Luciano Pirola

7.9k total citations · 3 hit papers
77 papers, 6.4k citations indexed

About

Luciano Pirola is a scholar working on Molecular Biology, Physiology and Epidemiology. According to data from OpenAlex, Luciano Pirola has authored 77 papers receiving a total of 6.4k indexed citations (citations by other indexed papers that have themselves been cited), including 35 papers in Molecular Biology, 34 papers in Physiology and 13 papers in Epidemiology. Recurrent topics in Luciano Pirola's work include Diet and metabolism studies (21 papers), Adipose Tissue and Metabolism (19 papers) and Metabolism, Diabetes, and Cancer (13 papers). Luciano Pirola is often cited by papers focused on Diet and metabolism studies (21 papers), Adipose Tissue and Metabolism (19 papers) and Metabolism, Diabetes, and Cancer (13 papers). Luciano Pirola collaborates with scholars based in France, Brazil and Poland. Luciano Pirola's co-authors include Matthias P. Wymann, Sara Fröjdö, Aneta Balcerczyk, Hubert Vidal, Anne Johnston, Vladimir L. Katanaev, Fiorella Altruda, Alberto Mantovani, Ornella Azzolino and Silvano Sozzani and has published in prestigious journals such as Science, Journal of Biological Chemistry and SHILAP Revista de lepidopterología.

In The Last Decade

Luciano Pirola

74 papers receiving 6.3k citations

Hit Papers

Central Role for G Protei... 1996 2026 2006 2016 2000 1996 1998 250 500 750 1000

Peers — A (Enhanced Table)

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

Name h Career Trend Papers Cites
Luciano Pirola France 34 3.6k 1.4k 965 747 725 77 6.4k
Weiping Chen China 42 3.2k 0.9× 876 0.6× 660 0.7× 1.1k 1.4× 610 0.8× 191 6.5k
Matteo Antonio Russo Italy 48 3.7k 1.0× 1.7k 1.2× 644 0.7× 1.6k 2.1× 615 0.8× 211 8.8k
Shailendra Giri United States 45 3.5k 1.0× 1.1k 0.8× 1.0k 1.0× 820 1.1× 323 0.4× 137 6.8k
Anu Kauppinen Finland 51 4.6k 1.3× 2.2k 1.6× 1.5k 1.6× 1.6k 2.1× 635 0.9× 125 9.9k
Bart van de Sluis Netherlands 39 3.4k 1.0× 2.6k 1.8× 1.2k 1.3× 947 1.3× 536 0.7× 96 7.7k
Hiroshi Sakaue Japan 44 4.3k 1.2× 1.6k 1.1× 489 0.5× 1.1k 1.4× 688 0.9× 146 6.9k
De‐Pei Liu China 49 4.1k 1.1× 1.3k 0.9× 797 0.8× 912 1.2× 306 0.4× 242 8.0k
Jay H. Chung United States 34 5.1k 1.4× 1.5k 1.1× 774 0.8× 824 1.1× 553 0.8× 73 7.7k
Minho Shong South Korea 50 4.1k 1.1× 1.5k 1.0× 1.4k 1.4× 1.2k 1.6× 541 0.7× 212 8.4k
Goo Taeg Oh South Korea 45 3.3k 0.9× 897 0.6× 1.3k 1.3× 989 1.3× 310 0.4× 175 6.9k

