Urine and plasma metabolites predict the development of diabetic nephropathy in individuals with Type 2 diabetes mellitus

Exploring the glycation association with dyslipidaemia: Novel approach for diabetic nephropathy

The transcription factor known as sterol regulatory element-binding protein (SREBP) and the glycation pathways, specifically the formation of Advanced Glycation End Products (AGEs), have a significant and deleterious impact on the kidney. They alter renal lipid metabolism and promote glomerulosclerosis, mesangial cell expansion, tubulointerstitial fibrosis, and inflammation, leading to diabetic nephropathy (DN) progression. Although several pieces of scientific evidence are reported for potential causes of glycation and lipotoxicity in DN, the underlying mechanism of renal lipid accumulation still needs to be fully understood. We provide a rationalized view on how AGEs exert multiple effects that cause SREBP activation and inflammation, contributing to DN through Receptor for AGEs (RAGE) signaling, AGE-R1-dependent downregulation of Sirtuin 1 (SIRT-1), and increased SREBP Cleavage Activating Protein (SCAP) glycosylation. This review emphasizes the association between glycation and the SREBP pathway and how it affects the onset of DN associated with obesity. Finally, we discuss the correlation of glycation and the SREBP pathway with insulin resistance (IR), oxidative stress, endoplasmic reticulum stress, inflammation, and existing and emerging therapeutic approaches toward better controlling obesity-related DN.

Microbiome-derived metabolites in early to mid-pregnancy and risk of gestational diabetes: a metabolome-wide association study

BACKGROUND

Pre-diagnostic disturbances in the microbiome-derived metabolome have been associated with an increased risk of diabetes in non-pregnant populations. However, the roles of microbiome-derived metabolites, the end-products of microbial metabolism, in gestational diabetes (GDM) remain understudied. We examined the prospective association of microbiome-derived metabolites in early to mid-pregnancy with GDM risk in a diverse population.

METHODS

We conducted a prospective discovery and validation study, including a case-control sample of 91 GDM and 180 non-GDM individuals within the multi-racial/ethnic The Pregnancy Environment and Lifestyle Study (PETALS) as the discovery set, a random sample from the PETALS (42 GDM, 372 non-GDM) as validation set 1, and a case-control sample (35 GDM, 70 non-GDM) from the Gestational Weight Gain and Optimal Wellness randomized controlled trial as validation set 2. We measured untargeted fasting serum metabolomics at gestational weeks (GW) 10-13 and 16-19 by gas chromatography/time-of-flight mass spectrometry (TOF-MS), liquid chromatography (LC)/quadrupole TOF-MS, and hydrophilic interaction LC/quadrupole TOF-MS. GDM was diagnosed using the 3-h, 100-g oral glucose tolerance test according to the Carpenter-Coustan criteria around GW 24-28.

RESULTS

Among 1362 annotated compounds, we identified 140 of gut microbiome metabolism origin. Multivariate enrichment analysis illustrated that carbocyclic acids and branched-chain amino acid clusters at GW 10-13 and the unsaturated fatty acids cluster at GW 16-19 were positively associated with GDM risk (FDR < 0.05). At GW 10-13, the prediction model that combined conventional risk factors and LASSO-selected microbiome-derived metabolites significantly outperformed the model with only conventional risk factors including fasting glucose (discovery AUC: 0.884 vs. 0.691; validation 1: 0.945 vs. 0.731; validation 2: 0.987 vs. 0.717; all P < 0.01). At GW 16-19, similar results were observed (discovery AUC: 0.802 vs. 0.691, P < 0.01; validation 1: 0.826 vs. 0.780; P = 0.10).

CONCLUSIONS

Dysbiosis in microbiome-derived metabolites is present early in pregnancy among individuals progressing to GDM.

The Potential Use of Targeted Proteomics and Metabolomics for the Identification and Monitoring of Diabetic Kidney Disease

Diabetic kidney disease (DKD) is a prevalent microvascular complication of diabetes mellitus and is associated with a significantly worse prognosis compared to diabetic patients without kidney involvement, other microvascular complications, or non-diabetic chronic kidney disease, due to its higher risk of cardiovascular events, faster progression to end-stage kidney disease, and increased mortality. In clinical practice, diagnosis is based on estimated glomerular filtration rate (eGFR) and albuminuria. However, given the limitations of these diagnostic markers, novel biomarkers must be identified. Omics is a new field of study involving the comprehensive analysis of various types of biological data at the molecular level. In different fields, they have shown promising results in (early) detection of diseases, personalized medicine, therapeutic monitoring, and understanding pathogenesis. DKD is primarily utilized in scientific research and has not yet been implemented in routine clinical practice. The aim of this review is to provide an overview of currently available data on targeted omics. After an extensive literature search, 25 different (panels of) omics were withheld and analyzed. Both serum/plasma and urine proteomics and metabolomics have been described with varying degrees of evidence. For all omics, there is still a relative paucity of data from large, prospective, longitudinal cohorts, presumably because of the heterogeneity of DKD and the lack of patient selection in studies, the complexity of omics technologies, and various practical and ethical considerations (e.g., limited accessibility, cost, and privacy concerns).

Metabolomic and lipidomic profiling of the spinal cord in type 2 diabetes mellitus rats with painful neuropathy

In this paper we investigated lipid and metabolite changes in diabetic neuropathy, using untargeted lipidomics and metabolomics analyses of the spinal cords from streptozotocin-treated diabetic rats.170 metabolites and 45 lipids were dysregulated in the painful diabetic neuropathy (PDN) phase. Pathway enrichment analysis revealed perturbations in starch and sucrose, tryptophan, pyrimidine, cysteine and methionine, thiamine, tyrosine, and nucleotides. The disturbance of tyrosine, tryptophan, methionine, triacylglycerol, and phosphatidylethanolamine metabolism indicated that pathological mechanisms in the PDN involved energy metabolism, oxidative stress, and neural reparative regeneration. These revelations offered potential biomarkers for PDN and enriched the comprehension of the complex molecular mechanisms characterizing PDN, establishing a solid foundation for subsequent inquiries into neural convalescence and recovery after PDN.

Glutaminolysis is a Potential Therapeutic Target for Kidney Diseases

Metabolic reprogramming contributes to the progression and prognosis of various kidney diseases. Glutamine is the most abundant free amino acid in the body and participates in more metabolic processes than other amino acids. Altered glutamine metabolism is a prominent feature in different kidney diseases. Glutaminolysis converts glutamine into the TCA cycle metabolite, alpha-ketoglutarate, via a cascade of enzymatic reactions. This metabolic pathway plays pivotal roles in inflammation, maladaptive repair, cell survival and proliferation, redox homeostasis, and immune regulation. Given the crucial role of glutaminolysis in bioenergetics and anaplerotic fluxes in kidney pathogenesis, studies on this cascade could provide a better understanding of kidney diseases, thus inspiring the development of potential methods for targeted therapy. Emerging evidence has shown that targeting glutaminolysis is a promising therapeutic strategy for ameliorating kidney disease. In this narrative review, equation including keywords related to glutamine, glutaminolysis and kidney are subjected to an exhaustive search on Pubmed database, we identified all relevant articles published before 1 April, 2024. Afterwards, we summarize the regulation of glutaminolysis in major kidney diseases and its underlying molecular mechanisms. Furthermore, we highlight therapeutic strategies targeting glutaminolysis and their potential clinical applications.

Advancements, Challenges, and clinical implications of integration of metabolomics technologies in diabetic nephropathy

BACKGROUND

Diabetic nephropathy (DN), a severe complication of diabetes, involves a range of renal abnormalities driven by metabolic derangements. Metabolomics, revealing dynamic metabolic shifts in diseases like DN and offering insights into personalized treatment strategies, emerges as a promising tool for improved diagnostics and therapies.

METHODS

We conducted an extensive literature review to examine how metabolomics contributes to the study of DN and the challenges associated with its implementation in clinical practice. We identified and assessed relevant studies that utilized metabolomics methods, including nuclear magnetic resonance (NMR) spectroscopy and mass spectrometry (MS) to assess their efficacy in diagnosing DN.

RESULTS

Metabolomics unveils key pathways in DN progression, highlighting glucose metabolism, dyslipidemia, and mitochondrial dysfunction. Biomarkers like glycated albumin and free fatty acids offer insights into DN nuances, guiding potential treatments. Metabolomics detects small-molecule metabolites, revealing disease-specific patterns for personalized care.

CONCLUSION

Metabolomics offers valuable insights into the molecular mechanisms underlying DN progression and holds promise for personalized medicine approaches. Further research in this field is warranted to elucidate additional metabolic pathways and identify novel biomarkers for early detection and targeted therapeutic interventions in DN.

Study on the potential mechanism of Qingxin Lianzi Yin Decoction on renoprotection in db/db mice via network pharmacology and metabolomics

BACKGROUND

Diabetic nephropathy (DN) was one of the most popular and most significant microvascular complications of diabetes mellitus. Qingxin Lianzi Yin Decoction (QXLZY) was a traditional Chinese classical formula, suitable for chronic urinary system diseases. QXLZY had good clinical efficacy in early DN, but the underlying molecular mechanism remained unrevealed.

