The DOI matches its Crossref record; 73 of the 74 references it cites were found in a registry or an index.
Opening up the “black box”: metabolic phenotyping and metabolome-wide association studies in epidemiology
Open accessAbstract
Background Metabolic phenotyping of humans allows information to be captured on the interactions between dietary, xenobiotic, other lifestyle and environmental exposures, and genetic variation, which together influence the balance between health and disease risks at both individual and population levels.Objectives We describe here the main procedures in large-scale metabolic phenotyping and their application to metabolome-wide association (MWA) studies.Methods By use of high-throughput technologies and advanced spectroscopic methods, application of metabolic profiling to large-scale epidemiologic sample collections, including metabolome-wide association (MWA) studies for biomarker discovery and identification.Discussion Metabolic profiling at epidemiologic scale requires optimization of experimental protocol to maximize reproducibility, sensitivity, and quantitative reliability, and to reduce analytical drift. Customized multivariate statistical modeling approaches are needed for effective data visualization and biomarker discovery with control for false-positive associations since 100s or 1,000s of complex metabolic spectra are being processed.Conclusion Metabolic profiling is an exciting addition to the armamentarium of the epidemiologist for the discovery of new disease-risk biomarkers and diagnostics, and to provide novel insights into etiology, biological mechanisms, and pathways.
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
“MWASs have been proposed as useful tools for discovering low-molecular-weight biomarkers that are predictive of either causal exposures or disease progression (59, 165, 166).”
From The food metabolome: a window over dietary exposure (2014) · cites this paper as [166]
In the citing paper
“The investigators showed that metabolite concentrations differed substantially between Asian and Western populations, suggesting important effects of diet and related risk factors, including the microbiota, on the risk of coronary artery disease and stroke.”
From The food metabolome: a window over dietary exposure (2014) · cites this paper as [166]
In the citing paper
“biomarkers were identified, namely alanine, which was directly correlated with blood pressure, and formate and hippurate, both of which were inversely correlated with blood pressure.”
From The food metabolome: a window over dietary exposure (2014) · cites this paper as [166]
In the citing paper
“The investigators showed that metabolite concentrations differed substantially between Asian and Western populations, suggesting important effects of diet and related risk factors, including the microbiota, on the risk of coronary artery disease and stroke.”
From The food metabolome: a window over dietary exposure (2014) · cites this paper as [166]
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
Kohonen, T. (1990) · Neurocomputing · doi:10.1016/s0925-2312(98)00030-7 · Cited by 10,686
Kohonen, T. (1990) · Proceedings of the IEEE · doi:10.1109/5.58325 · Cited by 8,414
