Plasma Arginine and Citrulline are Elevated in Diabetic Retinopathy

Ursodeoxycholic acid grafted chitosan oligosaccharide self-assembled micelles with enhanced oral absorption and antidiabetic effect of oleanolic acid

Oleanolic acid (OA) is a food-derived bioactive component with antidiabetic activity, but its water solubility and oral bioavailability are notably restricted. In this study, to overcome these limitations, ursodeoxycholic acid-modified chitosan oligosaccharide (UCOS) was synthesized to encapsulate OA in self-assembled nanomicelles (UCOS-OA). The encapsulation efficiency and drug loading of UCOS-OA were 86 % and 11 %, respectively. UCOS-OA exhibited enhanced gastrointestinal stability and prolonged intestinal retention time when compared with free OA, resulting in a 10.6-fold increase in oral bioavailability. The enhanced antidiabetic activity of UCOS-OA was confirmed in the type 2 diabetes mellitus mice model, as it significantly improved glycolipid metabolism disorders and mitigated liver injury. Furthermore, UCOS-OA ameliorated the dysbiosis of gut microbiota and fecal metabolites. In conclusion, UCOS serves as an effective polymeric carrier for encapsulating OA, thereby improving its bioavailability and antidiabetic activity. This work provides valuable insights for the advancement of oral delivery systems for bioactive compounds.

Multi-Omics Integration With Machine Learning Identified Early Diabetic Retinopathy, Diabetic Macula Edema and Anti-VEGF Treatment Response

Purpose

Identify optimal metabolic features and pathways across diabetic retinopathy (DR) stages, develop risk models to differentiate diabetic macular edema (DME), and predict anti-vascular endothelial growth factor (anti-VEGF) therapy response.

Methods

We analyzed 108 aqueous humor samples from 78 type 2 diabetes mellitus patients and 30 healthy controls. Ultra-high-performance liquid chromatography-high-resolution-mass-spectrometry detected lipidomics and metabolomics profiles. DME patients received ≥3 anti-VEGF treatments, categorized into strong and weak response groups. Machine learning (ML) screened prospective metabolic features, developing prediction models.

Results

Key metabolic features identified in the metabolomics and lipidomics datasets included n-acetyl isoleucine (odds ratio [OR] = 1.635), cis-aconitic acid (OR = 3.296), and ophthalmic acid (OR = 0.836) for DR. For early-DR, n-acetyl isoleucine (OR = 1.791) and decaethylene glycol (PEG-10) (OR = 0.170) were identified as key markers. L-kynurenine (OR = 0.875), niacinamide (OR = 0.843), and linoleoyl ethanolamine (OR = 0.941) were identified as significant indicators for DME. Trigonelline (OR = 1.441) and 4-methylcatechol-2-sulfate (OR = 1.121) emerged as predictors for strong response to anti-VEGF. Predictive models achieved R² values of 99.9%, 97.7%, 93.9%, and 98.4% for DR, early-DR, DME, and strong response groups in the calibration set, respectively, and validated well with R² values of 96.3%, 96.8%, 79.9%, and 96.3%.

Conclusions

This research used ML to identify differential metabolic features from metabolomics and lipidomics datasets in DR patients. It implies that metabolic indicators can effectively predict early disease progression and potential weak responders to anti-VEGF therapy in DME eyes.

Translational Relevance

The identified metabolic indicators may aid in predicting the early progression of DR and optimizing therapeutic strategies for DME.

Integrated metabolomic and transcriptomic analysis reveals the effects and mechanisms of Jinqi Jiangtang tablets on type 2 diabetes

BACKGROUND

Type 2 diabetes (T2DM) is one of the major metabolic diseases and poses a serious challenge to human life and global economic development. Jinqi Jiangtang Tablets (JQJT) is effective in ameliorating the effects of T2DM, but the mechanism of JQJT is unclear.

PURPOSE

This study integrated metabolomics and transcriptomics to reveal the mechanism by which JQJT improves T2DM.

