Suhre, Karsten

Genomic atlas of the human plasma proteome

Sun, B. B., Maranville, J. C., Peters, J. E., Stacey, D., Staley, J. R., Blackshaw, J., Burgess, S., Jiang, T., Paige, E., Surendran, P., et al. (2018) · Nature

Genetic Associations and EpidemiologyGenetic Mapping and Diversity in Plants and Animals

Cited by 2,268

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

Genetic Associations and EpidemiologyMetabolomics and Mass Spectrometry Studies

Cited by 1,515

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

Genetic Associations and EpidemiologyMetabolomics and Mass Spectrometry Studies

Cited by 1,017

Connecting genetic risk to disease end points through the human blood plasma proteome

Suhre, K., Arnold, M., Bhagwat, A. M., Cotton, R. J., Engelke, R., Raffler, J., Sarwath, H., Thareja, G., Wahl, A., DeLisle, R. K., et al. (2017) · Nature Communications

Genetic Associations and EpidemiologyBioinformatics and Genomic Networks

Cited by 800

Genetics meets metabolomics: a genome-wide association study of metabolite profiles in human serum

Gieger, C., Geistlinger, L., Altmaier, E., Hrabé de Angelis, M., Kronenberg, F., Meitinger, T., Mewes, H.-W., Wichmann, H.-E., Weinberger, K. M., Adamski, J., et al. (2008) · PLoS Genetics

Genetic Associations and EpidemiologyMetabolomics and Mass Spectrometry Studies

Cited by 737

Novel biomarkers for pre-diabetes identified by metabolomics

Wang‐Sattler, R., Yu, Z., Herder, C., Messias, A. C., Floegel, A., He, Y., Heim, K., Campillos, M., Holzapfel, C., Thorand, B., et al. (2012) · Molecular Systems Biology

Metabolomics and Mass Spectrometry StudiesDiabetes, Cardiovascular Risks, and Lipoproteins

Cited by 701

A genome-wide perspective of genetic variation in human metabolism

Illig, T., Gieger, C., Zhai, G., Römisch-Margl, W., Wang-Sattler, R., Prehn, C., Altmaier, E., Kastenmüller, G., Kato, B. S., Mewes, H.-W., et al. (2010) · Nature Genetics

Genetic Associations and EpidemiologyMetabolomics and Mass Spectrometry Studies

Cited by 653

Metabolomics enables precision medicine: “A White Paper, Community Perspective”

for “Precision Medicine and Pharmacometabolomics Task Group”-Metabolomics Society Initiative, Beger, R. D., Dunn, W., Schmidt, M. A., Gross, S. S., Kirwan, J. A., Cascante, M., Brennan, L., Wishart, D. S., Oresic, M., et al. (2016) · Metabolomics

Metabolomics and Mass Spectrometry StudiesHealth, Environment, Cognitive Aging

Cited by 592

Genetics meets proteomics: perspectives for large population-based studies

Suhre, K., McCarthy, M. I., & Schwenk, J. M. (2021) · Nature Reviews Genetics

Genetic Associations and EpidemiologyBioinformatics and Genomic Networks

Cited by 478

Genome‐wide mapping of plasma protein QTLs identifies putatively causal genes and pathways for cardiovascular disease

Yao, C., Chen, G., Song, C., Keefe, J., Mendelson, M., Huan, T., Sun, B. B., Laser, A., Maranville, J. C., Wu, H., et al. (2018) · Nature Communications

Genetic Associations and EpidemiologyGenetic Mapping and Diversity in Plants and Animals

Cited by 431

Biomarkers for Type 2 Diabetes and Impaired Fasting Glucose Using a Nontargeted Metabolomics Approach

Menni, C., Fauman, E., Erte, I., Perry, J. R. B., Kastenmüller, G., Shin, S.-Y., Petersen, A.-K., Hyde, C., Psatha, M., Ward, K. J., et al. (2013) · Diabetes

Metabolomics and Mass Spectrometry StudiesDiabetes, Cardiovascular Risks, and Lipoproteins

Cited by 416

Human serum metabolic profiles are age dependent

Yu, Z., Zhai, G., Singmann, P., He, Y., Xu, T., Prehn, C., Römisch‐Margl, W., Lattka, E., Gieger, C., Soranzo, N., et al. (2012) · Aging Cell

Metabolomics and Mass Spectrometry StudiesGenetics, Aging, and Longevity in Model Organisms

Cited by 340

The dynamic range of the human metabolome revealed by challenges

Krug, S., Kastenmüller, G., Stückler, F., Rist, M. J., Skurk, T., Sailer, M., Raffler, J., Römisch‐Margl, W., Adamski, J., Prehn, C., et al. (2012) · The FASEB Journal

