Genome-wide study for circulating metabolites identifies 62 loci and reveals novel systemic effects of LPA
Open accessAbstract
Genome-wide association studies have identified numerous loci linked with complex diseases, for which the molecular mechanisms remain largely unclear. Comprehensive molecular profiling of circulating metabolites captures highly heritable traits, which can help to uncover metabolic pathophysiology underlying established disease variants. We conduct an extended genome-wide association study of genetic influences on 123 circulating metabolic traits quantified by nuclear magnetic resonance metabolomics from up to 24,925 individuals and identify eight novel loci for amino acids, pyruvate and fatty acids. The LPA locus link with cardiovascular risk exemplifies how detailed metabolic profiling may inform underlying aetiology via extensive associations with very-low-density lipoprotein and triglyceride metabolism. Genetic fine mapping and Mendelian randomization uncover wide-spread causal effects of lipoprotein(a) on overall lipoprotein metabolism and we assess potential pleiotropic consequences of genetically elevated lipoprotein(a) on diverse morbidities via electronic health-care records. Our findings strengthen the argument for safe LPA -targeted intervention to reduce cardiovascular risk.
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
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?
To provide more conservative results accounting for the number of tested proteins, the study used a strategy described by Gao et al. and Kettunen et al. that accommodates the correlation between protein levels.
“To also provide the more conservative results accounting for the number of tested proteins, we used a strategy described by Gao et al.35 and Kettunen et al.36, which accommodates the correlation between protein levels.”
From Genetic determinants of plasma protein levels in the Estonian population (2024) · cites this paper as Kettunen et al. [36]
An MR study of body mass index (BMI) effects on type II diabetes yielded the same predicted relative risk reduction for a given difference in BMI among groups at different level of risk.
“an MR study of body mass index (BMI) effects on type II diabetes yielded the same predicted relative risk reduction for a given difference in BMI among groups at different level of risk 148 .”
From Integrating genomics with biomarkers and therapeutic targets to invigorate cardiovascular drug development (2021) · cites this paper as [148]
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
The self-check needs the paper’s full text.
Cites
A map of human genome variation from population-scale sequencing.
The 1000 Genomes Project Consortium (2010) · Nature · doi:10.1038/nature09534 · Cited by 7,877 · see citing works
The Genotype-Tissue Expression (GTEx) pilot analysis: Multitissue gene regulation in humans
The GTEx Consortium, Ardlie, K. G., Deluca, D. S., Segrè, A. V., Sullivan, T. J., Young, T. R., Gelfand, E. T., Trowbridge, C. A., Maller, J. B., Tukiainen, T., et al. (2015) · Science · doi:10.1126/science.1262110 · Cited by 5,062 · see citing works
Self-citationMendelian randomization: genetic anchors for causal inference in epidemiological studies
Davey Smith, G., & Hemani, G. (2014) · Human Molecular Genetics · doi:10.1093/hmg/ddu328 · Cited by 4,677 · see citing works
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 · see citing works
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 3,527 · see citing works
Self-citationThe NHGRI GWAS Catalog, a curated resource of SNP-trait associations
Welter, D., MacArthur, J., Morales, J., Burdett, T., Hall, P., Junkins, H., Klemm, A., Flicek, P., Manolio, T., Hindorff, L., et al. (2014) · Nucleic Acids Research · doi:10.1093/nar/gkt1229 · Cited by 2,707 · see citing works
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 · see citing works
Self-citationGenetic Investigation of ANthropometric Traits (GIANT) Consortium, Yang, J., DIAbetes Genetics Replication And Meta-analysis (DIAGRAM) Consortium, Ferreira, T., Morris, A. P., Medland, S. E., Madden, P. A. F., Heath, A. C., Martin, N. G., Montgomery, G. W., et al. (2012) · Nature Genetics · doi:10.1038/ng.2213 · Cited by 1,712 · see citing works
Matrix eQTL: ultra fast eQTL analysis via large matrix operations
Shabalin, A. A. (2012) · Bioinformatics · doi:10.1093/bioinformatics/bts163 · Cited by 1,694 · see citing works
An atlas of genetic influences on human blood metabolites
The Multiple Tissue Human Expression Resource (MuTHER) Consortium, Shin, S.-Y., Fauman, E. B., Petersen, A.-K., Krumsiek, J., Santos, R., Huang, J., Arnold, M., Erte, I., Forgetta, V., et al. (2014) · Nature Genetics · doi:10.1038/ng.2982 · Cited by 1,515 · see citing works
Self-citationGenetically Elevated Lipoprotein(a) and Increased Risk of Myocardial Infarction
Kamstrup, P. R. (2009) · JAMA · doi:10.1001/jama.2009.801 · Cited by 1,357 · see citing works
