The DOI matches its Crossref record; 57 of the 57 references it cites were found in a registry or an index.
Association Between a Literature-Based Genetic Risk Score and Cardiovascular Events in Women
Open accessAbstract
CONTEXT: While multiple genetic markers associated with cardiovascular disease have been identified by genome-wide association studies, their aggregate effect on risk beyond traditional factors is uncertain, particularly among women. OBJECTIVE: To test the predictive ability of a literature-based genetic risk score for cardiovascular disease. DESIGN, SETTING, AND PARTICIPANTS: Prospective cohort of 19,313 initially healthy white women in the Women's Genome Health Study followed up over a median of 12.3 years (interquartile range, 11.6-12.8 years). Genetic risk scores were constructed from the National Human Genome Research Institute's catalog of genome-wide association study results published between 2005 and June 2009. MAIN OUTCOME MEASURE: Incident myocardial infarction, stroke, arterial revascularization, and cardiovascular death. RESULTS: A total of 101 single nucleotide polymorphisms reported to be associated with cardiovascular disease or at least 1 intermediate cardiovascular disease phenotype at a published P value of less than 10(-7) were identified and risk alleles were added to create a genetic risk score. During follow-up, 777 cardiovascular disease events occurred (199 myocardial infarctions, 203 strokes, 63 cardiovascular deaths, 312 revascularizations). After adjustment for age, the genetic risk score had a hazard ratio (HR) for cardiovascular disease of 1.02 per risk allele (95% confidence interval [CI], 1.00-1.03/risk allele; P = .006). This corresponds to an absolute cardiovascular disease risk of 3% over 10 years in the lowest tertile of genetic risk (73-99 risk alleles) and 3.7% in the highest tertile (106-125 risk alleles). However, after adjustment for traditional factors, the genetic risk score did not improve discrimination or reclassification (change in c index from Expert Panel on Detection, Evaluation, and Treatment of High Blood Cholesterol in Adults [ATP III] risk score, 0; net reclassification improvement, 0.5%; [P = .24]). The genetic risk score was not associated with cardiovascular disease risk (ATP III-adjusted HR/allele, 1.00; 95% CI, 0.99-1.01). In contrast, self-reported family history remained significantly associated with cardiovascular disease in multivariable models. CONCLUSION: After adjustment for traditional cardiovascular risk factors, a genetic risk score comprising 101 single nucleotide polymorphisms was not significantly associated with the incidence of total cardiovascular disease.
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?
The source backs part of the claim, not all of it — or backs it for a different population, condition or comparison.
In the citing paper
“models to combine multiple markers into a cumulative risk score are often flawed and are usually no better than traditional risk factors such as family history, demographics and non-genetic clinical phenotypes.”
From Standards and guidelines for the interpretation of sequence variants: a joint consensus recommendation of the American College of Medical Genetics and Genomics and the Association for Molecular Pathology (2015) · cites this paper as [56]
Experimental — accuracy not yet measuredIn the citing paper
“In some conditions the process of genetic counselling can be optimized and extended with cascade screening, which identifies patients at risk and enables timely treatment of affected relatives, as is the case for familial hypercholesterolaemia.72,75”
From European Guidelines on cardiovascular disease prevention in clinical practice (version 2012) (2012) · cites this paper as [72]
In the citing paper
“These risk alleles, when aggregated into a polygenic risk score, are predictive of incident coronary events and provide a continuous and quantitative measure of genetic susceptibility.16–24”
