Diminished retinal complex lipid synthesis and impaired fatty acid β-oxidation associated with human diabetic retinopathy

Identification of serum biomarkers for chronic kidney disease using serum metabolomics

This study aimed to identify biomarkers for chronic kidney disease (CKD) by studying serum metabolomics. Serum samples were collected from 194 non-dialysis CKD patients and 317 healthy controls (HC). Using ultra-high-performance liquid chromatography-tandem mass spectrometry (UPLC-MS), untargeted metabolomics analysis was conducted. A random forest model was developed and validated in separate sets of HC and CKD patients. The serum metabolomic profiles of patients with chronic kidney disease (CKD) exhibited significant differences compared to healthy controls (HC). A total of 314 metabolites were identified as significantly different, with 179 being upregulated and 135 being downregulated in CKD patients. KEGG enrichment analysis revealed several key pathways, including arginine biosynthesis, phenylalanine metabolism, linoleic acid metabolism, and purine metabolism. The diagnostic efficacy of the classifier was high, with an area under the curve of 1 in the training and validation sets and 0.9435 in the cross-validation set. This study provides comprehensive insights into serum metabolism in non-dialysis CKD patients, highlighting the potential involvement of abnormal biological metabolism in CKD pathogenesis. Exploring metabolites may offer new possibilities for the management of CKD.

Role of Systemic Factors in Improving the Prognosis of Diabetic Retinal Disease and Predicting Response to Diabetic Retinopathy Treatment

Topic

To review clinical evidence on systemic factors that might be relevant to update diabetic retinal disease (DRD) staging systems, including prediction of DRD onset, progression, and response to treatment.

Clinical relevance

Systemic factors may improve new staging systems for DRD to better assess risk of disease worsening and predict response to therapy.

Methods

The Systemic Health Working Group of the Mary Tyler Moore Vision Initiative reviewed systemic factors individually and in multivariate models for prediction of DRD onset or progression (i.e., prognosis) or response to treatments (prediction).

Results

There was consistent evidence for associations of longer diabetes duration, higher glycosylated hemoglobin (HbA1c), and male sex with DRD onset and progression. There is strong trial evidence for the effect of reducing HbA1c and reducing DRD progression. There is strong evidence that higher blood pressure (BP) is a risk factor for DRD incidence and for progression. Pregnancy has been consistently reported to be associated with worsening of DRD but recent studies reflecting modern care standards are lacking. In studies examining multivariate prognostic models of DRD onset, HbA1c and diabetes duration were consistently retained as significant predictors of DRD onset. There was evidence of associations of BP and sex with DRD onset. In multivariate prognostic models examining DRD progression, retinal measures were consistently found to be a significant predictor of DRD with little evidence of any useful marginal increment in prognostic information with the inclusion of systemic risk factor data apart from retinal image data in multivariate models. For predicting the impact of treatment, although there are small studies that quantify prognostic information based on imaging data alone or systemic factors alone, there are currently no large studies that quantify marginal prognostic information within a multivariate model, including both imaging and systemic factors.

Conclusion

With standard imaging techniques and ways of processing images rapidly evolving, an international network of centers is needed to routinely capture systemic health factors simultaneously to retinal images so that gains in prediction increment may be precisely quantified to determine the usefulness of various health factors in the prognosis of DRD and prediction of response to treatment.

Financial Disclosures

Proprietary or commercial disclosure may be found in the Footnotes and Disclosures at the end of this article.

Ethanolamine as a biomarker and biomarker-based therapy for diabetic retinopathy in glucose-well-controlled diabetic patients

