The DOI matches its Crossref record; 95 of the 95 references it cites were found in a registry or an index.
Effects of pre-analytical processes on blood samples used in metabolomics studies
Open accessAbstract
Every day, analytical and bio-analytical chemists make sustained efforts to improve the sensitivity, specificity, robustness, and reproducibility of their methods. Especially in targeted and non-targeted profiling approaches, including metabolomics analysis, these objectives are not easy to achieve; however, robust and reproducible measurements and low coefficients of variation (CV) are crucial for successful metabolomics approaches. Nevertheless, all efforts from the analysts are in vain if the sample quality is poor, i.e. if preanalytical errors are made by the partner during sample collection. Preanalytical risks and errors are more common than expected, even when standard operating procedures (SOP) are used. This risk is particularly high in clinical studies, and poor sample quality may heavily bias the CV of the final analytical results, leading to disappointing outcomes of the study and consequently, although unjustified, to critical questions about the analytical performance of the approach from the partner who provided the samples. This review focuses on the preanalytical phase of liquid chromatography-mass spectrometry-driven metabolomics analysis of body fluids. Several important preanalytical factors that may seriously affect the profile of the investigated metabolome in body fluids, including factors before sample collection, blood drawing, subsequent handling of the whole blood (transportation), processing of plasma and serum, and inadequate conditions for sample storage, will be discussed. In addition, a detailed description of latent effects on the stability of the blood metabolome and a suggestion for a practical procedure to circumvent risks in the preanalytical phase will be given.
Overview
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Integrity signals
Registry
Cites
Statistics
Verification
Claims the paper makes
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
The self-check needs the paper’s full text.
Citations
Cites
Nicholson, J. K., Lindon, J. C., & Holmes, E. (1999) · Xenobiotica · doi:10.1080/004982599238047 · Cited by 3,895
The Human Serum Metabolome (HUSERMET) Consortium, Dunn, W. B., Broadhurst, D., Begley, P., Zelena, E., Francis-McIntyre, S., Anderson, N., Brown, M., Knowles, J. D., Halsall, A., et al. (2011) · Nature Protocols · doi:10.1038/nprot.2011.335 · Cited by 3,294
Newgard, C. B., An, J., Bain, J. R., Muehlbauer, M. J., Stevens, R. D., Lien, L. F., Haqq, A. M., Shah, S. H., Arlotto, M., Slentz, C. A., et al. (2009) · Cell Metabolism · doi:10.1016/j.cmet.2009.02.002 · Cited by 3,165
Metabolite profiles and the risk of developing diabetes
Wang, T. J., Larson, M. G., Vasan, R. S., Cheng, S., Rhee, E. P., McCabe, E., Lewis, G. D., Fox, C. S., Jacques, P. F., Fernandez, C., et al. (2011) · Nature Medicine · doi:10.1038/nm.2307 · Cited by 3,143
Metabolomic profiles delineate potential role for sarcosine in prostate cancer progression
Sreekumar, A., Poisson, L. M., Rajendiran, T. M., Khan, A. P., Cao, Q., Yu, J., Laxman, B., Mehra, R., Lonigro, R. J., Li, Y., et al. (2009) · Nature · doi:10.1038/nature07762 · Cited by 2,234
Bouatra, S., Aziat, F., Mandal, R., Guo, A. C., Wilson, M. R., Knox, C., Bjorndahl, T. C., Krishnamurthy, R., Saleem, F., Liu, P., et al. (2013) · PLoS ONE · doi:10.1371/journal.pone.0073076 · Cited by 1,449
Total homocysteine in plasma or serum: methods and clinical applications
Ueland, P. M., Refsum, H., Stabler, S. P., Malinow, M. R., Andersson, A., & Allen, R. H. (1993) · Clinical Chemistry · doi:10.1093/clinchem/39.9.1764 · Cited by 981
Large-Scale Human Metabolomics Studies: A Strategy for Data (Pre-) Processing and Validation
Bijlsma, S., Bobeldijk, I., Verheij, E. R., Ramaker, R., Kochhar, S., Macdonald, I. A., van Ommen, B., & Smilde, A. K. (2006) · Analytical Chemistry · doi:10.1021/ac051495j · Cited by 867
Clinical Applications of Metabolomics in Oncology: A Review
Spratlin, J. L., Serkova, N. J., & Eckhardt, S. G. (2009) · Clinical Cancer Research · doi:10.1158/1078-0432.ccr-08-1059 · Cited by 753
Metabolomics: A Global Biochemical Approach to Drug Response and Disease
Kaddurah-Daouk, R., Kristal, B. S., & Weinshilboum, R. M. (2008) · Annual Review of Pharmacology and Toxicology · doi:10.1146/annurev.pharmtox.48.113006.094715 · Cited by 670
Cited by
Metabolomics for clinical use and research in chronic kidney disease
Hocher, B., & Adamski, J. (2017) · Nature Reviews Nephrology · doi:10.1038/nrneph.2017.30 · Cited by 339
Burla, B., Arita, M., Arita, M., Bendt, A. K., Cazenave-Gassiot, A., Dennis, E. A., Ekroos, K., Han, X., Ikeda, K., Liebisch, G., et al. (2018) · Journal of Lipid Research · doi:10.1194/jlr.s087163 · Cited by 312
Promises and pitfalls of untargeted metabolomics
Gertsman, I., & Barshop, B. A. (2018) · Journal of Inherited Metabolic Disease · doi:10.1007/s10545-017-0130-7 · Cited by 271
Fisher, W. E., Cruz-Monserrate, Z., McElhany, A. L., Lesinski, G. B., Hart, P. A., Ghosh, R., Van Buren, G., Fishman, D. S., Rinaudo, J. A. S., Serrano, J., et al. (2018) · Pancreas · doi:10.1097/mpa.0000000000001171 · Cited by 34
Osakunor, D. N. M., Mduluza, T., Osei-Hyiaman, D., Burgess, K., Woolhouse, M. E. J., & Mutapi, F. (2020) · PLoS neglected tropical diseases · doi:10.1371/journal.pntd.0008866 · Cited by 20
Metadata
- Authors
- Yin, P., Lehmann, R., & Xu, G.
- Venue
- Analytical and Bioanalytical Chemistry
- Published
- 2015-03-04
- Volume
- 407
- Issue
- 17
- Pages
- 4879-4892
- ISSN
- 1618-2642 (ISSN-L)
- 1618-2650
- OpenAlex
- W1986202285
- Cited by
- 278
- References
- 95
- FWCI
- 11.45
- Type
- journal article
- Language
- en
- Open access
- hybrid
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review
How to cite
Cite CoChat for the checks on this page, and the paper for its own findings.
- This page
- CoChat. (2026). Citation check: Effects of pre-analytical processes on blood samples used in metabolomics studies. https://app.cochat.ai/papers/10.1007/s00216-015-8565-x
- The paper
- Yin, P., Lehmann, R., & Xu, G. (2015). Effects of pre-analytical processes on blood samples used in metabolomics studies. Analytical and Bioanalytical Chemistry. https://doi.org/10.1007/s00216-015-8565-x
Data sources
Bibliographic data: Semantic Scholar, OpenAlex, Crossref.
Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.