PURPOSE

This study aimed to establish the content determination method of QXLZY index components and explore the mechanism of QXLZY on DN by network pharmacology and metabolomics studies.

METHODS

Firstly, the content determination methods of QXLZY were established with calycosin-7-O-β-d-glucoside, acteoside, baicalin and glycyrrhizic acid as index components. Secondly, pharmacological experiments of QXLZY were evaluated using db/db mice. UHPLC-LTQ-Orbitrap MS was used to carry out untargeted urine metabolomics, serum metabolomics, and kidney metabolomics studies. Thirdly, employing network pharmacology, key components and targets were analyzed. Finally, targeted metabolomics studies were performed on the endogenous constituents in biological samples for validation based on untargeted metabolomics results.

RESULTS

A method for the simultaneous determination of multiple index components in QXLZY was established, which passed the comprehensive methodological verification. It was simple, feasible, and scientific. The QXLZY treatment alleviated kidney injury of db/db mice, included the degree of histopathological damage and the level of urinary microalbumin/creatinine ratio. Untargeted metabolomics studies had identified metabolic dysfunction in pathways associated with amino acid metabolism in db/db mice. Treatment with QXLZY could reverse metabolite abnormalities and influence the pathways related to energy metabolism and amino acid metabolism. It had been found that pathways with a high degree were involved in signal transduction, prominently on amino acids metabolism and lipid metabolism, analyzed by network pharmacology. Disorders of amino acid metabolism did occur in db/db mice. QXLZY could revert the levels of metabolites, such as quinolinic acid, arginine, and asparagine.

CONCLUSION

This study was the first time to demonstrate that QXLZY alleviated diabetes-induced pathological changes in the kidneys of db/db mice by correcting disturbances in amino acid metabolism. This work could provide a new experimental basis and theoretical guidance for the rational application of QXLZY on DN, exploring the new pharmacological effect of traditional Chinese medicine, and promoting in-depth research and development.

Deliverables from Metabolomics in Kidney Disease: Adenine, New Insights, and Implication for Clinical Decision-Making

BACKGROUND

Chronic kidney disease (CKD) presents a persistent global health challenge, characterized by complex pathophysiology and diverse progression patterns. Metabolomics has emerged as a valuable tool in unraveling the intricate molecular mechanisms driving CKD progression.

SUMMARY

This comprehensive review provides a summary of recent progress in the field of metabolomics in kidney disease with a focus on spatial metabolomics to shed important insights to enhancing our understanding of CKD progression, emphasizing its transformative potential in early disease detection, refined risk assessment, and the development of targeted interventions to improve patient outcomes.

KEY MESSAGE

Through an extensive analysis of metabolic pathways and small-molecule fluctuations, bulk and spatial metabolomics offers unique insights spanning the entire spectrum of CKD, from early stages to advanced disease states. Recent advances in metabolomics technology have enabled spatial identification of biomarkers to provide breakthrough discoveries in predicting CKD trajectory and enabling personalized risk assessment. Furthermore, metabolomics can help decipher the complex molecular intricacies associated with kidney diseases for exciting novel therapeutic approaches. A recent example is the identification of adenine as a key marker of kidney fibrosis for diabetic kidney disease using both untargeted and targeted bulk and spatial metabolomics. The metabolomics studies were critical to identify a new biomarker for kidney failure and to guide new therapeutics for diabetic kidney disease. Similar approaches are being pursued for acute kidney injury and other kidney diseases to enhance precision medicine decision-making.

Current updates on metabolites and its interlinked pathways as biomarkers for diabetic kidney disease: A systematic review

Diabetic kidney disease (DKD) is a major microvascular complication of diabetes mellitus (DM) that poses a serious risk as it can lead to end-stage renal disease (ESRD). DKD is linked to changes in the diversity, composition, and functionality of the microbiota present in the gastrointestinal tract. The interplay between the gut microbiota and the host organism is primarily facilitated by metabolites generated by microbial metabolic processes from both dietary substrates and endogenous host compounds. The production of numerous metabolites by the gut microbiota is a crucial factor in the pathogenesis of DKD. However, a comprehensive understanding of the precise mechanisms by which gut microbiota and its metabolites contribute to the onset and progression of DKD remains incomplete. This review will provide a summary of the current scenario of metabolites in DKD and the impact of these metabolites on DKD progression. We will discuss in detail the primary and gut-derived metabolites in DKD, and the mechanisms of the metabolites involved in DKD progression. Further, we will address the importance of metabolomics in helping identify potential DKD markers. Furthermore, the possible therapeutic interventions and research gaps will be highlighted.

Role of the Gut Bacteria-Derived Metabolite Phenylacetylglutamine in Health and Diseases

Over the past few decades, it has been well established that gut microbiota-derived metabolites can disrupt gut function, thus resulting in an array of diseases. Notably, phenylacetylglutamine (PAGln), a bacterial derived metabolite, has recently gained attention due to its role in the initiation and progression of cardiovascular and cerebrovascular diseases. This meta-organismal metabolite PAGln is a byproduct of amino acid acetylation of its precursor phenylacetic acid (PAA) from a range of dietary sources like egg, meat, dairy products, etc. The microbiota-dependent metabolism of phenylalanine produces PAA, which is a crucial intermediate that is catalyzed by diverse microbial catalytic pathways. PAA conjugates with glutamine and glycine in the liver and kidney to predominantly form phenylacetylglutamine in humans and phenylacetylglycine in rodents. PAGln is associated with thrombosis as it enhances platelet activation mediated through the GPCRs receptors α2A, α2B, and β2 ADRs, thereby aggravating the pathological conditions. Clinical evidence suggests that elevated levels of PAGln are associated with pathology of cardiovascular, cerebrovascular, and neurological diseases. This Review further consolidates the microbial/biochemical synthesis of PAGln and discusses its role in the above pathophysiologies.

Explainable Machine Learning-Based Prediction Model for Diabetic Nephropathy

The aim of this study is to analyze the effect of serum metabolites on diabetic nephropathy (DN) and predict the prevalence of DN through a machine learning approach. The dataset consists of 548 patients from April 2018 to April 2019 in the Second Affiliated Hospital of Dalian Medical University (SAHDMU). We select the optimal 38 features through a least absolute shrinkage and selection operator (LASSO) regression model and a 10-fold cross-validation. We compare four machine learning algorithms, including extreme gradient boosting (XGB), random forest, decision tree, and logistic regression, by AUC-ROC curves, decision curves, and calibration curves. We quantify feature importance and interaction effects in the optimal predictive model by Shapley additive explanation (SHAP) method. The XGB model has the best performance to screen for DN with the highest AUC value of 0.966. The XGB model also gains more clinical net benefits than others, and the fitting degree is better. In addition, there are significant interactions between serum metabolites and duration of diabetes. We develop a predictive model by XGB algorithm to screen for DN. C2, C5DC, Tyr, Ser, Met, C24, C4DC, and Cys have great contribution in the model and can possibly be biomarkers for DN.

Corn silk polysaccharides attenuate diabetic nephropathy through restoration of the gut microbial ecosystem and metabolic homeostasis

Introduction

The pathogenesis of diabetic nephropathy (DN) is complex, inflammation is the central link among the inducing factors in the existing research, and the gutkidney axis could scientifically explain the reasons for the accumulation of chronic low-grade inflammation. As both a medicine and food, corn silk contains abundant polysaccharides. Historical studies and modern research have both confirmed its intervention effect on diabetes and DN, but the mechanism of action is unclear.

Methods

In this study, a DN rat model was generated, and the therapeutic effect of corn silk polysaccharides (CSPs) was evaluated based on behavioral, histopathological and biochemical indicators. We attempted to fully understand the interactions between CSPs, the gut microbiota and the host at the systemic level from a gut microbiota metabolomics perspective to fundamentally elucidate the mechanisms of action that can be used to intervene in DN.

Results

Research has found that the metabolic pathways with a strong correlation with CSPs were initially identified as glycerophosphate, fatty acid, bile acid, tyrosine, tryptophan and phenylalanine metabolism and involved Firmicutes, Bacteroides, Lachnospiraceae-NK4A136- group and Dubosiella, suggesting that the effect of CSPs on improving DN is related to changes in metabolite profiles and gut microbiota characteristics.

Discussion

CSPs could be harnessed to treat the abnormal metabolism of endogenous substances such as bile acids and uremic toxins caused by changes in gut microbiota, thus alleviating kidney damage caused by inflammation. In view of its natural abundance, corn silk is safe and nontoxic and can be used for the prevention and treatment of diabetes and DN.