METHODS

The T2DM mouse model was established, and the effects of JQJT on improving T2DM were evaluated by determining the levels of blood lipids, fasting blood glucose (FBG), insulin metabolism and hepatic lipid accumulation in mice after JQJT administration for 8 weeks. Serum metabolites were detected using ultra-performance liquid chromatography/quadrupole time-of-flight-tandem mass spectrometry (UPLC-Q-TOF-MS) technology, and mouse liver differential genes were detected using transcriptomic technology. Correlation analysis was used to extract metabolites and RNA with correlations, and potential pathways were enriched and constructed using the common pathway analysis function of MetaboAnalyst 5.0. Finally, the expression of key target proteins and genes was verified by Western blot (WB) and Polymerase Chain Reaction (PCR) to further elucidate the mechanism by which JQJT improves T2DM.

RESULTS

JQJT reduced FBG and lipid levels, improved insulin resistance (IR) and hepatic lipoatrophy in mice. A total of 35 differentially abundant metabolites were identified by metabolomics, and 328 differential genes were detected by transcriptomics. The integrated metabolomics and transcriptomics results suggested that JQJT may ameliorate T2DM mainly by regulating glucose and lipid metabolic pathways. WB and PCR results showed that JQJT regulates the insulin signaling pathway, involved in fatty acid metabolism, glycogen synthesis and catabolism.

CONCLUSIONS

JQJT improved IR in T2DM mice by regulating the insulin signaling pathway, improving glycogen synthesis and glycolysis, and increasing hepatic triglyceride and fatty acid metabolism.

Causality of Blood Metabolites on Proliferative Diabetic Retinopathy: Insights From a Genetic Perspective

Background: Our goal was to examine the causal link between blood metabolites, their ratios, and the risk of developing proliferative diabetic retinopathy (PDR) from a genetic insight. Methods: Summary-level data about 1400 blood metabolites and their ratios, as well as PDR, were sourced from prior genome-wide association studies (GWAS). A two-sample univariate and multivariate Mendelian randomization (MR) approach was utilized. Additionally, metabolic pathway analysis and sensitivity analysis were also conducted. Results: After adjusting for multiple tests, four blood metabolites significantly correlated with PDR risk. Two ceramides, including glycosyl-N-palmitoyl-sphingosine (d18:1/16:0) (odds ratio [OR] = 1.12, 95% confidence interval (CI): 1.06-1.17, p < 0.001, false discovery rate (FDR) = 0.005) and glycosyl-N-behenoyl-sphingadienine (d18:2/22:0) (OR = 1.11, 95% CI: 1.06-1.16, p < 0.001, FDR = 0.017), were linked to increased risk. Additionally, 3-methylcytidine (OR = 1.05, 95% CI: 1.03-1.08, p < 0.001, FDR = 0.021) also posed a risk, whereas (N(1)+N(8))-acetylspermidine (OR = 0.91, 95% CI: 0.87-0.94, p < 0.001, FDR = 0.002) appeared protective. Multivariable MR analysis further confirmed a direct, protective effect of (N(1)+N(8))-acetylspermidine on PDR risk (OR = 0.94, 95% CI: 0.89-1.00, p = 0.040). The sensitivity analysis results indicated that evidence for heterogeneity and pleiotropy was absent. Conclusion: These metabolites have the potential to be used as biomarkers and are promising for future research into the mechanisms and drug targets for PDR.

Exploring the impact of metabolites function on heart failure and coronary heart disease: insights from a Mendelian randomization (MR) study

BACKGROUND

Heart failure (HF) and coronary heart disease (CHD) are major causes of morbidity and mortality worldwide. While traditional risk factors such as hypertension, diabetes, and smoking have been extensively studied, the role of metabolite functions in the development of these cardiovascular conditions has been less explored. This study employed a Mendelian randomization (MR) approach to investigate the impact of metabolite functions on HF and CHD.

METHODS

To assess the causal impacts of specific metabolite risk factors on HF and CHD, this study utilized genetic variants associated with these factors as instrumental variables. Comprehensive genetic and phenotypic data from diverse cohorts, including genome-wide association studies (GWAS) and cardiovascular disease registries, were incorporated into the research.

