Urine Nuclear Magnetic Resonance (NMR) Metabolomics in Age-Related Macular Degeneration

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.

Urine Metabolomics during a Legume Diet Intervention Suggests Altered Metabolic Signatures and Potential New Intake Markers: First Insights

Given the general increase in legume consumption worldwide, there is a need to characterize the resulting human metabolic adaptations in order to demonstrate potential legume diet/health relationships. A nuclear magnetic resonance (NMR) metabolomics urine study was carried out on a small cohort (n = 18) to characterize the excretory effects of a pilot longitudinal 8-week legume-based dietary intervention. Despite the expected high interindividual variability in the excreted metabolome, the results suggested a nonlinear metabolic response, with higher metabolic activity in the first 4 weeks and a tendency toward baseline at the end of the intervention. The excretion of isoleucine, leucine, and threonine increased, along with metabolite changes suggestive of activation of the tricarboxylic acid cycle (through anaplerosis), ketogenesis, fat catabolism, and glycoprotein biosynthesis. Gut microbiota adaptations were also suggested based on the increased excretion of 2-hydroxyisobutyrate, allantoin, and hippurate. Increased levels of trigonelline were consistent with its role as a legume intake marker, whereas malonate and pseudouridine were suggested as possible additional markers. Correlation of NMR data with nutritional parameters aided putative explanatory hypotheses to be advanced. Our results suggest a dynamic response to legume consumption, mainly through increased amino acid excretion and altered energy metabolism, while advancing potential new markers of legume intake. These results require confirmation in larger cohorts but pave the way for an informed interpretation of the effects of legume-based diets on human health.

Metabolomic profiling of COVID-19 using serum and urine samples in intensive care and medical ward cohorts

The COVID-19 pandemic remains a significant global health threat, with uncertainties persisting regarding the factors determining whether individuals experience mild symptoms, severe conditions, or succumb to the disease. This study presents an NMR metabolomics-based approach, analysing 80 serum and urine samples from COVID-19 patients (34 intensive care patients and 46 hospitalized patients) and 32 from healthy controls. Our research identifies discriminant metabolites and clinical variables relevant to COVID-19 diagnosis and severity. These discriminant metabolites play a role in specific pathways, mainly "Phenylalanine, tyrosine and tryptophan biosynthesis", "Phenylalanine metabolism", "Glycerolipid metabolism" and "Arginine and proline metabolism". We propose a three-metabolite diagnostic panel-comprising isoleucine, TMAO, and glucose-that effectively discriminates COVID-19 patients from healthy individuals, achieving high efficiency. Furthermore, we found an optimal biomarker panel capable of efficiently classify disease severity considering both clinical characteristics (obesity/overweight, dyslipidemia, and lymphocyte count) together with metabolites content (ethanol, TMAO, tyrosine and betaine).

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.

Beta-alanine promotes angiogenesis in laser-induced choroidal neovascularization mice models

AIM

To investigate the effect of β-alanine (BA) on laser-induced choroidal neovascularization (CNV) mice models.

METHODS

Laser-induced CNV mice models were established, and BA was administrated for one week and two weeks in advance, separately. Furthermore, retinal pigment epithelium (RPE)-choroid flat mounts were separated, and immunohistochemical staining was performed. The laser-induced CNV lesion areas were measured and compared. In addition, liver and kidney morphologies were observed to identify potential hepatorenal toxicity.

RESULTS

Enlarged CNV lesion areas were observed in the BA treated group. No significant differences were observed in the liver and kidney sections between groups.

CONCLUSION

BA treatment increase CNV lesion areas, suggesting the detrimental effects of BA as a nutritional supplement in age-related macular degeneration (AMD) population.

