Frontiers and hotspots of adipose tissue and NAFLD: a bibliometric analysis from 2002 to 2022

Front Physiol. 2023 Dec 21:14:1278952. doi: 10.3389/fphys.2023.1278952. eCollection 2023.

Abstract

Background: The annual incidence of non-alcoholic fatty liver disease (NAFLD) continues to rise steadily. In recent years, adipose tissue (AT) has gained recognition as a pivotal contributor to the pathogenesis of NAFLD. Employing bibliometric analysis, we examined literature concerning AT and NAFLD. Methods: Relevant literature on AT in NAFLD from 1980 to 2022 was extracted from the Web of Science Core Collection. These records were visualized using CiteSpace and VOSviewer regarding publications, countries/regions, institutions, authors, journals, references, and keywords. Results: Since 2002, a total of 3,330 papers have been included, exhibiting an annual surge in publications. Notably, the quality of publications is superior in the USA and Europe. Kenneth Cusi stands out as the author with the highest number of publications and H-index. Hepatology is the journal boasting the highest citation and H-index. The University of California System holds the highest centrality among institutions. References specifically delve into physiological processes associated with AT in NAFLD. Currently, lipid metabolism and inflammation constitute the principal research mechanisms in the AT-based regulation of NAFLD, with pertinent keywords including microRNA, T cell, hypoxia, sarcopenia, hepatokine, gut microbiota, and autophagy. The Mediterranean diet is among the most widely recommended dietary approaches for potential NAFLD treatment. Conclusion: This paper represents the inaugural bibliometric study on the effects of AT on NAFLD, offering valuable insights and directions for future research.

Keywords: CiteSpace; VOSviewer; adipose tissue; bibliometrics; hotspots; non-alcoholic fatty liver disease.

Grants and funding

The author(s) declare financial support was received for the research, authorship, and/or publication of this article. This work was supported by the National Science and Technology Major Special Project based on big data research and development of new Chinese medicine drugs (2019ZX09201004-001-021).