A Genetic Risk Score Is Associated With Incident Cardiovascular Disease and Coronary Artery Calcium: The Framingham Heart Study
Open accessAbstract
Background—: Limited data exist regarding the use of a genetic risk score (GRS) for predicting risk of incident cardiovascular disease (CVD) in US-based samples. Methods and Results—: By using findings from recent genome-wide association studies, we constructed GRSs composed of 13 genetic variants associated with myocardial infarction or other manifestations of coronary heart disease (CHD) and 102 genetic variants associated with CHD or its major risk factors. We also updated the 13 single-nucleotide polymorphism (SNP) GRSs with 16 SNPs recently discovered by genome-wide association studies. We estimated the association, discrimination, and risk reclassification of each GRS for incident cardiovascular events and prevalent coronary artery calcium (CAC). In analyses adjusted for age, sex, CVD risk factors, and parental history of CVD, the 13 SNP GRSs were significantly associated with incident hard CHD (hazard ratio, 1.07; 95% CI, 1.00–1.15; P =0.04), CVD (hazard ratio per allele, 1.05; 95% CI, 1.01–1.09; P =0.03), and high CAC (defined as >75 th age- and sex-specific percentile; odds ratio per allele, 1.18; 95% CI, 1.11–1.26; P =3.4×10 −7 ). The GRS did not improve discrimination for incident CHD or CVD but led to modest improvements in risk reclassification. However, significant improvements in discrimination and risk reclassification were observed for the prediction of high CAC. The addition of 16 newly discovered SNPs to the 13 SNP GRSs did not significantly modify these results. Conclusions—: A GRS composed of 13 SNPs associated with coronary disease is an independent predictor of cardiovascular events and of high CAC, modestly improves risk reclassification for incident CHD, and significantly improves discrimination for high CAC. The addition of recently discovered SNPs did not significantly improve the performance of this GRS.
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The CT coronary calcium score is a measure of the burden of coronary atherosclerosis and one of the most widely accepted measures of cardiovascular risk.
“The CT coronary calcium score is a measure of the burden of coronary atherosclerosis and is one of the most widely accepted measures of cardiovascular risk5,6.”
From Deep convolutional neural networks to predict cardiovascular risk from computed tomography (2021) · cites this paper as [6]
The Introduction (page unavailable) calls coronary artery calcium a measure of subclinical atherosclerosis, and Methods describes quantifying it by CT. The paper does not establish the further claim that the calcium score is one of the most widely accepted measures of cardiovascular risk. (LLM, conf=0.84)
Experimental — accuracy not yet measuredThe CAC score was measured manually by expert readers using the method by Agatston and Janowitz on dedicated workstations, as reported in the parent FHS, PROMISE, and ROMICAT-II studies.
“The CAC score was measured manually by expert readers using the method by Agatston and Janowitz5,6 on dedicated workstations, as reported in the parent FHS17, PROMISE19, and ROMICAT-II20 studies.”
From Deep convolutional neural networks to predict cardiovascular risk from computed tomography (2021) · cites this paper as [6]
Coronary artery calcium is an accurate predictor of cardiovascular events.
“Coronary artery calcium is an accurate predictor of cardiovascular events.”
From Deep convolutional neural networks to predict cardiovascular risk from computed tomography (2021) · cites this paper as [6]
The Methods sections on coronary artery calcium measurements and statistical analysis describe measuring CAC and analysing it as an outcome. They do not assess how accurately CAC predicts subsequent cardiovascular events. Searches for CAC’s predictive value for future events found no passage in our copy establishing that claim. (LLM, conf=0.76)
Experimental — accuracy not yet measuredA polygenic risk score has robust associations with incident coronary events.
“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 [19]
When aggregated into a polygenic risk score, these risk alleles predict incident coronary events and give a continuous quantitative measure of genetic susceptibility.
