Quantitative Proteomics and Weighted Correlation Network Analysis of Tear Samples in Adults and Children With Diabetes and Dry Eye

Systems biology of dry eye: Unraveling molecular mechanisms through multi-omics integration

Dry eye disease (DED) is a multifactorial condition with complex and incompletely understood molecular mechanisms. Advances in multi-omics technologies, including genomics, transcriptomics, proteomics, metabolomics, and microbiomics, have provided new insights into the pathophysiology of DED. Genomic analyses have identified key genetic variants linked to immune regulation and lacrimal gland function. Transcriptomic studies reveal upregulated inflammatory pathways in ocular surface tissues, implicating these as core drivers of chronic inflammation. Proteomic research highlights significant alterations in tear protein composition, especially proteins involved in inflammation and tissue repair. Metabolomics studies focus on disrupted lipid metabolism and oxidative stress, which are crucial in maintaining tear film stability. Furthermore, microbiome research has demonstrated reduced microbial diversity and increased pathogenic bacteria, exacerbating inflammatory responses. The integration of multi-omics data allows for the identification of novel biomarkers and therapeutic targets, enabling precision diagnostics and personalized treatments. Therefore, this review highlights the critical importance of multi-omics approaches in deepening our understanding of DED's complex molecular mechanisms and their potential to transform clinical management and therapeutic innovations in this challenging field.

Leptin Receptor Deficiency-Associated Diabetes Disrupts Lacrimal Gland Circadian Rhythms and Contributes to Dry Eye Syndrome

Purpose

This study investigated the impact of hyperglycemia in type 2 diabetes mellitus (T2DM) on the circadian rhythms and function of lacrimal glands (LGs) in contributing to dry eye syndrome. We assessed the effects of hyperglycemia on circadian gene expression, immune cell recruitment, neural activity, and metabolic pathways, and evaluated the effectiveness of insulin in restoring normal LG function.

Methods

Using a T2DM mouse model (db/db mice), circadian transcriptomic changes in LGs were analyzed through RNA sequencing over a 24-hour period. Rhythmic expression of core clock genes, immune and neural activity, and metabolic pathways were evaluated. The effects of insulin treatment on these parameters were also assessed.

Results

Hyperglycemia disrupted the circadian expression of core clock genes in LGs, leading to a 50% reduction in rhythmic gene expression. This was associated with altered immune cell recruitment, impaired neural activity, and metabolic changes. Insulin treatment lowered blood glucose levels but did not restore normal circadian function or tear secretion, exacerbating dry eye syndrome in diabetic mice.

Conclusions

T2DM significantly disrupts circadian rhythms and function in lacrimal glands, contributing to dry eye syndrome. The limited efficacy of insulin in restoring circadian regulation suggests that hyperglycemia-induced dysfunction in LGs is not solely dependent on blood glucose levels, highlighting the need for therapies targeting circadian rhythms in diabetic ocular complications.

Tear biomarkers

An extensive exploration of lacrimal fluid molecular biomarkers in understanding and diagnosing a spectrum of ocular and systemic diseases is presented. The chapter provides an overview of lacrimal fluid composition, elucidating the roles of proteins, lipids, metabolites, and nucleic acids within the tear film. Pooled versus single-tear analysis is discussed to underline the benefits and challenges associated with both approaches, offering insights into optimal strategies for tear sample analysis. Subsequently, an in-depth analysis of tear collection methods is presented, with a focus on Schirmer's test strips and microcapillary tubes methods. Alternative tear collection techniques are also explored, shedding light on their applicability and advantages. Variability factors, including age, sex, and diurnal fluctuations, are examined in the context of their impact on tear biomarker analysis. The main body of the chapter is dedicated to discussing specific biomarkers associated with ocular discomfort and a wide array of ocular diseases. From dry eye disease and thyroid-associated ophthalmopathy to keratoconus, age-related macular degeneration, diabetic retinopathy, and glaucoma, the intricate relationship between molecular biomarkers and these conditions is thoroughly dissected. Expanding beyond ocular pathologies, the chapter explores the applicability of tear biomarkers in diagnosing systemic diseases such as multiple sclerosis, amyotrophic lateral sclerosis, Alzheimer's disease, Parkinson's disease, and cancer. This broader perspective underscores the potential of lacrimal fluid analysis in offering non-invasive diagnostic tools for conditions with far-reaching implications.

