Jay J. Kuo

2.0k total citations
18 papers, 1.3k citations indexed

About

Jay J. Kuo is a scholar working on Nutrition and Dietetics, Endocrine and Autonomic Systems and Physiology. According to data from OpenAlex, Jay J. Kuo has authored 18 papers receiving a total of 1.3k indexed citations (citations by other indexed papers that have themselves been cited), including 10 papers in Nutrition and Dietetics, 9 papers in Endocrine and Autonomic Systems and 8 papers in Physiology. Recurrent topics in Jay J. Kuo's work include Regulation of Appetite and Obesity (9 papers), Biochemical Analysis and Sensing Techniques (9 papers) and Dietary Effects on Health (5 papers). Jay J. Kuo is often cited by papers focused on Regulation of Appetite and Obesity (9 papers), Biochemical Analysis and Sensing Techniques (9 papers) and Dietary Effects on Health (5 papers). Jay J. Kuo collaborates with scholars based in United States, Germany and Brazil. Jay J. Kuo's co-authors include John E. Hall, Alexandre A. da Silva, Lakshmi S. Tallam, Jiankang Liu, Jeffrey R. Henegar, Terry Dwyer, Rogério Baumgratz de Paula, M BRANDS, Sharyn M. Fitzgerald and Drew A. Hildebrandt and has published in prestigious journals such as Journal of Clinical Investigation, Nature Communications and The FASEB Journal.

In The Last Decade

Jay J. Kuo

18 papers receiving 1.3k citations

Peers — A (Enhanced Table)

Peers by citation overlap · career bar shows stage (early→late) cites · hero ref

Name h Career Trend Papers Cites
Jay J. Kuo United States 16 526 400 382 321 281 18 1.3k
Lakshmi S. Tallam United States 15 399 0.8× 352 0.9× 346 0.9× 264 0.8× 249 0.9× 19 1.1k
J. Fuller United States 11 478 0.9× 418 1.0× 280 0.7× 116 0.4× 498 1.8× 13 1.3k
Angela Doering Germany 17 108 0.2× 267 0.7× 118 0.3× 432 1.3× 183 0.7× 25 1.4k
Jaimar C. Rincon Venezuela 17 67 0.1× 155 0.4× 259 0.7× 204 0.6× 153 0.5× 43 1.1k
Helene Nørrelund Denmark 27 276 0.5× 754 1.9× 134 0.4× 479 1.5× 140 0.5× 67 2.0k
Ralf Nass United States 20 585 1.1× 655 1.6× 348 0.9× 108 0.3× 106 0.4× 45 1.5k
K G Alberti United Kingdom 15 164 0.3× 431 1.1× 65 0.2× 315 1.0× 213 0.8× 19 1.5k
F. Javier Salazar Spain 23 84 0.2× 633 1.6× 124 0.3× 607 1.9× 57 0.2× 86 1.7k
Petra Schling Germany 10 97 0.2× 405 1.0× 78 0.2× 576 1.8× 286 1.0× 13 1.2k
Jonathan Blau United States 10 43 0.1× 289 0.7× 324 0.8× 333 1.0× 127 0.5× 21 1.1k

Countries citing papers authored by Jay J. Kuo

Since Specialization
Citations

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

Fields of papers citing papers by Jay J. Kuo

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Jay J. Kuo. 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 Jay J. Kuo. The network helps show where Jay J. Kuo may publish in the future.

