Plasma glutamine and glutamic acid are potential biomarkers for predicting diabetic retinopathy

Liver lipid metabolism, oxidative stress, and inflammation in glutamine-supplemented ob/ob mice

Glutamine availability may be reduced in chronic diseases, such as type 2 diabetes mellitus (T2DM)-induced by obesity. Herein, the antioxidant, anti-inflammatory and lipid metabolism effects of chronic oral glutamine supplementation in its free and dipeptide form were assessed in ob/ob mice. Adult male C57BL/6J ob/ob mice were supplemented with L-alanyl-L-glutamine (DIP) or free L-glutamine (GLN) in the drinking water for 40 days, whilst C57BL/6J Wild-type lean (WT) and control ob/ob mice (CTRL) received fresh water only. Plasma and tissue (skeletal muscle and liver) glutamine levels, and insulin resistance parameters (e.g., GTT, ITT, insulin) were determined. Oxidative stress (e.g., GSH system, Nrf2 translocation), inflammatory (e.g., NFkB translocation, TNF-α gene expression) and lipid metabolism parameters (e.g., plasma and liver triglyceride levels, SRBP-1, FAS, ACC, and ChRBP gene expression) were also analyzed. CTRL ob/ob mice showed lower glutamine levels in plasma and tissue, as well as increased insulin resistance and fat in the liver. Conversely, chronic DIP supplementation restored glutamine levels in plasma and tissues, improved glucose homeostasis and reduced plasma and liver lipid levels. Also, Nrf2 restoration, reduced NFkB translocation, and lower TNF-α gene expression was observed in the DIP group. Interestingly, chronic free GLN only increased muscle glutamine stores but reduced overall insulin resistance, and attenuated plasma and liver lipid metabolic biomarkers. The results presented herein indicate that restoration of body glutamine levels reduces oxidative stress and inflammation in obese and T2DM ob/ob mice. This effect attenuated hepatic lipid metabolic changes observed in obesity.

Advances in molecular epidemiology of diabetic retinopathy: from genomics to gut microbiomics

Diabetic retinopathy (DR) remains a prevalent complication of diabetes mellitus and a leading cause of blindness worldwide. The growing global diabetic population underscores the urgency to deepen our understanding of DR pathogenesis and develop effective prevention strategies. This review synthesizes recent advancements in molecular epidemiology, spanning genomics, epigenomics, transcriptomics, proteomics, metabolomics, and gut microbiomics, elucidating genetic underpinnings, epigenetic modifications, transcriptional alterations, protein biomarkers, metabolic disruptions, and gut microbiota dysbiosis associated with DR. Highlighted are key findings from genome-wide association studies (GWAS), Mendelian randomization (MR) studies, candidate gene association studies, and advancements in epigenetic mechanisms, revealing intricate disease pathways and potential therapeutic targets. Additionally, insights into altered metabolic profiles and gut microbiota compositions in DR underscore their emerging roles in disease progression and complications. Challenges and future directions in molecular epidemiological research are discussed to accelerate the translation of these findings into clinical applications for personalized DR management. The integration of multi-omics research findings may provide novel perspectives for facilitating rapid and accurate disease diagnosis, enabling dynamic disease monitoring, and advancing targeted therapeutic strategies.

Metabolomics in the Diagnosis of Bacterial Infections

One of the essential factors in the appropriate treatment of infections is accurate and timely laboratory diagnosis. The correct diagnosis of infections plays a vital role in determining desirable therapy and controlling the spread of pathogens. Traditional methods of infection diagnosis are limited by several factors such as insufficient sensitivity and specificity, being time-consuming and laborious, having a low ability to distinguish infection from non-infectious inflammatory conditions and a low potential to predict treatment outcomes. Therefore, it is necessary to find innovative strategies for detecting specific biomarkers in order to diagnose infections. The rapid advancement of metabolomics makes it possible to determine the pattern of metabolite changes in the both of pathogen and the host during an infection. Metabolomics is a method used to assess the levels and type of metabolites in an organism. Metabolites are of low-molecular-weight compounds produced as a result of metabolic processes and pathways within cells. Metabolomics provides valuable data to detect accurate biomarkers of specific biochemical features directly related to certain phenotypes or conditions. This study aimed to review the applications and progress of metabolomics as a biomarker for the diagnosis of bacterial infections.

Metabolomic studies reveal and validate potential biomarkers of diabetic retinopathy in two Chinese datasets with type 2 diabetes: a cross-sectional study

BACKGROUND

Diabetic retinopathy (DR) is a major microvascular complication of diabetes mellitus and causes vision impairment and blindness. The presence of major risk factors for DR, such as high levels of HbA1c, does not predict all DR pathogenesis in the clinic, which suggests that uncovering the underlying mechanisms and identifying novel markers are needed. Previous evidence has shown that the serum metabolic signature of DR is unique and detectable compared with that of diabetes mellitus (DM). Here, we aimed to identify serum metabolites as reliable biomarkers for the presence of DR in type 2 DM (T2DM) patients.

METHODS

We performed untargeted and targeted metabolomic studies using liquid chromatography‒mass spectrometry (LC‒MS) and multiple reaction monitoring (MRM) methods on the serum samples of T2DM patients. For the discovery dataset, 39 DR patients and 39 non-DR (NDR) patients were included. For the validation dataset, 95 DR patients and 95 non-DR (NDR) patients were included. Receiver operating characteristic curve analysis was performed to evaluate the discriminating power of the metabolites. Binary logistic regression models were fit to evaluate the associations of metabolite peak areas or neurotransmitter concentrations with the presence of DR and adjusted for known risk factors.

RESULTS

A total of 7123 metabolites were tested. The 39 DR patients had a mean age of 56 years with an average diabetes duration of 12 years, and the 39 NDR patients had a mean age of 57 years with an average diabetes duration of 11 years. Nine serum candidate markers were further identified. Six out of nine markers were associated with DR after we adjusted for covariates, including blood pressure, HbA1c, diabetes duration, fasting blood glucose, triglyceride, eGFR etc. Among them, eicosapentaenoic acid (EPA) and L-tyrosine were validated in an independent, risk factor-matched sample set. The serum L-tyrosine concentration was decreased in DR group by 47% (-0.22 ± 0.87 vs. 0.48 ± 1.05, P < 0.001), of which the cutoff value was 0.10 mg/ml, with 86% sensitivity and 40% specificity (AUC = 0.62, 95% CI = 0.54-0.70, P = 0.005).

CONCLUSIONS

Low levels of circulating L-Tyrosine indicate retinopathy occurrence in T2DM population.

Plasma Metabolomics Identifies Key Metabolites and Improves Prediction of Diabetic Retinopathy: Development and Validation across Multinational Cohorts

PURPOSE

To identify longitudinal metabolomic fingerprints of diabetic retinopathy (DR) and to evaluate their usefulness in predicting DR development and progression.

DESIGN

Multicenter, multiethnic cohort study.

PARTICIPANTS

This study included 17 675 participants from the UK Biobank (UKB) who had baseline prediabetes or diabetes, identified in accordance with the 2021 American Diabetes Association guidelines, and were free of baseline DR and an additional 638 participants with type 2 diabetes mellitus from the Guangzhou Diabetic Eye Study (GDES) for external validation. Diabetic retinopathy was determined by ICD-10 codes in the UKB cohort and revised ETDRS grading criteria in the GDES cohort.

METHODS

Longitudinal DR metabolomic fingerprints were identified through nuclear magnetic resonance (NMR) assay in UKB participants. The predictive value of these fingerprints for predicting DR development were assessed in a fully withheld test set. External validation and extrapolation analyses of DR progression and microvascular damage were conducted in the GDES cohort using NMR technology. Model assessments included the concordance (C) statistic, net classification improvement (NRI), integrated discrimination improvement (IDI), calibration, and clinical usefulness in both cohorts.

MAIN OUTCOME MEASURES

DR development and progression and retinal microvascular damage.

RESULTS

Of 168 metabolites, 118 were identified as candidate metabolomic fingerprints for future DR development. These fingerprints significantly improved the predictability for DR development beyond traditional indicators (C statistic, 0.802 [95% confidence interval (CI), 0.760-0.843] vs. 0.751 [95% CI, 0.706-0.796]; P = 5.56 × 10-4). Glucose, lactate, and citrate were among the fingerprints validated in the GDES cohort. Using these parsimonious and replicable fingerprints yielded similar improvements for predicting DR development (C statistic, 0.807 [95% CI, 0.711-0.903] vs. 0.617 [95% CI, 0.494-0.740]; P = 1.68 × 10-4) and progression (C statistic, 0.797 [95% CI, 0.712-0.882] vs. 0.665 [95% CI, 0.545-0.784]; P = 0.003) in the external GDES cohort. Improvements in NRIs, IDIs, and clinical usefulness also were evident in both cohorts (all P < 0.05). In addition, lactate and citrate were associated with microvascular damage across macular and optic nerve head regions among Chinese GDES (all P < 0.05).