From Genetic Risk, Adherence to a Healthy Lifestyle, and Coronary Disease (2016) · cites this paper as [18]
In the citing paper
“Our finding that a polygenic risk score has robust associations with incident coronary events is well aligned with previous studies of both primary and secondary prevention populations.16–24”
From Genetic Risk, Adherence to a Healthy Lifestyle, and Coronary Disease (2016) · cites this paper as [18]
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
Grundy, S. M., Bilheimer, D. W., Chait, A., Clark, L. T., Denke, M. A., Havel, R. J., Hazzard, W. R., Hulley, S. B., Hunninghake, D. B., Kreisberg, R. A., et al. (1993) · JAMA · doi:10.1001/jama.269.23.3015 · Cited by 27,944
Detection, evaluation, and treatment of high blood cholesterol in adults (Adult Treatment Panel III)
Grundy, S. M., Becker, D. M., Clark, L. T., Cooper, R., Denke, M. A., Howard, J. L., Hunninghake, D. B., Illingworth, D. R., Luepker, R. V., McBride, P. E., et al. (2002) · Civil War Book Review · Cited by 12,198
Genome-wide association study of 14,000 cases of seven common diseases and 3,000 shared controls
The Wellcome Trust Case Control Consortium, Management Committee, Burton, P. R., Clayton, D. G., Cardon, L. R., Craddock, N., Deloukas, P., Duncanson, A., Kwiatkowski, D. P., McCarthy, M. I., et al. (2007) · Nature · doi:10.1038/nature05911 · Cited by 9,442
Pencina, M. J., D' Agostino, R. B., D' Agostino, R. B., & Vasan, R. S. (2008) · Statistics in Medicine · doi:10.1002/sim.2929 · Cited by 6,493
A second generation human haplotype map of over 3.1 million SNPs
The International HapMap Consortium (2007) · Nature · doi:10.1038/nature06258 · Cited by 4,438
A genome-wide association study identifies novel risk loci for type 2 diabetes
Sladek, R., Rocheleau, G., Rung, J., Dina, C., Shen, L., Serre, D., Boutin, P., Vincent, D., Belisle, A., Hadjadj, S., et al. (2007) · Nature · doi:10.1038/nature05616 · Cited by 2,993
Genome-wide association studies for complex traits: consensus, uncertainty and challenges
McCarthy, M. I., Abecasis, G. R., Cardon, L. R., Goldstein, D. B., Little, J., Ioannidis, J. P. A., & Hirschhorn, J. N. (2008) · Nature Reviews Genetics · doi:10.1038/nrg2344 · Cited by 2,891
Genome-wide association analysis identifies loci for type 2 diabetes and triglyceride levels
Saxena, R., Voight, B. F., Lyssenko, V., Burtt, N. P., de Bakker, P. I. W., Chen, H., Roix, J. J., Kathiresan, S., Hirschhorn, J. N., Daly, M. J., et al. (2007) · Science · doi:10.1126/science.1142358 · Cited by 2,843
Flagged first, then by citations
Cited by
Sorted by citations
Items 201–300 of 361
Is Cystic Fibrosis Genetic Medicine’s Canary?
Lindee, S., & Mueller, R. (2011) · Perspectives in biology and medicine · doi:10.1353/pbm.2011.0035 · Cited by 11
Priest, J. R., & Ashley, E. A. (2014) · Heart · doi:10.1136/heartjnl-2014-306111 · Cited by 11
Biomarkers for Coronary Heart Disease Clinical Risk Prediction: A Critical Appraisal
Wilkins, J. T., & Lloyd‐Jones, D. M. (2010) · Preventive Cardiology · doi:10.1111/j.1751-7141.2010.00081.x · Cited by 10
Risk Markers for Atherothrombotic Disease
Ridker, P. M., & Libby, P. (2012) · Elsevier eBooks · doi:10.1016/b978-1-4377-0398-6.00044-5 · Cited by 10
Lynn, J., Owens, A. P., & Bartunek, J. M. (2011) · BMJ Quality & Safety · doi:10.1136/bmjqs.2010.046532 · Cited by 10
Human Genomics in Cardiovascular Medicine
Morita, H. (2013) · Circulation Journal · doi:10.1253/circj.cj-13-0126 · Cited by 9
Iglesias, A. I., Mihăescu, R., Ioannidis, J. P. A., Khoury, M. J., Little, J., van Duijn, C. M., & Janssens, A. C. J. W. (2014) · Journal of Clinical Epidemiology · doi:10.1016/j.jclinepi.2013.10.006 · Cited by 8
Mansur, A. D. P., Takada, J. Y., Strunz, C. M. C., Avakian, S. D., César, L. A. M., & Ramires, J. A. F. (2013) · Clinics · doi:10.6061/clinics/2013(12)05 · Cited by 9
Telomere length as a risk marker for cardiovascular disease: the next big thing?
