Metabolomic biomarkers in diabetic kidney diseases--A systematic review

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.

Lipid metabolism disorder in diabetic kidney disease

Diabetic kidney disease (DKD), a significant complication associated with diabetes mellitus, presents limited treatment options. The progression of DKD is marked by substantial lipid disturbances, including alterations in triglycerides, cholesterol, sphingolipids, phospholipids, lipid droplets, and bile acids (BAs). Altered lipid metabolism serves as a crucial pathogenic mechanism in DKD, potentially intertwined with cellular ferroptosis, lipophagy, lipid metabolism reprogramming, and immune modulation of gut microbiota (thus impacting the liver-kidney axis). The elucidation of these mechanisms opens new potential therapeutic pathways for DKD management. This research explores the link between lipid metabolism disruptions and DKD onset.

Metabolome evidence of CKDu risks after chronic exposure to simulated Sri Lanka drinking water in zebrafish

It is still a serious public health issue that chronic kidney disease of uncertain etiology (CKDu) in Sri Lanka poses challenges in identification, prevention, and treatment. What environmental factors in drinking water cause kidney damage remains unclear. This study aimed to investigate the risks of various environmental factors that may induce CKDu, including water hardness, fluoride (HF), heavy metals (HM), microcystin-LR (MC-LR), and their combined exposure (HFMM). The research focused on comprehensive metabolome analysis, and correlation with transcriptomic and gut microbiota changes. Results revealed that chronic exposure led to kidney damage and pancreatic toxicity in adult zebrafish. Metabolomics profiling showed significant alterations in biochemical processes, with enriched metabolic pathways of oxidative phosphorylation, folate biosynthesis, arachidonic acid metabolism, FoxO signaling pathway, lysosome, pyruvate metabolism, and purine metabolism. The network analysis revealed significant changes in metabolites associated with renal function and diseases, including 20-Hydroxy-LTE4, PS(18:0/22:2(13Z,16Z)), Neuromedin N, 20-Oxo-Leukotriene E4, and phenol sulfate, which are involved in the fatty acyls and glycerophospholipids class. These metabolites were closely associated with the disrupted gut bacteria of g_ZOR0006, g_Pseudomonas, g_Tsukamurella, g_Cetobacterium, g_Flavobacterium, which belonged to dominant phyla of Firmicutes and Proteobacteria, etc., and differentially expressed genes (DEGs) such as egln3, ca2, jun, slc2a1b, and gls2b in zebrafish. Exploratory omics analyses revealed the shared significantly changed pathways in transcriptome and metabolome like calcium signaling and necroptosis, suggesting potential biomarkers for assessing kidney disease.

Tubular dysfunction impairs renal excretion of pseudouridine in diabetic kidney disease

Plasma nucleosides-pseudouridine (PU) and N2N2-dimethyl guanosine (DMG) predict the progression of type 2 diabetic kidney disease (DKD) to end-stage renal disease, but the mechanisms underlying this relationship are not well understood. We used a well-characterized model of type 2 diabetes (db/db mice) and control nondiabetic mice (db/m mice) to characterize the production and excretion of PU and DMG levels using liquid chromatography-mass spectrometry. The fractional excretion of PU and DMG was decreased in db/db mice compared with control mice at 24 wk before any changes to renal function. We then examined the dynamic changes in nucleoside metabolism using in vivo metabolic flux analysis with the injection of labeled nucleoside precursors. Metabolic flux analysis revealed significant decreases in the ratio of urine-to-plasma labeling of PU and DMG in db/db mice compared with db/m mice, indicating significant tubular dysfunction in diabetic kidney disease. We observed that the gene and protein expression of the renal tubular transporters involved with nucleoside transport in diabetic kidneys in mice and humans was reduced. In conclusion, this study strongly suggests that tubular handling of nucleosides is altered in early DKD, in part explaining the association of PU and DMG with human DKD progression observed in previous studies.NEW & NOTEWORTHY Tubular dysfunction explains the association between the nucleosides pseudouridine and N2N2-dimethyl guanosine and diabetic kidney disease.

Diabetic vascular diseases: molecular mechanisms and therapeutic strategies

Vascular complications of diabetes pose a severe threat to human health. Prevention and treatment protocols based on a single vascular complication are no longer suitable for the long-term management of patients with diabetes. Diabetic panvascular disease (DPD) is a clinical syndrome in which vessels of various sizes, including macrovessels and microvessels in the cardiac, cerebral, renal, ophthalmic, and peripheral systems of patients with diabetes, develop atherosclerosis as a common pathology. Pathological manifestations of DPDs usually manifest macrovascular atherosclerosis, as well as microvascular endothelial function impairment, basement membrane thickening, and microthrombosis. Cardiac, cerebral, and peripheral microangiopathy coexist with microangiopathy, while renal and retinal are predominantly microangiopathic. The following associations exist between DPDs: numerous similar molecular mechanisms, and risk-predictive relationships between diseases. Aggressive glycemic control combined with early comprehensive vascular intervention is the key to prevention and treatment. In addition to the widely recommended metformin, glucagon-like peptide-1 agonist, and sodium-glucose cotransporter-2 inhibitors, for the latest molecular mechanisms, aldose reductase inhibitors, peroxisome proliferator-activated receptor-γ agonizts, glucokinases agonizts, mitochondrial energy modulators, etc. are under active development. DPDs are proposed for patients to obtain more systematic clinical care requires a comprehensive diabetes care center focusing on panvascular diseases. This would leverage the advantages of a cross-disciplinary approach to achieve better integration of the pathogenesis and therapeutic evidence. Such a strategy would confer more clinical benefits to patients and promote the comprehensive development of DPD as a discipline.

