Amit V. Khera
- Genetics top 0.2%
- Cardiology and Cardiovascular Medicine top 0.5%
- Molecular Biology top 5%
- Surgery top 1%
- Epidemiology top 2%
- Co-authors
- Sekar KathiresanConnor A. EmdinPatrick T. EllinorPradeep NatarajanKrishna G. AragamMary E. HaasMark ChaffinEric S. Lander
- Topics
- Genetic Associations and Epidemiology (32 papers)Lipoproteins and Cardiovascular Health (22 papers)Cardiovascular Disease and Adiposity (12 papers)
- Partner nations
- United StatesSouth KoreaUnited Kingdom
In The Last Decade
Amit V. Khera
95 papers receiving 11.3k citations
Hit Papers
Peers
Comparison fields: 5 of 181
- Genetics 3.4k
- Cardiology and Cardiovascular Medicine 3.3k
- Molecular Biology 2.3k
- Surgery 2.2k
- Epidemiology 1.6k
Countries citing papers authored by Amit V. Khera
This map shows the geographic impact of Amit V. Khera's research. It shows the number of citations coming from papers published by authors working in each country. You can also color the map by specialization and compare the number of citations received by Amit V. Khera with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Amit V. Khera more than expected).
Fields of papers citing papers by Amit V. Khera
This network shows the impact of papers produced by Amit V. Khera. Nodes represent research fields, and links connect fields that are likely to share authors. Colored nodes show fields that tend to cite the papers produced by Amit V. Khera. The network helps show where Amit V. Khera may publish in the future.
Co-authorship network of co-authors of Amit V. Khera
This figure shows the co-authorship network connecting the top 25 collaborators of Amit V. Khera. A scholar is included among the top collaborators of Amit V. Khera based on the total number of citations received by their joint publications. Widths of edges represent the number of papers authors have co-authored together. Node borders signify the number of papers an author published with Amit V. Khera. Amit V. Khera is excluded from the visualization to improve readability, since they are connected to all nodes in the network.
All Works
| # | Work | Indexed citations |
|---|---|---|
| 1 | 0 | |
| 2 | 0 | |
| 3 | 1 | |
| 4 | 22 | |
| 5 | 7 | |
| 6 | 5 | |
| 7 | 8 | |
| 8 | 75 | |
| 9 | 5 | |
| 10 | Association between adiposity and cardiovascular outcomes: an umbrella review and meta-analysis of observational and Mendelian randomization studiesbreakdown → | 210 |
| 11 | 55 | |
| 12 | 29 | |
| 13 | 101 | |
| 14 | 9 | |
| 15 | 129 | |
| 16 | Lp(a) (Lipoprotein[a]) Concentrations and Incident Atherosclerotic Cardiovascular Diseasebreakdown → | 295 |
| 17 | 27 | |
| 18 | Genome-wide polygenic scores for common diseases identify individuals with risk equivalent to monogenic mutationsbreakdown → | 1631 |
| 19 | 1 | |
| 20 | 150 |
About Amit V. Khera
Amit V. Khera is a scholar working on Cardiology and Cardiovascular Medicine, Genetics and Endocrinology, Diabetes and Metabolism, having authored 104 papers that have together received 11.5k indexed citations. Recurring topics across this work include Genetic Associations and Epidemiology (32 papers), Lipoproteins and Cardiovascular Health (22 papers) and Cardiovascular Disease and Adiposity (12 papers). The work is most often cited by research in Cardiology and Cardiovascular Medicine (3.3k citations), Genetics (3.4k citations) and Endocrinology, Diabetes and Metabolism (1.3k citations). Amit V. Khera has collaborated with scholars based in United States, South Korea and United Kingdom. Frequent co-authors include Sekar Kathiresan, Connor A. Emdin, Sekar Kathiresan, Patrick T. Ellinor, Pradeep Natarajan, Krishna G. Aragam, Mary E. Haas, Mark Chaffin, Eric S. Lander and Steven A. Lubitz. Their work appears in journals such as New England Journal of Medicine, Cell and The Lancet.
Rankless uses publication and citation data sourced from OpenAlex, an open and comprehensive bibliographic database. While OpenAlex provides broad and valuable coverage of the global research landscape, it—like all bibliographic datasets—has inherent limitations. These include incomplete records, variations in author disambiguation, differences in journal indexing, and delays in data updates. As a result, some metrics and network relationships displayed in Rankless may not fully capture the entirety of a scholar's output or impact.