Differential metabolic markers associated with primary open-angle glaucoma and cataract in human aqueous humor

Implications of Ocular Confounding Factors for Aqueous Humor Proteomic and Metabolomic Analyses in Retinal Diseases

Purpose

To assess the impact of ocular confounding factors on aqueous humor (AH) proteomic and metabolomic analyses for retinal disease characterization.

Methods

This study recruited 138 subjects (eyes): 102 with neovascular age-related macular degeneration (nAMD), 18 with diabetic macular edema (DME), and 18 with cataract (control group). AH samples underwent analysis using Olink Target 96 proteomics and Metabolon's metabolomics platform Data analysis included correlation, differential abundance, and gene-set analysis.

Results

In total, 756 proteins and 408 metabolites were quantified in AH. Total AH protein concentration was notably higher in nAMD (3.2-fold) and DME (4.1-fold) compared to controls. Pseudophakic eyes showed higher total AH protein concentrations than phakic eyes (e.g., 1.6-fold in nAMD) and a specific protein signature indicative of matrix remodeling. Unexpectedly, pupil-dilating drugs containing phenylephrine/tropicamide increased several AH proteins, notably interleukin-6 (5.4-fold in nAMD). Correcting for these factors revealed functionally relevant protein correlation clusters and disease-relevant, differentially abundant proteins across the groups. Metabolomics analysis, for which the relevance of confounder adjustment was less apparent, suggested insufficiently controlled diabetes and chronic hyperglycemia in the DME group.

Conclusions

AH protein concentration, pseudophakia, and pupil dilation with phenylephrine/tropicamide are important confounding factors for AH protein analyses. When these factors are considered, AH analyses can more clearly reveal disease-relevant factors.

Translational Relevance

Considering AH protein concentration, lens status, and phenylephrine/tropicamide administration as confounders is crucial for accurate interpretation of AH protein data.

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.

3D superstructure based metabolite profiling for glaucoma diagnosis

Metabolome analysis has gained widespread application in disease diagnosis owing to its ability to provide comprehensive information, including disease phenotypes. In this study, we utilized 3D superstructures fabricated through evaporation-induced microprinting to analyze the metabolome for glaucoma diagnosis. 3D superstructures offer the following advantages: high hotspot density per unit volume of the structure extending from two to three dimensions, excellent signal repeatability due to the reproducibility and defect tolerance of 3D printing, and high thermal stability due to the PVP-enclosed capsule form. Leveraging the superior optical properties of the 3D superstructure, we aimed to classify patients with glaucoma. The signal obtained from the 3D superstructure was employed in a Deep Neural Network (DNN) classification model to accurately classify glaucoma patients. The sensitivity and specificity of the model were determined as 92.05% and 93.51%, respectively. Additionally, the fabrication of 3D superstructures can be performed at any stage, significantly reducing measurement time. Furthermore, their thermal stability allows for the analysis of smaller samples. One notable advantage of 3D superstructures is their versatility in accommodating different target materials. Consequently, they can be utilized for a wide range of metabolic analyses and disease diagnoses.

Critical Factors in Sample Collection and Preparation for Clinical Metabolomics of Underexplored Biological Specimens

This review article compiles critical pre-analytical factors for sample collection and extraction of eight uncommon or underexplored biological specimens (human breast milk, ocular fluids, sebum, seminal plasma, sweat, hair, saliva, and cerebrospinal fluid) under the perspective of clinical metabolomics. These samples are interesting for metabolomics studies as they reflect the status of living organisms and can be applied for diagnostic purposes and biomarker discovery. Pre-collection and collection procedures are critical, requiring protocols to be standardized to avoid contamination and bias. Such procedures must consider cleaning the collection area, sample stimulation, diet, and food and drug intake, among other factors that impact the lack of homogeneity of the sample group. Precipitation of proteins and removal of salts and cell debris are the most used sample preparation procedures. This review intends to provide a global view of the practical aspects that most impact results, serving as a starting point for the designing of metabolomic experiments.