Mainous, A. G., & Díaz, V. A. (2010) · Expert Review of Molecular Diagnostics · doi:10.1586/erm.10.69 · Cited by 9
Богачевская, С. А., & Николаевич, Б. А. (2017) · Social Aspects of Population Health · doi:10.21045/2071-5021-2017-53-1-1 · Cited by 10
Yang, M., Zhang, J., Su, S., Qin, B., Kang, L., Zhu, R., & Guan, H. (2018) · PLoS ONE · doi:10.1371/journal.pone.0184478 · Cited by 9
Additional value of a combined genetic risk score to standard cardiovascular stratification
Pereira, A., Mendonça, M. I., Borges, S., Sousa, A. C., Freitas, S., Henriques, E., Rodrigues, M. F., Freitas, A. I., Guerra, G., Freitas, C., et al. (2018) · Genetics and Molecular Biology · doi:10.1590/1678-4685-gmb-2017-0173 · Cited by 9
Boyd, S. D., Galli, S. J., Schrijver, I., Zehnder, J. L., Ashley, E. A., & Merker, J. D. (2014) · Genes · doi:10.3390/genes5030748 · Cited by 10
Scientific Contributions of Population-Based Studies to Cardiovascular Epidemiology in the GWAS Era
Lieb, W., & Vasan, R. S. (2018) · Frontiers in Cardiovascular Medicine · doi:10.3389/fcvm.2018.00057 · Cited by 8
Sanz, J., & Fuster, V. (2011) · Journal of the American College of Cardiology · doi:10.1016/j.jacc.2011.03.052 · Cited by 9
Bauer, A., Zierer, A., Gieger, C., Büyüközkan, M., Müller‐Nurasyid, M., Grallert, H., Meisinger, C., Strauch, K., Prokisch, H., Roden, M., et al. (2021) · Genetic Epidemiology · doi:10.1002/gepi.22389 · Cited by 8
Sex‐Specific Parental Effects on Offspring Lipid Levels
Predazzi, I. M., Sobota, R. S., Sanna, S., Bush, W. S., Bartlett, J., Lilley, J. S., Linton, M. F., Schlessinger, D., Cucca, F., Fazio, S., et al. (2015) · Journal of the American Heart Association · doi:10.1161/jaha.115.001951 · Cited by 8
Guevara‐Cruz, M., Vera, I., Flores‐López, A., Aguilar‐López, M., Smith, C. E., Parnell, L. D., Lee, Y., Lai, C., Tovar, A. R., Ordovás, J. M., et al. (2019) · Journal of Nutrition · doi:10.1093/jn/nxz060 · Cited by 9
Association of Genes Related to Oxidative Stress with the Extent of Coronary Atherosclerosis
Racis, M., Stanisławska‐Sachadyn, A., Sobiczewski, W., Wirtwein, M., Krzemiński, M., Krawczyńska, N., Limon, J., Rynkiewicz, A., & Gruchała, M. (2020) · Life · doi:10.3390/life10090210 · Cited by 8
An Introduction to Assessing Genomic Screening and Diagnostic Tests
Haddow, J. E., & Palomaki, G. E. (2011) · Nutrition Today · doi:10.1097/nt.0b013e3182261d7f · Cited by 8
Pencina, M. J. (2012) · Clinical Chemistry · doi:10.1373/clinchem.2012.188383 · Cited by 8
Reducing Cardiovascular Risk Using Genomic Information in the Era of Precision Medicine
Natarajan, P., & O’Donnell, C. J. (2016) · Circulation · doi:10.1161/circulationaha.116.021765 · Cited by 8
Cardiovascular Disease in Women
