AIM

To provide a comprehensive review of the advances in research on diabetes-associated dry eye (DADE), highlighting its pathophysiological mechanisms, risk factors, and demographic characteristics, laying the foundation for further investigation into its pathogenesis and treatment strategies.

METHODS

A systemic review of the documents related to DADE had been performed based on the Web of Science database prior to achieving the plain text files of authors, titles, journals, and abstracts which afterwards had been imported into Citespace and VOSviewer software for data cleansing. The visual analysis was implemented from the following aspects: journals' publications, author and national cooperation, keyword co-occurrence, timeline analysis, and burst detection.

RESULTS

The 318 documents in 167 journals had been incorporated with the overall annual citations and annual publications respectively growing significantly since 2014 and 2016. The keyword co-occurrence networks formed 4 clusters, with the representative keywords being dry eye, diabetes mellitus, prevalence, and diabetic retinopathy. Both the timeline map and the burst detection demonstrated that in the exploration of the pathogenesis of DADE, initial research was dedicated to Sjögren's syndrome, followed by cross-sectional statistical analysis of the pertinent contributing factors of DADE using online databases. Precisely the oxidative stress seemed to surge into the research spotlight presently. The key pathogenic mechanisms of DADE include inflammation, oxidative stress and corneal neuropathy, contributing to the development of dry eye symptoms.

CONCLUSION

Age, gender, diabetes duration, and diabetic retinopathy are strongly associated with the development of DADE, but the impact of other systemic factors require further investigation. With high prevalence of dry eye in Asia, valuable resources like the Korea National Health and Nutrition Examination Survey (KNHANES) database offer crucial data for developing risk prediction models for DADE. Building risk prediction models using machine learning algorithms is a promising future research direction, enabling physicians to identify high-risk individuals and implement early interventions.