CONCLUSIONS

Metabolomic profiling may be effective in identifying robust fingerprints for predicting future DR development and progression, providing novel insights into the early and advanced stages of DR pathophysiology.

FINANCIAL DISCLOSURE(S)

The author(s) have no proprietary or commercial interest in any materials discussed in this article.

Disentangling the consequences of type 2 diabetes on targeted metabolite profiles using causal inference and interaction QTL analyses

Circulating metabolite levels have been associated with type 2 diabetes (T2D), but the extent to which T2D affects metabolite levels and their genetic regulation remains to be elucidated. In this study, we investigate the interplay between genetics, metabolomics, and T2D risk in the UK Biobank dataset using the Nightingale panel composed of 249 metabolites, 92% of which correspond to lipids (HDL, IDL, LDL, VLDL) and lipoproteins. By integrating these data with large-scale T2D GWAS from the DIAMANTE meta-analysis through Mendelian randomization analyses, we find 79 metabolites with a causal association to T2D, all spanning lipid-related classes except for Glucose and Tyrosine. Twice as many metabolites are causally affected by T2D liability, spanning almost all tested classes, including branched-chain amino acids. Secondly, using an interaction quantitative trait locus (QTL) analysis, we describe four metabolites consistently replicated in an independent dataset from the Estonian Biobank, for which genetic loci in two different genomic regions show attenuated regulation in T2D cases compared to controls. The significant variants from the interaction QTL analysis are significant QTLs for the corresponding metabolites in the general population but are not associated with T2D risk, pointing towards consequences of T2D on the genetic regulation of metabolite levels. Finally, through differential level analyses, we find 165 metabolites associated with microvascular, macrovascular, or both types of T2D complications, with only a few discriminating between complication classes. Of the 165 metabolites, 40 are not causally linked to T2D in either direction, suggesting biological mechanisms specific to the occurrence of complications. Overall, this work provides a map of the consequences of T2D on Nightingale targeted metabolite levels and on their genetic regulation, enabling a better understanding of the T2D trajectory leading to complications.

Dipeptide alanine-glutamine ameliorates retinal neurodegeneration in an STZ-induced rat model

Introduction

Diabetic retinopathy (DR) is a common complication of diabetes. Retinal neuronal degeneration is an early event in DR, indicated by the declined electroretinogram (ERG). Dipeptide alanine-glutamine (Ala-Gln) is widely used as a nutritional supplement in the clinic and has anti-inflammatory effects on the gastrointestinal system. Studies also reported that glutamine has beneficial effects on diabetes. This study aimed to investigate the possible therapeutic effects of Ala-Gln in diabetic retinal neurodegeneration and to delineate its mechanism of action.

Methods

The Streptozotocin (STZ)-induced rat model was used as a DR model. ERG was used to measure the neuronal function of the retina. Western blot analysis was performed to test the expression of proteins. Immunofluorescence staining was used for the detection and localization of proteins.

Results

In diabetic rats, the amplitudes of ERG were declined, while Ala-Gln restored the declined ERG. Retinal levels of inflammatory factors were significantly decreased in Ala-Gln-treated diabetic rats. Ala-Gln mitigated the declined levels of glutamine synthetase and ameliorated the upregulated levels of glial fibrillary acidic protein (GFAP) in diabetic retinas. Moreover, Ala-Gln upregulated the glycolytic enzymes pyruvate kinase isozymes 2 (PKM2), lactate dehydrogenase A (LDHA) and LDHB and stimulated the mTOR signaling pathway in diabetic retinas. The mitochondrial function was improved after the treatment of Ala-Gln in diabetic retinas.

Discussion

Ala-Gln ameliorates retinal neurodegeneration by reducing inflammation and enhancing glucose metabolism and mitochondrial function in DR. Therefore, manipulation of metabolism by Ala-Gln may be a novel therapeutic avenue for retinal neurodegeneration in DR.

Analysis of metabolites associated with ADIPOQ genotypes in individuals with type 2 diabetes mellitus

Diabetes mellitus (DM) is a significant public health problem and it is known that the identification of molecular markers involved in glycemic control can impact disease control. Although the rs266729 polymorphism located in the promoter of the adiponectin gene (ADP) has been shown to be a candidate for involvement in glycemic control, the genotypic groups have never been characterized in terms of metabolomic aspects. Objective: Analyze the metabolites present in the rs266729 genotype groups. 127 diabetic individuals were compared according to the rs266729 genotype groups CC and GC + GG (RFLP-PCR). Blood plasma metabolites were classified by nuclear magnetic resonance (NMR), and the metabolic pathways of each group using the MetaboAnalyst tool. Insulin therapy (p = 0.049) was more frequent in the GC + GG rs266729 group. Lactate, alanine, glutamine, aspartate, lipid, lysine, isoleucine, citrulline, cholesterol, and fucose impacted the CC group and aspartate, beta-glucose, glutamate, pyruvate, proline, and 2-oxoglutarate impacted the CG + GG group. The glucose-alanine pathway, malate-aspartate transport, and urea cycle impacted the CC group (D-glucose, glutamic acid, L-alanine, oxoglutaric acid, and pyruvic acid). The glutamine/glutamate ratio is likely to be related to the causes of rs266729 influencing the risk of diabetes.

Indoxyl sulfate induces retinal microvascular injury via COX-2/PGE2 activation in diabetic retinopathy

BACKGROUND

Diabetic retinopathy (DR), the principal cause of acquired blindness among the working-age population, is the most frequent microvascular complication of diabetes. Although metabolic disorders are hypothesized to play a role in its pathogenesis, the underlying mechanism remains largely elusive.

METHODS

To elucidate the mechanism, we initially compared metabolite profiles of vitreous fluid between 23 patients with DR and 12 non-diabetic controls using liquid chromatography/tandem mass spectrometry, identifying the distinct metabolite indoxyl sulfate (IS). Subsequently, streptozotocin (STZ)-induced diabetic and IS-injected rat models were established to examine the effects of IS on retinal microvasculature. RNA sequencing was conducted to identify potential regulatory mechanisms in IS-treated human retinal endothelial cells (HREC). Finally, target gene knockdown in HREC and treatment of IS-injected rats with inhibitors (targeting IS production or downstream regulators) were employed to elucidate the detailed mechanisms and identify therapeutic targets for DR.

RESULTS

Metabolomics identified 172 significantly altered metabolites in the vitreous humor of diabetics, including the dysregulated tryptophan metabolite indoxyl sulfate (IS). IS was observed to breach the blood-retinal barrier and accumulate in the intraocular fluid of diabetic rats. Both in vivo and in vitro experiments indicated that elevated levels of IS induced endothelial apoptosis and disrupted cell junctions. RNA sequencing pinpointed prostaglandin E2 (PGE2) synthetase-cyclooxygenase 2 (COX-2) as a potential target of IS. Validation experiments demonstrated that IS enhanced COX-2 expression, which subsequently increased PGE2 secretion by promoting transcription factor EGR1 binding to COX-2 DNA following entry into cells via organic anion transporting polypeptides (OATP2B1). Furthermore, inhibition of COX-2 in vivo or silencing EGR1/OATP2B1 in HREC mitigated IS-induced microcapillary damage and the activation of COX-2/PGE2.

CONCLUSION

Our study demonstrated that indoxyl sulfate (IS), a uremic toxin originating from the gut microbiota product indole, increased significantly and contributed to retinal microvascular damage in diabetic retinopathy (DR). Mechanistically, IS impaired retinal microvascular integrity by inducing the expression of COX-2 and the production of PGE2. Consequently, targeting the gut microbiota or the PGE2 pathway may offer effective therapeutic strategies for the treatment of DR.

Metabolomics combined with intestinal microbiota reveals the mechanism of compound Qilian tablets against diabetic retinopathy

Background

Diabetic retinopathy (DR) is one of the common chronic complications of diabetes mellitus, which has developed into the leading cause of irreversible visual impairment in adults worldwide. Compound Qilian tablets (CQLT) is a traditional Chinese medicine (TCM) developed for treating DR, but its mechanism is still unclear. This study explored the mechanism of action of CQLT in treating DR through metabolomics and intestinal microbiota.

