The DOI matches its Crossref record; 52 of the 52 references it cites were found in a registry or an index.
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.
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.
Claims other papers make about this one
Each is checked against this paper’s own text: does it say what the citing paper says it does?
In the citing paper
“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
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
Cites
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
Cited by
Sorted by citations
Items 201–300 of 1,000
Di-(2-Ethylhexyl)-Phthalate (DEHP) Causes Impaired Adipocyte Function and Alters Serum Metabolites
Klöting, N., Hesselbarth, N., Gericke, M. T., Kunath, A., Biemann, R., Chakaroun, R., Kosacka, J., Kovacs, P. L., Kern, M., Stümvoll, M., et al. (2015) · PLoS ONE · doi:10.1371/journal.pone.0143190 · Cited by 86
Ried, J. S., Baurecht, H., Stückler, F., Krumsiek, J., Gieger, C., Heinrich, J., Kabesch, M., Prehn, C. P., Peters, A., Rodríguez, E., et al. (2013) · Allergy · doi:10.1111/all.12110 · Cited by 85
Self-citationEstablishment of Protocols for Global Metabolomics by LC-MS for Biomarker Discovery
Saigusa, D., Okamura, Y., Motoike, I. N., Katoh, Y., Yasuhiro, K., Saijo, R., Koshiba, S., Yasuda, J., Motohashi, H., Sugawara, J., et al. (2016) · PLoS ONE · doi:10.1371/journal.pone.0160555 · Cited by 86
Miyamoto, S., Taylor, S. L., Barupal, D. K., Taguchi, A., Wohlgemuth, G., Wikoff, W. R., Yoneda, K. Y., Gandara, D. R., Hanash, S., Kim, K., et al. (2015) · Metabolites · doi:10.3390/metabo5020192 · Cited by 85
Factor Structure Underlying Components of Allostatic Load
McCaffery, J. M., Marsland, A. L., Strohacker, K. A., Muldoon, M. F., & Manuck, S. B. (2012) · PLoS ONE · doi:10.1371/journal.pone.0047246 · Cited by 89
Xie, W., Wood, A. R., Lyssenko, V., Weedon, M. N., Knowles, J. W., Alkayyali, S., Assimes, T. L., Quertermous, T., Abbasi, F., Paananen, J., et al. (2013) · Diabetes · doi:10.2337/db12-0876 · Cited by 83
Lactate transporters as therapeutic targets in cancer and inflammatory diseases
Pucino, V., Cucchi, D., & Mauro, C. (2018) · Expert Opinion on Therapeutic Targets · doi:10.1080/14728222.2018.1511706 · Cited by 87
1H–13C NMR-based urine metabolic profiling in autism spectrum disorders
Mavel, S., Nadal‐Desbarats, L., Blasco, H., Bonnet‐Brilhault, F., Barthélémy, C., Montigny, F., Sarda, P., Laumonnier, F., Vourc’h, P., Andres, C. R., et al. (2013) · Talanta · doi:10.1016/j.talanta.2013.03.064 · Cited by 83
Coltell, Ó., Sorlí, J. V., Asensio, E. M., Barragán, R., González, J. I., Gimenez-Alba, I. M., Zanón‐Moreno, V., Estruch, R., Sabio, J. B. R., Pascual, E. C., et al. (2020) · Nutrients · doi:10.3390/nu12020310 · Cited by 83
Teumer, A., Qi, Q., Nethander, M., Aschard, H., Bandinelli, S., Beekman, M., Berndt, S. I., Bidlingmaier, M., Broer, L., CHARGE Longevity Working Group, et al. (2016) · Aging Cell · doi:10.1111/acel.12490 · Cited by 83
Integrated Metabolomics and Genomics
Shah, S. H., & Newgard, C. B. (2015) · Circulation Cardiovascular Genetics · doi:10.1161/circgenetics.114.000223 · Cited by 83
Alu insertion variants alter mRNA splicing
Payer, L. M., Steranka, J. P., Ardeljan, D., Walker, J., Fitzgerald, K. C., Calabresi, P. A., Cooper, T. A., & Burns, K. H. (2018) · Nucleic Acids Research · doi:10.1093/nar/gky1086 · Cited by 84