Quality of the Finnish Hospital Discharge Register: A systematic review
Sund, R. (2012) · Scandinavian Journal of Public Health · doi:10.1177/1403494812456637 · Cited by 1,189 · see citing works
Human metabolic individuality in biomedical and pharmaceutical research
CARDIoGRAM, Suhre, K., Shin, S.-Y., Petersen, A.-K., Mohney, R. P., Meredith, D., Wägele, B., Altmaier, E., Deloukas, P., Erdmann, J., et al. (2011) · Nature · doi:10.1038/nature10354 · Cited by 1,017 · see citing works
Self-citationSoininen, P., Kangas, A. J., Würtz, P., Suna, T., & Ala‐Korpela, M. (2015) · Circulation Cardiovascular Genetics · doi:10.1161/circgenetics.114.000216 · Cited by 890 · see citing works
Self-citationGWAMA: software for genome-wide association meta-analysis
Mägi, R., & Morris, A. P. (2010) · BMC Bioinformatics · doi:10.1186/1471-2105-11-288 · Cited by 603 · see citing works
High-throughput serum NMR metabonomics for cost-effective holistic studies on systemic metabolism
Soininen, P., Kangas, A. J., Würtz, P., Tukiainen, T., Tynkkynen, T., Laatikainen, R., Järvelin, M., Kähönen, M. A. P., Lehtimäki, T. J., Viikari, J. S. A., et al. (2009) · The Analyst · doi:10.1039/b910205a · Cited by 591 · see citing works
Self-citationGenome-wide association study identifies multiple loci influencing human serum metabolite levels
Kettunen, J., Tukiainen, T., Sarin, A.-P., Ortega-Alonso, A., Tikkanen, E., Lyytikäinen, L.-P., Kangas, A. J., Soininen, P., Würtz, P., Silander, K., et al. (2012) · Nature Genetics · doi:10.1038/ng.1073 · Cited by 553 · see citing works
Self-citationTrégouët, D., König, I. R., Erdmann, J., Munteanu, A., Braund, P. S., Hall, A. S., Großhennig, A., Linsel‐Nitschke, P., Perret, C., DeSuremain, M., et al. (2009) · Nature Genetics · doi:10.1038/ng.314 · Cited by 474 · see citing works
Khera, A. V., Everett, B. M., Caulfield, M. P., Hantash, F. M., Wohlgemuth, J., Ridker, P. M., & Mora, S. (2014) · Circulation · doi:10.1161/circulationaha.113.004406 · Cited by 461 · see citing works
Tsimikas, S., Viney, N. J., Hughes, S. G., Singleton, W., Graham, M. J., Baker, B. F., Burkey, J. L., Yang, Q., Marcovina, S. M., Geary, R. S., et al. (2015) · The Lancet · doi:10.1016/s0140-6736(15)61252-1 · Cited by 460 · see citing works
Matrix eQTL: Ultra fast eQTL analysis via large matrix operationsPreprint
Shabalin, A. A. (2011) · arXiv (Cornell University) · doi:10.17615/g28n-r219 · Cited by 452 · see citing works
The impact of low-frequency and rare variants on lipid levels
ENGAGE Consortium, Surakka, I., Horikoshi, M., Mägi, R., Sarin, A.-P., Mahajan, A., Lagou, V., Marullo, L., Ferreira, T., Miraglio, B., et al. (2015) · Nature Genetics · doi:10.1038/ng.3300 · Cited by 351 · see citing works
Self-citationChasman, D. I., Giulianini, F., MacFadyen, J., Barratt, B. J., Nyberg, F., & Ridker, P. M. (2012) · Circulation: Cardiovascular Genetics · doi:10.1161/circgenetics.111.961144 · Cited by 285 · see citing works
Service, S., DeYoung, J. A., Karayiorgou, M., Roos, J. L., Pretorious, H., Bedoya, G., Ospina, J., Ruiz‐Linares, A., Macedo, A., Palha, J. A., et al. (2006) · Nature Genetics · doi:10.1038/ng1770 · Cited by 273 · see citing works
Self-citationThe Genotype-Tissue Expression (GTEx) project
Lonsdale, J., Thomas, J., Salvatore, M., Phillips, R., Lo, E., Shad, S., Hasz, R., Walters, G., Garcia, F., Young, N., et al. (2013) · Nature Genetics · doi:10.1038/ng.2653 · Cited by 263 · see citing works
Genetic variation in metabolic phenotypes: study designs and applications
Suhre, K., & Gieger, C. (2012) · Nature Reviews Genetics · doi:10.1038/nrg3314 · Cited by 187 · see citing works
Self-citationHobbs, H. H., & White, A. L. (1999) · Current Opinion in Lipidology · doi:10.1097/00041433-199906000-00005 · Cited by 178 · see citing works
Deshmukh, H., Colhoun, H. M., Johnson, T., McKeigue, P., Betteridge, D. J., Durrington, P. N., Fuller, J., Livingstone, S. J., Charlton-Menys, V., Neil, A., et al. (2012) · Journal of Lipid Research · doi:10.1194/jlr.p021113 · Cited by 125 · see citing works
Lipoprotein(a): more interesting than ever after 50 years
Dubé, J. B., Boffa, M. B., Hegele, R. A., & Koschinsky, M. L. (2012) · Current Opinion in Lipidology · doi:10.1097/mol.0b013e32835111d8 · Cited by 105 · see citing works
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 75 · see citing works
Self-citationCobbaert, C. M., Jukema, J. W., Zwinderman, A. H., Withagen, A. J. A. M., Lindemans, J. W., & Bruschke, A. V. G. (1997) · Journal of the American College of Cardiology · doi:10.1016/s0735-1097(97)00353-7 · Cited by 64 · see citing works
Distribution and Medical Impact of Loss-of-Function Variants in the Finnish Founder Population
Lim, E. T., Würtz, P., Havulinna, A. S., Palta, P., Tukiainen, T., Rehnström, K., Esko, T., Mägi, R., Inouye, M., Lappalainen, T., et al. (2014) · PLoS Genetics · doi:10.1371/journal.pgen.1004494 · Cited by 49 · see citing works
Self-citation
Flagged first, then by citations
1 of the 33 references couldn’t be matched to a known paper.