RESULTS

Our results encompass 61 metabolic cell phenotypes, with ten providing strong evidence of the influence of metabolite functions on the occurrence of HF and CHD. We found that elevated levels of erucate (22:1n9), lower levels of α-tocopherol, an imbalanced citrulline-to-ornithine ratio, elevated γ-glutamyl glycine levels, and elevated 7-methylguanine levels independently increased the risk of these cardiovascular conditions. These findings were consistent across different populations and robust to sensitivity analyses.

CONCLUSION

This MR study provides valuable insights into the influence of metabolite functions on HF and CHD. However, further investigation is needed to fully understand the precise mechanisms by which these metabolite factors contribute to the onset of these conditions. Such research could pave the way for the development of targeted therapeutic strategies.

Levels of asymmetric dimethylarginine in plasma and aqueous humor: a key risk factor for the severity of fibrovascular proliferation in proliferative diabetic retinopathy

Introduction

Proliferative diabetic retinopathy (PDR) is a common diabetes complication, significantly impacting vision and quality of life. Previous studies have suggested a potential link between arginine pathway metabolites and diabetic retinopathy (DR). Connective tissue growth factor (CTGF) plays a role in the occurrence and development of fibrovascular proliferation (FVP) in PDR patients. However, the relationship between arginine pathway metabolites and FVP in PDR remains undefined. This study aimed to explore the correlation between four arginine pathway metabolites (arginine, asymmetric dimethylarginine[ADMA], ornithine, and citrulline) and the severity of FVP in PDR patients.

Methods

In this study, plasma and aqueous humor samples were respectively collected from 30 patients with age-related cataracts without diabetes mellitus (DM) and from 85 PDR patients. The PDR patients were categorized as mild-to-moderate or severe based on the severity of fundal FVP. The study used Kruskal-Wallis test to compare arginine, ADMA, ornithine, and citrulline levels across three groups. Binary logistic regression identified risk factors for severe PDR. Spearman correlation analysis assessed associations between plasma and aqueous humor metabolite levels, and between ADMA and CTGF levels in aqueous humor among PDR patients.

Results

ADMA levels in the aqueous humor were significantly greater in patients with severe PDR than in those with mild-to-moderate PDR(P=0.0004). However, the plasma and aqueous humor levels of arginine, ornithine, and citrulline did not significantly differ between mild-to-moderate PDR patients and severe PDR patients (P>0.05). Binary logistic regression analysis indicated that the plasma (P=0.01) and aqueous humor (P=0.006) ADMA levels in PDR patients were risk factors for severe PDR. Furthermore, significant correlations were found between plasma and aqueous humor ADMA levels (r=0.263, P=0.015) and between aqueous humor ADMA and CTGF levels (r=0.837, P<0.001).

Conclusion

Elevated ADMA levels in plasma and aqueous humor positively correlate with the severity of FVP in PDR, indicating ADMA as a risk factor for severe PDR.

Metabolomic analysis of retinopathy stages and amputation in type 2 diabetes

BACKGROUND

Diabetic retinopathy (DR) and limb amputation are frequent complications of diabetes that cannot always be explained by blood glucose control. Metabolomics is a science that is currently being explored in the search for biomarkers or profiles that identify clinical conditions of interest.

OBJECTIVE

This study aimed to analyze, using a metabolomic approach, peripheral blood samples from type 2 diabetes mellitus (DM2) individuals, compared with those with diabetic retinopathy and limb amputation.

METHODS

The sample consisted of 128 participants, divided into groups: control, DM2 without DR (DM2), non-proliferative DR (DRNP), proliferative DR (DRP), and DM2 amputated (AMP). Metabolites from blood plasma were classified by spectra using nuclear magnetic resonance (NMR), and the metabolic routes of each group using metaboanalyst.

RESULTS

We identified that the metabolism of phenylalanine, tyrosine, and tryptophan was discriminant for the DRP group. Histidine biosynthesis, on the other hand, was statistically associated with the AMP group. The results of this work consolidate metabolites such as glutamine and citrulline as discriminating for DRP, and the branched-chain amino acids as important for DR.