Metabolic and proteomic signatures of type 2 diabetes subtypes in an Arab population
Zaghlool, S. B., Halama, A., Stephan, N., Guðmundsdóttir, V., Gudnason, V. G., Jennings, L. L., Thangam, M., Ahlqvist, E., Malik, R. A., Albagha, O., et al. (2022) · Nature Communications · doi:10.1038/s41467-022-34754-z · Cited by 81
Self-citationAltmaier, E., Fobo, G., Heier, M., Thorand, B., Meisinger, C., Römisch‐Margl, W., Waldenberger, M., Gieger, C., Illig, T., Adamski, J., et al. (2014) · European Journal of Epidemiology · doi:10.1007/s10654-014-9910-7 · Cited by 78
Self-citationStatistical Spectroscopic Tools for Biomarker Discovery and Systems Medicine
Robinette, S. L., Lindon, J. C., & Nicholson, J. K. (2013) · Analytical Chemistry · doi:10.1021/ac4007254 · Cited by 81
A Systematic Evaluation of Blood Serum and Plasma Pre-Analytics for Metabolomics Cohort Studies
Jobard, É., Trédan, O., Postoly, D., André, F., Martin, A., Elena‐Herrmann, B., & Boyault, S. (2016) · International Journal of Molecular Sciences · doi:10.3390/ijms17122035 · Cited by 82
Genetic Architecture of the Cardiovascular Risk Proteome
Benson, M. D., Yang, Q., Ngo, D., Zhu, Y., Shen, D., Farrell, L. A., Sinha, S., Keyes, M. J., Vasan, R. S., Larson, M. G., et al. (2017) · Circulation · doi:10.1161/circulationaha.117.029536 · Cited by 78
Gallois, A., Mefford, J., Ko, A., Vaysse, A., Julienne, H., Ala‐Korpela, M., Laakso, M., Zaitlen, N., Pajukanta, P., & Aschard, H. (2019) · Nature Communications · doi:10.1038/s41467-019-12703-7 · Cited by 78
Liu, J., Lahousse, L., Nivard, M. G., Bot, M., Chen, L., van Klinken, J. B., Thesing, C. S., Beekman, M., van den Akker, E. B., Slieker, R. C., et al. (2020) · Nature Medicine · doi:10.1038/s41591-019-0722-x · Cited by 79
A MicroRNA Linking Human Positive Selection and Metabolic Disorders
Wang, L., Sinnott-Armstrong, N., Wagschal, A., Wark, A. R., Camporez, J. P., Perry, R. J., Ji, F., Sohn, Y., Oh, J. D., Wu, S., et al. (2020) · Cell · doi:10.1016/j.cell.2020.09.017 · Cited by 81
Zhou, Z., Shu, T., Sun, Y., Si, H., Peng, P., Su, L., & Zhang, X. (2021) · Biosensors and Bioelectronics · doi:10.1016/j.bios.2021.113530 · Cited by 78
Burwell, R. G., Dangerfield, P., Moulton, A., & Grivas, T. B. (2011) · Scoliosis · doi:10.1186/1748-7161-6-26 · Cited by 81
Genetic Influences on Metabolite Levels: A Comparison across Metabolomic Platforms
Yet, İ., Menni, C., Shin, S., Mangino, M., Soranzo, N., Adamski, J., Suhre, K., Spector, T. D., Kastenmüller, G., & Bell, J. T. (2016) · PLoS ONE · doi:10.1371/journal.pone.0153672 · Cited by 77
Self-citationWu, S., Alseekh, S., Inostroza, A. C., Fusari, C. M., Mutwil, M., Kooke, R., Keurentjes, J. J. B., Fernie, A. R., Willmitzer, L., & Brotman, Y. (2016) · PLoS Genetics · doi:10.1371/journal.pgen.1006363 · Cited by 76
Yuan, L., Zeng, G., Chen, L., Wang, G., Wang, X., Cao, X., Lü, M., Liu, X., Qian, G., Xiao, Y., et al. (2018) · International Journal of Biological Sciences · doi:10.7150/ijbs.23574 · Cited by 76
Circadian Variation of the Human Metabolome Captured by Real-Time Breath Analysis
Sinues, P., Tarokh, L., Li, X., Kohler, M., Brown, S. A., Zenobi, R., & Dallmann, R. (2014) · PLoS ONE · doi:10.1371/journal.pone.0114422 · Cited by 75
Systems genetics: From GWAS to disease pathways
van der Sijde, M. R., Ng, A., & Fu, J. (2014) · Biochimica et Biophysica Acta (BBA) - Molecular Basis of Disease · doi:10.1016/j.bbadis.2014.04.025 · Cited by 83