Diabetic retinopathy (DR) is the leading cause of blindness among the working-age population. Although controlling blood glucose levels effectively reduces the incidence and development of DR to less than 50%, there are currently no diagnostic biomarkers or effective treatments for DR development in glucose-well-controlled diabetic patients (GW-DR). In this study, we established a prospective GW-DR cohort by strictly adhering to glycemic control guidelines and maintaining regular retinal examinations over a median 2-year follow-up period. The discovery cohort encompassed 71 individuals selected from a pool of 292 recruited diabetic patients at baseline, all of whom consistently maintained hemoglobin A1c (HbA1c) levels below 7% without experiencing hypoglycemia. Within this cohort of 71 individuals, 21 subsequently experienced new-onset GW-DR, resulting in an incidence rate of 29.6%. In the validation cohort, we also observed a significant GW-DR incidence rate of 17.9%. Employing targeted metabolomics, we investigated the metabolic characteristics of serum in GW-DR, revealing a significant association between lower levels of ethanolamine and GW-DR risk. This association was corroborated in the validation cohort, exhibiting superior diagnostic performance in distinguishing GW-DR from diabetes compared to the conventional risk factor HbA1c, with AUCs of 0.954 versus 0.506 and 0.906 versus 0.521 in the discovery and validation cohorts, respectively. Furthermore, in a streptozotocin (STZ)-induced diabetic rat model, ethanolamine attenuated diabetic retinal inflammation, accompanied by suppression of microglial diacylglycerol (DAG)-dependent protein kinase C (PKC) pathway activation. In conclusion, we propose that ethanolamine is a potential biomarker and represents a viable biomarker-based therapeutic option for GW-DR.

AST/ALT ratio is an independent risk factor for diabetic retinopathy: A cross-sectional study

The aspartate to alanine transaminase (AST/ALT) ratio indicates oxidative stress and inflammatory reactions related to the occurrence of diabetic retinopathy (DR). Currently, there are no reports on the correlation between AST/ALT ratio and DR. Hence, this study aimed to explore the relationship between AST/ALT ratio and DR. This cross-sectional study utilized data from the Metabolic Management Center of the First People's Hospital in City. In total, 1365 patients with type 2 diabetes mellitus (T2DM) participated in the study, including 244 patients with DR and 1121 patients without DR. We collected the results of fundus photography, liver function, and other research data and grouped them according to tertiles of AST/ALT ratios. DR prevalence was the highest in the group with the highest AST/ALT ratio (22.12%, P = .004). Both univariate (OR = 2.25, 95% CI: 1.51-3.34, P < .001) and multivariable logistic regression analyses (adjusted for confounding factors) showed that the risk of DR increased by 36% when the AST/ALT ratio increased by 1 standard deviation (SD) (OR = 1.36, 95% CI: 1.16-1.59, P < .001), and 29.3% was mediated by the duration of diabetes. A sensitivity analysis confirmed the stability of the results. This study showed that an increase in AST/ALT ratio is an independent risk factor for DR.

Lipidomic studies revealing serological markers associated with the occurrence of retinopathy in type 2 diabetes

PURPOSE

The duration of type 2 diabetes mellitus (T2DM) and blood glucose levels have a significant impact on the development of T2DM complications. However, currently known risk factors are not good predictors of the onset or progression of diabetic retinopathy (DR). Therefore, we aimed to investigate the differences in the serum lipid composition in patients with T2DM, without and with DR, and search for potential serological indicators associated with the development of DR.

METHODS

A total of 622 patients with T2DM hospitalized in the Department of Endocrinology of the First Affiliated Hospital of Xi'an JiaoTong University were selected as the discovery set. One-to-one case-control matching was performed according to the traditional risk factors for DR (i.e., age, duration of diabetes, HbA1c level, and hypertension). All cases with comorbid chronic kidney disease were excluded to eliminate confounding factors. A total of 42 pairs were successfully matched. T2DM patients with DR (DR group) were the case group, and T2DM patients without DR (NDR group) served as control subjects. Ultra-performance liquid chromatography-mass spectrometry (LC-MS/MS) was used for untargeted lipidomics analysis on serum, and a partial least squares discriminant analysis (PLS-DA) model was established to screen differential lipid molecules based on variable importance in the projection (VIP) > 1. An additional 531 T2DM patients were selected as the validation set. Next, 1:1 propensity score matching (PSM) was performed for the traditional risk factors for DR, and a combined 95 pairings in the NDR and DR groups were successfully matched. The screened differential lipid molecules were validated by multiple reaction monitoring (MRM) quantification based on mass spectrometry.