Co-authorship network of co-authors of Jay J. Kuo

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

All Works

18 of 18 papers shown
1.
Sullivan, Katie, Dhanunjay Mukhi, Magaiver Andrade-Silva, et al.. (2025). Glutathione-specific gamma–glutamylcyclotransferase 1 ( CHAC1 ) increases kidney disease risk by modulating ferroptosis. Science Translational Medicine. 17(795). eadn3079–eadn3079. 3 indexed citations
2.
Muto, Yoshiharu, Eryn E. Dixon, Yasuhiro Yoshimura, et al.. (2022). Defining cellular complexity in human autosomal dominant polycystic kidney disease by multimodal single cell analysis. Nature Communications. 13(1). 6497–6497. 52 indexed citations
3.
Wu, Junnan, Archana Raman, Nathan J. Coffey, et al.. (2021). The key role of NLRP3 and STING in APOL1-associated podocytopathy. Journal of Clinical Investigation. 131(20). 94 indexed citations
4.
Kuo, Jay J., et al.. (2015). Renal Structure‐Function Relationship in the Obese ZSF1 Rat Model of Diabetic Nephropathy. The FASEB Journal. 29(S1). 1 indexed citations
5.
Kim, Enoch, A. Stewart Campbell, Olivier Schueller, et al.. (2011). A Small-Molecule Inhibitor of Enterocytic Microsomal Triglyceride Transfer Protein, SLx-4090: Biochemical, Pharmacodynamic, Pharmacokinetic, and Safety Profile. Journal of Pharmacology and Experimental Therapeutics. 337(3). 775–785. 34 indexed citations
6.
Silva, Alexandre A. da, Jay J. Kuo, Lakshmi S. Tallam, Jiankang Liu, & John E. Hall. (2005). Does Obesity Induce Resistance to the Long-Term Cardiovascular and Metabolic Actions of Melanocortin 3/4 Receptor Activation?. Hypertension. 47(2). 259–264. 24 indexed citations
7.
Hall, John E., Jeffrey R. Henegar, Terry Dwyer, et al.. (2004). Is obesity a major cause of chronic kidney disease?. Advances in Renal Replacement Therapy. 11(1). 41–54. 185 indexed citations
8.
Kuo, Jay J., Alexandre A. da Silva, Lakshmi S. Tallam, & John E. Hall. (2004). Role of Adrenergic Activity in Pressor Responses to Chronic Melanocortin Receptor Activation. Hypertension. 43(2). 370–375. 58 indexed citations
9.
Tallam, Lakshmi S., Jay J. Kuo, Alexandre A. da Silva, & John E. Hall. (2004). Cardiovascular, Renal, and Metabolic Responses to Chronic Central Administration of Agouti-Related Peptide. Hypertension. 44(6). 853–858. 15 indexed citations
10.
Silva, Alexandre A. da, Jay J. Kuo, Lakshmi S. Tallam, & John E. Hall. (2004). Role of Endothelin-1 in Blood Pressure Regulation in a Rat Model of Visceral Obesity and Hypertension. Hypertension. 43(2). 383–387. 36 indexed citations
11.
Silva, Alexandre A. da, Jay J. Kuo, & John E. Hall. (2004). Role of Hypothalamic Melanocortin 3/4-Receptors in Mediating Chronic Cardiovascular, Renal, and Metabolic Actions of Leptin. Hypertension. 43(6). 1312–1317. 91 indexed citations
12.
Kuo, Jay J., Alexandre A. da Silva, & John E. Hall. (2003). Hypothalamic Melanocortin Receptors and Chronic Regulation of Arterial Pressure and Renal Function. Hypertension. 41(3). 768–774. 89 indexed citations
13.
Hall, John E., Daniel W. Jones, Jay J. Kuo, et al.. (2003). Impact of the obesity epidemic on hypertension and renal disease. Current Hypertension Reports. 5(5). 386–392. 80 indexed citations
14.
Hall, John E., Jay J. Kuo, Alexandre A. da Silva, et al.. (2003). Obesity-associated hypertension and kidney disease. Current Opinion in Nephrology & Hypertension. 12(2). 195–200. 136 indexed citations
15.
Kuo, Jay J., et al.. (2003). Chronic cardiovascular and renal actions of leptin during hyperinsulinemia. American Journal of Physiology-Regulatory, Integrative and Comparative Physiology. 284(4). R1037–R1042. 15 indexed citations
16.
Kuo, Jay J., et al.. (2002). Chronic Cardiovascular and Renal Actions of Leptin. Hypertension. 39(2). 496–501. 232 indexed citations
17.
Kuo, Jay J., et al.. (2001). Inhibition of NO Synthesis Enhances Chronic Cardiovascular and Renal Actions of Leptin. Hypertension. 37(2). 670–676. 88 indexed citations
18.
Hall, John E., M BRANDS, Drew A. Hildebrandt, Jay J. Kuo, & Sharyn M. Fitzgerald. (2000). Role of sympathetic nervous system and neuropeptides in obesity hypertension. Brazilian Journal of Medical and Biological Research. 33(6). 605–618. 102 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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