The DOI matches its Crossref record; 107 of the 107 references it cites were found in a registry or an index.
Quantitative Serum Nuclear Magnetic Resonance Metabolomics in Large-Scale Epidemiology: A Primer on -Omic Technologies
Open accessAbstract
Detailed metabolic profiling in large-scale epidemiologic studies has uncovered novel biomarkers for cardiometabolic diseases and clarified the molecular associations of established risk factors. A quantitative metabolomics platform based on nuclear magnetic resonance spectroscopy has found widespread use, already profiling over 400,000 blood samples. Over 200 metabolic measures are quantified per sample; in addition to many biomarkers routinely used in epidemiology, the method simultaneously provides fine-grained lipoprotein subclass profiling and quantification of circulating fatty acids, amino acids, gluconeogenesis-related metabolites, and many other molecules from multiple metabolic pathways. Here we focus on applications of magnetic resonance metabolomics for quantifying circulating biomarkers in large-scale epidemiology. We highlight the molecular characterization of risk factors, use of Mendelian randomization, and the key issues of study design and analyses of metabolic profiling for epidemiology. We also detail how integration of metabolic profiling data with genetics can enhance drug development. We discuss why quantitative metabolic profiling is becoming widespread in epidemiology and biobanking. Although large-scale applications of metabolic profiling are still novel, it seems likely that comprehensive biomarker data will contribute to etiologic understanding of various diseases and abilities to predict disease risks, with the potential to translate into multiple clinical settings.
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Self-citationCohort Profile: The Avon Longitudinal Study of Parents and Children: ALSPAC mothers cohort
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Genetic variants in novel pathways influence blood pressure and cardiovascular disease risk
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Cited by
Lipids, 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-citationMetabolomic profiles predict individual multidisease outcomes
Buergel, T., Steinfeldt, J., Ruyoga, G., Pietzner, M., Bizzarri, D., Vojinovic, D., Upmeier zu Belzen, J., Loock, L., Kittner, P., Christmann, L., et al. (2022) · Nature Medicine · doi:10.1038/s41591-022-01980-3 · Cited by 406
High‐Throughput Metabolomics by 1D NMR
Vignoli, A., Ghini, V., Meoni, G., Licari, C., Takis, P. G., Tenori, L., Turano, P., & Luchinat, C. (2019) · Angewandte Chemie International Edition · doi:10.1002/anie.201804736 · Cited by 365
Deelen, 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-citationMetabolomics in Diabetes and Diabetic Complications: Insights from Epidemiological Studies
Jin, Q., & Ma, R. C. W. (2021) · Cells · doi:10.3390/cells10112832 · Cited by 252
Metadata
- Authors
- Würtz, P., Kangas, A. J., Soininen, P., Lawlor, D. A., Davey Smith, G., & Ala-Korpela, M.
- Venue
- American Journal of Epidemiology
- Publisher
- Oxford University Press (OUP)
- Published
- 2017-05-10
- Volume
- 186
- Issue
- 9
- Pages
- 1084-1096
- ISSN
- 0002-9262 (ISSN-L)
- 1476-6256
- OpenAlex
- W2587991149
- Cited by
- 686
- References
- 107
- FWCI
- 12.33
- Type
- journal article
- Language
- en
- Open access
- hybrid
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- Würtz, P., Kangas, A. J., Soininen, P., Lawlor, D. A., Davey Smith, G., & Ala-Korpela, M. (2017). Quantitative Serum Nuclear Magnetic Resonance Metabolomics in Large-Scale Epidemiology: A Primer on -Omic Technologies. American Journal of Epidemiology. https://doi.org/10.1093/aje/kwx016
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