Newby, L. K., & Douglas, P. S. (2012) · Elsevier eBooks · doi:10.1016/b978-1-4377-0398-6.00081-0 · Cited by 7
Labos, C., & Thanassoulis, G. (2018) · Current Cardiology Reports · doi:10.1007/s11886-018-0980-0 · Cited by 7
Neelankavil, J. P., Rau, C. D., & Wang, Y. (2015) · Journal of Cardiothoracic and Vascular Anesthesia · doi:10.1053/j.jvca.2015.01.031 · Cited by 6
Liang, Y., & Kelemen, A. (2017) · Medicine · doi:10.1097/md.0000000000007683 · Cited by 6
Rosa, R. S., Ribeiro, Í. J. D. S., da Silva, J. K., Souza, L. H. R., Cruz, D. P., Damasceno, R. O., Júnior, E. V. D. S., & Boery, R. N. S. D. O. (2021) · Revista CUIDARTE · doi:10.15649/cuidarte.1165 · Cited by 6
Sun, X., Pan, Y., Zhang, R., Anda‐Duran, I. D., Huang, Z., Li, C., Shi, M., Razavi, A. C., Bazzano, L., He, J., et al. (2022) · Genes · doi:10.3390/genes13081473 · Cited by 6
Genetics-based risk scores for prediction of premature coronary artery disease
Gupta, R. (2023) · Indian Heart Journal · doi:10.1016/j.ihj.2023.08.003 · Cited by 7
Clinical utility of novel biomarkers for cardiovascular disease risk stratification
Averna, M. R., & Noto, D. (2012) · Internal and Emergency Medicine · doi:10.1007/s11739-012-0830-9 · Cited by 6
Cline, J. L., & Beckie, T. M. (2011) · Biological Research For Nursing · doi:10.1177/1099800411424487 · Cited by 6
Phenotyping Patient-Derived Cells for Translational Studies in Cardiovascular Disease
Shaw, S. Y., & Brettman, A. D. (2011) · Circulation · doi:10.1161/circulationaha.111.043943 · Cited by 7
Genomic Risk Models Improve Prediction of Longitudinal Lipid Levels in Children and Young Adults
Wineinger, N. E., Harper, A. R., Libiger, O., Srinivasan, S. R., Chen, W., Berenson, G. S., & Schork, N. J. (2013) · Frontiers in Genetics · doi:10.3389/fgene.2013.00086 · Cited by 7
Epidemiology of Chronic Coronary Artery Disease
Wilson, P. W. F., & O’Donnell, C. J. (2017) · Elsevier eBooks · doi:10.1016/b978-0-323-42880-4.00001-7 · Cited by 6
Ethical implications of new genetic technologies
Lucassen, A. (2012) · Developmental Medicine & Child Neurology · doi:10.1111/j.1469-8749.2011.04182.x · Cited by 6
O'Donnell, M. P. (2011) · American Journal of Health Promotion · doi:10.4278/ajhp.25.6.iv · Cited by 6
Heng, H. H. (2013) · Cytogenetic and Genome Research · doi:10.1159/000347035 · Cited by 6
Almanac 2012: Cardiovascular risk scores
Pell, J. P. (2013) · Archivos de cardiología de México · doi:10.1016/j.acmx.2013.01.006 · Cited by 5
Pell, J. P. (2013) · Revista Portuguesa de Cardiologia (English Edition) · doi:10.1016/j.repce.2013.01.008 · Cited by 5
Consensus statement on the management of dyslipidaemias in adults
Béliard, S., Bonnet, F., Bouhanick, B., Bruckert, É., Cariou, B., Charrière, S., Durlach, V., Moulin, P., Valéro, R., & Vergès, B. L. (2017) · Annales d Endocrinologie · doi:10.1016/j.ando.2016.11.001 · Cited by 5