Cited by
Sorted by citations
The MR-Base platform supports systematic causal inference across the human phenome
Hemani, G., Zheng, J., Elsworth, B., Wade, K. H., Haberland, V., Baird, D., Laurin, C., Burgess, S., Bowden, J., Langdon, R., et al. (2018) · eLife · doi:10.7554/elife.34408 · Cited by 7,959 · see citing works
The GTEx Consortium atlas of genetic regulatory effects across human tissues
The GTEx Consortium, Aguet, F., Anand, S., Ardlie, K. G., Gabriel, S., Getz, G. A., Graubert, A., Hadley, K., Handsaker, R. E., Huang, K. H., et al. (2020) · Science · doi:10.1126/science.aaz1776 · Cited by 5,547 · see citing works
Võsa, U., Claringbould, A., Westra, H.-J., Bonder, M. J., Deelen, P., Zeng, B., Kirsten, H., Saha, A., Kreuzhuber, R., Yazar, S., et al. (2021) · Nature Genetics · doi:10.1038/s41588-021-00913-z · Cited by 2,607 · see citing works
Self-citationGenomic atlas of the plasma metabolome prioritizes metabolites implicated in human diseases
Chen, Y., Lu, T., Pettersson, U., Stewart, I. D., Butler‐Laporte, G., Nakanishi, T., Cerani, A., Liang, K. Y. H., Yoshiji, S., Willett, J., et al. (2023) · Nature Genetics · doi:10.1038/s41588-022-01270-1 · Cited by 980 · see citing works
Richardson, T. G., Sanderson, E., Palmer, T. M., Ala-Korpela, M., Ference, B. A., Davey Smith, G., & Holmes, M. V. (2020) · PLOS Medicine · doi:10.1371/journal.pmed.1003062 · Cited by 843 · see citing works
Self-citationThe MRC IEU OpenGWAS data infrastructurePreprint
Elsworth, B., Lyon, M., Alexander, T., Liu, Y., Matthews, P., Hallett, J., Bates, P., Palmer, T., Haberland, V., Smith, G. D., et al. (2020) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2020.08.10.244293 · Cited by 941 · see citing works
Zheng, J., Erzurumluoglu, A. M., Elsworth, B. L., Kemp, J. P., Howe, L., Haycock, P. C., Hemani, G., Tansey, K., Laurin, C., Early Genetics and Lifecourse Epidemiology (EAGLE) Eczema Consortium, et al. (2017) · Bioinformatics · doi:10.1093/bioinformatics/btw613 · Cited by 869 · see citing works
Genetics of 35 blood and urine biomarkers in the UK Biobank
Sinnott-Armstrong, N., Tanigawa, Y., Amar, D., Mars, N., Benner, C., Aguirre, M., Venkataraman, G. R., Wainberg, M., Ollila, H. M., Kiiskinen, T., et al. (2021) · Nature Genetics · doi:10.1038/s41588-020-00757-z · Cited by 744 · see citing works
Self-citationWürtz, P., Kangas, A. J., Soininen, P., Lawlor, D. A., Smith, G. D., & Ala‐Korpela, M. (2017) · American Journal of Epidemiology · doi:10.1093/aje/kwx016 · Cited by 660 · see citing works
Self-citationSanderson, E. C. M., Spiller, W., & Bowden, J. (2021) · Statistics in Medicine · doi:10.1002/sim.9133 · Cited by 603 · see citing works
Unraveling the polygenic architecture of complex traits using blood eQTL metaanalysis
Võsa, U., Claringbould, A., Westra, H.-J., Bonder, M. J., Deelen, P., Zeng, B., Kirsten, H., Saha, A., Kreuzhuber, R., Kasela, S., et al. (2018) · doi:10.1101/447367 · Cited by 2,607 · see citing works
Self-citationLipids, 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 · see citing works
Self-citationWhole-genome sequencing identifies common-to-rare variants associated with human blood metabolites
Long, T., Hicks, M. A., Yu, H., Biggs, W., Kirkness, E. F., Menni, C., Zierer, J., Small, K. S., Mangino, M., Messier, H., et al. (2017) · Nature Genetics · doi:10.1038/ng.3809 · Cited by 419 · see citing works
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 · see citing works
Self-citationBayesian weighted Mendelian randomization for causal inference based on summary statistics
Zhao, J., Ming, J., Hu, X., Chen, G., Liu, J., & Yang, C. (2019) · Bioinformatics · doi:10.1093/bioinformatics/btz749 · Cited by 352 · see citing works
Ference, B. A., Kastelein, J. J. P., Ginsberg, H. N., Chapman, M. J., Nicholls, S. J., Ray, K. K., Packard, C. J., Laufs, U., Brook, R. D., Oliver-Williams, C., et al. (2017) · JAMA · doi:10.1001/jama.2017.11467 · Cited by 304 · see citing works
Genetic Risk Scores for Diabetes Diagnosis and Precision Medicine
Udler, M. S., McCarthy, M. I., Florez, J. C., & Mahajan, A. (2019) · Endocrine Reviews · doi:10.1210/er.2019-00088 · Cited by 302 · see citing works
Genome-wide characterization of circulating metabolic biomarkers
Karjalainen, M. K., Karthikeyan, S., Oliver‐Williams, C., Sliz, E., Allara, E., Fung, W. T., Surendran, P., Zhang, W., Jousilahti, P., Kristiansson, K., et al. (2024) · Nature · doi:10.1038/s41586-024-07148-y · Cited by 268 · see citing works
Self-citationPhenotypic Characterization of Genetically Lowered Human Lipoprotein(a) Levels
Emdin, C. A., Khera, A. V., Natarajan, P., Klarin, D., Won, H.-H., Peloso, G. M., Stitziel, N. O., Nomura, A., Zekavat, S. M., Bick, A. G., et al. (2016) · Journal of the American College of Cardiology · doi:10.1016/j.jacc.2016.10.033 · Cited by 244 · see citing works
Waldeyer, C., Makarova, N., Zeller, T., Schnabel, R. B., Brunner, F. J., Jørgensen, T., Linneberg, A. R., Niiranen, T., Salomaa, V. V., Jousilahti, P., et al. (2017) · European Heart Journal · doi:10.1093/eurheartj/ehx166 · Cited by 244 · see citing works
Self-citationMunukka, E., Ahtiainen, J. P., Puigbó, P., Jalkanen, S., Pahkala, K., Keskitalo, A., Kujala, U. M., Pietilä, S., Hollmén, M., Elo, L., et al. (2018) · Frontiers in Microbiology · doi:10.3389/fmicb.2018.02323 · Cited by 237 · see citing works
