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Genetic risk, coronary heart disease events, and the clinical benefit of statin therapy: an analysis of primary and secondary prevention trials
Open accessAbstract
Background Genetic variants have been associated with the risk of coronary heart disease. In this study, we tested whether or not a composite of these variants could ascertain the risk of both incident and recurrent coronary heart disease events and identify those individuals who derive greater clinical benefit from statin therapy.Methods A community-based cohort study (the Malmo Diet and Cancer Study) and four randomised controlled trials of both primary prevention (JUPITER and ASCOT) and secondary prevention (CARE and PROVE IT-TIMI 22) with statin therapy, comprising a total of 48,421 individuals and 3477 events, were included in these analyses. We studied the association of a genetic risk score based on 27 genetic variants with incident or recurrent coronary heart disease, adjusting for traditional clinical risk factors. We then investigated the relative and absolute risk reductions in coronary heart disease events with statin therapy stratified by genetic risk. We combined data from the different studies using a meta-analysis.Findings When individuals were divided into low (quintile 1), intermediate (quintiles 2-4), and high (quintile 5) genetic risk categories, a significant gradient in risk for incident or recurrent coronary heart disease was shown. Compared with the low genetic risk category, the multivariable-adjusted hazard ratio for coronary heart disease for the intermediate genetic risk category was 1·34 (95% CI 1·22-1·47, p<0·0001) and that for the high genetic risk category was 1·72 (1·55-1·92, p<0·0001). In terms of the benefit of statin therapy in the four randomised trials, we noted a significant gradient (p=0·0277) of increasing relative risk reductions across the low (13%), intermediate (29%), and high (48%) genetic risk categories. Similarly, we noted greater absolute risk reductions in those individuals in higher genetic risk categories (p=0·0101), resulting in a roughly threefold decrease in the number needed to treat to prevent one coronary heart disease event in the primary prevention trials. Specifically, in the primary prevention trials, the number needed to treat to prevent one such event in 10 years was 66 in people at low genetic risk, 42 in those at intermediate genetic risk, and 25 in those at high genetic risk in JUPITER, and 57, 47, and 20, respectively, in ASCOT.Interpretation A genetic risk score identified individuals at increased risk for both incident and recurrent coronary heart disease events. People with the highest burden of genetic risk derived the largest relative and absolute clinical benefit from statin therapy.Funding National Institutes of Health.
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“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 [22]
In the citing paper
“For example, we previously showed that a high polygenic risk for CAD may be offset by either of two interventions: adherence to a healthy lifestyle or cholesterol-lowering therapy with statin medications.19–21”
From Genome-wide polygenic scores for common diseases identify individuals with risk equivalent to monogenic mutations (2018) · cites this paper as [20]
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“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 [22]
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1 retracted
Reduced dietary salt for the prevention of cardiovascular disease
