Mapping the evolution of liver aging research: A bibliometric analysis

Abstract

Background: With the increasing of the global aging population, healthy aging and prevention of age-related diseases have become increasingly important. The liver, a vital organ involved in metabolism, detoxification, digestion, and immunity, holds a pivotal role in the aging process of organisms. Although extensive research on liver aging has been carried out, no bibliometric analysis has been conducted to evaluate the scientific progress in this area.

Aim: To analyze basic knowledge, development trends, and current research frontiers in the field via bibliometric methods.

Methods: We conducted bibliometric analyses via a range of analytical tools including Python, the bibliometrix package in R, CiteSpace, and VOSviewer. We retrieved publication data on liver aging research from the Web of Science Core Collection Database. A scientific knowledge map was constructed to display the contributions from different authors, journals, countries, institutions, as well as patterns of co-occurrence keywords and co-cited references. Additionally, gene regulation pathways associated with liver aging were analyzed via the STRING database.

Results: We identified 4288 articles on liver aging, authored by 24034 contributors from 4092 institutions across 85 countries. Notably, the years 1991 and 2020 presented significant bursts in publication output. The United States led in terms of publications (n = 1008, 25.1%), citations (n = 55205), and international collaborations (multiple country publications = 214). Keywords such as "lipid metabolism", "fatty liver disease", "inflammation", "liver fibrosis" and "target" were prominent, highlighting the current research hotspots. Notably, the top 64 genes, each of which appeared in at least 8 articles, were involved in pathways essential for cell survival and aging, including the phosphatidylinositol 3-kinase/protein kinase B, Forkhead box O and p53 signaling pathways.

Conclusion: This study highlights key areas of liver aging and offers a comprehensive overview of research trends, as well as insights into potential value for collaborative pursuits and clinical implementations.

Keywords: Aging; Bibliometric; CiteSpace; Gene regulation; Liver; R language; VOSviewer.

Figure 1

Detailed flowchart of the search strategy for screening publications. KEGG: Kyoto Encyclopedia of Genes Genomes; GO: Gene Ontology; RPYS: Reference publication year spectroscopy.

Figure 2

The annual number and the cumulative publications from 1984-2023.

Figure 3

Analysis of publications by country and institution. A: The distribution of countries (log-transformed scale of article count); B: Network maps showing countries involved in the research on liver aging, the size of the node indicates the number of papers; C: Three-field plot of keyword, countries and journals; D: The top 10 institutions ranked by publication count; E: Network maps of 49 institutions with at least 25 articles published in liver aging research. The node size represents the number of articles.

Figure 4

Authorship analysis in liver aging research. A: Annual publication trends of the top 10 most prolific authors; B: Co-authorship network map of 65 authors, each with a minimum of 10 articles in liver aging research. The node size indicates the number of articles.

Figure 5

Dual-map overlay of journals in liver aging research. The left side represents the citing journals and the right side represents the cited journals, and the lines’ hues indicate the different disciplines.

Figure 6

Citation analysis in liver aging research. A: Network map of the top 48 co-cited references, each cited more than 45 times; B: Top 30 references with the strongest citation bursts, the red segment represents the period of emergence, with burst strength proportional of the segment length; C: Reference publication year spectroscopy (RPYS) revealing three citation peaks in 1990, 2000, and 2013.

Figure 7

Co-occurrence network of keywords via CiteSpace.

Figure 8

Keyword analysis in liver aging research. A: Cluster map of keywords via CiteSpace; B: Top 25 keywords with robust citation bursts, the red segment indicates the period of emergence, with the burst strength proportional to the segment length.

Figure 9

Research trends in liver aging research. A: Conceptual structure map of keyword based on multiple correspondence analysis; B: Timeline of the research trends in liver aging. MCA: Multiple correspondence analysis.

Figure 10

Gene analysis in liver aging research. A: Top 20 genes reported in publication abstracts; B: Kyoto Encyclopedia of Genes Genomes pathway enrichment analysis of the top 64 genes; C: Gene Ontology enrichment analysis of the top 64 genes. BP: Biological process; MF: Molecular function; CC: Cellular component; TNF: Tumor necrosis factor; SIRT1: Sirtuin 1; mTOR: Mechanistic target of rapamycin; HGF: Hepatocyte growth factor; STAT3: Signal transducer and activator of transcription 3; PGC: Peroxisome proliferator-activated receptor gamma coactivator 1; PI3K: Phosphatidylinositol 3-kinase; Akt: Protein kinase B; FoxO: Forkhead box O; PD-L1: Programmed death-ligand 1; PD-1: Programmed cell death protein-1.

Figure 11

Holistic model in liver aging research. The figure illustrates the stimuli of liver aging, cellular, structural and functional changes caused by liver aging, and interventions and targets for delaying liver aging. PQQ: Pyrroloquinoline quinone; NMN: Nicotinamide mononucleotide; FABP4: Fatty acid binding protein 4; RAC1, Ras-related C3 botulinum substrate 1; IGF2: Insulin-like growth factor 2; FOXO1: Forkhead box O1; CISD2: CDGSH iron–sulfur domain-containing protein 2; PCSK9: Proprotein convertase subtilisin/kexin type 9; MANF: Mesencephalic-astrocyte-derived neurotrophic factor; SIRT6: Sirtuin 6; SLC13A5: Solute carrier family 13 member 5; HO-1: Heme oxygenase 1; ICAM-1: Intercellular cell adhesion molecule-1; LSECs: Liver sinusoidal endothelial cells; HSCs: Hepatic stellate cells; α-SMA: Alpha-smooth muscle; ROS: Reactive oxygen species; KCs: Kupffer cells; DNMT1: DNA (cytosine-5-)- methyltransferase 1; D + Q: Dasatinib (D) and quercetin (Q); CD32b: Fc gamma receptor IIb; PPARα: Peroxisome proliferator activated receptor alpha; SASP: Senescence-associated secretory phenotype; IL: Interleukin.