Profile of plasma free amino acids, carnitine and acylcarnitines, and JAK2v617f mutation as potential metabolic markers in children with type 1 diabetic nephropathy

Fifty diabetic nephropathy (DN) children with type 1 diabetes mellitus (T1DM) and 50 healthy matched controls were included. Chromatographic assays of 14 amino acids, free carnitine and 27 carnitine esters using high-performance liquid chromatography/electrospray ionization-mass spectroscopy, and genetic testing for JAK2v617f mutation using real-time PCR were performed. Patients had significantly lower levels of tyrosine, branched-chain amino acids (BCAAs), and BCAA/AAA (aromatic chain amino acids) ratios, glycine, arginine, ornithine, free carnitine and some carnitine esters (C5, 6, 12 and 16) and higher phenylalanine, phenylalanine/tyrosine ratio and C18 compared with the controls and in the macro-albuminuria vs. the microalbuminuria group (p < 0.05 for all) except for free carnitine. Plasma carnitine was negatively correlated with eGFR (r = -0.488, p = 0.000). There were significant positive correlations between tyrosine with UACR ratio (r = 0.296, p = 0.037). The plasma BCAA/AAA ratio showed significant negative correlations with UACR (r = -0.484, p = 0.000). There was a significantly higher frequency of the JAK2V617F gene mutation in diabetic nephropathy patients compared with the control group and in macro-albuminuria than the microalbuminuria group (p = 0.000) for both. When monitoring children with T1DM, plasma free amino acids and acylcarnitine profiles should be considered, especially if they have tested positive for JAK2V617F for the early diagnosis of DN.

Lipidomics Profiling Reveals Serum Phospholipids Associated with Albuminuria in Early Type 2 Diabetic Kidney Disease

Early screening and administration of DKD are beneficial for renal outcomes of type 2 diabetic patients. However, the current early diagnosis using the albuminuria/creatine ratio (ACR) contains limitations. This study aimed to compare serum lipidome variation between type 2 diabetes and early DKD patients with increased albuminuria through an untargeted lipidomics method to explore the potential lipid biomarkers for DKD identification. 92 type 2 diabetic patients were enrolled and divided into two groups: DM group (ACR < 3 mg/mmol, n = 49) and early DKD group (3 mg/mmol ≤ ACR < 30 mg/mmol, n = 43). Fasting serum was analyzed through an ultraperformance liquid mass spectrometry tandem chromatography system (LC-MS). Orthogonal partial least-squares discriminant analysis (OPLS-DA) and univariate and multivariate analysis were performed to filter differentially depressed lipids. Receiver operating characteristic (ROC) curves were used to estimate the diagnostic capability of potential lipid biomarkers. We found that serum phospholipids including phosphatidylserine (PS), sphingomyelin (SM), and phosphatidylcholine (PC) were significantly upregulated in the DKD group and were highly correlated with the ACR. In addition, a panel of two phospholipids including PS(27:0)-H and PS(30:2e)-H showed good performance to help clinical lipids in early DKD identification, which increased the area under the curve (AUC) from 0.568 to 0.954. The study exhibited the serum lipidome variation in early DKD patients, and the increased phospholipids might participate in the development of albuminuria. The panel of PS(27:0)-H and PS(30:2e)-H could be a potential biomarker for DKD diagnosis.

Role of Epigenetics and Metabolomics in Predicting Endothelial Dysfunction in Type 2 Diabetes

Type 2 diabetes (T2D) is a worldwide health problem and cardiovascular disease (CVD) is a leading cause of morbidity and mortality in T2D patients, making the prevention of CVD onset a major priority. It is therefore crucial to optimize diagnosis and treatment to reduce this burden. Endothelial dysfunction is one of the most important prognostic factors for CVD progression, thus novel approaches to identify the early phase of endothelial dysfunction may lead to specific preventive measures to reduce the occurrence of CVD. Nowadays, multiomics approaches have provided unprecedented opportunities to stratify T2D patients into endotypes, improve therapeutic treatment and outcome and amend the survival prediction. Among omics strategies, epigenetics and metabolomics are gaining increasing interest. Recently, a dynamic correlation between metabolic pathways and gene expression through chromatin remodeling, such as DNA methylation, has emerged, indicating new perspectives on the regulatory networks impacting cellular processes. Thus, a better understanding of epigenetic-metabolite relationships can provide insight into the physiological processes altered early in the endothelium that ultimately head to disease development. Here, recent studies on epigenetics and metabolomics related to CVD prevention potentially useful to identify disease biomarkers, as well as new therapies hopefully targeting the early phase of endothelial dysfunction are highlighted.

Metabolites Potentially Derived from Gut Microbiota Associated with Podocyte, Proximal Tubule, and Renal and Cerebrovascular Endothelial Damage in Early Diabetic Kidney Disease in T2DM Patients

Complications due to type 2 diabetes mellitus (T2DM) such as diabetic kidney disease (DKD) and cerebral small vessel disease (CSVD) have a powerful impact on mortality and morbidity. Our current diagnostic markers have become outdated as T2DM-related complications continue to develop. The aim of the investigation was to point out the relationship between previously selected metabolites which are potentially derived from gut microbiota and indicators of endothelial, proximal tubule (PT), and podocyte dysfunction, and neurosonological indices. The study participants were 20 healthy controls and 90 T2DM patients divided into three stages: normoalbuminuria, microalbuminuria, and macroalbuminuria. Serum and urine metabolites were determined by untargeted and targeted metabolomic techniques. The markers of endothelial, PT and podocyte dysfunction were assessed by ELISA technique, and the neurosonological indices were provided by an ultrasound device with high resolution (MYLAB 8-ESAOTE Italy). The descriptive statistical analysis was followed by univariable and multivariable linear regression analyses. In conclusion, in serum, arginine (sArg), butenoylcarnitine (sBCA), and indoxyl sulfate (sIS) expressed a biomarker potential in terms of renal endothelial dysfunction and carotid atherosclerosis, whereas sorbitol (sSorb) may be a potential biomarker of blood-brain barrier (BBB) dysfunction. In urine, BCA and IS were associated with markers of podocyte damage, whereas PCS correlated with markers of PT dysfunction.

Quantitative, Targeted Analysis of Gut Microbiota Derived Metabolites Provides Novel Biomarkers of Early Diabetic Kidney Disease in Type 2 Diabetes Mellitus Patients

Diabetic kidney disease (DKD) is one of the most debilitating complications of type 2 diabetes mellitus (T2DM), as it progresses silently to end-stage renal disease (ESRD). The discovery of novel biomarkers of early DKD becomes acute, as its incidence is reaching catastrophic proportions. Our study aimed to quantify previously identified metabolites from serum and urine through untargeted ultra-high-performance liquid chromatography coupled with electrospray ionization-quadrupole-time of flight-mass spectrometry (UHPLC-QTOF-ESI+-MS) techniques, such as the following: arginine, dimethylarginine, hippuric acid, indoxyl sulfate, p-cresyl sulfate, L-acetylcarnitine, butenoylcarnitine and sorbitol. The study concept was based on the targeted analysis of selected metabolites, using the serum and urine of 20 healthy subjects and 90 T2DM patients with DKD in different stages (normoalbuminuria-uACR < 30 mg/g; microalbuminuria-uACR 30-300 mg/g; macroalbuminuria-uACR > 300 mg/g). The quantitative evaluation of metabolites was performed with pure standards, followed by the validation methods such as the limit of detection (LOD) and the limit of quantification (LOQ). The following metabolites from this study resulted as possible biomarkers of early DKD: in serum-arginine, dimethylarginine, hippuric acid, indoxyl sulfate, butenoylcarnitine and sorbitol and in urine-p-cresyl sulfate.

Urine myo-inositol as a novel prognostic biomarker for diabetic kidney disease: a targeted metabolomics study using nuclear magnetic resonance

BACKGROUND

As a leading cause of chronic kidney disease, clinical demand for noninvasive biomarkers of diabetic kidney disease (DKD) beyond proteinuria is increasing. Metabolomics is a popular method to identify mechanisms and biomarkers. We investigated urinary targeted metabolomics in DKD patients.

METHODS

We conducted a targeted metabolomics study of 26 urinary metabolites in consecutive patients with DKD stage 1 to 5 (n = 208) and healthy controls (n = 26). The relationships between estimated glomerular filtration rate (eGFR) or urine protein-creatinine ratio (UPCR) and metabolites were evaluated. Multivariate Cox analysis was used to estimate relationships between urinary metabolites and the target outcome, end-stage renal disease (ESRD). C statistics and time-dependent receiver operating characteristics (ROC) were used to assess diagnostic validity.

RESULTS

During a median 4.5 years of follow-up, 103 patients (44.0%) progressed to ESRD and 65 (27.8%) died. The median fold changes of nine metabolites belonged to monosaccharide and tricarboxylic acid (TCA) cycle metabolites tended to increase with DKD stage. Myo-inositol, choline, and citrates were correlated with eGFR and choline, while mannose and myo-inositol were correlated with UPCR. Elevated urinary monosaccharide and TCA cycle metabolites showed associations with increased morality and ESRD progression. The predictive power of ESRD progression was high, in the order of choline, myo-inositol, and citrate. Although urinary metabolites alone were less predictive than serum creatinine or UPCR, myo-inositol had additive effect with serum creatinine and UPCR. In time-dependent ROC, myo-inositol was more predictive than UPCR of 1-year ESRD progression prediction.

CONCLUSION

Myo-inositol can be used as an additive biomarker of ESRD progression in DKD.

Precision Medicine and/or Biomarker Based Therapy in T2DM: Ready for Prime Time?