Methods

Histopathologic examination of the pancreas and retina of Zucker diabetic fatty (ZDF) rats and immunohistochemistry were used to determine the expression levels of retinal nerve damage indicators ionized calcium binding adaptor molecule-1 (Iba-1) and glial fibrillary acidic protein (GFAP). Rat fecal samples were tested by LC-MS metabolomics to search for potential biomarkers and metabolic pathways for CQLT treatment of DR. Characteristic nucleic acid sequences of rat intestinal microbiota from each group were revealed using 16S rDNA technology to explore key microbes and related pathways for CQLT treatment of DR. At the same time, we investigated the effect of CQLT on the gluconeogenic pathway.

Results

After CQLT intervention, islet cell status was improved, Iba-1 and GFAP expression were significantly decreased, and abnormal retinal microvascular proliferation and exudation were ameliorated. Metabolomics results showed that CQLT reversed 20 differential metabolites that were abnormally altered in DR rats. Intestinal microbiota analysis showed that treatment with CQLT improved the abundance and diversity of intestinal flora. Functional annotation of metabolites and intestinal flora revealed that glycolysis/gluconeogenesis, alanine, aspartate and glutamate metabolism, starch and sucrose metabolism were the main pathways for CQLT in treating DR. According to the results of correlation analysis, there were significant correlations between Iba-1, GFAP, and intestinal microbiota and metabolites affected by CQLT. In addition, we found that CQLT effectively inhibited the gluconeogenesis process in diabetic mice.

Conclusion

In conclusion, CQLT could potentially reshape intestinal microbiota composition and regulate metabolite profiles to protect retinal morphology and function, thereby ameliorating the progression of DR.

Aberrant Neurovascular Coupling in Diabetic Retinopathy Using Arterial Spin Labeling (ASL) and Functional Magnetic Resonance Imaging (fMRI) methods

Background

Previous imaging studies have demonstrated that diabetic retinopathy (DR) is linked to structural and functional abnormalities in the brain. However, the extent to which DR patients exhibit abnormal neurovascular coupling remains largely unknown.

Methods

Thirty-one patients with DR and 31 sex- and age-matched healthy controls underwent resting-state functional magnetic resonance imaging (rs-fMRI) to calculate functional connectivity strength (FCS) and arterial spin-labeling imaging (ASL) to calculate cerebral blood flow (CBF). The study compared CBF-FCS coupling across the entire grey matter and CBF/FCS ratios (representing blood supply per unit of connectivity strength) per voxel between the two groups. Additionally, a support vector machine (SVM) method was employed to differentiate between diabetic retinopathy (DR) patients and healthy controls (HC).

Results

In DRpatients compared to healthy controls, there was a reduction in CBF-FCS coupling across the entire grey matter. Specifically, DR patients exhibited elevated CBF/FCS ratios primarily in the primary visual cortex, including the right calcarine fissure and surrounding cortex. On the other hand, reduced CBF/FCS ratios were mainly observed in premotor and supplementary motor areas, including the left middle frontal gyrus.

Conclusion

An elevated CBF/FCS ratio suggests that patients with DR may have a reduced volume of gray matter in the brain. A decrease in its ratio indicates a decrease in regional CBF in patients with DR. These findings suggest that neurovascular decoupling in the visual cortex, as well as in the supplementary motor and frontal gyrus, may represent a neuropathological mechanism in diabetic retinopathy.

Circulating glutamine/glutamate ratio is closely associated with type 2 diabetes and its associated complications

Objective

This study aims to conduct a comprehensive investigation of the serum amino acid profiles of individuals with type 2 diabetes (T2D) and its related complications.

Methods

Patients with T2D were enrolled in this study. Sixteen kinds of common amino acids in the fasting circulating were assessed through liquid chromatography-mass spectrometry (LC-MS). Subsequently, correlation, regression analyses, and receiver operating characteristic (ROC) curves were conducted to assess the associations between amino acids and clinical indicators.

Results

Thirteen different kinds of amino acids were identified in diabetic patients, as compared with normal controls. The Glutamine/Glutamate (Gln/Glu) ratio was negatively correlated with BMI, HbA1c, serum uric acid, and the triglyceride-glucose (TyG) index, while it was positively correlated with HDL-C. Logistic regression analyses indicated that Gln/Glu was a consistent protective factor for both T2D (OR = 0.65, 95% CI 0.50-0.86) and obesity (OR = 0.79, 95% CI 0.66-0.96). The ROC curves demonstrated that Gln/Glu, proline, valine, and leucine provided effective predictions for diabetes risk, with Gln/Glu exhibiting the highest AUC [0.767 (0.678-0.856)]. In patients with T2D, Gln was the only amino acid that displayed a negative correlation with HbA1c (r = -0.228, p = 0.017). Furthermore, HOMA-β exhibited a negative correlation with Glu (r = -0.301, p = 0.003) but a positive correlation with Gln/Glu (r = 0.245, p = 0.017). Notably, logistic regression analyses revealed an inverse correlation of Gln/Glu with the risk of diabetic kidney disease (OR = 0.74, 95% CI 0.55-0.98) and a positive association with the risk of diabetic retinopathy (OR = 1.53, 95% CI 1.08-2.15).

Conclusion

The Gln/Glu ratio exhibited a significant association with diabetes, common metabolic parameters, and diabetic complications. These findings shed light on the pivotal role of Gln metabolism in T2D and its associated complications.

Pharmacodynamic and targeted amino acid metabolomics researches on the improvement of diabetic retinopathy with Fufang Xueshuantong component compatibility

The Fufang Xueshuantong capsule (FXT) has significant preventive and therapeutic effects on diabetic retinopathy(DR), but the compatibility of its active components remains to be thoroughly explored. In this study, a zebrafish diabetic retinopathy model was established using high-mixed sugars, and the optimal ratios of notoginseng total saponins, total salvianolic acid, astragaloside, and harpagide were selected through orthogonal experiments. Furthermore, we used UPLC-QqQ/MS to detect the changes in amino acid content of DR zebrafish tissues after administration of FXT and its compatible formula to analyze the effects of FXT and its compatible formula on amino acid metabolites. The results showed that the final compatibility ratios of the components were 8: 5: 1: 6.6 by comprehensive evaluation of the indicators. FXT and its compatibility formula had beneficial effects on retinal vasodilatation, lipid accumulation in the liver, total glucose, and VEGF levels in DR zebrafish, and all of them could call back some amino acid levels in DR zebrafish. In this research, we determined the compatible formulation of the active ingredients in the FXT and investigated their efficacy in DR zebrafish for further clinical applications.

Transcriptome analysis combined with Mendelian randomization screening for biomarkers causally associated with diabetic retinopathy

Background

Diabetic retinopathy (DR) is considered one of the most severe complications of diabetes mellitus, but its pathogenesis is still unclear. We hypothesize that certain genes exert a pivotal influence on the progression of DR. This study explored biomarkers for the diagnosis and treatment of DR through bioinformatics analysis.

Methods

Within the GSE221521 and GSE189005 datasets, candidate genes were acquired from intersections of genes obtained using WGCNA and DESeq2 packages. Mendelian randomization (MR) analysis selected candidate biomarkers exhibiting causal relationships with DR. Receiver Operating Characteristic (ROC) analysis determined the diagnostic efficacy of biomarkers, the expression levels of biomarkers were verified in the GSE221521 and GSE189005 datasets, and a nomogram for diagnosing DR was constructed. Enrichment analysis delineated the roles and pathways associated with the biomarkers. Immune infiltration analysis analyzed the differences in immune cells between DR and control groups. The miRNet and networkanalyst databases were then used to predict the transcription factors (TFs) and miRNAs, respectively, of biomarkers. Finally, RT-qPCR was used to verify the expression of the biomarkers in vitro.

Results

MR analysis identified 13 candidate biomarkers that had causal relationships with DR. The ROC curve demonstrated favorable diagnostic performance of three biomarkers (OSER1, HIPK2, and DDRGK1) for DR, and their expression trends were consistent across GSE221521 and GSE189005 datasets. The calibration curves and ROC curves indicated good predictive performance of the nomogram. The biomarkers were enriched in pathways of immune, cancer, amino acid metabolism, and oxidative phosphorylation. Ten immune cell lines showed notable disparities between the DR and control groups. Among them, effector memory CD8+ T cells, plasmacytoid dendritic cells, and activated CD4+ T cells exhibited good correlation with biomarker expression. The TF-mRNA-miRNA network suggested that hsa-mir-92a-3p, GATA2, and RELA play important roles in biomarker targeting for DR. RT-qPCR results also demonstrated a notably high expression of HIPK2 in patients with DR, whereas notably low expression of OSER1.