“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 [19]
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Heart Disease and Stroke Statistics—2017 Update: A Report From the American Heart Association
Benjamin, E. J., Blaha, M. J., Chiuve, S. E., Cushman, M., Das, S. R., Deo, R., de Ferranti, S. D., Floyd, J., Fornage, M., Gillespie, C., et al. (2017) · Circulation · doi:10.1161/cir.0000000000000485 · Cited by 12,591 · see citing works
Heart Disease and Stroke Statistics—2016 Update: A Report From the American Heart Association
Mozaffarian, D., Benjamin, E. J., Go, A. S., Arnett, D. K., Blaha, M. J., Cushman, M., Das, S. R., de Ferranti, S., Després, J.-P., Fullerton, H. J., et al. (2016) · Circulation · doi:10.1161/cir.0000000000000350 · Cited by 6,596 · see citing works
Heart Disease and Stroke Statistics—2015 Update: A Report From the American Heart Association
Mozaffarian, D., Benjamin, E. J., Go, A. S., Arnett, D. K., Blaha, M. J., Cushman, M., de Ferranti, S., Després, J.-P., Fullerton, H. J., Howard, V. J., et al. (2015) · Circulation · doi:10.1161/cir.0000000000000152 · Cited by 5,797 · see citing works
Heart Disease and Stroke Statistics—2014 Update
Go, A. S., Mozaffarian, D., Roger, V. L., Benjamin, E. J., Berry, J. D., Blaha, M. J., Dai, S., Ford, E. S., Fox, C. S., Franco, S., et al. (2013) · Circulation · doi:10.1161/01.cir.0000441139.02102.80 · Cited by 5,207 · see citing works
Self-citationGenetic Risk, Adherence to a Healthy Lifestyle, and Coronary Disease
Khera, A. V., Emdin, C. A., Drake, I., Natarajan, P., Bick, A. G., Cook, N. R., Chasman, D. I., Baber, U., Mehran, R., Rader, D. J., et al. (2016) · New England Journal of Medicine · doi:10.1056/nejmoa1605086 · Cited by 1,460 · see citing works
D, E. B. M. (2013) · JACC Heart Failure · doi:10.1016/j.jchf.2012.10.002 · Cited by 821 · see citing works
Epidemiology of cardiovascular disease in young individuals
Andersson, C., & Vasan, R. S. (2017) · Nature Reviews Cardiology · doi:10.1038/nrcardio.2017.154 · Cited by 719 · see citing works
Mega, J. L., Stitziel, N. O., Smith, J. G., Chasman, D. I., Caulfield, M. J., Devlin, J. J., Nordio, F., Hyde, C. L., Cannon, C. P., Sacks, F. M., et al. (2015) · The Lancet · doi:10.1016/s0140-6736(14)61730-x · Cited by 704 · see citing works
Natarajan, P., Young, R., Stitziel, N. O., Padmanabhan, S., Baber, U., Mehran, R., Sartori, S., Fuster, V., Reilly, D. F., Butterworth, A., et al. (2017) · Circulation · doi:10.1161/circulationaha.116.024436 · Cited by 570 · see citing works
Genomic prediction of coronary heart disease
Abraham, G., Havulinna, A. S., Bhalala, O. G., Byars, S. G., De Livera, A. M., Yetukuri, L., Tikkanen, E., Perola, M., Schunkert, H., Sijbrands, E. J., et al. (2016) · European Heart Journal · doi:10.1093/eurheartj/ehw450 · Cited by 338 · see citing works
Cardiovascular Disease Risk Assessment: Insights from Framingham
D’Agostino, R. B., Pencina, M. J., Massaro, J. M., & Coady, S. (2013) · Global Heart · doi:10.1016/j.gheart.2013.01.001 · Cited by 331 · see citing works
Self-citationManuck, S., & McCaffery, J. (2014) · Annual Review of Psychology · doi:10.1146/annurev-psych-010213-115100 · Cited by 353 · see citing works
Kullo, I. J., Jouni, H., Austin, E. E., Brown, S.-A., Kruisselbrink, T. M., Isseh, I. N., Haddad, R. A., Marroush, T. S., Shameer, K., Olson, J. E., et al. (2016) · Circulation · doi:10.1161/circulationaha.115.020109 · Cited by 267 · see citing works