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.

A generalized covariate-adjusted top-scoring pair algorithm with applications to diabetic kidney disease stage classification in the Chronic Renal Insufficiency Cohort (CRIC) Study

BACKGROUND

The growing amount of high dimensional biomolecular data has spawned new statistical and computational models for risk prediction and disease classification. Yet, many of these methods do not yield biologically interpretable models, despite offering high classification accuracy. An exception, the top-scoring pair (TSP) algorithm derives parameter-free, biologically interpretable single pair decision rules that are accurate and robust in disease classification. However, standard TSP methods do not accommodate covariates that could heavily influence feature selection for the top-scoring pair. Herein, we propose a covariate-adjusted TSP method, which uses residuals from a regression of features on the covariates for identifying top scoring pairs. We conduct simulations and a data application to investigate our method, and compare it to existing classifiers, LASSO and random forests.

RESULTS

Our simulations found that features that were highly correlated with clinical variables had high likelihood of being selected as top scoring pairs in the standard TSP setting. However, through residualization, our covariate-adjusted TSP was able to identify new top scoring pairs, that were largely uncorrelated with clinical variables. In the data application, using patients with diabetes (n = 977) selected for metabolomic profiling in the Chronic Renal Insufficiency Cohort (CRIC) study, the standard TSP algorithm identified (valine-betaine, dimethyl-arg) as the top-scoring metabolite pair for classifying diabetic kidney disease (DKD) severity, whereas the covariate-adjusted TSP method identified the pair (pipazethate, octaethylene glycol) as top-scoring. Valine-betaine and dimethyl-arg had, respectively, ≥ 0.4 absolute correlation with urine albumin and serum creatinine, known prognosticators of DKD. Thus without covariate-adjustment the top-scoring pair largely reflected known markers of disease severity, whereas covariate-adjusted TSP uncovered features liberated from confounding, and identified independent prognostic markers of DKD severity. Furthermore, TSP-based methods achieved competitive classification accuracy in DKD to LASSO and random forests, while providing more parsimonious models.

CONCLUSIONS

We extended TSP-based methods to account for covariates, via a simple, easy to implement residualizing process. Our covariate-adjusted TSP method identified metabolite features, uncorrelated from clinical covariates, that discriminate DKD severity stage based on the relative ordering between two features, and thus provide insights into future studies on the order reversals in early vs advanced disease states.

The GenoDiabMar Registry: A Collaborative Research Platform of Type 2 Diabetes Patients

The GenoDiabMar registry is a prospective study that aims to provide data on demographic, biochemical, and clinical changes in type 2 diabetic (T2D) patients attending real medical outpatient consultations. This registry is also used to find new biomarkers related to the micro- and macrovascular complications of T2D, with a particular focus on diabetic nephropathy. With this purpose, longitudinal serum and urine samples, DNA banking, and data on 227 metabolomics profiles, 77 immunoglobulin G glycomics traits, and other emerging biomarkers were recorded in this cohort. In this study, we show a detailed longitudinal description of the clinical and analytical parameters of this registry, with a special focus on the progress of renal function and cardiovascular events. The main objective is to analyze whether there are differential risk factors for renal function deterioration between sexes, as well as to analyze cardiovascular events and mortality in this population. In total, 650 patients with a median age of 69 (14) with different grades of chronic kidney disease—G1−G2 (eGFR > 90−60 mL/min/1.73 m2) 50.3%, G3 (eGFR; 59−30 mL/min/1.73 m2) 31.4%, G4 (eGFR; 29−15 mL/min/1.73 m2) 10.8%, and G5 (eGFR < 15 mL/min/1.73 m2) 7.5%—were followed up for 4.7 (0.65) years. Regardless of albuminuria, women lost 0.93 (0.40−1.46) fewer glomerular filtration units per year than men. A total of 17% of the participants experienced rapid deterioration of renal function, 75.2% of whom were men, with differential risk factors between sexes—severe macroalbuminuria > 300 mg/g for men OR [IQ] 2.40 [1.29:4.44] and concomitant peripheral vascular disease 3.32 [1.10:9.57] for women. Overall mortality of 23% was detected (38% of which was due to cardiovascular etiology). We showed that kidney function declined faster in men, with different risk factors compared to women. Patients with T2D and kidney involvement have very high mortality and an important cardiovascular burden. This cohort is proposed as a great tool for scientific collaboration for studies, whether they are focused on T2D, or whether they are interested in comparing differential markers between diabetic and non-diabetic populations.

