Objectives: Biomarker research in Parkinsons lacks a systematic bibliometric review, despite a growing body of bibliometric studies. This study aimed to analyze publications on biomarkers that may play an important role in the diagnosis and treatment of Parkinsons disease.
Materials and Methods: In the study, bibliometric methods were used to analyze the related research in depth. Data were obtained from the Web of Science Core Collection database. Specific keywords consisting of MeSH terms were used as search criteria. The search was conducted in English and the type of publication was selected as article. Microsoft Excel, VOS viewer and the visualization and analytical capabilities of the Biblioshiny R package were used in the analysis, providing a versatile approach to achieve the study objectives.
Results: A total of 461 articles on Parkinsons disease biomarkers published between 2002 and 2023 in 170 journals met the criteria and were analyzed. Regarding publications and citations, the year 2022 stood out. Each document had an average of 6.28 co-authors. The main contributing countries were the United States, China and Italy, with a global co-authorship rate of 30.8%. Notable institutions included the University of Washington, Ruhr University Bochum and the Paracelsus Elena Clinic. "Journal of Neurochemistry," was the journal with the most publications. Through their total link strength, prominent terms such as "Parkinsons disease", "biomarker" and "alpha-synuclein" showed significant occurrences and thematic relevance.
Conclusion: The present study presents the inaugural bibliometric evaluation of biomarkers in Parkinsons disease. The gradual increase of papers since 2012 indicates persistent scholarly interest and foretells continued significance over the following ten years. Future research will be aided by the study's depiction of biomarker patterns, which provides insightful information. The term "Alpha-synuclein" stood out, highlighting its relevance in our research.
Key words: Bibliometrics; Parkinsons disease; Parkinson; Biomarker; Web of Science
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