Taylor, R. S., Ashton, K. E., Moxham, T., Hooper, L., & Ebrahim, S. B. J. (2013) · Cochrane Database of Systematic Reviews · doi:10.1002/14651858.cd009217.pub2 · Cited by 311
RetractedSacks, F. M., Pfeffer, M. A., Moye, L. A., Rouleau, J. L., Rutherford, J. D., Cole, T. G., Brown, L., Warnica, J. W., Arnold, J. M. O., Wun, C.-C., et al. (1996) · New England Journal of Medicine · doi:10.1056/nejm199610033351401 · Cited by 7,439
Self-citationRosuvastatin to Prevent Vascular Events in Men and Women with Elevated C-Reactive Protein
Ridker, P. M., Danielson, E., Fonseca, F. A. H., Genest, J., Gotto, A. M., Kastelein, J. J. P., Koenig, W., Libby, P., Lorenzatti, A. J., MacFadyen, J. G., et al. (2008) · New England Journal of Medicine · doi:10.1056/nejmoa0807646 · Cited by 6,674
Self-citationCholesterol Treatment Trialists' (CTT) Collaboration (2010) · The Lancet · doi:10.1016/s0140-6736(10)61350-5 · Cited by 6,550
Intensive versus Moderate Lipid Lowering with Statins after Acute Coronary Syndromes
Cannon, C. P., Braunwald, E., McCabe, C. H., Rader, D. J., Rouleau, J. L., Belder, R., Joyal, S. V., Hill, K. A., Pfeffer, M. A., & Skene, A. M. (2004) · New England Journal of Medicine · doi:10.1056/nejmoa040583 · Cited by 4,938
Self-citationSever, P., Dahlöf, B., Poulter, N. R., Wedel, H., Beevers, G., Caulfield, M. J., Collins, R., Kjeldsen, S. E., Kristinsson, A., McInnes, G. T., et al. (2003) · The Lancet · doi:10.1016/s0140-6736(03)12948-0 · Cited by 3,790
Self-citationRosuvastatin to Prevent Vascular Events in Men and Women With Elevated C-Reactive Protein
Ridker, P. M., Danielson, E., Fonseca, F. A. H., Genest, J. J., Gotto, A. M., Kastelein, J. J. P., Köenig, W., Libby, P., Lorenzatti, A. J., MacFadyen, J. G., et al. (2009) · Obstetrical & Gynecological Survey · doi:10.1097/01.ogx.0000344393.60303.0a · Cited by 3,641
Self-citationCollaborators, C. T. T. (. (2012) · The Lancet · doi:10.1016/s0140-6736(12)60367-5 · Cited by 2,833
Statins for the primary prevention of cardiovascular disease
Taylor, F., Huffman, M. D., Macedo, A. F., Moore, T. H., Burke, M., Davey Smith, G., Ward, K., Ebrahim, S., & Gay, H. C. (2013) · Cochrane Database of Systematic Reviews · doi:10.1002/14651858.cd004816.pub5 · Cited by 1,934
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Reisberg, S., Iljasenko, T., Läll, K., Fischer, K., & Vilo, J. (2017) · PLoS ONE · doi:10.1371/journal.pone.0179238 · Cited by 70
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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
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Tremblay, J., Haloui, M., Attaoua, R., Tahir, M. R., Hishmih, C., Harvey, F., Marois-Blanchet, F.-C., Long, C., Simon, P., Santucci, L., et al. (2021) · Diabetologia · doi:10.1007/s00125-021-05491-7 · Cited by 57
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Bending the Curve in Cardiovascular Disease Mortality
Goff, D. C., Khan, S. S., Lloyd‐Jones, D. M., Arnett, D. K., Carnethon, M. R., Labarthe, D. R., Loop, M. S., Luepker, R. V., McConnell, M. V., Mensah, G. A., et al. (2021) · Circulation · doi:10.1161/circulationaha.120.046501 · Cited by 60
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Knowles, J. W., Zarafshar, S., Pavlović, A., Goldstein, B. A., Tsai, S. A., Li, J., McConnell, M. V., Absher, D., Ashley, E. A., Kiernan, M., et al. (2017) · Frontiers in Cardiovascular Medicine · doi:10.3389/fcvm.2017.00053 · Cited by 55
Sun, Q., Rowland, B., Chen, J. C., Mikhaylova, A. V., Avery, C. L., Peters, U., Lundin, J. I., Matise, T. C., Buyske, S. G., Tao, R., et al. (2024) · Nature Communications · doi:10.1038/s41467-024-45135-z · Cited by 60
