Untargeted Metabolomics by Ultra-High-Performance Liquid Chromatography Coupled with Electrospray Ionization-Quadrupole-Time of Flight-Mass Spectrometry Analysis Identifies a Specific Metabolomic Profile in Patients with Early Chronic Kidney Disease

Targeted Analysis of Serum and Urinary Metabolites for Early Chronic Kidney Disease

Chronic kidney disease (CKD) has become one of the most rapidly advancing diseases of the century, contributing significantly to increased mortality and morbidity. Metabolomics presents a promising approach to understanding CKD pathogenesis and identifying novel biomarkers for early diagnosis. This study evaluated serum and urine metabolomic profiles in CKD patients with declining glomerular filtration rates (eGFR). Using targeted metabolomics, we quantified seven potential metabolites in blood and urine samples from 20 healthy individuals and 99 CKD patients staged by eGFR according to the KDIGO guidelines. The analysis was conducted using ultra-high performance liquid chromatography combined with electrospray ionization quadrupole time-of-flight mass spectrometry. The metabolites investigated included L-phenylalanine, L-methionine, arginine, indoxyl sulfate, kynurenic acid, and L-acetylcarnitine. Quantitative assessments were performed using pure standards and validated through methods such as the limit of detection (LOD) and limit of quantification (LOQ). The findings identified potential biomarkers for early CKD diagnosis: in serum, L-phenylalanine, L-methionine, arginine, kynurenic acid, and indoxyl sulfate, while L-acetylcarnitine was significant in urine. These biomarkers could provide valuable insights into CKD progression and support in developing more effective diagnostic tools for early intervention.

Plasma Metabolite Profiles Between In-Center Daytime Extended-Hours and Conventional Hemodialysis

Key Points

Significant differences in 39 plasma metabolites were observed between patients on extended-hours hemodialysis and those on conventional hemodialysis. Extended-hours hemodialysis had a lower lactate-to-pyruvate ratio and higher branched-chain amino acids than conventional hemodialysis. Extended-hours hemodialysis may have favorable metabolic and nutritional benefits for patients undergoing maintenance hemodialysis.

Background

Protein–energy wasting, characterized by disordered body protein catabolism resulting from metabolic and nutritional derangements, is associated with adverse clinical outcomes in patients undergoing hemodialysis. Extended-hours hemodialysis (≥6 hours per treatment session) offers both enhanced removal of uremic solutes and better fluid management, generally allowing more liberalized dietary protein and calorie intake. The aim of this study was to evaluate the difference in plasma metabolite profiles among patients receiving in-center daytime extended-hours hemodialysis and those receiving conventional hemodialysis.

Methods

Predialysis plasma samples were obtained from 188 patients on extended-hours hemodialysis (21.9 h/wk) and 286 patients on conventional hemodialysis (12.1 h/wk) in Japan in 2020 using capillary electrophoresis-mass spectrometry. Group differences were compared for 117 metabolites using Wilcoxon rank-sum tests with multiple comparisons and partial least squares discriminant analysis. In addition, propensity score–adjusted multiple regression analyses were performed to evaluate group differences for known uremic toxins, branched-chain amino acids, and lactate-to-pyruvate ratio (a possible surrogate marker of mitochondrial dysfunction).

Results

Significant differences were observed in 39 metabolites, largely consistent with the high variable importance for prediction in partial least squares discriminant analysis. Among known uremic toxins, uridine and hypoxanthine levels were significantly higher in the conventional hemodialysis group than in the extended-hours hemodialysis group, whereas trimethylamine N -oxide levels were higher in the extended-hours hemodialysis group than in the conventional hemodialysis group. Patients on extended-hours hemodialysis had higher levels of all branched-chain amino acids and a lower lactate-to-pyruvate ratio than those on conventional hemodialysis (significant difference of −8.6 [95% confidence interval, −9.8 to −7.4]).

Conclusions

Extended-hours hemodialysis was associated with a more favorable plasma metabolic and amino acid profile; however, concentrations of most uremic toxins did not significantly differ from those of conventional hemodialysis.