RESULTS

The discovery set showed no differences in traditional risk factors associated with the development of DR (i.e., age, disease duration, HbA1c, blood pressure, and glomerular filtration rate). In the DR group compared with the NDR group, the levels of three ceramides (Cer) and seven sphingomyelins (SM) were significantly lower, and one phosphatidylcholine (PC), two lysophosphatidylcholines (LPC), and two SMs were significantly higher. Furthermore, evaluation of these 15 differential lipid molecules in the validation sample set showed that three Cer and SM(d18:1/24:1) molecules were substantially lower in the DR group. After excluding other confounding factors (e.g., sex, BMI, lipid-lowering drug therapy, and lipid levels), multifactorial logistic regression analysis revealed that a lower abundance of two ceramides, i.e., Cer(d18:0/22:0) and Cer(d18:0/24:0), was an independent risk factor for the occurrence of DR in T2DM patients.

CONCLUSION

Disturbances in lipid metabolism are closely associated with the occurrence of DR in patients with T2DM, especially in ceramides. Our study revealed for the first time that Cer(d18:0/22:0) and Cer(d18:0/24:0) might be potential serological markers for the diagnosis of DR occurrence in T2DM patients, providing new ideas for the early diagnosis of DR.

Elovl4b knockout zebrafish as a model for ocular very-long-chain PUFA deficiency

Very-long-chain PUFAs (VLC-PUFAs) are a group of lipids with chain lengths >24 carbons, and the ELOVL4 (elongation of very-long-chain FA-4) enzyme is responsible for vertebrate VLC-PUFA biosynthesis. Studies on the role of VLC-PUFAs in vision have been hindered because of the need for adequate animal models to capture the global loss of VLC-PUFAs. Since homozygous Elovl4 ablation is lethal in neonatal mice because of catastrophic drying from the loss of their protective skin barrier, we established a zebrafish (Danio rerio) model of Elovl4 ablation. We generated Elovl4b KO zebrafish by creating a 56-bp deletion mutation in exon 2 of the Elovl4b gene using CRISPR-Cas9. We used GC-MS and LC-MS/MS to analyze the VLC-PUFA and lipid profiles from wild-type and Elovl4b KO fish eyes. We also performed histology and visual-behavioral tests. We found that heterozygous and homozygous Elovl4b KO zebrafish eyes had altered lipid profiles and a significantly lower C30 to C36 VLC-PUFA abundance than wild-type fish. Moreover, Elovl4b+/- and Elovl4b-/- KO larvae had significantly lower motor activity in response to light-dark cycles than their age-matched controls. Elovl4b-/- adult fish showed no obvious differences in gross retinal morphology and lamination compared with wild type, except for the presence of lipid droplets within the retinal pigment epithelial cell layer of Elovl4b-/- fish. Our data indicate that the loss of Elovl4b in zebrafish changes ocular lipid profiles and leads to visual abnormalities and subtle retinal changes. These findings highlight the use of zebrafish as a model for VLC-PUFA depletion and ELOVL4-related dysfunction.

Frontal Cortex Lipid Alterations During the Onset of Alzheimer's Disease

Background

Although sporadic Alzheimer's disease (AD) is a neurodegenerative disorder of unknown etiology, familial AD is associated with specific gene mutations. A commonality between these forms of AD is that both display multiple pathogenic events including cholinergic and lipid dysregulation.

Objective

We aimed to identify the relevant lipids and the activity of their related receptors in the frontal cortex and correlating them with cognition during the progression of AD.

Methods

MALDI-mass spectrometry imaging (MSI) and functional autoradiography was used to evaluate the distribution of phospholipids/sphingolipids and the activity of cannabinoid 1 (CB1), sphingosine 1-phosphate 1 (S1P1), and muscarinic M2/M4 receptors in the frontal cortex (FC) of people that come to autopsy with premortem clinical diagnosis of AD, mild cognitive impairment (MCI), and no cognitive impairment (NCI).

Results

MALDI-MSI revealed an increase in myelin-related lipids, such as diacylglycerol (DG) 36:1, DG 38:5, and phosphatidic acid (PA) 40:6 in the white matter (WM) in MCI compared to NCI, and a downregulation of WM phosphatidylinositol (PI) 38:4 and PI 38:5 levels in AD compared to NCI. Elevated levels of phosphatidylcholine (PC) 32:1, PC 34:0, and sphingomyelin 38:1 were observed in discrete lipid accumulations in the FC supragranular layers during disease progression. Muscarinic M2/M4 receptor activation in layers V-VI decreased in AD compared to MCI. CB1 receptor activity was upregulated in layers V-VI, while S1P1 was downregulated within WM in AD relative to NCI.

