Institute for Biomedical Research Dr. Pifarré Foundation IRBLleida, University of Lleida, 25198 Lleida, Spain;
| | - Dídac Mauricio
- Department of Endocrinology and Nutrition, Hospital de la Santa Creu i Sant Pau & Institut d’Investigació Biomèdica Sant Pau (IIB Sant Pau), 08041 Barcelona, Spain; (M.I.R.-L.); (J.J.)
- Center for Biomedical Research on Diabetes and Associated Metabolic Diseases (CIBERDEM), Instituto de Salud Carlos III, 28029 Madrid, Spain; (J.R.); (N.A.); (N.A.); (J.F.-N.)
- DAP-Cat Group, Unitat de Suport a la Recerca Barcelona, Institut Universitari d’Investigació en Atenció Primària Jordi Gol (IDIAP Jordi Gol), 08041 Barcelona, Spain
- Faculty of Medicine, University of Vic (UVIC/UCC), 08500 Vic, Spain
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6
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Wilson PWF, Jacobson TA, Martin SS, Jackson EJ, Le NA, Davidson MH, Vesper HW, Frikke-Schmidt R, Ballantyne CM, Remaley AT. Lipid measurements in the management of cardiovascular diseases: Practical recommendations a scientific statement from the national lipid association writing group. J Clin Lipidol 2021; 15:629-648. [PMID: 34802986 DOI: 10.1016/j.jacl.2021.09.046] [Citation(s) in RCA: 55] [Impact Index Per Article: 18.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/01/2021] [Revised: 09/13/2021] [Accepted: 09/14/2021] [Indexed: 01/31/2023]
Abstract
Lipoprotein measurements are pivotal in the management of patients at risk for atherosclerotic coronary heart disease (CHD) with myocardial infarction and coronary death as the main outcomes, and for atherosclerotic cardiovascular disease (ASCVD), which includes CHD and stroke. Recent developments and changes in guidelines affect optimization of using lipid measures as cardiovascular biomarkers. This scientific statement reviews the pre-analytical, analytical, post-analytical, and clinical aspects of lipoprotein measurements. Highlights include the following: i) It is acceptable to screen with nonfasting lipids. ii) non-high-density lipoprotein HDL-cholesterol (non-HDL-C) is measured reliably in either the fasting or the nonfasting state and can effectively guide ASCVD prevention. iii) low density lipoprotein cholesterol (LDL-C) can be estimated from total cholesterol, high density lipoprotein cholesterol (HDL-C), and triglyceride (TG) measurements. For patients with LDL-C>100 mg/dL and TG ≤150 mg/dL it is reasonable to use the Friedewald formula. However, for those with TG 150-400 mg/dL the Friedewald formula for LDL-C estimation is less accurate. The Martin/Hopkins method is recommended for LDL-C estimation throughout the range of LDL-C levels and up to TG levels of 399 mg/dL. For TG levels ≥400 mg/dL LDL-C estimating equations are currently not recommended and newer methods are being evaluated. iv) When LDL-C or TG screening results are abnormal the clinician should consider obtaining fasting lipids. v) Advanced lipoprotein tests using apolipoprotein B (apoB), LDL Particle Number (LDL-P) or remnant cholesterol may help to guide therapeutic decisions in select patients, but data are limited for patients already on lipid lowering therapy with low LDL-C levels. Better harmonization of advanced lipid measurement methods is needed. Lipid measurements are recommended 4-12 weeks after a change in lipid treatment. Lipid laboratory reports should denote desirable values and specifically identify extremely elevated LDL-C levels (≥190 mg/dL at any age or ≥160 mg/dL in children) as severe hypercholesterolemia. Potentially actionable abnormal lipid test results, including fasting triglycerides (TG) ≥500 mg/dL, should be reported as hypertriglyceridemia. Appropriate use and reporting of lipid tests should improve their utility in the management of persons at high risk for ASCVD events.
