The DOI matches its Crossref record; 85 of the 86 references it cites were found in a registry or an index.
Cardiovascular Disease
Open accessAbstract
ardiovascular disease, including stroke, is the leading cause of illness and death in the United States.There are an estimated 62 million people with cardiovascular disease and 50 million people with hypertension in this country.1 In 2000, approximately 946,000 deaths were attributable to cardiovascular disease, accounting for 39 percent of all deaths in the United States. 2 Epidemiologic studies and randomized clinical trials have provided compelling evidence that coronary heart disease is largely preventable.3 However, there is also reason to believe that there is a heritable component to the disease.In this review, I highlight what we know now about genetic factors in cardiovascular disease.As future genomic discoveries are translated to the care of patients with cardiovascular disease, it is likely that what we can do will change.c lessons learned from monogenic cardiovascular disorders
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 cited source says what the claim attributes to it.
In the citing paper
“High levels of low density lipoprotein cholesterol are a major risk factor for the development of arteriosclerosis and death from cardiovascular causes (41).”
From Expression of miR-33 from an SREBP2 Intron Inhibits Cholesterol Export and Fatty Acid Oxidation* (2010) · cites this paper as [41]
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
The ALLHAT Officers and Coordinators for the ALLHAT Collaborative Research Group (2002) · JAMA: The Journal of the American Medical Association · doi:10.1001/jama.288.23.2981 · Cited by 5,664
Sanguinetti, M. C., Jiang, C., Curran, M. E., & Keating, M. T. (1995) · Cell · doi:10.1016/0092-8674(95)90340-2 · Cited by 2,422
Prevalence of Hypertrophic Cardiomyopathy in a General Population of Young Adults
Maron, B. J., Gardin, J. M., Flack, J. M., Gidding, S. S., Kurosaki, T., & Bild, D. E. (1995) · Circulation · doi:10.1161/01.cir.92.4.785 · Cited by 2,274
Genetic basis and molecular mechanism for idiopathic ventricular fibrillation
Chen, Q., Kirsch, G. E., Zhang, D., Brugada, R., Brugada, J., Brugada, P., Potenza, D. R., Moya‐Mitjans, À., Borggrefe, M., BREITHARDT, G. E., et al. (1998) · Nature · doi:10.1038/32675 · Cited by 1,823
Pharmacogenomics — Drug Disposition, Drug Targets, and Side Effects
Evans, W. E., & McLeod, H. L. (2003) · New England Journal of Medicine · doi:10.1056/nejmra020526 · Cited by 1,778
Coassembly of KVLQT1 and minK (IsK) proteins to form cardiac IKS potassium channel
Sanguinetti, M. C., Curran, M. E., Zou, A., Shen, J., Specter, P. S., Atkinson, D. L., & Keating, M. T. (1996) · Nature · doi:10.1038/384080a0 · Cited by 1,765
Molecular Mechanisms of Human Hypertension
Lifton, R. P., Gharavi, A. G., & Geller, D. S. (2001) · Cell · doi:10.1016/s0092-8674(01)00241-0 · Cited by 1,686
SCN5A mutations associated with an inherited cardiac arrhythmia, long QT syndrome
Wang, Q. K., Shen, J., Splawski, I., Atkinson, D. L., Li, Z., Robinson, J. L., Moss, A. J., Towbin, J. A., & Keating, M. T. (1995) · Cell · doi:10.1016/0092-8674(95)90359-3 · Cited by 1,682
Berge, K. E., Tian, H., Graf, G. A., Yu, L., Grishin, N. V., Schultz, J. R., Kwiterovich, P. O., Shan, B., Barnes, R., & Hobbs, H. H. (2000) · Science · doi:10.1126/science.290.5497.1771 · Cited by 1,645
KvLQT1 and IsK (minK) proteins associate to form the IKS cardiac potassium current
Barhanin, J., Lesage, F., Guillemare, E., Fink, M., Lazdunski, M., & Romey, G. (1996) · Nature · doi:10.1038/384078a0 · Cited by 1,613
Research works printed but not linked: 3
- Goldstein JL, Hobbs HH, Brown MS. Familial hypercholesterolemia. In: Scriver CR, Beaudet AL, Sly WS, Valle D, eds. The metabolic & molecular bases of inherited disease. 8th ed. Vol. 2. New York: McGraw-Hill, 2001:2863-913.
- Kane JP, Havel RJ. Disorders of the biogenesis and secretion of lipoproteins containing the B apolipoproteins. In: Scriver CR, Beaudet AL, Sly WS, Valle D, eds. The metabolic & molecular bases of inherited disease. 8th ed. Vol. 2. New York: McGraw-Hill, 2001:2717-52.