Conclusion

OSER1, HIPK2, and DDRGK1 were identified as biomarkers for DR. The study findings provide novel insights into the pathogenesis of DR.

Integrated Analysis of Microbiome and Metabolome Reveals Disease-Specific Profiles in Inflammatory Bowel Diseases and Intestinal Behçet's Disease

The gut microbial and metabolic characteristics of intestinal Behçet's disease (BD), a condition sharing many clinical similarities with ulcerative colitis (UC) and Crohn's disease (CD), are largely unexplored. This study investigated the gut microbial and metabolic characteristics of intestinal BD as well as potential biomarkers, comparing them with those in UC, CD, and healthy controls. Colon tissue and stool samples from 100 patients (35 UC, 30 CD, and 35 intestinal BD) and 41 healthy volunteers were analyzed using 16S ribosomal RNA sequencing to assess microbial diversity, taxonomic composition, and functional profiling. Plasma metabolomic analyses were performed using gas chromatography and ultra-performance liquid chromatography-mass spectrometry. Results indicated reduced microbial diversity in CD but not in intestinal BD, with intestinal BD showing fewer changes compared to controls yet distinct taxonomic features from UC, CD, and controls. Common alterations across all diseases included a reduction in beneficial bacteria producing short-chain fatty acids. Intestinal BD-specific changes featured a decreased abundance of Bacteroides fragilis. Metabolomic profiles in intestinal BD were similar to those in CD but distinct from those in UC, displaying significant changes in energy metabolism and genetic information processing. This integrative analysis revealed both shared and unique profiles in intestinal BD compared with UC, CD, and controls, advancing our understanding of the distinctive features of these diseases.

Quercetin Alleviates Insulin Resistance and Repairs Intestinal Barrier in db/db Mice by Modulating Gut Microbiota

Type 2 diabetes mellitus (T2DM) is a chronic metabolic disease which seriously affects public health. Gut microbiota remains a dynamic balance state in healthy individuals, and its disorder may affect health status and even results in metabolic diseases. Quercetin, a natural flavonoid, has been shown to have biological activities that can be used in the prevention and treatment of metabolic diseases. This study aimed to explore the mechanism of quercetin in alleviating T2DM based on gut microbiota. db/db mice were adopted as the model for T2DM in this study. After 10 weeks of administration, quercetin could significantly decrease the levels of body weight, fasting blood glucose (FBG), serum insulin (INS), the homeostasis model assessment of insulin resistance (HOMA-IR), monocyte chemoattractant protein-1 (MCP-1), D-lactic acid (D-LA), and lipopolysaccharide (LPS) in db/db mice. 16S rRNA gene sequencing and untargeted metabolomics analysis were performed to compare the differences of gut microbiota and metabolites among the groups. The results demonstrated that quercetin decreased the abundance of Proteobacteria, Bacteroides, Escherichia-Shigella and Escherichia_coli. Moreover, metabolomics analysis showed that the levels of L-Dopa and S-Adenosyl-L-methionine (SAM) were significantly increased, but 3-Methoxytyramine (3-MET), L-Aspartic acid, L-Glutamic acid, and Androstenedione were significantly decreased under quercetin intervention. Taken together, quercetin could exert its hypoglycemic effect, alleviate insulin resistance, repair the intestinal barrier, remodel the intestinal microbiota, and alter the metabolites of db/db mice.

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.

Ferric particle-assisted LDI-MS platform for metabolic fingerprinting of diabetic retinopathy

OBJECTIVES

To explore the metabolic fingerprints of diabetic retinopathy (DR) in individuals with type 2 diabetes using a newly-developed laser desorption/ionization mass spectrometry (LDI-MS) platform assisted by ferric particles.

METHODS

Metabolic fingerprinting was performed using a ferric particle-assisted LDI-MS platform. A nested population-based case-control study was performed on 216 DR cases and 216 control individuals with type 2 diabetes.

RESULTS

DR cases and control individuals with type 2 diabetes were comparable for a list of clinical factors. The newly-developed LDI-MS platform allowed us to draw the blueprint of plasma metabolic fingerprints from participants with and without DR. The neural network afforded diagnostic performance with an average area under curve value of 0.928 for discovery cohort and 0.905 for validation cohort (95 % confidence interval: 0.902-0.954 and 0.845-0.965, respectively). Tandem MS and Fourier transform ion cyclotron resonance MS with ultrahigh resolution identified seven specific metabolites that were significantly associated with DR in fully adjusted models. Of these metabolites, dihydrobiopterin, phosphoserine, N-arachidonoylglycine, and 3-methylhistamine levels in plasma were first reported to show the associations.

CONCLUSIONS

This work advances the design of metabolic analysis for DR and holds the potential to promise as an efficient tool for clinical management of DR.

Relationship between circulating metabolites and diabetic retinopathy: a two-sample Mendelian randomization analysis

Diabetic retinopathy (DR) is the most frequent microvascular complication of diabetes mellitus, however, its underlying biological mechanisms remain poorly understood. We examined single nucleotide polymorphisms linked to 486 blood metabolites through extensive genome-wide association studies conducted on individuals of European ancestry. The FinnGen Biobank database served as a reference to define DR. Two-sample MR analysis was conducted to reveal the association between the levels of genetically predicted circulating metabolites and the susceptibility to DR. To validate the robustness of the obtained findings, sensitivity analyses with weighted median, weighted mode, and MR-Egger were conducted. 1-oleoylglycerophosphoethanolamine (odds ratio [OR] (OR per one standard deviation [SD] increase) = 0.414; 95% confidence interval [CI] 0.292-0.587; P = 7.613E-07, PFDR = 6.849E-06), pyroglutamine (OR per one SD increase = 0.414; 95% confidence interval [CI] 0.292-0.587; P = 8.31E-04, PFDR = 0.007), phenyllactate (PLA) (OR per one SD increase = 0.591; 95% confidence interval [CI] 0.418-0.836; P = 0.003, PFDR = 0.026), metoprolol acid metabolite (OR per one SD increase = 0.978; 95% confidence interval [CI] 0.962-0.993; P = 0.005, PFDR = 0.042), 10-undecenoate (OR per one SD increase = 0.788; 95% confidence interval [CI] 0.667-0.932; P = 0.005, PFDR = 0.049), erythritol (OR per one SD increase = 0.691; 95% confidence interval [CI] 0.513-0.932; P = 0.015, PFDR = 0.034), 1-stearoylglycerophosphoethanolamine (OR per one SD increase = 0.636; 95% confidence interval [CI] 0.431-0.937; P = 0.022, PFDR = 0.099), 1-arachidonoylglycerophosphoethanolamine (OR per one SD increase = 0.636; 95% confidence interval [CI] 0.431-0.937; P = 0.030, PFDR = 0.099) showed a significant causal relationship with DR and could have protective effects. stachydrine (OR per one SD increase = 1.146; 95% confidence interval [CI] 1.066-1.233; P = 2.270E-04, PFDR = 0.002), butyrylcarnitine (OR per one SD increase = 1.117; 95% confidence interval [CI] 1.023-1.219; P = 0.014, PFDR = 0.062), 5-oxoproline (OR per one SD increase = 1.569; 95% confidence interval [CI] 1.056-2.335; P = 0.026, PFDR = 0.082), and kynurenine (OR = 1.623; 95% CI 1.042-2.526; P = 0.041, PFDR = 0.097) were significantly associated with an increased risk of DR. This study identified metabolites have the potential to be considered prospective compounds for investigating the underlying mechanisms of DR and for selecting appropriate drug targets.

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.

The association between plasma free amino acids and type 2 diabetes mellitus complicated with infection in Chinese patients

BACKGROUND

Type 2 diabetes mellitus (T2DM), one of the most common public diseases threatening human health, is always accompanied by infection. Though there are still a variety of flaws in the treatment of some infectious diseases, metabolomics provides a fresh perspective to explore the relationship between T2DM and infection. Our research aimed to investigate the association between plasma free amino acids (PFAAs) and T2DM complicated with infection in Chinese patients.

METHODS

A cross-sectional study was conducted from May 2015 to August 2016. We retrieved the medical records of 1032 inpatients with T2DM from Liaoning Medical University First Affiliated Hospital and we used mass spectrometry to quantify 23 PFAAs. Infections contained 15 individual categories that could be retrieved from the database. Principal component analysis was used to extract factors of PFAAs. Multi-variable binary logistic regression was used to obtain odds ratios (OR) and their 95% confidence intervals (CI).

