Clinical Chemistry and Laboratory Medicine (CCLM)
Published in Association with the European Federation of Clinical Chemistry and Laboratory Medicine (EFLM)
Editor-in-Chief: Plebani, Mario
Ed. by Gillery, Philippe / Lackner, Karl J. / Lippi, Giuseppe / Melichar, Bohuslav / Payne, Deborah A. / Schlattmann, Peter / Tate, Jillian R.
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Rank 5 out of 30 in category Medical Laboratory Technology in the 2014 Thomson Reuters Journal Citation Report/Science Edition
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Anti-β2-Glycoprotein I ELISA: Methodology, Determination of Cut-off Values in 434 Healthy Caucasians and Evaluation of Monoclonal Antibodies as Possible International Standards
Antibodies against β2-glycoprotein I are among the most commonly detected subset of antiphospholipid antibodies. The inter-laboratory comparability of results is hardly possible due to methodological differences, lack of international standards and different cut-off values. We evaluated an ELISA for the detection of anti-β2-glycoprotein I using the analytical goals based on biological variations for similar analytes (immunoglobulins). By our ELISA we fulfilled the optimal (IgA, IgM) or minimal (IgG) analytical goals in longterm imprecision. The determination of cut-off values was based on the frequency distribution of results obtained on 434 healthy Caucasians. To aim at a better inter-laboratory comparability we tested two monoclonal antibodies as possible calibrators: HCAL (IgG) and EY2C9 (IgM). Binding properties determined by dilutional curves showed great similarities with polyclonal sera, used as in-house standards. Cut-off values were expressed by concentrations of IgG and IgM monoclonal antibodies (4.5 and 25.3 μg/l). Our study shows the possibility for a successful application of analytical goals based on biological variation even when data for a particular analyte are not available. The expression of cut-off values, obtained on a large scale Caucasian population, by the concentration of IgG and IgM monoclonal antibodies could make possible a more reliable inter-laboratory comparison of data.
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