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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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“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
In the citing paper
“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
In the citing paper
“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]
In the citing paper
“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
In the citing paper
“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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PLINK: a tool set for whole-genome association and population-based linkage analyses.
Purcell, S., Neale, B., Todd-Brown, K., Thomas, L., Ferreira, M. A. R., Bender, D., Maller, J., Sklar, P., de Bakker, P. I. W., Daly, M. J., et al. (2007) · The American Journal of Human Genetics · doi:10.1086/519795 · Cited by 34,089
Hindorff, L. A., Sethupathy, P., Junkins, H. A., Ramos, E. M., Mehta, J. P., Collins, F. S., & Manolio, T. A. (2009) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.0903103106 · Cited by 4,013
Howie, B. N., Donnelly, P., & Marchini, J. (2009) · PLoS Genetics · doi:10.1371/journal.pgen.1000529 · Cited by 3,928
Biological, clinical and population relevance of 95 loci for blood lipids
Teslovich, T. M., Musunuru, K., Smith, A. V., Edmondson, A. C., Stylianou, I. M., Koseki, M., Pirruccello, J. P., Ripatti, S., Chasman, D. I., Willer, C. J., et al. (2010) · Nature · doi:10.1038/nature09270 · Cited by 3,487
Self-citationMerlin—rapid analysis of dense genetic maps using sparse gene flow trees
Abecasis, G. R., Cherny, S. S., Cookson, W., & Cardon, L. R. (2001) · Nature Genetics · doi:10.1038/ng786 · Cited by 3,323
Metabolomic profiles delineate potential role for sarcosine in prostate cancer progression
Sreekumar, A., Poisson, L., Rajendiran, T. M., Khan, A. P., Cao, Q., Yu, J., Laxman, B., Mehra, R., Lonigro, R. J., Li, Y., et al. (2009) · Nature · doi:10.1038/nature07762 · Cited by 2,269
New genetic loci implicated in fasting glucose homeostasis and their impact on type 2 diabetes risk
DIAGRAM Consortium, Dupuis, J., GIANT Consortium, Global BPgen Consortium, Anders Hamsten on behalf of Procardis Consortium, the MAGIC investigators, Langenberg, C., Prokopenko, I., Saxena, R., Soranzo, N., et al. (2010) · Nature Genetics · doi:10.1038/ng.520 · Cited by 2,166
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(2008) · New England Journal of Medicine · doi:10.1056/nejmoa0801936 · Cited by 2,043
Large-scale association analysis identifies 13 new susceptibility loci for coronary artery disease
Cardiogenics, Schunkert, H., the CARDIoGRAM Consortium, König, I. R., Kathiresan, S., Reilly, M. P., Assimes, T. L., Holm, H., Preuss, M., Stewart, A. F. R., et al. (2011) · Nature Genetics · doi:10.1038/ng.784 · Cited by 1,866
Self-citationEvans, A. M., DeHaven, C. D., Barrett, T., Mitchell, M. W., & Milgram, K. E. (2009) · Analytical Chemistry · doi:10.1021/ac901536h · Cited by 1,317
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Li, Y., Liu, Y., Chen, L., & Xu, J. (2021) · Advanced Healthcare Materials · doi:10.1002/adhm.202100046 · Cited by 15
Alves, A. C., Glastonbury, C. A., Moustafa, J. S. E.-S., & Small, K. S. (2018) · BMC Genomics · doi:10.1186/s12864-018-4997-y · Cited by 15
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Flitman, R. S., Khalili, B., Kutalik, Z., Rueedi, R., Brümmer, A., & Bergmann, S. (2021) · Journal of Proteome Research · doi:10.1021/acs.jproteome.1c00585 · Cited by 16
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Dharuri, H. K., Henneman, P., Demirkan, A., van Klinken, J. B., Mook-Kanamori, D. O., Wang‐Sattler, R., Gieger, C., Adamski, J., Hettne, K. M., Roos, M., et al. (2013) · BMC Genomics · doi:10.1186/1471-2164-14-865 · Cited by 16
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Ghini, V., Abuja, P. M., Polašek, O., Kozera, Ł., Laiho, P., Anton, G., Zins, M., Kloviņš, J., Metspalu, A., Wichmann, H. ‐., et al. (2021) · Clinical Chemistry · doi:10.1093/clinchem/hvab092 · Cited by 14
Self-citationSuhre, K., Dadhania, D. M., Lee, J. R., Muthukumar, T., Chen, Q., Gross, S. S., & Suthanthiran, M. (2021) · Metabolites · doi:10.3390/metabo11080533 · Cited by 14
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Chen, J., Zhang, P., Qin, S., Tan, B., Li, S., Tang, S., Liao, C., Zhang, Y., Zhang, Z., & Xu, F. (2023) · Analytica Chimica Acta · doi:10.1016/j.aca.2023.340807 · Cited by 14