Simultaneous analyses of urinary eicosanoids and related mediators identified tetranor-prostaglandin E metabolite as a novel biomarker of diabetic nephropathy

Diabetic nephropathy is a major complication of diabetes mellitus, and thus novel biomarkers are desired to evaluate the presence and progression of diabetic nephropathy. In this study, we sought to identify possible metabolites related to diabetic nephropathy among urinary eicosanoids and related mediators. Using liquid chromatogram-tandem mass spectrometry, we optimized the lipid extraction from urine using the Monospin C18 as a solid-phase extraction cartridge and measured the urinary lipid mediators in 111 subjects with type 2 diabetes mellitus as well as 33 healthy subjects. We observed that 14 metabolites differed significantly among the clinical stages of nephropathy. Among them, levels of tetranor-prostaglandin E metabolite (tetranor-PGEM), an arachidonic acid metabolite, were significantly higher in subjects with stage 1 nephropathy than in healthy subjects and increased with the progression of nephropathy. We also observed that levels of maresin-1, a docosahexaenoic acid metabolite, and leukotriene B4-ethanolamide, an arachidonoyl ethanolamide metabolite, were significantly lower in subjects with stage 3–4 nephropathy than in healthy subjects and those with stage 1–2 nephropathy. Finally, using a comprehensive analysis of urinary eicosanoids and related mediators, we concluded that tetranor-PGEM was capable of discriminating clinical stages of nephropathy and thus useful as a novel biomarker for diabetic nephropathy.

Targeted metabolomics as a tool for the diagnosis of kidney disease in Type II diabetes mellitus

Background: Diabetic kidney disease (DKD) is an increasing health problem and an extra burden to health services. The study of characteristic metabolic alterations of DKD is crucial for a better understanding of pathogenesis to identify new potential biomarkers and drug targets. We hypothesized that metabolic profiling of amino acids, acylcarnitines, and organic acids are useful new biomarkers for the diagnosis of the early stages of DKDMethods: The hypothesis was testing in a case-control study of 232 patients with type 2 diabetes mellitus and 150 healthy controls. Patients were classified according to urinary albumin and estimated glomerular filtration rate (eGFR) into 100 with normoalbuminuria and 132 with microalbuminuria group. Eighteen AcylCNs and 17 amino acids were measured in the blood by tandem mass spectrometry while 17 urinary organic acids were quantitatively measured by gas chromatography - mass spectrometry.Results: Regression analysis found that dodecanoylcarnitines C12 (effect size 2.03 [95%CI 1.73-2.32]), triglylcarnitine C5:1 (2.01 [1.70-2.30]), and isovalerylcarnitine C5 (1.78 [1.48-2.07]) were stronger predictors of albumin/creatinine ratio than HbA1c (1.50 [1.20-1.78]) and hence they could serve as potential biomarkers for the diagnosis of the early stages of DKD.Conclusions: Targeted metabolic profiling offers a new, non-invasive approach for detecting biomarkers for the early diagnosis of DKD suggesting new pathogenetic phases that might be new targets for treatment.

Metabolomic Signature Between Metabolically Healthy Overweight/Obese and Metabolically Unhealthy Overweight/Obese: A Systematic Review

The clinical manifestations of overweight/obesity are heterogeneous and complex. In contrast to metabolically unhealthy overweight/obese (MUO), a particular sub-group of obese patients who are considered as metabolically healthy overweight/obese (MHO), display favorable metabolic profiles characterized by high levels of insulin sensitivity, normal blood pressure, as well as favorable lipid, inflammation, hormone, liver enzyme, and immune profiles. While only a few available studies focused on the metabolic files underlying the obese phenotypes, the current review aimed to perform a systematic review of available studies focusing on describing the metabolomic signature between MUO and MHO. We did the systematic search for literature on MEDLINE (PubMed), the Cochrane Library, EMBASE, and searched for the references of relevant manuscripts from inception to 29 May 2020. After critical selection, 20 studies were eligible for this systematic review and evaluated by using QUADOMICS for quality assessment. Eventually, 12 of 20 studies were classified as "high quality". Branched-chain amino acids (isoleucine, leucine, and valine), aromatic amino acids (phenylalanine and tyrosine), lipids (palmitic acid, palmitoleic acid, oleic acid, eicosapentaenoic acid, and docosahexaenoic acid), and acylcarnitines (propionyl carnitine) levels might be elevated in MUO. The current results suggested that MHO showed a favorable trend in the overall metabolic signature. More longitudinal studies are needed to elaborate deeply on the metabolic pathway and the relationship between metabolic patterns and the occurrence of the disease.

Indications for renal biopsy in patients with diabetes. Joint position statement of the Italian Society of Nephrology and the Italian Diabetes Society

AIMS

This joint document of the Italian Society of Nephrology and the Italian Diabetes Society reviews the main indications to perform a renal biopsy in diabetic patients, according to the recommendations of a panel of experts based on all available scientific evidence.