Identification of biomarkers to diagnose diseases and find adverse drug reactions by metabolomics
Saigusa, D., Matsukawa, N., Hishinuma, E., & Koshiba, S. (2020) · Drug Metabolism and Pharmacokinetics · doi:10.1016/j.dmpk.2020.11.008 · Cited by 79
A practical guide to metabolomic profiling as a discovery tool for human heart disease
Heather, L. C., Wang, X., West, J. A., & Griffin, J. L. (2012) · Journal of Molecular and Cellular Cardiology · doi:10.1016/j.yjmcc.2012.12.001 · Cited by 77
Insight in Genome-Wide Association of Metabolite Quantitative Traits by Exome Sequence Analyses
Demirkan, A., Henneman, P., Verhoeven, A., Dharuri, H. K., Amin, N., van Klinken, J. B., Karssen, L. C., de Vries, B., Meissner, A., Göraler, S., et al. (2015) · PLoS Genetics · doi:10.1371/journal.pgen.1004835 · Cited by 77
Republished: Value of biomarkers in osteoarthritis: current status and perspectives
Lotz, M., Pelletier, J., Christiansen, C., Brandi, M. L., Bruyère, O., Chapurlat, R. D., Collette, J., Cooper, C. C., Giacovelli, G., Kanis, J. А., et al. (2014) · Postgraduate Medical Journal · doi:10.1136/postgradmedj-2013-203726rep · Cited by 75
Genome-wide association studies with metabolomics
Adamski, J. (2012) · Genome Medicine · doi:10.1186/gm333 · Cited by 77
Self-citationHarada, S., Hirayama, A., Chan, Q. M., Kurihara, A., Fukai, K., Iida, M., Kato, S., Sugiyama, D., Kuwabara, K., Takeuchi, A., et al. (2018) · PLoS ONE · doi:10.1371/journal.pone.0191230 · Cited by 76
Urine Metabolite Profiles Predictive of Human Kidney Allograft Status
Suhre, K., Schwartz, J. E., Sharma, V. K., Chen, Q., Lee, J. R., Muthukumar, T., Dadhania, D. M., Ding, R., Iklé, D. N., Bridges, N. D., et al. (2015) · Journal of the American Society of Nephrology · doi:10.1681/asn.2015010107 · Cited by 73
Self-citationHeritability of skewed X-inactivation in female twins is tissue-specific and associated with age
Zito, A., Davies, M. N., Tsai, P.-C., Roberts, S., Ejarque, R. A., Nardone, S., Bell, J. T., Wong, C. C. Y., & Small, K. S. (2019) · Nature Communications · doi:10.1038/s41467-019-13340-w · Cited by 73
Self-citationPharmacogenomics: A road ahead for precision medicine in psychiatry
Pardiñas, A. F., Owen, M. J., & Walters, J. (2021) · Neuron · doi:10.1016/j.neuron.2021.09.011 · Cited by 80
Maroni, L., van de Graaf, S. F. J., Hohenester, S. D., Elferink, R. P. J. O., & Beuers, U. H. (2014) · Clinical Reviews in Allergy & Immunology · doi:10.1007/s12016-014-8423-1 · Cited by 72
Zhao, K., Lu, Z., Park, J. W., Zhou, Q., & Xing, Y. (2013) · Genome biology · doi:10.1186/gb-2013-14-7-r74 · Cited by 76
Targeting hepatic glutaminase activity to ameliorate hyperglycemia
Miller, R., Shi, Y., Lu, W., Pirman, D. A., Jatkar, A., Blatnik, M., Wu, H., Cárdenas, C., Wan, M., Foskett, J. K., et al. (2018) · Nature Medicine · doi:10.1038/nm.4514 · Cited by 72
An exome array study of the plasma metabolome
Rhee, E. P., Yang, Q., Yu, B., Liu, X., Cheng, S. C., Deik, A., Pierce, K. A., Bullock, K. H., Ho, J. E., Levy, D. A., et al. (2016) · Nature Communications · doi:10.1038/ncomms12360 · Cited by 71
Metabolic switch during adipogenesis: From branched chain amino acid catabolism to lipid synthesis
