Metabolomics in serum of patients with non-advanced age-related macular degeneration reveals aberrations in the glutamine pathway

Evidence for Genetic Causal Association Between the Gut Microbiome, Derived Metabolites, and Age-Related Macular Degeneration: A Mediation Mendelian Randomization Analysis

Background/Objectives: Despite substantial research, the causal relationships between gut microbiota (GM) and age-related macular degeneration (AMD) remain unclear. We aimed to explore these causal associations using Mendelian randomization (MR) and elucidate the potential mechanisms mediated by blood metabolites. Methods: We utilized the 211 GM dataset (n = 18,340) provided by the MiBioGen consortium. AMD outcome data were sourced from the MRC Integrated Epidemiology Unit (IEU) OpenGWAS Project. We performed bidirectional MR, two mediation analyses, and two-step MR to assess the causal links between GM and different stages of AMD (early, dry, and wet). Results: Our findings indicate that the Bacteroidales S24.7 group and genus Dorea are associated with an increased risk of early AMD, while Ruminococcaceae UCG011 and Parasutterella are linked to a higher risk of dry AMD. Conversely, Lachnospiraceae UCG004 and Anaerotruncus are protective against dry AMD. In the case of wet AMD, Intestinimonas and Sellimonas increase risk, whereas Anaerotruncus and Rikenellaceae RC9 reduce it. Additionally, various blood metabolites were implicated: valine, arabinose, creatine, lysine, alanine, and apolipoprotein A1 were associated with early AMD; glutamine and hyodeoxycholate-with a reduced risk of dry AMD; and androsterone sulfate, epiandrosterone sulfate, and lipopolysaccharide-with a reduced risk of wet AMD. Notably, the association between family Oxalobacteraceae and early AMD was mediated by valine, accounting for 19.1% of the association. Conclusions: This study establishes causal links between specific gut microbiota and AMD, mediated by blood metabolites, thereby enhancing our understanding of the gut-retina axis in AMD pathophysiology.

Analysis of biofluid metabolomic profiles to the discovery of biomarkers in age-related macular degeneration

OBJECTIVE

Age-related macular degeneration (AMD) is one of the leading causes of irreversible visual impairment and blindness in the elderly. As AMD is a multifactorial disease, it is critical to explore useful biomarkers and pathological pathways underlying it. The purpose of this study is to summarise current metabolic profiles and further identify potential metabolic biomarkers and therapeutic targets in AMD, which could facilitate clinical diagnosis and treatment.

METHODS AND ANALYSIS

Relevant metabolomics studies published before 10 December 2021 were generally reviewed from online resources by two investigators. Studies with sufficient information and data were included in this systematic review and repeatedly identified metabolites were extracted. Pathway and Kyoto Encyclopaedia of Genes and Genomes (KEGG) analyses were performed. The public Gene Expression Omnibus (GEO) database was used for coanalysis with differential metabolites to construct a pathway network via MetaboAnalyst V.5.0.

RESULTS

16 studies were included in our analysis. 24 metabolites were repeatedly detected and regarded as potential biomarkers for AMD. Pathway analysis implied a major role of phenylalanine, tyrosine and tryptophan pathways in AMD pathology. 11 KEGG pathways were enriched, meanwhile, 11 metabolic pathway clusters were identified by coanalysing the differential metabolites and gene profiles using the GEO database.

CONCLUSION

In this study, we summarised 16 metabolomic studies on AMD, and 24 metabolites were identified as potential biofluid biomarkers. This provided novel insights into the pathogenic mechanisms underlying AMD. Further studies are warranted to validate and expand an effective pattern for AMD diagnosis and treatment.

Association of Metabolomics With Incidence of Age-Related Macular Degeneration: The UK Biobank Study

Purpose

The purpose of this study was to identify serum metabolites associated with age-related macular degeneration (AMD) incidence and investigate whether metabolite profiles enhance AMD risk prediction.

Methods

In a prospective cohort study involving 240,317 UK Biobank participants, we assessed the associations of 168 metabolites with AMD incidence using Cox hazards models. Principal component analysis (PCA) captured 90% of the variance in metabolites. These principal components (PCs) were added to the Cox models, with the first PC selected to evaluate model performance using receiver operating characteristic (ROC) curves.

Results

During a median follow-up of 13.69 years, 5199 (2.16%) participants developed AMD. After accounting for demographic, lifestyle, multimorbidity, socioeconomic factors, and genetic predispositions to AMD, 42 metabolites were associated with AMD incidence. Very-low-density lipoprotein (VLDL)-related particles, low-density lipoprotein (LDL)-related particles, three additional lipids particles, and albumin were associated with decreased AMD incidence, whereas glucose increased the risk of AMD incidence. Compared to those in the lowest quartile, individuals in the highest quartile of protective metabolite scores exhibited lower risk of AMD incidence (hazard ratio [HR] = 0.869, 95% confidence interval [CI] = 0.803-0.940, false discovery rate [FDR]-adjusted P = 1.44 × 10-3). However, the AMD-associated metabolites did not enhance predictive performance (both areas under the curve [AUC] = 0.776).