Reduced glutathione level in the aqueous humor of patients with primary open-angle glaucoma and normal-tension glaucoma

Glaucoma is a leading cause of blindness worldwide in older people. Profiling the aqueous humor, including the metabolites it contains, is useful to understand physiological and pathological conditions in the eye. In the current study, we used mass spectrometry (MS) to characterize the aqueous humor metabolomic profile and biological features of patients with glaucoma. Aqueous humor samples were collected during trabeculectomy surgery or cataract surgery and analyzed with global metabolomics. We included 40 patients with glaucoma (32 with POAG, 8 with NTG) and 37 control subjects in a discovery study. VIP analysis revealed five metabolites that were elevated and three metabolites that were reduced in the glaucoma patients. The identified metabolomic profile had an area under the receiver operating characteristic curve of 0.953. Among eight selected metabolites, the glutathione level was significantly decreased in association with visual field defects. Moreover, in a validation study to confirm the reproducibility of our findings, the glutathione level was reduced in NTG and POAG patients compared with a cataract control group. Our findings demonstrate that aqueous humor profiling can help to diagnose glaucoma and that various aqueous humor metabolites are correlated with clinical parameters in glaucoma patients. In addition, glutathione is clearly reduced in the aqueous humor of glaucoma patients with both IOP-dependent and IOP-independent disease subtypes. These findings indicate that antioxidant agents in the aqueous humor reflect glaucomatous optic nerve damage and that excessive oxidative stress may be involved in the pathogenesis of glaucoma.

Adaptation of the AbsoluteIDQ p180 kit to the analysis of metabolites in the human aqueous humor

The aim of this study was to use the commercial kit AbsoluteIDQ p180 (Biocrates) for the quantification of metabolites in aqueous humor (AH), as well as to determine the optimal volume of AH that is necessary to obtain reliable and reproducible results. Different volumes of AH (10 µl, 20 µl, and 30 µl) were tested. Of the 188 metabolites measurable with the Biocrates kit, 69 were detected in AH. Depending on the volume used, 41, 51, and 63 metabolites were measured using 10 µl, 20 µl, and 30 µl of AH, respectively. The repeatability of the measurements improved with increasing AH volume. Considering only those metabolites that were obtained with a CV < 15%, 34 metabolites at 10 µl, 41 at 20 µl, and 44 at 30 µl AH were received. On this basis, it can be concluded that the tested method can be successfully applied to analyze metabolites in the human AH. To achieve the most comprehensive detection range and highest repeatability of measurements, it is recommended to use 30 µl AH.

The role of artificial intelligence in analysis of biofluid markers for diagnosis and management of glaucoma: A systematic review

PURPOSE

This review focuses on utility of artificial intelligence (AI) in analysis of biofluid markers in glaucoma. We detail the accuracy and validity of AI in the exploration of biomarkers to provide insight into glaucoma pathogenesis.

METHODS

A comprehensive search was conducted across five electronic databases including Embase, Medline, Cochrane Central Register of Controlled Trials, Cochrane Database of Systematic Reviews, and Web of Science. Studies pertaining to biofluid marker analysis using AI or bioinformatics in glaucoma were included. Identified studies were critically appraised and assessed for risk of bias using the Joanna Briggs Institute Critical Appraisal tools.

RESULTS

A total of 10,258 studies were screened and 39 studies met the inclusion criteria, including 23 cross-sectional studies (59%), nine prospective cohort studies (23%), six retrospective cohort studies (15%), and one case-control study (3%). Primary open angle glaucoma (POAG) was the most commonly studied subtype (55% of included studies). Twenty-four studies examined disease characteristics, 10 explored treatment decisions, and 5 provided diagnostic clarification. While studies examined at entire metabolomic or proteomic profiles to determine changes in POAG, there was heterogeneity in the data with over 175 unique, differentially expressed biomarkers reported. Discriminant analysis and artificial neural network predictive models displayed strong differentiating ability between glaucoma patients and controls, although these tools were untested in a clinical context.

CONCLUSION

The use of AI models could inform glaucoma diagnosis with high sensitivity and specificity. While insight into differentially expressed biomarkers is valuable in pathogenic exploration, no clear pathogenic mechanism in glaucoma has emerged.

Plasma metabolite profile for primary open-angle glaucoma in three US cohorts and the UK Biobank

Glaucoma is a progressive optic neuropathy and a leading cause of irreversible blindness worldwide. Primary open-angle glaucoma is the most common form, and yet the etiology of this multifactorial disease is poorly understood. We aimed to identify plasma metabolites associated with the risk of developing POAG in a case-control study (599 cases and 599 matched controls) nested within the Nurses' Health Studies, and Health Professionals' Follow-Up Study. Plasma metabolites were measured with LC-MS/MS at the Broad Institute (Cambridge, MA, USA); 369 metabolites from 18 metabolite classes passed quality control analyses. For comparison, in a cross-sectional study in the UK Biobank, 168 metabolites were measured in plasma samples from 2,238 prevalent glaucoma cases and 44,723 controls using NMR spectroscopy (Nightingale, Finland; version 2020). Here we show higher levels of diglycerides and triglycerides are adversely associated with glaucoma in all four cohorts, suggesting that they play an important role in glaucoma pathogenesis.