A Report of the Association for Molecular Pathology
Sepulveda, A. R., Jones, D., Ogino, S., Samowitz, W. S., Gulley, M. L., Edwards, R. H., Levenson, V., Pratt, V. M., Yang, B., Nafa, K., et al. (2009) · Cited by 5
Genetics Primer for the General Cardiologist
MacRae, C. A. (2011) · Circulation · doi:10.1161/circulationaha.111.018879 · Cited by 5
Scott, I. C., Seegobin, S., Steer, S. E., Tan, R., Forabosco, P., Hinks, A., Eyre, S., Morgan, A. W., Wilson, A. G., Hocking, L. J., et al. (2013) · PLoS Genetics · doi:10.1371/annotation/a86ab397-f58c-4144-a241-4957836a728a · Cited by 5
Sex-based Differences in Heart Failure Biomarkers
Mezcua, A. R., Aguado, N. G., de la Rosa, A. P. M., Álvarez, C. C., Rubio, C. J., Cabeza, A. P., Gómez‐Doblas, J. J., Jiménez-Navarro, M. F., Murri-Pierri, M., & García‐Pinilla, J. M. (2024) · Current Heart Failure Reports · doi:10.1007/s11897-024-00665-x · Cited by 4
Road-maps and revelations: on the somatic ethics of genetic susceptibility
Groves, C. (2013) · New Genetics and Society · doi:10.1080/14636778.2013.781424 · Cited by 4
China’s “Gene War of the Century” and Its Aftermath: The Contest Goes On
Guo, S. (2013) · Minerva · doi:10.1007/s11024-013-9237-7 · Cited by 4
Subgroup specific incremental value of new markers for risk prediction
Zhou, Q. M., Zheng, Y., & Cai, T. (2012) · Lifetime Data Analysis · doi:10.1007/s10985-012-9235-3 · Cited by 4
Libby, P. (2012) · Elsevier eBooks · doi:10.1016/b978-1-4377-2930-6.00008-2 · Cited by 4
THE HEART OF STRENGTH: THE STRONG BLACK WOMAN SCHEMA AND CARDIOVASCULAR DISEASE RISK
Abrams, J. A. (2015) · VCU Scholars Compass (Virginia Commonwealth University) · doi:10.25772/zsdp-0x86 · Cited by 4
Genetics Studies in Ischaemic Stroke
Markus, H. S. (2010) · Translational Stroke Research · doi:10.1007/s12975-010-0041-5 · Cited by 5
Using graded response model for the prediction of prostate cancer risk
Chen, S., Ip, E. H., Xu, J., Sun, J., & Hsu, F. (2012) · Human Genetics · doi:10.1007/s00439-012-1160-8 · Cited by 4
Yang, J., & Zhao, J. (2011) · Epidemiology Open Access · doi:10.4172/2161-1165.1000108 · Cited by 4
Ferguson, L. R., & Parslow, V. R. (2019) · Elsevier eBooks · doi:10.1016/b978-0-12-804572-5.00070-7 · Cited by 4
Genetic Risk Score and Cardiovascular Events in Women
Hólm, H. (2010) · JAMA · doi:10.1001/jama.2010.660 · Cited by 4
O’Donnell, M. P. (2011) · American Journal of Health Promotion · doi:10.4278/ajhp.25.4.iv · Cited by 4
Sample size for multivariable prognostic models
Jinks, R. (2012) · UCL Discovery (University College London) · Cited by 4
Genetics and Breast Cancer Risk Prediction—Are We There Yet?