RESULTS

Among 1032 inpatients,109 (10.6%) had infectious diseases. Six factors, accounting for 68.6% of the total variance, were extracted. Factor 4 consisted of Glu, Asp and Orn. Factor 5 consisted of Hcy and Pip. After adjusting for potential confounders, factor 4 was positively correlated with T2DM complicated with infection in Chinese T2DM patients (OR: 1.27, 95%CI: 1.06-1.52). Individual Hcy in factor 5 was positively associated with T2DM complicated with infection (OR: 1.33, 95%CI: 1.08-1.64). Furthermore, factor 4 (OR: 1.44, 95%CI: 1.11-1.87), Orn (OR: 1.01, 95%CI: 1.00-1.02) and Hcy (OR: 1.56, 95%CI: 1.14-3.14) were positively associated with bacterial infection in Chinese T2DM patients, while factor 5 (OR: 0.71, 95%CI: 0.50-1.00) was negatively associated with bacterial infection.

CONCLUSIONS

Urea cycle-related metabolites (Orn, Asp, Glu) and Hcy were positively associated with T2DM complicated with infection in China. Orn and Hcy were positively associated with bacterial infection in T2DM patients in China.

Application of Metabolomics and Traditional Chinese Medicine for Type 2 Diabetes Mellitus Treatment

Diabetes is a major global public health problem with high incidence and case fatality rates. Traditional Chinese medicine (TCM) is used to help manage Type 2 Diabetes Mellitus (T2DM) and has steadily gained international acceptance. Despite being generally accepted in daily practice, the TCM methods and hypotheses for understanding diseases lack applicability in the current scientific characterization systems. To date, there is no systematic evaluation system for TCM in preventing and treating T2DM. Metabonomics is a powerful tool to predict the level of metabolites in vivo, reveal the potential mechanism, and diagnose the physiological state of patients in time to guide the follow-up intervention of T2DM. Notably, metabolomics is also effective in promoting TCM modernization and advancement in personalized medicine. This review provides updated knowledge on applying metabolomics to TCM syndrome differentiation, diagnosis, biomarker discovery, and treatment of T2DM by TCM. Its application in diabetic complications is discussed. The combination of multi-omics and microbiome to fully elucidate the use of TCM to treat T2DM is further envisioned.

Diverse and Synergistic Actions of Phytochemicals in a Plant-Based Multivitamin/Mineral Supplement against Oxidative Stress and Inflammation in Healthy Individuals: A Systems Biology Approach Based on a Randomized Clinical Trial

Traditional clinical methodologies often fall short of revealing the complex interplay of multiple components and targets within the human body. This study was designed to explore the complex and synergistic effects of phytochemicals in a plant-based multivitamin/mineral supplement (PBS) on oxidative stress and inflammation in healthy individuals. Utilizing a systems biology framework, we integrated clinical with multi-omics analyses, including UPLC-Q-TOF-MS for 33 phytochemicals, qPCR for 42 differential transcripts, and GC-TOF-MS for 17 differential metabolites. A Gene Ontology analysis facilitated the identification of 367 biological processes linked to oxidative stress and inflammation. As a result, a comprehensive network was constructed consisting of 255 nodes and 1579 edges, featuring 10 phytochemicals, 26 targets, and 218 biological processes. Quercetin was identified as having the broadest target spectrum, succeeded by ellagic acid, hesperidin, chlorogenic acid, and quercitrin. Moreover, several phytochemicals were associated with key genes such as HMOX1, TNF, NFE2L2, CXCL8, and IL6, which play roles in the Toll-like receptor, NF-kappa B, adipocytokine, and C-type lectin receptor signaling pathways. This clinical data-driven network system approach has significantly advanced our comprehension of a PBS's effects by pinpointing pivotal phytochemicals and delineating their synergistic actions, thus illuminating potential molecular mechanisms.

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.

Fluorescent-Based Neurotransmitter Sensors: Present and Future Perspectives

Neurotransmitters (NTs) are endogenous low-molecular-weight chemical compounds that transmit synaptic signals in the central nervous system. These NTs play a crucial role in facilitating signal communication, motor control, and processes related to memory and learning. Abnormalities in the levels of NTs lead to chronic mental health disorders and heart diseases. Therefore, detecting imbalances in the levels of NTs is important for diagnosing early stages of diseases associated with NTs. Sensing technologies detect NTs rapidly, specifically, and selectively, overcoming the limitations of conventional diagnostic methods. In this review, we focus on the fluorescence-based biosensors that use nanomaterials such as metal clusters, carbon dots, and quantum dots. Additionally, we review biomaterial-based, including aptamer- and enzyme-based, and genetically encoded biosensors. Furthermore, we elaborate on the fluorescence mechanisms, including fluorescence resonance energy transfer, photon-induced electron transfer, intramolecular charge transfer, and excited-state intramolecular proton transfer, in the context of their applications for the detection of NTs. We also discuss the significance of NTs in human physiological functions, address the current challenges in designing fluorescence-based biosensors for the detection of NTs, and explore their future development.

Untargeted metabolomics reveals dynamic changes in metabolic profiles of rat supraspinatus tendon at three different time points after diabetes induction

Objective

To investigate the dynamic changes of metabolite composition in rat supraspinatus tendons at different stages of diabetes by untargeted metabolomics analysis.

Methods

A total of 80 Sprague-Dawley rats were randomly divided into normal (NG, n = 20) and type 2 diabetes mellitus groups (T2DM, n = 60) and subdivided into three groups according to the duration of diabetes: T2DM-4w, T2DM-12w, and T2DM-24w groups; the duration was calculated from the time point of T2DM rat model establishment. The three comparison groups were set up in this study, T2DM-4w group vs. NG, T2DM-12w group vs. T2DM-4w group, and T2DM-24w group vs. T2DM-12w group. The metabolite profiles of supraspinatus tendon were obtained using tandem mass spectrometry. Metabolomics multivariate statistics were used for metabolic data analysis and differential metabolite (DEM) determination. The intersection of the three comparison groups' DEMs was defined as key metabolites that changed consistently in the supraspinatus tendon after diabetes induction; then, Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analysis was performed.

Results

T2DM-4w group vs. NG, T2DM-12w group vs. T2DM-4w group, and T2DM-24w group vs. T2DM-12w group detected 94 (86 up-regulated and 8 down-regulated), 36 (13 up-regulated and 23 down-regulated) and 86 (24 up-regulated and 62 down-regulated) DEMs, respectively. Seven key metabolites of sustained changes in the supraspinatus tendon following induction of diabetes include D-Lactic acid, xanthine, O-acetyl-L-carnitine, isoleucylproline, propoxycarbazone, uric acid, and cytidine, which are the first identified biomarkers of the supraspinatus tendon as it progresses through the course of diabetes. The results of KEGG pathway enrichment analysis showed that the main pathway of supraspinatus metabolism affected by diabetes (p < 0.05) was purine metabolism. The results of the KEGG metabolic pathway vs. DEMs correlation network graph revealed that uric acid and xanthine play a role in more metabolic pathways.

Conclusion

Untargeted metabolomics revealed the dynamic changes of metabolite composition in rat supraspinatus tendons at different stages of diabetes, and the newly discovered seven metabolites, especially uric acid and xanthine, may provide novel research to elucidate the mechanism of diabetes-induced tendinopathy.

Bone marrow plasma metabonomics of idiopathic acquired aplastic anemia patients using 1H nuclear magnetic resonance spectroscopy

INTRODUCTION

Idiopathic acquired aplastic anemia (AA) is a bone marrow failure disorder where aberrant T-cell functions lead to depletion of hematopoietic stem and progenitor cells in the bone marrow (BM) microenvironment. T-cells undergo metabolic rewiring, which regulates their proliferation and differentiation. Therefore, studying metabolic variation in AA patients may aid us with a better understanding of the T-cell regulatory pathways governed by metabolites and their pathological engagement in the disease.

OBJECTIVE

To identify the differential metabolites in BM plasma of AA patients, AA follow-up (AAF) in comparison to normal controls (NC) and to identify potential disease biomarker(s).

METHODS

The study used 1D 1H NMR Carr-Purcell-Meiboom-Gill (CPMG) spectra to identify the metabolites present in the BM plasma samples of AA (n = 40), AAF (n = 16), and NC (n = 20). Metabolic differences between the groups and predictive biomarkers were identified by using multivariate analysis and receiver operating characteristic (ROC) module of Metaboanalyst V5.0 tool, respectively.