Prediagnostic Plasma Metabolomics and the Risk of Exfoliation Glaucoma

Purpose

The etiology of exfoliation glaucoma (XFG) is poorly understood. We aimed to identify a prediagnostic plasma metabolomic signature associated with XFG.

Methods

We conducted a 1:1 matched case-control study nested within the Nurses' Health Study and Health Professionals Follow-up Study. We collected blood samples in 1989-1990 (Nurses' Health Study) and 1993-1995 (Health Professionals Follow-up Study). We identified 205 incident XFG cases through 2016 (average time to diagnosis from blood draw = 11.8 years) who self-reported glaucoma and were confirmed as XFG cases with medical records. We profiled plasma metabolites using liquid chromatography-mass spectrometry. We evaluated 379 known metabolites (transformed for normality using probit scores) using multiple conditional logistic models. Metabolite set enrichment analysis was used to identify metabolite classes associated with XFG. To adjust for multiple comparisons, we used number of effective tests (NEF) and the false discovery rate (FDR).

Results

Mean age of cases (n = 205) at diagnosis was 71 years; 85% were women and more than 99% were Caucasian; controls (n = 205) reported eye examinations as of the matched cases' index date. Thirty-three metabolites were nominally significantly associated with XFG (P < 0.05), and 4 metabolite classes were FDR-significantly associated. We observed positive associations for lysophosphatidylcholines (FDR = 0.02) and phosphatidylethanolamine plasmalogens (FDR = 0.004) and inverse associations for triacylglycerols (FDR < 0.0001) and steroids (FDR = 0.03). In particular, the multivariable-adjusted odds ratio with each 1 standard deviation higher plasma cortisone levels was 0.49 (95% confidence interval, 0.32-0.74; NEF = 0.05).

Conclusions

In plasma from a decade before diagnosis, lysophosphatidylcholines and phosphatidylethanolamine plasmalogens were positively associated and triacylglycerols and steroids (e.g., cortisone) were inversely associated with XFG risk.

Research Progress and Potential Applications of Spermidine in Ocular Diseases

Spermidine, a natural polyamine, exists in almost all human tissues, exhibiting broad properties like anti-aging, autophagy induction, anti-inflammation, anti-oxidation, cell proliferation activation, and ion channel regulation. Considering that spermidine is already present in human nutrition, recent studies targeting supplementing exogenous sources of this polyamine appear feasible. The protective role of spermidine in various systems has been illuminated in the literature, while recent progress of spermidine administration in ocular diseases remains to be clarified. This study shows the current landscape of studies on spermidine and its potential to become a promising therapeutic agent to treat ocular diseases: glaucoma, optic nerve injury, age-related macular degeneration (AMD), cataracts, dry eye syndrome, and bacterial keratitis. It also has the potential to become a potent biomarker to predict keratoconus (KC), cataracts, uveitis, glaucoma, proliferative diabetic retinopathy (PDR), proliferative vitreoretinopathy (PVR), and retinopathy of prematurity (ROP). We also summarize the routes of administration and the effects of spermidine at different doses.

Metabolomics in Primary Open Angle Glaucoma: A Systematic Review and Meta-Analysis

Glaucoma is a leading cause of blindness worldwide. It is suggested that primary open angle glaucoma (POAG), the most common form of glaucoma, may be associated with significant metabolic alternations, but the systemic literature review and meta-analysis in the area have been missing. Altered metabolomic profiles in the aqueous humor and plasma may serve as possible biomarkers for early detection or treatment targets. In this article, we performed a systematic meta-analysis of the current literature surrounding the metabolomics of patients with POAG and metabolites associated with the disease. Results suggest several metabolites found to be specifically altered in patients with POAG, suggesting broad generalizability and pathways for future research.

Protein Biomarkers in Glaucoma: A Review

Glaucoma is a multifactorial disease. Early diagnosis of this disease can support treatment and reduce the effects of pathophysiological processes. A significant problem in the diagnosis of glaucoma is limited access to the tested material. Therefore, intensive research is underway to develop biomarkers for fast, noninvasive, and reliable testing. Biomarkers indicated in the formation of glaucoma include chemical compounds from different chemical groups, such as proteins, sugars, and lipids. This review summarizes our knowledge about protein and/or their protein-like derived biomarkers used for glaucoma diagnosis since 2000. The described possibilities resulting from a biomarker search may contribute to identifying a group of compounds strongly correlated with glaucoma development. Such a find would be of great importance in the diagnosis and treatment of this disorder, as current screening techniques have low sensitivity and are unable to diagnose early primary open-angle glaucoma.