CONCLUSIONS

The results demonstrate the relationship between the metabolism of ketone bodies, with acetoacetate metabolite being discriminating for the DRP group and histidine being a significant metabolite in the AMP group, when compared to the DM2 group.

Advances in Mass Spectrometry-Based Blood Metabolomics Profiling for Non-Cancer Diseases: A Comprehensive Review

Blood metabolomics profiling using mass spectrometry has emerged as a powerful approach for investigating non-cancer diseases and understanding their underlying metabolic alterations. Blood, as a readily accessible physiological fluid, contains a diverse repertoire of metabolites derived from various physiological systems. Mass spectrometry offers a universal and precise analytical platform for the comprehensive analysis of blood metabolites, encompassing proteins, lipids, peptides, glycans, and immunoglobulins. In this comprehensive review, we present an overview of the research landscape in mass spectrometry-based blood metabolomics profiling. While the field of metabolomics research is primarily focused on cancer, this review specifically highlights studies related to non-cancer diseases, aiming to bring attention to valuable research that often remains overshadowed. Employing natural language processing methods, we processed 507 articles to provide insights into the application of metabolomic studies for specific diseases and physiological systems. The review encompasses a wide range of non-cancer diseases, with emphasis on cardiovascular disease, reproductive disease, diabetes, inflammation, and immunodeficiency states. By analyzing blood samples, researchers gain valuable insights into the metabolic perturbations associated with these diseases, potentially leading to the identification of novel biomarkers and the development of personalized therapeutic approaches. Furthermore, we provide a comprehensive overview of various mass spectrometry approaches utilized in blood metabolomics research, including GC-MS, LC-MS, and others discussing their advantages and limitations. To enhance the scope, we propose including recent review articles supporting the applicability of GC×GC-MS for metabolomics-based studies. This addition will contribute to a more exhaustive understanding of the available analytical techniques. The Integration of mass spectrometry-based blood profiling into clinical practice holds promise for improving disease diagnosis, treatment monitoring, and patient outcomes. By unraveling the complex metabolic alterations associated with non-cancer diseases, researchers and healthcare professionals can pave the way for precision medicine and personalized therapeutic interventions. Continuous advancements in mass spectrometry technology and data analysis methods will further enhance the potential of blood metabolomics profiling in non-cancer diseases, facilitating its translation from the laboratory to routine clinical application.

Metabolites as Risk Factors for Diabetic Retinopathy in Patients With Type 2 Diabetes: A 12-Year Follow-up Study

CONTEXT

Diabetic retinopathy (DR) is a specific microvascular complication in patients with diabetes and the leading cause of blindness. Recent advances in omics, especially metabolomics, offer the possibility identifying novel potential biomarkers for DR.

OBJECTIVE

The aim was to identify metabolites associated with DR.

METHODS

We performed a 12-year follow-up study including 1349 participants with type 2 diabetes (1021 without DR, 328 with DR) selected from the METSIM cohort. Individuals who had retinopathy before the baseline study were excluded (n = 63). The diagnosis of retinopathy was based on fundus photography examination. We performed nontargeted metabolomics profiling to identify metabolites.

RESULTS

We found 17 metabolites significantly associated with incident DR after adjustment for confounding factors. Among amino acids, N-lactoyl isoleucine, N-lactoyl valine, N-lactoyl tyrosine, N-lactoyl phenylalanine, N-(2-furoyl) glycine, and 5-hydroxylysine were associated with an increased risk of DR, and citrulline with a decreased risk of DR. Among the fatty acids N,N,N-trimethyl-5-aminovalerate was associated with an increased risk of DR, and myristoleate (14:1n5), palmitoleate (16:1n7), and 5-dodecenoate (12:1n7) with a decreased risk of DR. Sphingomyelin (d18:2/24:2), a sphingolipid, was significantly associated with a decreased risk of DR. Carboxylic acid maleate and organic compounds 3-hydroxypyridine sulfate, 4-vinylphenol sulfate, 4-ethylcatechol sulfate, and dimethyl sulfone were significantly associated with an increased risk of DR.