DATA SYNTHESIS

Renal biopsy has a pivotal role in assessing the nature and severity of renal injury in patients with diabetic kidney disease (DKD). The procedure is mandatory in the presence of one of more of the following features: rapid onset or progression of albuminuria or sudden onset of nephrotic syndrome, rapid GFR decline with or without albuminuria, hematuria, active urine sediment, clinical and/or laboratory suspicion of other systemic diseases, and, in patients with type 1 diabetes, short diabetes duration and absence of retinopathy. Indeed, ~40% of diabetic individuals with kidney injury undergoing renal biopsy are affected by a non-diabetic renal disease (NDRD). Furthermore, the histological evaluation of patients with suspected classical diabetic nephropathy allows to define the extent of glomerular, tubulo-interstitial and vascular lesions, thus providing important prognostic (and potentially therapeutic) data. In the future, the indications for renal biopsy might be extended to the definition of the histological lesions underlying the "nonalbuminuric" DKD phenotypes, as well as to the evaluation of the response to treatment with the new anti-hyperglycemic drugs that provide cardiorenal protection.

CONCLUSIONS

In view of the heterogeneous clinical presentation and course of DKD and of the related heterogeneous histopathological patterns, a more extensive use of renal biopsy may be crucial to provide valuable information with important pathogenic, diagnostic, prognostic, and therapeutic implications.

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.

A GC-MS-Based Metabolomics Investigation of the Protective Effect of Liu-Wei-Di-Huang-Wan in Type 2 Diabetes Mellitus Mice

Materials and Methods

MKR mice were used for the development of diabetes with high-fat diet feeding. These mice were further injected with streptozocin (STZ) to aggravate kidney failure. Fasting blood glucose (FBG) and urinary albumin-to-creatinine ratio (ACR values) were determined to validate the successful establishment of diabetic models with desired kidney dysfunction. Metabolomics approach coupled with gas chromatography-mass spectrometry (GC-MS) and random forest (RF) algorithm was proposed to discover the metabolic differences among model group and control group as well as to examine the therapeutic efficacy of traditional Chinese medicine, Liu-Wei-Di-Huang-Wan (LWDHW), in diabetes and associated kidney failure.

Results

Some metabolites such as 3-hydroxybutyric acid, citric acid, hexadecanoic acid, and octadecanoic acid showed significant differences between the control group and model group. Treatment with LWDHW resulted in a significant decrease in FBG and ACR values. These results suggested that LWDHW could have beneficial effects in diabetes-associated renal failure.

Metabolomic Markers of Kidney Function Decline in Patients With Diabetes: Evidence From the Chronic Renal Insufficiency Cohort (CRIC) Study

RATIONALE & OBJECTIVE

Biomarkers that provide reliable evidence of future diabetic kidney disease (DKD) are needed to improve disease management. In a cross-sectional study, we previously identified 13 urine metabolites that had levels reduced in DKD compared with healthy controls. We evaluated associations of these 13 metabolites with future DKD progression.

STUDY DESIGN

Prospective cohort.

SETTING & PARTICIPANTS

1,001 Chronic Renal Insufficiency Cohort (CRIC) participants with diabetes with estimated glomerular filtration rates (eGFRs) between 20 and 70mL/min/1.73m² were followed up prospectively for a median of 8 (range, 2-10) years.

PREDICTORS

13 urine metabolites, age, race, sex, smoked more than 100 cigarettes in lifetime, body mass index, hemoglobin A_1c level, blood pressure, urinary albumin, and eGFR.

OUTCOMES

Annual eGFR slope and time to incident kidney failure with replacement therapy (KFRT; ie, initiation of dialysis or receipt of transplant).

ANALYTICAL APPROACH

Several clinical metabolite models were developed for eGFR slope as the outcome using stepwise selection and penalized regression, and further tested on the time-to-KFRT outcome. A best cross-validated (final) prognostic model was selected based on high prediction accuracy for eGFR slope and high concordance statistic for incident KFRT.

RESULTS

During follow-up, mean eGFR slope was-1.83±1.92 (SD) mL/min/1.73m² per year; 359 (36%) participants experienced KFRT. Median time to KFRT was 7.45 years from the time of entry to the CRIC Study. In our final model, after adjusting for clinical variables, levels of metabolites 3-hydroxyisobutyrate (3-HIBA) and 3-methylcrotonyglycine had a significant negative association with eGFR slope, whereas citric and aconitic acid were positively associated. Further, 3-HIBA and aconitic acid levels were associated with higher and lower risk for KFRT, respectively (HRs of 2.34 [95% CI, 1.51-3.62] and 0.70 [95% CI, 0.51-0.95]).

LIMITATIONS

Subgroups for whom metabolite signatures may not be optimal, nontargeted metabolomics by flow-injection analysis, and 2-stage modeling approaches.

CONCLUSIONS

Urine metabolites may offer insights into DKD progression. If replicated in future studies, aconitic acid and 3-HIBA could identify individuals with diabetes at high risk for GFR decline, potentially leading to improved clinical care and targeted therapies.