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Dissecting the polygenic basis of atherosclerosis via disease-associated cell state signatures
Örd, T., Lönnberg, T., Nurminen, V., Ravindran, A., Niskanen, H., Kiema, M., Õunap, K., Maria, M., Moreau, P. R., Mishra, P. P., et al. (2023) · The American Journal of Human Genetics · doi:10.1016/j.ajhg.2023.03.013 · Cited by 49
Clark, K., Leung, Y. Y., Lee, W., Voight, B. F., & Wang, L. (2022) · Journal of Alzheimer s Disease · doi:10.3233/jad-220025 · Cited by 48
Genome-wide association studies of late-onset cardiovascular disease
Smith, J. G., & Newton‐Cheh, C. (2015) · Journal of Molecular and Cellular Cardiology · doi:10.1016/j.yjmcc.2015.04.004 · Cited by 51
Self-citationYaeger, K., Martini, M. L., Yaniv, G., Oermann, E. K., & Costa, A. B. (2019) · Health Policy and Technology · doi:10.1016/j.hlpt.2019.05.006 · Cited by 47
Lanfear, D. E., Luzum, J. A., She, R., Gui, H., Donahue, M., O’Connor, C. M., Adams, K. F., Wijk, S. S., Zeld, N., Maeder, M. T., et al. (2020) · Circulation Heart Failure · doi:10.1161/circheartfailure.119.007012 · Cited by 47
Nazarzadeh, M., Pinho‐Gomes, A., Bidel, Z., Canoy, D., Dehghan, A., Smith-Byrne, K., Bennett, D., Smith, G. D., & Rahimi, K. (2021) · Genome Medicine · doi:10.1186/s13073-021-00849-3 · Cited by 46
Tamlander, M., Mars, N., Pirinen, M., FinnGen, Palotie, A. V., Daly, M. J., companies, P., Riley-Gills, B., Jacob, H. J., Paul, D. S., et al. (2022) · Communications Biology · doi:10.1038/s42003-021-02996-0 · Cited by 43
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Bandstein, M., Voisin, S. A. C., Nilsson, E., Schultes, B., Ernst, B., Thurnheer, M., Benedict, C., Mwinyi, J., & Schiöth, H. B. (2016) · Obesity Surgery · doi:10.1007/s11695-016-2072-9 · Cited by 46
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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 49
Muse, E. D., Chen, S., Liu, S., Fernandez, B., Schrader, B., Molparia, B., León, A. N., Lee, R., Pubbi, N., Mejia, N., et al. (2022) · npj Digital Medicine · doi:10.1038/s41746-022-00578-w · Cited by 43
Kiflen, M., Le, A., Mao, S., Lali, R., Narula, S., Xie, F., & Paré, G. (2022) · Circulation Genomic and Precision Medicine · doi:10.1161/circgen.121.003423 · Cited by 42
Wand, H., Kalia, S. S., Helm, B. M., Suckiel, S. A., Brockman, D., Vriesen, N., Goudar, R. K., Austin, J., & Yanes, T. (2023) · Journal of Genetic Counseling · doi:10.1002/jgc4.1668 · Cited by 45
Gu, A., Kamat, S., & Argulian, E. (2018) · Diabetes Research and Clinical Practice · doi:10.1016/j.diabres.2018.02.019 · Cited by 41
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Green, S., & Vogt, H. (2016) · BIBSYS Brage (BIBSYS (Norway)) · Cited by 40
Current State and Future of Polygenic Risk Scores in Cardiometabolic Disease: A Scoping Review
Phulka, J., Ashraf, M., Bajwa, B. K., Paré, G., & Laksman, Z. (2023) · Circulation Genomic and Precision Medicine · doi:10.1161/circgen.122.003834 · Cited by 41
Walton, G., Stockley, J. A., Griffiths, D., Sadhra, C., Purvis, T., & Sapey, E. (2016) · Journal of Clinical Medicine · doi:10.3390/jcm5100089 · Cited by 43
Morieri, M. L., Gao, H., Pigeyre, M., Shah, H., Sjaarda, J., Mendonça, C., Hastings, T., Buranasupkajorn, P., Motsinger‐Reif, A. A., Rotroff, D. M., et al. (2018) · Diabetes Care · doi:10.2337/dc18-0709 · Cited by 39
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Mazzarotto, F., Olivotto, I., & Walsh, R. (2020) · Cardiovascular Drugs and Therapy · doi:10.1007/s10557-020-06948-4 · Cited by 41
