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    Biological variation data for lipid cardiovascular risk assessment biomarkers. A systematic review applying the biological variation data critical appraisal checklist (BIVAC)
    (ELSEVIER, 2019-01-01) Diaz-Garzon, Jorge; Fernandez Calle, Pilar; Minchinela, Joana; Aarsand, Aasne K.; Bartlett, William A.; Aslan, Berna; Boned, Beatriz; Braga, Federica; Carobene, Anna; Coskun, Abdurrahman; Gonzalez-Lao, Elisabet; Jonker, Niels; Marques-Garcia, Fernando; Perich, Carmen; Ricos, Carmen; Simon, Margarita; Sandberg, Sverre
    Background: Biological variation (BV) data can be used to set analytical performance specifications (APS) for lipid assays. Poor performance will impact upon the efficacy of international guidelines for cardiovascular risk assessment (CVR) and relevant clinical decision limits. This systematic review applies the Biological Variation Data Critical Appraisal Checklist (BIVAC) to published studies of BV of CVR biomarkers enabling metanalysis of the data. Methods: Studies of BV of total cholesterol, HDL-cholesterol, LDL-cholesterol, triglycerides and apolipoproteins A(1) and B, retrieved using a systematic literature search, were evaluated and graded using the BIVAC. Meta analysis of CV1 and CVG estimates were performed utilizing weightings based upon BIVAC grades and the width of the data confidence intervals. Results: Applying the BIVAC, ten publications were graded as D, 43 as C, 5 as B and 1 as A (fully compliant). A total of 196 CV1 and 87 CVG estimates were available for the different lipid measurands. The meta-analysis-derived BV data estimates were generally concordant with those in the online 2014 BV database. Conclusions: Application of BIVAC identifies BV data suitable for many important applications including setting APS. Additionally, this review identifies a need for new BIVAC compliant studies to deliver BV reference data in different subpopulations.
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    European Biological Variation Study (EuBIVAS): within- and between-subject biological variation estimates of beta-isomerized C-terminal telopeptide of type I collagen (beta-CTX), N-terminal propeptide of type I collagen (PINP), osteocalcin, intact fibroblast growth factor 23 and uncarboxylated-unphosphorylated matrix-Gla protein-a cooperation between the EFLM Working Group on Biological Variation and the International Osteoporosis Foundation-International Federation of Clinical Chemistry Committee on Bone Metabolism
    (SPRINGER LONDON LTD, 2020-01-01) Cavalier, E.; Lukas, P.; Bottani, M.; Aarsand, A. K.; Ceriotti, F.; Coskun, A.; Diaz-Garzon, J.; Fernandez-Calle, P.; Guerra, E.; Locatelli, M.; Sandberg, S.; Carobene, A.; Metab, I. O. F.-I.F.C.C. Committee Bone; Che, European Federation Clinical
    We have calculated the biological variation (BV) of different bone metabolism biomarkers on a large, well-described cohort of subjects. BV is important to calculate reference change value (or least significant change) which allows evaluating if the difference observed between two consecutive measurements in a patient is biologically significant or not. Introduction Within-subject (CVI) and between-subject (CVG) biological variation (BV) estimates are essential in determining both analytical performance specifications (APS) and reference change values (RCV). Previously published estimates of BV for bone metabolism biomarkers are generally not compliant with the most up-to-date quality criteria for BV studies. We calculated the BV and RCV for different bone metabolism markers, namely beta-isomerized C-terminal telopeptide of type I collagen (beta-CTX), N-terminal propeptide of type I collagen (PINP), osteocalcin (OC), intact fibroblast growth factor 23 (iFGF-23), and uncarboxylated-unphosphorylated Matrix-Gla Protein (uCuP-MGP) using samples from the European Biological Variation Study (EuBIVAS). Methods In the EuBIVAS, 91 subjects were recruited from six European laboratories. Fasting blood samples were obtained weekly for ten consecutive weeks. The samples were run in duplicate on IDS iSYS or DiaSorin Liaison instruments. The results were subjected to outlier and variance homogeneity analysis before CV-ANOVA was used to obtain the BV estimates. Results We found no effect of gender upon the CV(I)estimates. The following CV(I)estimates with 95\% confidence intervals (95\% CI) were obtained: beta-CTX 15.1\% (14.4-16.0\%), PINP 8.8\% (8.4-9.3\%), OC 8.9\% (8.5-9.4\%), iFGF23 13.9\% (13.2-14.7\%), and uCuP-MGP 6.9\% (6.1-7.3\%). Conclusions The EuBIVAS has provided updated BV estimates for bone markers, including iFGF23, which have not been previously published, facilitating the improved follow-up of patients being treated for metabolic bone disease.