Relationship between eyelid margin irregularity and meibomian gland dropout

doi:10.1016/j.jtos.2020.11.007

The Ocular Surface

Volume 19, January 2021, Pages 31-37

https://doi.org/10.1016/j.jtos.2020.11.007 Get rights and content

Abstract

Purpose

To examine the relationship between lid margin abnormalities and meibomian gland loss in infrared meibography.

Methods

This study was a retrospective chart review of 170 patients with dry eye disease. A correlation analysis between eyelid margin abnormalities and meibomian gland dropout in infrared meibography was performed using data from 141 eyes. We graded the following eyelid margin abnormalities: irregular lid margin, vascular engorgement, plugging, anterior placement of the mucocutaneous junction, exposed terminal duct, and presence of tattoos. Multiple regression analyses were performed with meiboscore (meibomian gland dropout grade) as the dependent variable and age, sex, lid margin abnormality grades, and total grading score of lid margin abnormalities as the covariates. In addition, Meibomian glands structure were examined in those with eyelid margin dimpling using meibography in 25 eyes.

Results

In the multiple regression analysis, an irregular lid margin in the upper eyelid was associated with a higher meiboscore after controlling for age and sex (coefficients B = 1.379, p = 0.025). Other lid margin abnormalities did not significantly affect the meiboscore. In the lower eyelids, irregular lid margin (coefficients B = 0.602, p = 0.009) and total grading score of lid margin abnormality were associated with higher meiboscores (coefficients B = 0.100, p = 0.022). Of the 25 eyes with dimples, 21 (84%) showed focal or complete meibomian gland loss at the site.

Conclusions

Lid margin abnormalities were found to be associated with meibomian gland dropout.

Introduction

According to The Tear Film Ocular Surface Society Dry Eye Work Shop II (TFOS DEWS II), dry eye disease is defined as “a multifactorial disease of the ocular surface characterized by a loss of homeostasis of the tear film, and accompanied by ocular symptoms, in which tear film instability and hyperosmolarity, ocular surface inflammation and damage, and neuro-sensory abnormalities play etiological roles” [1]. The causes of dry eye disease are a lack of aqueous production or excessive evaporation of tear film, or both [2]. The tear film comprises an aqueous-mucin gel and lipid layer [3]. Of the 2 components, it is the lipid layer of the tear film that prevents evaporation of the aqueous layer [4], being formed by Meibum secreted from the meibomian gland. Meibomian gland dysfunction (MGD) is characterized by terminal duct obstruction and reduced/altered meibum secretions [5]. MGD is a leading cause of evaporative dry eye disease, and new diagnostic devices and treatment options for MGD were recently developed [6].

To diagnose MGD, evaluation of subjective symptoms, morphological changes in the eyelid margin, meibum secretion, infrared meibography, lipid layer thickness measurement, and other factors are required [7]. Although examining meibomian gland secretion and identifying abnormal eyelid margins remains the main method for clinically examining MGD, meibography has been also widely used for assessing MGD after introduction of meiboscopy [8], transillumination meibography [9], or infrared non-contact meibography [10], because it directly shows the morphological changes of the meibomian gland.

Not all clinicians have access to an infrared meibography system, but eyelid margin abnormalities can easily be detected by ophthalmologists using slit-lamp microscopy. Although it is predicted that abnormal eyelid margins indicate more meibomian gland loss (dropout) in meibography, there are no published reports confirming that this is the case.

The purpose of the present study was to examine the relationship between lid margin abnormalities, a factor traditionally used for the diagnosis of MGD, and meibomian gland loss, which can be confirmed by infrared meibography. Our findings revealed a correlation between eyelid margin abnormalities and meiboscores. Furthermore, we directly checked the meibomian glands at focal dimples in the lid margins using an infrared camera system.

Section snippets

Patients and methods

This study was a retrospective chart review of 170 patients (173 eyes) who visited Chuncheon Sacred Heart Hospital of Hallym University and were diagnosed with dry eye disease. This study followed the principles of the Declaration of Helsinki and was approved by the Institutional Review Board of Chuncheon Sacred Heart Hospital (CHUNCHEON 2019-11-013).