RESULTS

The AA and AAF samples were well discriminated from NC group as per Principal Component analysis (PCA). Further, we found significant alteration in the levels of 17 metabolites in AA involved in amino-acid (Leucine, serine, threonine, phenylalanine, lysine, histidine, valine, tyrosine, and proline), carbohydrate (Glucose, lactate and mannose), fatty acid (Acetate, glycerol myo-inositol and citrate), and purine metabolism (hypoxanthine) in comparison to NC. Additionally, biomarker analysis predicted Hypoxanthine and Acetate can be used as a potential biomarker.

CONCLUSION

The study highlights the significant metabolic alterations in the BM plasma of AA patients which may have implication in the disease pathobiology.

Evaluation of Antifungal Metabolites Produced by Lactic Acid Bacteria

This study aimed to select and characterize lactic acid bacteria (LAB) with potential antifungal activities against the filamentous fungi Alternaria alternata ATCC MYA-4642, Aspergillus flavus KACC 45470, Aspergillus niger KACC 42589, Cladosporium sphaerospermum ATCC MYA-4645, Penicillium chrysogenum ATCC MYA-4644, and Penicillium expansum KACC 40815. Initial screening of the antifungal activity has identified six LAB strains belonging to the genera Enterococcus and Leuconostoc, selected by their antagonistic activities against at least three of the filamentous fungi in the test panel. Preliminary prediction of bioactive compounds was carried out to narrow down the possible identity of the antagonistic metabolites produced by the studied LAB. Furthermore, metabolic profiles were assessed and used as a basis for the identification of key metabolites based on VIP scores and PCA plot scores. Key metabolites were identified to be β-phenyllactic acid, ⍺-hydroxyisobutyric acid, 1,3-butanediol, phenethylamine, and benzoic acid. Individual assessment of each metabolic compound against the test panel showed specificity inhibitory patterns; yet, combinations between them only showed additive, but not synergetic effects. The pH neutralization significantly reduced the antifungal activity of the cell-free supernatant (CFS), but no bioactive compounds were found to be stable in high temperatures and pressure. This study will be beneficial as an additional building block on the existing knowledge and future antifungal application of LAB produced metabolites. Furthermore, this study also provides a new bio-preservative perspective on unexplored antifungal metabolites produced by LAB as biocontrol agents.

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.

Comprehensive metabolic profiling of diabetic retinopathy

Diabetic retinopathy (DR) is an important complication of diabetes mellitus and a prevalent blind-causing ophthalmic disease. Despite years of efforts, rapid and accurate diagnosis of DR remains a challenging task. Metabolomics has been used as a diagnostic tool for disease progression and therapy monitoring. In this study, retinal tissues were collected from diabetic mice and age-matched non-diabetic mice. An unbiased metabolic profiling was performed to identify the altered metabolites and metabolic pathways in DR. 311 differential metabolites were identified between diabetic retinas and non-diabetic retinas under the criteria of variable importance in projection (VIP) > 1 and P < 0.05. These differential metabolites were highly enriched in purine metabolism, amino acid metabolism, glycerophospholipid metabolism, and pantaothenate and CoA biosynthesis. We then evaluated the sensitivity and specificity of purine metabolites as the candidate biomarkers for DR through the area under the receiver-operating characteristic curves (AUC-ROCs). Compared with other purine metabolites, adenosine, guanine, and inosine had higher sensitivity, specificity, and accuracy for DR prediction. In conclusion, this study sheds new light on the metabolic mechanism of DR, which can facilitate clinical diagnosis, therapy, and prognosis of DR in the future.

Serum disease-specific IgG Fc glycosylation as potential biomarkers for nonproliferative diabetic retinopathy using mass spectrometry

OBJECTIVE

To explore the potential of serum disease-specific immunoglobulin G (DSIgG) glycosylation as a biomarker for the diagnosis of nonproliferative diabetic retinopathy (NPDR).

METHODS

A total of 387 consecutive diabetic patients presenting in an eye clinic without proliferative diabetic retinopathy (DR) were included and divided into those with nondiabetic retinopathy (NDR) (n = 181) and NPDR (n = 206) groups. Serum was collected from all patients for DSIgG separation. The enriched glycopeptides of the tryptic digests of DSIgG were detected using matrix-assisted laser desorption ionization time-of-flight mass spectrometry (MALDI-TOF MS). Patients were randomly divided into discovery and validation sets (1:1). The differences in glycopeptide ratios between the groups were compared by using Student's t-test or the Mann-Whitney U test. The predictive ability of the model was assessed using the area under the receiver operating characteristic curve (AUC).

RESULTS

DSIgG1 G1FN/G0FN, G2N/G2, G2FN/G2N and DSIgG2 G1F/G0F, G1FN/G0FN, G2N/G1N, G2S/G2 were significantly different between NDR and NPDR patients (p < 0.05) in both the discovery and validation sets. The prediction model that was built comprising the seven glycopeptide ratios showed good NPDR prediction performance with an AUC of 0.85 in the discovery set and 0.87 in the validation set.

CONCLUSION

DSIgG Fc N-glycosylation ratios were associated with NPDR and can be used as potential biomarkers for the early diagnosis of DR.

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.

Evaluation of Small Molecules in Blank EDTA Plasma Tubes and Optimization of Metabolomic Workflow for Biomarker Studies Using Plasma Samples

As the first step of metabolomic analysis in biomarker identification studies, various types of blood collection tubes are used in clinical practice. However, little attention is paid to potential contamination caused by the blank tube itself. Here, we evaluated small molecules in blank EDTA plasma tubes through LC-MS-based untargeted metabolomic analysis and identified small molecules with markedly varied levels among different production batches or specifications. Our data demonstrate possible contamination and data interference caused by blank EDTA plasma tubes when employing large clinical cohorts for biomarker identification. Therefore, we propose a workflow of filtering metabolites in blank tubes prior to statistical analysis to improve the fidelity of biomarker identification.

Amino acid analysis as a method of discovering biomarkers for diagnosis of diabetes and its complications

Diabetes mellitus (DM) is a severe chronic diseases with a global prevalence of 9%, leading to poor health and high health care costs, and is a direct cause of millions of deaths each year. The rising epidemic of diabetes and its complications, such as retinal and peripheral nerve disease, is a huge burden globally. A better understanding of the molecular pathways involved in the development and progression of diabetes and its complications can facilitate individualized prevention and treatment. High diabetes mellitus incidence rate is caused mainly by lack of non-invasive and reliable methods for early diagnosis, such as plasma biomarkers. The incidence of diabetes and its complications in the world still grows so it is crucial to develop a new, faster, high specificity and more sensitive diagnostic technologies. With the advancement of analytical techniques, metabolomics can identify and quantify multiple biomarkers simultaneously in a high-throughput manner, and effective biomarkers can greatly improve the efficiency of diabetes and its complications. By providing information on potential metabolic pathways, metabolomics can further define the mechanisms underlying the progression of diabetes and its complications, help identify potential therapeutic targets, and improve the prevention and management of T2D and its complications. The application of amino acid metabolomics in epidemiological studies has identified new biomarkers of diabetes mellitus (DM) and its complications, such as branched-chain amino acids, phenylalanine and arginine metabolites. This study focused on the analysis of metabolic amino acid profiling as a method for identifying biomarkers for the detection and screening of diabetes and its complications. The results presented are all from recent studies, and in all cases analyzed, there were significant changes in the amino acid profile of patients in the experimental group compared to the control group. This study demonstrates the potential of amino acid profiles as a detection method for diabetes and its complications.

Genomic, metabolomic, and functional characterisation of beneficial properties of Pediococcus pentosaceus ST58, isolated from human oral cavity

Bacteriocins produced by lactic acid bacteria are proteinaceous antibacterial metabolites that normally exhibit bactericidal or bacteriostatic activity against genetically closely related bacteria. In this work, the bacteriocinogenic potential of Pediococcus pentosaceus strain ST58, isolated from oral cavity of a healthy volunteer was evaluated. To better understand the biological role of this strain, its technological and safety traits were deeply investigated through a combined approach considering physiological, metabolomic and genomic properties. Three out of 14 colonies generating inhibition zones were confirmed to be bacteriocin producers and, according to repPCR and RAPD-PCR, differentiation assays, and 16S rRNA sequencing it was confirmed to be replicates of the same strain, identified as P. pentosaceus, named ST58. Based on multiple isolation of the same strain (P. pentosaceus ST58) over the 26 weeks in screening process for the potential bacteriocinogenic strains from the oral cavity of the same volunteer, strain ST58 can be considered a persistent component of oral cavity microbiota. Genomic analysis of P. pentosaceus ST58 revealed the presence of operons encoding for bacteriocins pediocin PA-1 and penocin A. The produced bacteriocin(s) inhibited the growth of Listeria monocytogenes, Enterococcus spp. and some Lactobacillus spp. used to determine the activity spectrum. The highest levels of production (6400 AU/ml) were recorded against L. monocytogenes strains after 24 h of incubation and the antimicrobial activity was inhibited after treatment of the cell-free supernatants with proteolytic enzymes. Noteworthy, P. pentosaceus ST58 also presented antifungal activity and key metabolites potentially involved in these properties were identified. Overall, this strain can be of great biotechnological interest towards the development of effective bio-preservation cultures as well as potential health promoting microbes.