Zuber, V., Colijn, J. M., Klaver, C. C. W., & Burgess, S. (2020) · Nature Communications · doi:10.1038/s41467-019-13870-3 · Cited by 237 · see citing works
An exposome perspective: Early-life events and immune development in a changing world
Renz, H., Holt, P. G., Inouye, M., Logan, A. C., Prescott, S. L., & Sly, P. D. (2017) · Journal of Allergy and Clinical Immunology · doi:10.1016/j.jaci.2017.05.015 · Cited by 226 · see citing works
Lipoprotein(a) Concentration and Risks of Cardiovascular Disease and Diabetes
Gudbjartsson, D. F., Þorgeirsson, G., Sulem, P., Helgadóttir, A., Gylfason, A., Saemundsdottir, J., Björnsson, E., Norddahl, G. L., Jónasdóttir, Á., Jónasdóttir, A., et al. (2019) · Journal of the American College of Cardiology · doi:10.1016/j.jacc.2019.10.019 · Cited by 227 · see citing works
Circulating metabolites and general cognitive ability and dementia: evidence from 11 cohort studies
van der Lee, S. J., Teunissen, C. E., Pool, R., Shipley, M. J., Teumer, A., Chouraki, V., Melo van Lent, D., Tynkkynen, J., Fischer, K., Hernesniemi, J., et al. (2018) · Alzheimer's & Dementia · doi:10.1016/j.jalz.2017.11.012 · Cited by 197 · see citing works
Self-citationHuman longevity: 25 genetic loci associated in 389,166 UK biobank participants
Pilling, L. C., Kuo, C.-L., Sicinski, K., Tamosauskaite, J., Kuchel, G. A., Harries, L. W., Herd, P., Wallace, R., Ferrucci, L., & Melzer, D. (2017) · Aging · doi:10.18632/aging.101334 · Cited by 211 · see citing works
Vujkovic, M., Ramdas, S., Lorenz, K. M., Guo, X., Darlay, R., Cordell, H. J., He, J., Gindin, Y., Chung, C., Myers, R. P., et al. (2022) · Nature Genetics · doi:10.1038/s41588-022-01078-z · Cited by 205 · see citing works
Metabolic profiling of pregnancy: cross-sectional and longitudinal evidence
Wang, Q., Würtz, P., Auro, K. M., Mäkinen, V., Kangas, A. J., Soininen, P., Tiainen, M., Tynkkynen, T., Jokelainen, J. J., Santalahti, K., et al. (2016) · BMC Medicine · doi:10.1186/s12916-016-0733-0 · Cited by 205 · see citing works
Self-citationGene-Diet Interaction and Precision Nutrition in Obesity
Heianza, Y., & Qi, L. (2017) · International Journal of Molecular Sciences · doi:10.3390/ijms18040787 · Cited by 209 · see citing works
A cross-platform approach identifies genetic regulators of human metabolism and health
Lotta, L. A., Pietzner, M., Stewart, I. D., Wittemans, L. B. L., Li, C., Bonelli, R., Raffler, J., Biggs, E. K., Oliver‐Williams, C., Auyeung, V. P. W., et al. (2021) · Nature Genetics · doi:10.1038/s41588-020-00751-5 · Cited by 204 · see citing works
Kurilshikov, A., van den Munckhof, I. C. L., Chen, L., Bonder, M. J., Schraa, K., Rutten, J. H. W., Riksen, N. P., de Graaf, J., Oosting, M., Sanna, S., et al. (2019) · Circulation Research · doi:10.1161/circresaha.118.314642 · Cited by 178 · see citing works
Self-citationBorges, M. C., Haycock, P., Zheng, J., Hemani, G., Holmes, M. V., Smith, G. D., Hingorani, A. D., & Lawlor, D. A. (2022) · BMC Medicine · doi:10.1186/s12916-022-02399-w · Cited by 187 · see citing works
Holmes, M. V., Richardson, T. G., Ference, B. A., Davies, N. M., & Davey Smith, G. (2021) · Nature Reviews Cardiology · doi:10.1038/s41569-020-00493-1 · Cited by 184 · see citing works
Wang, Q., Holmes, M. V., Smith, G. D., & Ala‐Korpela, M. (2017) · Diabetes Care · doi:10.2337/dc17-1642 · Cited by 184 · see citing works
Self-citationExome sequencing of Finnish isolates enhances rare-variant association power
Locke, A. E., Steinberg, K. M., Chiang, C. W. K., Service, S. K., Havulinna, A. S., Stell, L., Pirinen, M., Abel, H., Chiang, C., Fulton, R. S., et al. (2019) · Nature · doi:10.1038/s41586-019-1457-z · Cited by 170 · see citing works
Self-citationGómez-Banoy, N., Guseh, J. S., Li, G., Rubio‐Navarro, A., Chen, T., Poirier, B., Putzel, G., Rosselot, C., Pabón, M. A., Camporez, J. P., et al. (2019) · Nature Medicine · doi:10.1038/s41591-019-0610-4 · Cited by 180 · see citing works
Genetic architecture of human plasma lipidome and its link to cardiovascular disease
Tabassum, R., Rämö, J. T., Ripatti, P., Koskela, J. T., Kurki, M., Karjalainen, J. M., Palta, P., Hassan, S., Núñez-Fontarnau, J., Kiiskinen, T., et al. (2019) · Nature Communications · doi:10.1038/s41467-019-11954-8 · Cited by 176 · see citing works
Self-citationBalling, M., Afzal, S., Varbo, A., Langsted, A., Davey Smith, G., & Nordestgaard, B. G. (2020) · Journal of the American College of Cardiology · doi:10.1016/j.jacc.2020.09.610 · Cited by 175 · see citing works
Frontiers of high-throughput metabolomics
Zampieri, M., Sekar, K., Zamboni, N., & Sauer, U. (2017) · Current Opinion in Chemical Biology · doi:10.1016/j.cbpa.2016.12.006 · Cited by 169 · see citing works
Identification of 12 genetic loci associated with human healthspan
Zenin, A., Tsepilov, Y., Sharapov, S., Getmantsev, E., Menshikov, L. I., Fedichev, P. O., & Aulchenko, Y. (2019) · Communications Biology · doi:10.1038/s42003-019-0290-0 · Cited by 170 · see citing works
Adiposity, metabolites, and colorectal cancer risk: Mendelian randomization study
Bull, C. J., Bell, J. A., Murphy, N., Sanderson, E. C. M., Smith, G. D., Timpson, N. J., Banbury, B. L., Albanes, D., Berndt, S. I., Bezieau, S., et al. (2020) · BMC Medicine · doi:10.1186/s12916-020-01855-9 · Cited by 167 · see citing works