CONCLUSION

Our study is the first large population-based longitudinal study to identify metabolites for DR. We found multiple metabolites associated with an increased and decreased risk for DR from several different metabolic pathways.

Advances and Perspectives in Relation to the Molecular Basis of Diabetic Retinopathy-A Review

Diabetes mellitus (DM) is a growing problem nowadays, and diabetic retinopathy (DR) is its predominant complication. Currently, DR diagnosis primarily relies on fundoscopic examination; however, novel biomarkers may facilitate that process and make it widely available. In this current review, we delve into the intricate roles of various factors and mechanisms in DR development, progression, prediction, and their association with therapeutic approaches linked to the underlying pathogenic pathways. Specifically, we focus on advanced glycation end products, vascular endothelial growth factor (VEGF), asymmetric dimethylarginine, endothelin-1, and the epigenetic regulation mediated by microRNAs (miRNAs) in the context of DR.

Recent Advances and Perspectives in Relation to the Metabolomics-Based Study of Diabetic Retinopathy

Diabetic retinopathy (DR), a prevalent microvascular complication of diabetes, is a major cause of acquired blindness in adults. Currently, a clinical diagnosis of DR primarily relies on fundus fluorescein angiography, with a limited availability of effective biomarkers. Metabolomics, a discipline dedicated to scrutinizing the response of various metabolites within living organisms, has shown noteworthy advancements in uncovering metabolic disorders and identifying key metabolites associated with DR in recent years. Consequently, this review aims to present the latest advancements in metabolomics techniques and comprehensively discuss the principal metabolic outcomes derived from analyzing blood, vitreous humor, aqueous humor, urine, and fecal samples.

Research progress on the pathogenesis of diabetic retinopathy

Diabetic retinopathy is one of the most common and serious microvascular complications of diabetes mellitus. There are many factors leading to diabetic retinopathy, and its pathogenesis is still unclear. At present, there are still no effective measures for the early treatment of diabetic retinopathy, and the treatment options available when diabetes progresses to advanced stages are very limited, and the treatment results are often unsatisfactory. Detailed studies on the molecular mechanisms of diabetic retinopathy pathogenesis and the development of new therapeutic agents are of great importance. This review describes the potential pathogenesis of diabetic retinopathy for experimental studies and clinical practice.

Untargeted Multiomics Approach Coupling Lipidomics and Metabolomics Profiling Reveals New Insights in Diabetic Retinopathy

Diabetic retinopathy (DR) is a microvascular complication of diabetes mellitus (DM) which is the main cause of vision loss in the working-age population. Currently known risk factors such as age, disease duration, and hemoglobin A1c lack sufficient efficiency to distinguish patients with early stages of DR. A total of 194 plasma samples were collected from patients with type 2 DM and DR (moderate to proliferative (PDR) or control (no or mild DR) matched for age, gender, diabetes duration, HbA1c, and hypertension. Untargeted lipidomic and metabolomic approaches were performed. Partial-least square methods were used to analyze the datasets. Levels of 69 metabolites and 85 lipid species were found to be significantly different in the plasma of DR patients versus controls. Metabolite set enrichment analysis indicated that pathways such as metabolism of branched-chain amino acids (methylglutaryl carnitine p = 0.004), the kynurenine pathway (tryptophan p < 0.001), and microbiota metabolism (p-Cresol sulfate p = 0.004) were among the most enriched deregulated pathways in the DR group. Moreover, Glucose-6-phosphate (p = 0.001) and N-methyl-glutamate (p < 0.001) were upregulated in DR. Subgroup analyses identified a specific signature associated with PDR, macular oedema, and DR associated with chronic kidney disease. Phosphatidylcholines (PCs) were dysregulated, with an increase of alkyl-PCs (PC O-42:5 p < 0.001) in DR, while non-ether PCs (PC 14:0-16:1, p < 0.001; PC 18:2-14:0, p < 0.001) were decreased in the DR group. Through an unbiased multiomics approach, we identified metabolites and lipid species that interestingly discriminate patients with or without DR. These features could be a research basis to identify new potential plasma biomarkers to promote 3P medicine.