Metabolomic biomarkers of the mediterranean diet in pregnant individuals: A prospective study

BACKGROUND AND AIMS

Metabolomic profiling is a systematic approach to identifying biomarkers for dietary patterns. Yet, metabolomic markers for dietary patterns in pregnant individuals have not been investigated. The aim of this study was to identify plasma metabolomic markers and metabolite panels that are associated with the Mediterranean diet in pregnant individuals.

METHODS

This is a prospective study of 186 pregnant individuals who had both dietary intake and metabolomic profiles measured from the Fetal Growth Studies-Singletons cohort. Dietary intakes during the peri-conception/1st trimester and the second trimester were accessed at 8-13 and 16-22 weeks of gestation, respectively. Adherence to the Mediterranean diet was measured by the alternate Mediterranean Diet (aMED) score. Fasting plasma samples were collected at 16-22 weeks and untargeted metabolomics profiling was performed using the mass spectrometry-based platforms. Metabolites individually or jointly associated with aMED scores were identified using linear regression and least absolute shrinkage and selection operator (LASSO) regression models with adjustment for potential confounders, respectively.

RESULTS

Among 459 annotated metabolites, 64 and 41 were individually associated with the aMED scores of the diet during the peri-conception/1st trimester and during the second trimester, respectively. Fourteen metabolites were associated with the Mediterranean diet in both time windows. Most Mediterranean diet-related metabolites were lipids (e.g., acylcarnitine, cholesteryl esters (CEs), linoleic acid, long-chain triglycerides (TGs), and phosphatidylcholines (PCs), amino acids, and sugar alcohols. LASSO regressions also identified a 10 metabolite-panel that were jointly associated with aMED score of the diet during the peri-conception/1st trimester (AUC: 0.74; 95% CI: 0.57, 0.91) and a 3 metabolites-panel in the 2nd trimester (AUC: 0.68; 95% CI: 0.50, 0.86).

CONCLUSION

We identified plasma metabolomic markers for the Mediterranean diet among pregnant individuals. Some of them have also been reported in previous studies among non-pregnant populations, whereas others are novel. The results from our study warrant replication in pregnant individuals by future studies.

CLINICAL TRIAL REGISTRATION NUMBER

This study was registered at ClinicalTrials.gov.

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.

[Blood metabolites in preterm infants with retinopathy of prematurity based on tandem mass spectrometry: a preliminary study]

OBJECTIVES

To study new biomarkers for the early diagnosis of retinopathy of prematurity (ROP) by analyzing the differences in blood metabolites based on liquid chromatography-tandem mass spectrometry (LC-MS/MS) and metabolomics.

METHODS

Dried blood spots were collected from 21 infants with ROP (ROP group) and 21 infants without ROP (non-ROP group) who were hospitalized in the Sixth Affiliated Hospital of Sun Yat-sen University from January 2013 to December 2016. LC-MS/MS was used to measure the metabolites, and orthogonal partial least squares-discriminant analysis was used to search for differentially expressed metabolites and biomarkers.

RESULTS

There was a significant difference in blood metabolic profiles between the ROP and non-ROP groups. The pattern recognition analysis, Score-plot, and weight analysis obtained 10 amino acids with a relatively large difference. Further statistical analysis showed that the ROP group had significant increases in blood levels of glutamic acid, leucine, aspartic acid, ornithine, and glycine compared with the non-ROP group (P<0.05). The receiver operating characteristic curve analysis showed that glutamic acid and ornithine had the highest value in diagnosing ROP.

CONCLUSIONS

Blood metabolites in preterm infants with ROP are different from those without ROP. Glutamic acid and ornithine are the metabolic markers for diagnosing ROP. LC-MS/MS combined with metabolomics analysis has a potential application value in the early identification and diagnosis of ROP.

The relationship between islet β-cell function and metabolomics in overweight patients with Type 2 diabetes

A cross-sectional study was performed using metabolomics in overweight patients with Type 2 diabetes (T2D) at different stages of the disease. We aimed to identify potential metabolites for assessing islet β-cell function in order to investigate the correlation between islet β-cell dysfunction and metabolite changes in overweight patients with T2D. We selected 60 overweight adults (24 ≤ body mass index [BMI] < 28 kg/m2) with T2D who had been admitted to our hospital. The participants were equally divided into three groups according to disease duration: H1 (duration ≤ 5 years), H2 (5 years < duration ≤ 10 years), and H3 (duration > 10 years). Questionnaires, physical examinations, laboratory tests, and imaging studies were administered to all participants. The modified homeostasis model of assessment (HOMA) index was calculated using fasting C-peptide levels, and metabolite assays were performed using mass spectrometry. The results showed that HOMA-β and visceral fat area (VFA) were negatively correlated with diabetes duration. The VFA was positively correlated with arginine, cysteine, methionine, proline, and succinyl/methylmalonylcarnitine levels. The HOMA-β was negatively correlated with the serine and tetradecanoyldiacylcarnitine levels, and positively correlated with the aspartic acid, cysteine, homocysteine, piperamide, proline, and valine levels. The HOMA-IR was negatively correlated with hydroxypalmitoylcarnitine levels and positively correlated with the myristoylcarnitine levels. Thus, at different stages of T2D progression in overweight patients, serine, aspartic acid, cysteine, homocysteine, piperamide, proline, valine, and tetradecanoyldiacylcarnitine may be associated with HOMA-β and represent potential novel biomarkers for evaluating islet β-cell function.

Circulatory Biomarkers and Diabetic Retinopathy in Racial and Ethnic Populations

Clinical staging systems for diagnosis and treatment of diabetic retinopathy (DR) must closely relate to endpoints that are both relevant for patients and feasible for physicians to implement. Current DR staging systems for clinical eye care and research provide detailed phenotypic characterization to predict patient outcomes in diabetes but have limitations. Biochemical biomarkers provide a rich pool of potential candidates for new DR staging systems that can be readily measured in accessible fluids. Circulating biomarkers that are specific to the retina and relate to angiogenesis and inflammation have been suggested as relevant for DR. Although there is a lack of multi-ethnic studies evaluating circulatory biomarkers in DR, variability in circulatory biomarkers have been reported in people from different ethnic and racial backgrounds. Therefore, there is a need for future studies to evaluate individual or combinations of biomarkers in diverse populations with DR from different ethnic and racial backgrounds.

Engineering At-Home Dilution and Filtration Methods to Enable Paper-Based Colorimetric Biosensing in Human Blood with Cell-Free Protein Synthesis

Diagnostic blood tests can guide the administration of healthcare to save and improve lives. Most clinical biosensing blood tests require a trained technician and specialized equipment to process samples and interpret results, which greatly limits test accessibility. Colorimetric paper-based diagnostics have an equipment-free readout, but raw blood obscures a colorimetric response which has motivated diverse efforts to develop blood sample processing techniques. This work uses inexpensive readily-available materials to engineer user-friendly dilution and filtration methods for blood sample collection and processing to enable a proof-of-concept colorimetric biosensor that is responsive to glutamine in 50 µL blood drop samples in less than 30 min. Paper-based user-friendly blood sample collection and processing combined with CFPS biosensing technology represents important progress towards the development of at-home biosensors that could be broadly applicable to personalized healthcare.

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.

