The heart’s fibrous web: A bibliometric analysis of cardiac fibrosis in Asia and Oceania

BACKGROUND: Cardiac fibrosis is a pathophysiologic condition in heart diseases. It refers to an excess deposition of extracellular matrix, which will cause hardening and stiffness in heart tissues, leading to heart failure. By considering the high prevalence of cardiac fibrosis globally, a bibliometric analysis is conducted to elucidate the research trend involving the unique perspectives in both the Asia and Oceania regions. METHODS: Data were retrieved from the Scopus database without restrictions on publication year. Documents related to cardiac fibrosis affiliated with countries in Asia and Oceania were identified using the United Nations geoscheme classifications. The dataset was curated using biblioMagika and OpenRefine. Then, bibliometric mapping was conducted using VOSviewer to analyze co-authorship and keyword co-occurrence networks. RESULTS: A total of 983 publications were identified from 1985 to 2025, with a marked increase in research output from 2010 onwards. China emerged as the leading contributor in terms of productivity and total citations, while Australia showed the highest citation impact per publication. High-impact institutions and authors were identified, and core themes included molecular mechanisms such as transforming growth factor beta signaling, extracellular matrix remodeling, and noncoding RNAs. CONCLUSION: This study is the first to systematically examine cardiac fibrosis research output across Asia and Oceania using bibliometric methods. It contributes novel insights into the regional dynamics, intellectual structure, and emerging priorities within the field. By identifying key actors, collaboration networks, and knowledge gaps, this research provides valuable guidance for scholars, funding agencies, and policymakers to advance cardiac fibrosis research in regional and global contexts. However, reliance on citation-based metrics may introduce temporal bias, as older publications tend to accumulate more citations over time. Future studies should employ multi-database strategies, altmetric indicators, and machine learning approaches to address these limitations and capture a broader spectrum of research impact.