Machine Learning-Based Diagnosis and Ranking of Risk Factors for Diabetic Retinopathy in Population-Based Studies from South India

This paper discusses the importance of investigating DR using machine learning and a computational method to rank DR risk factors by importance using different machine learning models. The dataset was collected from four large population-based studies conducted in India between 2001 and 2010 on the prevalence of DR and its risk factors. We deployed different machine learning models on the dataset to rank the importance of the variables (risk factors). The study uses a t-test and Shapely additive explanations (SHAP) to rank the risk factors. Then, it uses five machine learning models (K-Nearest Neighbor, Decision Tree, Support Vector Machines, Logistic Regression, and Naive Bayes) to identify the unimportant risk factors based on the area under the curve criterion to predict DR. To determine the overall significance of risk variables, a weighted average of each classifier's importance is used. The ranking of risk variables is provided to machine learning models. To construct a model for DR prediction, the combination of risk factors with the highest AUC is chosen. The results show that the risk factors glycosylated hemoglobin and systolic blood pressure were present in the top three risk factors for DR in all five machine learning models when the t-test was used for ranking. Furthermore, the risk factors, namely, systolic blood pressure and history of hypertension, were present in the top five risk factors for DR in all the machine learning models when SHAP was used for ranking. Finally, when an ensemble of the five machine learning models was employed, independently with both the t-test and SHAP, systolic blood pressure and diabetes mellitus duration were present in the top four risk factors for diabetic retinopathy. Decision Tree and K-Nearest Neighbor resulted in the highest AUCs of 0.79 (t-test) and 0.77 (SHAP). Moreover, K-Nearest Neighbor predicted DR with 82.6% (t-test) and 78.3% (SHAP) accuracy.

New insight of metabolomics in ocular diseases in the context of 3P medicine

Metabolomics refers to the high-through untargeted or targeted screening of metabolites in biofluids, cells, and tissues. Metabolome reflects the functional states of cells and organs of an individual, influenced by genes, RNA, proteins, and environment. Metabolomic analyses help to understand the interaction between metabolism and phenotype and reveal biomarkers for diseases. Advanced ocular diseases can lead to vision loss and blindness, reducing patients' quality of life and aggravating socio-economic burden. Contextually, the transition from reactive medicine to the predictive, preventive, and personalized (PPPM / 3P) medicine is needed. Clinicians and researchers dedicate a lot of efforts to explore effective ways for disease prevention, biomarkers for disease prediction, and personalized treatments, by taking advantages of metabolomics. In this way, metabolomics has great clinical utility in the primary and secondary care. In this review, we summarized much progress achieved by applying metabolomics to ocular diseases and pointed out potential biomarkers and metabolic pathways involved to promote 3P medicine approach in healthcare.

Role of ADMA in the pathogenesis of microvascular complications in type 2 diabetes mellitus

Diabetic microangiopathy is a typical and severe problem in diabetics, including diabetic retinopathy, diabetic nephropathy, diabetic neuropathy, and diabetic cardiomyopathy. Patients with type 2 diabetes and diabetic microvascular complications have significantly elevated levels of Asymmetric dimethylarginine (ADMA), which is an endogenous inhibitor of nitric oxide synthase (NOS). ADMA facilitates the occurrence and progression of microvascular complications in type 2 diabetes through its effects on endothelial cell function, oxidative stress damage, inflammation, and fibrosis. This paper reviews the association between ADMA and microvascular complications of diabetes and elucidates the underlying mechanisms by which ADMA contributes to these complications. It provides a new idea and method for the prevention and treatment of microvascular complications in type 2 diabetes.