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Affiliation(s)
- Peter W F Wilson
- Emory University School of Medicine, Atlanta, GA, United States; Atlanta Veterans Affairs Medical Center, Atlanta, GA, United States.
| | | | - Seth S Martin
- Johns Hopkins University School of Medicine, Baltimore, MD, United States
| | | | - N-Anh Le
- Atlanta Veterans Affairs Medical Center, Atlanta, GA, United States
| | | | - Hubert W Vesper
- Centers for Disease Control and Prevention, Atlanta, GA, United States
| | - Ruth Frikke-Schmidt
- Rigshospitalet, Copenhagen University Hospital, Copenhagen, Denmark; Department of Clinical Medicine, University of Copenhagen, Copenhagen, Denmark
| | | | - Alan T Remaley
- National Heart, Lung and Blood Institute, Bethesda, MD, United States
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7
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Langlois MR, Nordestgaard BG, Langsted A, Chapman MJ, Aakre KM, Baum H, Borén J, Bruckert E, Catapano A, Cobbaert C, Collinson P, Descamps OS, Duff CJ, von Eckardstein A, Hammerer-Lercher A, Kamstrup PR, Kolovou G, Kronenberg F, Mora S, Pulkki K, Remaley AT, Rifai N, Ros E, Stankovic S, Stavljenic-Rukavina A, Sypniewska G, Watts GF, Wiklund O, Laitinen P. Quantifying atherogenic lipoproteins for lipid-lowering strategies: consensus-based recommendations from EAS and EFLM. Clin Chem Lab Med 2021; 58:496-517. [PMID: 31855562 DOI: 10.1515/cclm-2019-1253] [Citation(s) in RCA: 91] [Impact Index Per Article: 30.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/04/2019] [Indexed: 12/15/2022]
Abstract
The joint consensus panel of the European Atherosclerosis Society (EAS) and the European Federation of Clinical Chemistry and Laboratory Medicine (EFLM) recently addressed present and future challenges in the laboratory diagnostics of atherogenic lipoproteins. Total cholesterol (TC), triglycerides (TG), high-density lipoprotein cholesterol (HDLC), LDL cholesterol (LDLC), and calculated non-HDLC (=total - HDLC) constitute the primary lipid panel for estimating risk of atherosclerotic cardiovascular disease (ASCVD) and can be measured in the nonfasting state. LDLC is the primary target of lipid-lowering therapies. For on-treatment follow-up, LDLC shall be measured or calculated by the same method to attenuate errors in treatment decisions due to marked between-method variations. Lipoprotein(a) [Lp(a)]-cholesterol is part of measured or calculated LDLC and should be estimated at least once in all patients at risk of ASCVD, especially in those whose LDLC declines poorly upon statin treatment. Residual risk of ASCVD even under optimal LDL-lowering treatment should be also assessed by non-HDLC or apolipoprotein B (apoB), especially in patients with mild-to-moderate hypertriglyceridemia (2-10 mmol/L). Non-HDLC includes the assessment of remnant lipoprotein cholesterol and shall be reported in all standard lipid panels. Additional apoB measurement can detect elevated LDL particle (LDLP) numbers often unidentified on the basis of LDLC alone. Reference intervals of lipids, lipoproteins, and apolipoproteins are reported for European men and women aged 20-100 years. However, laboratories shall flag abnormal lipid values with reference to therapeutic decision thresholds.