Smith, C. A., Want, E. J., O’Maille, G., Abagyan, R. A., & Siuzdak, G. E. (2006) · Analytical Chemistry · doi:10.1021/ac051437y · Cited by 5,296
Nicholson, J. K., Lindon, J. C., & Holmes, E. C. (1999) · Xenobiotica · doi:10.1080/004982599238047 · Cited by 3,991
Anderson, E. N. (1977) · eScholarship (California Digital Library) · Cited by 2,567
Orthogonal projections to latent structures (O‐PLS)
Trygg, J., & Wold, S. (2002) · Journal of Chemometrics · doi:10.1002/cem.695 · Cited by 2,523
Metabolite profiling for plant functional genomics
Fiehn, O., Kopka, J., Dörmann, P., Altmann, T., Trethewey, R. N., & Willmitzer, L. (2000) · Nature Biotechnology · doi:10.1038/81137 · Cited by 2,129
Nicholson, J. K., & Lindon, J. C. (2008) · Nature · doi:10.1038/4551054a · Cited by 1,931
Metabonomics: a platform for studying drug toxicity and gene function
Nicholson, J. K., Connelly, J. C., Lindon, J. C., & Holmes, E. C. (2002) · Nature Reviews Drug Discovery · doi:10.1038/nrd728 · Cited by 1,915
The Generalization of Student's Ratio
Hotelling, H. (1931) · The Annals of Mathematical Statistics · doi:10.1214/aoms/1177732979 · Cited by 1,561
Cited by
Sorted by citations
Items 101–139 of 139
Distinct urinary metabolic profiles associated with serum TSH and FT4 concentrations
Pietzner, M., Budde, K., Homuth, G., Völker, U., Völzke, H., Nauck, M. A., Wallaschofski, H., & Friedrich, N. (2015) · Metabolomics · doi:10.1007/s11306-015-0788-5 · Cited by 7
Grant, C. W., Juran, B. D., Ali, A. H., Schlicht, E. M., Bianchi, J. K., Hu, X. Y., Liang, Y., Jarrell, Z. R., Liu, K., Go, Y., et al. (2023) · Exposome · doi:10.1093/exposome/osac011 · Cited by 6
Molecular Fingerprints of Iron Parameters among a Population-Based Sample
Kaul, A., Masuch, A., Budde, K., Kastenmüller, G., Artati, A., Adamski, J., Völzke, H., Nauck, M. A., Friedrich, N., & Pietzner, M. (2018) · Nutrients · doi:10.3390/nu10111800 · Cited by 6
The Significance of Metabolomics in Human Health
Gomez‐Casati, D. F., Grisolia, M. J., & Busi, M. V. (2016) · Elsevier eBooks · doi:10.1016/b978-0-12-420196-5.00007-1 · Cited by 8
Athersuch, T. J. (2018) · Springer eBooks · doi:10.1007/978-3-319-89321-1_6 · Cited by 7
Уколов, А. И., Orlova, T. I., Савельева, Е. И., & Radilov, A. S. (2015) · Journal of Analytical Chemistry · doi:10.1134/s1061934815090178 · Cited by 6
Metabolomics in environmental analysis: Applications, advances, and emerging challenges
Peng, L., Yan, H., & Cai, Z. (2025) · TrAC Trends in Analytical Chemistry · doi:10.1016/j.trac.2025.118556 · Cited by 5
Systems mapping of metabolic genes through control theory
Liu, G., Kong, L., Wang, Z., Wang, C., & Wu, R. (2013) · Advanced Drug Delivery Reviews · doi:10.1016/j.addr.2013.04.007 · Cited by 5
Mapping of population disparities in the cholangiocarcinoma urinary metabolome
Alsaleh, M., Leftley, Z., O’Connor, T. P., Hughes, T., Barbera, T. A., Koomson, L. K., Zabron, A., Reeves, H. L., Cramp, M., Ryder, S. D., et al. (2021) · Scientific Reports · doi:10.1038/s41598-021-00530-0 · Cited by 5
Uzoni, A., Ciobanu, O., Sandu, R. E., Bugă, A. M., & Popa‐Wagner, A. (2015) · Discoveries · doi:10.15190/d.2015.31 · Cited by 5
Assessment of proteins of the immune system
Abraham, R. S., Barnidge, D. R., & Lanza, I. R. (2013) · Elsevier eBooks · doi:10.1016/b978-0-7234-3691-1.00106-9 · Cited by 5