Cook, N. R., & Paynter, N. P. (2010) · JNCI Journal of the National Cancer Institute · doi:10.1093/jnci/djq413 · Cited by 4
Self-citationThe limitations of opportunistic epidemiology, pseudopod epidemiology
Kuller, L. H. (2016) · European Journal of Epidemiology · doi:10.1007/s10654-016-0196-9 · Cited by 3
Hsieh, A., Luo, Y., Bao, B., & Chou, T. (2024) · BMC Urology · doi:10.1186/s12894-024-01524-6 · Cited by 3
The Role of Noncoding “Junk DNA” in Cardiovascular Disease
Vickers, K. C., Palmisano, B. T., & Remaley, A. T. (2010) · Clinical Chemistry · doi:10.1373/clinchem.2010.147603 · Cited by 3
Evaluation of an ensemble of genetic models for prediction of a quantitative trait
Milton, J. N., Steinberg, M. H., & Sebastiani, P. (2015) · Frontiers in Genetics · doi:10.3389/fgene.2014.00474 · Cited by 3
Pell, J. P. (2012) · Revista Portuguesa de Cardiologia · doi:10.1016/j.repc.2012.10.004 · Cited by 3
Hvor har den nye genforskningen brakt oss? - Where has the new genomic research brought us?
Elstad, J. I. (2014) · Tidsskrift for samfunnsforskning · doi:10.18261/issn1504-291x-2014-02-04 · Cited by 3
Daniels, L. B. (2011) · Bioanalysis · doi:10.4155/bio.11.147 · Cited by 3
Coronary Artery Disease and Myocardial Infarction
Damani, S. B., & Topol, E. J. (2012) · Elsevier eBooks · doi:10.1016/b978-0-12-382227-7.00046-x · Cited by 3
Genetic Risk Scores for the Clinical Rheumatologist
Wheeler, A. M., Riley, T. R., & Merriman, T. R. (2024) · JCR Journal of Clinical Rheumatology · doi:10.1097/rhu.0000000000002152 · Cited by 2
Nanna, M. G., Peterson, E. D., Chiswell, K., Overton, R. A., Nelson, A. J., Kong, D. F., & Návar, A. M. (2021) · Atherosclerosis · doi:10.1016/j.atherosclerosis.2021.02.004 · Cited by 2
Atherosclerosis, Vulnerable Plaques, and Acute Coronary Syndromes
Bentzon, J. F., & Falk, E. (2012) · Elsevier eBooks · doi:10.1016/b978-0-12-382227-7.00047-1 · Cited by 2
Genetische Analysen als Basis einer individualisierten Medizin bei koronarer Herzkrankheit
Kessler, T., Kaess, B. M., Bourier, F. J., Erdmann, J., & Schunkert, H. (2014) · Herz · doi:10.1007/s00059-013-4048-z · Cited by 2
Knopik, V. S. (2011) · Frontiers in Genetics · doi:10.3389/fgene.2011.00009 · Cited by 2
Genetic Risk Prediction for CKD: A Journey of a Thousand Miles
Kopp, J. B., & Winkler, C. A. (2011) · American Journal of Kidney Diseases · doi:10.1053/j.ajkd.2011.11.011 · Cited by 2
Genomics: Is It Ready for Primetime?
Dandona, S., Stewart, A. F. R., & Roberts, R. (2012) · Medical Clinics of North America · doi:10.1016/j.mcna.2012.01.018 · Cited by 2
Advances in DNA sequencing: Challenges and limitations of personal sequencing
Mehla, K., Chaudhary, S., Kumar, A., Kumar, V., Chauhan, P. S., Gupta, S., Singh, J., Kumar, P., Kumar, N., Jindal, A., et al. (2011) · African Journal of Agricultural Research · doi:10.5897/ajar10.008 · Cited by 2
Evaluation of genetic risk scores for prediction of dichotomous outcomes.
