Targeted metabolomics identifies reliable and stable metabolites in human serum and plasma samples

Michaela Breier, Simone Wahl, Cornelia Prehn, Marina Fugmann, Uta Ferrari, Michaela Weise, Friederike Banning, Jochen Seissler, Harald Grallert, Jerzy Adamski, Andreas Lechner, Michaela Breier, Simone Wahl, Cornelia Prehn, Marina Fugmann, Uta Ferrari, Michaela Weise, Friederike Banning, Jochen Seissler, Harald Grallert, Jerzy Adamski, Andreas Lechner

Abstract

Background: Information regarding the variability of metabolite levels over time in an individual is required to estimate the reproducibility of metabolite measurements. In intervention studies, it is critical to appropriately judge changes that are elicited by any kind of intervention. The pre-analytic phase (collection, transport and sample processing) is a particularly important component of data quality in multi-center studies.

Methods: Reliability of metabolites (within-and between-person variance, intraclass correlation coefficient) and stability (shipment simulation at different temperatures, use of gel-barrier collection tubes, freeze-thaw cycles) were analyzed in fasting serum and plasma samples of 22 healthy human subjects using a targeted LC-MS approach.

Results: Reliability of metabolite measurements was higher in serum compared to plasma samples and was good in most saturated short-and medium-chain acylcarnitines, amino acids, biogenic amines, glycerophospholipids, sphingolipids and hexose. The majority of metabolites were stable for 24 h on cool packs and at room temperature in non-centrifuged tubes. Plasma and serum metabolite stability showed good coherence. Serum metabolite concentrations were mostly unaffected by tube type and one or two freeze-thaw cycles.

Conclusion: A single time point measurement is assumed to be sufficient for a targeted metabolomics analysis of most metabolites. For shipment, samples should ideally be separated and frozen immediately after collection, as some amino acids and biogenic amines become unstable within 3 h on cool packs. Serum gel-barrier tubes can be used safely for this process as they have no effect on concentration in most metabolites. Shipment of non-centrifuged samples on cool packs is a cost-efficient alternative for most metabolites.

Conflict of interest statement

Competing Interests: The authors have declared that no competing interests exist.

Figures

Figure 1. Median ICC with confidence intervals…
Figure 1. Median ICC with confidence intervals of serum metabolites.
(A) Metabolites with median ICC-values below 0.65 and (B) metabolites with median ICC values above 0.65 are displayed.
Figure 2. Histogram of within-subject coefficient of…
Figure 2. Histogram of within-subject coefficient of variance (WCV) in serum with mark at CV = 0.25.
Figure 3. Stability of metabolites in plasma…
Figure 3. Stability of metabolites in plasma during shipment simulation.
Example of (A), (D) decreasing and (B), (C) increasing metabolite concentration of plasma samples at room temperature (RT) and on cool packs (CP). Stars in boxplots indicate significant difference in concentration compared to baseline (0 h). (Wilcoxon signed rank test, significance level p

Figure 4. Stability of metabolites in serum…

Figure 4. Stability of metabolites in serum during shipment simulation.

Example of (A)-(C) increasing and…

Figure 4. Stability of metabolites in serum during shipment simulation.
Example of (A)-(C) increasing and (D) decreasing metabolite concentration during transportation simulation of serum samples on cool packs (CP). Stars in boxplots indicate significant difference in concentration compared to baseline (0 h). (Wilcoxon signed rank test, significance level p

Figure 5. Effect of tube type on…

Figure 5. Effect of tube type on serum metabolites.