Halama, A., Horsch, M., Kastenmüller, G., Möller, G., Kumar, P., Prehn, C. P., Laumen, H., Hauner, H., de Angelis, M. H., Beckers, J., et al. (2015) · Archives of Biochemistry and Biophysics · doi:10.1016/j.abb.2015.09.013 · Cited by 71
Self-citationDiscovering Regulated Metabolite Families in Untargeted Metabolomics Studies
Treutler, H., Tsugawa, H., Porzel, A., Gorzolka, K., Tissier, A. F., Neumann, S., & Balcke, G. U. (2016) · Analytical Chemistry · doi:10.1021/acs.analchem.6b01569 · Cited by 71
Metabolite profiling identifies anandamide as a biomarker of nonalcoholic steatohepatitis
Kimberly, W. T., O’Sullivan, J., Nath, A. K., Keyes, M. J., Shi, X. D., Larson, M. G., Yang, Q., Long, M. T., Vasan, R. S., Peterson, R. T., et al. (2017) · JCI Insight · doi:10.1172/jci.insight.92989 · Cited by 72
Metabolic signatures of human breast cancer
Mishra, P., & Ambs, S. (2015) · Molecular & Cellular Oncology · doi:10.4161/23723556.2014.992217 · Cited by 70
Integrating lipidomics and genomics: emerging tools to understand cardiovascular diseases
Tabassum, R., & Ripatti, S. (2021) · Cellular and Molecular Life Sciences · doi:10.1007/s00018-020-03715-4 · Cited by 76
Systems Biology Analysis Reveals Eight SLC22 Transporter Subgroups, Including OATs, OCTs, and OCTNs
Engelhart, D. C., Granados, J. C., Shi, D., Saier, M. H., Baker, M. E., Abagyan, R. A., & Nigám, S. K. (2020) · International Journal of Molecular Sciences · doi:10.3390/ijms21051791 · Cited by 71
Adam, J., Brandmaier, S., Leonhardt, J., Scheerer, M. F., Mohney, R. P., Xu, T., Bi, J., Rotter, M., Troll, M., Chi, S., et al. (2016) · Diabetes · doi:10.2337/db16-0512 · Cited by 68
Self-citationRecent advances in metabolomics in neurological disease, and future perspectives
Zhang, A., Sun, H. J., & Wang, X. (2013) · Analytical and Bioanalytical Chemistry · doi:10.1007/s00216-013-7061-4 · Cited by 68
Network inference from glycoproteomics data reveals new reactions in the IgG glycosylation pathway
Benedetti, E., Pučić‐Baković, M., Keser, T., Wahl, A., Hassinen, A., Yang, J., Liu, L., Trbojević‐Akmačić, I., Razdorov, G., Štambuk, J., et al. (2017) · Nature Communications · doi:10.1038/s41467-017-01525-0 · Cited by 68
Self-citationMetabolite profiling reveals new insights into the regulation of serum urate in humans
Albrecht, E., Waldenberger, M., Krumsiek, J., Evans, A. M., Jeratsch, U., Breier, M., Adamski, J., Köenig, W., Kunze, S., Fuchs, C., et al. (2013) · Metabolomics · doi:10.1007/s11306-013-0565-2 · Cited by 67
Self-citationProteomic and metabolomic approaches in the search for biomarkers in chronic kidney disease
Cañadas‐Garre, M., Anderson, K., McGoldrick, J., Maxwell, A. P., & McKnight, A. J. (2018) · Journal of Proteomics · doi:10.1016/j.jprot.2018.09.020 · Cited by 66
Jin, K., Wilson, K. A., Beck, J. N., Nelson, C. S., Brownridge, G. W., Harrison, B. R., Djukovic, D., Raftery, D., Brem, R. B., Yu, S., et al. (2020) · PLoS Genetics · doi:10.1371/journal.pgen.1008835 · Cited by 66
Alami, M. M., Ouyang, Z., Zhang, Y., Shu, S., Yang, G., Mei, Z., & Wang, X. (2022) · International Journal of Molecular Sciences · doi:10.3390/ijms232415932 · Cited by 67
METLIN-CCS: an ion mobility spectrometry collision cross section database
Baker, E. S., Hoang, C., Uritboonthai, W., Heyman, H. M., Pratt, B. S., MacCoss, M. J., MacLean, B., Plumb, R. S., Aisporna, A., & Siuzdak, G. E. (2023) · Nature Methods · doi:10.1038/s41592-023-02078-5 · Cited by 66