Conclusions

Our findings reveal significant associations between specific metabolites and AMD incidence, highlighting the roles of lipoprotein subclasses, cholesterol subtypes, apolipoproteins, glucose, and albumin. Although metabolomics did not improve risk prediction, certain biomarkers may serve as promising therapeutic targets.

Reconstructing Molecular Networks by Causal Diffusion Do-Calculus Analysis with Deep Learning

Quantifying molecular regulations between genes/molecules causally from observed data is crucial for elucidating the molecular mechanisms underlying biological processes at the network level. Presently, most methods for inferring gene regulatory and biological networks rely on association studies or observational causal-analysis approaches. This study introduces a novel approach that combines intervention operations and diffusion models within a do-calculus framework by deep learning, i.e., Causal Diffusion Do-calculus (CDD) analysis, to infer causal networks between molecules. CDD can extract causal relations from observed data owing to its intervention operations, thereby significantly enhancing the accuracy and generalizability of causal network inference. Computationally, CDD has been applied to both simulated data and real omics data, which demonstrates that CDD outperforms existing methods in accurately inferring gene regulatory networks and identifying causal links from genes to disease phenotypes. Especially, compared with the Mendelian randomization algorithm and other existing methods, the CDD can reliably identify the disease genes or molecules for complex diseases with better performances. In addition, the causal analysis between various diseases and the potential factors in different populations from the UK Biobank database is also conducted, which further validated the effectiveness of CDD.

Serum metabolite biomarkers for the early diagnosis and monitoring of age-related macular degeneration

INTRODUCTION

Age-related macular degeneration (AMD) is a leading cause of irreversible blindness worldwide, with significant challenges for early diagnosis and treatment.

OBJECTIVES

To identify new biomarkers that are important for the early diagnosis and monitoring of the severity/progression of AMD.

METHODS

We investigated the diagnostic and monitoring potential of blood metabolites in a cohort of 547 individuals (167 healthy controls, 240 individuals with other eye diseases as eye disease controls, and 140 individuals with AMD) from 2 centers over three phases: discovery phase 1, discovery phase 2, and an external validation phase. The samples were analyzed via a mass spectrometry-based, widely targeted metabolomic workflow. In discovery phases 1 and 2, we built a machine learning algorithm to predict the probability of AMD. In the external validation phase, we further confirmed the performance of the biomarker panel identified by the algorithm. We subsequently evaluated the performance of the identified biomarker panel in monitoring the progression and severity of AMD.

RESULTS

We developed a clinically specific three-metabolite panel (hypoxanthine, 2-furoylglycine, and 1-hexadecyl-2-azelaoyl-sn-glycero-3-phosphocholine) via five machine learning models. The random forest model effectively discriminated patients with AMD from patents in the other two groups and showed acceptable calibration (area under the curve (AUC) = 1.0; accuracy = 1.0) in both discovery phases 1 and 2. An independent validation phase confirmed the diagnostic model's efficacy (AUC = 0.962; accuracy = 0.88). The three-biomarker panel model demonstrated an AUC of 1.0 in differentiating the severity of AMD via RF machine learning, which was consistent across both the discovery and external validation phases. Additionally, the biomarker concentrations remained stable under repeated freeze-thaw cycles (P > 0.05).

CONCLUSIONS

This study reveals distinct metabolite variations in the serum of AMD patients, paving the way for the development of the first routine laboratory test for AMD.

Causal influence of plasma metabolites on age-related macular degeneration: A Mendelian randomization study

Using genome-wide association study data from European populations, this research clarifies the causal relationship between plasma metabolites and age-related macular degeneration (AMD) and employs Metabo Analyst 5.0 for enrichment analysis to investigate their metabolic pathways. Employing Mendelian randomization analysis, this study leveraged single nucleotide polymorphisms significantly associated with plasma metabolites as instrumental variables. This approach established a causal link between metabolites and AMD. Analytical methods such as inverse-variance weighted, Mendelian randomization-Egger, and weighted median were applied to validate causality. Mendelian Randomization Pleiotropy Residual Sum and Outlier was utilized for outlier detection and correction, and Cochran's Q test was conducted to assess heterogeneity. To delve deeper into the metabolic characteristics of AMD, metabolic enrichment analysis was performed using Metabo Analyst 5.0. These combined methods provided a robust framework for elucidating the metabolic underpinnings of AMD. The 2-sample MR analysis, after meticulous screening, identified causal relationships between 88 metabolites and AMD. Of these, 16 metabolites showed a significant causal association. Following false discovery rate correction, 3 metabolites remained significantly associated, with androstenediol (3 beta, 17 beta) disulfate (2) exhibiting the most potent protective effect against AMD. Further exploration using Metabo Analyst 5.0 highlighted 4 metabolic pathways potentially implicated in AMD pathogenesis. This pioneering MR study has unraveled the causal connections between plasma metabolites and AMD. It identified several metabolites with a causal impact on AMD, with 3 maintaining significance after FDR correction. These insights offer robust causal evidence for future clinical applications and underscore the potential of these metabolites as clinical biomarkers in AMD screening, treatment, and prevention strategies.