Effect of glyphosate on renal function: A study integrating epidemiological and experimental evidence

Glyphosate, a widely used herbicide globally, has prompted concerns regarding its potential health impacts. This study aimed to explore the link between glyphosate exposure and renal function by combining NHANES, a zebrafish model, and metabolomics. A cross-sectional analysis of 2013-2014 NHANES data investigated the relationship between glyphosate exposure and renal function [albumin-to-creatinine ratio (ACR) and estimated glomerular filtration rate (eGFR)]. A subsequent zebrafish experiment was conducted to verify this association. Embryos (0.75 hpf-96 hpf) were exposed to different glyphosate concentrations dissolved in water (0, 30, 60, 90, 120 μg/mL). The underlying mechanism of the association between glyphosate and renal function was explored by the real-time quantitative polymerase chain reaction (RT-qPCR) and non-targeted metabolomics analysis [embryos (0.75 hpf-96 hpf) were exposed to 90 μg/mL glyphosate]. 1170 participants were enrolled in the NHANES study. The NHANES-based study found a positive association between glyphosate and ACR [0.07 (0.01, 0.13)]. Higher urinary glyphosate levels, particularly in the third quartile group, were negatively linked to eGFR [-3.72 (-5.98, -1.46)]. Further zebrafish experiments indicated that zebrafish exposed to 90 μg/mL glyphosate exhibited increased mortality rates, higher fluorescence intensity, up-regulated the havcr1 expression level, and cystic dilatation of the kidney. Non-targeted metabolomics analysis identified differential metabolites (e.g., 5-Hydroxyindole acetic acid) and pathways (e.g., ABC transporters) influenced by glyphosate. Glyphosate exposure is negatively associated with renal function in community adults. The damage to the kidneys caused by glyphosate may be mediated through the regulation of metabolic pathways, and the specific mechanisms require further experimental investigation.

Mapping the gut microbial structural variations in healthy aging within the Chinese population

Mapping gut microbial structural variants (SVs) during human aging may provide fundamental knowledge and mechanistic understanding of the gut microbiome's relationship with healthy aging. We characterize gut microbial SVs from 3,230 Chinese participants, identifying key SVs associated with aging, healthy aging, and age-related chronic diseases. Our findings reveal a pattern of copy number loss in aging-related SVs, with 35 core SVs consistently detected. Additionally, eight SVs distinguish healthy from unhealthy aging, regardless of age. Notably, a 3-kbp deletion SV of Bifidobacterium pseudocatenulatum, encoding plant polysaccharide degradation, is regulated by plant-based diet and contributes to healthy aging through bile acid metabolism. Our analysis also connects SVs to age-related diseases, such as chronic kidney disease, via genes in the methionine-homocysteine pathway. This study deepens our understanding of the gut microbiome's role in aging and could inform future efforts to enhance lifespan and healthspan.

Paper spray mass spectrometry combined with machine learning as a rapid diagnostic for chronic kidney disease

A new analytical method for chronic kidney disease (CKD) detection utilizing paper spray mass spectrometry (PS-MS) combined with machine learning is presented. The analytical protocol is rapid and simple, based on metabolic profile alterations in urine. Anonymized raw urine samples were deposited (10 μL each) onto pointed PS-MS sample strips. Without waiting for the sample to dry, 75 μL of acetonitrile and high voltage were applied to the strips, using high resolution mass spectrometry measurement (15 s per sample) with polarity switching to detect a wide range of metabolites. Random forest machine learning was used to classify the resulting data. The diagnostic performance for the potential diagnosis of CKD was evaluated for accuracy, sensitivity, and specificity, achieving results >96% for the training data and >91% for validation and test data sets. Metabolites selected by the classification model as up- or down-regulated in healthy or CKD samples were tentatively identified and in agreement with previously reported literature. The potential utilization of this approach to discriminate albuminuria categories (normo, micro, and macroalbuminuria) was also demonstrated. This study indicates that PS-MS combined with machine learning has the potential to be used as a rapid and simple diagnostic tool for CKD.

