Application of Mendelian randomization in cardiovascular disease: Bibliometric analysis and visualization from 2003 to 2024

孟德尔随机化 可视化 疾病 计算机科学 计算生物学 医学 生物 数据挖掘 内科学 遗传学 遗传变异 基因 基因型
作者
Sitong Guo,Dandan Xu,S. Qin,Chunxia Chen,Xiaoyu Chen
出处
期刊:Cardiology [Karger Publishers]
卷期号:: 1-24
标识
DOI:10.1159/000545277
摘要

Introduction: Mendelian randomization (MR) is an innovative epidemiological research method. In order to summarize and clarify the research status of MR related to cardiovascular disease (CVD), and point out the possible future development direction, we conducted a comprehensive and multi-dimensional bibliometric analysis of the literature published in this field from 2003 to 2024. Methods: We analyzed 1,870 articles published between 2003 and 2024 from the Web of Science Core Collection (WoSCC) using VOSviewer, R software, bibliometric online analysis tool and CiteSpace software. Results: CVD‐related MR research demonstrated an overall upward trend, with the United States leading in terms of publication output, followed by the United Kingdom and China. The most prolific institution in this field was the University of Bristol, and Smith GD, who had the highest number of publications (n = 103), was also affiliated with this institution. The European Heart Journal (36 publications, 5,023 citations) was the most cited journal. Related topics of frontiers will still focus on mendelian randomization, coronary heart disease, heart failure, c-reactive protein, cholesterol and body mass index. Conclusions: As the scope of MR studies continues to expand, especially the number of measurable features continues to increase, the need for rigorous methods and critical interpretation of MR findings becomes increasingly apparent. However, this ease of use can compromise the reliability of study results due to methodological flaws and publication bias, thereby affecting the perceived significance of the results. Nonetheless, with the emergence of large genetic datasets supporting two-sample MR, resources such as MR-Base and PhenoScanner, MR remains a powerful method for identifying potential pathogenic features in cardiometabolic and other diseases. In addition, it plays a crucial role in prioritizing drug targets for entry into clinical trials.

科研通智能强力驱动
Strongly Powered by AbleSci AI
科研通是完全免费的文献互助平台,具备全网最快的应助速度,最高的求助完成率。 对每一个文献求助,科研通都将尽心尽力,给求助人一个满意的交代。
实时播报
QIAO完成签到 ,获得积分20
刚刚
1秒前
小张完成签到,获得积分20
1秒前
守一倾风月完成签到,获得积分10
1秒前
wyy发布了新的文献求助10
2秒前
2秒前
3秒前
3秒前
要努力鸭发布了新的文献求助10
5秒前
香蕉觅云应助考尔菲德采纳,获得10
6秒前
水母发布了新的文献求助10
6秒前
打打应助守一倾风月采纳,获得10
6秒前
6秒前
ddsvdv发布了新的文献求助10
6秒前
7秒前
Lucas应助wyy采纳,获得10
7秒前
buer发布了新的文献求助10
8秒前
科研通AI6.2应助王彬采纳,获得10
8秒前
铁骨完成签到 ,获得积分10
8秒前
喵喵不二发布了新的文献求助10
9秒前
11秒前
无花果应助呆萌致远采纳,获得10
11秒前
moon发布了新的文献求助10
11秒前
11秒前
温酒会雨生完成签到,获得积分10
12秒前
12秒前
英姑应助M87采纳,获得10
12秒前
111完成签到,获得积分10
13秒前
ddsvdv完成签到,获得积分10
14秒前
14秒前
14秒前
MozzieMiao应助肥胖的红薯采纳,获得30
14秒前
碳土不凡完成签到 ,获得积分10
16秒前
老王完成签到,获得积分10
16秒前
californium252完成签到,获得积分10
16秒前
希望天下0贩的0应助huhu采纳,获得30
16秒前
南城发布了新的文献求助10
16秒前
东方元语应助狂野的慕青采纳,获得20
16秒前
思源应助一一采纳,获得10
17秒前
17秒前
高分求助中
(应助此贴封号)【重要!!请各用户(尤其是新用户)详细阅读】【科研通的精品贴汇总】 10000
An Introduction to Foreign Language Learning and Teaching 750
China Pluperfect I: Epistemology of Past and Outside in Chinese Art 520
Matrix Methods in Data Mining and Pattern Recognition Second Edition 510
Cosmos as Art Object: Studies in Plato's Timaeus and Other Dialogues 500
What is the Future of Psychotherapy in Digital Age? Technology, AI Bots, and Psychotherapy after Covid 444
煤炭地下气化渗流燃烧方法的研究 400
热门求助领域 (近24小时)
化学 材料科学 医学 生物 纳米技术 工程类 有机化学 化学工程 生物化学 计算机科学 内科学 物理 复合材料 催化作用 细胞生物学 无机化学 光电子学 物理化学 电极 基因
热门帖子
关注 科研通微信公众号,转发送积分 7631887
求助须知:如何正确求助?哪些是违规求助? 9206276
关于积分的说明 19744090
捐赠科研通 7201183
什么是DOI,文献DOI怎么找? 3274710
关于科研通互助平台的介绍 2436577
邀请新用户注册赠送积分活动 2271320