Metabolic implications of amino acid metabolites in chronic kidney disease progression: a metabolomics analysis using OPLS-DA and MBRole2.0 database.

BACKGROUND: As chronic kidney disease (CKD) progresses, metabolites undergo diverse transformations. Nevertheless, the impact of these metabolic changes on the etiology, progression, and prognosis of CKD remains uncertain. Our objective is to conduct a metabolomics analysis to scrutinize metabolites and identify significant metabolic pathways implicated in CKD progression, thereby pinpointing potential therapeutic targets for CKD management. METHODS: We recruited 145 patients with CKD and determined their mGFR by measuring the plasma iohexol clearance, whereupon we partitioned them into four groups based on their mGFR values. Non-targeted metabolomics analysis was conducted using UPLC-MS/MS assays. Differential metabolites were identified via one-way ANOVA, PCA, PLS-DA, and OPLS-DA analyses employing the MetaboAnalyst 5.0 platform. Ultimately, we performed differential metabolite pathway enrichment analysis, using both the MetaboAnalyst 5.0 platform and the MBRole2.0 database. RESULTS: According to the findings of the MBRole2.0 and MetaboAnalyst 5.0 enrichment analysis, six amino acid metabolism pathways were discovered to have significant roles in the progression of CKD, with the glycine, serine, and threonine metabolism pathway being the most prominent. The latter enriched 14 differential metabolites, of which six decreased while two increased concomitantly with renal function deterioration. CONCLUSIONS: The metabolic analysis unveiled that glycine, serine, and threonine metabolism plays a pivotal role in the progression of CKD. Specifically, glycine was found to increase while serine decreased with the deterioration of CKD.

Novel Metabolites to Improve Glomerular Filtration Rate Estimation.

INTRODUCTION: The glomerular filtration rate (GFR) is crucial for chronic kidney disease (CKD) diagnosis and therapy. Various studies have sought to recognize ideal endogenous markers to improve the estimated GFR for clinical practice. To screen out potential novel metabolites related to GFR (mGFR) measurement in CKD patients from the Chinese population, we identified more biomarkers for improving GFR estimation. METHODS: Fifty-three CKD participants were recruited from the Third Affiliated Hospital of Sun Yat-sen University in 2020. For each participant, mGFR was evaluated by utilizing the plasma clearance of iohexol and collecting serum samples for untargeted metabolomics analyses by ultrahigh-performance liquid chromatography-tandem mass spectroscopy. All participants were divided into four groups according to mGFR. The metabolite peak area data were uploaded to MetaboAnalyst 5.0 for one-way analysis of variance, principal component analysis, and partial least squares-discriminant analysis and confirmed the metabolites whose levels increased or decreased with mGFR and variable importance in projection (VIP) values >1. Metabolites were ranked by correlation with the original values of mGFR, and metabolites with a correlation coefficient >0.8 and VIP >2 were identified. RESULTS: We screened out 198 metabolites that increased or decreased with mGFR decline. After ranking by correlation with mGFR, the top 50 metabolites were confirmed. Further studies confirmed the 10 most highly correlated metabolites. CONCLUSION: We screened out the metabolites that increased or decreased with mGFR decline in CKD patients from the Chinese population, and 10 of them were highly correlated. They are potential novel metabolites to improve GFR estimation.

A Metabolomics study of metabolites associated with the glomerular filtration rate.