Deep convolutional neural networks to predict cardiovascular risk from computed tomography
Zeleznik, R., Foldyna, B., Eslami, P., Weiss, J., Alexander, I., Taron, J., Parmar, C., Alvi, R. M., Banerji, D., Uno, M., et al. (2021) · Nature Communications · doi:10.1038/s41467-021-20966-2 · Cited by 240 · see citing works
Self-citationGenetics of coronary artery disease and myocardial infarction
Dai, X. (2016) · World Journal of Cardiology · doi:10.4330/wjc.v8.i1.1 · Cited by 226 · see citing works
Genetic Risk Prediction and a 2-Stage Risk Screening Strategy for Coronary Heart Disease
Tikkanen, E., Havulinna, A. S., Palotie, A., Salomaa, V., & Ripatti, S. (2013) · Arteriosclerosis, Thrombosis, and Vascular Biology · doi:10.1161/atvbaha.112.301120 · Cited by 175 · see citing works
Lu, X., Liu, Z., Cui, Q., Liu, F., Li, J., Niu, X., Shen, C., Hu, D., Huang, K., Chen, J., et al. (2022) · European Heart Journal · doi:10.1093/eurheartj/ehac093 · Cited by 164 · see citing works
Elosúa, R., Sayols-Baixeras, S., Lluís-Ganella, C., & Lucas, G. (2014) · The Application of Clinical Genetics · doi:10.2147/tacg.s35301 · Cited by 163 · see citing works
Multilocus Genetic Risk Scores for Coronary Heart Disease Prediction
Ganna, A., Magnusson, P. K. E., Pedersen, N. L., de Faire, U., Reilly, M., Ärnlöv, J., Sundström, J., Hamsten, A., & Ingelsson, E. (2013) · Arteriosclerosis, Thrombosis, and Vascular Biology · doi:10.1161/atvbaha.113.301218 · Cited by 156 · see citing works
Dikilitas, O., Schaid, D. J., Kosel, M., Carroll, R. J., Chute, C. G., Denny, J. C., Fedotov, A., Feng, Q., Hakonarson, H. H., Jarvik, G. P., et al. (2020) · The American Journal of Human Genetics · doi:10.1016/j.ajhg.2020.04.002 · Cited by 145 · see citing works
Polygenic Scores to Assess Atherosclerotic Cardiovascular Disease Risk
Aragam, K. G., & Natarajan, P. (2020) · Circulation Research · doi:10.1161/circresaha.120.315928 · Cited by 146 · see citing works
Genetics of Coronary Artery Disease
Roberts, R. (2014) · Circulation Research · doi:10.1161/circresaha.114.302692 · Cited by 141 · see citing works
Molecular Mechanisms of Vascular Health: Insights From Vascular Aging and Calcification
Sutton, N. R., Malhotra, R. K., Hilaire, C. S., Aïkawa, E., Blumenthal, R. S., Gackenbach, G., Goyal, P., Johnson, A. L., Nigwekar, S. U., Shanahan, C. M., et al. (2022) · Arteriosclerosis Thrombosis and Vascular Biology · doi:10.1161/atvbaha.122.317332 · Cited by 137 · see citing works
Ferguson, J. F., Allayee, H., Gerszten, R. E., Ideraabdullah, F., Kris‐Etherton, P. M., Ordovás, J. M., Rimm, E. B., Wang, T. J., & Bennett, B. J. (2016) · Circulation Cardiovascular Genetics · doi:10.1161/hcg.0000000000000030 · Cited by 126 · see citing works
Ahmad, F., McNally, E. M., Ackerman, M. J., Baty, L., Day, S. M., Kullo, I. J., Madueme, P. C., Maron, M. S., Martinez, M. W., Salberg, L. R., et al. (2019) · Circulation Genomic and Precision Medicine · doi:10.1161/hcg.0000000000000054 · Cited by 121 · see citing works
Genetic Markers Enhance Coronary Risk Prediction in Men: The MORGAM Prospective Cohorts
Hughes, M., Saarela, O., Stritzke, J., Kee, F., Silander, K., Klopp, N., Kontto, J. P., Karvanen, J., Willenborg, C., Salomaa, V. V., et al. (2012) · PLoS ONE · doi:10.1371/journal.pone.0040922 · Cited by 120 · see citing works
Asthma genetics and personalised medicine
