Mapping the structure of depression biomarker research: A bibliometric analysis - Guo, Xiang-jie; Wu, Peng; Jia, Xiao; Dong, Yi-ming; Zhao, Chun-mei; Chen, Nian-nian; Zhang, Zhi-yong; Miao, Yu-ting; Yun, Ke-ming; Gao, Cai-rong; Ren, Yan (Frontiers in Psychiatry, 2022)
Background: Depression is a common mental disorder and the diagnosis is still based on the descriptions of symptoms. Biomarkers can reveal disease characteristics for diagnosis, prognosis, and treatment. In recent years, many biomarkers relevant to the mechanisms of depression have been identified. This study uses bibliometric methods and visualisation tools to analyse the literature on depression biomarkers and its hot topics, and research frontiers to provide references for future research. Methods: Scientific publications related to depression biomarkers published between 2009 and 2022 were obtained from the Web of Science database. The BICOMB software was used to extract high-frequency keywords and to construct binary word-document and co-word matrices. gCLUTO was used for bicluster and visual analyses of high-frequency keywords. Further graphical visualisations were generated using R, CiteSpace and VOSviewer software. Results: A total of 14,403 articles related to depression biomarkers were identified. The United States (34.81%) and China (15.68%), which together account for more than half of all publications, can be considered the research base for the field. Among institutions, the University of California, University of London, and Harvard University are among the top in terms of publication number. Three authors (Maes M, Penninx B.W.J.H., and Berk M) emerged as eminent researchers in the field. Finally, eight research hotspots for depression biomarkers were identified using reference co-citation analysis. Conclusion: This study used bibliometric methods to characterise the body of literature and subject knowledge in the field of depression biomarker research. Among the core biomarkers of depression, functional magnetic resonance imaging (fMRI), cytokines, and oxidative stress are relatively well established; however, research on machine learning, metabolomics, and microRNAs holds potential for future development. We found ‘microRNAs’ and ‘gut microbiota’ to be the most recent burst terms in the study of depression biomarkers and the likely frontiers of future research.