Rick B. Vega

9.5k total citations · 5 hit papers
53 papers, 7.6k citations indexed

About

Rick B. Vega is a scholar working on Molecular Biology, Physiology and Surgery. According to data from OpenAlex, Rick B. Vega has authored 53 papers receiving a total of 7.6k indexed citations (citations by other indexed papers that have themselves been cited), including 35 papers in Molecular Biology, 28 papers in Physiology and 10 papers in Surgery. Recurrent topics in Rick B. Vega's work include Adipose Tissue and Metabolism (24 papers), Mitochondrial Function and Pathology (11 papers) and Muscle metabolism and nutrition (8 papers). Rick B. Vega is often cited by papers focused on Adipose Tissue and Metabolism (24 papers), Mitochondrial Function and Pathology (11 papers) and Muscle metabolism and nutrition (8 papers). Rick B. Vega collaborates with scholars based in United States, Finland and Japan. Rick B. Vega's co-authors include Daniel P. Kelly, Janice M. Huss, Eric N. Olson, Richard C. Scarpulla, Rhonda Bassel‐Duby, R. Sanders Williams, Beverly A. Rothermel, John Yang, Timothy A. McKinsey and Gerald W. Dorn and has published in prestigious journals such as Cell, Proceedings of the National Academy of Sciences and Journal of Biological Chemistry.

In The Last Decade

Rick B. Vega

51 papers receiving 7.5k citations

Hit Papers

The Coactivator PGC-1 Cooperates with Peroxisome Prolifer... 2000 2026 2008 2017 2000 2012 2004 2016 2019 250 500 750

Peers

Rick B. Vega
Richard M. Mortensen United States
David R. Pimentel United States
Christopher Baines United States
Jason X.‐J. Yuan United States
Teresa C. Leone United States
Luc Bertrand Belgium
Adam R. Wende United States
Edward P. Feener United States
Richard M. Mortensen United States
Rick B. Vega
Citations per year, relative to Rick B. Vega Rick B. Vega (= 1×) peers Richard M. Mortensen

Countries citing papers authored by Rick B. Vega

Since Specialization
Citations

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

Fields of papers citing papers by Rick B. Vega

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Rick B. Vega. 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 Rick B. Vega. The network helps show where Rick B. Vega may publish in the future.