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.

Plasma Metabolites Associated with OCT Features of Age-Related Macular Degeneration

Purpose

The most widely used classifications of age-related macular degeneration (AMD) and its severity stages still rely on color fundus photographs (CFPs). However, AMD has a wide phenotypic variability that remains poorly understood and is better characterized by OCT. We and others have shown that patients with AMD have a distinct plasma metabolomic profile compared with controls. However, all studies to date have been performed solely based on CFP classifications. This study aimed to assess if plasma metabolomic profiles are associated with OCT features commonly seen in AMD.

Design

Prospectively designed, cross-sectional study.

Participants

Subjects with a diagnosis of AMD and a control group (> 50 years old) from Boston, United States, and Coimbra, Portugal.

Methods

All participants were imaged with CFP, used for AMD staging (Age-Related Eye Disease Study 2 classification scheme), and with spectral domain OCT (Spectralis, Heidelberg). OCT images were graded by 2 independent graders for the presence of characteristic AMD features, according to a predefined protocol. Fasting blood samples were collected for metabolomic profiling (using nontargeted high-resolution mass spectrometry by Metabolon Inc). Analyses were conducted using logistic regression models including the worst eye of each patient (AREDS2 classification) and adjusting for confounding factors. Each cohort (United States and Portugal) was analyzed separately and then results were combined by meta-analyses. False discovery rate (FDR) was used to account for multiple comparisons.

Main Outcome Measures

Plasma metabolite levels associated with OCT features.

Results

We included data on 468 patients, 374 with AMD and 94 controls, and on 725 named endogenous metabolites. Meta-analysis identified significant associations (FDR < 0.05) between plasma metabolites and 3 OCT features: hyperreflective foci (6), atrophy (6), and ellipsoid zone disruption (3). Most associations were seen with amino acids, and all but 1 metabolite presented specific associations with the OCT features assessed.

Conclusions

To our knowledge, we show for the first time that plasma metabolites have associations with specific OCT features seen in AMD. Our results support that the wide spectrum of presentations of AMD likely include different pathophysiologic mechanisms by identifying specific pathways associated with each OCT feature.

Financial Disclosures

Proprietary or commercial disclosure may be found after the references.

Association of lipid-lowering drugs and antidiabetic drugs with age-related macular degeneration: a meta-analysis in Europeans

BACKGROUND/AIMS

To investigate the association of commonly used systemic medications with prevalent age-related macular degeneration (AMD) in the general population.

METHODS

We included 38 694 adults from 14 population-based and hospital-based studies from the European Eye Epidemiology consortium. We examined associations between the use of systemic medications and any prevalent AMD as well as any late AMD using multivariable logistic regression modelling per study and pooled results using random effects meta-analysis.

RESULTS

Between studies, mean age ranged from 61.5±7.1 to 82.6±3.8 years and prevalence ranged from 12.1% to 64.5% and from 0.5% to 35.5% for any and late AMD, respectively. In the meta-analysis of fully adjusted multivariable models, lipid-lowering drugs (LLD) and antidiabetic drugs were associated with lower prevalent any AMD (OR 0.85, 95% CI=0.79 to 0.91 and OR 0.78, 95% CI=0.66 to 0.91). We found no association with late AMD or with any other medication.

CONCLUSION

Our study indicates a potential beneficial effect of LLD and antidiabetic drug use on prevalence of AMD across multiple European cohorts. Our findings support the importance of metabolic processes in the multifactorial aetiology of AMD.

Integrative metabolome and lipidome analyses of plasma in neovascular macular degeneration

Age-related macular degeneration (AMD) causes irreversible vision-loss among the elderly in industrial countries. Neovascular AMD (nAMD), which refers to late-stage AMD, is characterized by severe vision-threatening choroidal neovascularization (CNV). Herein, we constructed a global metabolic network of nAMD, based on untargeted metabolomic and lipidomic analysis of plasma samples collected from sixty subjects (30 nAMD patients and 30 age-matched controls). Among the nAMD and control groups, 62 and 44 significantly different metabolites were detected in the positive and negative ion modes, respectively. Grouping analysis further showed that lipid and lipid-like molecule-based superclasses contained the highest number of significantly different metabolites. Lipidomic analysis revealed that 53 lipids among the nAMD and control groups differed significantly; these belonged to four major lipid categories (glycerophospholipids, sphingolipids, glycerolipids, and fatty acids). A discriminative biomarker panel comprising 16 metabolites and lipids, which was constructed using multivariate statistical machine learning methods, could effectively identify nAMD cases. Among these 16 compounds, eight were lipids that belonged to three lipid categories (glycerophospholipids, sphingolipids, and prenol lipids). The top three biomarkers with the highest importance scores were all lipids (a glycerophospholipid and two sphingolipids), highlighting the crucial role played by glycerophospholipid and sphingolipid pathways in nAMD. These differences between the metabolic and lipid profiles of nAMD patients and elderly individuals without AMD provide a readout of the overall metabolic status of nAMD. Further insights into the identified discriminative biomarkers may pave the way for future diagnostic and therapeutic interventions for nAMD.