Increased High-Density Lipoprotein Levels Associated with Age-Related Macular Degeneration
Colijn, J. M., den Hollander, A. I., Demirkan, A., Cougnard-Grégoire, A., Verzijden, T., Kersten, E., Meester‐Smoor, M. A., Merle, B. M. J., Papageorgiou, G., Ahmad, S., et al. (2018) · Ophthalmology · doi:10.1016/j.ophtha.2018.09.045 · Cited by 158 · see citing works
Self-citationLipid lowering and Alzheimer disease risk: A mendelian randomization study
Williams, D. M., Finan, C., Schmidt, A. F., Burgess, S., & Hingorani, A. D. (2020) · Annals of Neurology · doi:10.1002/ana.25642 · Cited by 157 · see citing works
Genetic evidence of a causal effect of insulin resistance on branched-chain amino acid levels
Mahendran, Y., Jonsson, A. E., Have, C. T., Allin, K. H., Witte, D. R., Jørgensen, M. E., Grarup, N., Pedersen, O. B., Kilpeläinen, T. O., & Hansen, T. F. (2017) · Diabetologia · doi:10.1007/s00125-017-4222-6 · Cited by 154 · see citing works
The role of lipoprotein (a) in chronic kidney disease
Hopewell, J. C., Haynes, R., & Baigent, C. (2018) · Journal of Lipid Research · doi:10.1194/jlr.r083626 · Cited by 151 · see citing works
Rare and common genetic determinants of metabolic individuality and their effects on human health
Surendran, P., Stewart, I. D., Auyeung, V. P. W., Pietzner, M., Raffler, J., Wörheide, M. A., Li, C., Smith, R. F., Wittemans, L. B. L., Bomba, L., et al. (2022) · Nature Medicine · doi:10.1038/s41591-022-02046-0 · Cited by 150 · see citing works
Levin, M. G., Tsao, N. L., Singhal, P., Liu, C., Vy, H. M. T., Paranjpe, I., Backman, J., Bellomo, T. R., Bone, W. P., Biddinger, K. J., et al. (2022) · Nature Communications · doi:10.1038/s41467-022-34216-6 · Cited by 144 · see citing works
Cholesteryl ester transfer protein (CETP) as a drug target for cardiovascular disease
Schmidt, A. F., Hunt, N. B., Gordillo‐Marañón, M., Charoen, P., Drenos, F., Casas, J. P., Mika Kivimaki, B., Lawlor, D. A., Giambartolomei, C., Papacosta, O., et al. (2021) · Nature Communications · doi:10.1038/s41467-021-25703-3 · Cited by 145 · see citing works
Merino, J., Leong, A., Liu, C., Porneala, B., Walford, G. A., von Grotthuss, M., Wang, T. J., Flannick, J. A., Dupuis, J., Levy, D. A., et al. (2018) · Diabetologia · doi:10.1007/s00125-018-4599-x · Cited by 144 · see citing works
Hemani, G., Zheng, J., Wade, K. H., Laurin, C., Elsworth, B., Burgess, S., Bowden, J., Langdon, R., Tan, V. Y., Yarmolinsky, J., et al. (2016) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/078972 · Cited by 133 · see citing works
Metabolic profiling of alcohol consumption in 9778 young adults
Würtz, P., Cook, S., Wang, Q., Tiainen, M., Tynkkynen, T., Kangas, A. J., Soininen, P., Laitinen, J. H., Viikari, J. S. A., Kähönen, M. A. P., et al. (2016) · International Journal of Epidemiology · doi:10.1093/ije/dyw175 · Cited by 138 · see citing works
Self-citationNAFLD risk alleles in PNPLA3, TM6SF2, GCKR and LYPLAL1 show divergent metabolic effects
Sliz, E., Sebert, S. P., Würtz, P., Kangas, A. J., Soininen, P., Lehtimäki, T. J., Kähönen, M. A. P., Viikari, J. S. A., Männikkö, M., Ala‐Korpela, M., et al. (2018) · Human Molecular Genetics · doi:10.1093/hmg/ddy124 · Cited by 135 · see citing works
Self-citationMendelian randomization for studying the effects of perturbing drug targets
Gill, D., Georgakis, M. K., Walker, V. M., Schmidt, A. F., Gkatzionis, A., Freitag, D. F., Finan, C., Hingorani, A. D., Howson, J. M. M., Burgess, S., et al. (2021) · Wellcome Open Research · doi:10.12688/wellcomeopenres.16544.2 · Cited by 280 · see citing works
Metabolic characterization of menopause: cross-sectional and longitudinal evidence
Wang, Q., Ferreira, D. L. S., Nelson, S. M., Sattar, N., Ala-Korpela, M., & Lawlor, D. A. (2018) · BMC Medicine · doi:10.1186/s12916-018-1008-8 · Cited by 134 · see citing works
Self-citationAssessing the causal association of glycine with risk of cardio-metabolic diseases
Wittemans, L. B. L., Lotta, L. A., Oliver‐Williams, C., Stewart, I. D., Surendran, P., Karthikeyan, S., Day, F. R., Koulman, A., Imamura, F., Zeng, L., et al. (2019) · Nature Communications · doi:10.1038/s41467-019-08936-1 · Cited by 134 · see citing works
A practical view of fine-mapping and gene prioritization in the post-genome-wide association era
Broekema, R., Bakker, O. B., & Jonkers, I. H. (2020) · Open Biology · doi:10.1098/rsob.190221 · Cited by 127 · see citing works
Chen, V. L., Du, X., Chen, Y., Kuppa, A., Handelman, S. K., Vohnoutka, R. B., Peyser, P. A., Palmer, N. D., Bielak, L. F., Halligan, B. D., et al. (2021) · Nature Communications · doi:10.1038/s41467-020-20870-1 · Cited by 129 · see citing works
Röytiö, H., Mokkala, K., Vahlberg, T., & Laitinen, K. (2017) · British Journal Of Nutrition · doi:10.1017/s0007114517002100 · Cited by 128 · see citing works
Influence of puberty timing on adiposity and cardiometabolic traits: A Mendelian randomisation study
Bell, J. A., Carslake, D., Wade, K. H., Richmond, R. C., Langdon, R., Vincent, E. E., Holmes, M. V., Timpson, N. J., & Smith, G. D. (2018) · PLoS Medicine · doi:10.1371/journal.pmed.1002641 · Cited by 124 · see citing works
