Immune Inflammation at the Crossroads of Atherosclerosis and Ischemic Stroke: Mechanisms, Trends, and Translational Perspectives

Abstract

Background: Atherosclerosis is a chronic inflammatory disorder and a major cause of ischemic stroke. Immune-mediated mechanisms are increasingly recognized as central in this continuum, yet the global research landscape and its clinical translation remain insufficiently characterized.

Methods: We conducted a multi-level bibliometric analysis using the Web of Science Core Collection and MEDLINE. Searches targeted atherosclerosis, ischemic stroke, and immunity, restricted to English-language articles and reviews. After screening, 1760 WoSCC records and 708 human-only MEDLINE articles were analyzed with VOSviewer, CiteSpace, and Bibliometrix. Comparative assessment between China and the United States examined differences in research output, thematic focus, and methodological orientation.

Results: Global publications rose steadily from 1999 to 2025, peaking in 2022. Inflammation, atherosclerosis, and ischemic stroke were the dominant themes, with growing interest in causal inference (e.g., Mendelian randomization) and translational biomarkers. China showed rapid post-2015 growth with focus on immune-cell mechanisms, while the United States maintained leadership in scholarly impact, clinical orientation, and collaboration. Human-only studies confirmed these patterns and highlighted emerging topics such as microRNAs, COVID-19, insulin resistance, and lipoprotein(a).

Conclusions: Research has shifted from associative links to mechanistic insights and early translational strategies. However, gaps remain between molecular and clinical domains, and causal pathways are underdeveloped. Future work should emphasize molecular-clinical integration, expand immunological targets, apply multi-omics and AI approaches, and strengthen international collaboration-particularly between China and the United States-to advance precision prevention and intervention in atherosclerotic ischemic stroke.

Keywords: atherosclerosis; bibliometric analysis; immune‐mediated inflammation; ischemic stroke; translational immunology.

FIGURE 1

Literature Screening Flowchart.

FIGURE 2

Bibliometric summary and annual publication trends.

FIGURE 3

International, institutional, and author collaboration networks. (A) International collaboration map. (B) International collaboration network. (C) Institutional collaboration network. (D) Author collaboration network.

FIGURE 4

Keyword co‐occurrence and relevance networks. (A) Keyword co‐occurrence network. (B) Relevance network centered on “atherosclerosis.” (C) Relevance network centered on “ischemic stroke.” (D) Relevance network centered on “inflammation.”

FIGURE 5

Keyword co‐occurrence, citation bursts, and temporal evolution. (A) Keyword co‐occurrence clusters. (B) Top 25 keywords with the strongest citation bursts (1999–2025). (C) Temporal evolution of keyword clusters.

FIGURE 6

Conceptual structure, thematic distribution, and topic trends. (A) Conceptual structure map based on Multiple Correspondence Analysis (MCA). (B) Keyword tree map. (C) Thematic map. (D) Trend topics over time.

FIGURE 7

References with strongest citation bursts and thematic evolution. (A) Top 25 references with the strongest citation bursts (1999–2025). (B) Thematic evolution of research clusters over time.

FIGURE 8

Keyword networks, citation bursts, and temporal evolution in China and USA. (A) Keyword co‐occurrence network in China. (B) Top 25 keywords with the strongest citation bursts in China (2020–2025). (C) Temporal evolution of keyword clusters in China. (D) Keyword co‐occurrence network in the USA. (E) Top 25 keywords with the strongest citation bursts in the USA (2020–2025). (F) Temporal evolution of keyword clusters in the USA.

FIGURE 9

Conceptual structures, thematic distributions, and topic trends in China and USA. (A) Conceptual structure map for China. (B) Keyword tree map for China. (C) Trend topics over time for China. (D) Conceptual structure map for the USA. (E) Keyword tree map for the USA. (F) Trend topics over time for the USA.

FIGURE 10

Keyword networks, citation bursts, and temporal evolution in human studies. (A) Keyword co‐occurrence network (human studies). (B) Top 25 keywords with the strongest citation bursts in human studies (2020–2025). (C) Temporal evolution of keyword clusters in human studies.

FIGURE 11

Conceptual structures, thematic distributions, and topic trends in human studies. (A) Conceptual structure map for human studies. (B) Keyword tree map for human studies. (C) Trend topics over time in human studies.