Measures of disease activity in glaucoma

Glaucoma is the leading cause of irreversible blindness globally which significantly affects the quality of life and has a substantial economic impact. Effective detective methods are necessary to identify glaucoma as early as possible. Regular eye examinations are important for detecting the disease early and preventing deterioration of vision and quality of life. Current methods of measuring disease activity are powerful in describing the functional and structural changes in glaucomatous eyes. However, there is still a need for a novel tool to detect glaucoma earlier and more accurately. Tear fluid biomarker analysis and new imaging technology provide novel surrogate endpoints of glaucoma. Artificial intelligence is a post-diagnostic tool that can analyse ophthalmic test results. A detail review of currently used clinical tests in glaucoma include intraocular pressure test, visual field test and optical coherence tomography are presented. The advanced technologies for glaucoma measurement which can identify specific disease characteristics, as well as the mechanism, performance and future perspectives of these devices are highlighted. Applications of AI in diagnosis and prediction in glaucoma are mentioned. With the development in imaging tools, sensor technologies and artificial intelligence, diagnostic evaluation of glaucoma must assess more variables to facilitate earlier diagnosis and management in the future.

Metabolomics in Retinal Diseases: An Update

Retinal diseases are a leading cause of visual loss and blindness, affecting a significant proportion of the population worldwide and having a detrimental impact on quality of life, with consequent economic burden. The retina is highly metabolically active, and a number of retinal diseases are associated with metabolic dysfunction. To better understand the pathogenesis underlying such retinopathies, new technology has been developed to elucidate the mechanism behind retinal diseases. Metabolomics is a relatively new "omics" technology, which has developed subsequent to genomics, transcriptomics, and proteomics. This new technology can provide qualitative and quantitative information about low-molecular-weight metabolites (M.W. < 1500 Da) in a given biological system, which shed light on the physiological or pathological state of a cell or tissue sample at a particular time point. In this article we provide an extensive review of the application of metabolomics to retinal diseases, with focus on age-related macular degeneration (AMD), diabetic retinopathy (DR), retinopathy of prematurity (ROP), glaucoma, and retinitis pigmentosa (RP).

Candidate Glaucoma Biomarkers: From Proteins to Metabolites, and the Pitfalls to Clinical Applications

Glaucoma is an insidious group of eye diseases causing degeneration of the optic nerve, progressive loss of vision, and irreversible blindness. The number of people affected by glaucoma is estimated at 80 million in 2021, with 3.5% prevalence in people aged 40-80. The main biomarker and risk factor for the onset and progression of glaucoma is the elevation of intraocular pressure. However, when glaucoma is diagnosed, the level of retinal ganglion cell death usually amounts to 30-40%; hence, the urgent need for its early diagnosis. Molecular biomarkers of glaucoma, from proteins to metabolites, may be helpful as indicators of pathogenic processes observed during the disease's onset. The discovery of human glaucoma biomarkers is hampered by major limitations, including whether medications are influencing the expression of molecules in bodily fluids, or whether tests to validate glaucoma biomarker candidates should include human subjects with different types and stages of the disease, as well as patients with other ocular and neurodegenerative diseases. Moreover, the proper selection of the biofluid or tissue, as well as the analytical platform, should be mandatory. In this review, we have summarized current knowledge concerning proteomics- and metabolomics-based glaucoma biomarkers, with specificity to human eye tissue and fluid, as well the analytical approach and the main results obtained. The complex data published to date, which include at least 458 different molecules altered in human glaucoma, merit a new, integrative approach allowing for future diagnostic tests based on the absolute quantification of local and/or systemic biomarkers of glaucoma.

Metabolomics in Glaucoma: A Systematic Review

Purpose

Glaucoma remains a poorly understood disease, and identifying biomarkers for early diagnosis is critical to reducing the risk of glaucoma-related visual impairment and blindness. The aim of this review is to provide current metabolic profiles for glaucoma through a summary and analysis of reported metabolites associated with glaucoma.

Methods

We searched PubMed and Web of Science for metabolomics studies of humans on glaucoma published before November 11, 2020. Studies were included if they assessed the biomarkers of any types of glaucoma and performed mass spectrometry-based or nuclear magnetic resonance–based metabolomics approach. Pathway enrichment analysis and topology analysis were performed to generate a global view of metabolic signatures related to glaucoma using the MetaboAnalyst 3.0.