Prince, N., Liang, D., Tan, Y., Alshawabkeh, A. N., Angel, E. E., Busgang, S. A., Chu, S. H., Cordero, J. F., Curtin, P. C. P., Dunlop, A. L., et al. (2024) · Metabolomics · doi:10.1007/s11306-023-02082-y · Cited by 4
Wang, F., Wang, B., Chen, X., Liu, W., Wang, G., Li, X., Liu, X., Li, N., Zhang, J., Yin, T., et al. (2021) · Diabetes Metabolic Syndrome and Obesity · doi:10.2147/dmso.s324153 · Cited by 5
Noerman, S., Virtanen, J. K., Lehtonen, M., Brunius, C. F., & Hanhineva, K. (2022) · European Journal of Nutrition · doi:10.1007/s00394-022-03010-x · Cited by 4
Screening for New Markers to Assess Thyroid Hormone Action by OMICs Analysis of Human Samples
Friedrich, N., Pietzner, M., Engelmann, B., Homuth, G., Führer, D., Brabant, G. E., Wallaschofski, H., & Völker, U. (2020) · Experimental and Clinical Endocrinology & Diabetes · doi:10.1055/a-1144-2636 · Cited by 4
Current NMR strategies for biomarker discovery
Van, Q. N. (2013) · Elsevier eBooks · doi:10.1016/b978-0-12-818607-7.00007-4 · Cited by 4
Fradin, M., Noël, L.-P., Talbot-Lachance, G., Snell, P., Voyer, K., & Rhéaume, C. (2024) · Digital Twin · doi:10.12688/digitaltwin.17936.1 · Cited by 3
Metabolomics Approaches in Toxicology
Lin, Y. S., Shireman, L. M., & McFeely, S. J. (2017) · Elsevier eBooks · doi:10.1016/b978-0-12-801238-3.64207-1 · Cited by 2
Metabolomics Approach to Identify Biomarkers of Epidemic Diseases
Mina, P. R. (2023) · Springer eBooks · doi:10.1007/978-3-031-39094-4_4 · Cited by 2
Drug Discovery for Trematodiases: Challenges and Progress
Caffrey, C. R., Utzinger, J., & Keiser, J. (2012) · doi:10.1002/9783527652969.ch20 · Cited by 2
Prediction of complex phenotypes using the Drosophila metabolomePreprint
Rohde, P. D., Kristensen, T. N., Sarup, P. M., Muñoz, J., & Malmendal, A. (2020) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2020.06.11.145623 · Cited by 2
Comprehensive metabolic characterization of serum osteocalcin action in a large non-diabetic sample
Entenmann, L., Pietzner, M., Artati, A., Hannemann, A., Henning, A., Kastenmüller, G., Völzke, H., Nauck, M. A., Adamski, J., Wallaschofski, H., et al. (2017) · PLoS ONE · doi:10.1371/journal.pone.0184721 · Cited by 1
Genomic Analysis of B-Cell Lymphomas
Davis, R. E. (2013) · Humana Press eBooks · doi:10.1007/978-1-62703-408-1_2
Čuperlović‐Culf, M. (2011) · Advances in bioinformatics and biomedical engineering book series · doi:10.4018/978-1-61350-435-2.ch001
Roux, A. (2011) · OpenGrey (Institut de l'Information Scientifique et Technique)
Mazurak, V. C. (2018) · MDPI eBooks · doi:10.3390/books978-3-03842-892-3
Mapping of Population Disparities in the Cholangiocarcinoma Urinary MetabolomePreprint
Alsaleh, M., Leftley, Z., Connor, T. O., Hughes, T., Barbera, T. A., Koomson, L. K., Zabron, A., Reeves, H. L., Cramp, M., Ryder, S. D., et al. (2021) · Research Square · doi:10.21203/rs.3.rs-682483/v2
O’Connell, T. M., & Jia, W. P. (2012) · Wiley eBooks · doi:10.1002/9781119946045.ch4
Mapping of Population Disparities in the Cholangiocarcinoma Urinary MetabolomePreprint
Alsaleh, M., Leftley, Z., Connor, T. O., Hughes, T., Barbera, T. A., Koomson, L. K., Zabron, A., Reeves, H. L., Cramp, M., Ryder, S. D., et al. (2021) · Research Square · doi:10.21203/rs.3.rs-682483/v1
Yap, I. K. S. (2011) · International e-Journal of Science Medicine & Education · doi:10.56026/imu.5.1.17
