Carsten Schmitz‐Peiffer

4.0k total citations · 1 hit paper
54 papers, 3.1k citations indexed

About

Carsten Schmitz‐Peiffer is a scholar working on Molecular Biology, Physiology and Surgery. According to data from OpenAlex, Carsten Schmitz‐Peiffer has authored 54 papers receiving a total of 3.1k indexed citations (citations by other indexed papers that have themselves been cited), including 48 papers in Molecular Biology, 18 papers in Physiology and 14 papers in Surgery. Recurrent topics in Carsten Schmitz‐Peiffer's work include Metabolism, Diabetes, and Cancer (30 papers), Protein Kinase Regulation and GTPase Signaling (20 papers) and Adipose Tissue and Metabolism (15 papers). Carsten Schmitz‐Peiffer is often cited by papers focused on Metabolism, Diabetes, and Cancer (30 papers), Protein Kinase Regulation and GTPase Signaling (20 papers) and Adipose Tissue and Metabolism (15 papers). Carsten Schmitz‐Peiffer collaborates with scholars based in Australia, United States and Norway. Carsten Schmitz‐Peiffer's co-authors include Trevor J. Biden, Edward W. Kraegen, Lisa Selbie, Donald J. Chisholm, Nicholas D. Oakes, Rosanna Cazzolli, Allan Watkinson, Jonathan P. Whitehead, Lee Carpenter and James G. Burchfield and has published in prestigious journals such as Journal of Biological Chemistry, The Journal of Immunology and PLoS ONE.

In The Last Decade

Carsten Schmitz‐Peiffer

51 papers receiving 3.1k citations

Hit Papers

Ceramide Generation Is Su... 1999 2026 2008 2017 1999 100 200 300 400 500

Peers — A (Enhanced Table)

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

Name h Career Trend Papers Cites
Carsten Schmitz‐Peiffer Australia 31 2.1k 1.3k 581 545 521 54 3.1k
Yoshikazu Tamori Japan 30 1.6k 0.8× 1.1k 0.8× 678 1.2× 635 1.2× 568 1.1× 67 2.9k
Marcin Baranowski Poland 29 1.6k 0.8× 1.0k 0.8× 447 0.8× 493 0.9× 400 0.8× 78 2.6k
Laura F. Michael United States 22 2.5k 1.2× 2.1k 1.6× 850 1.5× 544 1.0× 503 1.0× 33 4.2k
Jonathan J. Fillmore United States 12 1.4k 0.7× 1.5k 1.1× 477 0.8× 957 1.8× 257 0.5× 14 2.8k
Daniel J. Fazakerley Australia 30 2.2k 1.0× 1.2k 0.9× 510 0.9× 528 1.0× 481 0.9× 67 3.3k
Yingjiang Zhou United States 21 1.4k 0.7× 708 0.6× 499 0.9× 873 1.6× 677 1.3× 34 2.9k
Yun Chau Long Singapore 25 1.8k 0.9× 857 0.7× 530 0.9× 567 1.0× 302 0.6× 42 2.8k
Jacqueline Stöckli Australia 28 2.1k 1.0× 1.1k 0.8× 686 1.2× 497 0.9× 721 1.4× 56 3.2k
Harold F. Sims United States 35 2.3k 1.1× 794 0.6× 548 0.9× 462 0.8× 411 0.8× 62 3.8k
Ryan S. Streeper United States 22 1.6k 0.8× 1.2k 0.9× 407 0.7× 712 1.3× 292 0.6× 30 3.1k

Countries citing papers authored by Carsten Schmitz‐Peiffer

Since Specialization
Citations

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

Fields of papers citing papers by Carsten Schmitz‐Peiffer

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Carsten Schmitz‐Peiffer. 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 Carsten Schmitz‐Peiffer. The network helps show where Carsten Schmitz‐Peiffer may publish in the future.