Systems Biology Technologies Enable Personalized Traditional Chinese Medicine: A Systematic Review
Wang, X., Zhang, A., Sun, H. J., & Wang, P. (2012) · The American Journal of Chinese Medicine · doi:10.1142/s0192415x12500826 · Cited by 66
Seminal plasma metabolomics approach for the diagnosis of unexplained male infertility
Qiao, S., Wu, W., Chen, M., Tang, Q., Xia, Y., Jia, W. P., & Wang, X. (2017) · PLoS ONE · doi:10.1371/journal.pone.0181115 · Cited by 66
Lipidome‐ and Genome‐Wide Study to Understand Sex Differences in Circulatory Lipids
Tabassum, R., Ruotsalainen, S., Ottensmann, L., Gerl, M. J., Klose, C. D., Tukiainen, T., Pirinen, M., Simons, K., Widén, E., & Ripatti, S. (2022) · Journal of the American Heart Association · doi:10.1161/jaha.122.027103 · Cited by 69
Fine Mapping and Functional Analysis Reveal a Role of SLC22A1 in Acylcarnitine Transport
Kim, H. I., Raffler, J., Lu, W., Lee, J., Abbey, D., Saleheen, D., Rabinowitz, J. D., Bennett, M. J., Hand, N. J., Brown, C. D., et al. (2017) · The American Journal of Human Genetics · doi:10.1016/j.ajhg.2017.08.008 · Cited by 66
Self-citationHarshfield, E. L., Koulman, A., Ziemek, D., Marney, L. C., Fauman, E. B., Paul, D. S., Stacey, D., Rasheed, A., Lee, J., Shah, N., et al. (2019) · Journal of Proteome Research · doi:10.1021/acs.jproteome.8b00786 · Cited by 64
r2VIM: A new variable selection method for random forests in genome-wide association studies
Szymczak, S., Holzinger, E. R., Dasgupta, A., Malley, J. D., Molloy, A. M., Mills, J. L., Brody, L. C., Stambolian, D., & Wilson, J. (2016) · BioData Mining · doi:10.1186/s13040-016-0087-3 · Cited by 63
Suhre, K. (2024) · Cell Genomics · doi:10.1016/j.xgen.2024.100506 · Cited by 65
Self-citationMetabolome 2.0: quantitative genetics and network biology of metabolic phenotypes
Dumas, M. (2012) · Molecular BioSystems · doi:10.1039/c2mb25167a · Cited by 63
Long term conservation of human metabolic phenotypes and link to heritability
Yousri, N. A., Kastenmüller, G., Gieger, C., Shin, S., Yet, İ., Menni, C., Peters, A., Meisinger, C., Mohney, R. P., Illig, T., et al. (2014) · Metabolomics · doi:10.1007/s11306-014-0629-y · Cited by 65
Self-citationGoldberg, J., Daniel, M., van Heuvel, Y., Victor, M., Beyer, C., Clarner, T., & Kipp, M. (2013) · Cellular and Molecular Neurobiology · doi:10.1007/s10571-013-9975-y · Cited by 62
Metabolomic epidemiology offers insights into disease aetiology
Fuller, H., Zhu, Y., Nicholas, J. C., Chatelaine, H., Drzymalla, E., Sarvestani, A. K., Julián‐Serrano, S., Tahir, U. A., Sinnott-Armstrong, N., Raffield, L. M., et al. (2023) · Nature Metabolism · doi:10.1038/s42255-023-00903-x · Cited by 63
Halama, A., Riesen, N., Möller, G., de Angelis, M. H., & Adamski, J. (2013) · Journal of Internal Medicine · doi:10.1111/joim.12117 · Cited by 61
Self-citationMixing omics: combining genetics and metabolomics to study rheumatic diseases
Menni, C., Zierer, J., Valdes, A. M., & Spector, T. D. (2017) · Nature Reviews Rheumatology · doi:10.1038/nrrheum.2017.5 · Cited by 61
Self-citationAssociations between the Maternal Exposome and Metabolome during Pregnancy
Chen, M., Guan, Y., Huang, R., Duan, J., Zhou, J., Chen, T., Wang, X., Xia, Y., & London, S. J. (2022) · Environmental Health Perspectives · doi:10.1289/ehp9745 · Cited by 60
From Metabonomics to Pharmacometabonomics: The Role of Metabolic Profiling in Personalized Medicine