The Role of The Metabolism/Exposome in Chronic Kidney Disease: Discovery for Precision Nutrition
Pathmasiri, W., Schroder, M., McRitchie, S., & Sumner, S. (2022) · Springer eBooks · doi:10.1007/978-3-031-11942-2_3
Сучасні молекулярно-генетичні методи визначення віку у криміналістиці
Милостива, Д. Ф., & Бабченко, А. В. (2025) · International Science Journal of Jurisprudence & Philosophy · doi:10.46299/j.isjjp.20250403.01
Untargeted Metabolomics Profiling of a PFAS-Exposed Flemish Population
Delgado‐Povedano, M. D. M., Sher, H., Jacobs, L., van de Lavoir, M., Robeyns, R., Colles, A., Govarts, E., Hond, E. D., Poma, G., van Nuijs, A. L. N., et al. (2026) · Metabolites · doi:10.3390/metabo16020135
Byambajav, T., Chan, Q., Okami, Y., Kadota, A., Miyagawa, N., Okuda, N., Nakagawa, H., Sakata, K., Saitoh, S., Okayama, A., et al. (2026) · European Journal of Nutrition · doi:10.1007/s00394-026-04068-7
Zhou, T., Yang, J., Zhang, Q., Zhang, H., Chen, L., Luo, S., Xiao, Q., Meng, Q., Jiang, J., Labasangzhu, L., et al. (2026) · Metabolomics · doi:10.1007/s11306-026-02517-2
Cellular and biochemical effects induced by antiretroviral drugs.
Anwar, K. (2013) · ResearchSpace (University of KwaZulu-Natal)
Metadata
- Authors
- Bictash, M., Ebbels, T. M., Chan, Q., Loo, R. L., Yap, I. K. S., Brown, I. J., de Iorio, M., Daviglus, M. L., Holmes, E., Stamler, J., Nicholson, J. K., & Elliott, P.
- Affiliations
- Imperial College London (GB)
- Medway School of Pharmacy (GB)
- Northwestern University (US)
- Corresponding author
- Nicholson, Jeremy K.
- Elliott, Paul
- ORCID
- Venue
- Journal of Clinical Epidemiology
- Publisher
- Elsevier BV
- Published
- 2010-09
- Volume
- 63
- Issue
- 9
- Pages
- 970-979
- ISSN
- 0895-4356 (ISSN-L)
- PMC
- PMC4048926
- OpenAlex
- W2130686160
- Cited by
- 140
- References
- 74
- Citations by year
- 2026: 3 · 2025: 3 · 2024: 4 · 2023: 5 · 2022: 6 · 2021: 10 · 2020: 6 · 2019: 12 · 2018: 9 · 2017: 10 · 2016: 10 · 2015: 10 · 2014: 11 · 2013: 11 · 2012: 15
- FWCI
- 1.13
- Type
- journal article
- Language
- en
- Open access
- green
- www.ncbi.nlm.nih.gov
- Locations
- Journal of Clinical Epidemiology (publishedVersion)
- PubMed (publishedVersion)
- PubMed Central (acceptedVersion)
- Murdoch University Research Portal (submittedVersion)
- UCL Discovery (University College London) (submittedVersion)
- Kent Academic Repository (University of Kent) (submittedVersion)
- Funders
- Ministry of Education, Culture, Sports, Science and Technology
- National Heart, Lung, and Blood Institute (090357003)
- Topics
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Metabolomics and Mass Spectrometry Studies
- Physical Sciences › Environmental Science › Health, Toxicology and Mutagenesis › Health, Environment, Cognitive Aging
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Clinical Biochemistry › Metabolism and Genetic Disorders
- Keywords
- metabolic phenotyping, metabolome-wide association study, metabolite profiling, biomarker discovery, high-throughput metabolomics, false positive association, multivariate statistical modeling, genetic variation
- MeSH terms
- Humans, Phenotype, Reproducibility of Results, Biomarkers, Molecular Epidemiology, Risk Assessment, Metabolomics, Metabolome
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
