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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
Self-citationSLCO1B1 Variants and Statin-Induced Myopathy — A Genomewide Study
(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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Luo, L., Ramanathan, R., Horlbogen, L., Mathialagan, S., Costales, C., Vourvahis, M., Holliman, C. L., & Rodrigues, A. D. (2020) · Analytical Chemistry · doi:10.1021/acs.analchem.0c01144 · Cited by 33
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Liang, Q., Wang, C., Li, B., & Zhang, A. (2015) · RSC Advances · doi:10.1039/c5ra13417j · Cited by 32
Lv, H. (2012) · Mass Spectrometry Reviews · doi:10.1002/mas.21354 · Cited by 32
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Robinson, N., Sottas, P., & Schumacher, Y. O. (2017) · Medicine and sport science/Medicine and sport · doi:10.1159/000460722 · Cited by 32
Avitabile, M., Succoio, M., Testori, A., Cardinale, A., Vaksman, Z., Lasorsa, V. A., Cantalupo, S., Esposito, M., Cimmino, F., Montella, A., et al. (2019) · Carcinogenesis · doi:10.1093/carcin/bgz153 · Cited by 32
Ullah, F., Rauf, W., Khan, K., Khan, S., Bell, K. M., Souza, V. C. D. O., Tariq, M., Bakhshalizadeh, S., Touraine, P. A., Katsanis, N., et al. (2021) · Human Genetics · doi:10.1007/s00439-021-02380-2 · Cited by 32
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Chan, S. Y., White, K., & Loscalzo, J. (2012) · Current Opinion in Cardiology · doi:10.1097/hco.0b013e3283515b31 · Cited by 33
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Lou, B., Wu, P., Liu, Y., & Wang, J. (2014) · High Altitude Medicine & Biology · doi:10.1089/ham.2013.1130 · Cited by 31
Hedjazi, L., Gauguier, D., Zalloua, P., Nicholson, J. K., Dumas, M., & Cazier, J. (2015) · Analytical Chemistry · doi:10.1021/acs.analchem.5b00145 · Cited by 31
Uemoto, Y., Ohtake, T., Sasago, N., Takeda, M., Abe, T., Sakuma, H., Kojima, T., & Sasaki, S. (2017) · BMC Genomics · doi:10.1186/s12864-017-4275-4 · Cited by 31
Frey, S., Eichler, A., Stonawski, V., Kriebel, J., Wahl, S., Gallati, S., Goecke, T. W., Fasching, P. A., Beckmann, M. W., Kratz, O., et al. (2018) · Frontiers in Behavioral Neuroscience · doi:10.3389/fnbeh.2018.00125 · Cited by 31
Yee, S. W., Stecuła, A., Chien, H., Zou, L., Feofanova, E. V., van Borselen, M., Cheung, K. W. K., Yousri, N. A., Suhre, K., Kinchen, J. M., et al. (2019) · PLoS Genetics · doi:10.1371/journal.pgen.1008208 · Cited by 32
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McClay, J. L., Vunck, S. A., Batman, A. M., Crowley, J. J., Vann, R. E., Beardsley, P. M., & van den Oord, E. J. C. G. (2015) · Journal of Neuroimmune Pharmacology · doi:10.1007/s11481-015-9605-1 · Cited by 27
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Nogal, A., Alkis, T., Lee, Y., Kifer, D., Hu, J., Murphy, R. A., Huang, Z., Wang-Sattler, R., Kastenmüler, G., Linkohr, B., et al. (2023) · Cardiovascular Research · doi:10.1093/cvr/cvad147 · Cited by 23
Self-citationSu, S., Duan, J., Cui, W., Shang, E., Liu, P., Bai, G., Guo, S., Qian, D., & Tang, Y. (2013) · Evidence-based Complementary and Alternative Medicine · doi:10.1155/2013/901943 · Cited by 26
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Treppner, M., Binder, H., & Hess, M. (2022) · Human Genetics · doi:10.1007/s00439-021-02417-6 · Cited by 29
Fang, S., Holmes, M. V., Gaunt, T. R., Smith, G. D., & Richardson, T. G. (2022) · eLife · doi:10.7554/elife.73951 · Cited by 27
Mohr, A. E., Jasbi, P., Bowes, D. A., Dirks, B., Whisner, C. M., Arciero, K. M., Poe, M., Gu, H., Gumpricht, E., Sweazea, K. L., et al. (2022) · Frontiers in Nutrition · doi:10.3389/fnut.2022.1036080 · Cited by 27