Systemic Metabolomics in a Framework of Genetics and Lifestyle in Age-Related Macular Degeneration

Insights into the pathogenesis of age-related macular degeneration (AMD), a leading cause of blindness, point towards a complex interplay of genetic and lifestyle factors triggering various systemic pathways. This study aimed to characterize metabolomic profiles for AMD and to evaluate their position in the trias with genetics and lifestyle. This study included 5923 individuals from five European studies. Blood metabolomics were assessed using a nuclear magnetic resonance platform of 146 metabolites. Associations were studied using regression analyses. A genetic risk score (GRS) was calculated using β-values of 49 AMD variants, a lifestyle risk score (LRS) using smoking and diet data, and a metabolite risk score (MRS) using metabolite values. We identified 61 metabolites associated with early-intermediate AMD, of which 94% were lipid-related, with higher levels of HDL-subparticles and apolipoprotein-A1, and lower levels of VLDL-subparticles, triglycerides, and fatty acids (false discovery rate (FDR) p-value < 1.4 × 10^-2). Late AMD was associated with lower levels of the amino acids histidine, leucine, valine, tyrosine, and phenylalanine, and higher levels of the ketone bodies acetoacetate and 3-hydroxybutyrate (FDR p-value < 1.5 × 10^-3). A favorable lifestyle characterized by a healthy diet was associated with higher levels of amino acids and lower levels of ketone bodies, while an unfavorable lifestyle, including smoking, showed opposite effects (FDR p-value < 2.7 × 10^-2). The MRS mediated 5% of the effect of the GRS and 20% of that of the LRS on late AMD. Our findings show that metabolomic profiles differ between AMD stages and show that blood metabolites mostly reflect lifestyle. The severity-specific profiles spur further interest into the systemic effects related to disease conversion.

Vitreous Fatty Amides and Acyl Carnitines Are Altered in Intermediate Age-Related Macular Degeneration

Purpose

Age-related macular degeneration (AMD) is the leading cause of visual impairment worldwide. In this study, we aimed to investigate the vitreous humor metabolite profiles of patients with intermediate AMD using untargeted metabolomics.

Methods

We performed metabolomics using high-resolution liquid chromatography mass spectrometry on the vitreous humor of 31 patients with intermediate AMD and 30 controls who underwent vitrectomy for epiretinal membrane with or without cataract surgery. Univariate analyses after false discovery rate correction were performed to discriminate the metabolites and identify the significant metabolites of intermediate AMD. For biologic interpretation, enrichment and pathway analysis were conducted using MetaboAnalyst 5.0.

Results

Of the 858 metabolites analyzed in the vitreous humor, 258 metabolites that distinguished patients with AMD from controls were identified (P values < 0.05). Ascorbic acid and uric acid levels increased in the AMD group (all P values < 0.05). The acyl carnitines, such as acetyl L-carnitine (1.37-fold), and fatty amides, such as anandamide (0.9-fold) and docosanamide (0.67-fold), were higher in patients with intermediate AMD. In contrast, nicotinamide (-0.55-fold), and succinic acid (-1.69-fold) were lower in patients with intermediate AMD. The metabolic pathway related oxidation of branched chain fatty acids and carnitine synthesis showed enrichment.

Conclusions

Multiple metabolites related to fatty amides and acyl carnitine were found to be increased in the vitreous humor of patients with intermediate AMD, whereas succinic acid and nicotinamide were reduced, suggesting that altered metabolites related to fatty amides and acyl carnitines and energy metabolism may be implicated in the etiology of AMD.

Amino Acids Metabolism in Retinopathy: From Clinical and Basic Research Perspective

Retinopathy, including age-related macular degeneration (AMD), diabetic retinopathy (DR), and retinopathy of prematurity (ROP), are the leading cause of blindness among seniors, working-age populations, and children. However, the pathophysiology of retinopathy remains unclear. Accumulating studies demonstrate that amino acid metabolism is associated with retinopathy. This study discusses the characterization of amino acids in DR, AMD, and ROP by metabolomics from clinical and basic research perspectives. The features of amino acids in retinopathy were summarized using a comparative approach based on existing high-throughput metabolomics studies from PubMed. Besides taking up a large proportion, amino acids appear in both human and animal, intraocular and peripheral samples. Among them, some metabolites differ significantly in all three types of retinopathy, including glutamine, glutamate, alanine, and others. Studies on the mechanisms behind retinal cell death caused by glutamate accumulation are on the verge of making some progress. To develop potential therapeutics, it is imperative to understand amino acid-induced retinal functional alterations and the underlying mechanisms. This review delineates the significance of amino acid metabolism in retinopathy and provides possible direction to discover therapeutic targets for retinopathy.