Co-authorship network of co-authors of Rick B. Vega

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

All Works

20 of 20 papers shown
1.
Koren, Michael, Rick B. Vega, Nikhil Agrawal, et al.. (2025). An Oral PCSK9 Inhibitor for Treatment of Hypercholesterolemia. Journal of the American College of Cardiology. 85(21). 1996–2007. 7 indexed citations
3.
Chung, Heaseung Sophia, Manik Garg, Ventzislava A. Hristova, et al.. (2024). Longitudinal clinical and proteomic diabetes signatures in women with a history of postgestational diabetes. JCI Insight. 10(2).
4.
Whytock, Katie L., Maria F. Pino, Yifei Sun, et al.. (2023). Comprehensive interrogation of human skeletal muscle reveals a dissociation between insulin resistance and mitochondrial capacity. American Journal of Physiology-Endocrinology and Metabolism. 325(4). E291–E302. 10 indexed citations
5.
Hinkley, J. Matthew, Robert A. Standley, Giovanna Distéfano, et al.. (2023). Exercise and ageing impact the kynurenine/tryptophan pathway and acylcarnitine metabolite pools in skeletal muscle of older adults. The Journal of Physiology. 601(11). 2165–2188. 14 indexed citations
6.
Rubenstein, Aliza B., J. Matthew Hinkley, Venugopalan D. Nair, et al.. (2022). Skeletal muscle transcriptome response to a bout of endurance exercise in physically active and sedentary older adults. American Journal of Physiology-Endocrinology and Metabolism. 322(3). E260–E277. 23 indexed citations
7.
Sakamoto, Tomoya, Kirill Batmanov, Shibiao Wan, et al.. (2022). The nuclear receptor ERR cooperates with the cardiogenic factor GATA4 to orchestrate cardiomyocyte maturation. Nature Communications. 13(1). 1991–1991. 25 indexed citations
8.
Lavin, Kaleen M., Paul M. Coen, Liliana C. Baptista, et al.. (2022). State of Knowledge on Molecular Adaptations to Exercise in Humans: Historical Perspectives and Future Directions. Comprehensive physiology. 12(2). 3193–3279. 6 indexed citations
9.
Gan, Zhenji, Tingting Fu, Daniel P. Kelly, & Rick B. Vega. (2018). Skeletal muscle mitochondrial remodeling in exercise and diseases. Cell Research. 28(10). 969–980. 201 indexed citations
10.
Maurya, Santosh K., José Luís Herrera, Sanjaya Kumar Sahoo, et al.. (2018). Sarcolipin Signaling Promotes Mitochondrial Biogenesis and Oxidative Metabolism in Skeletal Muscle. Cell Reports. 24(11). 2919–2931. 93 indexed citations
11.
Zimmer, Michael, Pradeep Bista, Diana Lee, et al.. (2017). CAT‐2003: A novel sterol regulatory element‐binding protein inhibitor that reduces steatohepatitis, plasma lipids, and atherosclerosis in apolipoprotein E*3‐Leiden mice. Hepatology Communications. 1(4). 311–325. 17 indexed citations
12.
Vega, Rick B., John P. Konhilas, Daniel P. Kelly, & Leslie A. Leinwand. (2017). Molecular Mechanisms Underlying Cardiac Adaptation to Exercise. Cell Metabolism. 25(5). 1012–1026. 221 indexed citations
13.
Aubert, Grégory, Ola J. Martin, Julie L. Horton, et al.. (2016). The Failing Heart Relies on Ketone Bodies as a Fuel. Circulation. 133(8). 698–705. 549 indexed citations breakdown →
14.
Liu, Jing, Xijun Liang, Danxia Zhou, et al.. (2016). Coupling of mitochondrial function and skeletal muscle fiber type by a miR‐499/Fnip1/ AMPK circuit. EMBO Molecular Medicine. 8(10). 1212–1228. 99 indexed citations
15.
Horton, Julie L., Ola J. Martin, Ling‐Ping Lai, et al.. (2016). Mitochondrial protein hyperacetylation in the failing heart. JCI Insight. 1(2). 168 indexed citations
16.
Sugarman, Eliot, Ada Koo, Eigo Suyama, et al.. (2013). Identification of Inhibitors of Triacylglyceride Accumulation in Muscle Cells: Comparing HTS Results from 1536-Well Plate-Based and High-Content Platforms. SLAS DISCOVERY. 19(1). 77–87. 5 indexed citations
17.
Aubert, Grégory, Rick B. Vega, & Daniel P. Kelly. (2012). Perturbations in the gene regulatory pathways controlling mitochondrial energy production in the failing heart. Biochimica et Biophysica Acta (BBA) - Molecular Cell Research. 1833(4). 840–847. 82 indexed citations
18.
Vega, Rick B., Rhonda Bassel‐Duby, & Eric N. Olson. (2003). Control of Cardiac Growth and Function by Calcineurin Signaling. Journal of Biological Chemistry. 278(39). 36981–36984. 118 indexed citations
19.
Vega, Rick B., John Yang, Beverly A. Rothermel, Rhonda Bassel‐Duby, & R. Sanders Williams. (2002). Multiple Domains of MCIP1 Contribute to Inhibition of Calcineurin Activity. Journal of Biological Chemistry. 277(33). 30401–30407. 127 indexed citations
20.
Rothermel, Beverly A., Rick B. Vega, John Yang, et al.. (2000). A Protein Encoded within the Down Syndrome Critical Region Is Enriched in Striated Muscles and Inhibits Calcineurin Signaling. Journal of Biological Chemistry. 275(12). 8719–8725. 356 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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