Everett, J. R. (2016) · Frontiers in Pharmacology · doi:10.3389/fphar.2016.00297 · Cited by 60
The genome revolution and its role in understanding complex diseases
Hofker, M. H., Fu, J., & Wijmenga, C. (2014) · Biochimica et Biophysica Acta (BBA) - Molecular Basis of Disease · doi:10.1016/j.bbadis.2014.05.002 · Cited by 65
Application of Metabolomics to Diagnosis of Insulin Resistance
Milburn, M. V., & Lawton, K. A. (2013) · Annual Review of Medicine · doi:10.1146/annurev-med-061511-134747 · Cited by 60
Self-citationComputational approaches for systems metabolomics
Krumsiek, J., Bartel, J., & Theis, F. J. (2016) · Current Opinion in Biotechnology · doi:10.1016/j.copbio.2016.04.009 · Cited by 63
Genetic regulation of mouse liver metabolite levels
Ghazalpour, A., Bennett, B. J., Shih, D. M., Che, N., Orozco, L. D., Pan, C., Hagopian, R., He, A., Kayne, P. S., Yang, W.-P., et al. (2014) · Molecular Systems Biology · doi:10.15252/msb.20135004 · Cited by 57
Metabolite Modulation in Human Plasma in the Early Phase of Acclimatization to Hypobaric Hypoxia
Liao, W., Liu, B., Chen, J., Cui, J., Gao, Y., Liu, F., Xu, G., Sun, B.-D., Zhang, E., Yuan, Z., et al. (2016) · Scientific Reports · doi:10.1038/srep22589 · Cited by 58
Olson, M. V. (2012) · Annual Review of Genomics and Human Genetics · doi:10.1146/annurev-genom-090711-163825 · Cited by 58
Testing multiple biological mediators simultaneously
Boca, S. M., Sinha, R. R., Cross, A. J., Moore, S. C., & Sampson, J. N. (2013) · Bioinformatics · doi:10.1093/bioinformatics/btt633 · Cited by 57
The Peroxisomal Enzyme L-PBE Is Required to Prevent the Dietary Toxicity of Medium-Chain Fatty Acids
Ding, J., Loizides‐Mangold, U., Rando, G., Zoete, V., Michielin, O. A., Reddy, J. K., Wahli, W., Riezman, H., & Thorens, B. (2013) · Cell Reports · doi:10.1016/j.celrep.2013.08.032 · Cited by 57
Livshits, G., Malkin, I., Bowyer, R. C. E., Verdi, S., Bell, J. T., Menni, C., Williams, F. M. K., & Steves, C. J. (2018) · Pain · doi:10.1097/j.pain.0000000000001364 · Cited by 57
Epigenome-wide association meta-analysis of DNA methylation with coffee and tea consumption
Karabegović, I., Portilla-Fernández, E., Li, Y., Ma, J., Maas, S. C. E., Sun, D., Hu, E. A., Kühnel, B., Zhang, Y., Ambatipudi, S., et al. (2021) · Nature Communications · doi:10.1038/s41467-021-22752-6 · Cited by 57
Glycomics meets genomics, epigenomics and other high throughput omics for system biology studies
Zoldoš, V., Horvat, T., & Lauc, G. (2013) · Current Opinion in Chemical Biology · doi:10.1016/j.cbpa.2012.12.007 · Cited by 56
Wang, J.-K., Li, Y., Zhao, X.-L., Liu, Y.-B., Tan, J., Xing, Y.-Y., Adi, D., Wang, Y.-T., Fu, Z.-Y., Ma, Y.-T., et al. (2022) · Circulation · doi:10.1161/circulationaha.121.056491 · Cited by 56
Leiter, E. H., Strobel, M. C., O’Neill, A. C., Schultz, D. W., Schile, A. J., & Reifsnyder, P. C. (2013) · Journal of Diabetes Research · doi:10.1155/2013/165327 · Cited by 59
Role of plasma kallikrein in diabetes and metabolism
Zhou, Q., Fickweiler, W., & Feener, E. P. (2013) · Thrombosis and Haemostasis · doi:10.1160/th13-02-0179 · Cited by 56
Diving Through the “-Omics”: The Case for Deep Phenotyping and Systems Epidemiology
Haring, R., & Wallaschofski, H. (2012) · OMICS A Journal of Integrative Biology · doi:10.1089/omi.2011.0108 · Cited by 57