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Affiliation(s)
- Michel R Langlois
- Department of Laboratory Medicine, AZ St-Jan, Ruddershove 10, 8000 Brugge, Belgium.,University of Ghent, Ghent, Belgium
| | - Børge G Nordestgaard
- Herlev and Gentofte Hospital, Copenhagen University Hospital, University of Copenhagen, Copenhagen, Denmark
| | - Anne Langsted
- Herlev and Gentofte Hospital, Copenhagen University Hospital, University of Copenhagen, Copenhagen, Denmark
| | - M John Chapman
- National Institute for Health and Medical Research (INSERM), Paris, France.,Endocrinology-Metabolism Service, Pitié-Salpetriere University Hospital, Paris, France
| | - Kristin M Aakre
- Hormone Laboratory, Haukeland University Hospital, Bergen, Norway
| | - Hannsjörg Baum
- Institute for Laboratory Medicine, Mikrobiologie und Blutdepot, Regionale Kliniken Holding RKH GmbH, Ludwigsburg, Germany
| | - Jan Borén
- Institute of Medicine, Sahlgrenska Academy at Göteborg University, Gothenburg, Sweden.,Wallenberg Laboratory for Cardiovascular and Metabolic Research, Sahlgrenska University Hospital, Gothenburg, Sweden
| | - Eric Bruckert
- Department of Endocrinology and Prevention of Cardiovascular Disease, Pitié-Salpetriere University Hospital, Paris, France
| | - Alberico Catapano
- Department of Pharmacological and Biomolecular Sciences, University of Milan, Milan, Italy.,IRCCS Multimedica, Milan, Italy
| | - Christa Cobbaert
- Department of Clinical Chemistry and Laboratory Medicine, Leiden University Medical Center, Leiden, The Netherlands
| | - Paul Collinson
- Department of Clinical Blood Sciences, St George's University Hospitals NHS Foundation Trust and St George's University of London, London, UK.,Department of Cardiology, St George's University Hospitals NHS Foundation Trust and St George's University of London, London, UK
| | - Olivier S Descamps
- Department of Internal Medicine, Centres Hospitaliers Jolimont, Haine-Saint-Paul, Belgium.,Department of Cardiology, UCL Cliniques Universitaires Saint-Luc, Brussels, Belgium
| | - Christopher J Duff
- Department of Clinical Biochemistry, University Hospitals of North Midlands NHS Trust, Stoke-on-Trent, UK
| | | | | | - Pia R Kamstrup
- Herlev and Gentofte Hospital, Copenhagen University Hospital, University of Copenhagen, Copenhagen, Denmark
| | - Genovefa Kolovou
- Cardiology Department, Onassis Cardiac Surgery Center, Athens, Greece
| | - Florian Kronenberg
- Department of Medical Genetics, Molecular and Clinical Pharmacology, Division of Genetic Epidemiology, Medical University of Innsbruck, Innsbruck, Austria
| | - Samia Mora
- Division of Preventive Medicine, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA.,Division of Cardiovascular Medicine, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA
| | - Kari Pulkki
- Department of Clinical Chemistry, University of Turku and Turku University Hospital, Turku, Finland
| | - Alan T Remaley
- Lipoprotein Metabolism Section, Cardiovascular-Pulmonary Branch, National Heart, Lung, and Blood Institute, National Institutes of Health, Bethesda, MD, USA
| | - Nader Rifai
- Boston Children's Hospital, Harvard Medical School, Boston, MA, USA
| | - Emilio Ros
- Lipid Clinic, Department of Endocrinology and Nutrition, Institut d'Investigacions Biomèdiques August Pi Sunyer, Hospital Clínic, Barcelona, Spain.,Ciber Fisiopatología de la Obesidad y Nutrición (CIBEROBN), Instituto de Salud Carlos III (ISCIII), Madrid, Spain
| | - Sanja Stankovic
- Center for Medical Biochemistry, Clinical Center of Serbia, Belgrade, Serbia
| | | | - Grazyna Sypniewska
- Department of Laboratory Medicine, Collegium Medicum, NC University, Bydgoszcz, Poland
| | - Gerald F Watts
- Lipid Disorders Clinic, Department of Cardiology, Royal Perth Hospital, University of Western Australia, Perth, Australia
| | - Olov Wiklund
- Institute of Medicine, Sahlgrenska Academy at Göteborg University, Gothenburg, Sweden.,Wallenberg Laboratory for Cardiovascular and Metabolic Research, Sahlgrenska University Hospital, Gothenburg, Sweden
| | - Päivi Laitinen
- Department of Clinical Chemistry, HUSLAB, Helsinki University Hospital, Helsinki, Finland
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8
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Correia M, Kagenaar E, van Schalkwijk DB, Bourbon M, Gama-Carvalho M. Machine learning modelling of blood lipid biomarkers in familial hypercholesterolaemia versus polygenic/environmental dyslipidaemia. Sci Rep 2021; 11:3801. [PMID: 33589716 PMCID: PMC7884847 DOI: 10.1038/s41598-021-83392-w] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/18/2020] [Accepted: 01/29/2021] [Indexed: 11/08/2022] Open
Abstract
Familial hypercholesterolaemia increases circulating LDL-C levels and leads to premature cardiovascular disease when undiagnosed or untreated. Current guidelines support genetic testing in patients complying with clinical diagnostic criteria and cascade screening of their family members. However, most of hyperlipidaemic subjects do not present pathogenic variants in the known disease genes, and most likely suffer from polygenic hypercholesterolaemia, which translates into a relatively low yield of genetic screening programs. This study aims to identify new biomarkers and develop new approaches to improve the identification of individuals carrying monogenic causative variants. Using a machine-learning approach in a paediatric dataset of individuals, tested for disease causative genes and with an extended lipid profile, we developed new models able to classify familial hypercholesterolaemia patients with a much higher specificity than currently used methods. The best performing models incorporated parameters absent from the most common FH clinical criteria, namely apoB/apoA-I, TG/apoB and LDL1. These parameters were found to contribute to an improved identification of monogenic individuals. Furthermore, models using only TC and LDL-C levels presented a higher specificity of classification when compared to simple cut-offs. Our results can be applied towards the improvement of the yield of genetic screening programs and corresponding costs.