Infante, T., Forte, E., Schiano, C., Punzo, B., Cademartiri, F., Cavaliere, C., Salvatore, M., & Claudio Napoli, A. (2019) · PLoS ONE · doi:10.1371/journal.pone.0210909 · Cited by 39
Polygenic risk for coronary heart disease acts through atherosclerosis in type 2 diabetes
Lu, T., Forgetta, V., Yu, O. H. Y., Mokry, L. E., Gregory, M., Thanassoulis, G., Greenwood, C. M. T., & Richards, J. B. (2020) · Cardiovascular Diabetology · doi:10.1186/s12933-020-0988-9 · Cited by 38
Maamari, D. J., Brockman, D., Aragam, K. G., Pelletier, R. C., Folkerts, E. K., Neben, C. L., Okumura, S., Hull, L. E., Philippakis, A., Natarajan, P., et al. (2022) · JACC Advances · doi:10.1016/j.jacadv.2022.100068 · Cited by 38
Vassy, J. L., Posner, D. C., Ho, Y.-L. A., GAGNON, D. R., Galloway, A., Tanukonda, V., Houghton, S. C., Madduri, R., McMahon, B. H., Tsao, P. S., et al. (2023) · JAMA Cardiology · doi:10.1001/jamacardio.2023.0857 · Cited by 40
Misra, S., Aguilar-Salinas, C. A., Chikowore, T., Konradsen, F., Ronald, C. W., Mbau, L. K., Mohan, V., Morton, R. W., Nyirenda, M., Tapela, N. M., et al. (2023) · The Lancet Diabetes & Endocrinology · doi:10.1016/s2213-8587(23)00164-x · Cited by 43
Selker, H. P., Eichler, H., Stockbridge, N., McElwee, N. E., Dere, W. H., Cohen, T., Erban, J. K., Seyfert‐Margolis, V., Honig, P. K., Kaitin, K. I., et al. (2019) · Clinical Pharmacology & Therapeutics · doi:10.1002/cpt.1347 · Cited by 43
de Villiers, C. B., Kroese, M., & Moorthie, S. A. (2020) · Journal of Medical Genetics · doi:10.1136/jmedgenet-2019-106763 · Cited by 41
Manikpurage, H. D., Eslami, A., Perrot, N., Li, Z., Couture, C., Mathieu, P., Bossé, Y., Arsenault, B. J., & Thériault, S. (2021) · Circulation Genomic and Precision Medicine · doi:10.1161/circgen.121.003452 · Cited by 39
Wood, G., Taylor, E., Ng, V., Murrell, A., Patil, A., van der Touw, T., Sigal, R., Wolden, M., & Smart, N. (2022) · British Journal of Sports Medicine · doi:10.1136/bjsports-2021-103999 · Cited by 45
Howe, L. J., Dudbridge, F., Schmidt, A. F., Finan, C., Denaxas, S., Asselbergs, F. W., Hingorani, A. D., & Patel, R. S. (2020) · Human Molecular Genetics · doi:10.1093/hmg/ddaa052 · Cited by 37
Ye, Y.-X., Chen, J.-X., Li, Y., Lai, Y.-W., Lu, Q., Xia, P., Franco, O. H., Liu, G., & Pan, A. (2024) · American Journal of Clinical Nutrition · doi:10.1016/j.ajcnut.2024.06.014 · Cited by 32
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Torkamani, A., & Topol, E. J. (2019) · Cell · doi:10.1016/j.cell.2019.03.051 · Cited by 38
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Villar, D., Frost, S., Deloukas, P., & Tinker, A. (2020) · Open Biology · doi:10.1098/rsob.200088 · Cited by 34
Bienfait, K., Chhibber, A., Marshall, J.-C. A., Armstrong, M., Cox, C., Shaw, P. M., & Paulding, C. (2021) · Human Genetics · doi:10.1007/s00439-021-02282-3 · Cited by 41
Jordan, D. M., & Do, R. (2018) · Annual Review of Genomics and Human Genetics · doi:10.1146/annurev-genom-083117-021136 · Cited by 35
Yu, X., Du, J.-L., Jiang, M. L., Ren, Y., Zhang, F.-L., Kong, F.-L., & Li, F. (2024) · Frontiers in Endocrinology · doi:10.3389/fendo.2024.1327903 · Cited by 36
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 34
Orho‐Melander, M. (2015) · Journal of Internal Medicine · doi:10.1111/joim.12407 · Cited by 39
Cardiovascular Risk Assessment
Pletcher, M. J., & Moran, A. E. (2017) · Medical Clinics of North America · doi:10.1016/j.mcna.2017.03.002 · Cited by 42
Genetic Risk of Coronary Artery Disease, Features of Atherosclerosis, and Coronary Plaque Burden