Selection in Europeans on Fatty Acid Desaturases Associated with Dietary Changes
Buckley, M. T., Racimo, F., Allentoft, M. E., Jensen, M. K., Jonsson, A. E., Huang, H., Hormozdiari, F., Sikora, M., Marnetto, D., Eskin, E., et al. (2017) · Molecular Biology and Evolution · doi:10.1093/molbev/msx103 · Cited by 121 · see citing works
Cholesterol Auxotrophy as a Targetable Vulnerability in Clear Cell Renal Cell Carcinoma
Riscal, R., Bull, C. J., Mesaros, C., Finan, J. M., Carens, M., Ho, E. S., Xu, J. P., Godfrey, J., Brennan, P., Johansson, M., et al. (2021) · Cancer Discovery · doi:10.1158/2159-8290.cd-21-0211 · Cited by 120 · see citing works
Omega-3 and omega-6 fatty acid levels in depressive and anxiety disorders
Thesing, C. S., Bot, M., Milaneschi, Y., Giltay, E. J., & Penninx, B. W. J. H. (2017) · Psychoneuroendocrinology · doi:10.1016/j.psyneuen.2017.10.005 · Cited by 110 · see citing works
Heritability estimates for 361 blood metabolites across 40 genome-wide association studies
Hagenbeek, F. A., Pool, R., van Dongen, J., Draisma, H. H. M., Hottenga, J., Willemsen, G. A. H. M., Abdellaoui, A., Fedko, I. O., den Braber, A., Visser, P. J., et al. (2020) · Nature Communications · doi:10.1038/s41467-019-13770-6 · Cited by 110 · see citing works
Self-citationMetabolomic Consequences of Genetic Inhibition of PCSK9 Compared With Statin Treatment
Sliz, E., Kettunen, J., Holmes, M. V., Williams, C. O., Boachie, C., Wang, Q., Männikkö, M., Sebert, S., Walters, R., Lin, K., et al. (2018) · Circulation · doi:10.1161/circulationaha.118.034942 · Cited by 109 · see citing works
Self-citationMarnetto, D., Pärna, K., Läll, K., Molinaro, L., Montinaro, F., Haller, T., Metspalu, M., Mägi, R., Fischer, K., & Pagani, L. (2020) · Nature Communications · doi:10.1038/s41467-020-15464-w · Cited by 98 · see citing works
Self-citationFall, T., Salihović, S., Brandmaier, S., Nowak, C., Ganna, A., Gustafsson, S., Broeckling, C. D., Prenni, J. E., Kastenmüller, G., Peters, A., et al. (2016) · Diabetologia · doi:10.1007/s00125-016-4041-1 · Cited by 108 · see citing works
Self-citationNavarese, E. P., Vine, D., Proctor, S., Grzelakowska, K., Berti, S., Kubica, J., & Raggi, P. (2023) · Arteriosclerosis, Thrombosis, and Vascular Biology · doi:10.1161/atvbaha.123.319297 · Cited by 107 · see citing works
Zekavat, S. M., Ruotsalainen, S., Handsaker, R. E., Alver, M., Bloom, J., Poterba, T., Seed, C., Ernst, J., Chaffin, M. D., Engreitz, J., et al. (2018) · Nature Communications · doi:10.1038/s41467-018-04668-w · Cited by 105 · see citing works
Self-citationExtra-cardiac BCAA catabolism lowers blood pressure and protects from heart failure
Murashige, D., Jung, J. W., Neinast, M. D., Levin, M. G., Chu, Q., Lambert, J. P., Garbincius, J. F., Kim, B., Hoshino, A., Martí-Pàmies, Í., et al. (2022) · Cell Metabolism · doi:10.1016/j.cmet.2022.09.008 · Cited by 107 · see citing works
Burgess, S. (2018) · Apollo (University of Cambridge) · doi:10.17863/cam.26250 · Cited by 118 · see citing works
Genetics of 38 blood and urine biomarkers in the UK BiobankPreprint
Sinnott-Armstrong, N., Tanigawa, Y., Amar, D., Mars, N., Aguirre, M., Venkataraman, G. R., Wainberg, M., Ollila, H. M., Pirruccello, J. P., Qian, J., et al. (2019) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/660506 · Cited by 102 · see citing works
Self-citationGagnon, É., Mitchell, P. L., Manikpurage, H. D., Abner, E., Taba, N., Esko, T., Ghodsian, N., Thériault, S., Mathieu, P., & Arsenault, B. J. (2023) · Journal of Translational Medicine · doi:10.1186/s12967-022-03799-5 · Cited by 110 · see citing works
Self-citationCholesteryl Ester Transfer Protein Influences High-Density Lipoprotein Levels and Survival in Sepsis
Trinder, M., Genga, K. R., Kong, H. J., Blauw, L. L., Lo, C., Li, X., Cirstea, M. S., Wang, Y., Rensen, P. C. N., Russell, J. A., et al. (2018) · American Journal of Respiratory and Critical Care Medicine · doi:10.1164/rccm.201806-1157oc · Cited by 98 · see citing works
A genome-wide association analysis reveals new pathogenic pathways in gout
Major, T. J., Takei, R., Matsuo, H., Leask, M., Sumpter, N., Topless, R., Shirai, Y., Wang, W., Cadzow, M., Phipps-Green, A., et al. (2024) · Nature Genetics · doi:10.1038/s41588-024-01921-5 · Cited by 93 · see citing works
Shen, X., Klarić, L., Sharapov, S., Mangino, M., Zheng, N., Wu, D., Trbojević‐Akmačić, I., Pučić‐Baković, M., Rudan, I., Polašek, O., et al. (2017) · Nature Communications · doi:10.1038/s41467-017-00453-3 · Cited by 95 · see citing works
Mulugeta, A., Zhou, A., King, C. E., & Hyppönen, E. (2019) · Molecular Psychiatry · doi:10.1038/s41380-019-0486-1 · Cited by 95 · see citing works
Metabolomics based markers predict type 2 diabetes in a 14-year follow-up study
Liu, J., Semiz, S., van der Lee, S. J., van der Spek, A., Verhoeven, A., van Klinken, J. B., Sijbrands, E. J. G., Harms, A. C., Hankemeier, T., van Dijk, K. W., et al. (2017) · Metabolomics · doi:10.1007/s11306-017-1239-2 · Cited by 92 · see citing works
Self-citationGeorgakis, M. K., Malik, R., Anderson, C. D., Parhofer, K. G., Hopewell, J. C., & Dichgans, M. (2019) · Brain · doi:10.1093/brain/awz413 · Cited by 91 · see citing works
Gu, Y., Jin, Q., Hu, J., Wang, X., Yu, W., Wang, Z., Wang, C., Liu, Y., Chen, Y., & Yuan, W. (2023) · Journal of Translational Medicine · doi:10.1186/s12967-023-04165-9 · Cited by 90 · see citing works
