The DOI matches its Crossref record; 36 of the 38 references it cites were found in a registry or an index.
Reliability of Serum Metabolite Concentrations over a 4-Month Period Using a Targeted Metabolomic Approach
Open accessAbstract
Metabolomics is a promising tool for discovery of novel biomarkers of chronic disease risk in prospective epidemiologic studies. We investigated the between- and within-person variation of the concentrations of 163 serum metabolites over a period of 4 months to evaluate the metabolite reliability expressed by the intraclass-correlation coefficient (ICC: the ratio of between-person variance and total variance). The analyses were performed with the BIOCRATES AbsoluteIDQ™ targeted metabolomics technology, including acylcarnitines, amino acids, glycerophospholipids, sphingolipids and hexose in 100 healthy individuals from the European Prospective Investigation into Cancer and Nutrition (EPIC)-Potsdam study who had provided two fasting blood samples 4 months apart. Overall, serum reliability of metabolites over a 4-month period was good. The median ICC of the 163 metabolites was 0.57. The highest ICC was observed for hydroxysphingomyelin C14:1 (ICC = 0.85) and the lowest was found for acylcarnitine C3:1 (ICC = 0). Reliability was high for hexose (ICC = 0.76), sphingolipids (median ICC = 0.66; range: 0.24-0.85), amino acids (median ICC = 0.58; range: 0.41-0.72) and glycerophospholipids (median ICC = 0.58; range: 0.03-0.81). Among acylcarnitines, reliability of short and medium chain saturated compounds was good to excellent (ICC range: 0.50-0.81). Serum reliability was lower for most hydroxyacylcarnitines and monounsaturated acylcarnitines (ICC range: 0.11-0.45 and 0.00-0.63, respectively). For most of the metabolites a single measurement may be sufficient for risk assessment in epidemiologic studies with healthy subjects.
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.
Claims other papers make about this one
Each is checked against this paper’s own text: does it say what the citing paper says it does?
In the citing paper
“On the basis of a previous evaluation of the reliability of measurements (23), we excluded metabolites below the limit of detection (n = 30) and those with very high analytic variance (n = 6).”
From Amino acids, lipid metabolites, and ferritin as potential mediators linking red meat consumption to type 2 diabetes (2015) · cites this paper as [23]
In the citing paper
“However, FFQs showed good reliability and relative validity to assess meat intake, and investigated metabolites showed good reliability in validation studies (23).”
From Amino acids, lipid metabolites, and ferritin as potential mediators linking red meat consumption to type 2 diabetes (2015) · cites this paper as [23]
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
The Design and Analysis of Clinical Experiments
Fleiss, J. L. (1999) · Wiley eBooks · doi:10.1002/9781118032923 · Cited by 4,819
Fundamentals of Biostatistics.
(1969) · Annals of Internal Medicine · doi:10.7326/0003-4819-70-4-873 · Cited by 4,573
Zelena, E., Dunn, W. B., Broadhurst, D., Francis‐McIntyre, S., Carroll, K. M., Begley, P., O’Hagan, S., Knowles, J. D., Halsall, A., HUSERMET Consortium, et al. (2009) · Analytical Chemistry · doi:10.1021/ac8019366 · Cited by 815
Metabolomics-based methods for early disease diagnostics
Gowda, G. A. N., Zhang, S., Gu, H., Asiago, V. M., Shanaiah, N., & Raftery, D. (2008) · Expert Review of Molecular Diagnostics · doi:10.1586/14737159.8.5.617 · Cited by 705
Mihalik, S. J., Goodpaster, B. H., Kelley, D. E., Chace, D. H., Vockley, J., Toledo, F. G. S., & DeLany, J. P. (2010) · Obesity · doi:10.1038/oby.2009.510 · Cited by 653
A genome-wide perspective of genetic variation in human metabolism
Illig, T., Gieger, C., Zhai, G., Römisch-Margl, W., Wang-Sattler, R., Prehn, C., Altmaier, E., Kastenmüller, G., Kato, B. S., Mewes, H.-W., et al. (2010) · Nature Genetics · doi:10.1038/ng.507 · Cited by 653 · cited 1×
“This metabolomic approach has already been successfully applied in the human KORA cohort study [7,8], and covers acylcarnitines, amino acids, glycerophospholipids, sphingolipids and hexoses, constituting a biologically relevant panel of 163 metabolites.”
MethodAdams, S. H., Hoppel, C. L., Lok, K. H., Zhao, L., Wong, S. W., Minkler, P. E., Hwang, D. H., Newman, J. W., & Garvey, W. T. (2009) · The Journal of Nutrition · doi:10.3945/jn.108.103754 · Cited by 598 · cited 1×
“Increased acylcarnitine concentrations have been associated with type 2 diabetes mellitus previously [18,19].”