- Seidman CE, Seidman JG. Hypertrophic cardiomyopathy. In: Scriver CR, Beaudet AL, Sly WS, Valle D, eds. The metabolic & molecular bases of inherited disease. 8th ed. Vol. 4. New York: McGraw-Hill, 2001:5433-50.
Cited by
Sorted by citations
Items 401–432 of 432
Soriano-Machorro, R. N., Sánchez-Cervantes, J. L., Rodríguez-Mazahua, L., & Guarneros-Nolasco, L. R. (2023) · Studies in big data · doi:10.1007/978-3-031-40688-1_12
Development of Cardiovascular Biomaterials From Collagenous Tissues
Baradwaj, G., Aherkar, K., Mythreyi, R., Gopenath, T. S., & Basalingappa, K. M. (2023) · Engineering materials · doi:10.1007/978-981-99-6698-1_17
Jan, H., & Tang, C. (2023) · Research Square · doi:10.21203/rs.3.rs-3708860/v1
Digitalis Purpurea: Hope for Myocardial Infarction Induced by Obesity - A Review
Passi, K., Kumar, R., Khurana, D., & Dhanda, N. (2023) · Current Indian Science · doi:10.2174/012210299x261528231116134608
HeartBeatNet: Unleashing the Power of Attention in Cardiology
Singh, G., Mehta, A., & Arora, V. (2023) · Communications in computer and information science · doi:10.1007/978-3-031-48984-6_2
Three-dimensional printing of grafts and implants
Dasgupta, N., Singh, V., Ranjan, S., Mishra, T., & Mishra, B. N. (2024) · Elsevier eBooks · doi:10.1016/b978-0-12-820301-9.00010-0
Exercise or Extra Fries? Behavioral Drivers of Obesity Over the Life CyclePreprint
Bairoliya, N., Miller, R., & Nygaard, V. M. (2024) · SSRN Electronic Journal · doi:10.2139/ssrn.4730783
Unnisa, S. M., Seereen, S., Lekhana, J., Archana, G., Kumar, A. H., Nikhil, S. M. S., & Chinnaeswraiah, M. (2024) · International Journal of Pharmaceutical Investigation · doi:10.5530/ijpi.14.2.46
A continuous hemodynamic parameters algorithm based on the wearable tonometric device
Fang, J., Zhou, S., Miao, Y., Du, C., Ding, B., Deng, M., & Pan, T. (2024) · IEEE International Conference on Micro/Nano Sensors for AI, Healthcare, and Robotics (NSENS) · doi:10.1109/nsens62142.2024.10561394
Murkey, J. A., Gaston, S. A., Alhasan, D. M., Jackson, W. B., & Jackson, C. L. (2024) · BMJ Public Health · doi:10.1136/bmjph-2023-000726
Internet of Medical Things: A revolution in cardiovascular disease Prevalence and Diagnosis
Mehta, S., Reddy, N. A. S., Sharma, A., Mahajan, A., & Sharma, A. (2023) · International Conference on Intelligent Computing, Communication & Convergence (ICI3C) · doi:10.1109/ici3c60830.2023.00046 · Cited by 1
Hypertension, obesity, and the microbiome in cardiovascular disease
Gupta, A., & Vakilna, R. (2024) · Elsevier eBooks · doi:10.1016/b978-0-443-18979-1.00006-x
Özgören, Ö. (2025) · Medical Records · doi:10.37990/medr.1534407
Enhancing Cardiovascular Health Prediction: A Machine Learning Perspective
Dodda, R., Bonam, A. R., Sakinala, S., & Dendhi, P. R. (2025) · Communications in computer and information science · doi:10.1007/978-3-031-80839-5_7
Suemae, M., Chaimay, B., & Woradet, S. (2025) · Journal of Health Research · doi:10.56808/2586-940x.1121
Mathew, A. C., Joy, J., Chandy, J. A., & Lam, A. (2025) · Clinical Epidemiology and Global Health · doi:10.1016/j.cegh.2025.102080
Jamali, A. G., Ilyas, A., Ahmed, F. W., Mansoor, A., Hassan, S. Z., Singla, S., Singla, B., Kettaneh, K., Mim, R. J., Habib, M., et al. (2025) · Cureus · doi:10.7759/cureus.88395
Backhaus, S. J., Treiber, J. M., Wolter, J. S., Kriechbaum, S. D., Fischer, U., Schuster, A., Püntmann, V. O., Nagel, E. C., Sossalla, S. T., & Rolf, A. (2025) · European Heart Journal Open · doi:10.1093/ehjopen/oeaf109
