Tear film osmolarity and dry eye disease: a review of the literature

Abstract

Objective

To evaluate the evidence in the peer-reviewed literature regarding the use of tear osmolarity as a physiological marker to diagnose, grade severity, and track therapeutic response in dry eye disease (DED). In addition, to review the evidence for the role of tear osmolarity in the pathophysiology of DED and ocular surface disease.

Methods

A literature review of all publications after the year 2000, which included the keywords “tear osmolarity”, was conducted. Relevant articles were graded according to quality of evidence and research, using the University of Michigan Practice Guideline and the Grading of Recommendations Assessment, Development, and Evaluation (GRADE) rating systems. Articles were further categorized by the nature of any reported financial support and by the overall impression they provided related to tear osmolarity.

Results

A total of 164 articles were identified as relevant to the search criteria, although some were editorials, and some were written in a foreign language. Of the total, it was possible to grade 159, and an overall impression was generated for 163. A positive impression of tear osmolarity in DED diagnosis was evident in 72% (117/163) of all articles, with a neutral impression in a further 21% (35/163); 7% had a negative impression. The percentage of positive impressions appeared independent of the quality of research; 73% (38/52) of articles graded high/moderate quality supported the use of tear film osmolarity measurement in DED diagnosis. Impressions were also independent of the source of financial support, with 72% (75/104) of independent studies positive.

Conclusion

The literature broadly supports the use of tear film osmolarity as an objective numerical measure for diagnosing, grading severity, and managing treatment of DED.

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Keywords:

• TearLab
• osmometer
• keratoconjunctivitis sicca
• dry eye diagnosis

Acknowledgments

The literature review and preparation of this manuscript was supported with funding to Science in Vision, Akron, NY, USA from TearLab Corporation, San Diego, CA, USA.

Disclosure

CJ Rapuano is a consultant for TearLab Corporation, but received no financial support for this work. No author has any proprietary or financial interest in the products or methods discussed. The authors report no other conflicts of interest in this work.