Mapping the evolving landscape of lecanemab research in Alzheimer's Disease: A bibliometric analysis

Lecanemab, a monoclonal antibody that targets amyloid-beta aggregates, has emerged as a promising therapeutic for Alzheimer's disease (AD). AD is a progressive neurodegenerative disorder characterized by cognitive decline and amyloid pathology. Research on the use of lecanemab in treating AD has increased; however, no relevant bibliometric analyses have been conducted. To address this gap, this study employed bibliometric methods to search for the relevant literature and analyze research trends investigating AD and lecanemab.

We performed a literature search of the Web of Science core database for studies investigating AD and lecanemab, published from database inception up to April 3rd, 2025. After rigorous screening, Excel, VOSviewer, and CiteSpace were used to perform a bibliometric analysis of publications, citations, and collaboration networks among countries, institutions, and authors, along with cluster and burst analyses of keywords. Coremine was used for text mining entries significantly related to AD and lecanemab.

The number of studies published on AD and lecanemab has increased annually. The countries with the highest publication output were the United States, the United Kingdom, and China. The leading institutions that produced the most articles were Eisai Inc. (Bunkyo City, Tokyo, Japan), Uppsala University (Uppsala, Sweden), and Harvard Medical School (Boston, MA, USA). The top three authors were Lars Lannfelt, Shobha Dhadda, and Michio Kanekiyo. The most prolific journals included The Journal of Alzheimer's Disease, Alzheimer's and Dementia, and Ageing Research Reviews. The most cited article was "Lecanemab in Early Alzheimer's Disease," by Van Dyck et al., published in The New England Journal of Medicine in 2023, which has accrued 172 citations. The 10 most frequently occurring keywords were Alzheimer's disease, lecanemab, dementia, aducanumab, amyloid-beta, immunotherapy, tau, a-beta, mouse model, and donanemab. Text mining revealed that drugs, anatomical structures, chemical molecules, genes, diseases, and procedures were significantly associated with both AD and lecanemab.

The bibliometric and text mining analysis revealed trends in research investigating the correlation between lecanemab and AD. It analyzed the cooperation among countries, regions, and authors, highlighting recent research hotspots. These data offer objective insights for scientific research and clinical practice on lecanemab and AD. These findings provide a roadmap for prioritizing clinical trials, optimizing drug development strategies, and addressing knowledge gaps in amyloid-targeted therapies.