Jürgen Eckel

15.6k total citations · 1 hit paper
212 papers, 11.8k citations indexed

About

Jürgen Eckel is a scholar working on Molecular Biology, Physiology and Epidemiology. According to data from OpenAlex, Jürgen Eckel has authored 212 papers receiving a total of 11.8k indexed citations (citations by other indexed papers that have themselves been cited), including 105 papers in Molecular Biology, 95 papers in Physiology and 65 papers in Epidemiology. Recurrent topics in Jürgen Eckel's work include Adipose Tissue and Metabolism (81 papers), Adipokines, Inflammation, and Metabolic Diseases (61 papers) and Metabolism, Diabetes, and Cancer (59 papers). Jürgen Eckel is often cited by papers focused on Adipose Tissue and Metabolism (81 papers), Adipokines, Inflammation, and Metabolic Diseases (61 papers) and Metabolism, Diabetes, and Cancer (59 papers). Jürgen Eckel collaborates with scholars based in Germany, Israel and Norway. Jürgen Eckel's co-authors include Henrike Sell, Kristin Eckardt, Silja Raschke, H. Reinauer, Annika Taube, Tania Romacho, D. Margriet Ouwens, Sven W. Görgens, Manuela Elsen and Christiane Habich and has published in prestigious journals such as Circulation, PLoS ONE and Circulation Research.

In The Last Decade

Jürgen Eckel

212 papers receiving 11.5k citations

Hit Papers

Dipeptidyl Peptidase 4 Is a Novel Adipokine Potentially L... 2011 2026 2016 2021 2011 100 200 300 400
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.

  1. Dipeptidyl Peptidase 4 Is a Novel Adipokine Potentially Linking Obesity to the Metabolic Syndrome
    2011 481 citations Daniela Lamers, Susanne Famulla et al. Diabetes profile →

Peers

Jürgen Eckel
Nora Klöting Germany
Andrea L. Hevener United States
Mathias Faßhauer Germany
Paul Cohen United States
Gérard Ailhaud France
Kazuhisa Maeda Japan
Max Lafontan France
Francesc Villarroya Spain
Bjørn Richelsen Denmark
Brian N. Finck United States
Nora Klöting Germany
Jürgen Eckel
Citations per year, relative to Jürgen Eckel Jürgen Eckel (= 1×) peers Nora Klöting

Countries citing papers authored by Jürgen Eckel

Since Specialization
Citations

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

Fields of papers citing papers by Jürgen Eckel

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Jürgen Eckel

This figure shows the co-authorship network connecting the top 25 collaborators of Jürgen Eckel. A scholar is included among the top collaborators of Jürgen Eckel 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 Jürgen Eckel. Jürgen Eckel 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.
Köhrer, Karl, et al.. (2023). Positive allosteric GABAA receptor modulation counteracts lipotoxicity-induced gene expression changes in hepatocytes in vitro. Frontiers in Physiology. 14. 1106075–1106075. 1 indexed citations
2.
Valencia, Inés, Concepción Peiró, Óscar Lorenzo, et al.. (2020). DPP4 and ACE2 in Diabetes and COVID-19: Therapeutic Targets for Cardiovascular Complications?. Frontiers in Pharmacology. 11. 1161–1161. 83 indexed citations
3.
Mutt, Shivaprakash Jagalur, Jari Jokelainen, Ghulam Shere Raza, et al.. (2018). Adipokines and inflammatory markers in elderly subjects with high risk of type 2 diabetes and cardiovascular disease. Scientific Reports. 8(1). 12816–12816. 33 indexed citations
4.
Märker, Tina, Henrike Sell, Jennifer Kriebel, et al.. (2012). Heat Shock Protein 60 as a Mediator of Adipose Tissue Inflammation and Insulin Resistance. Diabetes. 61(3). 615–625. 55 indexed citations
5.
Greulich, Sabrina, Luuk J. Rijzewijk, Jacqueline T. Jonker, et al.. (2011). Plasma omentin levels in relation to cardiac function in patients with type 2 diabetes and healthy controls: effect of pioglitazone versus metformin. Diabetologia. 54. 1 indexed citations
6.
7.
Eckardt, Kristin, et al.. (2011). The adipokine zinc-α2-glycoprotein activates AMP kinase in human primary skeletal muscle cells. Archives of Physiology and Biochemistry. 117(2). 88–93. 20 indexed citations
8.
Famulla, Susanne, Daniela Lamers, Sonja Hartwig, et al.. (2010). Pigment epithelium-derived factor (PEDF) is one of the most abundant proteins secreted by human adipocytes and induces insulin resistance and inflammatory signaling in muscle and fat cells. International Journal of Obesity. 35(6). 762–772. 134 indexed citations
9.
Lamers, Daniela, Raphaela Schlich, Sabrina Greulich, et al.. (2010). Oleic acid and adipokines synergize in inducing proliferation and inflammatory signalling in human vascular smooth muscle cells. Journal of Cellular and Molecular Medicine. 15(5). 1177–1188. 53 indexed citations
10.
11.
Schwenk, Robert W., Joost J.F.P. Luiken, & Jürgen Eckel. (2007). FIP2 and Rip11 specify Rab11a-mediated cellular distribution of GLUT4 and FAT/CD36 in H9c2-hIR cells. Biochemical and Biophysical Research Communications. 363(1). 119–125. 25 indexed citations
12.
Sell, Henrike, Daniela Dietze‐Schroeder, & Jürgen Eckel. (2006). The adipocyte–myocyte axis in insulin resistance. Trends in Endocrinology and Metabolism. 17(10). 416–422. 101 indexed citations
13.
Sasson, Shlomo & Jürgen Eckel. (2006). Disparate effects of 12-lipoxygenase and 12-hydroxyeicosatetraenoic acid in vascular endothelial and smooth muscle cells and in cardiomyocytes. Archives of Physiology and Biochemistry. 112(2). 119–129. 13 indexed citations
14.
Schwenk, Robert W. & Jürgen Eckel. (2006). A novel method to monitor insulin-stimulated GTP-loading of Rab11a in cardiomyocytes. Cellular Signalling. 19(4). 825–830. 14 indexed citations
15.
16.
Rizk, Nasser, Hans‐Georg Joost, & Jürgen Eckel. (2001). Increased Hypothalamic Expression of the p75 Tumor Necrosis Factor Receptor in New Zealand Obese Mice. Hormone and Metabolic Research. 33(9). 520–524. 12 indexed citations
17.
Bock, Manja, et al.. (1996). Acute and chronic effects of troglitazone (CS-045) on isolated rat ventricular cardiomyocytes. Diabetologia. 39(7). 766–774. 64 indexed citations
18.
Csermely, Péter, et al.. (1996). Insulin-induced phosphorylation of a 38 kDa DNA-binding protein in ventricular cardiomyocytes: possible implication of nuclear protein phosphatase activity. Molecular and Cellular Endocrinology. 120(2). 107–114. 2 indexed citations
19.
Kolter, Thomas, et al.. (1992). Contraction-induced translocation of the glucose transporter Glut4 in isolated ventricular cardiomyocytes. Biochemical and Biophysical Research Communications. 189(2). 1207–1214. 49 indexed citations
20.
Eckel, Jürgen & H. Reinauer. (1985). Glucose uptake in isolated heart cells: studies on the role of insulin.. PubMed. 80 Suppl 2. 103–6. 3 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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