Approximately 30-40% of people with type 2 diabetes mellitus develop chronic kidney disease. This is characterised by elevated blood pressure, declining kidney function and enhanced cardiovascular morbidity and mortality. Increased albuminuria and decreasing estimated glomerular function has to be evaluated regularly to diagsnose kidney disease. New biomarkers may facilitate early diagnosis and provide infomation on undlying pathology thereby supporting early precision intervention for the optimal benefit. A number of biomarkers have been suggested but are not yet implemented in clinical practice. iI the future such bimarkers may pave the way for personalized treatment.

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.

Metabolite Profiling of the Gut–Renal–Cerebral Axis Reveals a Particular Pattern in Early Diabetic Kidney Disease in T2DM Patients

Type 2 diabetes mellitus (T2DM) represents an important microvascular disease concerning the kidney and the brain. Gut dysbiosis and microbiota-derived metabolites may be in relation with early pathophysiological changes in diabetic kidney disease (DKD). The aim of the study was to find new potential gut-derived biomarkers involved in the pathogenesis of early DKD, with a focus on the complex interconnection of these biomarkers with podocyte injury, proximal tubule dysfunction, renal and cerebrovascular endothelial dysfunction. The study design consisted of metabolite profiling of serum and urine of 90 T2DM patients (subgroups P1-normoalbuminuria, P2-microalbuminuria, P3-macroalbuminuria) and 20 healthy controls (group C), based on ultra-high-performance liquid chromatography coupled with electrospray ionization-quadrupole-time of flight-mass spectrometry analysis (UHPLC-QTOF-ESI+-MS). By multivariate and univariate analyses of serum and urine, which included Partial Least Squares Discriminant Analysis (PLSDA), Variable Importance Plots (VIP), Random Forest scores, One Way ANOVA and Biomarker analysis, there were discovered metabolites belonging to nitrogen metabolic pathway and retinoic acid signaling pathway which differentiate P1 group from P2, P3, C groups. Tyrosine, phenylalanine, indoxyl sulfate, serotonin sulfate, and all-trans retinoic acid express the metabolic fingerprint of P1 group vs. P2, P3, C groups, revealing a particular pattern in early DKD in T2DM patients.

Intestinal microbiome diversity of diabetic and non-diabetic kidney disease: Current status and future perspective

A significant portion of the health burden of diabetic kidney disease (DKD) is caused by both type 1 and type 2 diabetes which leads to morbidity and mortality globally. It is one of the most common diabetic complications characterized by loss of renal function with high prevalence, often leading to acute kidney disease (AKD). Inflammation triggered by gut microbiota is commonly associated with the development of DKD. Interactions between the gut microbiota and the host are correlated in maintaining metabolic and inflammatory homeostasis. However, the fundamental processes through which the gut microbiota affects the onset and progression of DKD are mainly unknown. In this narrative review, we summarised the potential role of the gut microbiome, their pathogenicity between diabetic and non-diabetic kidney disease (NDKD), and their impact on host immunity. A well-established association has already been seen between gut microbiota, diabetes and kidney disease. The gut-kidney interrelationship is confirmed by mounting evidence linking gut dysbiosis to DKD, however, it is still unclear what is the real cause of gut dysbiosis, the development of DKD, and its progression. In addition, we also try to distinguish novel biomarkers for early detection of DKD and the possible therapies that can be used to regulate the gut microbiota and improve the host immune response. This early detection and new therapies will help clinicians for better management of the disease and help improve patient outcomes.

Metabolomic profiles in serum and urine uncover novel biomarkers in children with nephrotic syndrome

BACKGROUND

Nephrotic syndrome is common in children and adults worldwide, and steroid-sensitive nephrotic syndrome (SSNS) accounts for 80%. Aberrant metabolism involvement in early SSNS is sparsely studied, and its pathogenesis remains unclear. Therefore, the goal of this study was to investigate the changes in initiated SSNS patients-related metabolites through serum and urine metabolomics and discover the novel potential metabolites and metabolic pathways.

METHODS

Serum samples (27 SSNS and 56 controls) and urine samples (17 SSNS and 24 controls) were collected. Meanwhile, the non-targeted analyses were performed by ultra-high-performance liquid chromatography-quadrupole time of flight-mass spectrometry (UHPLC-QTOF-MS) to determine the changes in SSNS. We applied the causal inference model, the DoWhy model, to assess the causal effects of several selected metabolites. An ultraperformance liquid chromatography-tandem mass spectrometry (UPLC-MS/MS) was used to validate hits (D-mannitol, dulcitol, D-sorbitol, XMP, NADPH, NAD, bilirubin, and α-KG-like) in 41 SSNS and 43 controls. In addition, the metabolic pathways were explored.

RESULTS

Compared to urine, the metabolism analysis of serum samples was more clearly discriminated at SSNS. 194 differential serum metabolites and five metabolic pathways were obtained in the SSNS group. Eight differential metabolites were identified by establishing the diagnostic model for SSNS, and four variables had a positive causal effect. After validation by targeted MS, except XMP, others have similar trends like the untargeted metabolic analysis.

CONCLUSION

With untargeted metabolomics analysis and further targeted quantitative analysis, we found seven metabolites may be new biomarkers for risk prediction and early diagnosis for SSNS.

Novel biomarkers for diabetic kidney disease

Although diabetic kidney disease (DKD) remains one of the leading causes of reduced lifespan in patients with diabetes mellitus; its prevalence has failed to decline over the past 30 years. To identify those at high risk of developing DKD and disease progression at an early stage, extensive research has been ongoing in the search for prognostic and surrogate endpoint biomarkers for DKD. Although biomarkers are not used routinely in clinical practice or prospective clinical trials, many biomarkers have been developed to improve the early identification and prognostication of patients with DKD. Novel biomarkers that capture one specific mechanism of the DKD disease process have been developed, and studies have evaluated the prognostic value of assay-based biomarkers either in small sets or in combinations involving multiple biomarkers. More recently, several studies have assessed the prognostic value of omics- based biomarkers that include proteomics, metabolomics, and transcriptomics. This review will first describe the biomarkers used in current practice and their limitations, and then summarize the current status of novel biomarkers for DKD with respect to assay- based protein biomarkers, proteomics, metabolomics, and transcriptomics.

Serum Metabolomics Benefits Discrimination Kidney Disease Development in Type 2 Diabetes Patients

Background

Diabetic kidney disease (DKD) is the primary cause of end-stage renal disease, raising a considerable burden worldwide. Recognizing novel biomarkers by metabolomics can shed light on new biochemical insight to benefit DKD diagnostics and therapeutics. We hypothesized that serum metabolites can serve as biomarkers in the progression of DKD.

Methods

A cross-sectional study of 1,043 plasma metabolites by untargeted LC/MS among 89 participants identified associations between proteinuria severity and metabolites difference. Pathway analysis from differently expressed metabolites was used to determine perturbed metabolism pathways. The results were replicated in an independent, cross-sectional cohort of 83 individuals. Correlation and prediction values were used to examine the association between plasma metabolites level and proteinuria amount.

Results

Diabetes, and diabetic kidney disease with different ranges of proteinuria have shown different metabolites patterns. Cysteine and methionine metabolism pathway, and Taurine and hypotaurine metabolism pathway were distinguishable in the existence of DKD in DC (diabetes controls without kidney disease), and DKD with different ranges of proteinuria. Two interesting tetrapeptides (Asn-Met-Cys-Ser and Asn-Cys-Pro-Pro) circulating levels were elevated with the DKD proteinuria progression.

Conclusions

These findings underscore that serum metabolomics provide us biochemical perspectives to identify some clinically relevant physiopathologic biomarkers of DKD progression.

Metabolomics as a tool for the early diagnosis and prognosis of diabetic kidney disease

Diabetic kidney disease (DKD) is one of the most prevalent comorbidities of diabetes mellitus and the leading cause of the end-stage renal disease (ESRD). DKD results from chronic exposure to hyperglycemia, leading to progressive alterations in kidney structure and function. The early development of DKD is clinically silent and when albuminuria is detected the lesions are often at advanced stages, leading to rapid kidney function decline towards ESRD. DKD progression can be arrested or substantially delayed if detected and addressed at early stages. A major limitation of current methods is the absence of albuminuria in non-albuminuric phenotypes of diabetic nephropathy, which becomes increasingly prevalent and lacks focused therapy. Metabolomics is an ever-evolving omics technology that enables the study of metabolites, downstream products of every biochemical event that occurs in an organism. Metabolomics disclosures complex metabolic networks and provide knowledge of the very foundation of several physiological or pathophysiological processes, ultimately leading to the identification of diseases' unique metabolic signatures. In this sense, metabolomics is a promising tool not only for the diagnosis but also for the identification of pre-disease states which would confer a rapid and personalized clinical practice. Herein, the use of metabolomics as a tool to identify the DKD metabolic signature of tubule interstitial lesions to diagnose or predict the time-course of DKD will be discussed. In addition, the proficiency and limitations of the currently available high-throughput metabolomic techniques will be discussed.