A lipidomic study on the lens epithelial cells of patients with age related cataracts

Age related cataracts (ARC) represent the main reason for blindness globally. The lens epithelial cells (LECs) participate not only in the metabolism of many substances in the lens but also in maintaining lens transparency. This study used lipidomics to investigate the metabolic differences in LECs of ARC patients with different severity, aiming at identifying potential metabolic biomarkers of ARC. Patients diagnosed with ARC and underwent cataract surgery at Shanghai Tongren Hospital were selected to participate in this study, which were classified as mild ARC group and severe ARC group. During their cataract surgery, anterior lens capsules(LCs) containing LECs were obtained. The lipidomics of LECs were analyzed using the liquid chromatography‑mass spectrometry (LC-MS). Potential pathways of lipids were searched for using databases such as the Kyoto Encyclopedia of Genes and Genomes (KEGG) and MetaboAnalyst platform. In LEC lipids, 26 lipids have been identified as potential biomarkers between mild ARC and severe ARC, with AUC values of 0.67-0.94. The pathway analysis results revealed that the Glycerophospholipid (GPL) metabolism was significantly influenced, indicating that these metabolic markers contribute significantly to regulating this pathway. The LEC metabolic spectrum demonstrates a proficient ability to differentiate between patients with varying levels of cataracts. Herein, we have successfully identified potential metabolic biomarkers and pathways that have proven to be valuable in enhancing our understanding of ARC pathogenesis. The finding has translational value for developing new cataract treatment methods in the future.

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.

Metabolomic-derived endotypes of age-related macular degeneration (AMD): a step towards identification of disease subgroups

Age-related macular degeneration (AMD) is a leading cause of blindness worldwide, with a complex pathophysiology and phenotypic diversity. Here, we apply Similarity Network Fusion (SNF) to cluster AMD patients into putative metabolomics-derived endotypes. Using a discovery cohort of 163 AMD patients from Boston, US, and a validation cohort of 214 patients from Coimbra, Portugal, we identified four distinct metabolomics-derived endotypes with varying retinal structural and functional characteristics, confirmed across both cohorts. Patients clustered into Endotype 1 exhibited a milder form of AMD and were characterized by low levels of amino acids in specific metabolic pathways. Meanwhile, patients clustered into both Endotype 3 and 4 were associated with more severe AMD and exhibited low levels of fatty acid metabolites and elevated levels of sphingomyelins and fatty acid metabolites, respectively. These preliminary findings indicate that metabolomics-derived endotyping may offer a refined strategy for categorizing AMD patients based on their specific pathophysiological underpinnings, rather than relying solely on traditional observational clinical indicators.

Integrating genetics and metabolomics from multi-ethnic and multi-fluid data reveals putative mechanisms for age-related macular degeneration

Age-related macular degeneration (AMD) is a leading cause of blindness in older adults. Investigating shared genetic components between metabolites and AMD can enhance our understanding of its pathogenesis. We conduct metabolite genome-wide association studies (mGWASs) using multi-ethnic genetic and metabolomic data from up to 28,000 participants. With bidirectional Mendelian randomization analysis involving 16,144 advanced AMD cases and 17,832 controls, we identify 108 putatively causal relationships between plasma metabolites and advanced AMD. These metabolites are enriched in glycerophospholipid metabolism, lysophospholipid, triradylcglycerol, and long chain polyunsaturated fatty acid pathways. Bayesian genetic colocalization analysis and a customized metabolome-wide association approach prioritize putative causal AMD-associated metabolites. We find limited evidence linking urine metabolites to AMD risk. Our study emphasizes the contribution of plasma metabolites, particularly lipid-related pathways and genes, to AMD risk and uncovers numerous putative causal associations between metabolites and AMD risk.

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.

Magnetic resonance spectroscopy ex vivo: A short historical review

Over the last half century, there have been several periods during which magnetic resonance spectroscopy (MRS) has been used ex vivo, for a variety of reasons, on samples such as microorganisms, cells, animal or human tissue, tissue extracts or biological fluids. These studies began in the days before the acronym MRS had been invented, when all such methods were still called nuclear magnetic resonance (NMR), and have extended to the present day. I will describe the historical development of NMR methods used ex vivo, their influences on the development of MRS in vivo, and their longer-term uses. All the interpretations will be personal, based on what I saw, or discussed with colleagues at the time.