Applications of artificial intelligence and bioinformatics methodologies in the analysis of ocular biofluid markers: a scoping review

PURPOSE

This scoping review summarizes the applications of artificial intelligence (AI) and bioinformatics methodologies in analysis of ocular biofluid markers. The secondary objective was to explore supervised and unsupervised AI techniques and their predictive accuracies. We also evaluate the integration of bioinformatics with AI tools.

METHODS

This scoping review was conducted across five electronic databases including EMBASE, Medline, Cochrane Central Register of Controlled Trials, Cochrane Database of Systematic Reviews, and Web of Science from inception to July 14, 2021. Studies pertaining to biofluid marker analysis using AI or bioinformatics were included.

RESULTS

A total of 10,262 articles were retrieved from all databases and 177 studies met the inclusion criteria. The most commonly studied ocular diseases were diabetic eye diseases, with 50 papers (28%), while glaucoma was explored in 25 studies (14%), age-related macular degeneration in 20 (11%), dry eye disease in 10 (6%), and uveitis in 9 (5%). Supervised learning was used in 91 papers (51%), unsupervised AI in 83 (46%), and bioinformatics in 85 (48%). Ninety-eight papers (55%) used more than one class of AI (e.g. > 1 of supervised, unsupervised, bioinformatics, or statistical techniques), while 79 (45%) used only one. Supervised learning techniques were often used to predict disease status or prognosis, and demonstrated strong accuracy. Unsupervised AI algorithms were used to bolster the accuracy of other algorithms, identify molecularly distinct subgroups, or cluster cases into distinct subgroups that are useful for prediction of the disease course. Finally, bioinformatic tools were used to translate complex biomarker profiles or findings into interpretable data.

CONCLUSION

AI analysis of biofluid markers displayed diagnostic accuracy, provided insight into mechanisms of molecular etiologies, and had the ability to provide individualized targeted therapeutic treatment for patients. Given the progression of AI towards use in both research and the clinic, ophthalmologists should be broadly aware of the commonly used algorithms and their applications. Future research may be aimed at validating algorithms and integrating them in clinical practice.

Dry eye disease in the young: A narrative review

Dry eye disease (DED), a multifactorial ocular disease that significantly impacts quality of life, is most commonly reported in adults. This review describes the prevalence, risk factors, diagnosis and management of DED in children. A literature search, conducted from January 2000-December 2022, identified 54 relevant publications. Using similar diagnostic criteria to those reported in adults, namely standardized questionnaires and evaluation of tear film homeostatic signs, the prevalence of DED in children ranged from 5.5% to 23.1 %. There was limited evidence for the influence of ethnicity in children, however some studies reported an effect of sex in older children. Factors independently associated with DED included digital device use, duration of digital device use, outdoor time and urban living, Rates of DED were higher in children with ocular allergy and underlying systemic diseases. Compared with similar studies in adults, the prevalence of a prior DED diagnosis or a diagnosis based on signs and symptoms was lower in children, but symptoms were commonly reported. Treatment options were similar to those in adults, including lifestyle modifications, blinking, management of lid disease and unpreserved lubricants in mild disease with escalating treatment with severity. Management requires careful exploration of symptoms, medical history and the diagnosis and management of ocular comorbidities such as allergy and anterior blepharitis. Appropriately powered population-based studies are required to understand the prevalence of and risk factors for DED in children. Development of age-appropriate thresholds for signs and symptoms of DED would support better diagnosis of disease and understanding of natural history.