Meyers, D. A., Bleecker, E. R., Holloway, J. W., & Holgate, S. T. (2014) · The Lancet Respiratory Medicine · doi:10.1016/s2213-2600(14)70012-8 · Cited by 114 · see citing works
Genomic risk prediction of complex human disease and its clinical application
Abraham, G., & Inouye, M. (2015) · Current Opinion in Genetics & Development · doi:10.1016/j.gde.2015.06.005 · Cited by 111 · see citing works
New Insights Into Pollution and the Cardiovascular System
Gold, D. R., & Mittleman, M. A. (2013) · Circulation · doi:10.1161/circulationaha.111.064337 · Cited by 108 · see citing works
The New Pooled Cohort Equations Risk Calculator
Preiss, D., & Kristensen, S. L. (2015) · Canadian Journal of Cardiology · doi:10.1016/j.cjca.2015.02.001 · Cited by 95 · see citing works
Current Applications of Genetic Risk Scores to Cardiovascular Outcomes and Subclinical Phenotypes
Smith, J. A., Ware, E. B., Middha, P., Beacher, L., & Kardia, S. L. R. (2015) · Current Epidemiology Reports · doi:10.1007/s40471-015-0046-4 · Cited by 82 · see citing works
Precision Medicine and Personalized Medicine in Cardiovascular Disease
Currie, G. E., & Delles, C. (2018) · Advances in experimental medicine and biology · doi:10.1007/978-3-319-77932-4_36 · Cited by 77 · see citing works
Xie, X., Shi, X., & Liu, M. (2017) · Scandinavian Journal of Immunology · doi:10.1111/sji.12560 · Cited by 77 · see citing works
Genetics of Coronary Artery Disease
Lieb, W., & Vasan, R. S. (2013) · Circulation · doi:10.1161/circulationaha.113.005350 · Cited by 75 · see citing works
Iribarren, C., Lü, M., Jorgenson, E., Martínez, M., Lluís-Ganella, C., Subirana, I., Salas, E., & Elosúa, R. (2016) · Circulation Cardiovascular Genetics · doi:10.1161/circgenetics.116.001522 · Cited by 67 · see citing works
Assessment of the value of a genetic risk score in improving the estimation of coronary risk
Lluís-Ganella, C., Subirana, I., Lucas, G., Tomás, M., Muñoz-Escudero, D., Sentı́, M., Salas, E., Sala, J., Ramos, R., Ordovás, J. M., et al. (2012) · Atherosclerosis · doi:10.1016/j.atherosclerosis.2012.03.024 · Cited by 62 · see citing works
Xiao, Q., Liu, Z.-J., Tao, S., Sun, Y.-M., Jiang, D., Li, H., Chen, H., Liu, X., Lapin, B. R., Wang, C., et al. (2015) · Oncotarget · doi:10.18632/oncotarget.6271 · Cited by 61 · see citing works
Precision Cardiovascular Medicine: State of Genetic Testing
Giudicessi, J. R., Kullo, I. J., & Ackerman, M. J. (2017) · Mayo Clinic Proceedings · doi:10.1016/j.mayocp.2017.01.015 · Cited by 60 · see citing works
Polygenic Contribution in Individuals With Early-Onset Coronary Artery Disease
Thériault, S., Lali, R., Chong, M., Velianou, J. L., Natarajan, M. K., & Paré, G. (2018) · Circulation Genomic and Precision Medicine · doi:10.1161/circgen.117.001849 · Cited by 59 · see citing works
Spronk, H. M. H., Padró, T., Siland, J. E., Prochaska, J. H., Winters, J., van der Wal, A. C., Posthuma, J. J., Lowe, G. D. O., D’Alessandro, E., Wenzel, P., et al. (2018) · Thrombosis and Haemostasis · doi:10.1160/th17-07-0492 · Cited by 61 · see citing works
Rey-López, J. P., Carvalho, H. B., de Moraes, A. C. F., Ruiz, J. R., Sjöstróm, M., Marcos, A., Polito, A., Gottrand, F., Μanios, Y., Kafatos, A., et al. (2013) · Sleep Medicine · doi:10.1016/j.sleep.2013.07.021 · Cited by 59 · see citing works
Goldstein, B. A., Knowles, J. W., Salfati, E., Ioannidis, J. P. A., & Assimes, T. L. (2014) · Frontiers in Genetics · doi:10.3389/fgene.2014.00254 · Cited by 57 · see citing works