Stars in boxplots indicate significant differences…

Figure 5. Effect of tube type on serum metabolites.
Stars in boxplots indicate significant differences in concentration between methionine sulfoxide in serum W tubes with clotting activator and serum gel-barrier tubes. (Friedman test, significance level p
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References
    1. Ma J, Folsom AR, Eckfeldt JH, Lewis L, Chambless LE (1995) Short- and long-term repeatability of fatty acid composition of human plasma phospholipids and cholesterol esters. The Atherosclerosis Risk in Communities (ARIC) Study Investigators. Am J Clin Nutr 62: 572–578. - PubMed
    1. Floegel A, Drogan D, Wang-Sattler R, Prehn C, Illig T, et al. (2011) Reliability of serum metabolite concentrations over a 4-month period using a targeted metabolomic approach. PLoS One 6: e21103. - PMC - PubMed
    1. Widjaja A, Morris RJ, Levy JC, Frayn KN, Manley SE, et al. (1999) Within- and between-subject variation in commonly measured anthropometric and biochemical variables. Clin Chem 45: 561–566. - PubMed
    1. Giltay EJ, Geleijnse JM, Schouten EG, Katan MB, Kromhout D (2003) High stability of markers of cardiovascular risk in blood samples. Clin Chem 49: 652–655. - PubMed
    1. Clark S, Youngman LD, Palmer A, Parish S, Peto R, et al. (2003) Stability of plasma analytes after delayed separation of whole blood: implications for epidemiological studies. Int J Epidemiol 32: 125–130. - PubMed
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This study was supported in part by a grant (01GI0925) from the German Federal Ministry of Education and Research (BMBF) (URL:http://www.bmbf.de/en/) to the German Center for Diabetes Research (DZD e.V.). The work leading to this publication has received support from the Innovative Medicines Initiative Joint Undertaking (URL:http://www.imi.europa.eu/) under grant agreement n°115317 (DIRECT), resources of which are composed of financial contribution from the Helmholtz association (URL:http://www.helmholtz.de/en/), the European Union’s Seventh Framework Programme (URL:http://cordis.europa.eu/fp7/home_en.html) (FP7/2007–2013) and EFPIA companies’ (URL:http://www.efpia.eu/about-us/membership) in kind contribution. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.
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Figure 4. Stability of metabolites in serum…
Figure 4. Stability of metabolites in serum during shipment simulation.
Example of (A)-(C) increasing and (D) decreasing metabolite concentration during transportation simulation of serum samples on cool packs (CP). Stars in boxplots indicate significant difference in concentration compared to baseline (0 h). (Wilcoxon signed rank test, significance level p

Figure 5. Effect of tube type on…

Figure 5. Effect of tube type on serum metabolites.

Stars in boxplots indicate significant differences…

Figure 5. Effect of tube type on serum metabolites.
Stars in boxplots indicate significant differences in concentration between methionine sulfoxide in serum W tubes with clotting activator and serum gel-barrier tubes. (Friedman test, significance level p
Similar articles
Cited by
References
    1. Ma J, Folsom AR, Eckfeldt JH, Lewis L, Chambless LE (1995) Short- and long-term repeatability of fatty acid composition of human plasma phospholipids and cholesterol esters. The Atherosclerosis Risk in Communities (ARIC) Study Investigators. Am J Clin Nutr 62: 572–578. - PubMed
    1. Floegel A, Drogan D, Wang-Sattler R, Prehn C, Illig T, et al. (2011) Reliability of serum metabolite concentrations over a 4-month period using a targeted metabolomic approach. PLoS One 6: e21103. - PMC - PubMed
    1. Widjaja A, Morris RJ, Levy JC, Frayn KN, Manley SE, et al. (1999) Within- and between-subject variation in commonly measured anthropometric and biochemical variables. Clin Chem 45: 561–566. - PubMed
    1. Giltay EJ, Geleijnse JM, Schouten EG, Katan MB, Kromhout D (2003) High stability of markers of cardiovascular risk in blood samples. Clin Chem 49: 652–655. - PubMed
    1. Clark S, Youngman LD, Palmer A, Parish S, Peto R, et al. (2003) Stability of plasma analytes after delayed separation of whole blood: implications for epidemiological studies. Int J Epidemiol 32: 125–130. - PubMed
Show all 31 references
Publication types
MeSH terms
Grant support
This study was supported in part by a grant (01GI0925) from the German Federal Ministry of Education and Research (BMBF) (URL:http://www.bmbf.de/en/) to the German Center for Diabetes Research (DZD e.V.). The work leading to this publication has received support from the Innovative Medicines Initiative Joint Undertaking (URL:http://www.imi.europa.eu/) under grant agreement n°115317 (DIRECT), resources of which are composed of financial contribution from the Helmholtz association (URL:http://www.helmholtz.de/en/), the European Union’s Seventh Framework Programme (URL:http://cordis.europa.eu/fp7/home_en.html) (FP7/2007–2013) and EFPIA companies’ (URL:http://www.efpia.eu/about-us/membership) in kind contribution. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.
[x]
Cite
Copy Download .nbib
Format: AMA APA MLA NLM
Figure 5. Effect of tube type on…
Figure 5. Effect of tube type on serum metabolites.
Stars in boxplots indicate significant differences in concentration between methionine sulfoxide in serum W tubes with clotting activator and serum gel-barrier tubes. (Friedman test, significance level p

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