Fu, W., He, H., Xu, J., Wu, P., Zhang, Q., Wei, M., Duan, L., Wang, G., Wang, L., Cao, Z., et al. (2025) · Frontiers in Cardiovascular Medicine · doi:10.3389/fcvm.2025.1643542
Analysing Heart-Brain Coupling: A Correlation Study of BCG, EEG, and ECG Signals
Wu, H., Xu, J., Lu, X., Liu, Q., Chen, J., & Qi, J. (2025) · IEEE International Conference on Bioinformatics and Biomedicine (BIBM) · doi:10.1109/bibm66473.2025.11356298
Chanet, A. (2011) · theses.fr (ABES) · doi:10.70675/c9b0983czed0bz4b1dzb975z2866bc9e5763
Li, P., Li, J., Niu, S., Timimi, F. K., Cheung, J., Otley, C. C., Makhni, S., Li, F., Feng, J., Hu, X., et al. (2026) · Journal of Biomedical Informatics · doi:10.1016/j.jbi.2026.105031
Cho, H.-M., Han, S., Seong, J., & Youn, I. (2026) · BioData Mining · doi:10.1186/s13040-026-00544-2
Clustering Performance on Heart Disease Data: Effects of Distance Metrics and Scaling
Akbas, I., Taşpınar, Y. S., & Koklu, M. (2026) · Journal of Technology and System Information · doi:10.47134/jtsi.v3i1.5336
Guan, X., Wang, H., Lai, Y., Jin, J., Li, J., Zhao, Q., Zhang, D., Geng, S., Mu, G., Wang, Y., et al. (2026) · PLOS Digital Health · doi:10.1371/journal.pdig.0001335
Translational Nanomedicine and Delivery Tools for Cardiovascular Disease Therapy and Diagnostics
Miller, M. A., & Williams, R. M. (2026) · Advances in experimental medicine and biology · doi:10.1007/978-3-032-22285-5_10
Bogari, N. M., Ekram, S. N., Amin, A. A., Alotaibi, M. S. M., Almalki, N. A. R., Amer, S., Obaid, R., & Allam, R. M. (2026) · Diagnostics · doi:10.3390/diagnostics16162489
Progress in Synthesis and Pharmacology of Telmisartan
Megha, Srivastava, R., & Singh, R. (2026) · Chemistry & Biodiversity · doi:10.1002/cbdv.71667
Ijaz, A., & Shabana (2026) · Egyptian Journal of Medical Human Genetics · doi:10.1186/s43042-026-00921-w
Application of in-silico approaches to cardiovascular disease
Du, J. (2014) · Research Publications (Maastricht University) · doi:10.26481/dis.20141008jd
Metadata
- Authors
- Nabel, E. G.
- Affiliations
- National Institutes of Health (US)
- Corresponding author
- Nabel, Elizabeth G.
- Venue
- New England Journal of Medicine
- Publisher
- Massachusetts Medical Society
- Published
- 2003-07-03
- Volume
- 349
- Issue
- 1
- Pages
- 60-72
- ISSN
- 0028-4793 (ISSN-L)
- 1533-4406
- OpenAlex
- W4245959283
- Cited by
- 431
- References
- 86
- Citations by year
- 2026: 11 · 2025: 22 · 2024: 45 · 2023: 37 · 2022: 26 · 2021: 20 · 2020: 9 · 2019: 9 · 2018: 14 · 2017: 10 · 2016: 17 · 2015: 15 · 2014: 19 · 2013: 14 · 2012: 14
- FWCI
- 2.11
- Type
- journal article
- Language
- en
- Open access
- bronze
- doi.org
- Locations
- New England Journal of Medicine (publishedVersion)
- PubMed (publishedVersion)
- Topics
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Genetics › Genetic Associations and Epidemiology
- Health Sciences › Medicine › Cardiology and Cardiovascular Medicine › Cardiovascular Health and Risk Factors
- Health Sciences › Medicine › Cardiology and Cardiovascular Medicine › Cardiac electrophysiology and arrhythmias
- Keywords
- cardiovascular disease, genetic factors, heritability, coronary artery disease, hypertension, stroke, cardiovascular mortality, cardiovascular disease prevention, genomics
- MeSH terms
- Arrhythmias, Cardiac, Cardiovascular Diseases, Coronary Disease, Genetic Testing, Humans, Hypercholesterolemia, Hypertension, Cholesterol, LDL, Mutation, Cardiomyopathies, Polymorphism, Genetic, Thrombosis, Gene Expression Profiling
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