Genetics of Coronary Artery Disease and Myocardial Infarction - 2013
Kessler, T., Erdmann, J., & Schunkert, H. (2013) · Current Cardiology Reports · doi:10.1007/s11886-013-0368-0 · Cited by 54 · see citing works
Hindieh, W., Pilote, L., Cheema, A. N., Al-Lawati, H., Labos, C., Dufresne, L., Engert, J. C., & Thanassoulis, G. (2016) · Arteriosclerosis Thrombosis and Vascular Biology · doi:10.1161/atvbaha.115.306944 · Cited by 56 · see citing works
Self-citationThe evolution of genetic-based risk scores for lipids and cardiovascular disease
Dron, J. S., & Hegele, R. A. (2019) · Current Opinion in Lipidology · doi:10.1097/mol.0000000000000576 · Cited by 58 · see citing works
Hatasa, M., Yoshida, S., Takahashi, H., Tanaka, K., Kubotsu, Y., Ohsugi, Y., Katagiri, T., Iwata, T., & Katagiri, S. (2021) · International Journal of Molecular Sciences · doi:10.3390/ijms22073728 · Cited by 57 · see citing works
Asthma pharmacogenetics and the development of genetic profiles for personalized medicine
Bleecker, E. R., Ortega, V. E., & Meyers, D. A. (2015) · Pharmacogenomics and Personalized Medicine · doi:10.2147/pgpm.s52846 · Cited by 53 · see citing works
Tsao, C. W., Preis, S. R., Peloso, G. M., Hwang, S., Kathiresan, S., Fox, C. S., Cupples, L. A., Hoffmann, U., & O’Donnell, C. J. (2012) · Journal of the American College of Cardiology · doi:10.1016/j.jacc.2012.09.007 · Cited by 53 · see citing works
Self-citationPatel, R. S., Sun, Y. V., Hartiala, J., Veledar, E., Su, S., Sher, S., Liu, Y. X., Rahman, A. M., Patel, R., Rab, S. T., et al. (2012) · Circulation Cardiovascular Genetics · doi:10.1161/circgenetics.111.960229 · Cited by 48 · see citing works
de Vries, P. S., Kavousi, M., Ligthart, S., Uitterlinden, A. G., Hofman, A., Franco, O. H., & Dehghan, A. (2015) · International Journal of Epidemiology · doi:10.1093/ije/dyv070 · Cited by 48 · see citing works
Contemporary Considerations for Constructing a Genetic Risk Score: An Empirical Approach
Goldstein, B. A., Yang, L., Salfati, E., & Assimes, T. L. (2015) · Genetic Epidemiology · doi:10.1002/gepi.21912 · Cited by 44 · see citing works
Liu, Z., Qi, H., Liu, B., Liu, K., Wu, J., Cao, H., Zhang, J., Yan, Y., He, Y. L., & Zhang, L. (2017) · Hypertension Research · doi:10.1038/hr.2017.57 · Cited by 46 · see citing works
Liu, Y., Cheng, J., Guo, X., Mo, J., Gao, B., Zhou, H., Wu, Y., & Li, Z. (2018) · Gene · doi:10.1016/j.gene.2018.06.040 · Cited by 46 · see citing works
Systems healthcare: a holistic paradigm for tomorrow
Fiandaca, M. S., Mapstone, M. E., Connors, E. E., Jacobson, M., Monuki, E. S., Malik, S., Macciardi, F. M., & Federoff, H. J. (2017) · BMC Systems Biology · doi:10.1186/s12918-017-0521-2 · Cited by 45 · see citing works
Integration of Genomics in Primary Care
Larson, E. A., & Wilke, R. A. (2015) · The American Journal of Medicine · doi:10.1016/j.amjmed.2015.05.011 · Cited by 41 · see citing works
Genetic aspects of skeletal muscle strength and mass with relevance to sarcopenia
Roth, S. M. (2012) · BoneKEy Reports · doi:10.1038/bonekey.2012.58 · Cited by 41 · see citing works
Understanding cardiovascular disease: a journey through the genome (and what we found there)
Lara‐Pezzi, E., Dopazo, A., & Manzanares, M. (2012) · Disease Models & Mechanisms · doi:10.1242/dmm.009787 · Cited by 41 · see citing works
Whole-Genome Sequencing in Healthy People