Conclusions

FC WM lipidomic alterations are associated with myelin dyshomeostasis in prodromal AD, suggesting WM lipid maintenance as a potential therapeutic target for dementia.

Explainable Artificial Intelligence Paves the Way in Precision Diagnostics and Biomarker Discovery for the Subclass of Diabetic Retinopathy in Type 2 Diabetics

Diabetic retinopathy (DR), a common ocular microvascular complication of diabetes, contributes significantly to diabetes-related vision loss. This study addresses the imperative need for early diagnosis of DR and precise treatment strategies based on the explainable artificial intelligence (XAI) framework. The study integrated clinical, biochemical, and metabolomic biomarkers associated with the following classes: non-DR (NDR), non-proliferative diabetic retinopathy (NPDR), and proliferative diabetic retinopathy (PDR) in type 2 diabetes (T2D) patients. To create machine learning (ML) models, 10% of the data was divided into validation sets and 90% into discovery sets. The validation dataset was used for hyperparameter optimization and feature selection stages, while the discovery dataset was used to measure the performance of the models. A 10-fold cross-validation technique was used to evaluate the performance of ML models. Biomarker discovery was performed using minimum redundancy maximum relevance (mRMR), Boruta, and explainable boosting machine (EBM). The predictive proposed framework compares the results of eXtreme Gradient Boosting (XGBoost), natural gradient boosting for probabilistic prediction (NGBoost), and EBM models in determining the DR subclass. The hyperparameters of the models were optimized using Bayesian optimization. Combining EBM feature selection with XGBoost, the optimal model achieved (91.25 ± 1.88) % accuracy, (89.33 ± 1.80) % precision, (91.24 ± 1.67) % recall, (89.37 ± 1.52) % F1-Score, and (97.00 ± 0.25) % the area under the ROC curve (AUROC). According to the EBM explanation, the six most important biomarkers in determining the course of DR were tryptophan (Trp), phosphatidylcholine diacyl C42:2 (PC.aa.C42.2), butyrylcarnitine (C4), tyrosine (Tyr), hexadecanoyl carnitine (C16) and total dimethylarginine (DMA). The identified biomarkers may provide a better understanding of the progression of DR, paving the way for more precise and cost-effective diagnostic and treatment strategies.

Retinoid X Receptor Activation Prevents Diabetic Retinopathy in Murine Models

Previously, the RXR agonist UAB126 demonstrated therapeutic potential to treat obese mice by controlling blood glucose levels (BGL) and altering the expression of genes associated with lipid metabolism and inflammatory response. The purpose of the study was to assess the effects of UAB126 on the progression of diabetic retinopathy (DR) in rodent models of type 1 diabetes (T1D), streptozotocin-induced, and type 2 diabetes (T2D), in db/db mice. UAB126 treatment was delivered either by oral gavage for 6 weeks or by topical application of eye drops for 2 weeks. At the end of the treatment, the retinal function of diabetic mice was assessed by electroretinography (ERG), and their retinal tissue was harvested for protein and gene expression analyses. Bone-marrow cells were isolated and differentiated into bone marrow-derived macrophages (BMDMs). The glycolysis stress test and the 2-DG glucose uptake analysis were performed. Our results demonstrated that in the UAB126-treated diabetic BMDMs, the ECAR rate and the 2-DG uptake were improved as compared to untreated diabetic BMDMs. In UAB126-treated diabetic mice, hyperglycemia was reduced and associated with the preservation of ERG amplitudes and enhanced AMPK activity. Retinas from diabetic mice treated with topical UAB126 demonstrated an increase in Rxr and Ppar and the expression of genes associated with lipid metabolism. Altogether, our data indicate that RXR activation is beneficial to preclinical models of DR.

