Literature DB >> 29336935

Update on the laboratory investigation of dyslipidemias.

I Ramasamy1.   

Abstract

The role of the clinical laboratory is evolving to provide more information to clinicians to assess cardiovascular disease (CVD) risk and target therapy more effectively. Current routine methods to measure LDL-cholesterol (LDL-C), the Friedewald calculation, ultracentrifugation, electrophoresis and homogeneous direct methods have established limitations. Studies suggest that LDL and HDL size or particle concentration are alternative methods to predict future CVD risk. At this time there is no consensus role for lipoprotein particle or subclasses in CVD risk assessment. LDL and HDL particle concentration are measured by several methods, namely gradient gel electrophoresis, ultracentrifugation-vertical auto profile, nuclear magnetic resonance and ion mobility. It has been suggested that HDL functional assays may be better predictors of CVD risk. To assess the issue of lipoprotein subclasses/particles and HDL function as potential CVD risk markers robust, simple, validated analytical methods are required. In patients with small dense LDL particles, even a perfect measure of LDL-C will not reflect LDL particle concentration. Non-HDL-C is an alternative measurement and includes VLDL and CM remnant cholesterol and LDL-C. However, apolipoprotein B measurement may more accurately reflect LDL particle numbers. Non-fasting lipid measurements have many practical advantages. Defining thresholds for treatment with new measurements of CVD risk remain a challenge. In families with genetic variants, ApoCIII and lipoprotein (a) may be additional risk factors. Recognition of familial causes of dyslipidemias and diagnosis in childhood will result in early treatment. This review discusses the limitations in current laboratory technologies to predict CVD risk and reviews the evidence for emergent approaches using newer biomarkers in clinical practice. Crown
Copyright © 2018. Published by Elsevier B.V. All rights reserved.

Entities:  

Keywords:  Apolipoprotein A; Apolipoprotein B; Apolipoprotein CIII; Apolipoprotein E; Cardiovascular disease risk; Familial dyslipoproteinemias; HDL-cholesterol; HDL-function; HDL-particle; HDL-subclass; LDL-cholesterol; LDL-particle; LDL-subclass; Lipoprotein (a); Lipoprotein measurement; Non-HDL-cholesterol

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Year:  2018        PMID: 29336935     DOI: 10.1016/j.cca.2018.01.015

Source DB:  PubMed          Journal:  Clin Chim Acta        ISSN: 0009-8981            Impact factor:   3.786


  9 in total

Review 1.  Which Lipids Should Be Analyzed for Diagnostic Workup and Follow-up of Patients with Hyperlipidemias?

Authors:  Michel R Langlois; Børge G Nordestgaard
Journal:  Curr Cardiol Rep       Date:  2018-08-17       Impact factor: 2.931

2.  Comparison of low-density lipoprotein cholesterol level calculated using the modified Martin/Hopkins estimation or the Friedewald formula with direct homogeneous assay measured low-density lipoprotein cholesterol.

Authors:  Istvan Reiber; Laszlo Mark; Gyorgy Paragh; Peter P Toth
Journal:  Arch Med Sci       Date:  2020-08-03       Impact factor: 3.318

3.  Apolipoprotein B/A1 Ratio as a Diagnostic Alternative to Triglycerides and HDL-Cholesterol for the Prediction of Metabolic Syndrome among Hypertensives in Kazakhstan.

Authors:  Alma Nurtazina; Dana Kozhakhmetova; Daulet Dautov; Aizhan Shakhanova; Vijay Kumar Chattu
Journal:  Diagnostics (Basel)       Date:  2020-07-23

4.  Integrated Lipidomics and Proteomics Point to Early Blood-Based Changes in Childhood Preceding Later Development of Psychotic Experiences: Evidence From the Avon Longitudinal Study of Parents and Children.

Authors:  Francisco Madrid-Gambin; Melanie Föcking; Sophie Sabherwal; Meike Heurich; Jane A English; Aoife O'Gorman; Tommi Suvitaival; Linda Ahonen; Mary Cannon; Glyn Lewis; Ismo Mattila; Caitriona Scaife; Sean Madden; Tuulia Hyötyläinen; Matej Orešič; Stanley Zammit; Gerard Cagney; David R Cotter; Lorraine Brennan
Journal:  Biol Psychiatry       Date:  2019-01-30       Impact factor: 13.382

5.  Faecal bacterial and short-chain fatty acids signature in hypercholesterolemia.

Authors:  A B Granado-Serrano; M Martín-Garí; V Sánchez; M Riart Solans; R Berdún; I A Ludwig; L Rubió; E Vilaprinyó; M Portero-Otín; J C E Serrano
Journal:  Sci Rep       Date:  2019-02-11       Impact factor: 4.379

6.  Comparison of Two Homogeneous LDL-Cholesterol Assays Using Fresh Hypertriglyceridemic Serum and Quantitative Ultracentrifugation Fractions.

Authors:  Megumi Yano; Akira Matsunaga; Sadako Harada; Bo Zhang; Emi Kawachi; Mikiko Tadera; Keijiro Saku
Journal:  J Atheroscler Thromb       Date:  2019-03-19       Impact factor: 4.928

7.  Evaluation of a new equation for estimating low-density lipoprotein cholesterol through the comparison with various recommended methods.

Authors:  Eduardo Martínez-Morillo; María García-García; María Angeles Luengo Concha; Luis Rello Varas
Journal:  Biochem Med (Zagreb)       Date:  2020-12-15       Impact factor: 2.313

8.  The Role of Fasting LDL-C Levels in Their Non-fasting Reduction in Patients With Coronary Heart Disease.

Authors:  Qiuzhen Lin; Yan Fu; XueYan Zang; Qiming Liu; Ling Liu
Journal:  Front Cardiovasc Med       Date:  2021-06-16

Review 9.  CVD Risk Stratification in the PCSK9 Era: Is There a Role for LDL Subfractions?

Authors:  Christian Abendstein Kjellmo; Anders Hovland; Knut Tore Lappegård
Journal:  Diseases       Date:  2018-05-27
  9 in total

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