Background: Sleep is an important biological process and has been linked to many diseases; however, very little is known about which and how genes control and regulate sleep. Although technology has seen significant development, this issue has still not been adequately resolved. Therefore, we conducted a bibliometric analysis to assess the progress in research on sleep quality and associated genes over the past 2 decades. Through our statistical data and discussions, we aimed to provide researchers with better research directions and ideas, thus promoting the advancement of this field.

Methods: On December 29, 2022, we utilized bibliometric techniques, such as co-cited and cluster analysis and keyword co-occurrence, using tools such as CiteSpace, VOSviewer, and the Online Analysis Platform of Literature Metrology (http://bibliometric.com/), to conduct a thorough examination of the relevant publications extracted from the Web of Science Core Collection (WoSCC). Our analysis aimed to identify the emerging trends and hot spots in this field while also predicting their potential development in future.

Results: Cluster analysis of the co-cited literature revealed the most popular terms relating to sleep quality and associated genes in the manner of cluster labels; these included genome-wide association studies (GWAS), circadian rhythms, obstructive sleep apnea (OSA), DNA methylation, and depression. Keyword burst detection suggested that obstructive sleep apnea, circadian clock, circadian genes, and polygenic risk score were newly emergent research hot spots.

Conclusion: Based on this bibliometric analysis of the publications in the last 20 years, a comprehensive analysis of the literature clarified the contributions, changes in research hot spots, and evolution of research techniques regarding sleep quality and associated genes. This research can provide medical staff and researchers with revelations into future directions of the study on the pathological mechanisms of sleep-related diseases.

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http://www.ncbi.nlm.nih.gov/pmc/articles/PMC10663257PMC
http://dx.doi.org/10.3389/fneur.2023.1264177DOI Listing

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