Metabolomic analysis in ophthalmology

Metabolomics studies in common multifactorial eye disorders: a review of biomarker discovery for age-related macular degeneration, glaucoma, diabetic retinopathy and myopia

Introduction

Multifactorial Eye disorders are a significant public health concern and have a huge impact on quality of life. The pathophysiological mechanisms underlying these eye disorders were not completely understood since functional and low-throughput biological tests were used. By identifying biomarkers linked to eye disorders, metabolomics enables early identification, tracking of the course of the disease, and personalized treatment.

Methods

The electronic databases of PubMed, Scopus, PsycINFO, and Web of Science were searched for research related to Age-Related macular degeneration (AMD), glaucoma, myopia, and diabetic retinopathy (DR). The search was conducted in August 2023. The number of cases and controls, the study's design, the analytical methods used, and the results of the metabolomics analysis were all extracted. Using the QUADOMICS tool, the quality of the studies included was evaluated, and metabolic pathways were examined for distinct metabolic profiles. We used MetaboAnalyst 5.0 to undertake pathway analysis of differential metabolites.

Results

Metabolomics studies included in this review consisted of 36 human studies (5 Age-related macular degeneration, 10 Glaucoma, 13 Diabetic retinopathy, and 8 Myopia). The most networked metabolites in AMD include glycine and adenosine monophosphate, while methionine, lysine, alanine, glyoxylic acid, and cysteine were identified in glaucoma. Furthermore, in myopia, glycerol, glutamic acid, pyruvic acid, glycine, cysteine, and oxoglutaric acid constituted significant metabolites, while glycerol, glutamic acid, lysine, citric acid, alanine, and serotonin are highly networked metabolites in cases of diabetic retinopathy. The common top metabolic pathways significantly enriched and associated with AMD, glaucoma, DR, and myopia were arginine and proline metabolism, methionine metabolism, glycine and serine metabolism, urea cycle metabolism, and purine metabolism.

Conclusion

This review recapitulates potential metabolic biomarkers, networks and pathways in AMD, glaucoma, DR, and myopia, providing new clues to elucidate disease mechanisms and therapeutic targets. The emergence of advanced metabolomics techniques has significantly enhanced the capability of metabolic profiling and provides novel perspectives on the metabolism and underlying pathogenesis of these multifactorial eye conditions. The advancement of metabolomics is anticipated to foster a deeper comprehension of disease etiology, facilitate the identification of novel therapeutic targets, and usher in an era of personalized medicine in eye research.

Acylcarnitines in Ophthalmology: Promising Emerging Biomarkers

Several common ocular diseases are leading causes of irreversible visual impairment. Over the last decade, various mainly untargeted metabolic studies have been performed to show that metabolic dysfunction plays an important role in the pathogenesis of ocular diseases. A number of metabolites in plasma/serum, aqueous or vitreous humor, or in tears have been found to differ between patients and controls; among them are L-carnitine and acylcarnitines, which are essential for mitochondrial fatty acid oxidation. The metabolic profile of carnitines regarding a variety of diseases has attracted researchers' interest. In this review, we present and discuss recent advances that have been made in the identification of carnitines as potential metabolic biomarkers in common ocular diseases, such as age-related macular degeneration, diabetic retinopathy, retinopathy of prematurity, central retinal vein occlusion, primary open-angle glaucoma, rhegmatogenous retinal detachment, and dry eye syndrome.

Metabolomic Alterations in the Tear Fluids of Patients With Superior Limbic Keratoconjunctivitis

Purpose

Superior limbic keratoconjunctivitis (SLK) is a bilateral, chronic inflammatory disease that recurs for up to several years; however, the fundamental processes involved in its pathogenic mechanisms remain unknown. We aimed to investigate the metabolomic alterations in the tear fluids of patients with superior limbic keratoconjunctivitis (SLK) compared with those of healthy volunteers (Ctrl group).

Methods

We performed a cross-sectional study involving 42 subjects. Tear fluid was taken from one eye of 24 SLK patients (40.13 ± 14.55 years, 83.33% female) and 18 healthy volunteers (Ctrl, 39.89 ± 9.2 years, 72.22% female) using Schirmer strips. After the liquid extraction of tear metabolites, samples were infused into the QE HFX Orbitrap mass spectrometer in both positive and negative ion mode. Metabolites were quantitatively analyzed and matched with entries in the HMDB database. Metabolic differences between the SLK group and the control group were identified based on multivariate statistical analysis. Open database sources, including SMPDB and MetaboAnalyst, were used to identify metabolic pathways.

Results

Among 179 metabolites retained for annotation, 133 metabolites were finally identified, among which 50 were found to be significantly changed in SLK patients. Of these 50 metabolites, 31 metabolites significantly increased and 19 metabolites decreased in SLK patients. The altered metabolites are mainly involved in α linolenic acid and linoleic acid metabolism, ketone body metabolism, butyrate metabolism, mitochondrial electron transport chain, carnitine synthesis, and so on. The most significantly changed pathway was linoleic acid metabolism. To explore the utility of tear biomarkers, a model combining 9 metabolites (phenol, ethyl glucuronide, eicosapentaenoic acid, 12-keto-leukotriene B4, linoleic acid, hypoxanthine, triethanolamine, 1-nitrohexane, and terephthalic acid) was selected as a candidate biomarker.