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.

Recent Advances in Proteomics-Based Approaches to Studying Age-Related Macular Degeneration: A Systematic Review

Age-related macular degeneration (AMD) is a common ocular disease characterized by degeneration of the central area of the retina in the elderly population. Progression and response to treatment are influenced by genetic and non-genetic factors. Proteomics is a powerful tool to study, at the molecular level, the mechanisms underlying the progression of the disease, to identify new therapeutic targets and to establish biomarkers to monitor progression and treatment effectiveness. In this work, we systematically review the use of proteomics-based approaches for the study of the molecular mechanisms underlying the development of AMD, as well as the progression of the disease and on-treatment patient monitoring. The Preferred Reporting Items for Systematic Reviews and Meta-Analysis (PRISMA) reporting guidelines were followed. Proteomic approaches have identified key players in the onset of the disease, such as complement components and proteins involved in lipid metabolism and oxidative stress, but also in the progression to advanced stages, including factors related to extracellular matrix integrity and angiogenesis. Although anti-vascular endothelial growth factor (anti-VEGF)-based therapy has been crucial in the treatment of neovascular AMD, it is necessary to deepen our understanding of the underlying disease mechanisms to move forward to next-generation therapies for later-stage forms of this multifactorial disease.

Systematic Review of NMR-Based Metabolomics Practices in Human Disease Research

Nuclear magnetic resonance (NMR) spectroscopy is one of the principal analytical techniques for metabolomics. It has the advantages of minimal sample preparation and high reproducibility, making it an ideal technique for generating large amounts of metabolomics data for biobanks and large-scale studies. Metabolomics is a popular “omics” technology and has established itself as a comprehensive exploratory biomarker tool; however, it has yet to reach its collaborative potential in data collation due to the lack of standardisation of the metabolomics workflow seen across small-scale studies. This systematic review compiles the different NMR metabolomics methods used for serum, plasma, and urine studies, from sample collection to data analysis, that were most popularly employed over a two-year period in 2019 and 2020. It also outlines how these methods influence the raw data and the downstream interpretations, and the importance of reporting for reproducibility and result validation. This review can act as a valuable summary of NMR metabolomic workflows that are actively used in human biofluid research and will help guide the workflow choice for future research.

Urinary Mass Spectrometry Profiles in Age-Related Macular Degeneration

We and others have shown that patients with different severity stages of age-related macular degeneration (AMD) have distinct plasma metabolomic profiles compared to controls. Urine is a biofluid that can be obtained non-invasively and, in other fields, urine metabolomics has been proposed as a feasible alternative to plasma biomarkers. However, no studies have applied urinary mass spectrometry (MS) metabolomics to AMD. This study aimed to assess urinary metabolomic profiles of patients with different stages of AMD and a control group. We included two prospectively designed, multicenter, cross-sectional study cohorts: Boston, US (n = 185) and Coimbra, Portugal (n = 299). We collected fasting urine samples, which were used for metabolomic profiling (Ultrahigh Performance Liquid chromatography—Mass Spectrometry). Multivariable logistic and ordinal logistic regression models were used for analysis, accounting for gender, age, body mass index and use of AREDS supplementation. Results from both cohorts were then meta-analyzed. No significant differences in urine metabolites were seen when comparing patients with AMD and controls. When disease severity was considered as an outcome, six urinary metabolites differed significantly (p < 0.01). In particular, two of the metabolites identified have been previously shown by our group to also differ in the plasma of patients of AMD compared to controls and across severity stages. While there are fewer urinary metabolites associated with AMD than plasma metabolites, this study identified some differences across stages of disease that support previous work performed with plasma, thus highlighting the potential of these metabolites as future biomarkers for AMD.

