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Standardization of C-Peptide Measurements

¹ Department of Pathology & Anatomical Sciences, University of Missouri School of Medicine, Columbia, MO; ² Department of Statistics, University of Missouri School of Medicine, Columbia, MO; ³ Department of Medicine, Washington University School of Medicine, St. Louis, MO; ⁴ Centers for Disease Control and Prevention, Division of Environmental Health Laboratory Sciences, Centers for Environmental Health, Chamblee, GA; ⁵ Benaroya Research Institute, Seattle, WA; ⁶ University of Washington and VA Medical Center, Seattle, WA; ⁷ Department of Medicine and GCRC Analytical Core Laboratory, Albert Einstein College of Medicine of Yeshiva University, Bronx, NY.

^aAddress correspondence to this author at: Diabetes Diagnostic Laboratory, M767, Department of Pathology & Anatomical Sciences, University of Missouri School of Medicine, 1 Hospital Dr., Columbia, MO 65212. Fax 573-884-8823; e-mail LittleR{at}health.missouri.edu

Background: C-peptide is a marker of insulin secretion in diabetic patients. We assessed within- and between-laboratory imprecision of C-peptide assays and determined whether serum calibrators with values assigned by mass spectrometry could be used to harmonize C-peptide results.

Methods: We sent 40 different serum samples to 15 laboratories, which used 9 different routine C-peptide assay methods. We also sent matched plasma samples to another laboratory for C-peptide analysis with a reference mass spectrometry method. Each laboratory analyzed 8 of these samples in duplicate on each of 4 days to evaluate within- and between-day imprecision. The same 8 samples were also used to normalize the results for the remaining samples to the mass spectrometry reference method.

Results: Within- and between-run CVs ranged from <2% to >10% and from <2% to >18%, respectively. Normalizing the results with serum samples significantly improved the comparability among laboratories and methods. After normalization, the differences among laboratories in mean response were no longer statistically significant (P = 0.24), with least-squares means of 0.93–1.02.

Conclusions: C-peptide results generated by different methods and laboratories do not always agree, especially at higher C-peptide concentrations. Within-laboratory imprecision also varied, with some methods giving much more consistent results than others. These data show that calibrating C-peptide measurement to a reference method can increase comparability between laboratories.

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