Kynurenine/Tryptophan Ratio Predicts Angiotensin Receptor Blocker Responsiveness in Patients with Diabetic Kidney Disease

Albuminuria is a measurement and determinant factor for diabetic kidney disease (DKD). Angiotensin receptor blocker (ARB) is recommended for albuminuria in DKD with variable response. To find surrogate markers to predict the therapeutic effect of ARB, we carried out a prospective study to correlate plasma metabolites and the progression of renal function/albuminuria in DKD patients. A total of 56 type 2 diabetic patients with various stages of chronic kidney disease and albuminuria were recruited. ARB was prescribed once albuminuria was established. Urinary albumin-to-creatinine ratio (UACR) was determined before and six months after ARB treatment, with a ≥30% reduction of UACR considered an ARB responder. Plasma levels of 145 metabolites were measured before ARB treatment; only those associated with albuminuria were selected and compared between ARB responders and non-responders. Both lower tryptophan (Trp ≤ 46.75 μmol/L) levels and a higher kynurenine/tryptophan ratio (KTR ≥ 68.5 × 10⁻³) were significantly associated with macroalbuminuria (MAU), but only KTR (≥54.7 × 10⁻³) predicts ARB responsiveness (sensitivity 90.0%, specificity 50%) in MAU. Together, these data suggest that the kynurenine/tryptophan ratio predicts angiotensin receptor blocker responsiveness in patients with diabetic kidney disease.

DESI-MSI and METASPACE indicates lipid abnormalities and altered mitochondrial membrane components in diabetic renal proximal tubules

INTRODUCTION

Diabetic kidney disease (DKD) is the most prevalent complication in diabetic patients, which contributes to high morbidity and mortality. Urine and plasma metabolomics studies have been demonstrated to provide valuable insights for DKD. However, limited information on spatial distributions of metabolites in kidney tissues have been reported.

OBJECTIVES

In this work, we employed an ambient desorption electrospray ionization-mass spectrometry imaging (DESI-MSI) coupled to a novel bioinformatics platform (METASPACE) to characterize the metabolome in a mouse model of DKD.

METHODS

DESI-MSI was performed for spatial untargeted metabolomics analysis in kidneys of mouse models (F1 C57BL/6J-Ins2Akita male mice at 17 weeks of age) of type 1 diabetes (T1D, n = 5) and heathy controls (n = 6).

RESULTS

Multivariate analyses (i.e., PCA and PLS-DA (a 2000 permutation test: P < 0.001)) showed clearly separated clusters for the two groups of mice on the basis of 878 measured m/z's in kidney cortical tissues. Specifically, mice with T1D had increased relative abundances of pseudouridine, accumulation of free polyunsaturated fatty acids (PUFAs), and decreased relative abundances of cardiolipins in cortical proximal tubules when compared with healthy controls.

CONCLUSION

Results from the current study support potential key roles of pseudouridine and cardiolipins for maintaining normal RNA structure and normal mitochondrial function, respectively, in cortical proximal tubules with DKD. DESI-MSI technology coupled with METASPACE could serve as powerful new tools to provide insight on fundamental pathways in DKD.

Metabolomic Changes of Human Proximal Tubular Cell Line in High Glucose Environment

Hyperglycemia causes mitochondrial damage renal tubular cells, which contribute to the progression of diabetic kidney disease. However, the metabolic aberration of renal tubular cells in an hyperglycemic milieu has not been fully elucidated. In this study, human proximal renal tubular cell line (HK-2 cell) are incubated in glucose and mannitol at 5 mM or 25 mM. Cellular metabolome was determined by capillary electrophoresis time of flight mass spectrometer (CE-TOF/MS) and capillary electrophoresis-triple quadrupole mass spectrometry (CE-QqQMS). A total of 116 metabolites were quantified. Principal component analysis (PCA) revealed excellent clustering of metabolomic changes for different treatment conditions, and exposure to glucose at 5 and 25 mM lead to distinct metabolomic profiles as compared to samples treated with serum-free medium or mannitol as osmotic control. Hierarchical clustering analysis showed a number of characteristic changes in metabolic profile following exposure to 5 mM or 25 mM glucose. Notably, lactate-to-pyruvate ratio was significantly increased, while cellular levels of citric acid, α-ketoglutaric acid (i.e. 2-oxoglutaric acid), and fumaric acid were significantly reduced after exposure to glucose at 25 mM but not 5 mM. Moreover, cellular levels of reduced glutathione and total glutathione were significantly decreased, and S-adenosylmethionine (SAM) to S-adenosylhomocysteine (SAH) ratio was significantly increased after exposure to glucose 25 mM but not 5 mM. We conclude that in response to high glucose, HK-2 cells characteristic metabolomic changes, including increase in lactate-to-pyruvate ratio, reduction in Krebs cycle metabolites, reduction in glutathione antioxidant activity, and increase in cellular methylation potential. Our results may shed light on the pathogenesis of diabetic kidney disease, but the expression of glucose metabolism-related protein and enzyme activity in HK-2 cells after hyperglycemia condition need to be confirmed by further studies.