Nanotechnology for Age-Related Macular Degeneration

Age-related macular degeneration (AMD) is a degenerative eye disease that is the leading cause of irreversible vision loss in people 50 years and older. Today, the most common treatment for AMD involves repeated intravitreal injections of anti-vascular endothelial growth factor (VEGF) drugs. However, the existing expensive therapies not only cannot cure this disease, they also produce a variety of side effects. For example, the number of injections increases the cumulative risk of endophthalmitis and other complications. Today, a single intravitreal injection of gene therapy products can greatly reduce the burden of treatment and improve visual effects. In addition, the latest innovations in nanotherapy provide the best drug delivery alternative for the treatment of AMD. In this review, we discuss the development of nano-drug delivery systems and gene therapy strategies for AMD in recent years. In addition, we discuss some novel targeting strategies and the potential application of these delivery methods in the treatment of AMD. Finally, we also propose that the combination of CRISPR/Cas9 technology with a new non-viral delivery system may be promising as a therapeutic strategy for the treatment of AMD.

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).

Carbon Nanotubes Induce Metabolomic Profile Disturbances in Zebrafish: NMR-Based Metabolomics Platform

The present study aims to investigate the metabolic effects of single-walled carbon nanotubes (SWCNT) on zebrafish (Danio rerio) using ¹H nuclear magnetic resonance (¹H-NMR) spectroscopy. However, there is no significant information available regarding the characterization of organic molecules, and metabolites with SWCNT exposure. Noninvasive biofluid methods have improved our understanding of SWCNT metabolism in zebrafish in recent years. Here, we used targeted metabolomics to quantify a set of metabolites within biological systems. SWCNT at various concentrations was given to zebrafish, and the metabolites were extracted using two immiscible solvent systems, methanol and chloroform. Metabolomics profiling was used in association with univariate and multivariate data analysis to determine metabolomic phenotyping. The metabolites, malate, oxalacetate, phenylaniline, taurine, sn-glycero-3-phosphate, glycine, N-acetyl mate, lactate, ATP, AMP, valine, pyruvate, ADP, serine, niacinamide are significantly impacted. The metabolism of amino acids, energy and nucleotides are influenced by SWCNT which might indicate a disturbance in metabolic reaction networks. In conclusion, using high-throughput analytical methods, we provide a perspective of metabolic impacts and the underlying associated metabolic pathways.

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.

Serum Metabolomics Reveals Personalized Metabolic Patterns for Macular Neovascular Disease Patient Stratification

The macular neovascular disease is a group disorder with complex pathogenesis of neovascularization for vision impairment and irreversible blindness, posing great challenges to precise diagnosis and management. We prospectively recruited participants with age-related macular degeneration (AMD), polypoidal choroidal vasculopathy (PCV), and pathological myopia (PM) and compared with cataract patients without fundus diseases as a control group. The serum metabolome was profiled by gas chromatography coupled with time-of-flight mass spectrometry (GC-TOFMS) analysis. Multivariate statistical methods as well as data mining were performed for interpretation of macular neovascularization. A total of 446 participants with macular neovascularization and 138 cataract subjects as the control group were enrolled in this study. By employing GC-TOFMS, 131 metabolites were identified and 33 differentiating metabolites were highlighted in patients with macular neovascularization. For differential diagnosis, three panels of specific metabolomics-based biomarkers provided areas under the curve of 0.967, 0.938, and 0.877 in the discovery phase (n = 328) and predictive values of 87.3%, 79%, and 85.7% in the test phase (n = 256). Personalized pathway dysregulation scores measurement using Lilikoi package in R language revealed the pentose phosphate pathway and mitochondrial electron transport chain as the most important pathways in AMD; purine metabolism and glycolysis were identified as the major disturbed pathways in PCV, while the altered thiamine metabolism and purine metabolism may contribute to PM phenotypes. Serum metabolomics are powerful for characterizing metabolic disturbances of the macular neovascular disease. Differences in metabolic pathways may reflect an underlying macular neovascular disease and serve as therapeutic targets for macular neovascular treatment.