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Short-term estimation and application of biological variation of small dense low-density lipoproteins in healthy individuals

  • Julio Lara-Riegos , Eduardo Brambila EMAIL logo , Ana Ake-Ku , Vanessa Villegas-Hernández , Carmen Quintero-Carrilo , Rolffy Ortiz-Andrade , Rubén Yza-Villanueva , Julio Torres-Romero and Patricia Lozano-Zarain
Published/Copyright: July 17, 2013
Clinical Chemistry and Laboratory Medicine (CCLM)
From the journal Clinical Chemistry and Laboratory Medicine (CCLM) Volume 51 Issue 11

Abstract

Background: A number of methods have been developed to measure small dense low-density lipoprotein cholesterol (sd-LDL-C) to evaluate atherogenic risk in the population. However, to our knowledge there are no reports about the biologic variability of these lipoproteins. Therefore, the aim of this work was to estimate sd-LDL-C biological variability, and with this information establish quality specifications, index of individuality (II) and reference change values (RCV).

Methods: To estimate within- and between-subject biological variability, sd-LDL-C in serum was measured in 24 individuals (11 female and 13 male) for 5 consecutive days and then, at 2 and 3 weeks. Quality specifications, II, and RCVs were estimated according to procedures described.

Results: Total within- and between-subject biological variability, expressed as coefficient of variation, was 9.1% and 20%. Meanwhile, within- and between-biological variability in female and men was 10.9% and 6.7%, and 22% and 17%, respectively. Desirable quality specification to the sd-LDL-C method was 4.6% for analytical imprecision, bias 5.5% and total allowable error of 11.4%; the II was 0.46 and the RCV (calculated at 95% and 99% of significance) was 27.1% and 35.7%, for the total data.

Conclusions: Short-term biological variability components were determined, and then used to estimate quality specifications, II and RCV for sd-LDL-C precipitation assay. To our knowledge, this is one of the first reports about sd-LDL-C biological variability, so that this information can be used as a starting point to develop long-term studies of biological variability for sd-LDL-C.

Keywords: analytical variability; analytical variation; biological variability; lipids; quality assurance; quality control
Corresponding author: Eduardo Brambila, Clinical Chemistry Research Laboratory, Chemical Sciences Faculty, Autonomous University of Puebla, Puebla, México, Pedro Hinojosa 17, Lomas de Loreto, Puebla, México 72260, Phone/Fax: +52 222 2341761, E-mail:

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Received: 2013-05-16
Accepted: 2013-06-25
Published Online: 2013-07-17
Published in Print: 2013-11-01

©2013 by Walter de Gruyter Berlin Boston

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https://doi.org/10.1515/cclm-2013-0370
Keywords for this article
analytical variability; analytical variation; biological variability; lipids; quality assurance; quality control

Articles in the same Issue

  1. Letter to the Editor
  2. Elevated level of cell-free plasma DNA is associated with advanced-stage breast cancer and metastasis
  3. Is procalcitonin a reliable marker of sepsis in critically ill septic patients undergoing continuous veno-venous hemodiafiltration with “high cut-off” membranes (HCO-CVVHDF)?
  4. Challenging our serological algorithm for celiac disease (CD) diagnosis by the ESPGHAN guidelines
  5. Preanalytical quality control in a university hospital in China
  6. Racial differences and relationships between gestational thyrotropin and free thyroxine in a multiracial Asian population
  7. Analytical performance and method comparison study of the total homocysteine immunoassay on the AIA 600II analyser
  8. Easy verification of clinical chemistry reference intervals
  9. The Elecsys® Vitamin B12 assay is not affected by anti-intrinsic factor antibodies
  10. Chemiluminescence-based cobalamin assay errors: background and perspectives
  11. Congress Abstracts
  12. Abstracts IV Italian Great Network Congress Rome, 14th–18th October 2013*)
  13. Masthead
  14. Masthead
  15. Editorial
  16. Making colorectal cancer screening FITTER for purpose with quantitative faecal immunochemical tests for haemoglobin (FIT)
  17. From “panic” to “critical” values: which path toward harmonization?
  18. Review
  19. Cerebrospinal fluid analyses for the diagnosis of subarachnoid haemorrhage and experience from a Swedish study. What method is preferable when diagnosing a subarachnoid haemorrhage?
  20. Opinion Paper
  21. False myths and legends in laboratory diagnostics
  22. General Clinical Chemistry and Laboratory Medicine
  23. National survey on critical values notification of 599 institutions in China
  24. Influence of physical properties of cuvette surface on measurement of serum lipase
  25. Red cell indices: differentiation between β-thalassemia trait and iron deficiency anemia and application to sickle-cell disease and sickle-cell thalassemia
  26. Measurement of immature platelets with Abbott CD-Sapphire and Sysmex XE-5000 in haematology and oncology patients
  27. Performance characteristics of consensus approaches for small and minor paroxysmal nocturnal hemoglobinuria clone determination by flow cytometry
  28. Comparison of PR3-ANCA specific assay performance for the diagnosis of granulomatosis with polyangiitis (Wegener’s)
  29. The integration of the detection of systemic sclerosis-associated antibodies in a routine laboratory setting: comparison of different strategies
  30. Reference Values and Biological Variations
  31. Reference interval studies: what is the maximum number of samples recommended?
  32. Short-term estimation and application of biological variation of small dense low-density lipoproteins in healthy individuals
  33. Cancer Diagnostics
  34. Diagnostic inconsistency of faecal immunochemical tests for haemoglobin in population screening of colorectal cancer
  35. Cardiovascular Diseases
  36. Comparison of the 99th percentiles of three troponin I assays in a large reference population
  37. Assessment of plasma aminothiol levels and the association with recurrent atherothrombotic events in patients hospitalized for an acute coronary syndrome: a prospective study
  38. Diabetes
  39. The relationship between estimated average glucose and fasting plasma glucose
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