Relation Between Upper and Lower Lids' Meibomian Gland Morphology, Tear Film, and Dry Eye
Overview
Affiliations
Purpose: To analyze relations between upper lid (UL) and lower lid (LL) meibomian gland (MG) morphology and tear film and the MG criteria ability to predict dry eye.
Methods: MG, lipid layer, and non-invasive break-up time (NIBUT) were evaluated of the OD of 20 randomly selected subjects (female = 10; median age = 44.5 years, interquartiles = 39.5 to 55 years). Subjects were grouped into nine Ocular Surface Disease Index (OSDI)- and 11 OSDI+ by the OSDI. Non-contact infrared meibography and image analysis were performed to evaluate MG loss, MG thickness, and MG bent angle.
Results: MG loss (Pearson correlation; r = 0.647, p = 0.003) and MG bent angle (r = 0.489, p = 0.027) were significantly correlated between lids, but MG thickness was not (r = -0.059, p = 0.413). MG loss was significantly (t-test; p = 0.048) less in the UL (median = 26.9%; LL = 32.3%), thicker in the LL (p < 0.001) and were more bent in the LL (p = 0.001). MG loss was significantly correlated to lipid-layer thickness (r < -0.597, p < 0.003) and NIBUT (r < -0.453, p < 0.030), whereas MG thickness and bent angle of the UL only were related to NIBUT (r < -0.563, p < 0.018). Combining MG loss of both lids (linear regression analysis) resulted in the best predictive ability of OSDI± (area under the receiver operative characteristic curve = 0.929, p = 0.001).
Conclusions: MG scores between lids were significantly different but correlated. MG loss was significantly correlated to tear film characteristics including lipid layer thickness and stability. MG thickness and bent angle of the UL were related to NIBUT. The combination of both lids' MG loss showed best predictive ability of dry eye.
Zheng F, Su J, Wang J, Zhan Q, Su M, Ding S Invest Ophthalmol Vis Sci. 2024; 65(3):24.
PMID: 38502139 PMC: 10959198. DOI: 10.1167/iovs.65.3.24.
Morphological changes in the meibomian gland in children with tic disorders.
Qian S, Dou R, Wang Q, Huang F, Zhao Y, Zhuo R Quant Imaging Med Surg. 2023; 13(10):6374-6383.
PMID: 37869316 PMC: 10585551. DOI: 10.21037/qims-22-390.
Srivastav S, Ali M, Basu S, Singh S Front Med (Lausanne). 2023; 10:1195568.
PMID: 37731719 PMC: 10507340. DOI: 10.3389/fmed.2023.1195568.
Automatic segmentation and quantified analysis of meibomian glands from infrared images.
Vunnava K, Shetty R, Prabhu S, Tiwari P, Kummelil M Indian J Ophthalmol. 2023; 71(4):1426-1431.
PMID: 37026276 PMC: 10276665. DOI: 10.4103/IJO.IJO_2930_22.
Yang Q, Liu L, Li J, Yan H, Cai H, Sheng M BMC Ophthalmol. 2023; 23(1):44.
PMID: 36721131 PMC: 9887780. DOI: 10.1186/s12886-023-02795-7.