Integrated Metabolomics and Transcriptomics Reveal Metabolic Patterns in Retina of STZ-Induced Diabetic Retinopathy Mouse Model

Diabetic retinopathy (DR), as the leading cause of vision loss in the working-age population, exhibits unique metabolite profiles in human plasma and vitreous. However, those in retina are not fully understood. Here, we utilized liquid and gas chromatography-tandem mass spectrometry technology to explore metabolite characteristics of streptozotocin (STZ)-induced diabetic mice retina. A total of 145 metabolites differed significantly in diabetic retinas compared with controls. These metabolites are mainly enriched in the Warburg effect, and valine, leucine and isoleucine degradation pathways. To further identify underlying regulators, RNA sequencing was performed to integrate metabolic enzyme alterations with metabolomics in STZ-induced diabetic retina. Retinol metabolism and tryptophan metabolism are the shared pathways enriched by metabolome and transcriptome. Additionally, transcriptomic analysis identified 71 differentially expressed enzyme-related genes including Hk2, Slc7a5, Aldh1a3 and Tph integrated with altered metabolic pathways. In addition, single nucleotide polymorphisms within 6 out of 71 genes are associated with increased diabetes risk. This study lays the foundation for mechanism research and the therapeutic target development of DR.

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 in Diabetic Retinopathy: From Potential Biomarkers to Molecular Basis of Oxidative Stress

Diabetic retinopathy (DR), the leading cause of blindness in working-age adults, is one of the most common complications of diabetes mellitus (DM) featured by metabolic disorders. With the global prevalence of diabetes, the incidence of DR is expected to increase. Prompt detection and the targeting of anti-oxidative stress intervention could effectively reduce visual impairment caused by DR. However, the diagnosis and treatment of DR is often delayed due to the absence of obvious signs of retina imaging. Research progress supports that metabolomics is a powerful tool to discover potential diagnostic biomarkers and therapeutic targets for the causes of oxidative stress through profiling metabolites in diseases, which provides great opportunities for DR with metabolic heterogeneity. Thus, this review summarizes the latest advances in metabolomics in DR, as well as potential diagnostic biomarkers, and predicts molecular targets through the integration of genome-wide association studies (GWAS) with metabolomics. Metabolomics provides potential biomarkers, molecular targets and therapeutic strategies for controlling the progress of DR, especially the interventions at early stages and precise treatments based on individual patient variations.

Metabolism and Vascular Retinopathies: Current Perspectives and Future Directions

The retina is one of the most metabolically active organs in the body. Although it is an extension of the brain, the metabolic needs of the retina and metabolic exchanges between the different cell types in the retina are not the same as that of the brain. Retinal photoreceptors convert most of the glucose into lactate via aerobic glycolysis which takes place in their cytosol, yet there are immense numbers of mitochondria in photoreceptors. The present article is a focused review of the metabolic dysregulation seen in retinopathies with underlying vascular abnormalities with aberrant mitochondrial metabolism and Hypoxia-inducible factor (HIF) dependent pathogenesis. Special emphasis has been paid to metabolic exchanges between different cell types in retinopathy of prematurity (ROP), age-related macular degeneration (AMD), and diabetic retinopathy (DR). Metabolic similarities between these proliferative retinopathies have been discussed.

A Hypothesis From Metabolomics Analysis of Diabetic Retinopathy: Arginine-Creatine Metabolic Pathway May Be a New Treatment Strategy for Diabetic Retinopathy

Diabetic retinopathy is one of the serious complications of diabetes, which the leading causes of blindness worldwide, and its irreversibility renders the existing treatment methods unsatisfactory. Early detection and timely intervention can effectively reduce the damage caused by diabetic retinopathy. Metabolomics is a branch of systems biology and a powerful tool for studying pathophysiological processes, which can help identify the characteristic metabolic changes marking the progression of diabetic retinopathy, discover potential biomarkers to inform clinical diagnosis and treatment. This review provides an update on the known metabolomics biomarkers of diabetic retinopathy. Through comprehensive analysis of biomarkers, we found that the arginine biosynthesis is closely related to diabetic retinopathy. Meanwhile, creatine, a metabolite with arginine as a precursor, has attracted our attention due to its important correlation with diabetic retinopathy. We discuss the possibility of the arginine-creatine metabolic pathway as a therapeutic strategy for diabetic retinopathy.

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.