Multi-level analysis reveals the association between diabetes, body mass index, and HbA1c in an Iraqi population

Type 2 diabetes (T2D) known as a complex metabolic disorder may cause health problems and changes in blood biochemical markers. A growing number of studies have looked into several biomarkers and their connections with T2D risk. However, few have explored the interconnection of these biomarkers, as well as the prospective alterations in the diabetes biomarker correlation network. We conducted a secondary analysis in order to introduce a multi-level approach to establish a relationship between diabetes, pre-diabetes, blood biochemical markers, age, and body mass index (BMI). The dataset was obtained from the Mendeley Data (available at https://data.mendeley.com/datasets/wj9rwkp9c2/1 . In this study, three groups were established: non-diabetic (n = 103), pre-diabetic (n = 53), and diabetic (n = 844). According to the Heatmap analysis, non-diabetic and pre-diabetic individuals had the lowest BMI, age, and HbA1c. Diabetes and pre-diabetes were correlated with BMI (r = 0.58 and - 0.27, respectively), age (r = 0.47 and - 0.28, respectively), and HbA1c (r = 0.55 and - 0.21, respectively) using Pearson analysis. Using multivariate analysis, we found that diabetes, BMI, age, HbA1c, cholesterol, triglyceride, LDL, VLDL, and HDL were all associated. Network analysis revealed a connection between BMI and diabetes at the highest cut-off point. Moreover, receiver operating characteristic (ROC) analysis validated the network findings, revealing that BMI (area under the ROC curve, AUC = 0.95), HbA1c (AUC = 0.94), and age (AUC = 0.84) were the best predictors of diabetes. In conclusion, our multi-step study revealed that identifying significant T2D predictors, such as BMI and HbA1c, required a series of mathematical analyses.

Retinopathy of prematurity: Metabolic risk factors

At preterm birth, the retina is incompletely vascularized. Retinopathy of prematurity (ROP) is initiated by the postnatal suppression of physiological retinal vascular development that would normally occur in utero. As the neural retina slowly matures, increasing metabolic demand including in the peripheral avascular retina, leads to signals for compensatory but pathological neovascularization. Currently, only late neovascular ROP is treated. ROP could be prevented by promoting normal vascular growth. Early perinatal metabolic dysregulation is a strong but understudied risk factor for ROP and other long-term sequelae of preterm birth. We will discuss the metabolic and oxygen needs of retina, current treatments, and potential interventions to promote normal vessel growth including control of postnatal hyperglycemia, dyslipidemia and hyperoxia-induced retinal metabolic alterations. Early supplementation of missing nutrients and growth factors and control of supplemental oxygen promotes physiological retinal development. We will discuss the current knowledge gap in retinal metabolism after preterm birth.

AMINO ACIDS METABOLOMIC SIGNATURE OF BLOOD PRESSURE VARIABILITY In Type 2 Diabetes

CONTEXT

Accumulating data supports the key role of disrupted amino acids (AAs) metabolism in diabetes. Conflicting data regarding the relevance of serum AAs in diabetes and hypertension suggest that their relationship needs further investigation.

OBJECTIVE

To investigate serum AAs as biomarkers of increased BP variability evaluated during 24-hour ambulatory BP monitoring in the presence of type 2 diabetes.

DESIGN

Cross-sectional.

SUBJECTS AND METHODS

We analyzed serum AAs using targeted metabolomics (ultrahigh-performance liquid chromatography/mass spectrometry) in patients with type 2 diabetes (n=80). BP variability was assessed using 24-hour ambulatory BP monitoring. Participants were divided into two groups based on the 24-hour diastolic BP variability median value.

RESULTS

Aspartic acid, isoleucine, leucine, and phenylalanine were significantly lower, while glutamine was significantly higher in the group with higher diastolic BP variability (p-value <0.05 and variable importance in the projection >1). Corresponding pathways identified as disrupted in patients with diabetes and a higher 24-hour diastolic BP variability were phenylalanine, tyrosine, and tryptophan biosynthesis, phenylalanine metabolism, and alanine, aspartate, and glutamate metabolism (pathway impact value >0).

CONCLUSIONS

We identified specific changes in serum AAs and target AAs pathways in relation to increased 24-hour diastolic BP variability in patients with type 2 diabetes.

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.

Interpretable machine learning-derived nomogram model for early detection of diabetic retinopathy in type 2 diabetes mellitus: a widely targeted metabolomics study

Objective

Early identification of diabetic retinopathy (DR) is key to prioritizing therapy and preventing permanent blindness. This study aims to propose a machine learning model for DR early diagnosis using metabolomics and clinical indicators.

Methods

From 2017 to 2018, 950 participants were enrolled from two affiliated hospitals of Wenzhou Medical University and Anhui Medical University. A total of 69 matched blocks including healthy volunteers, type 2 diabetes, and DR patients were obtained from a propensity score matching-based metabolomics study. UPLC-ESI-MS/MS system was utilized for serum metabolic fingerprint data. CART decision trees (DT) were used to identify the potential biomarkers. Finally, the nomogram model was developed using the multivariable conditional logistic regression models. The calibration curve, Hosmer–Lemeshow test, receiver operating characteristic curve, and decision curve analysis were applied to evaluate the performance of this predictive model.

Results

The mean age of enrolled subjects was 56.7 years with a standard deviation of 9.2, and 61.4% were males. Based on the DT model, 2-pyrrolidone completely separated healthy controls from diabetic patients, and thiamine triphosphate (ThTP) might be a principal metabolite for DR detection. The developed nomogram model (including diabetes duration, systolic blood pressure and ThTP) shows an excellent quality of classification, with AUCs (95% CI) of 0.99 (0.97–1.00) and 0.99 (0.95–1.00) in training and testing sets, respectively. Furthermore, the predictive model also has a reasonable degree of calibration.

Conclusions

The nomogram presents an accurate and favorable prediction for DR detection. Further research with larger study populations is needed to confirm our findings.

Serum Untargeted Metabolomics Reveal Potential Biomarkers of Progression of Diabetic Retinopathy in Asians

Purpose: To reveal molecular mechanisms of diabetic retinopathy (DR) in Asians and facilitate the identification of new therapeutic targets through untargeted metabolomics. To determine the differences in serum metabolites and metabolic pathways between different stages of diabetic retinopathy in patients with type 2 diabetic mellitus (T2DM) and proliferative DR (PDR) and non-proliferative DR (NPDR) and identify differential metabolites between T2DM and DR (NPDR and PDR) patients. Methods: This prospective observational registration study described the differential metabolites between 45 T2DM patients and 15 control cases with no significant differences in clinical characteristics. Their biospecimens and clinical information were collected and recorded in their medical reports. DR phenotypes of the subjects were verified by retina specialists. Serum metabolites were analyzed using high-resolution mass spectrometry with liquid chromatography. Untargeted metabolomics was performed on serum samples from 15 T2DM patients, 15 non-proliferative diabetic retinopathy patients, 15 proliferative diabetic retinopathy patients, and 15 diabetic controls. Discriminatory metabolic features were identified through partial least squares discriminant analysis (PLS-DA), hierarchical clustering analysis (HCA), and generalized linear regression models. Result: Through untargeted metabolomics, 931 features (523 in positive and 408 in negative modes) with 102 common metabolites highly relevant to the presence of DR were detected. In the adjusted analysis, 67 metabolic features differed significantly between T2DM and NPDR patients. Pathway analysis revealed alterations in metabolisms of amino acids and fatty acids. Glutamate, phosphatidylcholine, and 13-hydroperoxyoctadeca-9,11-dienoic acid (13-PHODE) were key contributors to these pathway differences. A total of 171 features distinguished PDR patients from T2DM patients, and pathway analysis revealed alterations in amino acid metabolism, fatty acid metabolism, nitrogen metabolism, and tricarboxylic acid cycle. Aspartate, glutamate, glutamine, ornithine, N-acetyl-l-glutamate, N-acetyl-l-aspartate, citrate, succinate, N-(L-arginino)succinate, 2-oxoglutarate, 13-hydroperoxyoctadeca-9,11-dienoic acid, methionine, lysine, threonine, phenylalanine, N(pi)-methyl-l-histidine, phosphatidylcholine, and linoleate were major contributors to the pathway differences. Between NPDR patients and PDR patients, there were 79 significant differential metabolites. Enrichment pathway analysis showed changes in amino acid metabolism, fatty acid metabolism, pantothenate, and CoA biosynthesis. Aspartate, glutamine, N-acetyl-l-glutamate, N-acetyl-l-aspartate, pantothenate, dihomo-gamma-linolenate, docosahexaenoic acid, and icosapentaenoic acid were key factors for the differences of these pathways. Conclusion: This study demonstrated that the pathways of arginine biosynthesis metabolism, linoleic acid metabolism, alanine, aspartate, and glutamate metabolism, as well as d-glutamine and d-glutamate metabolism, were dysregulated in DR patients of the Asian population. Increased levels of glutamate, aspartate, glutamine, N-acetyl-l-glutamate, and N-acetyl-l-aspartate and decreased levels of dihomo-gamma-linolenate, docosahexaenoic, and icosapentaenoic were considered as the metabolic profile that could distinguish PDR from NPDR in Asians. Phosphatidylcholine and 13-PHODE were identified as two major novel metabolite markers in advanced stages of DR in our study.