Targeted Clinical Metabolite Profiling Platform for the Stratification of Diabetic Patients

Several small molecule biomarkers have been reported in the literature for prediction and diagnosis of (pre)diabetes, its co-morbidities, and complications. Here, we report the development and validation of a novel, quantitative method for the determination of a selected panel of 34 metabolite biomarkers from human plasma. We selected a panel of metabolites indicative of various clinically-relevant pathogenic stages of diabetes. We combined these candidate biomarkers into a single ultra-high-performance liquid chromatography-tandem mass spectrometry (UHPLC-MS/MS) method and optimized it, prioritizing simplicity of sample preparation and time needed for analysis, enabling high-throughput analysis in clinical laboratory settings. We validated the method in terms of limits of detection (LOD) and quantitation (LOQ), linearity (R²), and intra- and inter-day repeatability of each metabolite. The method’s performance was demonstrated in the analysis of selected samples from a diabetes cohort study. Metabolite levels were associated with clinical measurements and kidney complications in type 1 diabetes (T1D) patients. Specifically, both amino acids and amino acid-related analytes, as well as specific bile acids, were associated with macro-albuminuria. Additionally, specific bile acids were associated with glycemic control, anti-hypertensive medication, statin medication, and clinical lipid measurements. The developed analytical method is suitable for robust determination of selected plasma metabolites in the diabetes clinic.

Metabolite aberrations in early diabetes detected in rat kidney using mass spectrometry imaging

Diabetic kidney disease is a serious complication of diabetes that can ultimately lead to end-stage renal disease. The pathogenesis of diabetic kidney disease is complex, and fundamental research is still required to provide a better understanding of the driving forces behind it. We report regional metabolic aberrations from an untargeted mass spectrometry imaging study of kidney tissue using an insulinopenic rat model of diabetes. Diabetes was induced by intravenous injection of streptozotocin, and kidneys were harvested 2 weeks thereafter. Imaging was performed using nanospray desorption electrospray ionization connected to a high-mass-resolving mass spectrometer. No histopathological changes were observed in the kidney sections; however, mass spectrometry imaging revealed a significant increase in several 18-carbon unsaturated non-esterified fatty acid species and monoacylglycerols. Notably, these 18-carbon acyl chains were also constituents of several increased diacylglycerol species. In addition, a number of short- and long-chain acylcarnitines were found to be accumulated while several amino acids were depleted. This study presents unique regional metabolic data indicating a dysregulated energy metabolism in renal mitochondria as an early response to streptozotocin-induced type I diabetes. Graphical abstract.

Urinary metabolites predict prolonged duration of delayed graft function in DCD kidney transplant recipients

Extending kidney donor criteria, including donation after circulatory death (DCD), has resulted in increased rates of delayed graft function (DGF) and primary nonfunction. Here, we used Nuclear Magnetic Resonance (NMR) spectroscopy to analyze the urinary metabolome of DCD transplant recipients at multiple time points (days 10, 42, 180, and 360 after transplantation). The aim was to identify markers that predict prolonged duration of functional DGF (fDGF). Forty-seven metabolites were quantified and their levels were evaluated in relation to fDGF. Samples obtained at day 10 had a different profile than samples obtained at the other time points. Furthermore, at day 10 there was a statistically significant increase in eight metabolites and a decrease in six metabolites in the group with fDGF (N = 53) vis-à-vis the group without fDGF (N = 22). In those with prolonged fDGF (≥21 days) (N = 17) urine lactate was significantly higher and pyroglutamate lower than in those with limited fDGF (<21 days) (N = 36). In order to further distinguish prolonged fDGF from limited fDGF, the ratios of all metabolites were analyzed. In a logistic regression analysis, the sum of branched-chain amino acids (BCAAs) over pyroglutamate and lactate over fumarate, predicted prolonged fDGF with an AUC of 0.85. In conclusion, kidney transplant recipients with fDGF can be identified based on their altered urinary metabolome. Furthermore, two ratios of urinary metabolites, lactate/fumarate and BCAAs/pyroglutamate, adequately predict prolonged duration of fDGF.

Circulating metabolic biomarkers of renal function in diabetic and non-diabetic populations

Using targeted NMR spectroscopy of 227 fasting serum metabolic traits, we searched for novel metabolic signatures of renal function in 926 type 2 diabetics (T2D) and 4838 non-diabetic individuals from four independent cohorts. We furthermore investigated longitudinal changes of metabolic measures and renal function and associations with other T2D microvascular complications. 142 traits correlated with glomerular filtration rate (eGFR) after adjusting for confounders and multiple testing: 59 in diabetics, 109 in non-diabetics with 26 overlapping. The amino acids glycine and phenylalanine and the energy metabolites citrate and glycerol were negatively associated with eGFR in all the cohorts, while alanine, valine and pyruvate depicted opposite association in diabetics (positive) and non-diabetics (negative). Moreover, in all cohorts, the triglyceride content of different lipoprotein subclasses showed a negative association with eGFR, while cholesterol, cholesterol esters (CE), and phospholipids in HDL were associated with better renal function. In contrast, phospholipids and CEs in LDL showed positive associations with eGFR only in T2D, while phospholipid content in HDL was positively associated with eGFR both cross-sectionally and longitudinally only in non-diabetics. In conclusion, we provide a wide list of kidney function-associated metabolic traits and identified novel metabolic differences between diabetic and non-diabetic kidney disease.

