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
An atlas of genetic influences on human blood metabolites
The Multiple Tissue Human Expression Resource (MuTHER) Consortium, Shin, S.-Y., Fauman, E. B., Petersen, A.-K., Krumsiek, J., Santos, R., Huang, J., Arnold, M., Erte, I., Forgetta, V., et al. (2014) · Nature Genetics · doi:10.1038/ng.2982 · Cited by 1,515
Metabolomics enables precision medicine: “A White Paper, Community Perspective”
for “Precision Medicine and Pharmacometabolomics Task Group”-Metabolomics Society Initiative, Beger, R. D., Dunn, W., Schmidt, M. A., Gross, S. S., Kirwan, J. A., Cascante, M., Brennan, L., Wishart, D. S., Oresic, M., et al. (2016) · Metabolomics · doi:10.1007/s11306-016-1094-6 · Cited by 592
The food metabolome: a window over dietary exposure
Scalbert, A., Brennan, L., Manach, C., Andres-Lacueva, C., Dragsted, L. O., Draper, J., Rappaport, S. M., van der Hooft, J. J., & Wishart, D. S. (2014) · The American Journal of Clinical Nutrition · doi:10.3945/ajcn.113.076133 · Cited by 519
Miyagi, Y., Higashiyama, M., Gochi, A., Akaike, M., Ishikawa, T., Miura, T., Saruki, N., Bando, E., Kimura, H., Imamura, F., et al. (2011) · PLoS ONE · doi:10.1371/journal.pone.0024143 · Cited by 439
The exposome in practice: Design of the EXPOsOMICS project
Vineis, P., Chadeau‐Hyam, M., Gmuender, H., Gulliver, J., Herceg, Z., Kleinjans, J. C. S., Kogevinas, M., Kyrtopoulos, S. Α., Nieuwenhuijsen, M. J., Phillips, D. H., et al. (2016) · International Journal of Hygiene and Environmental Health · doi:10.1016/j.ijheh.2016.08.001 · Cited by 353
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