The inclusion criteria were age of ≥20 years and at least mild dry eye symptoms (an Ocular Surface Disease Index [OSDI] score ≥13) and low tear

Relationship between eyelid margin abnormalities and meibomian gland dropout in infrared meibography

Mean age ( $\pm$ standard deviation) of the 141 patients was 54.8 $\pm$ 14.6 (range, 20–86 years). There were 37 males and 104 females. Table 1 shows the distribution of the meiboscores of the meibomian glands in the upper and lower eyelids according to the grades of 6 abnormalities in the upper and lower eyelid margin. In the upper eyelids, the meiboscores of patients with higher grades of irregular lid margins seemed to be higher than the meiboscores of those without irregular lid margins (p = 0.065,

Discussion

In 2008, Arita et al. introduced noncontact infrared meibography and demonstrated a correlation between lid margin abnormalities and meibomian gland dropout (meiboscore) [10]. In their report, 4 lid margin abnormalities (irregular lid margin, vascular engorgement, plugging of meibomian gland orifices, and anterior and posterior placement of the mucocutaneous junction) were scored from 0 through 4 according to the number of these abnormalities present in each eye. As a result, the meiboscore was

Financial disclosure

The authors have no commercial or proprietary interest in any concept or product described in this article.

Acknowledgement

This work was supported by a grant of the Korea Health Technology R&D Project through the Korea Health Industry Development Institute (KHIDI), funded by the Ministry of Health & Welfare, Republic of Korea (grant number: HI17C0659) and the National Research Foundation of Korea (NRF) grant funded by the Korea government (Ministry of Science and ICT) (No. 2017R1A1A2A10000681, 2020R1A2C1005009).

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Citation Excerpt :
A diagnosis of MGD requires evaluation of subjective symptoms, morphologic changes in the eyelid margin, meibum secretion, infrared meibography, lipid layer thickness measurement, and other factors [2]. Although examining MG secretion and identifying abnormal eyelid margins [3] are the main methods for clinical evaluation of MGD, meibography has become widely used to assess the morphologic changes of the MG [4] since the introduction of transillumination meibography [5] and non-contact infrared meibography [6]. Non-contact infrared meibography uses infrared light and an infrared camera to provide MG images with reduced discomfort for the patient.
Develop a deep learning-based automated method to segment meibomian glands (MG) and eyelids, quantitatively analyze the MG area and MG ratio, estimate the meiboscore, and remove specular reflections from infrared images.
A total of 1600 meibography images were captured in a clinical setting. 1000 images were precisely annotated with multiple revisions by investigators and graded 6 times by meibomian gland dysfunction (MGD) experts. Two deep learning (DL) models were trained separately to segment areas of the MG and eyelid. Those segmentation were used to estimate MG ratio and meiboscores using a classification-based DL model. A generative adversarial network was implemented to remove specular reflections from original images.
The mean ratio of MG calculated by investigator annotation and DL segmentation was consistent 26.23% vs 25.12% in the upper eyelids and 32.34% vs. 32.29% in the lower eyelids, respectively. Our DL model achieved 73.01% accuracy for meiboscore classification on validation set and 59.17% accuracy when tested on images from independent center, compared to 53.44% validation accuracy by MGD experts. The DL-based approach successfully removes reflection from the original MG images without affecting meiboscore grading.
DL with infrared meibography provides a fully automated, fast quantitative evaluation of MG morphology (MG Segmentation, MG area, MG ratio, and meiboscore) which are sufficiently accurate for diagnosing dry eye disease. Also, the DL removes specular reflection from images to be used by ophthalmologists for distraction-free assessment.
Higher incidence of meibomian gland dysfunction in postmenopausal women with primary acquired nasolacrimal duct obstruction
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Morphological features of the eyelid margin in relation to conjunctival goblet cells and MUC5AC in tear film and clinical tests of dry eye
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Unsupervised Learning Based on Meibography Enables Subtyping of Dry Eye Disease and Reveals Ocular Surface Features
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FLARelationship between eyelid margin irregularity and meibomian gland dropout

Abstract

Purpose

Methods

Results

Conclusions

Introduction

Section snippets

Patients and methods

Relationship between eyelid margin abnormalities and meibomian gland dropout in infrared meibography

Discussion

Financial disclosure

Acknowledgement

Ocul Surf

Ocul Surf

Ocul Surf

Ophthalmology

Ophthalmology

Am J Ophthalmol

Contact Lens Anterior Eye

The definition and classification of dry eye disease: report of the definition and classification subcommittee of the international dry eye WorkShop

Ocul Surf

Structure and function of the tear film

Adv Exp Med Biol

The international workshop on meibomian gland dysfunction: executive summary

Invest Ophthalmol Vis Sci

The contribution of meibomian disease to dry eye

Ocul Surf

FLA
Relationship between eyelid margin irregularity and meibomian gland dropout