Skip to main page content
U.S. flag

An official website of the United States government

Dot gov

The .gov means it’s official.
Federal government websites often end in .gov or .mil. Before sharing sensitive information, make sure you’re on a federal government site.

Https

The site is secure.
The https:// ensures that you are connecting to the official website and that any information you provide is encrypted and transmitted securely.

Access keys NCBI Homepage MyNCBI Homepage Main Content Main Navigation
. 2017 Mar:466:105-111.
doi: 10.1016/j.cca.2017.01.005. Epub 2017 Jan 6.

Analytes related to erythrocyte metabolism are reliable biomarkers for preanalytical error due to delayed plasma processing in metabolomics studies

Mahim Jain  1 , Adam D Kennedy  2 , Sarah H Elsea  1 , Marcus J Miller  3
Affiliations

Analytes related to erythrocyte metabolism are reliable biomarkers for preanalytical error due to delayed plasma processing in metabolomics studies

Mahim Jain et al. Clin Chim Acta. 2017 Mar.

Abstract

Background: Delaying plasma separation after phlebotomy (processing delay) can cause perturbations of numerous small molecule analytes. This poses a major challenge to the clinical application of metabolomics analyses. In this study, we further define the analyte changes that occur during processing delays and generate a model for the post hoc detection of this preanalytical error.

Methods: Using an untargeted metabolomics platform we analyzed EDTA-preserved plasma specimens harvested after processing delays lasting from minutes to days. Identified biomarkers were tested on (i) a test-set of samples exposed to either minimal (n=28) or long delays (n=40) and (ii) samples collected in a clinical setting for metabolomics analysis (n=141).

Results: A total of 149 of 803 plasma analytes changed significantly during processing delays lasting 0-20h. Biomarkers related to erythrocyte metabolism, e.g., 5-oxoproline, lactate, and an ornithine/arginine ratio, were the strongest predictors of plasma separation delays, providing 100% diagnostic accuracy in the test set. Together these biomarkers could accurately predict processing delays >2h in a pilot study and we found evidence of sample mishandling in 4 of 141 clinically derived specimens.

Conclusions: Our study highlights the widespread effects of processing delays and proposes that erythrocyte metabolism creates a reproducible signal that can identify mishandled specimens in metabolomics studies.

Keywords: Clinical metabolomics; Phlebotomy; Preanalytical error; Quality control; Whole blood stability.

PubMed Disclaimer

Conflict of interest statement

Declaration of interest

Mahim Jain, Sarah H. Elsea and Marcus J. Miller are members of the Department of Molecular and Human Genetics at Baylor College of Medicine, and this department, alone or as part of a joint venture with Miraca Holdings, offers a number of clinical tests on a fee-for-service basis, but these in no way conflict with the research reported here. Adam D. Kennedy is an employee of Metabolon, Inc. and, as such, has affiliations with or financial involvement with Metabolon, Inc. The authors have no other relevant affiliations or financial involvement with any organization or entity with a financial interest in or financial conflict with the subject matter or materials discussed in the manuscript apart from those disclosed.

Figures

Fig. 1
Fig. 1
Analyte perturbations during processing delays. The heat maps show the average fold change values across two independent assays (0–20 h and 0–4 day studies) for a subset of relevant analytes (i.e., named compounds that underwent a significant fold change (FDR < 0.01) during the 0–20 h time course assay). *GPE = glycero-3-phosphoethanolamine; GPI= glycerophosphoinositol.
Fig. 2
Fig. 2
Utility of single biomarkers in the detection of processing delays. (A) The %GAP calculation used to determine test-set diagnostic performance is shown for 5-oxoproline. (−) indicates TESTneg and (+) indicates TESTpos samples. (B) The %GAP was calculated for all biomarkers identified in the 0–20 h time course assay. %GAP values are plotted in terms of p-values (heteroscedastic Student's t-test) to illustrate the utility of the %GAP approach as opposed to standard analyses of population mean differences. Dark circles indicate analytes with a positive %GAP value, that were thus capable of 100% diagnostic accuracy in the test set.
Fig. 3
Fig. 3
A ratio of ornithine to arginine is a strong biomarker for processing delays. (A) Ornithine, (B) arginine, and (C) ornithine/arginine values are shown for the test-set. (−) indicates TESTneg and (+) indicates TESTpos samples.
See this image and copyright information in PMC

Similar articles

See all similar articles

Cited by

See all "Cited by" articles

References

    1. Plebani M, Sciacovelli L, Aita A, Padoan A, Chiozza ML. Quality indicators to detect pre-analytical errors in laboratory testing. Clin. Chim. Acta. 2014;432:44–48. - PubMed
    1. Kellogg MD, Ellervik C, Morrow D, Hsing A, Stein E, Sethi AA. Preanalytical considerations in the design of clinical trials and epidemiological studies. Clin. Chem. 2015;61(6):797–803. - PubMed
    1. Yin P, Lehmann R, Xu G. Effects of pre-analytical processes on blood samples used in metabolomics studies. Anal. Bioanal. Chem. 2015;407(17):4879–4892. - PMC - PubMed
    1. Patti GJ, Yanes O, Siuzdak G. Innovation: metabolomics: the apogee of the omics trilogy. Nat. Rev. Mol. Cell Biol. 2012;13(4):263–269. - PMC - PubMed
    1. Miller MJ, Kennedy AD, Eckhart AD, Burrage LC, Wulff JE, Miller LA, et al. Untargeted metabolomic analysis for the clinical screening of inborn errors of metabolism. J. Inherit. Metab. Dis. 2015;38(6):1029–1039. - PMC - PubMed

LinkOut - more resources