Kennedy, A. D., Pappan, K. L., Donti, T., Evans, A. M., Wulff, J. E., Miller, L. A. D., Sutton, V. R., Sun, Q., Miller, M. J., & Elsea, S. H. (2017) · Molecular Genetics and Metabolism · doi:10.1016/j.ymgme.2017.04.005 · Cited by 57
Watrous, J. D., Henglin, M., Claggett, B. L., Lehmann, K. A., Larson, M. G., Cheng, S. C., & Jain, M. (2017) · Analytical Chemistry · doi:10.1021/acs.analchem.6b04337 · Cited by 56
Hysi, P. G., Mangino, M., Christofidou, P., Falchi, M., Karoly, E. D., NIHR Bioresource Investigators, Mohney, R. P., Valdes, A. M., Spector, T. D., & Menni, C. (2022) · Metabolites · doi:10.3390/metabo12010061 · Cited by 59
Self-citationBiochemical insights from population studies with genetics and metabolomics
Suhre, K., Raffler, J., & Kastenmüller, G. (2015) · Archives of Biochemistry and Biophysics · doi:10.1016/j.abb.2015.09.023 · Cited by 55
Self-citationLécuyer, L., Dalle, C., Lyan, B., Demidem, A., Rossary, A., Vasson, M., Pétéra, M., Lagrée, M., Ferreira, T., Centeno, D., et al. (2019) · Cancer Epidemiology Biomarkers & Prevention · doi:10.1158/1055-9965.epi-19-0154 · Cited by 55
Tan, Y., Liu, X., Lu, C. C., He, X., Li, J., Xiao, C., Jiang, M., Yang, J., Zhou, K., Zhang, Z., et al. (2014) · Journal of Ethnopharmacology · doi:10.1016/j.jep.2014.02.011 · Cited by 53
Yu, B., Zheng, Y., Alexander, D. C., Manolio, T. A., Alonso, Á., Nettleton, J. A., & Boerwinkle, E. (2013) · Genetic Epidemiology · doi:10.1002/gepi.21752 · Cited by 53
Metabolic profiling in human exposome studies
Athersuch, T. J., & Keun, H. C. (2015) · Mutagenesis · doi:10.1093/mutage/gev060 · Cited by 56
Pharmacometabolomics informs pharmacogenomics
Neavin, D. R., Kaddurah-Daouk, R. F., & Weinshilboum, R. M. (2016) · Metabolomics · doi:10.1007/s11306-016-1066-x · Cited by 53
Metabolomics strategy reveals therapeutical assessment of limonin on nonbacterial prostatitis
Zhang, A., Wang, H., Sun, H. J., Zhang, Y., An, N., Yan, G., Meng, X., & Wang, X. (2015) · Food & Function · doi:10.1039/c5fo00489f · Cited by 52
Mass Spectrometry-based Metabolomics in Translational Research
Kim, S. J., Song, H. E., Lee, H. Y., & Yoo, H. J. (2021) · Advances in experimental medicine and biology · doi:10.1007/978-981-33-6064-8_19 · Cited by 52
Tahir, U. A., Katz, D. H., Ávila-Pacheco, J., Bick, A. G., Pampana, A., Robbins, J., Yu, Z., Chen, Z., Benson, M. D., Cruz, D. E., et al. (2022) · Nature Communications · doi:10.1038/s41467-022-32275-3 · Cited by 52
Metabolic biomarkers for chronic kidney disease
Breit, M., & Weinberger, K. M. (2015) · Archives of Biochemistry and Biophysics · doi:10.1016/j.abb.2015.07.018 · Cited by 54
Genetic and Environmental Factors in Age-Related Hearing Impairment
Momi, S. K., Wolber, L. E., Fabiane, S. M., MacGregor, A. J., & Williams, F. M. K. (2015) · Twin Research and Human Genetics · doi:10.1017/thg.2015.35 · Cited by 52
The role of metabolomics in neurological disease
Hassan‐Smith, G., Wallace, G. R., Douglas, M. R., & Sinclair, A. J. (2012) · Journal of Neuroimmunology · doi:10.1016/j.jneuroim.2012.01.009 · Cited by 52
A Genome-Wide Assessment of Variability in Human Serum Metabolism
Hong, M., Karlsson, R., Magnusson, P. K. E., Lewis, M. R., Isaacs, W., Zheng, L. S., Xu, J., Grönberg, H., Ingelsson, E., Yudi Pawitan, Y., et al. (2012) · Human Mutation · doi:10.1002/humu.22267 · Cited by 52
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
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