BackgroundRecruitment Procedures of EPIC-Germany
Boeing, H., Korfmann, A., & Bergmann, M. M. (1999) · Annals of Nutrition and Metabolism · doi:10.1159/000012787 · Cited by 307
Fleiss, J. L. (1999) · doi:10.1002/9781118032923.ch1 · Cited by 296 · cited 4×
“To address this issue, the present study aimed to evaluate the reliability, expressed by the intraclass-correlation coefficient (ICC) [5], as the ratio of between-person variance and total variance, of 163 targeted metabolites in fasting serum samples over a 4-month period within a sub-sample of the European Prospecti…”
BackgroundMethodInfluentialRömisch‐Margl, W., Prehn, C. P., Bogumil, R., Röhring, C., Suhre, K., & Adamski, J. (2011) · Metabolomics · doi:10.1007/s11306-011-0293-4 · Cited by 292
Flagged first, then by citations
2 of the 38 references couldn’t be matched to a known paper.
Research works printed but not linked: 1
- KM Weinberger, 2008, [Metabolomics in diagnosing metabolic diseases]., Ther Umsch, 65, 487
Cited by
Sorted by citations
Items 101–134 of 134
Intra- and inter-individual variation of the human lipidome
Anh, N. H., Beyene, H. B., Mocciaro, G., Hahnefeld, L., Lamichhane, S., Fabritius, M., Avela, H. F., Wang, Y., Dickens, A. M., Gurke, R., et al. (2025) · TrAC Trends in Analytical Chemistry · doi:10.1016/j.trac.2025.118368 · Cited by 7
Veiga, S. J., Wahrheit, J., Rodríguez‐Martín, A., & Sonntag, D. (2018) · Methods in molecular biology · doi:10.1007/978-1-4939-7816-8_28 · Cited by 6
Sources of Variability in Serum Lipidomic Measurements and Implications for Epidemiologic Studies
Naudin, S., Sampson, J. N., Moore, S. C., & Stolzenberg‐Solomon, R. Z. (2022) · American Journal of Epidemiology · doi:10.1093/aje/kwac106 · Cited by 6
Duisters, K. L. W., Ogino, S., Andou, T., Ito, K., Akabane, T., Harms, A. C., Moerland, M., Hashimoto, Y., Ando, A., Ohtsu, Y., et al. (2019) · Clinical Pharmacology & Therapeutics · doi:10.1002/cpt.1606 · Cited by 5
“Floegel et al.(10) link the between variance proportion in plasma to biomolecule family (high for hexose, sphingolipids, amino acids, and glycerophospholipids; low for most acylcarnitines).”
BackgroundMetabolomics: An Emerging Tool for Precision Medicine
Meiliana, A., Dewi, N. M., & Wijaya, A. (2021) · The Indonesian Biomedical Journal · doi:10.18585/inabj.v13i1.1309 · Cited by 5
“The 4 months reproducibility is good, and data showed a single measurement is sufficient for risk assessment in epidemiologic studies using healthy subjects.(99) Hence, we could collect data on metabolite properties to create a dietary biomarker classification (i.”
BackgroundLiu, L., Cai, H., Yang, H., Wang, S., Li, Y., Huang, Y., Gao, M., Zhang, X., Zhang, X., Wang, H., et al. (2024) · Metabolism · doi:10.1016/j.metabol.2024.156085 · Cited by 5
Darragh, I. A. J., O’Driscoll, L., & Egan, B. (2023) · International Journal of Sport Nutrition and Exercise Metabolism · doi:10.1123/ijsnem.2022-0177 · Cited by 4
MetaboVariation: Exploring Individual Variation in Metabolite Levels
Gupta, S., Gormley, I. C., & Brennan, L. (2023) · Metabolites · doi:10.3390/metabo13020164 · Cited by 3
“With respect to the two metabolites with the highest number of individuals flagged, the ICCs previously reported were good, indicating that our method is capable of picking up individual variation that may be missed with an ICC approach [28,29].”