Retinoid X Receptor activation prevents diabetic retinopathy in murine models

Previously, the RXR agonist UAB126 demonstrated therapeutic potential to treat obese mice by controlling blood glucose levels (BGL) and altering the expression of genes associated with lipid metabolism and inflammatory response. The purpose of the study was to assess UAB126 effect in progression of diabetic retinopathy (DR) in rodent models of Type1 diabetes (T1D), streptozotocin-induced, and Type2 diabetes (T2D), the db/db mice. UAB126 treatment was delivered either by oral gavage for 6 weeks or by topical application of eye drops for 2 weeks. At the end of the treatment, the retinal function of diabetic mice was assessed by electroretinography (ERG), and their retinal tissue was harvested for protein and gene expression analyses. Bone-marrow cells were isolated and differentiated into bone marrow-derived macrophages (BMDMs). The glycolysis stress test and the 2-DG glucose uptake analysis were performed. Our results demonstrated that in the UAB126-treated diabetic BMDMs, the ECAR rate and the 2-DG uptake were improved as compared to untreated diabetic BMDMs. In UAB126-treated diabetic mice, hyperglycemia was reduced and associated with the preservation of ERG amplitudes and enhanced AMPK activity. Retinas from diabetic mice treated with topical UAB126 demonstrated an increase in Rxr and Ppar, and expression of genes associated with lipid metabolism. Altogether, our data indicate that RXR activation is beneficial to preclinical models of DR.

Integration of Vitreous Lipidomics and Metabolomics for Comprehensive Understanding of the Pathogenesis of Proliferative Diabetic Retinopathy

As a vision-threatening complication of diabetes mellitus (DM), proliferative diabetic retinopathy (PDR) is associated with sustained metabolic disorders. Herein, we collected the vitreous cavity fluid of 49 patients with PDR and 23 control subjects without DM for metabolomics and lipidomics analyses. Multivariate statistical methods were performed to explore relationships between samples. For each group of metabolites, gene set variation analysis scores were generated, and we constructed a lipid network by using weighted gene co-expression network analysis. The association between lipid co-expression modules and metabolite set scores was investigated using the two-way orthogonal partial least squares (O2PLS) model. A total of 390 lipids and 314 metabolites were identified. Multivariate statistical analysis revealed significant vitreous metabolic and lipid differences between PDR and controls. Pathway analysis showed that 8 metabolic processes might be associated with the development of PDR, and 14 lipid species were found to be altered in PDR patients. Combining metabolomics and lipidomics, we identified fatty acid desaturase 2 (FADS2) as an important potential contributor to the pathogenesis of PDR. Collectively, this study integrates vitreous metabolomics and lipidomics to comprehensively unravel metabolic dysregulation and identifies genetic variants associated with altered lipid species in the mechanistic pathways for PDR.

The essential role of docosahexaenoic acid and its derivatives for retinal integrity

The fatty acid composition of photoreceptor outer segment (POS) phospholipids diverges from other membranes, being highly enriched in polyunsaturated fatty acids (PUFAs). The most abundant PUFA is docosahexaenoic acid (DHA, C22:6n-3), an omega-3 PUFA that amounts to over 50% of the POS phospholipid fatty acid side chains. Interestingly, DHA is the precursor of other bioactive lipids such as elongated PUFAs and oxygenated derivatives. In this review, we present the current view on metabolism, trafficking and function of DHA and very long chain polyunsaturated fatty acids (VLC-PUFAs) in the retina. New insights on pathological features generated from PUFA deficient mouse models with enzyme or transporter defects and corresponding patients are discussed. Not only the neural retina, but also abnormalities in the retinal pigment epithelium are considered. Furthermore, the potential involvement of PUFAs in more common retinal degeneration diseases such as diabetic retinopathy, retinitis pigmentosa and age-related macular degeneration are evaluated. Supplementation treatment strategies and their outcome are summarized.

A Metabolism-Based Interpretable Machine Learning Prediction Model for Diabetic Retinopathy Risk: A Cross-Sectional Study in Chinese Patients with Type 2 Diabetes

The burden of diabetic retinopathy (DR) is increasing, and the sensitive biomarkers of the disease were not enough. Studies have found that the metabolic profile, such as amino acid (AA) and acylcarnitine (AcylCN), in the early stages of DR patients might have changed, indicating the potential of metabolites to become new biomarkers. We are amid to construct a metabolite-based prediction model for DR risk. This study was conducted on type 2 diabetes (T2D) patients with or without DR. Logistic regression and extreme gradient boosting (XGBoost) prediction models were constructed using the traditional clinical features and the screening features, respectively. Assessing the predictive power of the models in terms of both discrimination and calibration, the optimal model was interpreted using the Shapley Additive exPlanations (SHAP) to quantify the effect of features on prediction. Finally, the XGBoost model incorporating AA and AcylCN variables had the best comprehensive evaluation (ROCAUC = 0.82, PRAUC = 0.44, Brier score = 0.09). C18 : 1OH lower than 0.04 μmol/L, C18 : 1 lower than 0.70 μmol/L, threonine higher than 27.0 μmol/L, and tyrosine lower than 36.0 μmol/L were associated with an increased risk of developing DR. Phenylalanine higher than 52.0 μmol/L was associated with a decreased risk of developing DR. In conclusion, our study mainly used AAs and AcylCNs to construct an interpretable XGBoost model to predict the risk of developing DR in T2D patients which is beneficial in identifying high-risk groups and preventing or delaying the onset of DR. In addition, our study proposed possible risk cut-off values for DR of C18 : 1OH, C18 : 1, threonine, tyrosine, and phenylalanine.