Yoo, W., Smith, S. A., & Coughlin, S. S. (2015) · Europe PMC (PubMed Central) · Cited by 2
Novel Biomarkers and the Assessment of Cardiovascular Risk
Nambi, V., Brautbar, A., & Ballantyne, C. M. (2011) · Elsevier eBooks · doi:10.1016/b978-1-4377-1366-4.00005-6 · Cited by 2
Methods to Assess Genetic Risk Prediction
Schulz, C., & Padmanabhan, S. (2017) · Methods in molecular biology · doi:10.1007/978-1-4939-6625-7_2 · Cited by 2
Cardiovascular precision medicine: Bad news from the front?
Lourenço, A. P., & Leite-Moreira, A. F. (2017) · Porto Biomedical Journal · doi:10.1016/j.pbj.2017.03.006 · Cited by 2
Genetic Customization of Anti-aging Treatments
Nisticò, S. P., DUCA, E. D., & Garoia, F. (2018) · Journal of Clinical & Experimental Dermatology Research · doi:10.4172/2155-9554.1000443 · Cited by 2
You, D., Qin, N., Zhang, M., Dai, J., Du, M., Wei, Y., Zhang, R., Hu, Z., Christiani, D. C., Zhao, Y., et al. (2020) · Gene · doi:10.1016/j.gene.2020.144570 · Cited by 2
At the heart of genetic testing
Strauss, S. (2010) · Nature Biotechnology · doi:10.1038/nbt1010-1003 · Cited by 2
Renal Aging and Transplantation
Higgins, J., & Kim, S. K. (2015) · Springer eBooks · doi:10.1007/978-3-319-23246-1_13 · Cited by 1
Li, Q., Fu, Y., Cui, X.-J., Wang, Z.-T., Tan, L., & Initiative, A. D. N. (2024) · Frontiers in Aging Neuroscience · doi:10.3389/fnagi.2024.1339163 · Cited by 1
Gene–Lifestyle Synergy in Diabetic Retinopathy Development
Yang, S., Xin, Z., Liu, B., Huang, Y., Zhu, Z., Tan, S., He, M., Tham, Y. C., Cheng, C., & Wang, W. (2026) · Ophthalmology · doi:10.1016/j.ophtha.2026.04.017 · Cited by 1
Methodisch-statistische Herausforderungen an die genombasierte Vorhersage von Erkrankungen
Foraita, R., Jäger, M., & Pigeot, I. (2014) · Bundesgesundheitsblatt - Gesundheitsforschung - Gesundheitsschutz · doi:10.1007/s00103-014-2091-4 · Cited by 1
Genetic Epidemiology of Atherosclerotic Vascular Disease
O’Donnell, C. J., & Fu, Y. (2015) · Springer eBooks · doi:10.1007/978-3-642-37078-6_47 · Cited by 1
Genetics and Cardiovascular Risk Prediction: A Step Toward Personalized Medicine?
Elosúa, R., Lucas, G., & Lluís-Ganella, C. (2013) · Current Cardiovascular Risk Reports · doi:10.1007/s12170-012-0285-6 · Cited by 1
The Lambda Limit: The Incompletability of Science
Hunt, G. (2012) · St Mary's University Repository (St Mary's University Twickenham London) · Cited by 1
Does the 9p21 Genetic Variant Have a Role in Cardiovascular Risk Prediction?