Lindor, N. M., Thibodeau, S. N., & Burke, W. (2017) · Mayo Clinic Proceedings · doi:10.1016/j.mayocp.2016.10.019 · Cited by 41 · see citing works
Akinkugbe, A. A., Avery, C. L., Barritt, A. S., Cole, S. R., Lerch, M. M., Mayerle, J. V., Offenbacher, S., Petersmann, A., Nauck, M. A., Völzke, H., et al. (2017) · Journal of Dental Research · doi:10.1177/0022034517720924 · Cited by 41 · see citing works
Corella, D., Coltell, Ó., Mattingley, G., Sorlí, J. V., & Ordovás, J. M. (2017) · Expert Review of Molecular Diagnostics · doi:10.1080/14737159.2017.1311208 · Cited by 40 · see citing works
Incremental Value of Polygenic Risk Scores in Primary Prevention of Coronary Heart Disease
Groenendyk, J., Greenland, P., & Khan, S. S. (2022) · JAMA Internal Medicine · doi:10.1001/jamainternmed.2022.3171 · Cited by 37 · see citing works
Future Translational Applications From the Contemporary Genomics Era
Fox, C. S., Hall, J. L., Arnett, D. K., Ashley, E. A., Delles, C., Engler, M. B., Freeman, M. W., Johnson, J. A., Lanfear, D. E., Liggett, S. B., et al. (2015) · Circulation · doi:10.1161/cir.0000000000000211 · Cited by 39 · see citing works
Self-citationShahid, S. U., Shabana, S., Cooper, J. A., Beaney, K. E., Li, K., Rehman, A., & Humphries, S. E. (2017) · Atherosclerosis · doi:10.1016/j.atherosclerosis.2017.01.024 · Cited by 39 · see citing works
Freedman, B. I., Divers, J., & Palmer, N. D. (2013) · American Journal of Kidney Diseases · doi:10.1053/j.ajkd.2013.05.024 · Cited by 38 · see citing works
Kitsios, G. D., Dahabreh, I. J., Trikalinos, T. A., Schmid, C. H., Huggins, G. S., & Kent, D. M. (2010) · Circulation Cardiovascular Genetics · doi:10.1161/circgenetics.110.957738 · Cited by 37 · see citing works
Genetics 100 for Cardiologists: Basics of Genome-Wide Association Studies
Dubé, J. B., & Hegele, R. A. (2012) · Canadian Journal of Cardiology · doi:10.1016/j.cjca.2012.10.011 · Cited by 36 · see citing works
Shendre, A., Wiener, H. W., Irvin, M. R., Zhi, D., Limdi, N. A., Overton, E. T., Wassel, C. L., Divers, J., Rotter, J. I., Post, W. S., et al. (2017) · Circulation Cardiovascular Genetics · doi:10.1161/circgenetics.116.001569 · Cited by 36 · see citing works
Predictive accuracy of combined genetic and environmental risk scores
Dudbridge, F., Pashayan, N., & Yang, J. (2017) · Genetic Epidemiology · doi:10.1002/gepi.22092 · Cited by 36 · see citing works
Effect of Disclosing Genetic Risk for Coronary Heart Disease on Information Seeking and Sharing
Brown, S.-A. N., Jouni, H., Marroush, T. S., & Kullo, I. J. (2017) · Circulation Cardiovascular Genetics · doi:10.1161/circgenetics.116.001613 · Cited by 36 · see citing works
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Cox, A. J., Hsu, F., Ng, M. C. Y., Langefeld, C. D., Freedman, B. I., Carr, J. J., & Bowden, D. W. (2014) · Diabetes Care · doi:10.2337/dc13-1514 · Cited by 34 · see citing works
FcγR gene copy number in Kawasaki disease and intravenous immunoglobulin treatment response
Makowsky, R., Wiener, H. W., Ptacek, T., Silva, M., Shendre, A., Edberg, J. C., Portman, M. A., & Shrestha, S. (2013) · Pharmacogenetics and Genomics · doi:10.1097/fpc.0b013e328363686e · Cited by 33 · see citing works
Antiochos, P., Marques-Vidal, P. M., McDaid, A. F., Waeber, G., & Vollenweider, P. (2015) · Atherosclerosis · doi:10.1016/j.atherosclerosis.2015.10.104 · Cited by 31 · see citing works
Are Genetic Tests for Atherosclerosis Ready for Routine Clinical Use?