Co-authorship network of co-authors of Carsten Schmitz‐Peiffer

This figure shows the co-authorship network connecting the top 25 collaborators of Carsten Schmitz‐Peiffer. A scholar is included among the top collaborators of Carsten Schmitz‐Peiffer 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 Carsten Schmitz‐Peiffer. Carsten Schmitz‐Peiffer 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.
Brandon, Amanda E., Tamara Pulpitel, Carsten Schmitz‐Peiffer, et al.. (2025). An Ad Libitum‐Fed Diet That Matches the Beneficial Lifespan Effects of Caloric Restriction but Acts via Opposite Effects on the Energy‐Splicing Axis. Aging Cell. 24(12). e70269–e70269. 1 indexed citations
2.
Sue, Nancy, Ebru Boslem, Kwan Yi Chu, et al.. (2025). ER stress disrupts insulin release in murine models of type 2 diabetes by impairing retromer action and constitutive secretion. Cell Reports. 44(5). 115691–115691. 1 indexed citations
3.
4.
Small, Lewin, Mark Larance, Darren N. Saunders, et al.. (2024). Liver proteomics identifies a disconnect between proteins associated with de novo lipogenesis and triglyceride storage. Journal of Lipid Research. 65(12). 100687–100687.
5.
Schmitz‐Peiffer, Carsten. (2018). Anarchy in the UPR: a Ca2+-insensitive PKC inhibits SERCA activity to promote ER stress. Bioscience Reports. 38(2). 4 indexed citations
6.
Windmill, Kelly, Andrew Sanigorski, Kathryn Aston‐Mourney, et al.. (2016). Pathways of Acetyl-CoA Metabolism Involved in the Reversal of Palmitate-Induced Glucose Production by Metformin and Salicylate. Experimental and Clinical Endocrinology & Diabetes. 124(10). 602–612. 2 indexed citations
7.
Liao, Bing, Katy Raddatz, Ling Zhong, et al.. (2014). Proteomic analysis of livers from fat‐fed mice deficient in either PKCδ or PKCε identifies Htatip2 as a regulator of lipid metabolism. PROTEOMICS. 14(21-22). 2578–2587. 17 indexed citations
10.
Luo, Xiao, Louise J. Hutley, Julie A. Webster, et al.. (2011). Identification of BMP and Activin Membrane-Bound Inhibitor (BAMBI) as a Potent Negative Regulator of Adipogenesis and Modulator of Autocrine/Paracrine Adipogenic Factors. Diabetes. 61(1). 124–136. 61 indexed citations
11.
Brummer, Tilman, Carsten Schmitz‐Peiffer, & Roger J. Daly. (2010). Docking proteins. FEBS Journal. 277(21). 4356–4369. 38 indexed citations
13.
Schmitz‐Peiffer, Carsten & Trevor J. Biden. (2008). Protein Kinase C Function in Muscle, Liver, and β-Cells and Its Therapeutic Implications for Type 2 Diabetes. Diabetes. 57(7). 1774–1783. 115 indexed citations
14.
Cazzolli, Rosanna, Todd W. Mitchell, James G. Burchfield, et al.. (2007). Dilinoleoyl-phosphatidic acid mediates reduced IRS-1 tyrosine phosphorylation in rat skeletal muscle cells and mouse muscle. Diabetologia. 50(8). 1732–1742. 18 indexed citations
15.
Ludowyke, Russell I., Zehra Elgundi, Tanya A. Kranenburg, et al.. (2006). Phosphorylation of Nonmuscle Myosin Heavy Chain IIA on Ser1917 Is Mediated by Protein Kinase CβII and Coincides with the Onset of Stimulated Degranulation of RBL-2H3 Mast Cells. The Journal of Immunology. 177(3). 1492–1499. 45 indexed citations
16.
Rolph, Michael S., Timothy R. Young, Bennett O. V. Shum, et al.. (2006). Regulation of Dendritic Cell Function and T Cell Priming by the Fatty Acid-Binding Protein aP2. The Journal of Immunology. 177(11). 7794–7801. 73 indexed citations
17.
Burchfield, James G., William E. Hughes, Valerie C. Wasinger, et al.. (2004). Akt Mediates Insulin-stimulated Phosphorylation of Ndrg2. Journal of Biological Chemistry. 279(18). 18623–18632. 71 indexed citations
18.
Schmitz‐Peiffer, Carsten & Jonathan P. Whitehead. (2003). IRS‐1 Regulation in Health and Disease. IUBMB Life. 55(7). 367–374. 61 indexed citations
19.
Schmitz‐Peiffer, Carsten. (2000). Signalling aspects of insulin resistance in skeletal muscle. Cellular Signalling. 12(9-10). 583–594. 210 indexed citations
20.
Schmitz‐Peiffer, Carsten, Martin L. Reeves, & Richard M. Denton. (1992). Characterization of the cyclic nucleotide phosphodiesterase isoenzymes present in rat epididymal fat cells. Cellular Signalling. 4(1). 37–49. 33 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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