Biomarkers of diabetic kidney disease

Diabetic kidney disease (DKD) remains one of the leading causes of reduced lifespan in diabetes. The quest for both prognostic and surrogate endpoint biomarkers for advanced DKD and end-stage renal disease has received major investment and interest in recent years. However, at present no novel biomarkers are in routine use in the clinic or in trials. This review focuses on the current status of prognostic biomarkers. First, we emphasise that albuminuria and eGFR, with other routine clinical data, show at least modest prediction of future renal status if properly used. Indeed, a major limitation of many current biomarker studies is that they do not properly evaluate the marginal increase in prediction on top of these routinely available clinical data. Second, we emphasise that many of the candidate biomarkers for which there are numerous sporadic reports in the literature are tightly correlated with each other. Despite this, few studies have attempted to evaluate a wide range of biomarkers simultaneously to define the most useful among these correlated biomarkers. We also review the potential of high-dimensional panels of lipids, metabolites and proteins to advance the field, and point to some of the analytical and post-analytical challenges of taking initial studies using these and candidate approaches through to actual clinical biomarker use.

Translational science in albuminuria: a new view of de novo albuminuria under chronic RAS suppression

The development of de novo albuminuria during chronic renin–angiotensin system (RAS) suppression is a clinical entity that remains poorly recognized in the biomedical literature. It represents a clear increment in global cardiovascular (CV) and renal risk that cannot be counteracted by RAS suppression. Although not specifically considered, it is clear that this entity is present in most published and ongoing trials dealing with the different forms of CV and renal disease. In this review, we focus on the mechanisms promoting albuminuria, and the predictors and new markers of de novo albuminuria, as well as the potential treatment options to counteract the excretion of albumin. The increase in risk that accompanies de novo albuminuria supports the search for early markers and predictors that will allow practising physicians to assess and prevent the development of de novo albuminuria in their patients.

Metabolomics window into diabetic complications

Diabetes has become a major global health problem. The elucidation of characteristic metabolic alterations during the diabetic progression is critical for better understanding its pathogenesis, and identifying potential biomarkers and drug targets. Metabolomics is a promising tool to reveal the metabolic changes and the underlying mechanism involved in the pathogenesis of diabetic complications. The present review provides an update on the application of metabolomics in diabetic complications, including diabetic coronary artery disease, diabetic nephropathy, diabetic retinopathy and diabetic neuropathy, and this review provides notes on the prevention and prediction of diabetic complications.

Proteomic and metabolomic characterization of streptozotocin-induced diabetic nephropathy in TIMP3-deficient mice

AIMS

The tissue inhibitor of metalloproteinase TIMP3 is a stromal protein that restrains the activity of both protease and receptor in the extracellular matrix and has been found to be down-regulated in diabetic nephropathy (DN), the leading cause of end-stage renal disease in developed countries.

METHODS

In order to gain deeper insights on the association of loss of TIMP3 and DN, we performed differential proteomic analysis of kidney and blood metabolic profiling of wild-type and Timp3-knockout mice before and after streptozotocin (STZ) treatment, widely used to induce insulin deficiency and hyperglycemia.

RESULTS

Kidney proteomic data and blood metabolic profiles suggest significant alterations of peroxisomal and mitochondrial fatty acids β-oxidation in Timp3-knockout mice compared to wild-type mice under basal condition. These alterations were exacerbated in response to STZ treatment.

CONCLUSIONS

Proteomic and metabolomic approaches showed that loss of TIMP3 alone or in combination with STZ treatment results in significant alterations of kidney lipid metabolism and peripheral acylcarnitine levels, supporting the idea that loss of TIMP3 may generate a phenotype more prone to DN.

Urinary lysophopholipids are increased in diabetic patients with nephropathy

Diabetic nephropathy (DN) is a major cause of chronic kidney disease that frequently leads to end stage renal failure. Lysophosphatidic acid (LPA) and lysophosphatidylcholine (LPC) are lysophospholipid mediators shown to accumulate in kidney and to promote renal inflammation and tubulo-interstitial fibrosis in diabetic rodent models. Here we assessed whether LPA and LPC were associated to the development of nephropathy in diabetic human patients. Several molecular species of LPA and LPC were quantified by LC/MS-MS in urine and plasma from type 2 diabetic patients with (cases; n=41) or without (controls, n=41) nephropathy symptoms (micro/macro-albuminuria and eGFR<60ml/min/1.73m²). Cases and controls were matched for sex, age and diabetes duration. Six species were detected in urine for both LPA and LPC, LPA16:0, LPA20:4, LPC16:0, LPC18:0, LPC18:1, and LPC18:2 that were significantly more concentrated in cases than in controls. Total LPC and LPA (sum of detected species) were significantly and exclusively associated with albuminuria (P<0.0001 and P=0.0009 respectively) and were significantly higher in the 3rd when compared to the 1st albuminuria tertile in cases. Plasma lysophospholipids showed a different species profile urine and their concentrations were not different between cases and controls. In conclusion, urine concentration of lysophospholipids increases in diabetic patients with DN as the likely result of their co-excretion with albumin combined with possible local production by kidney. Because LPA and LPC are known to promote renal inflammation and tubulo-interstitial fibrosis, their increased production in DN could participate to the development of kidney damage associated with diabetes.