Tear Fluid Biomarkers and Quality of Life in People with Type 2 Diabetes and Dry Eye Disease

Dry eye disease (DED) can be extremely distressing and is common in type 2 diabetes (T2D). To investigate potential biomarkers of DED in T2D, panels of proteins in tears, alongside clinical signs and symptoms of DED, were assessed. Patients were classified into four groups: T2D + DED (n = 47), T2D-only (n = 41), DED-only (n = 17) and healthy controls (n = 17). All patients underwent the Ocular Surface Disease Index (OSDI) and Dry Eye-Related Quality of Life (DEQS) questionnaires, tear evaporation rate (TER), fluorescein tear break-up time (fTBUT), corneal fluorescein staining (CFS) and Schirmer 1 test assessments. Six metabolic proteins and 14 inflammatory cytokines were analyzed with multiplex bead analysis. Interleukin (IL)-6 and IL-8 concentrations in tears were significantly higher in the T2D + DED group, and these biomarkers were positively correlated with CFS. In addition, tear IL-6 was negatively correlated with fTBUT in the T2D + DED group. Clinical signs of DED in the T2D + DED group were similar to the DED-only group. The T2D + DED group had more patients with moderate and severe DED (versus the DED-only group), suggesting a different pathogenesis for DED in T2D versus DED-only. Therefore, IL-6 and IL-8 could potentially be diagnostic biomarkers of DED in T2D.

Development and assessment of diabetic nephropathy prediction model using hub genes identified by weighted correlation network analysis

Diabetic nephropathy (DN) is one microvascular complication of diabetes. About 30% of diabetic patients can develop DN, which is closely related to the high incidence and mortality of heart diseases, and then develop end-stage renal diseases. Therefore, early detection and screening of high-risk patients with DN is important. Herein, we explored the differences of serum transcriptomics between DN and non-DN in type II diabetes mellitus (T2DM) patients. We obtained 110 target genes using weighted correlation network analysis. Gene Ontology enrichment analysis indicates these target genes are mainly related to membrane adhesion, alpha-amino acid biosynthesis, metabolism, and binding, terminus, inhibitory synapse, clathrinid-sculpted vesicle, kinase activity, hormone binding, receptor activity, and transporter activity. Kyoto Encyclopedia of Genes and Genomes analysis indicates the process of DN in diabetic patients can involve synaptic vesicle cycle, cysteine and methionine metabolism, N-Glycan biosynthesis, osteoclast differentiation, and cAMP signaling pathway. Next, we detected the expression levels of hub genes in a retrospective cohort. Then, we developed a risk score tool included in the prediction model for early DN in T2DM patients. The prediction model was well applied into clinical practice, as confirmed by internal validation and several other methods. A novel DN risk model with relatively high prediction accuracy was established based on clinical characteristics and hub genes of serum detection. The estimated risk score can help clinicians develop individualized intervention programs for DN in T2DM. External validation data are required before individualized intervention measures.

Exploring Human Milk Dynamics: Interindividual Variation in Milk Proteome, Peptidome, and Metabolome

Human milk is a dynamic biofluid, and its detailed composition receives increasing attention. While most studies focus on changes over time or differences between maternal characteristics, interindividual variation receives little attention. Nevertheless, a comprehensive insight into this can help interpret human milk studies and help human milk banks provide targeted milk for recipients. This study aimed to map interindividual variation in the human milk proteome, peptidome, and metabolome and to investigate possible explanations for this variation. A set of 286 milk samples was collected from 29 mothers in the third month postpartum. Samples were pooled per mother, and proteins, peptides, and metabolites were analyzed. A substantial coefficient of variation (>100%) was observed for 4.6% and 36.2% of the proteins and peptides, respectively. In addition, using weighted correlation network analysis (WGCNA), 5 protein and 11 peptide clusters were obtained, showing distinct characteristics. With this, several associations were found between the different data sets and with specific sample characteristics. This study provides insight into the dynamics of human milk protein, peptide, and metabolite composition. In addition, it will support future studies that evaluate the effect size of a parameter of interest by enabling a comparison with natural variability.

Tear function in patients with diabetes mellitus: A systematic review and meta-analysis

PURPOSE

To examine tear function in patients with diabetes mellitus (DM).

DESIGN

Systematic review and meta-analysis.

METHOD

We searched Embase and PubMed from database inception to March 16, 2022. We included observational studies that compared tear function between patients with and without DM. Tear function was measured using invasive tear breakup time (ITBUT) and Schirmer's 1 test. Pooled results are presented as standard mean difference (SMD) with 95% confidence interval (CI) based on random-effects models.

