Adipokine and Hepatokines in Metabolic Dysfunction-Associated Steatotic Liver Disease (MASLD): Current and Developing Trends

Abstract

Background/Objectives: Metabolic dysfunction-associated steatotic liver disease (MASLD) represents a major global health challenge characterized by complex adipose-liver interactions mediated by adipokines and hepatokines. Despite rapid field evolution, a comprehensive understanding of research trends and translational advances remains fragmented. This study systematically maps the scientific landscape through bibliometric analysis, identifying emerging domains and future clinical translation directions. Methods: A comprehensive bibliometric analysis of 1002 publications from 2004 to 2025 was performed using thematic mapping, temporal trend evaluation, and network analysis. Analysis included geographical and institutional distributions, thematic cluster identification, and research paradigm evolution assessment, focusing specifically on adipokine-hepatokine signaling mechanisms and clinical implications. Results: The United States and China are at the forefront of research output, whereas European institutions significantly contribute to mechanistic discoveries. The thematic map analysis reveals the motor/basic themes residing at the heart of the field, such as insulin resistance, fatty liver, metabolic syndrome, steatosis, fetuin-A, and other related factors that drive innovation. Basic clusters include metabolic foundations (obesity, adipose tissue, FGF21) and adipokine-centered subjects (adiponectin, leptin, NASH). New themes focus on inflammation, oxidative stress, gut microbiota, lipid metabolism, and hepatic stellate cells. Niche areas show targeted fronts such as exercise therapies, pediatric/novel adipokines (chemerin, vaspin, omentin-1), and advanced molecular processes that focus on AMPK and endoplasmic-reticulum stress. Temporal analysis shows a shift from single liver studies to whole models that include the gut microbiota, mitochondrial dysfunction, and interactions between other metabolic systems. The network analysis identifies nine major clusters: cardiovascular-metabolic links, adipokine-inflammatory pathways, hepatokine control, and new therapeutic domains such as microbiome interventions and cellular stress responses. Conclusions: In summary, this study delineates current trends and emerging areas within the field and elucidates connections between mechanistic research and clinical translation to provide guidance for future research and development in this rapidly evolving area.

Keywords: AMPK; MASLD; adipokine; fetuin; hepatokine; insulin resistance; non-invasive biomarkers; oxidative stress.

Figure 1

(A) temporal publication trends (2004–2025) and (B) top ten journal contributions (%) in the field.

Figure 2

(A) Annual publications from the 10 most prolific journals in the field over about 20 years; IJMS = International Journal of Molecular Sciences; WJGastro = World Journal of Gastroenterology; Metab.: Clin. and Exp. = Metabolism: Clinical and Experimental; Liver Intern. = Liver International; J Hepat. = Journal of Hepatology. (B) Journal co-citation network generated via VOSviewer; nodes colored by citation impact (blue = low, yellow = high).

Figure 3

Geopolitical distribution of research output. The top ten countries for productivity in the field (A). Country-level collaboration network (B). The data were analyzed using Bibliometrix and Biblioshiny.

Figure 4

The top ten institutions for productivity in the field (A). Institution-level collaboration network (B). The data were analyzed using Bibliometrix and Biblioshiny.

Figure 5

Author productivity (A) and networks (B). The data were analyzed using Bibliometrix and Biblioshiny.

Figure 6

Thematic map analysis of research areas in the field. The horizontal axis (Centrality) measures the relevance of the theme in the general context, while the vertical axis (Density) indicates the level of development of the theme. The quadrants are divided into motor themes, basic themes, emerging or declining themes, and niche themes. The sizes of the circles represent the frequency or relative importance of each theme within the analyzed dataset. The data were analyzed using Bibliometrix and Biblioshiny.

Figure 7

Temporal evolution of research topics (2014–2025). This sliding window analysis (3-year intervals) displays term emergence patterns using the thematic evolution function. Node size reflects normalized frequency within each period. Lines show the conceptual lineage between terms across periods. The data were analyzed using Bibliometrix and Biblioshiny.

Figure 8

Clustered co-occurrence map extracted from the keywords of 1002 publications. In the map, the sizes of the frames indicate the frequency with which the keyword occurred. The co-occurrence strength between pairs of keywords is indicated by the proximity of two nodes and the thickness of the line connecting them. The color of the circle denotes keyword clusters, which are usually composed of co-occurring terms and can be regarded as broad research subjects in the field.