The DOI matches its Crossref record.
High-throughput serum NMR metabonomics for cost-effective holistic studies on systemic metabolism
Abstract
A high-throughput proton (1H) nuclear magnetic resonance (NMR) metabonomics approach is introduced to characterise systemic metabolic phenotypes. The methodology combines two molecular windows that contain the majority of the metabolic information available by 1H NMR from native serum, e.g. serum lipids, lipoprotein subclasses as well as various low-molecular-weight metabolites. The experimentation is robotics-controlled and fully automated with a capacity of about 150-180 samples in 24 h. To the best of our knowledge, the presented set-up is unique in the sense of experimental high-throughput, cost-effectiveness, and automated multi-metabolic data analyses. As an example, we demonstrate that the NMR data as such reveal associations between systemic metabolic phenotypes and the metabolic syndrome (n = 4407). The high-throughput of up to 50,000 serum samples per year is also paving the way for this technology in large-scale clinical and epidemiological studies. In contradiction to single 'biomarkers', the application of this holistic NMR approach and the integrated computational methods provides a data-driven systems biology approach to biomedical research.
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
Cited by
Kettunen, J., Demirkan, A., Würtz, P., Draisma, H. H. M., Haller, T., Rawal, R., Vaarhorst, A., Kangas, A. J., Lyytikäinen, L.-P., Pirinen, M., et al. (2016) · Nature Communications · doi:10.1038/ncomms11122 · Cited by 738
Self-citationMeasuring biological age using omics data
Rutledge, J., Oh, H., & Wyss-Coray, T. (2022) · Nature Reviews Genetics · doi:10.1038/s41576-022-00511-7 · Cited by 570
Genome-wide association study identifies multiple loci influencing human serum metabolite levels
Kettunen, J., Tukiainen, T., Sarin, A.-P., Ortega-Alonso, A., Tikkanen, E., Lyytikäinen, L.-P., Kangas, A. J., Soininen, P., Würtz, P., Silander, K., et al. (2012) · Nature Genetics · doi:10.1038/ng.1073 · Cited by 553
Self-citationLipids, lipoproteins, and metabolites and risk of myocardial infarction and stroke
Holmes, M. V., Millwood, I. Y., Kartsonaki, C., Hill, M. R., Bennett, D. A., Boxall, R., Guo, Y., Xu, X., Bian, Z., Hu, R., et al. (2018) · Journal of the American College of Cardiology · doi:10.1016/j.jacc.2017.12.006 · Cited by 508
Self-citationDeelen, J., Kettunen, J., Fischer, K., van der Spek, A., Trompet, S., Kastenmüller, G., Boyd, A., Zierer, J., van den Akker, E. B., Ala-Korpela, M., et al. (2019) · Nature Communications · doi:10.1038/s41467-019-11311-9 · Cited by 362
Self-citationFischer, K., Kettunen, J., Würtz, P., Haller, T., Havulinna, A. S., Kangas, A. J., Soininen, P., Esko, T., Tammesoo, M.-L., Mägi, R., et al. (2014) · PLoS Medicine · doi:10.1371/journal.pmed.1001606 · Cited by 344
Self-citationWürtz, P., Wang, Q., Kangas, A. J., Richmond, R. C., Skarp, J., Tiainen, M., Tynkkynen, T., Soininen, P., Havulinna, A. S., Kaakinen, M., et al. (2014) · PLoS Medicine · doi:10.1371/journal.pmed.1001765 · Cited by 280
Self-citationLong-term leisure-time physical activity and serum metabolome
Kujala, U. M., Mäkinen, V.-P., Heinonen, I., Soininen, P., Kangas, A. J., Leskinen, T. H., Rahkila, P., Würtz, P., Kovanen, V., Cheng, S., et al. (2013) · Circulation · doi:10.1161/circulationaha.112.105551 · Cited by 239