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Affiliation(s)
- Marta Correia
- University of Lisboa, Faculty of Sciences, BioISI-Biosystems & Integrative Sciences Institute, Campo Grande, 1749-016, Lisboa, Portugal
- National Institute of Health Doutor Ricardo Jorge, Padre Cruz Av., 1649-016, Lisboa, Portugal
| | - Eva Kagenaar
- Amsterdam University College, Science Park 113, 1098 XG, Amsterdam, The Netherlands
| | | | - Mafalda Bourbon
- University of Lisboa, Faculty of Sciences, BioISI-Biosystems & Integrative Sciences Institute, Campo Grande, 1749-016, Lisboa, Portugal
- National Institute of Health Doutor Ricardo Jorge, Padre Cruz Av., 1649-016, Lisboa, Portugal
| | - Margarida Gama-Carvalho
- University of Lisboa, Faculty of Sciences, BioISI-Biosystems & Integrative Sciences Institute, Campo Grande, 1749-016, Lisboa, Portugal.
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9
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Jakubauskas D, Jansen M, Lyngsø J, Cheng Y, Pedersen JS, Cárdenas M. Toward reliable low-density lipoprotein ultrastructure prediction in clinical conditions: A small-angle X-ray scattering study on individuals with normal and high triglyceride serum levels. NANOMEDICINE-NANOTECHNOLOGY BIOLOGY AND MEDICINE 2020; 31:102318. [PMID: 33091569 DOI: 10.1016/j.nano.2020.102318] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 04/15/2020] [Revised: 08/23/2020] [Accepted: 10/05/2020] [Indexed: 10/23/2022]
Abstract
Atherosclerosis is the main killer in the west and therefore a major health challenge today. Total serum cholesterol and lipoprotein concentrations, used as clinical markers, fail to predict the majority of cases, especially between the risk scale extremes, due to the high complexity in lipoprotein structure and composition. In particular, low-density lipoprotein (LDL) plays a key role in atherosclerosis development, with LDL size being a parameter considered for determining the risk for cardiovascular diseases. Determining LDL size and structural parameters is challenging to address experimentally under physiological-like conditions. This article describes the biochemistry and ultrastructure of normolipidemic and hypertriglyceridemic LDL fractions and subfractions using small-angle X-ray scattering. Our results conclude that LDL particles of hypertriglyceridemic compared to healthy individuals 1) have lower LDL core melting temperature, 2) have lower cholesteryl ester ordering in their core, 3) are smaller, rounder and more spherical below melting temperature, and 4) their protein-containing shell is thinner above melting temperature.
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Affiliation(s)
- Dainius Jakubauskas
- Biofilms - Research center for Biointerfaces, Dept. of Biomedical Science, Faculty of Health and Society, Malmo University, Malmo, Sweden.
| | - Martin Jansen
- Institute of Clinical Chemistry and Laboratory Medicine, Medical Centre, University of Freiburg, Freiburg im Breisgau, Germany.
| | - Jeppe Lyngsø
- Department of Chemistry and Interdisciplinary Nanoscience Center (iNANO), Aarhus University, Aarhus, Denmark.
| | - Yuanji Cheng
- Department of Materials Science and Applied Mathematics, Faculty of Technology and Society, Malmo University, Malmo, Sweden.
| | - Jan Skov Pedersen
- Department of Chemistry and Interdisciplinary Nanoscience Center (iNANO), Aarhus University, Aarhus, Denmark.
| | - Marité Cárdenas
- Biofilms - Research center for Biointerfaces, Dept. of Biomedical Science, Faculty of Health and Society, Malmo University, Malmo, Sweden.