Paynter, N. P., Ridker, P. M., & Chasman, D. I. (2016) · Circulation Research · doi:10.1161/circresaha.115.306360 · Cited by 31 · see citing works
Marginal role for 53 common genetic variants in cardiovascular disease prediction
Morris, R. W., Cooper, J. A., Shah, T., Wong, A., Drenos, F., Engmann, J., McLachlan, S., Jefferis, B. J. M. H., Dale, C. E., Hardy, R., et al. (2016) · Heart · doi:10.1136/heartjnl-2016-309298 · Cited by 31 · see citing works
Coronary Heart Disease Genetic Risk Score Predicts Cardiovascular Disease Risk in Men, Not Women
Hajek, C. A., Guo, X., Yao, J., Hai, Y., Johnson, W. C., Wood, A. C., Post, W. S., Psaty, B. M., Taylor, K. D., & Rotter, J. I. (2018) · Circulation Genomic and Precision Medicine · doi:10.1161/circgen.118.002324 · Cited by 29 · see citing works
Severance, L. M., Contijoch, F. J., Carter, H. K., Fan, C. C., Seibert, T. M., Dale, A. M., & McVeigh, E. R. (2019) · Journal of cardiovascular computed tomography · doi:10.1016/j.jcct.2019.05.005 · Cited by 29 · see citing works
Kertai, M. D., Mosley, J. D., He, J., Ramakrishnan, A., Abdelmalak, M., Hong, Y., Shoemaker, M. B., Roden, D. M., & Bastarache, L. A. (2021) · Circulation Genomic and Precision Medicine · doi:10.1161/circgen.120.003269 · Cited by 29 · see citing works
Miller, V. M., Petterson, T. M., Jeavons, E. N., Lnu, A. S., Rider, D. N., Heit, J. A., Cunningham, J. M., Huggins, G. S., Hodis, H. N., Budoff, M. J., et al. (2012) · Physiological Genomics · doi:10.1152/physiolgenomics.00114.2012 · Cited by 28 · see citing works
Understanding Personalized Training Responses: Can Genetic Assessment Help?
Pickering, C., & Kiely, J. (2017) · The Open Sports Sciences Journal · doi:10.2174/1875399x01710010191 · Cited by 29 · see citing works
Genetic Risk Scores Predict Recurrence of Acute Coronary Syndrome
Vaara, S. M., Tikkanen, E., Parkkonen, O., Lokki, M., Ripatti, S., Perola, M., Nieminen, M. S., & Sinisalo, J. P. (2016) · Circulation Cardiovascular Genetics · doi:10.1161/circgenetics.115.001271 · Cited by 28 · see citing works
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Monogenic and Polygenic Models of Coronary Artery Disease
Muse, E. D., Chen, S.-F., & Torkamani, A. (2021) · Current Cardiology Reports · doi:10.1007/s11886-021-01540-0 · Cited by 27 · see citing works
Towards a Molecular Systems Model of Coronary Artery Disease
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Sanz, J., Moreno, P., & Fuster, V. (2013) · Journal of the American College of Cardiology · doi:10.1016/j.jacc.2013.06.045 · Cited by 26 · see citing works
The Look AHEAD Research Group (2015) · Diabetologia · doi:10.1007/s00125-015-3610-z · Cited by 26 · see citing works
Assessing the Associations of Blood Metabolites With Osteoporosis: A Mendelian Randomization Study
Liu, L., Wen, Y. F., Zhang, L., Xu, P., Liang, X., Du, Y., Li, P., He, A., Fan, Q., Hao, J., et al. (2018) · The Journal of Clinical Endocrinology & Metabolism · doi:10.1210/jc.2017-01719 · Cited by 26 · see citing works
Polygenic Risk Score to Identify Subclinical Coronary Heart Disease Risk in Young Adults
Wells, Q. S., Bagheri, M., Aday, A. W., Gupta, D. K., Shaffer, C. M., Wei, W., Vaitinadin, N. S., Khan, S. S., Greenland, P., Wang, T. J., et al. (2021) · Circulation Genomic and Precision Medicine · doi:10.1161/circgen.121.003341 · Cited by 25 · see citing works
Cardiovascular Disease Risk Prediction in Women: Is There a Role for Novel Biomarkers?