Frontiers in diabetic retinal disease

Diabetic retinal disease (DRD) remains a leading cause of vision loss and blindness globally. Although treatments can be effective when given at vision-threatening stages of DRD, there is a lack of knowledge about the earliest mechanisms leading to the development of clinically evident DRD. Recent advances in retinal imaging methods for patients with diabetes allow a more precise and granular characterization of the different stages of DRD than is provided by the classic Diabetic Retinopathy Severity Scale based on fundus photographs. In addition, recent clinical studies have yielded more information on how to adjust blood glucose levels, lipid levels and blood pressure to minimize the risk of DRD. Given the incomplete success of current therapies, there is a critical need for better understanding of the mechanisms underlying DRD and novel treatment targets that address the entire neurovascular retina. Moreover, the causes for interindividual variability in the development of DRD in patients with similar glycemic history and other metabolic factors are not yet clarified either. Finally, greater focus on patients' experience with visual disabilities and treatment effects should be addressed in research in this field.

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.

Integrated Analysis of Metabolomics and Lipidomics in Plasma of T2DM Patients with Diabetic Retinopathy

Diabetic retinopathy (DR) is a major cause of blindness worldwide and may be non-proliferative (NPDR) or proliferative (PDR). To Investig.gate the metabolomic and lipidomic characteristics of plasma in DR patients, plasma samples were collected from patients with type 2 diabetes mellitus (DR group) with PDR (n = 27), NPDR (n = 18), or no retinopathy (controls, n = 21). Levels of 54 and 41 metabolites were significantly altered in the plasma of DR patients under positive and negative ion modes, respectively. By subgroup analysis, 74 and 29 significantly changed plasma metabolites were detected in PDR patients compared with NPDR patients under positive and negative ion modes, respectively. KEGG analysis indicated that pathways such as biosynthesis of amino acids and neuroactive ligand-receptor interaction were among the most enriched pathways in altered metabolites in the DR group and PDR subgroup. Moreover, a total of 26 and 41 lipids were significantly changed in the DR group and the PDR subgroup, respectively. The panel using the 29-item index could discriminate effectively between diabetic patients with and without retinopathy, and the panel of 22 items showed effective discrimination between PDR and NPDR. These results provide a basis for further research into the therapeutic targets associated with these metabolite and lipid alterations.

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.

Diabetic retinopathy: Involved cells, biomarkers, and treatments

Diabetic retinopathy (DR), a leading cause of vision loss and blindness worldwide, is caused by retinal neurovascular unit dysfunction, and its cellular pathology involves at least nine kinds of retinal cells, including photoreceptors, horizontal and bipolar cells, amacrine cells, retinal ganglion cells, glial cells (Müller cells, astrocytes, and microglia), endothelial cells, pericytes, and retinal pigment epithelial cells. Its mechanism is complicated and involves loss of cells, inflammatory factor production, neovascularization, and BRB impairment. However, the mechanism has not been completely elucidated. Drug treatment for DR has been gradually advancing recently. Research on potential drug targets relies upon clear information on pathogenesis and effective biomarkers. Therefore, we reviewed the recent literature on the cellular pathology and the diagnostic and prognostic biomarkers of DR in terms of blood, protein, and clinical and preclinical drug therapy (including synthesized molecules and natural molecules). This review may provide a theoretical basis for further DR research.