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10
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Puzo J. Lifestyle intervention for hypercholesterolemic children. Is it worth it? CLINICA E INVESTIGACION EN ARTERIOSCLEROSIS : PUBLICACION OFICIAL DE LA SOCIEDAD ESPANOLA DE ARTERIOSCLEROSIS 2020; 32:63-65. [PMID: 32171436 DOI: 10.1016/j.arteri.2020.02.001] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/10/2023]
Affiliation(s)
- José Puzo
- Servicio de Bioquímica. Unidad de Lípidos. Hospital San Jorge; IIS Aragón. Dpto. de Medicina y Psiquiatria. Universidad de Zaragoza. Huesca
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11
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Quantifying atherogenic lipoproteins for lipid-lowering strategies: Consensus-based recommendations from EAS and EFLM. Atherosclerosis 2020; 294:46-61. [DOI: 10.1016/j.atherosclerosis.2019.12.005] [Citation(s) in RCA: 65] [Impact Index Per Article: 16.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 12/06/2019] [Accepted: 12/12/2019] [Indexed: 12/22/2022]
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12
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Delatour V, Clouet-Foraison N, Gaie-Levrel F, Marcovina SM, Hoofnagle AN, Kuklenyik Z, Caulfield MP, Otvos JD, Krauss RM, Kulkarni KR, Contois JH, Remaley AT, Vesper HW, Cobbaert CM, Gillery P. Comparability of Lipoprotein Particle Number Concentrations Across ES-DMA, NMR, LC-MS/MS, Immunonephelometry, and VAP: In Search of a Candidate Reference Measurement Procedure for apoB and non-HDL-P Standardization. Clin Chem 2018; 64:1485-1495. [PMID: 30087138 DOI: 10.1373/clinchem.2018.288746] [Citation(s) in RCA: 24] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/08/2018] [Accepted: 07/10/2018] [Indexed: 12/15/2022]
Abstract
BACKGROUND Despite the usefulness of standard lipid parameters for cardiovascular disease risk assessment, undiagnosed residual risk remains high. Advanced lipoprotein testing (ALT) was developed to provide physicians with more predictive diagnostic tools. ALT methods separate and/or measure lipoproteins according to different parameters such as size, density, charge, or content, and equivalence of results across methods has not been demonstrated. METHODS Through a split-sample study, 25 clinical specimens (CSs) were assayed in 10 laboratories before and after freezing using the major ALT methods for non-HDL particles (non-HDL-P) or apolipoprotein B-100 (apoB-100) measurements with the intent to assess their comparability in the current state of the art. RESULTS The overall relative standard deviation (CV) of non-HDL-P and apoB-100 concentrations measured by electrospray differential mobility analysis, nuclear magnetic resonance, immunonephelometry, LC-MS/MS, and vertical autoprofile in the 25 frozen CSs was 14.1%. Within-method comparability was heterogeneous, and CV among 4 different LC-MS/MS methods was 11.4% for apoB-100. No significant effect of freezing and thawing was observed. CONCLUSIONS This study demonstrates that ALT methods do not yet provide equivalent results for the measurement of non-HDL-P and apoB-100. The better agreement between methods harmonized to the WHO/IFCC reference material suggests that standardizing ALT methods by use of a common commutable calibrator will improve cross-platform comparability. This study provides further evidence that LC-MS/MS is the most suitable candidate reference measurement procedure to standardize apoB-100 measurement, as it would provide results with SI traceability. The absence of freezing and thawing effect suggests that frozen serum pools could be used as secondary reference materials.