Metabolomics study of treatment response to conbercept of patients with neovascular age-related macular degeneration and polypoidal choroidal vasculopathy

Background: Neovascular age-related macular degeneration (nAMD) and polypoidal choroidal vasculopathy (PCV) are major causes of blindness in aged people. 30% of the patients show unsatisfactory response to anti-vascular endothelial growth factor (anti-VEGF) drugs. This study aims to investigate the relationship between serum metabolome and treatment response to anti-VEGF therapy.

Methods: A prospective longitudinal study was conducted between March 2017 and April 2019 in 13 clinical sites in China. The discovery group were enrolled from Shanghai General Hospital. The validation group consisted of patients from the other 12 sites. Participants received at least one intravitreal injection of 0.5 mg anti-VEGF drug, conbercept, and were divided into two groups - responders and non-responders. Serum samples of both groups were processed for UHPLC-MS/MS analysis. We constructed principal component analysis (PCA) and partial least squares discriminant analysis (PLS-DA) models to investigate the metabolic differences between two groups using SIMCA-P. Area under curve (AUC) was calculated to screen the biomarkers to predict treatment response. Metabolites sub-classes and enriched pathways were obtained using MetaboAnalyst5.0.

Results: 219 eyes from 219 patients (nAMD = 126; PCV = 93) were enrolled. A total of 248 metabolites were detected. PCA and PLS-DA models of the discovery group demonstrated that the metabolic profiles of responders and non-responders clearly differed. Eighty-five differential metabolites were identified, including sub-classes of diacylglycerophosphocholines, lysophosphatidylcholine (LPC), fatty acids, phosphocholine, etc. Responders and non-responders differed most significantly in metabolism of LPC (p = 7.16 × 10^-19) and diacylglycerophosphocholine (p = 6.96 × 10^-17). LPC 18:0 exhibited the highest AUC, which is 0.896 with 95% confidence internal between 0.833 and 0.949, to discriminate responders. The predictive accuracy of LPC 18:0 was 72.4% in the validation group.

Conclusions: This study suggests that differential metabolites may be useful for guiding treatment options for nAMD and PCV. Metabolism of LPC and diacylglycerophosphocholine were found to affect response to conbercept treatment. LPC 18:0 was a potential biomarker to discriminate responders from non-responders.

Artificial Intelligence Analysis of Biofluid Markers in Age-Related Macular Degeneration: A Systematic Review

This systematic review explores the use of artificial intelligence (AI) in the analysis of biofluid markers in age-related macular degeneration (AMD). We detail the accuracy and validity of AI in diagnostic and prognostic models and biofluid markers that provide insight into AMD pathogenesis and progression. This review was conducted in accordance with the Preferred Reporting Items for a Systematic Review and Meta-analysis guidelines. A comprehensive search was conducted across 5 electronic databases including Cochrane Central Register of Controlled Trials, Cochrane Database of Systematic Reviews, EMBASE, Medline, and Web of Science from inception to July 14, 2021. Studies pertaining to biofluid marker analysis using AI or bioinformatics in AMD were included. Identified studies were assessed for risk of bias and critically appraised using the Joanna Briggs Institute Critical Appraisal tools. A total of 10,264 articles were retrieved from all databases and 37 studies met the inclusion criteria, including 15 cross-sectional studies, 15 prospective cohort studies, five retrospective cohort studies, one randomized controlled trial, and one case–control study. The majority of studies had a general focus on AMD (58%), while neovascular AMD (nAMD) was the focus in 11 studies (30%), and geographic atrophy (GA) was highlighted by three studies. Fifteen studies examined disease characteristics, 15 studied risk factors, and seven guided treatment decisions. Altered lipid metabolism (HDL-cholesterol, total serum triglycerides), inflammation (c-reactive protein), oxidative stress, and protein digestion were implicated in AMD development and progression. AI tools were able to both accurately differentiate controls and AMD patients with accuracies as high as 87% and predict responsiveness to anti-VEGF therapy in nAMD patients. Use of AI models such as discriminant analysis could inform prognostic and diagnostic decision-making in a clinical setting. The identified pathways provide opportunity for future studies of AMD development and could be valuable in the advancement of novel treatments.

