Global trends and prospects of ocular biomarkers in Alzheimer’s disease: a bibliometric analysis - Shen, Yufei; Zhang, Xiaoling; Xu, Congying; Zhu, Zhuoying (Frontiers in Aging Neuroscience, 2025)
BackgroundAlzheimer’s disease (AD) diagnosis necessitates the development of novel biomarkers that ensure high diagnostic accuracy and cost-effectiveness in blood tests. Recent studies have identified a significant association between ocular symptoms and the pathological processes of AD, suggesting the potential for effective ocular biomarkers. This bibliometric analysis aims to explore recent advancements and research trends in ocular biomarkers for the early diagnosis of AD.MethodsArticles related to AD and ocular biomarkers were retrieved from the Web of Science Core Collection (WoSCC) database. These articles were analyzed using bibliometric tools such as VOSviewer, R-bibliometrix, and CiteSpace.ResultsA total of 623 papers were included in the analysis, revealing a steady increase in publications since 2012. The United States produced the most publications, followed by China and Italy. Notably, authors affiliated with Complutense University of Madrid in Spain and Sapienza University of Rome in Italy made significant contributions, demonstrating robust internal collaborations. The Journal of Alzheimer’s Disease published the most articles pertaining to ocular science and neuroscience. Keyword analysis indicates evolving trends in ocular markers for AD from 2005 to 2024, transitioning from diagnostic techniques (e.g., “spectroscopy,” “cerebrospinal fluid”) to pathological mechanisms (e.g., “oxidative stress”) and advanced imaging technologies (e.g., “optical coherence tomography angiography”).ConclusionThe bibliometric analysis highlights key research hotspots related to ocular markers for AD, documenting the shift from basic diagnostic techniques to advanced imaging methods and the discovery of novel biomarkers. Future research may investigate the potential of Optical Coherence Tomography Angiography, tear component analysis, eye movement assessments, and artificial intelligence to enhance early detection of AD.