Liu, D., Zhang, H., Yang, Y., Liu, T., Guo, Z., Fan, W., Wang, Z., Yang, X., Zhang, B., Liu, H., et al. (2023) · Advanced Science · doi:10.1002/advs.202300148 · Cited by 26
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Self-citationRied, J. S., Shin, S., Krumsiek, J., Illig, T., Theis, F. J., Spector, T. D., Adamski, J., Wichmann, H. ‐., Strauch, K., Soranzo, N., et al. (2014) · Human Molecular Genetics · doi:10.1093/hmg/ddu301 · Cited by 27
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Lamina, C., Friedel, S., Coassin, S., Rueedi, R., Yousri, N. A., Seppälä, I., Gieger, C., Schönherr, S., Forer, L., Erhart, G., et al. (2016) · Human Molecular Genetics · doi:10.1093/hmg/ddw211 · Cited by 26
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Marklund, M., Morris, A. P., Mahajan, A., Ingelsson, E., Lindgren, C. M., Lind, L., & Risérus, U. (2018) · Nutrients · doi:10.3390/nu10111791 · Cited by 28
Hettne, K. M., Dharuri, H. K., Zhao, J., Wolstencroft, K., Belhajjame, K., Soiland‐Reyes, S., Mina, E., Thompson, M., Cruickshank, D. G., Verdes-Montenegro, L. L., et al. (2014) · Journal of Biomedical Semantics · doi:10.1186/2041-1480-5-41 · Cited by 25
Homuth, G., Teumer, A., Völker, U., & Nauck, M. A. (2012) · Journal of Endocrinology · doi:10.1530/joe-12-0144 · Cited by 25
Rifkin, S. B., Shrubsole, M. J., Cai, Q., Smalley, W. E., Ness, R. M., Swift, L. L., Zheng, W., & Murff, H. J. (2017) · British Journal Of Nutrition · doi:10.1017/s0007114517001490 · Cited by 25
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Aschard, H., Guillemot, V., Vilhjálmsson, B. J., Patel, C. J., Skurnik, D., Ye, C., Wolpin, B. M., Kraft, P. L., & Zaitlen, N. (2017) · Nature Genetics · doi:10.1038/ng.3975 · Cited by 28
Koh, K. X., Tan, G. H., Low, S. H. H., Omar, M. F. M., Han, M. J., Iacopetta, B. J., Soo, R. A., Beloueche‐Babari, M., Bhattacharya, B., & Soong, R. (2017) · Oncotarget · doi:10.18632/oncotarget.22655 · Cited by 25
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Jia, Q., Han, Y. Y., Huang, P., Woodward, N. C., Gukasyan, J., Kettunen, J., Ala‐Korpela, M., Anufrieva, O., Wang, Q., Perola, M., et al. (2019) · Journal of the American Heart Association · doi:10.1161/jaha.119.011922 · Cited by 26
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Murcy, F., Borowczyk, C., Gourion‐Arsiquaud, S., Torrino, S., Ouahrouche, N., Barouillet, T., Dussaud, S., Couralet, M., Vaillant, N., Merlin, J., et al. (2024) · Nature Cardiovascular Research · doi:10.1038/s44161-024-00566-1 · Cited by 24
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Arnold, M., Büyüközkan, M., Doraiswamy, P. M., Nho, K. T., Wu, T., Gudnason, V. G., Launer, L. J., Wang‐Sattler, R., Adamski, J., Initiative, T. A. D. N., et al. (2025) · Nature Communications · doi:10.1038/s41467-025-57032-0 · Cited by 24
Self-citationVihervaara, T., Suoniemi, M., & Laaksonen, R. (2013) · Drug Discovery Today · doi:10.1016/j.drudis.2013.09.008 · Cited by 24
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Gurwitz, D., & McLeod, H. L. (2013) · Pharmacogenomics · doi:10.2217/pgs.13.35 · Cited by 24
Jensen, O., Gebauer, L., Brockmöller, J., & Dücker, C. (2022) · International Journal of Molecular Sciences · doi:10.3390/ijms23042007 · Cited by 24
Wahl, S., Krug, S., Then, C., Kirchhofer, A., Kastenmüller, G., Brand, T., Skurk, T., Claussnitzer, M., Huth, C., Heier, M., et al. (2013) · Metabolomics · doi:10.1007/s11306-013-0586-x · Cited by 24
Self-citation
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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