Biomarkers as Predictive Factors of Anti-VEGF Response

Age-related macular degeneration is the main cause of irreversible vision in developed countries, and intravitreal anti-vascular endothelial growth factor (anti-VEGF) injections are the current gold standard treatment today. Although anti-VEGF treatment results in important improvements in the course of this disease, there is a considerable number of patients not responding to the standardized protocols. The knowledge of how a patient will respond or how frequently retreatment might be required would be vital in planning treatment schedules, saving both resource utilization and financial costs, but today, there is not an ideal biomarker to use as a predictive response to ranibizumab therapy. Whole blood and blood mononuclear cells are the samples most studied; however, few reports are available on other important biofluid samples for studying this disease, such as aqueous humor. Moreover, the great majority of studies carried out to date were focused on the search for SNPs in genes related to AMD risk factors, but miRNAs, proteomic and metabolomics studies have rarely been conducted in anti-VEGF-treated samples. Here, we propose that genomic, proteomic and/or metabolomic markers could be used not alone but in combination with other methods, such as specific clinic characteristics, to identify patients with a poor response to anti-VEGF treatment to establish patient-specific treatment plans.

Plasma Metabolomic Profiles Associated with Three-Year Progression of Age-Related Macular Degeneration

Plasma metabolomic profiles have been shown to be associated with age-related macular degeneration (AMD) and its severity stages. However, all studies performed to date have been cross-sectional and have not assessed progression of AMD. This prospective, longitudinal, pilot study analyzes, for the first time, the association between plasma metabolomic profiles and progression of AMD over a 3-year period. At baseline and 3 years later, subjects with AMD (n = 108 eyes) and controls (n = 45 eyes) were imaged with color fundus photos for AMD staging and tested for retinal function with dark adaptation (DA). Fasting plasma samples were also collected for metabolomic profiling. AMD progression was considered present if AMD stage at 3 years was more advanced than at baseline (n = 26 eyes, 17%). Results showed that, of the metabolites measured at baseline, eight were associated with 3-year AMD progression (p < 0.01) and 19 (p < 0.01) with changes in DA. Additionally, changes in the levels (i.e., between 3 years and baseline) of 6 and 17 metabolites demonstrated significant associations (p < 0.01) with AMD progression and DA, respectively. In conclusion, plasma metabolomic profiles are associated with clinical and functional progression of AMD at 3 years. These findings contribute to our understanding of mechanisms of AMD progression and the identification of potential therapeutics for this blinding disease.

Plasma Metabolomics of Intermediate and Neovascular Age-Related Macular Degeneration Patients

To characterize metabolites and metabolic pathways altered in intermediate and neovascular age-related macular degeneration (IAMD and NVAMD), high resolution untargeted metabolomics was performed via liquid chromatography-mass spectrometry on plasma samples obtained from 91 IAMD patients, 100 NVAMD patients, and 195 controls. Plasma metabolite levels were compared between: AMD patients and controls, IAMD patients and controls, and NVAMD and IAMD patients. Partial least-squares discriminant analysis and linear regression were used to identify discriminatory metabolites. Pathway analysis was performed to determine metabolic pathways altered in AMD. Among the comparisons, we identified 435 unique discriminatory metabolic features. Using computational methods and tandem mass spectrometry, we identified 11 metabolic features whose molecular identities had been previously verified and confirmed the molecular identities of three additional discriminatory features. Included among the discriminatory metabolites were acylcarnitines, phospholipids, amino acids, and steroid metabolites. Pathway analysis revealed that lipid, amino acid, and vitamin metabolism pathways were altered in NVAMD, IAMD, or AMD in general, including the carnitine shuttle pathway which was significantly altered in all comparisons. Finally, few discriminatory features were identified between IAMD patients and controls, suggesting that plasma metabolic profiles of IAMD patients are more similar to controls than to NVAMD patients.

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).

REP1 deficiency causes systemic dysfunction of lipid metabolism and oxidative stress in choroideremia

Choroideremia (CHM) is an X-linked recessive chorioretinal dystrophy caused by mutations in CHM, encoding for Rab escort protein 1 (REP1). Loss of functional REP1 leads to the accumulation of unprenylated Rab proteins and defective intracellular protein trafficking, the putative cause for photoreceptor, retinal pigment epithelium (RPE), and choroidal degeneration. CHM is ubiquitously expressed, but adequate prenylation is considered to be achieved, outside the retina, through the isoform REP2. Recently, the possibility of systemic features in CHM has been debated; therefore, in this study, whole metabolomic analysis of plasma samples from 25 CHM patients versus age- and sex-matched controls was performed. Results showed plasma alterations in oxidative stress-related metabolites, coupled with alterations in tryptophan metabolism, leading to significantly raised serotonin levels. Lipid metabolism was disrupted with decreased branched fatty acids and acylcarnitines, suggestive of dysfunctional lipid oxidation, as well as imbalances of several sphingolipids and glycerophospholipids. Targeted lipidomics of the chmru848 zebrafish provided further evidence for dysfunction, with the use of fenofibrate over simvastatin circumventing the prenylation pathway to improve the lipid profile and increase survival. This study provides strong evidence for systemic manifestations of CHM and proposes potentially novel pathomechanisms and targets for therapeutic consideration.