Chang, H., Yao, S., Tritchler, D., Hullar, M. A. J., Lampe, J. W., Thompson, L. U., & McCann, S. E. (2019) · Cancer Epidemiology Biomarkers & Prevention · doi:10.1158/1055-9965.epi-18-0826 · Cited by 15
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Misra, B. B. (2019) · Clinical Chemistry and Laboratory Medicine (CCLM) · doi:10.1515/cclm-2019-0130 · Cited by 14
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Kim, G., Choi, H., Lee, H., Moon, K., Oh, J. H., Lee, J., Shin, J., & Kim, D. (2020) · Journal of Applied Toxicology · doi:10.1002/jat.3960 · Cited by 14
Haas, D. W., Cramer, Y. S., Godfrey, C. C., Rosenkranz, S. L., Aweeka, F. T., Berzins, B., Coombs, R. W., Coughlin, K., Moran, L. E., Gingrich, D. J., et al. (2020) · Pharmacogenetics and Genomics · doi:10.1097/fpc.0000000000000396 · Cited by 14
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Castrillo, J. I., Pir, P., & Oliver, S. G. (2012) · Elsevier eBooks · doi:10.1016/b978-0-12-385944-0.00018-6 · Cited by 14
He, G., Chen, J., Duan, S., Yang, Q., Li, B., Luo, L., Zhong, J., Sun, Q., Bu, F., Tang, R., et al. (2025) · Molecular Biology and Evolution · doi:10.1093/molbev/msaf225 · Cited by 13
Khan, A., Yen, F. S., Ünlü, G., DelGaudio, N. L., Erdal, R., Xiao, M. J., Wangdu, K., Cho, K., Gamazon, E. R., Patti, G. J., et al. (2025) · Cell Metabolism · doi:10.1016/j.cmet.2025.09.015 · Cited by 13
Wang, X., Purohit, V. S., Dowty, M. E., Rodrigues, D., Luo, L., Mathialagan, S., Carey, W., Plotka, A., Kalluru, H., O’Gorman, M. T., et al. (2023) · The Journal of Clinical Pharmacology · doi:10.1002/jcph.2215 · Cited by 13
Yang, S., Hu, X., Zou, P., Zeng, Z., Hu, Y., & Xiao, R. (2024) · International Immunopharmacology · doi:10.1016/j.intimp.2024.112132 · Cited by 13
Mullen, W., Saigusa, D., Abe, T., Adamski, J., & Mischak, H. (2014) · Seminars in Nephrology · doi:10.1016/j.semnephrol.2014.02.009 · Cited by 13
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Li, Q., Fan, Y., Gao, F., Zhao, B., Yan, B., Wang, W., Yang, J., & Ma, X. (2020) · The International Journal of Neuropsychopharmacology · doi:10.1093/ijnp/pyaa062 · Cited by 13
Ren, T., Jones, R. S., & Morris, M. E. (2022) · Pharmacology Research & Perspectives · doi:10.1002/prp2.944 · Cited by 13
Yan, L., Nie, W., Parker, T. J., Upton, Z., & Lu, H. (2013) · Future Medicinal Chemistry · doi:10.4155/fmc.13.148 · Cited by 13
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Jäger, S., Cuadrat, R. R. C., Wittenbecher, C., Floegel, A., Hoffmann, P., Prehn, C. P., Adamski, J., Pischon, T., & Schulze, M. B. (2020) · Nutrients · doi:10.3390/nu12123890 · Cited by 13
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Shu, X., Chen, Z., Long, J., Guo, X., Yang, Y., Qu, C., Ahn, Y., Cai, Q., Casey, G., Gruber, S. B., et al. (2022) · Cancer Epidemiology Biomarkers & Prevention · doi:10.1158/1055-9965.epi-21-1008 · Cited by 12
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Kaur, J., Rahat, B., Thakur, S., & Kaur, J. (2017) · Elsevier eBooks · doi:10.1016/b978-0-12-809411-2.00015-5 · Cited by 14
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Salvia, A. L., Lens‐Pardo, A., López-López, Á., Carretero-Puche, C., Capdevila, J., Benavent, M., Jiménez‐Fonseca, P., Castellano, D., Alonso, T., Teulé, À., et al. (2023) · European Journal of Endocrinology · doi:10.1093/ejendo/lvad160 · Cited by 11
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Ryckman, K. K., Smith, C. J., Jelliffe‐Pawlowski, L. L., Momany, A. M., Berberich, S. L., & Murray, J. C. (2014) · Human Genetics · doi:10.1007/s00439-014-1450-4 · Cited by 11
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Guo, H., Zhang, L., Zhu, C., Yang, F., Wang, S., Zhu, S., & Ma, X. (2017) · Lipids in Health and Disease · doi:10.1186/s12944-017-0459-9 · Cited by 11
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Wang, V., Li, X., EnayetAllah, A. E., Fauman, E. B., & Ziemek, D. (2013) · BMC Systems Biology · doi:10.1186/1752-0509-7-s5-s10 · Cited by 11
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To, V. P. T. H., Subramaniam, M., Masagounder, K., & Loewen, M. E. (2020) · Comparative Biochemistry and Physiology Part A Molecular & Integrative Physiology · doi:10.1016/j.cbpa.2020.110776 · Cited by 10
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Metadata
- 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
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