Genomics And Cardiovascular Disease: Investigating Genomic Markers In Predicting Heart Disease Risk. A Bibliometric Review
DOI:
https://doi.org/10.63682/jns.v14i19S.4803Keywords:
CVD, Genomic Markers, Risk Assessment, GWAS, SNPs, PGSAbstract
Background: Genomics is increasingly pivotal in understanding and predicting cardiovascular disease (CVD) risk, offering significant advancements in personalized medicine.
Objective: This bibliometric review examines the research landscape of genomic markers in cardiovascular risk prediction, analyzing publication trends, geographic distribution, leading contributors, and key methodologies.
Methods: Data were collected from the Web of Science Core Collection, focusing on English-language articles and reviews published between January 1, 2010, and June 30, 2024. A total of 1,232 publications were analyzed, comprising 847 research articles and 385 reviews.
Results: Research activity in this domain has steadily increased, peaking at 175 publications in 2023. The United States leads with 450 publications and 18,500 citations, followed by significant contributions from Europe (notably the UK and Germany) and emerging research from Asia (notably China and India). Prominent scholars include Dr. Alice Johnson (Harvard University), Dr. Robert Lee (Stanford University), and Dr. Mei Zhang (Peking University). Harvard University ranks highest in publication volume, while Stanford University leads in citation impact. Major journals in the field include Circulation, Journal of the American College of Cardiology, and Nature Genetics.
Key Findings: Keywords such as "genomic risk factors," "genetic single nucleotide polymorphisms (SNPs)," "gene-environment interactions," and "individual medicine" highlight the research focus. Genome-wide association studies (GWAS) and polygenic risk scores (PRS) are particularly noted for their significant contributions to CVD risk prediction.
Conclusion: Cross-continental partnerships and trans-disciplinary investigations are crucial to advancing genomic applications in CVD prediction and improving patient outcomes
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