Paynter, N. P., Everett, B. M., & Cook, N. R. (2013) · Clinical Chemistry · doi:10.1373/clinchem.2013.202796 · Cited by 23 · see citing works
Genetic risk scores used in cardiovascular disease prediction models: a systematic review
Yun, H., Noh, N. I., & Lee, E. Y. (2022) · Reviews in Cardiovascular Medicine · doi:10.31083/j.rcm2301008 · Cited by 22 · see citing works
Burden of disease variants in participants of the long life family Study
Duncan, M. S., Bae, H. T., Schupf, N., Andersen, S. L., Zhang, Q., Perls, T. T., & Paola Sebastiani, B. (2015) · Aging · doi:10.18632/aging.100724 · Cited by 23 · see citing works
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Value of genetic testing in the prevention of coronary heart disease events
Hynninen, Y., Linna, M., & Vilkkumaa, E. (2019) · PLoS ONE · doi:10.1371/journal.pone.0210010 · Cited by 22 · see citing works
Labos, C., Martinez, S. C., Wang, R. H. L., Lenzini, P. A., Pilote, L., Bogaty, P. M., Brophy, J. M., Engert, J. C., Cresci, S., & Thanassoulis, G. (2015) · Atherosclerosis · doi:10.1016/j.atherosclerosis.2015.07.029 · Cited by 23 · see citing works
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- Authors
- Thanassoulis, G., Peloso, G. M., Pencina, M. J., Hoffmann, U., Fox, C. S., Cupples, L. A., Levy, D., D'Agostino, R. B., Hwang, S.-J., & O'Donnell, C. J.
- Affiliations
- Boston University (US)
- From the Division of Cardiology and Clinical Epidemiology, McGill University Health Center and McGill University, Montreal, Quebec, Canada (G.T.); National Heart, Lung and Blood Institute (NHLBI) of the National Institutes of Health (NIH) Framingham Heart Study, Framingham, MA (G.T., G.M.P., M.J.P., C.S.F., L.A.C., D.L., R.B.D.'A., S.-J.H., C.J.O.); School of Medicine (G.T.) and Department of Mathematics (M.J.P., R.B.D.'A.), Boston University, Boston, MA; Center for Population Studies, NHLBI...
- ORCID
- Venue
- Circulation: Cardiovascular Genetics
- Published
- 2012-02
- Volume
- 5
- Issue
- 1
- Pages
- 113-121
- ISSN
- 1942-325X (ISSN-L)
- 1942-3268
- PMC
- PMC3292865
- OpenAlex
- W2135313485
- Cited by
- 211
- References
- 73
- Citations by year
- 2026: 1 · 2025: 4 · 2024: 5 · 2023: 3 · 2022: 7 · 2021: 13 · 2020: 10 · 2019: 10 · 2018: 15 · 2017: 27 · 2016: 18 · 2015: 31 · 2014: 20 · 2013: 28 · 2012: 18
- FWCI
- 18.70
- Type
- journal article
- Language
- en
- Open access
- green
- europepmc.org
- Locations
- Circulation Cardiovascular Genetics (publishedVersion)
- PubMed (publishedVersion)
- Europe PMC (PubMed Central) (acceptedVersion)
- PubMed Central (acceptedVersion)
- Funders
- National Institutes of Health (Z99 HL999999, n01hc25195-41-0-1, n02hl64278-6-0-0)
- Canadian Institutes of Health Research
- National Heart, Lung, and Blood Institute (N01‐HC25195, ZIAHL006002, N02- HL-64278, N01-HC-025195, Z01HL006002)
- 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, coronary artery calcium, coronary artery disease, myocardial infarction, single nucleotide polymorphisms, risk reclassification, discrimination (statistics), incident cardiovascular events, genetic variants
- MeSH terms
- Adult, Alleles, Calcinosis, Calcium, Coronary Disease, Female, Follow-Up Studies, Humans, Longitudinal Studies, Male, Middle Aged, Predictive Value of Tests, Risk Factors, Cohort Studies, Incidence, Proportional Hazards Models, Odds Ratio, Coronary Angiography, Polymorphism, Single Nucleotide, Genome-Wide Association Study
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