Results

In total, 18 articles were included in this review, among which 13 studies were focused on open-angle glaucoma (OAG). Seventeen metabolites related to OAG were repeatedly identified, including seven amino acids (arginine, glycine, alanine, lysine, methionine, phenylalanine, tyrosine), two phosphatidylcholine (PC aa C34:2, PC aa C36:4), three complements (acetylcarnitine, propionylcarnitine, butyrylcarnitine), carnitine, glutamine, hypoxanthine, spermine, and spermidine. The pathway analysis implied a major role of amino metabolism in OAG pathophysiology and revealed the metabolic characteristics between different biological samples.

Conclusions

In this review, we summarize existing metabolomic studies related to glaucoma biomarker identification and point out a series of metabolic disorders in OAG patients, providing information on the molecular mechanism changes in glaucoma. Additional studies are needed to validate existing findings, and future research will need to explore the potential overlap between different biological fluids.

Metabolomic Profiling of Aqueous Humor and Plasma in Primary Open Angle Glaucoma Patients Points Towards Novel Diagnostic and Therapeutic Strategy

Glaucoma is the second leading cause of blindness globally characterized by progressive loss of retinal ganglion cells (RGCs) and irreversible visual deficiency. As the most common type of glaucoma, primary open angle glaucoma (POAG) is currently an unmet medical need with limited therapy by lowering intraocular pressure (IOP). However, some patients continue to progress even though their IOP are controlled. Although early diagnosis and prompt treatment are crucial in preventing irreversible visual impairment, there are currently no biomarkers for screening POAG. Metabolomics has the advantages of illustrating the final downstream products of the genome and establishing the closest link to the phenotype. So far, there is no study investigating the metabolomic profiles in both aqueous humor and plasma of POAG patients. Therefore, to explore diagnostic biomarkers, unveil underlying pathophysiology and potential therapeutic strategies, a widely targeted metabolomic approach was applied using ultrahigh-resolution mass spectrometry with C18 liquid chromatography to characterize the metabolomic profiles in both aqueous humor and plasma of 28 POAG patients and 25 controls in our study. Partial least squares-discriminant analysis (PLS-DA) was performed to determine differentially expressed metabolites (DEMs) between POAG and age-matched controls. The area under the receiver operating characteristic curve (AUC) was calculated to assess the prediction accuracy of the DEMs. The correlation of DEMs with the clinical parameters was determined by Pearson correlation, and the metabolic pathways were analyzed using MetaboAnalyst 4.0. PLS-DA significantly separated POAG from controls with 22 DEMs in the aqueous humor and 11 DEMs in the plasma. Additionally, univariate ROC analysis and correlation analysis with clinical parameters revealed cyclic AMP (AUC = 0.87), 2-methylbenzoic acid (AUC = 0.75), 3'-sialyllactose (AUC = 0.73) in the aqueous humor and N-lac-phe (AUC = 0.76) in the plasma as potential biomarkers for POAG. Moreover, the metabolic profiles pointed towards the alteration in the purine metabolism pathway. In conclusion, the study identified potential and novel biomarkers for POAG by crosslinking the metabolomic profiles in aqueous humor and plasma and correlating with the clinical parameters. These findings have important clinical implications given that no biomarkers are currently available for glaucoma in the clinic, and the study provided new insights in exploring diagnostic biomarkers and potential therapeutic strategies of POAG by targeting metabolic pathways.

Metabolomic analysis in ophthalmology

Modern science takes into account phenotype complexity and establishes approaches to track changes on every possible level. Many "omics" studies have been developed over the last decade. Metabolomic analysis enables dynamic measurement of the metabolic response of a living system to a variety of stimuli or genetic modifications. Important targets of metabolomics is biomarker development and translation to the clinic for personalized diagnosis and a greater understanding of disease pathogenesis. The current review highlights the major aspects of metabolomic analysis and its applications for the identification of relevant predictive, diagnostic and prognostic biomarkers for some ocular diseases including dry eye, keratoconus, retinal diseases, macular degeneration, and glaucoma. To date, possible biomarker candidates for dry eye disease are lipid metabolites and androgens, for keratoconus cytokeratins, urea, citrate cycle, and oxidative stress metabolites. Palmitoylcarnitine, sphingolipids, vitamin D related metabolites, and steroid precursors may be used for distinguishing glaucoma patients from healthy controls. Dysregulation of amino acid and carnitine metabolism is critical in the development and progression of diabetic retinopathy. Further work is needed to discover and validate metabolic biomarkers as a powerful tool for understanding the molecular mechanisms of ocular diseases, to provide knowledge on their etiology and pathophysiology and opportunities for personalized clinical intervention at an early stage.