Christiansen, M. K., Nissen, L., Winther, S., Møller, P. L., Frost, L., Johansen, J. K., Jensen, H. K., Gudbjartsson, D. F., Hólm, H., Stefánsson, K., et al. (2020) · Journal of the American Heart Association · doi:10.1161/jaha.119.014795 · Cited by 37
Arnold, N., & Köenig, W. (2020) · Cardiovascular Drugs and Therapy · doi:10.1007/s10557-020-07105-7 · Cited by 34
2019 ESC Guidelines for the diagnosis and management of chronic coronary syndromes
(2020) · Revista Española de Cardiología (English Edition) · doi:10.1016/j.rec.2020.05.011 · Cited by 36
Hindy, G., Wiberg, F., Almgren, P., Melander, O., & Orho‐Melander, M. (2018) · Circulation Genomic and Precision Medicine · doi:10.1161/circgen.117.001856 · Cited by 33
Self-citationPersonalized Management of Cardiovascular Disorders
Jain, K. K. (2017) · Medical Principles and Practice · doi:10.1159/000481403 · Cited by 37
Polygenic Risk Scoring for Coronary Heart Disease
Natarajan, P. (2018) · Journal of the American College of Cardiology · doi:10.1016/j.jacc.2018.08.1041 · Cited by 35
Mendelian Randomization Studies Promise to Shorten the Journey to FDA Approval
Roberts, R. (2018) · JACC Basic to Translational Science · doi:10.1016/j.jacbts.2018.08.001 · Cited by 31
Advances and Applications of Polygenic Scores for Coronary Artery Disease
Patel, A. P., & Khera, A. V. (2022) · Annual Review of Medicine · doi:10.1146/annurev-med-042921-112629 · Cited by 35
Long-Term Effects of an Intensive Prevention Program After Acute Myocardial Infarction
Osteresch, R., Fach, A., Frielitz, F., Meyer, S., Schmucker, J., Rühle, S., Retzlaff, T., Hadwiger, M., Härle, T., Elsässer, A., et al. (2021) · The American Journal of Cardiology · doi:10.1016/j.amjcard.2021.05.034 · Cited by 33
Polygenic risk scores in coronary artery disease
Rao, A. S., & Knowles, J. W. (2019) · Current Opinion in Cardiology · doi:10.1097/hco.0000000000000629 · Cited by 33
Inouye, M., Abraham, G., Nelson, C. P., Wood, A., Sweeting, M. J., Dudbridge, F., Lai, F. Y., Kaptoge, S. K., Brożyńska, M., Wang, T., et al. (2018) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/250712 · Cited by 33
Multiomics in atherosclerotic cardiovascular disease
Nordestgaard, L. T., Wolford, B. N., de Gonzalo‐Calvo, D., Sopić, M., Devaux, Y., Matic, L. P., Wettinger, S. B., Schmid, J. A., Amigó, N., Masana, L., et al. (2025) · Atherosclerosis · doi:10.1016/j.atherosclerosis.2025.120414 · Cited by 40
Validation of a genetic risk score for atrial fibrillation: A prospective multicenter cohort study
Muse, E. D., Wineinger, N. E., Spencer, E. G., Peters, M., Henderson, R., Zhang, Y., Barrett, P. M., Rivera, S. P., Wohlgemuth, J. G., Devlin, J. J., et al. (2018) · PLoS Medicine · doi:10.1371/journal.pmed.1002525 · Cited by 32
Self-citationJarmul, J. A., Pletcher, M. J., Lich, K. H., Wheeler, S. B., Weinberger, M., Avery, C. L., Jonas, D. E., Earnshaw, S., & Pignone, M. P. (2018) · Circulation Cardiovascular Quality and Outcomes · doi:10.1161/circoutcomes.117.004171 · Cited by 32
Polygenic Score Models for Alzheimer’s Disease: From Research to Clinical Applications
Zhou, X., Li, Y. Y. T., Fu, A. K. Y., & Ip, N. Y. (2021) · Frontiers in Neuroscience · doi:10.3389/fnins.2021.650220 · Cited by 32
Hypertension genomics and cardiovascular prevention
Ng, F. L., Warren, H. R., & Caulfield, M. J. (2018) · Annals of Translational Medicine · doi:10.21037/atm.2018.06.34 · Cited by 31
Self-citationAntiochos, 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
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
Self-citationMarginal 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 32
Precision Psychiatry—Will Genomic Medicine Lead the Way?