Paynter, N. P. (2010) · Current Cardiovascular Risk Reports · doi:10.1007/s12170-010-0150-4 · Cited by 1
Self-citationActualizing the Benefits of Genomic Discovery in Pediatric Nephrology
Sampson, M. G. (2015) · Journal of Pediatric Genetics · doi:10.1055/s-0035-1557113 · Cited by 1
Epidemiology of Atherosclerotic Vascular Diseases
Andersson, C., & O’Donnell, C. J. (2015) · Springer eBooks · doi:10.1007/978-3-642-37078-6_46 · Cited by 1
Subgroup Specific Incremental Value of New Markers for Risk Prediction
Zhou, Q. M., Zheng, Y., & Cai, T. (2013) · Lecture notes in statistics · doi:10.1007/978-1-4614-8981-8_12 · Cited by 1
Primary Prevention and Cardiovascular Risk Assessment in Women
Peterson, L.-A., Freaney, P. M., & Gulati, M. (2022) · Contemporary cardiology · doi:10.1007/978-3-030-98824-1_10 · Cited by 1
Pell, J. P. (2013) · Srce i krvni sudovi · doi:10.5937/siks1302168p · Cited by 1
Genomic Biomarkers in Human Population Studies
Danik, J. S., Paynter, N. P., & Ridker, P. M. (2012) · Elsevier eBooks · doi:10.1016/b978-0-12-382227-7.00021-5 · Cited by 1
Self-citationCumulative Genetic and Environmental Predictors of Youth Substance Use
Carrano, J. (2012) · Cited by 1
Role of Cardiac Imaging in Cardiovascular Diseases in Females
Zhou, W., Lodhi, F. K., & Srichai, M. B. (2017) · Current Radiology Reports · doi:10.1007/s40134-017-0242-8 · Cited by 1
Progress in ASCVD Risk Assessment in African Americans and Hispanic Americans
Wilson, P. W. F. (2021) · Contemporary cardiology · doi:10.1007/978-3-030-81034-4_12 · Cited by 1
Beyond genome wide discovery: an exploration of novel genetic variants for coronary heart disease
Patel, R. S. (2012) · ORCA Online Research @Cardiff (Cardiff University) · Cited by 1
Minnier, J. (2012) · Digital Access to Scholarship at Harvard (DASH) (Harvard University) · Cited by 1
Metadata
- Authors
- Paynter, N. P.
- Affiliations
- Brigham and Women's Hospital (US)
- Corresponding author
- Paynter, Nina P.
- ORCID
- Paynter, Nina P.
- Venue
- JAMA
- Publisher
- American Medical Association (AMA)
- Published
- 2010-02-17
- Volume
- 303
- Issue
- 7
- Pages
- 631
- ISSN
- 0002-9955 (ISSN-L)
- 0098-7484
- PMC
- PMC2845522
- OpenAlex
- W2015060037
- Cited by
- 361
- References
- 42
- Citations by year
- 2026: 3 · 2025: 1 · 2024: 5 · 2023: 3 · 2022: 8 · 2021: 5 · 2020: 9 · 2019: 6 · 2018: 10 · 2017: 22 · 2016: 22 · 2015: 26 · 2014: 32 · 2013: 45 · 2012: 74
- FWCI
- 37.27
- Type
- journal article
- Language
- en
- Open access
- green
- www.ncbi.nlm.nih.gov
- Locations
- JAMA (publishedVersion)
- PubMed (publishedVersion)
- PubMed Central (acceptedVersion)
- Funders
- National Institutes of Health (5r01hl043851-10)
- National Heart, Lung, and Blood Institute (R01HL080467, HL043851, R01‐HL043851)
- National Cancer Institute (CA 047988, R01CA047988)
- Topics
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Genetics › Genetic Associations and Epidemiology
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Genetics › Nutrition, Genetics, and Disease
- Health Sciences › Medicine › Cardiology and Cardiovascular Medicine › Cardiovascular Health and Risk Factors
- Keywords
- polygenic risk score, cardiovascular disease, women, myocardial infarction, stroke, cardiovascular mortality, arterial revascularization, single nucleotide polymorphisms, family history, cardiovascular risk prediction, reclassification, risk discrimination, traditional cardiovascular risk factors, women's cardiovascular health
- MeSH terms
- Alleles, Cardiovascular Diseases, Female, Genetic Markers, Humans, Middle Aged, Phenotype, Prospective Studies, Risk, Genetic Predisposition to Disease, Polymorphism, Single Nucleotide
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