Conclusion

The results reveal that SLK has a specific metabolomic profile, of which some key elements can serve as potential biomarkers of SLK for diagnostic and prognostic purposes. The findings of this study are novel and provide a basis for further investigations of the mechanism of SLK.

Metabolomics of various samples advancing biomarker discovery and pathogenesis elucidation for diabetic retinopathy

Diabetic retinopathy (DR) is a universal microvascular complication of diabetes mellitus (DM), which is the main reason for global sight damage/loss in middle-aged and/or older people. Current clinical analyses, like hemoglobin A1c, possess some importance as prognostic indicators for DR severity, but no effective circulating biomarkers are used for DR in the clinic currently, and studies on the latent pathophysiology remain lacking. Recent developments in omics, especially metabolomics, continue to disclose novel potential biomarkers in several fields, including but not limited to DR. Therefore, based on the overview of metabolomics, we reviewed progress in analytical technology of metabolomics, the prominent roles and the current status of biomarkers in DR, and the update of potential biomarkers in various DR-related samples via metabolomics, including tear as well as vitreous humor, aqueous humor, retina, plasma, serum, cerebrospinal fluid, urine, and feces. In this review, we underscored the in-depth analysis and elucidation of the common biomarkers in different biological samples based on integrated results, namely, alanine, lactate, and glutamine. Alanine may participate in and regulate glucose metabolism through stimulating N-methyl-D-aspartate receptors and subsequently suppressing insulin secretion, which is the potential pathogenesis of DR. Abnormal lactate could cause extensive oxidative stress and neuroinflammation, eventually leading to retinal hypoxia and metabolic dysfunction; on the other hand, high-level lactate may damage the structure and function of the retinal endothelial cell barrier via the G protein-coupled receptor 81. Abnormal glutamine indicates a disturbance of glutamate recycling, which may affect the activation of Müller cells and proliferation via the PPP1CA-YAP-GS-Gln-mTORC1 pathway.

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 approach to biomarkers of dry eye disease using 1H-NMR in rats

Dry eye disease (DED) is a chronic and progressive lesion on the ocular surface and induces symptoms, such as burning sensation, itchy eyes, heavy eyes, tired eyes, dry feeling, facial flushing, and blurred vision. The present study was performed to develop DED biomarkers using metabolomics in a rat model. DED was induced by injecting scopolamine and exposing rats to a dry condition. Scopolamine (12 mg/kg/day for 7 days) was subcutaneously injected to male Sprague-Dawley rats. The rats were placed in dry condition with air-flow and dehumidifier. Tear volume and tear breakup time (TBUT) were measured, and eyes were examined through fluorescein staining to assess DED. Mucosal damage and immune reactions were also determined. Plasma and urinary endogenous metabolites were determined using ¹H-NMR analysis. Compared with control tear and TBUT levels were significantly decreased in the DED group whereas corneal damage was significantly increased. The levels of interleukins (IL-6) and IL-1β significantly elevated in the cornea and lacrimal glands in the DED group. TNF-α was numerically increased but not significantly different between groups. Pattern recognition using principal component analysis (PCA) and orthogonal projections to latent structure-discriminant analysis (OPLS-DA) of the NMR spectra in global profiling revealed different clusters between DED and control groups. Target profiling demonstrated that PCA and OPLS-DA score plots were separated between DED and controls in plasma and urine. Subsequently, 9 plasma metabolites were selected to examine different clustering between groups, and 26 urinary metabolites were also selected. Plasma metabolites showed a non-significant rising tendency in the DED group. Urinary phenylalanine, phenylacetate, pantothenate, glycine, succinate, methanol, valine, propylene glycol, histidine, threonine, lactate, and acetate were significantly different between control and DED rats. These results may contribute to understanding the metabolic regulation that is involved in DED and might be useful for potential biomarkers related to DED in rats.

Topic Evolution Analysis for Omics Data Integration in Cancers

One of the vital challenges for cancer diseases is efficient biomarkers monitoring formation and development are limited. Omics data integration plays a crucial role in the mining of biomarkers in the human condition. As the link between omics study on biomarkers discovery and cancer diseases is deepened, defining the principal technologies applied in the field is a must not only for the current period but also for the future. We utilize topic modeling to extract topics (or themes) as a probabilistic distribution of latent topics from the dataset. To predict the future trend of related cases, we utilize the Prophet neural network to perform a prediction correction model for existing topics. A total of 2,318 pieces of literature (from 2006 to 2020) were retrieved from MEDLINE with the query on “omics” and “cancer.” Our study found 20 topics covering current research types. The topic extraction results indicate that, with the rapid development of omics data integration research, multi-omics analysis (Topic 11) and genomics of colorectal cancer (Topic 10) have more studies reported last 15 years. From the topic prediction view, research findings in multi-omics data processing and novel biomarker discovery for cancer prediction (Topic 2, 3, 10, 11) will be heavily focused in the future. From the topic visuallization and evolution trends, metabolomics of breast cancer (Topic 9), pharmacogenomics (Topic 15), genome-guided therapy regimens (Topic 16), and microRNAs target genes (Topic 17) could have more rapidly developed in the study of cancer treatment effect and recurrence prediction.