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Affiliation(s)
- Vincent Delatour
- Laboratoire National de Métrologie et d'Essais (LNE), Paris, France;
| | | | | | - Santica M Marcovina
- Northwest Lipid Metabolism and Diabetes Research Laboratories, University of Washington, Seattle, WA
| | - Andrew N Hoofnagle
- Department of Laboratory Medicine, University of Washington, Seattle, WA
| | - Zsuzsanna Kuklenyik
- Centers for Disease Control and Prevention, Division of Laboratory Sciences, Atlanta, GA
| | | | - James D Otvos
- Laboratory Corporation of America® Holdings, Morrisville, NC
| | | | | | | | - Alan T Remaley
- Lipoprotein Metabolism Section, National Heart, Lung, and Blood Institute, Bethesda, MD
| | - Hubert W Vesper
- Centers for Disease Control and Prevention, Division of Laboratory Sciences, Atlanta, GA
| | - Christa M Cobbaert
- Department of Clinical Chemistry and Laboratory Medicine, Leiden University Medical Center, Leiden, the Netherlands
| | - Philippe Gillery
- University Hospital of Reims, Laboratory of Pediatric Biology and Research, Reims, France
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13
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Langlois MR, Chapman MJ, Cobbaert C, Mora S, Remaley AT, Ros E, Watts GF, Borén J, Baum H, Bruckert E, Catapano A, Descamps OS, von Eckardstein A, Kamstrup PR, Kolovou G, Kronenberg F, Langsted A, Pulkki K, Rifai N, Sypniewska G, Wiklund O, Nordestgaard BG. Quantifying Atherogenic Lipoproteins: Current and Future Challenges in the Era of Personalized Medicine and Very Low Concentrations of LDL Cholesterol. A Consensus Statement from EAS and EFLM. Clin Chem 2018; 64:1006-1033. [PMID: 29760220 DOI: 10.1373/clinchem.2018.287037] [Citation(s) in RCA: 154] [Impact Index Per Article: 25.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/22/2018] [Accepted: 04/09/2018] [Indexed: 01/25/2023]
Abstract
BACKGROUND The European Atherosclerosis Society-European Federation of Clinical Chemistry and Laboratory Medicine Consensus Panel aims to provide recommendations to optimize atherogenic lipoprotein quantification for cardiovascular risk management. CONTENT We critically examined LDL cholesterol, non-HDL cholesterol, apolipoprotein B (apoB), and LDL particle number assays based on key criteria for medical application of biomarkers. (a) Analytical performance: Discordant LDL cholesterol quantification occurs when LDL cholesterol is measured or calculated with different assays, especially in patients with hypertriglyceridemia >175 mg/dL (2 mmol/L) and low LDL cholesterol concentrations <70 mg/dL (1.8 mmol/L). Increased lipoprotein(a) should be excluded in patients not achieving LDL cholesterol goals with treatment. Non-HDL cholesterol includes the atherogenic risk component of remnant cholesterol and can be calculated in a standard nonfasting lipid panel without additional expense. ApoB more accurately reflects LDL particle number. (b) Clinical performance: LDL cholesterol, non-HDL cholesterol, and apoB are comparable predictors of cardiovascular events in prospective population studies and clinical trials; however, discordance analysis of the markers improves risk prediction by adding remnant cholesterol (included in non-HDL cholesterol) and LDL particle number (with apoB) risk components to LDL cholesterol testing. (c) Clinical and cost-effectiveness: There is no consistent evidence yet that non-HDL cholesterol-, apoB-, or LDL particle-targeted treatment reduces the number of cardiovascular events and healthcare-related costs than treatment targeted to LDL cholesterol. SUMMARY Follow-up of pre- and on-treatment (measured or calculated) LDL cholesterol concentration in a patient should ideally be performed with the same documented test method. Non-HDL cholesterol (or apoB) should be the secondary treatment target in patients with mild to moderate hypertriglyceridemia, in whom LDL cholesterol measurement or calculation is less accurate and often less predictive of cardiovascular risk. Laboratories should report non-HDL cholesterol in all standard lipid panels.