RESULTS

We included 59 studies (7,234 eyes) comparing the tear function between patients with and without DM. This meta-analysis indicated that patients with DM had worse tear function than those without DM (ITBUT: SMD: -0.98, 95% CI: -1.27 to -0.69; Schirmer's 1 test: SMD: -0.45, 95% CI: -0.64 to -0.26), and the results remained consistent in patients with different types of DM (e.g., type 1 DM and type 2 DM) and from different ethnic backgrounds (e.g., Asian vs. non-Asian). Patients with DM under poor glycemic control had worse tear function than those of the non-DM group (ITBUT: SMD: -1.26, 95% CI: -1.86 to -0.66; Schirmer's 1 test: SMD: -0.25, 95% CI: -0.48 to -0.02), whereas there were no significant differences in tear function between patients with DM under optimal glycemic control and non-DM groups.

CONCLUSIONS

We found that patients with type 1 or type 2 DM had significantly reduced tear function. The level of tear function could be determined by glycemic control, and therefore, our findings suggest that glycemic control in patients with DM is critical for maintaining tear function.

SYSTEMATIC REVIEW REGISTRATION

https://www.crd.york.ac.uk/prospero, identifier CRD42021250498.

Longitudinal Tear Protein Changes Correlate with Ocular Chronic GVHD Development in Allogeneic Hematopoietic Stem Cell Transplant Patients

Ocular graft-versus-host disease (oGVHD) is a manifestation of chronic GVHD, frequently occurring in patients after allogeneic hematopoietic stem cell transplant (HSCT). We analyzed tear protein changes before and after allogeneic HSCT, and correlated their levels with the oGVHD development. This retrospective study included 102 patients, and data were recorded before the conditioning treatment, and after 3 to 6 months postoperatively. Tear protein analysis was performed with the Agilent-2100 Bioanalyzer on individual tears sampled by aspiration. Total protein (TP), Lysozyme-C (LYS-C), Lactoferrin (LACTO), Lipocalin-1 (LIPOC-1), Transferrin (TRANSF), Albumin (ALB), and Zinc-alpha-2-glycoprotein (ZAG-2) levels were retrieved and statistically analyzed. Following HSCT forty-three patients developed oGVHD. TP, LACTO, LYS-C, and ZAG-2 levels significantly decreased post-HSCT as compared to pre HSCT levels. In univariate analysis, TP, LACTO, and ZAG-2 decrease was associated with an increased development of oGVHD (OR = 4.49; 95% CI, 1.9 to 10.5; p < 0.001; OR = 3.08; 95% CI 1.3 to 7.6; p = 0.01; OR = 11.1; 95% CI 2.7 to 46.6; p < 0.001, respectively). TRANSF post-HSCT levels significantly increased (OR 15.7; 95% CI, 4.1 to 52.2; p = 0.0001). No pre-post-HSCT changes were shown in ALB and LIPOC-1 levels. Data suggest that TP content, LACTO, TRANSF, and ZAG-2 pre-post changes might be significant predictors of oGVHD development.

Ocular Surface Microbiota in Diabetic Patients With Dry Eye Disease

Purpose

To investigate the ocular surface (OS) commensal bacteria profiles of patients with diabetes mellitus (DM) and dry eye disease (DED).

Methods

In the present study, subjects were assigned to four groups: 37 to the diabetic mellitus with dry eye disease (DM with DED) group, 22 to the diabetes mellitus (DM)-only group, 34 to the dry eye disease (DED)-only group, and 22 to the control group. Tear fluid was collected using Schirmer's tear secretion test paper. 16S ribosomal ribonucleic acid (rRNA) gene sequencing was used to analyze the bacterial microbiota.

Results

The DM with DED group showed the highest operational taxonomic unit (OTU) numbers and alpha diversity and the most different beta diversity. The groups shared the four most abundant phyla, accounting for over 96% of the total abundance. At the genus level, there were 10 types of overlap in the core microbiota in the groups. They showed significant differences between the groups. Additionally, the DM with DED group and the control group showed four unique core genera, respectively. Unclassified Clostridiales and Lactobacillus were the core microbiota members of the DM with DED group, the DM-only group, and the DED-only group, but not the control group.

Conclusions

In the present study, our results showed that the patients in the DM with DED group had a more complex and comprehensive ocular surface microbial composition. To the best of our knowledge, this is the first study to reveal the microbial profile of dry eye disease in patients with diabetes mellitus.