Metabolomics and Mass Spectrometry StudiesHealth, Environment, Cognitive Aging

Cited by 327

Metabolomic markers reveal novel pathways of ageing and early development in human populations

Menni, C., Kastenmüller, G., Petersen, A. K., Bell, J. T., Psatha, M., Tsai, P.-C., Gieger, C., Schulz, H., Erte, I., John, S., et al. (2013) · International Journal of Epidemiology

Metabolomics and Mass Spectrometry StudiesBirth, Development, and Health

Cited by 282

A metabolome-wide association study of kidney function and disease in the general population

Sekula, P., Goek, O.-N., Quaye, L., Barrios, C., Levey, A. S., Römisch-Margl, W., Menni, C., Yet, I., Gieger, C., Inker, L. A., et al. (2016) · Journal of the American Society of Nephrology

Metabolomics and Mass Spectrometry StudiesChronic Kidney Disease and Diabetes

Cited by 203

Mining the Unknown: A Systems Approach to Metabolite Identification Combining Genetic and Metabolic Information

Krumsiek, J., Suhre, K., Evans, A. M., Mitchell, M. W., Mohney, R. P., Milburn, M. V., Wägele, B., Römisch-Margl, W., Illig, T., Adamski, J., et al. (2012) · PLoS Genetics

Metabolomics and Mass Spectrometry StudiesBioinformatics and Genomic Networks

Cited by 197

Epigenetics meets metabolomics: an epigenome-wide association study with blood serum metabolic traits

Petersen, A.-K., Zeilinger, S., Kastenmüller, G., Römisch-Margl, W., Brugger, M., Peters, A., Meisinger, C., Strauch, K., Hengstenberg, C., Pagel, P., et al. (2014) · Human Molecular Genetics

Epigenetics and DNA MethylationGenetic Associations and Epidemiology

Cited by 183

Associations of circulating plasma microRNAs with age, body mass index and sex in a population-based study

Ameling, S., Kacprowski, T., Chilukoti, R. K., Malsch, C., Liebscher, V., Suhre, K., Pietzner, M., Friedrich, N., Homuth, G., Hammer, E., et al. (2015) · BMC Medical Genomics

MicroRNA in disease regulationExtracellular vesicles in disease

Cited by 181

Genome-wide association study of caffeine metabolites provides new insights to caffeine metabolism and dietary caffeine-consumption behavior

Cornelis, M. C., Kacprowski, T., Menni, C., Gustafsson, S., Pivin, E., Adamski, J., Artati, A., Eap, C. B., Ehret, G., Friedrich, N., et al. (2016) · Human Molecular Genetics

Coffee research and impactsGenetic Associations and Epidemiology

Cited by 175

Metabolites associate with kidney function decline and incident chronic kidney disease in the general population

Goek, O.-N., Prehn, C., Sekula, P., Römisch-Margl, W., Döring, A., Gieger, C., Heier, M., Koenig, W., Wang-Sattler, R., Illig, T., et al. (2013) · Nephrology Dialysis Transplantation

Metabolomics and Mass Spectrometry StudiesChronic Kidney Disease and Diabetes

Cited by 143

Serum Metabolite Concentrations and Decreased GFR in the General Population

Goek, O.-N., Döring, A., Gieger, C., Heier, M., Koenig, W., Prehn, C., Römisch-Margl, W., Wang-Sattler, R., Illig, T., Suhre, K., et al. (2012) · American Journal of Kidney Diseases

Metabolomics and Mass Spectrometry StudiesChronic Kidney Disease and Diabetes

Cited by 121

Questionnaire-based self-reported nutrition habits associate with serum metabolism as revealed by quantitative targeted metabolomics

Altmaier, E., Kastenmüller, G., Römisch-Margl, W., Thorand, B., Weinberger, K. M., Illig, T., Adamski, J., Döring, A., & Suhre, K. (2011) · European Journal of Epidemiology

Metabolomics and Mass Spectrometry StudiesNutritional Studies and Diet

Cited by 84

Metformin Effect on Nontargeted Metabolite Profiles in Patients With Type 2 Diabetes and in Multiple Murine Tissues

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

Metabolism, Diabetes, and CancerMetabolomics and Mass Spectrometry Studies

Cited by 68

Machine Learning Approaches Reveal Metabolic Signatures of Incident Chronic Kidney Disease in Individuals With Prediabetes and Type 2 Diabetes

Huang, J., Huth, C., Covic, M., Troll, M., Adam, J., Zukunft, S., Prehn, C., Wang, L., Nano, J., Scheerer, M. F., et al. (2020) · Diabetes

Chronic Kidney Disease and DiabetesMetabolomics and Mass Spectrometry Studies

Cited by 60