Countries citing papers authored by Luciano Pirola

Since Specialization
Citations

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

Fields of papers citing papers by Luciano Pirola

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Luciano Pirola

This figure shows the co-authorship network connecting the top 25 collaborators of Luciano Pirola. A scholar is included among the top collaborators of Luciano Pirola 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 Luciano Pirola. Luciano Pirola 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.
Strigini, Maura, et al.. (2025). The Impact of Ketogenic Nutrition on Obesity and Metabolic Health: Mechanisms and Clinical Implications. Nutrition Reviews. 83(10). 1957–1972. 3 indexed citations
3.
Pirola, Luciano. (2024). Elafibranor, a dual PPARα and PPARδ agonist, reduces alcohol-associated liver disease: Lessons from a mouse model. World Journal of Gastroenterology. 31(4). 99312–99312. 1 indexed citations
4.
Balcerczyk, Aneta, Assia Eljaafari, & Luciano Pirola. (2024). Adipose stem cells drive T cell infiltration in obesity. Trends in Endocrinology and Metabolism. 35(11). 931–933. 3 indexed citations
5.
Costa‐Silva, João Henrique, Arthur Bassot, Carol Góis Leandro, et al.. (2023). Diet enriched in saturated fatty acids induces liver oxidative stress and elicits inflammatory pathways prior to metabolic disruption in perinatal protein undernutrition. Nutrition Research. 118. 104–115.
6.
Watson, Julia, et al.. (2023). Adipokines in obesity and metabolic-related-diseases. Biochimie. 212. 48–59. 42 indexed citations
7.
Panthu, Baptiste, et al.. (2022). Citrullination in the pathology of inflammatory and autoimmune disorders: recent advances and future perspectives. Cellular and Molecular Life Sciences. 79(2). 94–94. 91 indexed citations
9.
Panthu, Baptiste, et al.. (2020). The Epigenetic Profile of Tumor Endothelial Cells. Effects of Combined Therapy with Antiangiogenic and Epigenetic Drugs on Cancer Progression. International Journal of Molecular Sciences. 21(7). 2606–2606. 33 indexed citations
10.
Balcerczyk, Aneta, et al.. (2020). Effects of ketogenic diet and ketone bodies on the cardiovascular system: Concentration matters. World Journal of Diabetes. 11(12). 584–595. 54 indexed citations
11.
Loizon, Emmanuelle, Hubert Vidal, João Henrique Costa‐Silva, et al.. (2019). Maternal physical activity-induced adaptive transcriptional response in brain and placenta of mothers and rat offspring. Journal of Developmental Origins of Health and Disease. 11(2). 108–117. 10 indexed citations
12.
Robert, Maud, Guillaume Vial, Jennifer Rieusset, et al.. (2016). Adipocytes, like their progenitors, contribute to inflammation of adipose tissues through promotion of Th-17 cells and activation of monocytes, in obese subjects. Adipocyte. 5(3). 275–282. 19 indexed citations
13.
Junien, Claudine, Polina Panchenko, Luciano Pirola, et al.. (2016). Épigénétique et réponses transgénérationnelles aux impacts de l’environnement. médecine/sciences. 32(1). 35–44. 10 indexed citations
15.
Fröjdö, Sara, Christine Durand, Laurent Molin, et al.. (2011). Phosphoinositide 3-kinase as a novel functional target for the regulation of the insulin signaling pathway by SIRT1. Molecular and Cellular Endocrinology. 335(2). 166–176. 107 indexed citations
16.
Pirola, Luciano, Aneta Balcerczyk, Jun Okabe, & Assam El‐Osta. (2010). Epigenetic phenomena linked to diabetic complications. Nature Reviews Endocrinology. 6(12). 665–675. 193 indexed citations
17.
Fröjdö, Sara, Hubert Vidal, & Luciano Pirola. (2008). Alterations of insulin signaling in type 2 diabetes: A review of the current evidence from humans. Biochimica et Biophysica Acta (BBA) - Molecular Basis of Disease. 1792(2). 83–92. 158 indexed citations
18.
Pirola, Luciano, Anne Johnston, & Ellen Van Obberghen‐Schilling. (2003). Modulators of insulin action and their role in insulin resistance. International Journal of Obesity. 27(S3). S61–S64. 12 indexed citations
19.
Hirsch, Emilio, Vladimir L. Katanaev, Cecília Garlanda, et al.. (2000). Central Role for G Protein-Coupled Phosphoinositide 3-Kinase γ in Inflammation. Science. 287(5455). 1049–1053. 1037 indexed citations breakdown →
20.
Wymann, Matthias P. & Luciano Pirola. (1998). Structure and function of phosphoinositide 3-kinases. Biochimica et Biophysica Acta (BBA) - Molecular and Cell Biology of Lipids. 1436(1-2). 127–150. 561 indexed citations breakdown →

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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