Title |
Are Myocardial Infarction–Associated Single-Nucleotide Polymorphisms Associated With Ischemic Stroke?
|
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Published in |
Stroke, February 2012
|
DOI | 10.1161/strokeaha.111.632075 |
Pubmed ID | |
Authors |
Yu-Ching Cheng, Christopher D. Anderson, Silvia Bione, Keith Keene, Jane M. Maguire, Michael Nalls, Asif Rasheed, Marion Zeginigg, John Attia, Ross Baker, Simona Barlera, Alessandro Biffi, Ebony Bookman, Thomas G. Brott, Robert D. Brown, Fang Chen, Wei-Min Chen, Emilio Ciusani, John W. Cole, Lynelle Cortellini, John Danesh, Kimberly Doheny, Luigi Ferrucci, Maria Grazia Franzosi, Philippe Frossard, Karen L. Furie, Jonathan Golledge, Graeme J. Hankey, Dena Hernandez, Elizabeth G. Holliday, Fang-Chi Hsu, Jim Jannes, Ayeesha Kamal, Muhammad Saleem Khan, Steven J. Kittner, Simon A. Koblar, Martin Lewis, Lisa Lincz, Antonella Lisa, Mar Matarin, Pablo Moscato, Josyf C. Mychaleckyj, Eugenio A. Parati, Silvia Parolo, Elizabeth Pugh, Natalia S. Rost, Michael Schallert, Helena Schmidt, Rodney J. Scott, Jonathan W. Sturm, Sunaina Yadav, Moazzam Zaidi, Giorgio B. Boncoraglio, Christopher Royce Levi, James F. Meschia, Jonathan Rosand, Michele Sale, Danish Saleheen, Reinhold Schmidt, Pankaj Sharma, Bradford Worrall, Braxton D. Mitchell |
Abstract |
Ischemic stroke (IS) shares many common risk factors with coronary artery disease (CAD). We hypothesized that genetic variants associated with myocardial infarction (MI) or CAD may be similarly involved in the etiology of IS. To test this hypothesis, we evaluated whether single-nucleotide polymorphisms (SNPs) at 11 different loci recently associated with MI or CAD through genome-wide association studies were associated with IS. |
X Demographics
Geographical breakdown
Country | Count | As % |
---|---|---|
United States | 1 | 100% |
Demographic breakdown
Type | Count | As % |
---|---|---|
Practitioners (doctors, other healthcare professionals) | 1 | 100% |
Mendeley readers
Geographical breakdown
Country | Count | As % |
---|---|---|
United States | 1 | 2% |
Italy | 1 | 2% |
Australia | 1 | 2% |
Unknown | 54 | 95% |
Demographic breakdown
Readers by professional status | Count | As % |
---|---|---|
Researcher | 9 | 16% |
Student > Ph. D. Student | 7 | 12% |
Professor > Associate Professor | 5 | 9% |
Student > Master | 5 | 9% |
Student > Bachelor | 4 | 7% |
Other | 16 | 28% |
Unknown | 11 | 19% |
Readers by discipline | Count | As % |
---|---|---|
Medicine and Dentistry | 22 | 39% |
Biochemistry, Genetics and Molecular Biology | 8 | 14% |
Agricultural and Biological Sciences | 5 | 9% |
Nursing and Health Professions | 2 | 4% |
Computer Science | 2 | 4% |
Other | 5 | 9% |
Unknown | 13 | 23% |