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Review
. 2019 Jan 22:25:643-655.
doi: 10.12659/MSM.913026.

Identification of Recent Trends in Research on Vitamin D: A Quantitative and Co-Word Analysis

Affiliations
Review

Identification of Recent Trends in Research on Vitamin D: A Quantitative and Co-Word Analysis

Aolin Yang et al. Med Sci Monit. .

Abstract

BACKGROUND In recent years, many studies on vitamin D have been published. We combed these data for hot spot analyses and predicted future research topic trends. MATERIAL AND METHODS Articles (4625) concerning vitamin D published in the past 3 years were selected as a study sample. Bibliographic Items Co-occurrence Matrix Builder (BICOMB) software was used to screen high-frequency Medical Subject Headings (MeSH) terms and construct a MeSH terms-source article matrix and MeSH terms co-occurrence matrix. Then, Graphical Clustering Toolkit (gCLUTO) software was employed to analyze the matrix by double-clustering and visual analysis to detect the trends on the subject. RESULTS Ninety high-frequency major MeSH terms were obtained from 4625 articles and divided into 5 clusters, and we generated a visualized matrix and a mountain map. Strategic coordinates were established by the co-occurrence matrix of the MeSH terms based on the above classification, and the 5 clusters described above were further divided into 7 topics. We classified the vitamin D-related diseases into 12 categories and analyzed their distribution. CONCLUSIONS The analysis of strategic coordinates revealed that the epidemiological study of vitamin D deficiency and vitamin D-related diseases is a hot research topic. The use of vitamin D in the prevention and treatment of some diseases, especially diabetes, was found to have a significant potential future research value.

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Conflict of interest statement

Conflict of interest

None.

Figures

Figure 1
Figure 1
Changes in the number of vitamin D-related papers (A) and journals (B) from 1997 to 2017.
Figure 2
Figure 2
Visualized matrix of bi-clustering of highly frequent major MeSH terms and PubMed Unique Identifiers (PMIDs) of articles on vitamin D. The rows represent the high-frequency major MeSH terms, listed on the right. The bottom of the matrix shows the PMID for each source article.
Figure 3
Figure 3
Mountain visualization of bi-clustering of highly frequent major MeSH terms and articles on vitamin D. The 90 high-frequency terms, listed on the right, are clustered in peaks that represent 5 clusters numbered from 0 to 4.
Figure 4
Figure 4
Strategic diagram of the clusters.
Figure 5
Figure 5
Vitamin D-related disease distribution from 2002 to 2005 (A) and from 2015 to 2018 (B). The numbers of publications are 707 (A) and 2351 (B), respectively.
Figure 6
Figure 6
Vitamin D-related endocrine and metabolic system diseases (A) and neoplasms (B) distribution (2015–2018). The number of publications is 406 (A) and 242 (B), respectively.

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