Zuber, V., Gill, D., Ala‐Korpela, M., Langenberg, C., Butterworth, A. S., Bottolo, L., & Burgess, S. (2020) · International Journal of Epidemiology · doi:10.1093/ije/dyaa216 · Cited by 87 · see citing works
Self-citationJones, H. J., Borges, M. C., Carnegie, R., Mongan, D., Rogers, P. J., Lewis, S. J., Thompson, A. D., & Zammit, S. (2021) · The Lancet Psychiatry · doi:10.1016/s2215-0366(21)00286-8 · Cited by 85 · see citing works
Hemani, G., Bowden, J., Haycock, P., Zheng, J., Davis, O., Flach, P., Gaunt, T., & Smith, G. D. (2017) · doi:10.1101/173682 · Cited by 85 · see citing works
Coras, R., Murillo‐Saich, J., & Gumá, M. (2020) · Cells · doi:10.3390/cells9040827 · Cited by 85 · see citing works
Cupido, A. J., Asselbergs, F. W., Natarajan, P., CHARGE Inflammation Working Group, Ridker, P. M., Hovingh, G. K., & Schmidt, A. F. (2021) · British Journal of Clinical Pharmacology · doi:10.1111/bcp.15191 · Cited by 83 · see citing works
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 · see citing works
Self-citationLiu, 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 78 · see citing works
Self-citationHazelwood, E., Sanderson, E. C. M., Tan, V. Y., Ruth, K. S., Frayling, T. M., Dimou, N. L., Gunter, M. J., Dossus, L., Newton, C. L., Ryan, N. A. J., et al. (2022) · BMC Medicine · doi:10.1186/s12916-022-02322-3 · Cited by 78 · see citing works
Leptin alters energy intake and fat mass but not energy expenditure in lean subjects
Chrysafi, P., Perakakis, N., Farr, O. M., Stefanakis, K., Peradze, N., Sala‐Vila, A., & Mantzoros, C. S. (2020) · Nature Communications · doi:10.1038/s41467-020-18885-9 · Cited by 79 · see citing works
Beynon, R. A., Richmond, R. C., Ferreira, D. L. S., Ness, A., May, M., Smith, G. D., Vincent, E. E., Adams, C. D., Ala‐Korpela, M., Würtz, P., et al. (2018) · International Journal of Cancer · doi:10.1002/ijc.31929 · Cited by 78 · see citing works
Self-citationMetabolomics Insights into Osteoporosis Through Association With Bone Mineral Density
Zhang, X., Xu, H., Li, G. H., Long, M. T., Cheung, C., Vasan, R. S., Hsu, Y., Kiel, D. P., & Liu, C. (2020) · Journal of Bone and Mineral Research · doi:10.1002/jbmr.4240 · Cited by 78 · see citing works
The Estonian Biobank’s journey from biobanking to personalized medicine
Milani, L. A., Alver, M., Laur, S., Reisberg, S., Haller, T., Aasmets, O., Abner, E., Alavere, H., Allik, A., Annilo, T., et al. (2025) · Nature Communications · doi:10.1038/s41467-025-58465-3 · Cited by 95 · see citing works
Self-citationYousri, N. A., Fakhro, K. A., Robay, A., Rodríguez-Flores, J. L., Mohney, R. P., Zeriri, H., Odeh, T., Kader, S. A., Al‐Dous, E., Thareja, G., et al. (2018) · Nature Communications · doi:10.1038/s41467-017-01972-9 · Cited by 76 · see citing works
Ottestad, I. O., Ulven, S. M., Øyri, L. K. L., Sandvei, K. S., Gjevestad, G. O., Bye, A., Sheikh, N. A., Biong, A. S., Andersen, L. F., & Holven, K. B. (2018) · British Journal Of Nutrition · doi:10.1017/s0007114518001307 · Cited by 75 · see citing works
Yuan, S., Wang, L., Zhang, H., Xu, F., Zhou, X., Yu, L., Sun, J., Chen, J., Ying, H., Xu, X., et al. (2023) · EBioMedicine · doi:10.1016/j.ebiom.2023.104488 · Cited by 73 · see citing works
Verma, A., Lucas, A., Verma, S. S., Zhang, Y., Josyula, N. S., Khan, A., Hartzel, D. N., Lavage, D. R., Leader, J. B., Ritchie, M. D., et al. (2018) · The American Journal of Human Genetics · doi:10.1016/j.ajhg.2018.02.017 · Cited by 73 · see citing works
Richardson, T. G., Zheng, J., Smith, G. D., Timpson, N. J., Gaunt, T. R., Relton, C. L., & Hemani, G. (2017) · The American Journal of Human Genetics · doi:10.1016/j.ajhg.2017.09.003 · Cited by 68 · see citing works
Schmidt, J. A., Fensom, G. K., Rinaldi, S., Scalbert, A., Appleby, P. N., Achaintre, D., Gicquiau, A., Gunter, M. J., Ferrari, P., Kaaks, R., et al. (2019) · International Journal of Cancer · doi:10.1002/ijc.32314 · Cited by 71 · see citing works
Balling, M., Langsted, A., Afzal, S., Varbo, A., Smith, G. D., & Nordestgaard, B. G. (2019) · Atherosclerosis · doi:10.1016/j.atherosclerosis.2019.05.011 · Cited by 70 · see citing works
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 70 · see citing works
Self-citationYarmolinsky, J., Díez‐Obrero, V., Richardson, T. G., Pigeyre, M., Sjaarda, J., Paré, G., Walker, V. M., Vincent, E. E., Tan, V. Y., Obón‐Santacana, M., et al. (2022) · PLoS Medicine · doi:10.1371/journal.pmed.1003897 · Cited by 70 · see citing works
- Authors
- Kettunen, J., Demirkan, A., Würtz, P., Draisma, H. H. M., Haller, T., Rawal, R., Vaarhorst, A., Kangas, A. J., Lyytikäinen, L.-P., Pirinen, M., Pool, R., Sarin, A.-P., Soininen, P., Tukiainen, T., Wang, Q., Tiainen, M., Tynkkynen, T., Amin, N., Zeller, T., Beekman, M., Deelen, J., van Dijk, K. W., Esko, T., Hottenga, J.-J., van Leeuwen, E. M., Lehtimäki, T., Mihailov, E., Rose, R. J., de Craen, A. J. M., Gieger, C., Kähönen, M., Perola, M., Blankenberg, S., Savolainen, M. J., Verhoeven, A., Viikari, J., Willemsen, G., Boomsma, D. I., van Duijn, C. M., Eriksson, J., Jula, A., Järvelin, M.-R., Kaprio, J., Metspalu, A., Raitakari, O., Salomaa, V., Slagboom, P. E., Waldenberger, M., Ripatti, S., & Ala-Korpela, M.
