Human metabolic individuality in biomedical and pharmaceutical research
Open accessAbstract
Genome-wide association studies (GWAS) have identified many risk loci for complex diseases, but effect sizes are typically small and information on the underlying biological processes is often lacking. Associations with metabolic traits as functional intermediates can overcome these problems and potentially inform individualized therapy. Here we report a comprehensive analysis of genotype-dependent metabolic phenotypes using a GWAS with non-targeted metabolomics. We identified 37 genetic loci associated with blood metabolite concentrations, of which 25 show effect sizes that are unusually high for GWAS and account for 10-60% differences in metabolite levels per allele copy. Our associations provide new functional insights for many disease-related associations that have been reported in previous studies, including those for cardiovascular and kidney disorders, type 2 diabetes, cancer, gout, venous thromboembolism and Crohn's disease. The study advances our knowledge of the genetic basis of metabolic individuality in humans and generates many new hypotheses for biomedical and pharmaceutical research.
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These mQTL associations had much larger effect sizes than most GWAS findings for disease traits.
“Comparing to most GWAS findings, these associations showed much larger effect sizes than those for disease traits (Suhre et al., 2011)”
From Integrative Analysis of Multi-omics Data for Discovery and Functional Studies of Complex Human Diseases (2016) · cites this paper as Suhre et al., 2011
A non-targeted genome-metabolome-wide study of more than 250 serum metabolites from 60 known pathways, combined with SNP data from 2,820 individuals, found 37 mQTLs significant at a stringent genome-wide threshold.
“A non-targeted genome-metabolome-wide study analyzed more than 250 metabolites from 60 known pathways in human serum samples. Combined with genome-wide SNP data from the same 2,820 individuals with metabolomic data, 37 mQTLs were significant at a stringent genome-wide threshold.”
From Integrative Analysis of Multi-omics Data for Discovery and Functional Studies of Complex Human Diseases (2016) · cites this paper as Suhre et al., 2011
Genome-wide analysis studies have identified loci associated with variations in endogenous metabolite concentrations, and many of these genes coded for metabolic enzymes.
“A number of loci associated with variations in the concentration of endogenous metabolites could be identified in genome-wide analysis studies, and many of these genes were coding for metabolic enzymes (180-182).”
From The food metabolome: a window over dietary exposure (2014) · cites this paper as [180]
Other polymorphisms associated with metabolite levels had previously been shown to associate with cardiovascular disorders, gout and type-2 diabetes, all of which involve changes in core metabolic pathways.
“Other polymorphisms had previously been shown to associate with cardiovascular disorders, gout and type-2 diabetes, all of which are known to involve changes in core metabolic pathways (Suhre et al., 2011).”
From Cellular Metabolism and Disease: What Do Metabolic Outliers Teach Us? (2012) · cites this paper as Suhre et al., 2011
Metabolite GWAS also found associations with drug toxicity, Crohn's disease and other conditions not conventionally thought to be related to intermediary metabolism.
“However, there were also associations with drug toxicity, Crohn’s disease and other conditions not conventionally thought to be related to intermediary metabolism (Suhre et al., 2011).”
From Cellular Metabolism and Disease: What Do Metabolic Outliers Teach Us? (2012) · cites this paper as Suhre et al., 2011
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“We combine high-throughput metabolomics and genotyping data in Gaussian graphical models (GGMs) [21] and in genome-wide association studies (GWAS) [5] in order to produce testable predictions of the unknown metabolites’ identities.”
BackgroundMethodResultInfluentialSelf-citationDraisma, H. H. M., Pool, R., Kobl, M., Jansen, R., Petersen, A.-K., Vaarhorst, A. A. M., Yet, İ., Haller, T., Demirkan, A., Esko, T., et al. (2015) · Nature Communications · doi:10.1038/ncomms8208 · Cited by 196 · see citing works
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Cerebrospinal fluid metabolomics identifies 19 brain-related phenotype associations
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- Authors
- CARDIoGRAM, Suhre, K., Shin, S.-Y., Petersen, A.-K., Mohney, R. P., Meredith, D., Wägele, B., Altmaier, E., Deloukas, P., Erdmann, J., Grundberg, E., Hammond, C. J., de Angelis, M. H., Kastenmüller, G., Köttgen, A., Kronenberg, F., Mangino, M., Meisinger, C., Meitinger, T., Mewes, H.-W., Milburn, M. V., Prehn, C., Raffler, J., Ried, J. S., Römisch-Margl, W., Samani, N. J., Small, K. S., -Erich Wichmann, H., Zhai, G., Illig, T., Spector, T. D., Adamski, J., Soranzo, N., & Gieger, C.
