Evaporative dry eye disease due to meibomian gland dysfunction: Preferred practice pattern guidelines for diagnosis and treatment

Evaporative dry eye (EDE) due to meibomian gland dysfunction (MGD) is one of the common clinical problems encountered in ophthalmology. It is a major cause of dry eye disease (DED) and of ocular morbidity. In EDE, inadequate quantity or quality of lipids produced by the meibomian glands leads to faster evaporation of the preocular tear film and symptoms and signs of DED. Although the diagnosis is made using a combination of clinical features and special diagnostic test results, the management of the disease might be challenging as it is often difficult to distinguish EDE from other subtypes of DED. This is critical because the approach to the treatment of DED is guided by identifying the underlying subtype and cause. The traditional treatment of MGD consists of warm compresses, lid massage, and improving lid hygiene, all measures aimed at relieving glandular obstruction and facilitating meibum outflow. In recent years, newer diagnostic imaging modalities and therapies for EDE like vectored thermal pulsation and intense pulsed light therapy have emerged. However, the multitude of management options may confuse the treating ophthalmologist, and a customized rather than a generalized approach is necessary for these patients. This review aims to provide a simplified approach to diagnose EDE due to MGD and to individualize treatment for each patient. The review also emphasizes the role of lifestyle modifications and appropriate counseling so that patients can have realistic expectations and enjoy a better quality of life.

Tear film microbiome in Sjogren’s and non-Sjogren’s aqueous deficiency dry eye

Purpose: To understand the bacterial microbiome changes associated with Sjogren’s syndrome (SS) and non?Sjogren’s syndrome (NSS) aqueous?deficient dry eyes compared to healthy eyes. Methods: Bacterial microbiome was generated from the deoxyribonucleic acid of tear film samples in healthy (n = 33), SS (n = 17), and NSS (n = 28) individuals. Sequencing of the V3?V4 region of the 16S rRNA gene was performed on the Illumina HiSeq2500 platform. Quantitative Insights Into Microbial Ecology (QIIME) pipeline was used to assign taxa to sequences. Statistical analysis was performed in R to assess the alpha diversity and beta diversity indices. Significant changes between the healthy, SS, and NSS cohorts were depicted by principal coordinate analysis (PCoA), differential abundance, and network analysis. Results: Tear microbiome was generated in healthy, SS, and NSS samples. Phyla Actinobacteria, Firmicutes, and Bacteroidetes showed significant changes in SS and NSS compared to healthy. Genera Lactobacillus and Bacillus were predominantly present in all samples. PCoA and heat map analysis showed distinct clusters for SS and NSS from the healthy cohort. Genera Prevotella, Coriobacteriaceae UCG?003, Enterococcus, Streptomyces, Rhodobacter, Ezakiella, and Microbacterium significantly increased in abundance in SS and NSS compared to a healthy cohort. Bacteria–bacteria interaction in SS, NSS, and healthy cohorts was predicted by CoNet network analysis. This analysis predicted a major hub of interaction for the pro?inflammatory bacterium Prevotella in the SS and NSS cohorts. Conclusion: The results of the study indicate significant changes in the phyla and genera in SS and NSS compared to healthy. Both discriminative analysis and network analysis indicated a possible association of predominant pro?inflammatory bacteria with SS and NSS.