The potential use of biomarkers in the prevention of Type 2 diabetes
Steinbrecher, A., & Pischon, T. (2013) · Expert Review of Endocrinology & Metabolism · doi:10.1586/eem.13.11 · Cited by 3
Adamski, J. (2020) · Elsevier eBooks · doi:10.1016/b978-0-12-812784-1.00001-3 · Cited by 3
van Roekel, E. H., Bours, M. J. L., van Delden, L., Breukink, S. O., Aquarius, M., Keulen, E. T. P., Gicquiau, A., Viallon, V., Rinaldi, S., Vineis, P., et al. (2021) · Scientific Reports · doi:10.1038/s41598-021-92279-9 · Cited by 3
Breeur, M., Ferrari, P., Dossus, L., Jenab, M., Johansson, M., Rinaldi, S., Travis, R. C., His, M., Key, T., Schmidt, J. A., et al. (2022) · medRxiv · doi:10.1101/2022.04.11.22273693 · Cited by 3
Intra-individual variability of the human seminal plasma metabolome
Blaurock, J., Grunewald, S., & Engel, K. M. (2025) · Asian Journal of Andrology · doi:10.4103/aja2024110 · Cited by 2
“23,24 The CVW covered a similar range as in our work.”
InfluentialWinkvist, A., Johansson, I., Ellegård, L., & Lindqvist, H. M. (2024) · Nutrition Journal · doi:10.1186/s12937-024-00929-1 · Cited by 3
“Previous research has shown this to be the case: Floegel and colleagues [28] used repeated fasting serum samples collected 4 months apart and demonstrated that reliability for most of the 163 metabolites evaluated was good.”
ResultBarko, P. C., Jambhekar, A., Swanson, K. S., Ridgway, M. D., & Williams, D. A. (2024) · PLoS ONE · doi:10.1371/journal.pone.0303500 · Cited by 2
“Untargeted metabolomics investigations have been utilized to study the influence of nutrition and various diseases including diabetes mellitus, obesity, chronic renal disease, mammary carcinoma, and exocrine pancreatic insufficiency on circulating metabolomes in cats [8–14].”
MethodLin, Y., Li, Y., & Caldwell, G. W. (2021) · Methods in pharmacology and toxicology · doi:10.1007/978-1-0716-1542-3_20 · Cited by 2
Masucci, J. A., Liang, F., Bingol, K., Windisch, V., & Caldwell, G. W. (2021) · Methods in pharmacology and toxicology · doi:10.1007/978-1-0716-1542-3_21 · Cited by 2
Goerdten, J., Rattner, J. I., Merdas, M., Achaintre, D., Li, Y., Russo, P., Veidebaum, T., Molnár, D., Lissner, L., Henauw, S. D., et al. (2025) · Journal of Nutrition · doi:10.1016/j.tjnut.2025.10.039 · Cited by 1
“There are several studies examining the reproducibility of blood metabolites, which show moderate-to-excellent reproducibility for short-and long-term exposure [6 – 9].”
Alkylresorcinols and Their Metabolites as Biomarkers of Whole-Grain Rye and Wheat Intake
Landberg, R., Kamal‐Eldin, A., Marklund, M., Adlercreutz, H., & Åman, P. (2014) · Elsevier eBooks · doi:10.1016/b978-1-891127-81-6.50010-9 · Cited by 1
Metabolomics and Its Role in the Study of Mammalian Systems
Dunn, W. B., Mamas, M. A., & Heazell, A. E. P. (2013) · Wiley eBooks · doi:10.1002/9783527655861.ch14 · Cited by 1
Metabolic signatures related to diet in human urine and blood specimens
Lindelöf, M. (2012)
Human samples for health assessments
Whitfield, P. D., & Doherty, M. K. (2013) · Elsevier eBooks · doi:10.1533/9780857098818.2.83
Saleem, M. (2017) · TSpace (University of Toronto)
Metzler, L. A. P., Goy, R. W., Metzger, J. M., Emborg, M. E., & Kapoor, A. (2025) · Frontiers in Genetics · doi:10.3389/fgene.2025.1655325
“…two exclusion criteria were applied: (1) only metabolites detected in at least 80% of all samples were retained and (2) metabolites with a coef fi cient of variation (CV) greater than 20% across replicates were excluded to minimize the in fl uence of technical variability (Floegel et al., 2011).”