- Affiliations
- Helmholtz Munich (DE)
- Wellcome Sanger Institute (GB)
- Oxford Brookes University (GB)
- St Thomas' Hospital (GB)
- King's College London (GB)
- University Medical Center Freiburg (DE)
- Innsbruck Medical University (AT)
- TUM Klinikum (DE)
- Corresponding author
- Suhre, Karsten
- Soranzo, Nicole
- ORCID
- Suhre, Karsten
- Shin, So-Youn
- Mohney, Robert P.
- Meredith, David
- Deloukas, Panos
- Erdmann, Jeanette
- Grundberg, Elin
- Hammond, Christopher J.
- Kastenmüller, Gabi
- Köttgen, Anna
- Kronenberg, Florian
- Mangino, Massimo
- Meisinger, Christa
- Meitinger, Thomas
- Mewes, Hans-Werner
- Prehn, Cornelia
- Raffler, Johannes
- Samani, Nilesh J.
- Small, Kerrin S.
- Zhai, Guangju
- Spector, Tim D.
- Adamski, Jerzy
- Soranzo, Nicole
- Gieger, Christian
- Venue
- Nature
- Published
- 2011-08-31
- Volume
- 477
- Issue
- 7362
- Pages
- 54-60
- ISSN
- 0028-0836 (ISSN-L)
- 1476-4687
- PMC
- PMC3832838
- OpenAlex
- W2166711199
- Cited by
- 1,017
- References
- 52
- Citations by year
- 2026: 31 · 2025: 34 · 2024: 53 · 2023: 55 · 2022: 69 · 2021: 63 · 2020: 73 · 2019: 48 · 2018: 69 · 2017: 68 · 2016: 84 · 2015: 82 · 2014: 89 · 2013: 88 · 2012: 95
- FWCI
- 57.27
- Type
- journal article
- Language
- en
- Open access
- green
- www.ncbi.nlm.nih.gov
- Licence
- www.springer.com/tdm (tdm)
- Locations
- Nature (publishedVersion)
- PubMed (publishedVersion)
- Europe PMC (PubMed Central) (acceptedVersion)
- PubMed Central (acceptedVersion)
- RePEc: Research Papers in Economics (submittedVersion)
- IRIS (submittedVersion)
- Research Portal (King's College London) (submittedVersion)
- Leicester Research Archive (University of Leicester) (submittedVersion)
- mediaTUM – the media and publications repository of the Technical University Munich (Technical University Munich) (submittedVersion)
- mediaTUM (Technical University of Munich) (submittedVersion, other-oa)
- Site cant be reached (submittedVersion)
- Explore Bristol Research (submittedVersion)
- SERVAL (Université de Lausanne) (submittedVersion)
- OPUS (Augsburg University) (submittedVersion)
- Funders
- Wellcome Trust (091746/Z/10/Z, 091746)
- Cancer Research UK
- British Heart Foundation
- National Institutes of Health (n01ag12100-57-0-2, n01hc55021-47-0-1, n01hc55020-39-0-0, n01hc55016-37-0-0, n01hc055022-024, n01hc55019-35-0-1, n01hc55018-38-0-1, n01hc55015-48-0-1)
- Canadian Institutes of Health Research (MOP--82810, #MOP77682, MOP172605)
- Biotechnology and Biological Sciences Research Council
- National Institute on Aging (N01-AG12100)
- National Heart, Lung, and Blood Institute (N01-HC55022, N01-HC-55018, N01 HC 55020, N01 HC 55019, R01HL087676, R01HL103931, R01HL087647, HL087647, N01-HC55016, P01HL076491, P01HL098055, P01HL087018, N01‐HC‐55015, N01-HC55021, R01HL089650)
- National Institute of Diabetes and Digestive and Kidney Diseases (R01DK080732)
- Topics
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Genetics › Genetic Associations and Epidemiology
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Metabolomics and Mass Spectrometry Studies
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Genetics › Nutrition, Genetics, and Disease
- Keywords
- blood metabolites, genetic loci, untargeted metabolomics, endophenotypes, type 2 diabetes, cardiovascular disease, kidney disease, cancer, gout, venous thromboembolism, Crohn's disease, gene-environment interaction, TwinsUK
- MeSH terms
- Adolescent, Adult, Aged, Aged, 80 and over, Blood, Child, Chronic Disease, Coronary Artery Disease, Diabetes Mellitus, Drug Industry, Female, Genotype, Humans, Male, Metabolism, Middle Aged, Pharmacogenetics, Risk Factors, Genetic Variation, Biomedical Research, Renal Insufficiency, Venous Thromboembolism, Genome-Wide Association Study, Metabolomics, Young Adult, Genetic Loci
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 ›
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