Pathophysiologic Mechanisms and Potential Biomarkers in Diabetic Kidney Disease

Although diabetic kidney disease (DKD) remains the leading cause of end-stage kidney disease eventually requiring chronic kidney replacement therapy, the prevalence of DKD has failed to decline over the past 30 years. In order to reduce disease prevalence, extensive research has been ongoing to improve prediction of DKD onset and progression. Although the most commonly used markers of DKD are albuminuria and estimated glomerular filtration rate, their limitations have encouraged researchers to search for novel biomarkers that could improve risk stratification. Considering that DKD is a complex disease process that involves several pathophysiologic mechanisms such as hyperglycemia induced inflammation, oxidative stress, tubular damage, eventually leading to kidney damage and fibrosis, many novel biomarkers that capture one specific mechanism of the disease have been developed. Moreover, the increasing use of high-throughput omic approaches to analyze biological samples that include proteomics, metabolomics, and transcriptomics has emerged as a strong tool in biomarker discovery. This review will first describe recent advances in the understanding of the pathophysiology of DKD, and second, describe the current clinical biomarkers for DKD, as well as the current status of multiple potential novel biomarkers with respect to protein biomarkers, proteomics, metabolomics, and transcriptomics.

Plasma and Vitreous Metabolomics Profiling of Proliferative Diabetic Retinopathy

Purpose

To determine the differences of metabolites and metabolic pathways between patients with proliferative diabetic retinopathy (PDR) and without diabetes (nondiabetic controls) in plasma and vitreous, respectively, and to characterize the relationship between plasma and vitreous metabolic profiles.

Methods

Liquid chromatography/tandem mass spectrometry technology was performed to distinct metabolite profiles of plasma and vitreous. A total of 139 plasma samples from 88 patients with PDR and 51 nondiabetic controls, as well as 74 vitreous samples from 51 patients with PDR and 23 nondiabetic controls, were screened. Pathway analysis was performed using MetaboAnalyst 5.0. Pearson correlation analysis was used to investigate the correlation of metabolites in vitreous and plasma.

Results

After adjusting for age, fasting blood glucose, and urea, in vitreous metabolomes, a total of 76 features distinguished patients with PDR from controls. Fifteen differential metabolites were found in plasma metabolites. Pantothenate and CoA biosynthesis was the common metabolic pathway altered in both plasma and vitreous. Aromatic amino acid metabolism pathways were dysregulated in vitreous of PDR. For four metabolic features, there were positive correlations between vitreous and plasma.

Conclusions

Despite great differences between the metabolic profiles of plasma and vitreous in PDR cases, there are also similarities in the change of metabolites and metabolic pathways. Exploring the relationship of metabolomics between vitreous and plasma may help provide new understanding of the mechanism of PDR.

Old and New Biomarkers Associated with Endothelial Dysfunction in Chronic Hyperglycemia

Chronic hyperglycemia and vascular damage are strictly related. Biomarkers of vascular damage have been intensively studied in the recent years in the quest of reliable cardiovascular risk assessment tools able to facilitate risk stratification and early detection of vascular impairment. The present study is a narrative review with the aim of revising the available evidence on current and novel markers of hyperglycemia-induced vascular damage. After a discussion of classic tools used to investigate endothelial dysfunction, we provide an in-depth description of novel circulating biomarkers (chemokines, extracellular vesicles, and epigenetic and metabolomic biomarkers). Appropriate use of a single as well as a cluster of the discussed biomarkers might enable in a near future (a) the prompt identification of targeted and customized treatment strategies and (b) the follow-up of cardiovascular treatment efficacy over time in clinical research and/or in clinical practice.

A Contemporary Insight of Metabolomics Approach for Type 1 Diabetes: Potential for Novel Diagnostic Targets

High-throughput omics has been widely applied in metabolic disease, type 1 diabetes (T1D) was one of the most typical diseases. Effective prevention and early diagnosis are very important because of infancy and persistent characteristics of T1D. The occurrence and development of T1D is a chronic and continuous process, in which the production of autoantibodies (ie serum transformation) occupies the central position. Metabolomics can evaluate the metabolic characteristics of serum before seroconversion, the changes with age and T1D complications. And the addition of natural drug metabolomics is more conducive to the systematic and comprehensive diagnosis and treatment of T1D. This paper reviewed the metabolic changes and main pathogenesis from pre-diagnosis to treatment in T1D. The metabolic spectrum of significant abnormal energy and glucose-related metabolic pathway, down-regulation of lipid metabolism and up-regulation of some antioxidant pathways has appeared before seroconversion, indicating that the body has been in the dual state of disease progression and disease resistance before T1D onset. Some metabolites (such as methionine) are closely related to age, and the types of autoantibodies produced are age-specific. Some metabolites may jointly predict DN with eGFR, and metabolomics can further contribute to the pathogenesis based on the correlation between DN and DR. Many natural drug components have been proved to act on abnormal metabolic pathways of T1D and have a positive impact on some metabolic levels, which is very important for further finding therapeutic targets and developing new drugs with small side effects. Metabolomics can provide auxiliary value for the diagnosis of T1D and provide a new direction to reveal the pathogenesis of T1D and find new therapeutic targets. The development of T1D metabolomics shows that high-throughput research methods are expected to be introduced into clinical practice.

Urinary metabolite profiling and risk of progression of diabetic nephropathy in 2670 individuals with type 1 diabetes

AIMS/HYPOTHESIS

This prospective, observational study examines associations between 51 urinary metabolites and risk of progression of diabetic nephropathy in individuals with type 1 diabetes by employing an automated NMR metabolomics technique suitable for large-scale urine sample collections.

METHODS

We collected 24-h urine samples for 2670 individuals with type 1 diabetes from the Finnish Diabetic Nephropathy study and measured metabolite concentrations by NMR. Individuals were followed up for 9.0 ± 5.0 years until their first sign of progression of diabetic nephropathy, end-stage kidney disease or study end. Cox regressions were performed on the entire study population (overall progression), on 1999 individuals with normoalbuminuria and 347 individuals with macroalbuminuria at baseline.

RESULTS

Seven urinary metabolites were associated with overall progression after adjustment for baseline albuminuria and chronic kidney disease stage (p < 8 × 10^-4): leucine (HR 1.47 [95% CI 1.30, 1.66] per 1-SD creatinine-scaled metabolite concentration), valine (1.38 [1.22, 1.56]), isoleucine (1.33 [1.18, 1.50]), pseudouridine (1.25 [1.11, 1.42]), threonine (1.27 [1.11, 1.46]) and citrate (0.84 [0.75, 0.93]). 2-Hydroxyisobutyrate was associated with overall progression (1.30 [1.16, 1.45]) and also progression from normoalbuminuria (1.56 [1.25, 1.95]). Six amino acids and pyroglutamate were associated with progression from macroalbuminuria.

CONCLUSIONS/INTERPRETATION

Branched-chain amino acids and other urinary metabolites were associated with the progression of diabetic nephropathy on top of baseline albuminuria and chronic kidney disease. We found differences in associations for overall progression and progression from normo- and macroalbuminuria. These novel discoveries illustrate the utility of analysing urinary metabolites in entire population cohorts.

Metabolomic Profiling of Amino Acids in Human Plasma Distinguishes Diabetic Kidney Disease From Type 2 Diabetes Mellitus

Background: Diabetic kidney disease (DKD) is a highly prevalent complication in patients with type 2 diabetes mellitus (T2DM). Patients with DKD exhibit changes in plasma levels of amino acids (AAs) due to insulin resistance, reduced protein intake, and impaired renal transport of AAs. The role of AAs in distinguishing DKD from T2DM and healthy controls has yet to be elucidated. This study aimed to investigate the metabolomic profiling of AAs in the plasma of patients with DKD.

Methods: We established an ultra-performance liquid chromatography tandem mass spectrometry (UPLC-MS/MS) method to detect the plasma levels of the 20 AAs in healthy controls (n = 112), patients with T2DM (n = 101), and patients with DKD (n = 101). The key AAs associated with DKD were identified by orthogonal partial least-squares discriminant analysis (OPLS-DA) models with loading plots, shared and unique structures (SUS) plots, and variable importance in projection (VIP) values. The discrimination accuracies of these key AAs were then determined by analyses of receiver-operating characteristic (ROC) curves.

Results: Metabolomic profiling of plasma revealed significant alterations in levels of the 20 AAs in patients with DKD when compared to those in either patients with T2DM or healthy controls. Metabolomic profiling of the 20 AAs showed a visual separation of patients with DKD from patients with T2DM and healthy controls in OPLS-DA models. Based on loading plots, SUS plots, and VIP values in the OPLS-DA models, we identified valine and cysteine as potential contributors to the progression of DKD from patients with T2DM. Histidine was identified as a key mediator that could distinguish patients with DKD from healthy controls. Plasma levels of histidine and valine were decreased significantly in patients with DKD with a decline in kidney function, and had excellent performance in distinguishing patients with DKD from patients with T2DM and healthy controls according to ROC curves.

Conclusion: Plasma levels of histidine and valine were identified as the main AAs that can distinguish patients with DKD. Our findings provide new options for the prevention, treatment, and management of DKD.

