Langenberg, Claudia

Association between alcohol and cardiovascular disease: Mendelian randomisation analysis based on individual participant data

Holmes, M. V., Dale, C. E., Zuccolo, L., Silverwood, R. J., Guo, Y., Ye, Z., Prieto-Merino, D., Dehghan, A., Trompet, S., Wong, A., et al. (2014) · BMJ

Alcohol Consumption and Health EffectsGenetic Associations and Epidemiology

Cited by 669

Mapping the proteo-genomic convergence of human diseases

Pietzner, M., Wheeler, E., Carrasco-Zanini, J., Cortes, A., Koprulu, M., Wörheide, M. A., Oerton, E., Cook, J., Stewart, I. D., Kerrison, N. D., et al. (2021) · Science

Genetic Associations and EpidemiologyBioinformatics and Genomic Networks

Cited by 657

Genome-wide meta-analysis identifies 11 new loci for anthropometric traits and provides insights into genetic architecture

Berndt, S. I., Gustafsson, S., Mägi, R., Ganna, A., Wheeler, E., Feitosa, M. F., Justice, A. E., Monda, K. L., Croteau-Chonka, D. C., Day, F. R., et al. (2013) · Nature Genetics

Genetic Associations and EpidemiologyGenetics and Physical Performance

Cited by 646

Rare and low-frequency coding variants alter human adult height

Marouli, E., Graff, M., Medina-Gomez, C., Lo, K. S., Wood, A. R., Kjaer, T. R., Fine, R. S., Lu, Y., Schurmann, C., Highland, H. M., et al. (2017) · Nature

Genetic Associations and EpidemiologyGenetics and Physical Performance

Cited by 610

Genetic drivers of heterogeneity in type 2 diabetes pathophysiology

Suzuki, K., Hatzikotoulas, K., Southam, L., Taylor, H. J., Yin, X., Lorenz, K. M., Mandla, R., Huerta-Chagoya, A., Melloni, G. E. M., Kanoni, S., et al. (2024) · Nature

Genetic Associations and EpidemiologyGenetic Mapping and Diversity in Plants and Animals

Cited by 606

Genomic analyses identify hundreds of variants associated with age at menarche and support a role for puberty timing in cancer risk

Study, T. L. C., Day, F. R., The InterAct Consortium, kConFab/AOCS Investigators, Endometrial Cancer Association Consortium, Ovarian Cancer Association Consortium, PRACTICAL consortium, Thompson, D. J., Helgason, H., Chasman, D. I., et al. (2017) · Nature Genetics

Hypothalamic control of reproductive hormonesGenetic Associations and Epidemiology

Cited by 587

Ultra-High-Throughput Clinical Proteomics Reveals Classifiers of COVID-19 Infection

Messner, C. B., Demichev, V., Wendisch, D., Michalick, L., White, M., Freiwald, A., Textoris-Taube, K., Vernardis, S. I., Egger, A.-S., Kreidl, M., et al. (2020) · Cell Systems

Advanced Proteomics Techniques and ApplicationsCOVID-19 Clinical Research Studies

Cited by 561

Differences in the prospective association between individual plasma phospholipid saturated fatty acids and incident type 2 diabetes: the EPIC-InterAct case-cohort study

Forouhi, N. G., Koulman, A., Sharp, S. J., Imamura, F., Kröger, J., Schulze, M. B., Crowe, F. L., Huerta, J. M., Guevara, M., Beulens, J. W., et al. (2014) · The Lancet Diabetes & Endocrinology

Fatty Acid Research and HealthNutritional Studies and Diet

Cited by 557

Rare variant in scavenger receptor BI raises HDL cholesterol and increases risk of coronary heart disease

Zanoni, P., Khetarpal, S. A., Larach, D. B., Hancock-Cerutti, W. F., Millar, J. S., Cuchel, M., DerOhannessian, S., Kontush, A., Surendran, P., Saleheen, D., et al. (2016) · Science

Cholesterol and Lipid MetabolismLipoproteins and Cardiovascular Health

Cited by 545

Estimating excess 1-year mortality associated with the COVID-19 pandemic according to underlying conditions and age: a population-based cohort study

Banerjee, A., Pasea, L., Harris, S., Gonzalez-Izquierdo, A., Torralbo, A., Shallcross, L., Noursadeghi, M., Pillay, D., Sebire, N., Holmes, C., et al. (2020) · The Lancet

COVID-19 epidemiological studiesCOVID-19 Clinical Research Studies

Cited by 518

Plasma protein patterns as comprehensive indicators of health

Williams, S. A., Kivimaki, M., Langenberg, C., Hingorani, A. D., Casas, J. P., Bouchard, C., Jonasson, C., Sarzynski, M. A., Shipley, M. J., Alexander, L., et al. (2019) · Nature Medicine

Advanced Biosensing Techniques and ApplicationsHealth, Environment, Cognitive Aging

Cited by 506

Refining the accuracy of validated target identification through coding variant fine-mapping in type 2 diabetes

Mahajan, A., Wessel, J., Willems, S. M., Zhao, W., Robertson, N. R., Chu, A. Y., Gan, W., Kitajima, H., Taliun, D., Rayner, N. W., et al. (2018) · Nature Genetics

Genetic Associations and EpidemiologyGenomics and Rare Diseases

Cited by 442

Genetic Predisposition to an Impaired Metabolism of the Branched-Chain Amino Acids and Risk of Type 2 Diabetes: A Mendelian Randomisation Analysis