Serum lipidomic determinants of human diabetic neuropathy in type 2 diabetes

OBJECTIVE

The serum lipidomic profile associated with neuropathy in type 2 diabetes is not well understood. Obesity and dyslipidemia are known neuropathy risk factors, suggesting lipid profiles early during type 2 diabetes may identify individuals who develop neuropathy later in the disease course. This retrospective cohort study examined lipidomic profiles 10 years prior to type 2 diabetic neuropathy assessment.

METHODS

Participants comprised members of the Gila River Indian community with type 2 diabetes (n = 69) with available stored serum samples and neuropathy assessment 10 years later using the combined Michigan Neuropathy Screening Instrument (MNSI) examination and questionnaire scores. A combined MNSI index was calculated from examination and questionnaire scores. Serum lipids (435 species from 18 classes) were quantified by mass spectrometry.

RESULTS

The cohort included 17 males and 52 females with a mean age of 45 years (SD = 9 years). Participants were stratified as with (high MNSI index score > 2.5407) versus without neuropathy (low MNSI index score ≤ 2.5407). Significantly decreased medium-chain acylcarnitines and increased total free fatty acids, independent of chain length and saturation, in serum at baseline associated with incident peripheral neuropathy at follow-up, that is, participants had high MNSI index scores, independent of covariates. Participants with neuropathy also had decreased phosphatidylcholines and increased lysophosphatidylcholines at baseline, independent of chain length and saturation. The abundance of other lipid classes did not differ significantly by neuropathy status.

INTERPRETATION

Abundance differences in circulating acylcarnitines, free fatty acids, phosphatidylcholines, and lysophosphatidylcholines 10 years prior to neuropathy assessment are associated with neuropathy status in type 2 diabetes.

Acylcarnitines: Can They Be Biomarkers of Diabetic Nephropathy?

Diabetic nephropathy (DN), one of the most serious microvascular complications of diabetes mellitus (DM), may progress to end-stage renal disease (ESRD). Current biochemical biomarkers, such as urinary albumin excretion rate (UAER), have limitations for early screening and monitoring of DN. Recent studies have identified some metabolites as candidate biomarkers for early detection of DN. In this review, we summarize the role of dysregulated acylcarnitines (AcylCNs) in DN pathophysiology. Lower abundance of short- and medium-chain AcylCNs and higher long-chain AcylCNs often occurred in DM with normal albuminuria and microalbuminuria, compared with advanced stages of DN. The increase of long-chain AcylCNs was supposed to be an adaptive compensation in fat acids (FAs) oxidation in the early stage of DN. Conversely, the decrease of long-chain AcylCNs was due to incomplete oxidation of FAs in advanced stage of DN. Thus, AcylCNs may serve as sensitive biomarkers in predicting the risk of DN.

Relationship Between Acylcarnitine and the Risk of Retinopathy in Type 2 Diabetes Mellitus

OBJECTIVE

Diabetic retinopathy is a common complication of type 2 diabetes mellitus (T2DM). Due to the limited effectiveness of current prevention and treatment methods, new biomarkers are urgently needed for the prevention and diagnosis of DR. This study aimed to explore the relationships between plasma acylcarnitine with DR in T2DM.

METHODS

From May 2015 to August 2016, data of 1032 T2DM patients were extracted from tertiary hospitals. Potential non-linear associations were tested by binary logistic regression models, and ORs and 95% CIs of the research variables were obtained. Correlation heat map was used to analyze the correlation between variables. The change of predictive ability was judged by the area under the receiver operating characteristic curve.

RESULTS

Of the 1032 patients with T2DM, 162 suffered from DR. After adjusting for several confounding variables, C2 (OR:0.55, 95%CI:0.39-0.76), C14DC (OR:0.64, 95%CI:0.49-0.84), C16 (OR:0.64, 95%CI:0.49-0.84), C18:1OH (OR:0.51, 95%CI:0.36-0.71) and C18:1 (OR:0.60, 95%CI:0.44-0.83) were negatively correlated with DR. The area under the curve increased from 0.794 (95% CI 0.745 to 0.842) to 0.840 (95% CI 0.797 to 0.833) when C2, C14DC, C18:1OH and C18:1 added to the traditional risk factor model.

CONCLUSION

There was a negative correlation between C2, C14DC, C16, C18:1OH, and C18:1 and the risk of retinopathy in patients with T2DM. C2, C14DC, C18:1OH, and C18:1 may be new predictors and diagnostic markers of DR.

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.