Profiling of Plasma Metabolites Suggests Altered Mitochondrial Fuel Usage and Remodeling of Sphingolipid Metabolism in Individuals With Type 2 Diabetes and Kidney Disease

Introduction

Pathophysiology of diabetic kidney disease (DKD) is incompletely understood. We aim to elucidate metabolic abnormalities associated with DKD in type 2 diabetes mellitus (T2DM) by targeted plasma metabolomics.

Methods

A total of 126 T2DM participants with early DKD (urinary albumin-to-creatinine ratio [ACR] 30−299 mg/g and eGFR ≥ 60 ml/min/1.73 m²), 154 overt DKD (ACR ≥ 300 mg/g or eGFR < 60 ml/min/1.73 m²), and 129 non-DKD T2DM controls (ACR < 30 mg/g and eGFR ≥ 60 ml/min/1.73 m²) were included in discovery study. Findings were subsequently validated in 149 T2DM with macroalbuminuria (ACR ≥ 300 mg/g) and 149 matched non-DKD T2DM controls. Plasma amino acid, acylcarnitine, Krebs cycle organic acid, and sphingolipids/ceramide levels were quantified by liquid chromatography−mass spectrometry and gas chromatography−mass spectrometry.

Results

Of 123 metabolites included in the data analysis, 24 differed significantly between DKD and controls in the same direction in both discovery and validation subpopulations. A number of short acylcarnitines including their dicarboxylic derivatives (C2−C6) were elevated in DKD, suggesting abnormalities in fatty acids and amino acids metabolic pathways. Five phosphatidylcholines were lower whereas 4 metabolites in the sphingomyelin−ceramide subfamily were higher in DKD. Principal component regression revealed that long-chain ceramides were independently associated with ACR but not eGFR. Conversely, essential amino acids catabolism and short dicarboxylacylcarnitine accumulation were associated with eGFR but not ACR.

Discussion

DKD is associated with altered fuel substrate use and remodeling of sphingolipid metabolism in T2DM with DKD. Associations of albuminuria and impaired filtration function with distinct metabolomic signatures suggest different pathophysiology underlying these 2 manifestations of DKD.

Technical Challenges in Mass Spectrometry-Based Metabolomics

Metabolomics is a strategy for analysis, and quantification of the complete collection of metabolites present in biological samples. Metabolomics is an emerging area of scientific research because there are many application areas including clinical, agricultural, and medical researches for the biomarker discovery and the metabolic system analysis by employing widely targeted analysis of a few hundred preselected metabolites from 10-100 biological samples. Further improvement in technologies of mass spectrometry in terms of experimental design for larger scale analysis, computational methods for tandem mass spectrometry-based elucidation of metabolites, and specific instrumentation for advanced bioanalysis will enable more comprehensive metabolome analysis for exploring the hidden secrets of metabolism.

Using Metabolomic Profiles as Biomarkers for Insulin Resistance in Childhood Obesity: A Systematic Review

A growing body of evidence has shown the intimate relationship between metabolomic profiles and insulin resistance (IR) in obese adults, while little is known about childhood obesity. In this review, we searched available papers addressing metabolomic profiles and IR in obese children from inception to February 2016 on MEDLINE, Web of Science, the Cochrane Library, ClinicalTrials.gov, and EMASE. HOMA-IR was applied as surrogate markers of IR and related metabolic disorders at both baseline and follow-up. To minimize selection bias, two investigators independently completed this work. After critical selection, 10 studies (including 2,673 participants) were eligible and evaluated by using QUADOMICS for quality assessment. Six of the 10 studies were classified as "high quality." Then we generated all the metabolites identified in each study and found amino acid metabolism and lipid metabolism were the main affected metabolic pathways in obese children. Among identified metabolites, branched-chain amino acids (BCAAs), aromatic amino acids (AAAs), and acylcarnitines were reported to be associated with IR as biomarkers most frequently. Additionally, BCAAs and tyrosine seemed to be relevant to future metabolic risk in the long-term follow-up cohorts, emphasizing the importance of early diagnosis and prevention strategy. Because of limited scale and design heterogeneity of existing studies, future studies might focus on validating above findings in more large-scale and longitudinal studies with elaborate design.

Quantification of 18 amino acids in human plasma: application in renal transplant patient plasma by targeted UHPLC–MS/MS

Aim: Quantification of amino acids in human plasma has become an important and essential analysis parameter in life science. In this paper, we developed a targeted UHPLC–MS/MS method for 18 amino acids in the renal transplant patients. Methods & results: Plasma in small volume (150 μl) was pretreated by a one-step protein precipitant extraction for analysis. Detection was executed by MS/MS in the MRM mode. Assays were validated according to current bioanalytical guidelines, with good linearity (R > 0.99), intraday and interday precision (CV < 11.6%, RE ≤ ± 14.8%), extraction recovery (between 77.4 and 117.6%), matrix effect (73.3–118.0%) and stability (RE≤ ±14.7%). Conclusion: The method was successfully applicable for amino acid analysis in the renal transplant patient.