Association of Human Plasma Metabolomics with Delayed Dark Adaptation in Age-Related Macular Degeneration

The purpose of this study was to analyze the association between plasma metabolite levels and dark adaptation (DA) in age-related macular degeneration (AMD). This was a cross-sectional study including patients with AMD (early, intermediate, and late) and control subjects older than 50 years without any vitreoretinal disease. Fasting blood samples were collected and used for metabolomic profiling with ultra-performance liquid chromatography-mass spectrometry (LC-MS). Patients were also tested with the AdaptDx (MacuLogix, Middletown, PA, USA) DA extended protocol (20 min). Two measures of dark adaptation were calculated and used: rod-intercept time (RIT) and area under the dark adaptation curve (AUDAC). Associations between dark adaption and metabolite levels were tested using multilevel mixed-effects linear modelling, adjusting for age, gender, body mass index (BMI), smoking, race, AMD stage, and Age-Related Eye Disease Study (AREDS) formulation supplementation. We included a total of 71 subjects: 53 with AMD (13 early AMD, 31 intermediate AMD, and 9 late AMD) and 18 controls. Our results revealed that fatty acid-related lipids and amino acids related to glutamate and leucine, isoleucine and valine metabolism were associated with RIT (p < 0.01). Similar results were found when AUDAC was used as the outcome. Fatty acid-related lipids and amino acids are associated with DA, thus suggesting that oxidative stress and mitochondrial dysfunction likely play a role in AMD and visual impairment in this condition.

Metabolomics in Age-Related Macular Degeneration: A Systematic Review

Purpose

Age-related macular degeneration (AMD) is one of the leading causes of blindness among the elderly, and the exact pathogenesis of the AMD remains unclear. The purpose of this review is to summarize potential metabolic biomarkers and pathways of AMD that might facilitate risk predictions and clinical diagnoses of AMD.

Methods

We obtained relevant publications of metabolomics studies of human beings by systematically searching the MEDLINE (PubMed) database before June 2020. Studies were included if they performed mass spectrometry-based or nuclear magnetic resonance-based metabolomics approach for humans. In addition, AMD was assessed from fundus photographs based on standardized protocols. The metabolic pathway analysis was performed using MetaboAnalyst 3.0.

Results

Thirteen studies were included in this review. Repeatedly identified metabolites including phenylalanine, adenosine, hypoxanthine, tyrosine, creatine, citrate, carnitine, proline, and maltose have the possibility of being biomarkers of AMD. Validation of the biomarker panels was observed in one study. Dysregulation of metabolic pathways involves lipid metabolism, carbohydrate metabolism, nucleotide metabolism, amino acid metabolism, and translation, which might play important roles in the development and progression of AMD.

Conclusions

This review summarizes the potential metabolic biomarkers and pathways related to AMD, providing opportunities for the construction of diagnostic or predictive models for AMD and the discovery of new therapeutic targets.

Integrating Metabolomics, Genomics, and Disease Pathways in Age-Related Macular Degeneration: The EYE-RISK Consortium

PURPOSE

The current study aimed to identify metabolites associated with age-related macular degeneration (AMD) by performing the largest metabolome association analysis in AMD to date, as well as aiming to determine the effect of AMD-associated genetic variants on metabolite levels and investigate associations between the identified metabolites and activity of the complement system, one of the main AMD-associated disease pathways.

DESIGN

Case-control association analysis of metabolomics data.

PARTICIPANTS

Five European cohorts consisting of 2267 AMD patients and 4266 control participants.

METHODS

Metabolomics was performed using a high-throughput proton nuclear magnetic resonance metabolomics platform, which allows quantification of 146 metabolite measurements and 79 derivative values. Metabolome-AMD associations were studied using univariate logistic regression analyses. The effect of 52 AMD-associated genetic variants on the identified metabolites was investigated using linear regression. In addition, associations between the identified metabolites and activity of the complement pathway (defined by the C3d-to-C3 ratio) were investigated using linear regression.

MAIN OUTCOME MEASURES

Metabolites associated with AMD.

RESULTS

We identified 60 metabolites that were associated significantly with AMD, including increased levels of large and extra-large high-density lipoprotein (HDL) subclasses and decreased levels of very low-density lipoprotein (VLDL), amino acids, and citrate. Of 52 AMD-associated genetic variants, 7 variants were associated significantly with 34 of the identified metabolites. The strongest associations were identified for genetic variants located in or near genes involved in lipid metabolism (ABCA1, CETP, APOE, and LIPC) with metabolites belonging to the large and extra-large HDL subclasses. Also, 57 of 60 metabolites were associated significantly with complement activation levels, independent of AMD status. Increased large and extra-large HDL levels and decreased VLDL and amino acid levels were associated with increased complement activation.