Self-citationMetabolomics View on Gut Microbiome Modulation by Polyphenol-Rich Foods
Moco, S., Martin, F.-P. J., & Rezzi, S. (2012) · Journal of Proteome Research · doi:10.1021/pr300581s · Cited by 234
Circulating metabolites and general cognitive ability and dementia: evidence from 11 cohort studies
van der Lee, S. J., Teunissen, C. E., Pool, R., Shipley, M. J., Teumer, A., Chouraki, V., Melo van Lent, D., Tynkkynen, J., Fischer, K., Hernesniemi, J., et al. (2018) · Alzheimer's & Dementia · doi:10.1016/j.jalz.2017.11.012 · Cited by 197
Metabolomic Profiling of Statin Use and Genetic Inhibition of HMG-CoA Reductase
Würtz, P., Wang, Q., Soininen, P., Kangas, A. J., Fatemifar, G., Tynkkynen, T., Tiainen, M., Perola, M., Tillin, T., Hughes, A. D., et al. (2016) · Journal of the American College of Cardiology · doi:10.1016/j.jacc.2015.12.060 · Cited by 188
Self-citationMetabolic characterization of menopause: cross-sectional and longitudinal evidence
Wang, Q., Ferreira, D. L. S., Nelson, S. M., Sattar, N., Ala-Korpela, M., & Lawlor, D. A. (2018) · BMC Medicine · doi:10.1186/s12916-018-1008-8 · Cited by 134
Self-citationAho, V., Ollila, H. M., Kronholm, E., Bondia-Pons, I., Soininen, P., Kangas, A. J., Hilvo, M., Seppälä, I., Kettunen, J., Oikonen, M., et al. (2016) · Scientific Reports · doi:10.1038/srep24828 · Cited by 126
Self-citationWewer Albrechtsen, N. J., Færch, K., Jensen, T. M., Witte, D. R., Pedersen, J., Mahendran, Y., Jonsson, A. E., Galsgaard, K. D., Winther-Sørensen, M., Torekov, S. S., et al. (2018) · Diabetologia · doi:10.1007/s00125-017-4535-5 · Cited by 119
Effects of hormonal contraception on systemic metabolism: cross-sectional and longitudinal evidence
Wang, Q., Würtz, P., Auro, K., Morin-Papunen, L., Kangas, A. J., Soininen, P., Tiainen, M., Tynkkynen, T., Joensuu, A., Havulinna, A. S., et al. (2016) · International Journal of Epidemiology · doi:10.1093/ije/dyw147 · Cited by 117
Self-citationWang, L., Tang, Y., Liu, S., Mao, S., Ling, Y., Liu, D., He, X., & Wang, X. (2013) · PLoS ONE · doi:10.1371/journal.pone.0065675 · Cited by 97
“0, as well as other existing databases and previous literature reports [35,36,37].”
BackgroundLipoprotein signatures of cholesteryl ester transfer protein and HMG-CoA reductase inhibition
Kettunen, J., Holmes, M. V., Allara, E., Anufrieva, O., Ohukainen, P., Oliver-Williams, C., Wang, Q., Tillin, T., Hughes, A. D., Kähönen, M., et al. (2019) · PLOS Biology · doi:10.1371/journal.pbio.3000572 · Cited by 47
Self-citationNikolaev, Y., Ripin, N., Soste, M., Picotti, P., Iber, D., & Allain, F. H.-T. (2019) · Nature Methods · doi:10.1038/s41592-019-0495-7 · Cited by 20
Michelsen, T. M., Skytte, H. N., Gunnes, N., Holven, K. B., Christensen, J. J., & Roland, M. C. P. (2024) · Journal of Reproductive Immunology · doi:10.1016/j.jri.2024.104397 · Cited by 2
Metadata
- Authors
- Soininen, P., Kangas, A. J., Würtz, P., Tukiainen, T., Tynkkynen, T., Laatikainen, R., Järvelin, M.-R., Kähönen, M., Lehtimäki, T., Viikari, J., Raitakari, O. T., Savolainen, M. J., & Ala-Korpela, M.
- Venue
- The Analyst
- Publisher
- Royal Society of Chemistry (RSC)
- Published
- 2009
- Volume
- 134
- Issue
- 9
- Pages
- 1781
- ISSN
- 0003-2654 (ISSN-L)
- 1364-5528
- OpenAlex
- W2102732029
- Cited by
- 589
- References
- 0
- FWCI
- 6.12
- 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
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.