High-performance liquid chromatography-tandem mass spectrometry method for the analysis of N-oleoyl glycine and N-oleoyl alanine in brain and plasma

Interest has increased in the role of N-acyl amino acids in a variety of disease states and as potential pharmacotherapies. Recently, N-oleoyl glycine and N-oleoyl alanine have shown promise in reducing the rewarding effects of drugs of abuse and alleviating withdrawal signs in rodent models. Previously published methods for the quantitation of these analytes by high-performance liquid chromatography-tandem mass spectrometry (HPLC-MS/MS) in tissue were part of extensive lipidomic panels which may result in limited sensitivity and selectivity and also reported low recovery. Presented is a method for the extraction and HPLC-MS/MS analysis of N-oleoyl glycine and N-oleoyl alanine. The bias and precision of the assay were determined to be within ± 20%. The method was shown to be reliable and robust, with over 90% recovery for the low-level analytes. Increasing concentrations of N-oleoyl glycine and N-oleoyl alanine were quantitated in mouse brain and plasma following exogenous administration. This method was developed to serve to support studies investigating the pharmacokinetics and involvement of N-oleoyl glycine and N-oleoyl alanine in drug dependence and other diseases.

CKD Urine Metabolomics: Modern Concepts and Approaches

One of the primary challenges regarding chronic kidney disease (CKD) diagnosis is the absence of reliable methods to detect early-stage kidney damage. A metabolomic approach is expected to broaden the current diagnostic modalities by enabling timely detection and making the prognosis more accurate. Analysis performed on urine has several advantages, such as the ease of collection using noninvasive methods and its lower protein and lipid content compared with other bodily fluids. This review highlights current trends in applied analytical methods, major discoveries concerning pathways, and investigated populations in the context of urine metabolomic research for CKD over the past five years. Also, we are presenting approaches, instrument upgrades, and sample preparation modifications that have improved the analytical parameters of methods. The onset of CKD leads to alterations in metabolism that are apparent in the molecular composition of urine. Recent works highlight the prevalence of alterations in the metabolic pathways related to the tricarboxylic acid cycle and amino acids. Including diverse patient cohorts, using numerous analytical techniques with modifications and the appropriate annotation and explanation of the discovered biomarkers will help develop effective diagnostic models for different subtypes of renal injury with clinical applications.

Metabolomic Investigation of Blood and Urinary Amino Acids and Derivatives in Patients with Type 2 Diabetes Mellitus and Early Diabetic Kidney Disease

Diabetic kidney disease (DKD) is the leading cause of end-stage renal disease; however, few biomarkers of its early identification are available. The aim of the study was to assess new biomarkers in the early stages of DKD in type 2 diabetes mellitus (DM) patients. This cross-sectional pilot study performed an integrated metabolomic profiling of blood and urine in 90 patients with type 2 DM, classified into three subgroups according to albuminuria stage from P1 to P3 (30 normo-, 30 micro-, and 30 macroalbuminuric) and 20 healthy controls using high-performance liquid chromatography and mass spectrometry (UPLC-QTOF-ESI* MS). From a large cohort of separated and identified molecules, 33 and 39 amino acids and derivatives from serum and urine, respectively, were selected for statistical analysis using Metaboanalyst 5.0. online software. The multivariate and univariate algorithms confirmed the relevance of some amino acids and derivatives as biomarkers that are responsible for the discrimination between healthy controls and DKD patients. Serum molecules such as tiglylglycine, methoxytryptophan, serotonin sulfate, 5-hydroxy lysine, taurine, kynurenic acid, and tyrosine were found to be more significant in the discrimination between group C and subgroups P1-P2-P3. In urine, o-phosphothreonine, aspartic acid, 5-hydroxy lysine, uric acid, methoxytryptophan, were among the most relevant metabolites in the discrimination between group C and DKD group, as well between subgroups P1-P2-P3. The identification of these potential biomarkers may indicate their involvement in the early DKD and 2DM progression, reflecting kidney injury at specific sites along the nephron, even in the early stages of DKD.