Stein, M. B., & Smoller, J. W. (2018) · JAMA Psychiatry · doi:10.1001/jamapsychiatry.2018.0375 · Cited by 31
Medici, M., Visser, T. J., & Peeters, R. P. (2017) · Best Practice & Research Clinical Endocrinology & Metabolism · doi:10.1016/j.beem.2017.04.002 · Cited by 32
Sadler, M. C., Apostolov, A. M., Cevallos, C., Auwerx, C., Ribeiro, D. M., Altman, R. B., & Kutalik, Z. (2025) · Nature Communications · doi:10.1038/s41467-025-58152-3 · Cited by 32
Liu, Y., Ma, H., Zhu, Q., Zhang, B., Yan, H., Li, H., Meng, J., Lai, W., Li, L., Yu, D., et al. (2019) · Journal of Lipid Research · doi:10.1194/jlr.p091009 · Cited by 31
Polygenic Risk Scores in Coronary Artery Disease and Atrial Fibrillation
Gladding, P. A., Legget, M. E., Fatkin, D., Larsen, P., & Doughty, R. N. (2019) · Heart Lung and Circulation · doi:10.1016/j.hlc.2019.12.004 · Cited by 28
Metadata
- Authors
- 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., Poulter, N. R., Sever, P. S., Ridker, P. M., Braunwald, E., Melander, O., Kathiresan, S., & Sabatine, M. S.
- Affiliations
- Brigham and Women's Hospital (US)
- Washington University in St. Louis (US)
- Broad Institute (US)
- Queen Mary University of London (GB)
- Quest Diagnostics (United States) (US)
- Pfizer (United States) (US)
- Imperial College London (GB)
- Lund University (SE)
- Corresponding author
- Mega, Jessica L
- Stitziel, Nathan O
- ORCID
- Venue
- The Lancet
- Publisher
- Elsevier BV
- Published
- 2015-06
- Volume
- 385
- Issue
- 9984
- Pages
- 2264-2271
- ISSN
- 0099-5355 (ISSN-L)
- 0140-6736
- PMC
- PMC4608367
- OpenAlex
- W2171705978
- Cited by
- 704
- References
- 46
- Citations by year
- 2026: 40 · 2025: 48 · 2024: 51 · 2023: 69 · 2022: 78 · 2021: 83 · 2020: 78 · 2019: 59 · 2018: 62 · 2017: 53 · 2016: 57 · 2015: 25
- FWCI
- 12.58
- Type
- journal article
- Language
- en
- Open access
- green
- www.ncbi.nlm.nih.gov
- Locations
- The Lancet (publishedVersion)
- PubMed (publishedVersion)
- Europe PMC (PubMed Central) (acceptedVersion)
- PubMed Central (acceptedVersion)
- Digital Access to Scholarship at Harvard (DASH) (Harvard University) (acceptedVersion)
- Lund University Publications (Lund University) (submittedVersion)
- Spiral (Imperial College London) (submittedVersion, cc-by-nc-nd)
- Funders
- National Heart, Lung, and Blood Institute (NHLBI) of the National Institutes of Health (NIH) (RC1 HL099634)
- NIH and the NHLBI (grant K08 HL114642)
- Topics
- Health Sciences › Medicine › Cardiology and Cardiovascular Medicine › Lipoproteins and Cardiovascular Health
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Genetics › Genetic Associations and Epidemiology
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Genetics › Nutrition, Genetics, and Disease
- Keywords
- coronary artery disease, polygenic risk score, genetic variants, statin therapy, primary prevention, secondary prevention, number needed to treat, risk stratification, JUPITER trial, ASCOT trial, CARE trial, PROVE IT-TIMI 22, recurrent cardiovascular events, Malmö Diet and Cancer Study, cardiovascular risk prediction, treatment benefit stratification, LDL cholesterol lowering
- MeSH terms
- Coronary Disease, Humans, Primary Prevention, Recurrence, Treatment Outcome, Risk Assessment, Hydroxymethylglutaryl-CoA Reductase Inhibitors, Secondary Prevention, Numbers Needed To Treat
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