Serum metabolomics of end-stage renal disease patients with depression: potential biomarkers for diagnosis

Background

End-stage renal disease (ESRD) is the final stage during the development of renal failure. Depression is the most common psychiatric disorder in patients with ESRD, which in turn aggravates the progression of renal failure, however, its underlying mechanism remains unclear. This study aimed to reveal the pathogenesis and to discover novel peripheral biomarkers for ESRD patients with depression through metabolomic analysis.

Methods

Ultra-high-performance liquid chromatography coupled with mass spectrometry (UPLC-MS) was used to explore changes of serum metabolites among healthy controls, ESRD patients with or without depression. The differential metabolites between groups were subjected to clustering analysis, pathway analysis, receiver operating characteristic (ROC) curve analysis.

Results

A total of 57 significant serum differential metabolites were identified between ESRD patients with or without depression, which were involved in 19 metabolic pathways, such as energy metabolism, glycerolipid metabolism, and glutamate-centered metabolism. Moreover, the area under the ROC curve of gentisic acid, uric acid, 5-hydroxytryptamine, 2-phosphoglyceric acid, leucyl-phenylalanine, propenyl carnitine, naloxone, pregnenolone, 6-thioxanthene 5'-monophosphate, hydroxyl ansoprazole, zileuton O-glucuronide, cabergoline, PA(34:2), PG(36:1), probucol and their combination was greater than 0.90.

Conclusions

Inflammation, oxidative stress and energy metabolism abnormalities, glycerolipid metabolism, and glutamate-centered metabolism are associated with the pathogenesis of ESRD with depression, which may be promising targets for therapy. Furthermore, the identified differential metabolites may serve as biomarkers for the diagnosis of ESRD patients with depression.

Metabolomics in Diabetes and Diabetic Complications: Insights from Epidemiological Studies

The increasing prevalence of diabetes and its complications, such as cardiovascular and kidney disease, remains a huge burden globally. Identification of biomarkers for the screening, diagnosis, and prognosis of diabetes and its complications and better understanding of the molecular pathways involved in the development and progression of diabetes can facilitate individualized prevention and treatment. With the advancement of analytical techniques, metabolomics can identify and quantify multiple biomarkers simultaneously in a high-throughput manner. Providing information on underlying metabolic pathways, metabolomics can further identify mechanisms of diabetes and its progression. The application of metabolomics in epidemiological studies have identified novel biomarkers for type 2 diabetes (T2D) and its complications, such as branched-chain amino acids, metabolites of phenylalanine, metabolites involved in energy metabolism, and lipid metabolism. Metabolomics have also been applied to explore the potential pathways modulated by medications. Investigating diabetes using a systems biology approach by integrating metabolomics with other omics data, such as genetics, transcriptomics, proteomics, and clinical data can present a comprehensive metabolic network and facilitate causal inference. In this regard, metabolomics can deepen the molecular understanding, help identify potential therapeutic targets, and improve the prevention and management of T2D and its complications. The current review focused on metabolomic biomarkers for kidney and cardiovascular disease in T2D identified from epidemiological studies, and will also provide a brief overview on metabolomic investigations for T2D.

How Perturbated Metabolites in Diabetes Mellitus Affect the Pathogenesis of Hypertension?

The presence of hypertension (HTN) in type 2 diabetes mellitus (DM) is a common phenomenon in more than half of the diabetic patients. Since HTN constitutes a predictor of vascular complications and cardiovascular disease in type 2 DM patients, it is of significance to understand the molecular and cellular mechanisms of type 2 DM binding to HTN. This review attempts to understand the mechanism via the perspective of the metabolites. It reviewed the metabolic perturbations, the biological function of perturbated metabolites in two diseases, and the mechanism underlying metabolic perturbation that contributed to the connection of type 2 DM and HTN. DM-associated metabolic perturbations may be involved in the pathogenesis of HTN potentially in insulin, angiotensin II, sympathetic nervous system, and the energy reprogramming to address how perturbated metabolites in type 2 DM affect the pathogenesis of HTN. The recent integration of the metabolism field with microbiology and immunology may provide a wider perspective. Metabolism affects immune function and supports immune cell differentiation by the switch of energy. The diverse metabolites produced by bacteria modified the biological process in the inflammatory response of chronic metabolic diseases either. The rapidly evolving metabolomics has enabled to have a better understanding of the process of diseases, which is an important tool for providing some insight into the investigation of diseases mechanism. Metabolites served as direct modulators of biological processes were believed to assess the pathological mechanisms involved in diseases.

Non-invasive metabolic biomarkers for early diagnosis of diabetic nephropathy: Meta-analysis of profiling metabolomics studies

AIM

Diabetic nephropathy (DN) is one of the worst complications of diabetes. Despite a growing number of DN metabolite profiling studies, most studies are suffering from inconsistency in their findings. The main goal of this meta-analysis was to reach to a consensus panel of significantly dysregulated metabolites as potential biomarkers in DN.

DATA SYNTHESIS

To identify the significant dysregulated metabolites, meta-analysis was performed by "vote-counting rank" and "robust rank aggregation" strategies. Bioinformatics analyses were performed to identify the most affected genes and pathways. Among 44 selected studies consisting of 98 metabolite profiles, 17 metabolites (9 up-regulated and 8 down-regulated metabolites), were identified as significant ones by both the meta-analysis strategies (p-value<0.05 and OR>2 or <0.5) and selected as DN metabolite meta-signature. Furthermore, enrichment analyses confirmed the involvement of various effective biological pathways in DN pathogenesis, such as urea cycle, TCA cycle, glycolysis, and amino acid metabolisms. Finally, by performing a meta-analysis over existing time-course studies in DN, the results indicated that lactic acid, hippuric acid, allantoin (in urine), and glutamine (in blood), are the topmost non-invasive early diagnostic biomarkers.

CONCLUSION

The identified metabolites are potentially involved in diabetic nephropathy pathogenesis and could be considered as biomarkers or drug targets in the disease.

PROSPERO REGISTRATION NUMBER

CRD42020197697.

Circulating short and medium chain fatty acids are associated with normoalbuminuria in type 1 diabetes of long duration

A substantial number of subjects with Type 1 Diabetes (T1D) of long duration never develop albuminuria or renal function impairment, yet the underlying protective mechanisms remain unknown. Therefore, our study included 308 Joslin Kidney Study subjects who had T1D of long duration (median: 24 years), maintained normal renal function and had either normoalbuminuria or a broad range of albuminuria within the 2 years preceding the metabolomic determinations. Serum samples were subjected to global metabolomic profiling. 352 metabolites were detected in at least 80% of the study population. In the logistic analyses adjusted for multiple testing (Bonferroni corrected α = 0.000028), we identified 38 metabolites associated with persistent normoalbuminuria independently from clinical covariates. Protective metabolites were enriched in Medium Chain Fatty Acids (MCFAs) and in Short Chain Fatty Acids (SCFAs) and particularly involved odd-numbered and dicarboxylate Fatty Acids. One quartile change of nonanoate, the top protective MCFA, was associated with high odds of having persistent normoalbuminuria (OR (95% CI) 0.14 (0.09, 0.23); p < 10-12). Multivariable Random Forest analysis concordantly indicated to MCFAs as effective classifiers. Associations of the relevant Fatty Acids with albuminuria seemed to parallel associations with tubular biomarkers. Our findings suggest that MCFAs and SCFAs contribute to the metabolic processes underlying protection against albuminuria development in T1D that are independent from mechanisms associated with changes in renal function.

A Metabolic Reprogramming of Glycolysis and Glutamine Metabolism Is a Requisite for Renal Fibrogenesis-Why and How?

Chronic Kidney Disease (CKD) is characterized by organ remodeling and fibrosis due to failed wound repair after on-going or severe injury. Key to this process is the continued activation and presence of matrix-producing renal fibroblasts. In cancer, metabolic alterations help cells to acquire and maintain a malignant phenotype. More recent evidence suggests that something similar occurs in the fibroblast during activation. To support these functions, pro-fibrotic signals released in response to injury induce metabolic reprograming to meet the high bioenergetic and biosynthetic demands of the (myo)fibroblastic phenotype. Fibrogenic signals such as TGF-β1 trigger a rewiring of cellular metabolism with a shift toward glycolysis, uncoupling from mitochondrial oxidative phosphorylation, and enhanced glutamine metabolism. These adaptations may also have more widespread implications with redirection of acetyl-CoA directly linking changes in cellular metabolism and regulatory protein acetylation. Evidence also suggests that injury primes cells to these metabolic responses. In this review we discuss the key metabolic events that have led to a reappraisal of the regulation of fibroblast differentiation and function in CKD.

Advances in Current Diabetes Proteomics: From the Perspectives of Label- free Quantification and Biomarker Selection

BACKGROUND

Due to its prevalence and negative impacts on both the economy and society, the diabetes mellitus (DM) has emerged as a worldwide concern. In light of this, the label-free quantification (LFQ) proteomics and diabetic marker selection methods have been applied to elucidate the underlying mechanisms associated with insulin resistance, explore novel protein biomarkers, and discover innovative therapeutic protein targets.

OBJECTIVE

The purpose of this manuscript is to review and analyze the recent computational advances and development of label-free quantification and diabetic marker selection in diabetes proteomics.

