Saaket Agrawal

1.6k total citations · 1 hit paper
13 papers, 308 citations indexed

About

Saaket Agrawal is a scholar working on Cardiology and Cardiovascular Medicine, Epidemiology and Physiology. According to data from OpenAlex, Saaket Agrawal has authored 13 papers receiving a total of 308 indexed citations (citations by other indexed papers that have themselves been cited), including 6 papers in Cardiology and Cardiovascular Medicine, 5 papers in Epidemiology and 3 papers in Physiology. Recurrent topics in Saaket Agrawal's work include Cardiovascular Disease and Adiposity (5 papers), Genetic Associations and Epidemiology (3 papers) and Adipose Tissue and Metabolism (3 papers). Saaket Agrawal is often cited by papers focused on Cardiovascular Disease and Adiposity (5 papers), Genetic Associations and Epidemiology (3 papers) and Adipose Tissue and Metabolism (3 papers). Saaket Agrawal collaborates with scholars based in United States, United Kingdom and China. Saaket Agrawal's co-authors include Amit V. Khera, Patrick T. Ellinor, Anthony Philippakis, Kenney Ng, Puneet Batra, Marcus D. R. Klarqvist, Minxian Wang, Nathaniel Diamant, Melina Claussnitzer and Aniruddh P. Patel and has published in prestigious journals such as Circulation, Nature Medicine and Nature Communications.

In The Last Decade

Saaket Agrawal

12 papers receiving 306 citations

Hit Papers

align trajectories

What are hit papers?

Hit papers significantly outperform the citation benchmark for their cohort. A paper qualifies if it has ≥500 total citations, achieves ≥1.5× the top-1% citation threshold for papers in the same subfield and year (this is the minimum needed to enter the top 1%, not the average within it), or reaches the top citation threshold in at least one of its specific research topics.

A multi-ancestry polygenic risk score improves risk prediction for coronary artery disease

2023 98 citations Aniruddh P. Patel, Minxian Wang et al. Nature Medicine profile →

Peers

Saaket Agrawal

CCM

EPIDE

GENET

PHYSI

EDM

Noriko Takebe Japan

Maen D. Abou Ziki United States

Beth S. Sutton United States

Tonia de las Heras Gala Germany

Nirja Kaka India

Kofi Agyenim‐Boateng Ghana

Genevieve M Leyden United Kingdom

Lorenzo Salgado-Goytia Mexico

Tianzhichao Hou China

Won Seon Jeon South Korea

Noriko Takebe Japan

Saaket Agrawal

75 ×0.8

CCM

103 ×1.1

EPIDE

41 ×0.4

GENET

63 ×0.9

PHYSI

120 ×2.4

EDM

Citations per year, relative to Saaket Agrawal Saaket Agrawal (= 1×) peers Noriko Takebe

Countries citing papers authored by Saaket Agrawal

Since Specialization

Citations

This map shows the geographic impact of Saaket Agrawal'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 Saaket Agrawal with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Saaket Agrawal more than expected).

Fields of papers citing papers by Saaket Agrawal

Since Specialization

Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Saaket Agrawal. 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 Saaket Agrawal. The network helps show where Saaket Agrawal may publish in the future.

Co-authorship network of co-authors of Saaket Agrawal

This figure shows the co-authorship network connecting the top 25 collaborators of Saaket Agrawal. A scholar is included among the top collaborators of Saaket Agrawal 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 Saaket Agrawal. Saaket Agrawal is excluded from the visualization to improve readability, since they are connected to all nodes in the network.

All Works

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13 of 13 papers shown

1.

Agrawal, Saaket, Egbert Madamba, Ricki Bettencourt, et al.. (2024). The impact of genetic risk on the prevalence of advanced fibrosis and cirrhosis in prospectively assessed patients with type 2 diabetes. Alimentary Pharmacology & Therapeutics. 60(3). 369–377. 12 indexed citations

2.

Santoiemma, Phillip P., Susan E. Cohn, Samuel W. M. Gatesy, et al.. (2024). The global population stru cture of Lacticaseibacillus rhamnosus and its application to an investigation of a rare case of infective endocarditis. PLoS ONE. 19(8). e0300843–e0300843.

3.

Agrawal, Saaket, Jian’an Luan, Beryl B. Cummings, et al.. (2024). Relationship of Fat Mass Ratio, a Biomarker for Lipodystrophy, With Cardiometabolic Traits. Diabetes. 73(7). 1099–1111. 10 indexed citations

4.

Agrawal, Saaket, Marcus D. R. Klarqvist, Nathaniel Diamant, et al.. (2023). BMI-adjusted adipose tissue volumes exhibit depot-specific and divergent associations with cardiometabolic diseases. Nature Communications. 14(1). 266–266. 43 indexed citations

5.

Patel, Aniruddh P., Minxian Wang, Yunfeng Ruan, et al.. (2023). A multi-ancestry polygenic risk score improves risk prediction for coronary artery disease. Nature Medicine. 29(7). 1793–1803. 98 indexed citations breakdown →

6.

Huang, Daniel Q., Saaket Agrawal, Amit V. Khera, et al.. (2023). Genomic analysis of lean individuals with NAFLD identifies monogenic disorders in a prospective cohort study. JHEP Reports. 5(4). 100692–100692. 15 indexed citations

7.

Klarqvist, Marcus D. R., Saaket Agrawal, Nathaniel Diamant, et al.. (2022). Silhouette images enable estimation of body fat distribution and associated cardiometabolic risk. npj Digital Medicine. 5(1). 105–105. 8 indexed citations

8.

Agrawal, Saaket, Minxian Wang, Marcus D. R. Klarqvist, et al.. (2022). Inherited basis of visceral, abdominal subcutaneous and gluteofemoral fat depots. Nature Communications. 13(1). 3771–3771. 75 indexed citations

9.

Agrawal, Saaket, Marcus D. R. Klarqvist, Connor A. Emdin, et al.. (2021). Selection of 51 predictors from 13,782 candidate multimodal features using machine learning improves coronary artery disease prediction. Patterns. 2(12). 100364–100364. 29 indexed citations

10.

Agrawal, Saaket, Marcus D. R. Klarqvist, Nathaniel Diamant, et al.. (2021). Abstract 12760: Association of Machine Learning-Derived Measures of Body Fat Distribution in >40,000 Individuals With Cardiometabolic Diseases. Circulation. 1 indexed citations

11.

Agrawal, Saaket, Marcus D. R. Klarqvist, Nathaniel Diamant, et al.. (2021). Abstract 12760: Association of Machine Learning-Derived Measures of Body Fat Distribution in >40,000 Individuals With Cardiometabolic Diseases. Circulation. 144(Suppl_1). 1 indexed citations

12.

Agrawal, Saaket, Tatiana Abramova, Minoli A. Perera, et al.. (2020). Impact of CYP2C9‐Interacting Drugs on Warfarin Pharmacogenomics. Clinical and Translational Science. 13(5). 941–949. 12 indexed citations

13.

Agrawal, Saaket, et al.. (2020). Fatal Lactobacillus endocarditis in a patient with transcatheter aortic valve replacement. BMJ Case Reports. 13(11). e236835–e236835. 4 indexed citations

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.

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