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Affiliation(s)
- Michel R Langlois
- Department of Laboratory Medicine, AZ St-Jan, Brugge, and University of Ghent, Belgium;
| | - M John Chapman
- National Institute for Health and Medical Research (INSERM), and Endocrinology-Metabolism Service, Pitié-Salpetriere University Hospital, Paris, France
| | - Christa Cobbaert
- Department of Clinical Chemistry and Laboratory Medicine, Leiden University Medical Center, Leiden, the Netherlands
| | - Samia Mora
- Divisions of Preventive and Cardiovascular Medicine, Brigham and Women's Hospital, Harvard Medical School, Boston, MA
| | - Alan T Remaley
- Lipoprotein Metabolism Section, Cardiovascular-Pulmonary Branch, National Heart, Lung, and Blood Institute, National Institutes of Health, Bethesda, MD
| | - Emilio Ros
- Lipid Clinic, Department of Endocrinology and Nutrition, Institut d'Investigacions Biomèdiques August Pi Sunyer, Hospital Clínic, Barcelona and Ciber Fisiopatología de la Obesidad y Nutrición (CIBEROBN), Instituto de Salud Carlos III (ISCIII), Spain
| | - Gerald F Watts
- Lipid Disorders Clinic, Department of Cardiology, Royal Perth Hospital, University of Western Australia, Perth, Australia
| | - Jan Borén
- Sahlgrenska University Hospital, Gothenburg, Sweden
| | - Hannsjörg Baum
- Institute for Laboratory Medicine, Blutdepot und Krankenhaushygiene, Regionale Kliniken Holding RKH GmbH, Ludwigsburg, Germany
| | - Eric Bruckert
- Pitié-Salpetriere University Hospital, Paris, France
| | - Alberico Catapano
- Department of Pharmacological and Biomolecular Sciences, University of Milan, Italy
| | | | | | - Pia R Kamstrup
- Herlev and Gentofte Hospital, Copenhagen University Hospital, University of Copenhagen, Copenhagen, Denmark
| | - Genovefa Kolovou
- Cardiology Department, Onassis Cardiac Surgery Center, Athens, Greece
| | - Florian Kronenberg
- Department of Medical Genetics, Molecular and Clinical Pharmacology, Division of Genetic Epidemiology, Medical University of Innsbruck, Innsbruck, Austria
| | - Anne Langsted
- Herlev and Gentofte Hospital, Copenhagen University Hospital, University of Copenhagen, Copenhagen, Denmark
| | - Kari Pulkki
- Department of Clinical Chemistry, University of Turku and Turku University Hospital, Turku, Finland
| | - Nader Rifai
- Boston Children's Hospital, Harvard Medical School, Boston, MA
| | - Grazyna Sypniewska
- Department of Laboratory Medicine, Collegium Medicum, NC University, Bydgoszcz, Poland
| | - Olov Wiklund
- Sahlgrenska University Hospital, Gothenburg, Sweden
| | - Børge G Nordestgaard
- Herlev and Gentofte Hospital, Copenhagen University Hospital, University of Copenhagen, Copenhagen, Denmark
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14
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Bermúdez-López M, Betriu À, Valdivielso JM, Bretones Del Pino T, Arroyo D, Fernández E. Beyond the traditional lipid parameters in chronic kidney disease. Nefrologia 2017; 38:109-113. [PMID: 29137894 DOI: 10.1016/j.nefro.2017.09.008] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/16/2017] [Revised: 09/12/2017] [Accepted: 09/17/2017] [Indexed: 11/29/2022] Open
Affiliation(s)
- Marcelino Bermúdez-López
- Grupo de Investigación Translacional Vascular y Renal, Instituto de Investigación Biomédica de Lleida (IRBLleida), Lleida, España.
| | - Àngels Betriu
- Grupo de Investigación Translacional Vascular y Renal, Instituto de Investigación Biomédica de Lleida (IRBLleida), Lleida, España
| | - Jose M Valdivielso
- Grupo de Investigación Translacional Vascular y Renal, Instituto de Investigación Biomédica de Lleida (IRBLleida), Lleida, España
| | | | - David Arroyo
- Grupo de Investigación Translacional Vascular y Renal, Instituto de Investigación Biomédica de Lleida (IRBLleida), Lleida, España
| | - Elvira Fernández
- Grupo de Investigación Translacional Vascular y Renal, Instituto de Investigación Biomédica de Lleida (IRBLleida), Lleida, España
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