Global Prevalence of Meibomian Gland Dysfunction: A Systematic Review and Meta-Analysis

Hassanzadeh, Samira; Varmaghani, Mehdi; Zarei‐Ghanavati, Siamak; Shandiz, Javad Heravian; Khorasani, Abbas Azimi

doi:10.1080/09273948.2020.1755441

Cited by 64 publications

(43 citation statements)

References 39 publications

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“…In recent years, the incidence of MGD in the population has increased, making it the most common disease in ophthalmology clinics. The estimated pooled prevalence of MGD worldwide was 35.8% ( 24 ). Consistent with this, a global survey of dry eye patients found that 35% of the population had dry eye, of which 53% were caused by MGD ( 25 ).…”

Section: Discussionmentioning

confidence: 99%

Artificial Intelligence to Detect Meibomian Gland Dysfunction From in-vivo Laser Confocal Microscopy

et al. 2021

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Background: In recent years, deep learning has been widely used in a variety of ophthalmic diseases. As a common ophthalmic disease, meibomian gland dysfunction (MGD) has a unique phenotype in in-vivo laser confocal microscope imaging (VLCMI). The purpose of our study was to investigate a deep learning algorithm to differentiate and classify obstructive MGD (OMGD), atrophic MGD (AMGD) and normal groups.Methods: In this study, a multi-layer deep convolution neural network (CNN) was trained using VLCMI from OMGD, AMGD and healthy subjects as verified by medical experts. The automatic differential diagnosis of OMGD, AMGD and healthy people was tested by comparing its image-based identification of each group with the medical expert diagnosis. The CNN was trained and validated with 4,985 and 1,663 VLCMI images, respectively. By using established enhancement techniques, 1,663 untrained VLCMI images were tested.Results: In this study, we included 2,766 healthy control VLCMIs, 2,744 from OMGD and 2,801 from AMGD. Of the three models, differential diagnostic accuracy of the DenseNet169 CNN was highest at over 97%. The sensitivity and specificity of the DenseNet169 model for OMGD were 88.8 and 95.4%, respectively; and for AMGD 89.4 and 98.4%, respectively.Conclusion: This study described a deep learning algorithm to automatically check and classify VLCMI images of MGD. By optimizing the algorithm, the classifier model displayed excellent accuracy. With further development, this model may become an effective tool for the differential diagnosis of MGD.

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Section: Discussionmentioning

confidence: 99%

Artificial Intelligence to Detect Meibomian Gland Dysfunction From in-vivo Laser Confocal Microscopy

et al. 2021

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“…The identification of male sex as a potential risk factor for MGD is not novel and has been previously reported. 14,36 It should also be noted that while the male and female cohorts were age matched, there were age differences between patients with MGD compared to non-MGD dry eye. This mean difference in age was seven years, with slightly older patients in the MGD group, however, LDL and non-HDL cholesterol levels were unchanged.…”

Section: Discussionmentioning

confidence: 99%

“…[10][11][12] The prevalence of MGD in the population varies from 21% to 71% and is impacted by variables such as sex, race, systemic and ocular diseases, and the presence or absence of dry eye symptoms. 13,14 In contrast to aqueous deficient dry eye, clinical signs of MGD include the presence of foamy tears, lid margin hyperemia/telangiectasias, meibomian orifice plugging, increased meibum viscosity, and a shift in the mucocutaneous junction. [15][16][17] Additional changes in the tear film include thinning of the lipid layer, measured by interferometry, and a shortening in the tear film breakup time.…”

Section: Introductionmentioning

confidence: 99%

The Association Between Risk Factors for Metabolic Syndrome and Meibomian Gland Disease in a Dry Eye Cohort

Mussi¹,

Haque²,

Robertson³

2021

OPTH

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Purpose: Risk factors for metabolic syndrome include abdominal obesity, insulin resistance, hypertension, high triglycerides and/or low high-density lipoprotein cholesterol, and hyperglycemia. Risk factors for metabolic syndrome have been associated with dry eye disease; however, their association with meibomian gland disease (MGD), a subtype of dry eye, is unclear. In the present study, we investigated risk factors for metabolic syndrome in a dry eye cohort with and without MGD. Methods: This retrospective case-control study evaluated electronic medical records at a major urban outpatient medical center to identify patients with a known diagnosis of dry eye disease with and without MGD. Males and females were matched for age, smoking status, race, ethnicity, and body mass index (BMI). Patient demographics, anthropometric measurements, medical history, clinical findings, and serologies were analyzed. A diagnosis of MGD was based on clinical signs noted in the medical record. Results: MGD was not associated with BMI, smoking, type 2 diabetes mellitus, hypertension or hyperlipidemia in this dry eye cohort. MGD was associated with male sex and increasing age. While increasing age was weakly correlated with decreased low density lipoprotein cholesterol and non-high density lipoprotein cholesterol, serum lipid levels were not associated with MGD. Conclusion: Importantly, we found that risk factors for metabolic syndrome are not specifically associated with an increase in MGD when compared to non-MGD dry eye subjects. While risk factors for metabolic syndrome are associated with dry eye disease, they likely reflect a chronic systemic state of low-grade inflammation that negatively impacts the function of both lacrimal and meibomian glands.

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“…A recent study reported an MGD prevalence of 35.8% [45]. Furthermore, according to Craig et al [19], the symptoms of MGD occurred earlier within the natural history of disorder progression, even in patients in their 20s, indicating the broad negative effects of MGD.…”

Section: Research Hotspotsmentioning

confidence: 96%

Research progress on meibomian gland dysfunction from 2011 to 2020: a bibliometric and visualized analysis

Liu

Zhao

Kong

2021

Preprint

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Background To explore the research areas, hotspots, and progress of meibomian gland dysfunction through bibliometrics. Methods Related publications were retrieved from the Web of Science Core Collection from 2011 to 2020. VOSviewer1.6.16, Citespace.5.7.R2, and GraphPad Prism 8 were used to visualize the distribution of countries, research institutions, journals, authors, keywords, and annual publication numbers in this field. Results A total number of 716 relevant publications were retrieved. The United States and Keio University ranked the first among the countries and organizations with the most publications. Cornea, Investigative Ophthalmology & Visual Science, and Ocular Surface were the top three journals with the highest publication counts and citations. The authors who contributed to this topic mainly formed three clusters which manifested the research areas, and the extracted keywords mainly formed four clusters which manifested the hotspots were explored. Conclusions The research areas and hotspots of meibomian gland dysfunction were as follow: (1) Pathogenesis or potential etiology of meibomian gland dysfunction; (2) Diagnosis of meibomian gland dysfunction; (3) Therapy of meibomian gland dysfunction and the International Workshop`s dedication to it; (4) Epidemiology of meibomian gland dysfunction.

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Global Prevalence of Meibomian Gland Dysfunction: A Systematic Review and Meta-Analysis

Cited by 64 publications

References 39 publications

Artificial Intelligence to Detect Meibomian Gland Dysfunction From in-vivo Laser Confocal Microscopy

Artificial Intelligence to Detect Meibomian Gland Dysfunction From in-vivo Laser Confocal Microscopy

The Association Between Risk Factors for Metabolic Syndrome and Meibomian Gland Disease in a Dry Eye Cohort

Research progress on meibomian gland dysfunction from 2011 to 2020: a bibliometric and visualized analysis

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