CONCLUSIONS

Lipoprotein levels were associated with AMD-associated genetic variants, whereas decreased essential amino acids may point to nutritional deficiencies in AMD. We observed strong associations between the vast majority of the AMD-associated metabolites and systemic complement activation levels, independent of AMD status. This may indicate biological interactions between the main AMD disease pathways and suggests that multiple pathways may need to be targeted simultaneously for successful treatment of AMD.

Metabolomic Profiling of the Aqueous Humor in Patients with Wet Age-Related Macular Degeneration Using UHPLC-MS/MS

Assessing metabolomic alterations in age-related macular degeneration (AMD) can provide insights into its pathogenesis. We compared the metabolomic profiles of the aqueous humor between wet AMD patients (n = 26) and age- and sex-matched patients undergoing cataract surgery without AMD as controls (n = 20). A global untargeted metabolomics study was performed using ultra-high-performance liquid chromatography tandem mass spectrometry. Univariate analysis after the false discovery correction showed 18 significantly altered metabolites among the 291 metabolites measured. These differential metabolomic profiles pointed to three interconnected metabolic pathways: a compromised carnitine-associated mitochondrial oxidation pathway (carnitine, deoxycarnitine, N6-trimethyl-l-lysine), an altered carbohydrate metabolism pathway (cis-aconitic acid, itaconatic acid, and mesaconic acid), which plays a role in senescence and immunity, and an activated osmoprotection pathway (glycine betaine, creatine), which potentially contributes to the pathogenesis of the disease. These results suggested that metabolic dysfunction in AMD is mitochondrial-centered and may provide new insights into the pathophysiology of wet AMD and novel therapeutic strategies.

Glucose Metabolic Characterization of Human Aqueous Humor in Relation to Wet Age-Related Macular Degeneration

Purpose

Energy compromise underpins wet age-related macular degeneration (wAMD) pathogenesis, but the relationship between glucose metabolism and the disease remains unclear. Here, we characterized aqueous humor (AH) to elucidate glucose-related metabolic signatures in patients with wAMD.

Methods

In total, 25 eyes of 25 patients with wAMD were divided into phakic (15 eyes), pseudophakic (10 eyes), and intravitreal injections of ranibizumab (13 eyes) wAMD groups. Twenty patients with cataract (21 eyes) served as controls. Ultrahigh-performance liquid chromatography tandem mass spectrometry was used to quantitatively characterize AH.

Results

Twenty-one metabolites related to glucose metabolism were identified in AH from 45 patients. Tricarboxylic acid (TCA)-related metabolic substrates, including citrate, were detected in AH and were significantly increased in AMD (P < 0.01) and AMD pseudophakic groups (P < 0.05). In contrast, α-ketoglutarate levels were decreased in the AMD group (P < 0.05). The α-ketoglutarate/citrate ratio was significantly decreased, corresponding to 71.71% and 93.6% decreases in the AMD (phakic and pseudophakic) groups as compared with controls (P < 0.001), revealing a significant positive correlation with glutamine. A lower mean glutamine and higher glutamate level were detected in AMD cases compared with controls. No significant differences were observed for lactic acid or other Krebs cycle metabolites. Intravitreal injection significantly alleviated mean central foveal thickness but did not significantly alter metabolites.

Conclusions

Compromised glucose TCA cycle and altered glutamine metabolism are implicated in the AH metabolism in wAMD. These findings highlight potential treatments for alleviating wAMD from a metabolic perspective.

A Plasma Metabolomic Profiling of Exudative Age-Related Macular Degeneration Showing Carnosine and Mitochondrial Deficiencies

To determine the plasma metabolomic profile of exudative age-related macular degeneration (AMD), we performed a targeted metabolomics study on the plasma from patients (n = 40, mean age = 81.1) compared to an age- and sex-matched control group (n = 40, mean age = 81.8). All included patients had documented exudative AMD, causing significant visual loss (mean logMAR visual acuity = 0.63), compared to the control group. Patients and controls did not differ in terms of body mass index and co-morbidities. Among the 188 metabolites analyzed, 150 (79.8%) were accurately measured. The concentrations of 18 metabolites were significantly modified in the AMD group, but only six of them remained significantly different after Benjamini–Hochberg correction. Valine, lysine, carnitine, valerylcarnitine and proline were increased, while carnosine, a dipeptide disclosing anti-oxidant and anti-glycating properties, was, on average, reduced by 50% in AMD compared to controls. Moreover, carnosine was undetectable for 49% of AMD patients compared to 18% in the control group (p-value = 0.0035). Carnitine is involved in the transfer of fatty acids within the mitochondria; proline, lysine and valerylcarnitine are substrates for mitochondrial electrons transferring flavoproteins, and proline is one of the main metabolites supplying energy to the retina. Overall, our results reveal six new metabolites involved in the plasma metabolomic profile of exudative AMD, suggesting mitochondrial energetic impairments and carnosine deficiency.