- Affiliations
- University of Eastern Finland (FI)
- Leiden University Medical Center (NL)
- University of Oulu (FI)
- Amsterdam Neuroscience (NL)
- University of Tartu (EE)
- Helmholtz Munich (DE)
- Tampere University (FI)
- University of Helsinki (FI)
- Amsterdam Public Health (NL)
- Harvard University (US)
- Erasmus MC (NL)
- Universität Hamburg (DE)
- Tampere University Hospital (FI)
- Oulu University Hospital (FI)
- University of Turku (FI)
- Finnish Institute for Health and Welfare (FI)
- Corresponding author
- Kettunen, Johannes
- ORCID
- Kettunen, Johannes
- Demirkan, Ayşe
- Würtz, Peter
- Draisma, Harmen H.M.
- Haller, Toomas
- Rawal, Rajesh
- Kangas, Antti J.
- Lyytikäinen, Leo-Pekka
- Pirinen, Matti
- Pool, René
- Sarin, Antti-Pekka
- Tukiainen, Taru
- Wang, Qin
- Tiainen, Mika
- Tynkkynen, Tuulia
- Amin, Najaf
- Zeller, Tanja
- Beekman, Marian
- Deelen, Joris
- van Dijk, Ko Willems
- Esko, Tõnu
- Hottenga, Jouke-Jan
- Lehtimäki, Terho
- Rose, Richard J.
- Gieger, Christian
- Kähönen, Mika
- Perola, Markus
- Blankenberg, Stefan
- Savolainen, Markku J.
- Verhoeven, Aswin
- Viikari, Jorma
- Willemsen, Gonneke
- Boomsma, Dorret I.
- Eriksson, Johan
- Jula, Antti
- Järvelin, Marjo-Riitta
- Kaprio, Jaakko
- Metspalu, Andres
- Raitakari, Olli
- Salomaa, Veikko
- Slagboom, P. Eline
- Ripatti, Samuli
- Venue
- Nature Communications
- Published
- 2016-03-23
- Volume
- 7
- Issue
- 1
- Pages
- 11122-11122
- ISSN
- 2041-1723 (ISSN-L)
- PMC
- PMC4814583
- OpenAlex
- W2310542696
- Cited by
- 738
- References
- 33
- Citations by year
- 2026: 35 · 2025: 76 · 2024: 118 · 2023: 89 · 2022: 86 · 2021: 84 · 2020: 75 · 2019: 75 · 2018: 52 · 2017: 37 · 2016: 9 · 2014: 1
- FWCI
- 46.12
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Locations
- Nature Communications (publishedVersion, cc-by)
- PubMed Central (publishedVersion, cc-by)
- PubMed (publishedVersion)
- EUR Research Repository (Erasmus University Rotterdam) (publishedVersion)
- Explore Bristol Research (publishedVersion, cc-by)
- Tampere University Institutional Repository (Tampere University) (publishedVersion, cc-by)
- Työväentutkimus Vuosikirja (submittedVersion, cc-by)
- PuSH - Publication Server of Helmholtz Zentrum München (submittedVersion, cc-by)
- Digital Access to Scholarship at Harvard (DASH) (Harvard University) (submittedVersion, cc-by)
- EUR Research Repository (Erasmus University Rotterdam) (submittedVersion)
- Data Archiving and Networked Services (DANS) (submittedVersion)
- RePEc: Research Papers in Economics (submittedVersion)
- Leiden Repository (Leiden University) (submittedVersion)
- DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
- Site cant be reached (submittedVersion, cc-by)
- Bristol Research (University of Bristol) (submittedVersion, cc-by)
- VU Research Portal (submittedVersion, other-oa)
- Spiral (Imperial College London) (submittedVersion, cc-by)
- Tampere University Institutional Repository (Tampere University) (submittedVersion, cc-by)
- STM:n Hallinnonalan avoin julkaisuarkisto (Julkari) (submittedVersion)
- Data Archiving and Networked Services (DANS) (submittedVersion, other-oa)
- Funders
- Novo Nordisk Fonden (NNF15OC0015998)
- 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 › Genetic Mapping and Diversity in Plants and Animals
- Keywords
- circulating metabolites, lipoprotein(a), LPA gene, genetic loci, amino acids, pyruvate, fatty acids, triglyceride metabolism, lipoprotein metabolism, cardiovascular risk, pleiotropy, Mendelian randomization, genetic fine-mapping
- MeSH terms
- Adult, Aged, Cardiovascular Diseases, Chromosome Mapping, Female, Humans, Lipoproteins, VLDL, Male, Middle Aged, Magnetic Resonance Spectroscopy, Triglycerides, Lipoprotein(a), Genetic Predisposition to Disease, Genome-Wide Association Study, Metabolomics, Young Adult, Mendelian Randomization Analysis
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