METHODS

Web of Science database, PubMed database and Google Scholar were utilized for searching label-free quantification, computational advances, feature selection and diabetes proteomics.

RESULTS

In this study, we systematically review the computational advances of label-free quantification and diabetic marker selection methods which were applied to get the understanding of DM pathological mechanisms. Firstly, different popular quantification measurements and proteomic quantification software tools which have been applied to the diabetes studies are comprehensively discussed. Secondly, a number of popular manipulation methods including transformation, pretreatment (centering, scaling, and normalization), missing value imputation methods and a variety of popular feature selection techniques applied to diabetes proteomic data are overviewed with objective evaluation on their advantages and disadvantages. Finally, the guidelines for the efficient use of the computationbased LFQ technology and feature selection methods in diabetes proteomics are proposed.

CONCLUSION

In summary, this review provides guidelines for researchers who will engage in proteomics biomarker discovery and by properly applying these proteomic computational advances, more reliable therapeutic targets will be found in the field of diabetes mellitus.

Aldh1l2 knockout mouse metabolomics links the loss of the mitochondrial folate enzyme to deregulation of a lipid metabolism observed in rare human disorder

Background

Mitochondrial folate enzyme ALDH1L2 (aldehyde dehydrogenase 1 family member L2) converts 10-formyltetrahydrofolate to tetrahydrofolate and CO₂ simultaneously producing NADPH. We have recently reported that the lack of the enzyme due to compound heterozygous mutations was associated with neuro-ichthyotic syndrome in a male patient. Here, we address the role of ALDH1L2 in cellular metabolism and highlight the mechanism by which the enzyme regulates lipid oxidation.

Methods

We generated Aldh1l2 knockout (KO) mouse model, characterized its phenotype, tissue histology, and levels of reduced folate pools and applied untargeted metabolomics to determine metabolic changes in the liver, pancreas, and plasma caused by the enzyme loss. We have also used NanoString Mouse Inflammation V2 Code Set to analyze inflammatory gene expression and evaluate the role of ALDH1L2 in the regulation of inflammatory pathways.

Results

Both male and female Aldh1l2 KO mice were viable and did not show an apparent phenotype. However, H&E and Oil Red O staining revealed the accumulation of lipid vesicles localized between the central veins and portal triads in the liver of Aldh1l2^-/- male mice indicating abnormal lipid metabolism. The metabolomic analysis showed vastly changed metabotypes in the liver and plasma in these mice suggesting channeling of fatty acids away from β-oxidation. Specifically, drastically increased plasma acylcarnitine and acylglycine conjugates were indicative of impaired β-oxidation in the liver. Our metabolomics data further showed that mechanistically, the regulation of lipid metabolism by ALDH1L2 is linked to coenzyme A biosynthesis through the following steps. ALDH1L2 enables sufficient NADPH production in mitochondria to maintain high levels of glutathione, which in turn is required to support high levels of cysteine, the coenzyme A precursor. As the final outcome, the deregulation of lipid metabolism due to ALDH1L2 loss led to decreased ATP levels in mitochondria.

Conclusions

The ALDH1L2 function is important for CoA-dependent pathways including β-oxidation, TCA cycle, and bile acid biosynthesis. The role of ALDH1L2 in the lipid metabolism explains why the loss of this enzyme is associated with neuro-cutaneous diseases. On a broader scale, our study links folate metabolism to the regulation of lipid homeostasis and the energy balance in the cell.

Supplementary Information

The online version contains supplementary material available at 10.1186/s40246-020-00291-3.

Identification of biomarkers for early diagnosis of diabetic nephropathy disease using direct flow through mass spectrometry

BACKGROUND AND AIMS

Albuminuria is not an effective marker for early diagnosis of diabetic renal complication with several subjects progressing to chronic kidney disease without any albuminuria. A biomarker that can predict early changes of the diabetic kidney will be useful in effective management of type 2 diabetes. Mass spectrometry based metabolomics approach offers tremendous promise for the identification of novel metabolite biomarkers.

METHODS

A case-control approach was carried out to identify renal biomarkers among Asian Indian subjects in a hospital setting. A total of 29 subjects were included in the study that included groups of diabetic controls, diabetic subjects with eGFR >90 ml/min/1.72 m² and diabetic subjects with eGFR between 60 and 89 ml/min/1.72 m² and eGFR between 15 and 30 ml/min/1.72 m². We employed an un-targeted mass spectrometry method for the identification of plasma metabolites.

RESULTS

A total of 1414 and 975 metabolites were identified in the positive and negative ion mode respectively. 19 metabolites were up regulated and 18 metabolites were down regulated in CKD2 and CKD4 groups when compared to control. Correlation analysis of the differential metabolites revealed Pregnenolone sulfate, creatinine and ganglioside GA1 to be negatively correlated and hexyl glucoside, all-trans-carophyll yellow and PG to be positively correlated with eGFR.

CONCLUSION

We have identified Pregnenolone sulfate, GA1, PG and all-trans-Carophyll yellow as biomarkers for early identification of diabetic nephropathy. These markers could aid in better management of diabetic nephropathy that may result delaying the progression of the disease.

Metabolomics of the diabetic nephropathy: behind the fingerprint of development and progression indicators

Current diagnostic methods are not very sensitive to detect the initial stages diabetic nephropathy of type 2. In this work, a review of metabolomic approximation studies for the identification of biomarkers of this disease with potential to differentiate between early stages, evaluate and direct treatment and help slow kidney damage. Using public (Pubmed and Google Scholar) and private (Scopus and Web of Knowledge) databases, a systematic search of the information published related to metabolomics of diabetic nephropathy in different biospecimens (urine, serum, plasma and blood) was made. Later, the MetaboAnalyst 4.0 software was used to identify the metabolic pathways associated with these metabolites. Groups of potential metabolites were identified for monitoring diabetic nephropathy with the available literature data. In the urine, oxide-3-hydroxyisovalerate, TMAO, aconite and citrate and hydroxypropionate derivatives are highlighted; meanwhile, in the serum: citrate, creatinine, arginine and its derivatives; and in the plasma: amino acids such as histidine, methionine and arginine has a potential contribution. Using MetaboAnalyst 4.0 the metabolic pathways related to these metabolites were related. The search for biomarkers to measure the progression of diabetic nephropathy, together with analytical strategies for their detection and quantification, are the starting point for designing new methods of clinical chemistry analysis. The association between the metabolic pathway dysfunction could be useful for the overall assessment of the treatment and clinical follow-up of this disease.

A Targeted Multiomics Approach to Identify Biomarkers Associated with Rapid eGFR Decline in Type 1 Diabetes

BACKGROUND

Individuals with type 1 diabetes (T1D) demonstrate varied trajectories of estimated glomerular filtration rate (eGFR) decline. The molecular pathways underlying rapid eGFR decline in T1D are poorly understood, and individual-level risk of rapid eGFR decline is difficult to predict.

METHODS

We designed a case-control study with multiple exposure measurements nested within 4 well-characterized T1D cohorts (FinnDiane, Steno, EDC, and CACTI) to identify biomarkers associated with rapid eGFR decline. Here, we report the rationale for and design of these studies as well as results of models testing associations of clinical characteristics with rapid eGFR decline in the study population, upon which "omics" studies will be built. Cases (n = 535) and controls (n = 895) were defined as having an annual eGFR decline of ≥3 and <1 mL/min/1.73 m2, respectively. Associations of demographic and clinical variables with rapid eGFR decline were tested using logistic regression, and prediction was evaluated using area under the curve (AUC) statistics. Targeted metabolomics, lipidomics, and proteomics are being performed using high-resolution mass-spectrometry techniques.

RESULTS

At baseline, the mean age was 43 years, diabetes duration was 27 years, eGFR was 94 mL/min/1.73 m2, and 62% of participants were normoalbuminuric. Over 7.6-year median follow-up, the mean annual change in eGFR in cases and controls was -5.7 and 0.6 mL/min/1.73 m2, respectively. Younger age, longer diabetes duration, and higher baseline HbA1c, urine albumin-creatinine ratio, and eGFR were significantly associated with rapid eGFR decline. The cross-validated AUC for the predictive model incorporating these variables plus sex and mean arterial blood pressure was 0.74 (95% CI: 0.68-0.79; p < 0.001).

CONCLUSION

Known risk factors provide moderate discrimination of rapid eGFR decline. Identification of blood and urine biomarkers associated with rapid eGFR decline in T1D using targeted omics strategies may provide insight into disease mechanisms and improve upon clinical predictive models using traditional risk factors.

A low-cost laboratory-based method for predicting newly diagnosed biopsy-proven diabetic nephropathy in people with type 2 diabetes

AIMS

To identify significant prognostic factors for newly diagnosed biopsy-proven diabetic nephropathy using routine laboratory measures, from which to derive a low-cost explanatory model, and to use this model to examine associations between the potential low-cost test panels and the risk of diabetic nephropathy in people with type 2 diabetes with normal kidney function.