Additional file 1 of Biomarkers of mammographic density in premenopausal women
His, M., Lajous, M., Gómez‐Flores‐Ramos, L., Monge, A., Dossus, L., Viallon, V., Gicquiau, A., Biessy, C., Gunter, M. J., & Rinaldi, S. (2021) · Figshare · doi:10.6084/m9.figshare.15047689
Breeur, M., Ferrari, P., Dossus, L., Jenab, M., Johansson, M., Rinaldi, S., Travis, R. C., His, M., Key, T. J., Schmidt, J. A., et al. (2022) · Figshare · doi:10.6084/m9.figshare.21358175
Breeur, M., Ferrari, P., Dossus, L., Jenab, M., Johansson, M., Rinaldi, S., Travis, R. C., His, M., Key, T. J., Schmidt, J. A., et al. (2022) · Figshare · doi:10.6084/m9.figshare.21358172
Winkvist, A., Johansson, I., Ellegård, L., & Lindqvist, H. M. (2024) · Figshare · doi:10.6084/m9.figshare.25329685
Longitudinal, Intra-Individual Stability of Untargeted Plasma and Cerebrospinal Fluid Metabolites
Rocha, B. L., Jonaitis, E. M., Hamwi, A., & Engelman, C. D. (2025) · Metabolites · doi:10.3390/metabo16010035
A metabolomic exploration of processed meat intake : dietary biomarkers and metabolic effects
Wedekind, R. (2020) · theses.fr (ABES) · doi:10.70675/9feb21c7z42f5z4b13z84edz05b650b15f0f
Han, S., Goodrich, J. A., Wang, H., Walker, D. I., Wright, R. O., Chatzi, L., Conti, D. V., & Setiawan, V. W. (2026) · Cancer Epidemiology Biomarkers & Prevention · doi:10.1158/1055-9965.epi-26-0048
Larnder, A., Murphy, R. A., Srour, B., Dechamp, N., Galan, P., Herçberg, S., Bourhis, L., Díaz, C., Genilloud, O., Touvier, M., et al. (2026) · American Journal of Clinical Nutrition · doi:10.1016/j.ajcnut.2026.101342 · Cited by 1
“Limitations include the use of a limited, noncancer and non-diet-specific metabolomics panel, lack of information on molecular subtypes of breast cancer, and single-time-point metabolite measurements, though prior studies suggest stability over time [66,67].”
Dewi, D. L., Manik, E., Damayanti, E., Anwar, M., Suratno, & Iryanto, S. B. (2026) · Metabolomics · doi:10.1007/s11306-026-02459-9
“…time between blood drawn to actual metabolomics analysis, blood matrices, extraction methods, column type that might influence the results of identified metabolites (Emwas et al., 2025; Floegel et al., 2011; Guerra et al., 2026; Sampson et al., 2013; Wallace et al., 2010 ; Xiao et al., 2014).”
Metabolites Associated With Long-Term Risk of Peripheral Artery Disease: The ARIC Study
Bansah, E. C., Hu, X. F., Ballew, S. H., Grams, M. E., Nambi, V., Selvin, E., Coresh, J., Boerwinkle, E., Yu, B., & Matsushita, K. (2026) · Arteriosclerosis Thrombosis and Vascular Biology · doi:10.1161/atvbaha.126.324549
Metadata
- Authors
- Floegel, A., Drogan, D., Wang-Sattler, R., Prehn, C., Illig, T., Adamski, J., Joost, H.-G., Boeing, H., & Pischon, T.
- Affiliations
- German Institute of Human Nutrition (DE)
- Helmholtz Munich (DE)
- Max Delbrück Center (DE)
- Corresponding author
- Floegel, Anna
- ORCID
- Venue
- PLoS ONE
- Publisher
- Public Library of Science (PLoS)
- Published
- 2011-06-15
- Volume
- 6
- Issue
- 6
- Pages
- e21103
- ISSN
- 1932-6203 (ISSN-L)
- PMC
- PMC3115978
- OpenAlex
- W2042418561
- Cited by
- 135
- References
- 38
- Citations by year
- 2026: 4 · 2025: 5 · 2024: 5 · 2023: 5 · 2022: 9 · 2021: 14 · 2020: 9 · 2019: 7 · 2018: 14 · 2017: 15 · 2016: 14 · 2015: 6 · 2014: 13 · 2013: 7 · 2012: 7
- FWCI
- 3.63
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Locations
- PLoS ONE (publishedVersion, cc-by)
- PubMed Central (publishedVersion, cc-by)
- PubMed (publishedVersion)
- MDC Repository (Max-Delbrueck-Center for Molecular Medicine) (acceptedVersion)
- RePEc: Research Papers in Economics (submittedVersion)
- DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
- Figshare (submittedVersion, cc-by)
- Site cant be reached (submittedVersion, cc-by)
- Funders
- Bundesministerium für Bildung und Forschung (01GI0922)
- Topics
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Metabolomics and Mass Spectrometry Studies
- Health Sciences › Health Professions › Medical Laboratory Technology › Clinical Laboratory Practices and Quality Control
- Physical Sciences › Environmental Science › Health, Toxicology and Mutagenesis › Health, Environment, Cognitive Aging
- Keywords
- serum metabolites, intraclass correlation coefficient, between-person variation, within-person variability, targeted metabolomics, acylcarnitines, sphingolipids, glycerophospholipids, amino acids, hexoses, biomarkers, chronic disease risk
- MeSH terms
- Female, Humans, Male, Middle Aged, Reproducibility of Results, Serum, Metabolomics
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
