Peter van der Kraan

1.1k total citations · 1 hit paper
13 papers, 831 citations indexed

About

Peter van der Kraan is a scholar working on Molecular Biology, Rheumatology and Pharmacology. According to data from OpenAlex, Peter van der Kraan has authored 13 papers receiving a total of 831 indexed citations (citations by other indexed papers that have themselves been cited), including 10 papers in Molecular Biology, 9 papers in Rheumatology and 3 papers in Pharmacology. Recurrent topics in Peter van der Kraan's work include Osteoarthritis Treatment and Mechanisms (7 papers), TGF-β signaling in diseases (4 papers) and Bone Metabolism and Diseases (4 papers). Peter van der Kraan is often cited by papers focused on Osteoarthritis Treatment and Mechanisms (7 papers), TGF-β signaling in diseases (4 papers) and Bone Metabolism and Diseases (4 papers). Peter van der Kraan collaborates with scholars based in Netherlands, United States and United Kingdom. Peter van der Kraan's co-authors include Ali Mobasheri, Margaret P. Rayman, Jérémie Sellam, Ursula Fearon, Oreste Gualillo, Csaba Matta, Pieter Buma, Wojciech Madej, Peter van Lent and A. van Caam and has published in prestigious journals such as International Journal of Molecular Sciences, Annals of the Rheumatic Diseases and The Journal of Pathology.

In The Last Decade

Peter van der Kraan

10 papers receiving 814 citations

Hit Papers

The role of metabolism in... 2017 2026 2020 2023 2017 100 200 300 400 500
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. The role of metabolism in the pathogenesis of osteoarthritis
    2017 550 citations Ali Mobasheri, Margaret P. Rayman et al. Nature Reviews Rheumatology profile →

Peers — A (Enhanced Table)

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

Name h Career Trend Papers Cites
Peter van der Kraan Netherlands 8 560 319 185 166 108 13 831
Abdul Haseeb United States 10 492 0.9× 365 1.1× 183 1.0× 192 1.2× 104 1.0× 14 908
Edith Charlier Belgium 11 574 1.0× 413 1.3× 164 0.9× 224 1.3× 85 0.8× 15 903
Sophie Neuville Belgium 9 580 1.0× 403 1.3× 161 0.9× 228 1.4× 85 0.8× 18 889
Céline Deroyer Belgium 11 555 1.0× 387 1.2× 148 0.8× 229 1.4× 76 0.7× 23 890
Olivier Malaise Belgium 7 497 0.9× 323 1.0× 131 0.7× 202 1.2× 70 0.6× 13 727
Stefan Söder Germany 10 524 0.9× 235 0.7× 122 0.7× 170 1.0× 106 1.0× 13 716
Klaus Bobacz Austria 14 547 1.0× 334 1.0× 176 1.0× 96 0.6× 170 1.6× 30 915
Martijn H. J. van den Bosch Netherlands 17 655 1.2× 442 1.4× 249 1.3× 142 0.9× 132 1.2× 40 1.1k
H.-J. Im United States 8 379 0.7× 356 1.1× 129 0.7× 232 1.4× 87 0.8× 9 763
Cleo Bonnet United Kingdom 9 609 1.1× 310 1.0× 249 1.3× 92 0.6× 195 1.8× 13 1.0k

Countries citing papers authored by Peter van der Kraan

Since Specialization
Citations

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

Fields of papers citing papers by Peter van der Kraan

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Peter van der Kraan. 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 Peter van der Kraan. The network helps show where Peter van der Kraan may publish in the future.