METHOD

A population-based case-control study was undertaken to test the association between diabetic nephropathy and 47 laboratory variables using a 'hypothesis-free' strategy and five routinely recorded factors in diabetes care (BMI, systolic and diastolic blood pressure, HbA_1c , fasting glucose). Factors that were significant after Bonferroni correction were included in different test panels and used to develop diabetic nephropathy (outcome) explanatory models. Models were derived using risk-set sampling among 950 biopsy-proven diabetic nephropathy cases newly diagnosed in the period between 2012 and 2018 and among 4750 age- and gender-matched controls.

RESULTS

A total of 15 Bonferroni-corrected significant laboratory predictors in the three test panels (blood cell, serum electrolytes and blood coagulation) were identified through multivariable analysis and used to develop the three explanatory models. The optimism-adjusted C-statistics and calibration slope were 0.725 (95% CI 0.723-0.728) and 0.978 (95% CI 0.912-0.999) for the blood cell model, 0.688 (95% CI 0.686-0.690) and 0.923 (95% CI 0.706-0.977) for the serum electrolytes model, 0.648 (95% CI 0.639-0.658) and 0.914 (95% CI 0.641-1.115) for the blood coagulation model, respectively.

CONCLUSIONS

A total of 15 predictors were significantly associated with newly diagnosed biopsy-proven diabetic nephropathy in type 2 diabetes. The blood cell model appeared to be the low-cost model with the best predictive ability.

Role of aromatic amino acids in pathogeneses of diabetic nephropathy in Chinese patients with type 2 diabetes

BACKGROUND

We aimed to estimate the associations between aromatic amino acids (AAAs) and diabetic nephropathy (DN) in patients with type 2 diabetes (T2D).

METHODS

We collected clinical and metabolomic data from 132 healthy subjects (HS group), 132 type 2 diabetes patients without diabetic nephropathy (T2D group) and 132 diabetic nephropathy patients (DN group) in tertiary hospital from May 2015 to August 2016. The odds ratio (OR) and 95% confidence interval (CI) were obtained by logistic regression.

RESULTS

The odds ratio of tyrosine for DN increased gradually. High tyrosine was associated with an increased OR of DN (model 3, OR:0.329, 95%CI, 0.144-0.750) when comparing extreme quantiles.

CONCLUSION

In Chinese patients with T2D, elevated tyrosine was associated with increased risk of DN.

Differential metabolomic signatures of declining renal function in Types 1 and 2 diabetes

BACKGROUND

Chronic kidney disease (CKD) shows different clinical features in Types1 (T1D) and 2 diabetes (T2D). Metabolomics have recently provided useful contribution to the identification of biomarkers of CKD progression in either form of the disease. However, no studies have so far compared plasma metabolomics between T1D and T2D in order to identify differential signatures of progression of estimated glomerular filtration rate (eGFR) decline.

METHODS

We used two large cohorts of T1D (from Finland) and T2D (from Italy) patients followed up to 7 and 3 years, respectively. In both groups, progression was defined as the top quartile of yearly decline in eGFR. Pooled data from the two groups were analysed by univariate and bivariate random forest (RF), and confirmed by bivariate partial least squares (PLS) analysis, the response variables being type of diabetes and eGFR progression.

RESULTS

In progressors, yearly eGFR loss was significantly larger in T2D [-5.3 (3.0), median (interquartile range)mL/min/1.73 m2/year] than T1D [-3.7 (3.1) mL/min/1.73 m2/year ; P = 0.018]. Out of several hundreds, bivariate RF extracted 22 metabolites associated with diabetes type (all higher in T1D than T2D except for 5-methylthioadenosine, pyruvate and β-hydroxypyruvate) and 13 molecules associated with eGFR progression (all higher in progressors than non-progressors except for sphyngomyelin). Three of the selected metabolites (histidylphenylalanine, leucylphenylalanine, tryptophylasparagine) showed a significant interaction between disease type and progression. Only eight metabolites were common to both bivariate RF and PLS.

CONCLUSIONS

Identification of metabolomic signatures of CKD progression is partially dependent on the statistical model. Dual analysis identified molecules specifically associated with progressive renal impairment in both T1D and T2D.

Identification of metabolic markers in patients with type 2 Diabetes by Ultrafast gas chromatography coupled to electronic nose. A pilot study

Metabolomics is a potential tool for the discovery of new biomarkers in the early diagnosis of diseases. An ultra-fast gas chromatography system equipped to an electronic nose detector (FGC eNose) was used to identify the metabolomic profile of Volatile Organic Compounds (VOCs) in type 2 diabetes (T2D) urine from Mexican population. A cross-sectional, comparative, and clinical study with translational approach was performed. We recruited twenty T2D patients and twenty-one healthy subjects. Urine samples were taken and analyzed by FGC eNose. Eighty-eight compounds were identified through Kovats's indexes. A natural variation of 30% between the metabolites, expressed by study groups, was observed in Principal Component 1 and 2 with a significant difference (p < 0.001). The model, performed through a Canonical Analysis of Principal coordinated (CAP), allowed a correct classification of 84.6% between healthy and T2D patients, with a 15.4% error. The metabolites 2-propenal, 2-propanol, butane- 2,3-dione and 2-methylpropanal, were increased in patients with T2D, and they were strongly correlated with discrimination between clinically healthy people and T2D patients. This study identified metabolites in urine through FGC eNose that can be used as biomarkers in the identification of T2D patients. However, more studies are needed for its implementation in clinical practice.

Metabolomics window into the role of acute kidney injury after coronary artery bypass grafting in diabetic nephropathy progression

Introduction

Metabolomics has emerged as a valuable tool to discover novel biomarkers and study the pathophysiology of diabetic nephropathy (DN). However, the effect of postoperative acute kidney injury (AKI) on diabetes mellitus (DM) to chronic DN progression has not been evaluated from the perspective of metabolomics.

Methods

A group of type 2 diabetes mellitus (T2DM) inpatients, who underwent off-pump coronary artery bypass grafting (CABG), were enrolled in our study. According to whether postoperative AKI occurred, patients were grouped in either the AKI group (AKI, n = 44) or the non-AKI group (NAKI, n = 44). Urine samples were collected from these patients before and 24 h after operation. Six patients from the AKI group and six patients from the NAKI group were chosen as the pilot cohort for untargeted metabolomics analysis, with the goal of identifying postoperative AKI-related metabolites. To understand the possible role of these metabolites in the chronic development of renal injury among T2DM patients, trans-4-hydroxy-L-proline and azelaic acid were quantified by targeted metabolomics analysis among 38 NAKI patients, 38 AKI patients, 46 early DN patients (DN-micro group), and 34 overt DN patients (DN-macro group).

Results

Untargeted metabolomics screened 61 statistically distinguishable metabolites in postoperative urine samples, compared with preoperative urine samples. Via Venn diagram analysis, nine of 61 were postoperative AKI-related metabolites, including trans-4-hydroxy-L-proline, uridine triphosphate, p-aminobenzoate, caffeic acid, adrenochrome, δ-valerolactam, L-norleucine, 5′-deoxy-5′-(methylthio) adenosine, and azelaic acid. By targeted metabolomics analysis, the level of trans-4-hydroxy-L-proline increased gradually from the NAKI group to the AKI, DN-micro, and DN-macro groups. For azelaic acid, the highest level was found in the NAKI and DN-micro groups, followed by the DN-macro group. The AKI group exhibited the lowest level of azelaic acid.

Conclusions

The detection of urinary trans-4-hydroxy-L-proline after AKI could be treated as an early warning of chronic DN progression and might be linked to renal fibrosis. Urinary azelaic acid can be used to monitor renal function noninvasively in DM and DN patients. Our results identified markers of AKI on DM and the chronic progression of DN. In addition, the progression of DN was associated with AKI-like episodes occurring in DM.

Positive interaction of mangiferin with selected oral hypoglycemic drugs: a therapeutic strategy to alleviate diabetic nephropathy in experimental rats

Diabetic nephropathy (DN) is one of the notorious diabetes associated complications. Despite many therapeutic strategies available, metabolic control of DN continues to poses a challenge. In this study, the interactions of mangiferin with selected oral hypoglycemic drugs, metformin and gliclazide to effectively alleviate the symptoms of renal injury in DN are evaluated. Male Sprague Dawley rats were used as experimental model and type II diabetes was induced by administration of high fat diet and low dose streptozotocin. Oral intervention of mangiferin with metformin and gliclazide for a period of 28 days was given to diabetic rats. At the end of the treatment period, biochemical parameters, kidney function markers, anti-oxidant enzymes levels, oxidative stress mediated gene expression and histology were analysed. Significant reduction in the serum biochemical markers (glucose, urea and creatinine) were observed in the groups treated with combination drugs. Marked improvement in the combination treated groups in terms of inflammation and oxidative damage in the gene (TNFα, NFκB, TGFβ, VEGF, PKC) and protein expression (NFκB, VEGF) were noted in the kidney tissue alleviating the symptoms of DN. These results were further corroborated with histopathological results. Scientific data in the present study reveals that the combinations of mangiferin with the oral hypoglycemic drugs have been favorable in alleviating renal injury. Hence, a combination therapy to alleviate the vascular complication, diabetic nephropathy may be considered as a possible therapeutic strategy by including natural phytocompounds as an add on therapy to conventional oral hypoglycemic drugs.