Lotta, L. A., Scott, R. A., Sharp, S. J., Burgess, S., Luan, J., Tillin, T., Schmidt, A. F., Imamura, F., Stewart, I. D., Perry, J. R. B., et al. (2016) · PLOS Medicine

Genetic Associations and EpidemiologyDiabetes, Cardiovascular Risks, and Lipoproteins

Cited by 417

Impact of common genetic determinants of hemoglobin A1c on type 2 diabetes risk and diagnosis in ancestrally diverse populations: A transethnic genome-wide meta-analysis

Wheeler, E., Leong, A., Liu, C.-T., Hivert, M.-F., Strawbridge, R. J., Podmore, C., Li, M., Yao, J., Sim, X., Hong, J., et al. (2017) · PLOS Medicine

Genetic Associations and EpidemiologyGenetic Mapping and Diversity in Plants and Animals

Cited by 408

Metabolomic profiles predict individual multidisease outcomes

Buergel, T., Steinfeldt, J., Ruyoga, G., Pietzner, M., Bizzarri, D., Vojinovic, D., Upmeier zu Belzen, J., Loock, L., Kittner, P., Christmann, L., et al. (2022) · Nature Medicine

Metabolomics and Mass Spectrometry StudiesHealth, Environment, Cognitive Aging

Cited by 406

Large-scale genomic analyses link reproductive aging to hypothalamic signaling, breast cancer susceptibility and BRCA1-mediated DNA repair

The PRACTICAL Consortium, Day, F. R., kConFab Investigators, AOCS Investigators, Scotland, G., EPIC-InterAct Consortium, Study, L. C., Ruth, K. S., Thompson, D. J., Lunetta, K. L., et al. (2015) · Nature Genetics

Genetic Associations and EpidemiologyBRCA gene mutations in cancer

Cited by 402

Association Between Low-Density Lipoprotein Cholesterol–Lowering Genetic Variants and Risk of Type 2 Diabetes: A Meta-analysis

Lotta, L. A., Sharp, S. J., Burgess, S., Perry, J. R. B., Stewart, I. D., Willems, S. M., Luan, J., Ardanaz, E., Arriola, L., Balkau, B., et al. (2016) · JAMA

Lipoproteins and Cardiovascular HealthGenetic Associations and Epidemiology

Cited by 394

PCSK9 genetic variants and risk of type 2 diabetes: a mendelian randomisation study

Schmidt, A. F., Swerdlow, D. I., Holmes, M. V., Patel, R. S., Fairhurst-Hunter, Z., Lyall, D. M., Hartwig, F. P., Horta, B. L., Hyppönen, E., Power, C., et al. (2017) · The Lancet Diabetes & Endocrinology

Lipoproteins and Cardiovascular HealthGenetic Associations and Epidemiology

Cited by 380

Rare MTNR1B variants impairing melatonin receptor 1B function contribute to type 2 diabetes

The Meta-Analysis of Glucose and Insulin-Related Traits Consortium (MAGIC), Bonnefond, A., Clément, N., Fawcett, K., Yengo, L., Vaillant, E., Guillaume, J.-L., Dechaume, A., Payne, F., Roussel, R., et al. (2012) · Nature Genetics

Circadian rhythm and melatoninGenetic Associations and Epidemiology

Cited by 363

Genome-Wide Association Identifies Nine Common Variants Associated With Fasting Proinsulin Levels and Provides New Insights Into the Pathophysiology of Type 2 Diabetes

Strawbridge, R. J., Dupuis, J., Prokopenko, I., Barker, A., Ahlqvist, E., Rybin, D., Petrie, J. R., Travers, M. E., Bouatia-Naji, N., Dimas, A. S., et al. (2011) · Diabetes

Genetic Associations and EpidemiologyDiabetes and associated disorders

Cited by 356

Genetic variation near IRS1 associates with reduced adiposity and an impaired metabolic profile

Kilpeläinen, T. O., Zillikens, M. C., Stančákova, A., Finucane, F. M., Ried, J. S., Langenberg, C., Zhang, W., Beckmann, J. S., Luan, J., Vandenput, L., et al. (2011) · Nature Genetics

Genetic Associations and EpidemiologyAdipokines, Inflammation, and Metabolic Diseases

Cited by 335

Genetic evidence of assortative mating in humans

Robinson, M. R., Kleinman, A., Graff, M., Vinkhuyzen, A. A. E., Couper, D., Miller, M. B., Peyrot, W. J., Abdellaoui, A., Zietsch, B. P., Nolte, I. M., et al. (2017) · Nature Human Behaviour

Genetic Associations and EpidemiologyGenetic and phenotypic traits in livestock

Cited by 326

Trans-ancestry meta-analyses identify rare and common variants associated with blood pressure and hypertension

CHARGE-Heart Failure Consortium, Surendran, P., EchoGen Consortium, METASTROKE Consortium, GIANT Consortium, EPIC-InterAct Consortium, Study, L. C., Wellcome Trust Case Control Consortium, Understanding Society Scientific Group, EPIC-CVD Consortium, et al. (2016) · Nature Genetics

Genetic Associations and EpidemiologyBlood Pressure and Hypertension Studies

Cited by 312

Human gain-of-function MC4R variants show signaling bias and protect against obesity

Lotta, L. A., Mokrosiński, J., Mendes de Oliveira, E., Li, C., Sharp, S. J., Luan, J., Brouwers, B., Ayinampudi, V., Bowker, N., Kerrison, N., et al. (2019) · Cell

Regulation of Appetite and ObesityReceptor Mechanisms and Signaling

Cited by 284