Co-authorship network of co-authors of Peter van der Kraan

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

All Works

13 of 13 papers shown
1.
Vitters, E.L., Birgitte Walgreen, Peter van der Kraan, et al.. (2025). CD8+ T cells drive myofibroblast activation and contraction via JAK/STAT3 and TGFβ signaling. iScience. 28(12). 113904–113904.
2.
Blom, Arjen B., Juliana P. Vago, E.L. Vitters, et al.. (2023). Innate Immunity and Sex: Distinct Inflammatory Profiles Associated with Murine Pain in Acute Synovitis. Cells. 12(14). 1913–1913.
3.
Akker, Guus van den, E.L. Vitters, Marije I. Koenders, et al.. (2021). Osteoarthritis-Related Inflammation Blocks TGF-β’s Protective Effect on Chondrocyte Hypertrophy via (de)Phosphorylation of the SMAD2/3 Linker Region. International Journal of Molecular Sciences. 22(15). 8124–8124. 20 indexed citations
4.
Vinuesa, Amaya García de, Gonzalo Sánchez‐Duffhues, A. van Caam, et al.. (2021). Cripto favors chondrocyte hypertrophy via TGF‐β SMAD1/5 signaling during development of osteoarthritis. The Journal of Pathology. 255(3). 330–342. 12 indexed citations
5.
Sánchez‐Duffhues, Gonzalo, A. van Caam, Kirsten Lodder, et al.. (2021). Cripto favours chondrocyte hypertrophy via TGF-beta SMAD1/5 signaling in experimental osteoarthritis. Bone Reports. 14. 101043–101043. 1 indexed citations
6.
Blom, Arjen B., E.J. Geven, Johannes Roth, et al.. (2019). AB0038 S100A8/9 ASSOCIATES WITH PAIN PERCEPTION IN OA PATIENTS AND INDUCES NOCICEPTIVE PAIN BUT NOT ALLODYNIA IN EXPERIMENTAL SYNOVITIS. Annals of the Rheumatic Diseases. 78. 1486–1486.
7.
Bot, Ilze, et al.. (2019). FRI0527 HIGH LDL LEVELS LESSEN BONE DESTRUCTION DURING ANTIGEN-INDUCED ARTHRITIS BY INHIBITING OSTEOCLAST FORMATION AND FUNCTION. Annals of the Rheumatic Diseases. 78. 958–958. 1 indexed citations
8.
Caam, A. van, Wojciech Madej, Amaya García de Vinuesa, et al.. (2017). TGFβ1-induced SMAD2/3 and SMAD1/5 phosphorylation are both ALK5-kinase-dependent in primary chondrocytes and mediated by TAK1 kinase activity. Arthritis Research & Therapy. 19(1). 112–112. 50 indexed citations
9.
Cremers, Niels A. J., Martijn H. J. van den Bosch, Irene Di Ceglie, et al.. (2017). S100A8/A9 increases the mobilization of pro-inflammatory Ly6Chigh monocytes to the synovium during experimental osteoarthritis. Arthritis Research & Therapy. 19(1). 217–217. 37 indexed citations
10.
Mobasheri, Ali, Margaret P. Rayman, Oreste Gualillo, et al.. (2017). The role of metabolism in the pathogenesis of osteoarthritis. Nature Reviews Rheumatology. 13(5). 302–311. 550 indexed citations breakdown →
11.
Kraan, Peter van der, Csaba Matta, & Ali Mobasheri. (2016). Age-Related Alterations in Signaling Pathways in Articular Chondrocytes: Implications for the Pathogenesis and Progression of Osteoarthritis - A Mini-Review. Gerontology. 63(1). 29–35. 41 indexed citations
12.
Madej, Wojciech, Pieter Buma, & Peter van der Kraan. (2016). Inflammatory conditions partly impair the mechanically mediated activation of Smad2/3 signaling in articular cartilage. Arthritis Research & Therapy. 18(1). 17 indexed citations
13.
Meurs, Joyce B. J. van, Peter van Lent, Reinout Stoop, et al.. (1999). Cleavage of aggrecan at the Asn341-Phe342 site coincides with the initiation of collagen damage in murine antigen-induced arthritis: A pivotal role for stromelysin 1 in matrix metalloproteinase activity. Arthritis & Rheumatism. 42(10). 2074–2084. 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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