The DOI matches its Crossref record; 40 of the 40 references it cites were found in a registry or an index.
Investigations of the Effects of Gender, Diurnal Variation, and Age in Human Urinary Metabolomic Profiles
Abstract
Metabolomics may have the capacity to revolutionize disease diagnosis through the identification of scores of metabolites that vary during environmental, pathogenic, or toxicological insult. NMR spectroscopy has become one of the main tools for measuring these changes since an NMR spectrum can accurately identify metabolites and their concentrations. The predominant approach in analyzing NMR data has been through the technique of spectral binning. However, identification of spectral areas in an NMR spectrum is insufficient for diagnostic evaluation, since it is unknown whether areas of interest are strictly caused by metabolic changes or are simply artifacts. In this paper, we explore differences in gender, diurnal variation, and age in a human population. We use the example of gender differences to compare traditional spectral binning techniques (NMR spectral areas) to novel targeted profiling techniques (metabolites and their concentrations). We show that targeted profiling produces robust models, generates accurate metabolite concentration data, and provides data that can be used to help understand metabolic differences in a healthy population. Metabolites relating to mitochondrial energy metabolism were found to differentiate gender and age. Dietary components and some metabolites related to circadian rhythms were found to differentiate time of day urine collection. The mechanisms by which these differences arise will be key to the discovery of new diagnostic tests and new understandings of the mechanism of disease.
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
Creatine and Creatinine Metabolism
Wyss, M., & Kaddurah-Daouk, R. (2000) · Physiological Reviews · doi:10.1152/physrev.2000.80.3.1107 · Cited by 2,950
Metabonomics: a platform for studying drug toxicity and gene function
Nicholson, J. K., Connelly, J., Lindon, J. C., & Holmes, E. (2002) · Nature Reviews Drug Discovery · doi:10.1038/nrd728 · Cited by 1,914
Metabolomics by numbers: acquiring and understanding global metabolite data
Goodacre, R., Vaidyanathan, S., Dunn, W. B., Harrigan, G. G., & Kell, D. B. (2004) · Trends in Biotechnology · doi:10.1016/j.tibtech.2004.03.007 · Cited by 1,249
Gut microorganisms, mammalian metabolism and personalized health care
Nicholson, J. K., Holmes, E., & Wilson, I. D. (2005) · Nature Reviews Microbiology · doi:10.1038/nrmicro1152 · Cited by 945
Targeted Profiling: Quantitative Analysis of 1H NMR Metabolomics Data
Weljie, A. M., Newton, J., Mercier, P., Carlson, E., & Slupsky, C. M. (2006) · Analytical Chemistry · doi:10.1021/ac060209g · Cited by 900
Self-citationLarge-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
Gender differences in fat metabolism
Blaak, E. (2001) · Current Opinion in Clinical Nutrition and Metabolic Care · doi:10.1097/00075197-200111000-00006 · Cited by 709
Kinetics, Pharmacokinetics, and Regulation of l‐Carnitine and Acetyl‐l‐carnitine Metabolism
REBOUCHE, C. J. (2004) · Annals of the New York Academy of Sciences · doi:10.1196/annals.1320.003 · Cited by 425
Mannitol production by lactic acid bacteria: a review
Wisselink, H. W., Weusthuis, R. A., Eggink, G., Hugenholtz, J., & Grobben, G. J. (2002) · International Dairy Journal · doi:10.1016/s0958-6946(01)00153-4 · Cited by 396
Cited by
The human circadian metabolome
Dallmann, R., Viola, A. U., Tarokh, L., Cajochen, C., & Brown, S. A. (2012) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.1114410109 · Cited by 566
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
Analytical and statistical approaches to metabolomics research
Issaq, H. J., Van, Q. N., Waybright, T. J., Muschik, G. M., & Veenstra, T. D. (2009) · Journal of Separation Science · doi:10.1002/jssc.200900152 · Cited by 227
Rist, M. J., Roth, A., Frommherz, L., Weinert, C. H., Krüger, R., Merz, B., Bunzel, D., Mack, C., Egert, B., Bub, A., et al. (2017) · PLOS ONE · doi:10.1371/journal.pone.0183228 · Cited by 202
Llorach, R., Urpi-Sarda, M., Jauregui, O., Monagas, M., & Andres-Lacueva, C. (2009) · Journal of Proteome Research · doi:10.1021/pr900470a · Cited by 144
Metadata
- Authors
- Slupsky, C. M., Rankin, K. N., Wagner, J., Fu, H., Chang, D., Weljie, A. M., Saude, E. J., Lix, B., Adamko, D. J., Shah, S., Greiner, R., Sykes, B. D., & Marrie, T. J.
- Venue
- Analytical Chemistry
- Publisher
- American Chemical Society (ACS)
- Published
- 2007-08-17
- Volume
- 79
- Issue
- 18
- Pages
- 6995-7004
- ISSN
- 0003-2700 (ISSN-L)
- 1520-6882
- OpenAlex
- W2083113920
- Cited by
- 401
- References
- 40
- FWCI
- 8.74
- Type
- journal article
- Language
- en
- Open access
- closed
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: Investigations of the Effects of Gender, Diurnal Variation, and Age in Human Urinary Metabolomic Profiles. https://app.cochat.ai/papers/10.1021/ac0708588
- The paper
- Slupsky, C. M., Rankin, K. N., Wagner, J., Fu, H., Chang, D., Weljie, A. M., Saude, E. J., Lix, B., Adamko, D. J., Shah, S., Greiner, R., Sykes, B. D., & Marrie, T. J. (2007). Investigations of the Effects of Gender, Diurnal Variation, and Age in Human Urinary Metabolomic Profiles. Analytical Chemistry. https://doi.org/10.1021/ac0708588
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.