BACKGROUND: Chronic kidney disease (CKD) is a global public health issue. The diagnosis of CKD would be considerably enhanced by discovering novel biomarkers used to determine the glomerular filtration rate (GFR). Small molecule metabolites related to kidney filtration function that might be utilized as biomarkers to measure GFR more accurately could be found via a metabolomics analysis of blood samples taken from individuals with varied glomerular filtration rates. METHODS: An untargeted metabolomics study of 145 plasma samples was performed using ultrahigh-performance liquid chromatography tandem mass spectrometry (UPLC-MS/MS). The 145 samples were divided into four groups based on the patient's measured glomerular filtration rates (mGFRs) determined by the iohexol plasma clearance rate. The data were analyzed using random forest analyses and six other unique statistical analyses. Principal component analysis (PCA) was conducted using R software. RESULTS: A large number of metabolites involved in various metabolic pathways changed significantly between groups with different GFRs. These included metabolites involved in tryptophan or pyrimidine metabolism. The top 30 metabolites that best distinguished between the four groups in a random forest plot analysis included 13 amino acids, 9 nucleotides, and 3 carbohydrates. A panel of metabolites (including hydroxyaparagine, pseudouridine, C-glycosyltryptophan, erythronate, N-acetylalanine, and 7-methylguanidine) for estimating GFR was selected for future testing in targeted analyses by combining the candidate lists with the six other statistical analyses. Both hydroxyasparagine and N,N-dimethyl-proline-proline are unique biomarkers shown to be inversely associated with kidney function that have not been reported previously. In contrast, 1,5-anhydroglucitol (1,5-AG) decreases with impaired renal function. CONCLUSIONS: This global untargeted metabolomics study of plasma samples from patients with different degrees of renal function identified potential metabolite biomarkers related to kidney filtration. These novel potential metabolites provide more insight into the underlying pathophysiologic processes that may contribute to the progression of CKD, lead to improvements in the estimation of GFR and provide potential therapeutic targets to improve kidney function.

Metabolic profiling identifies the significance of caffeine metabolism in CKD.

Background: With the development of chronic kidney disease (CKD), there are various changes in metabolites. However, the effect of these metabolites on the etiology, progression and prognosis of CKD remains unclear. Objective: We aimed to identify significant metabolic pathways in CKD progression by screening metabolites through metabolic profiling, thus identifying potential targets for CKD treatment. Methods: Clinical data were collected from 145 CKD participants. GFR (mGFR) was measured by the iohexol method and participants were divided into four groups according to their mGFR. Untargeted metabolomics analysis was performed via UPLC-MS/MSUPLC-MSMS/MS assays. Metabolomic data were analyzed by MetaboAnalyst 5.0, one-way ANOVA, principal component analysis (PCA), and partial least squares discriminant analysis (PLS-DA) to identify differential metabolites for further analysis. The open database sources of MBRole2.0, including KEGG and HMDB, were used to identify significant metabolic pathways in CKD progression. Results: Four metabolic pathways were classified as important in CKD progression, among which the most significant was caffeine metabolism. A total of 12 differential metabolites were enriched in caffeine metabolism, four of which decreased with the deterioration of the CKD stage, and two of which increased with the deterioration of the CKD stage. Of the four decreased metabolites, the most important was caffeine. Conclusion: Caffeine metabolism appears to be the most important pathway in the progression of CKD as identified by metabolic profiling. Caffeine is the most important metabolite that decreases with the deterioration of the CKD stage.

Identification of Metabolite Markers Associated with Kidney Function.

Background: Chronic kidney disease (CKD) is a global public health problem. Identifying new biomarkers that can be used to calculate the glomerular filtration rate (GFR) would greatly improve the diagnosis and understanding of CKD at the molecular level. A metabolomics study of blood samples derived from patients with widely divergent glomerular filtration rates could potentially discover small molecule metabolites associated with varying kidney function. Methods: Using ultrahigh-performance liquid chromatography-tandem mass spectrometry (UPLC-MS/MS), serum was analyzed from 53 participants with a spectrum of measured GFR (by iohexol plasma clearance) ranging from normal to severe renal insufficiency. An untargeted metabolomics assay (N » 214) was conducted at the Calibra-Metabolon Joint Laboratory. Results: From a large number of metabolomics-derived metabolites, the top 30 metabolites correlated to increasing renal insufficiency according to mGFR were selected by the random forest method. Significant differences in metabolite profiles with increasing stages of CKD were observed. Combining candidate lists from six other unique statistical analyses, six novel, potential metabolites that were reproducibly strongly associated with mGFR were selected, including erythronate, gulonate, C-glycosyltryptophan, N-acetylserine, N6-carbamoylthreonyladenosine, and pseudouridine. In addition, hydroxyasparagine were strongly associated with mGFR and CKD, which were unique to this study. Conclusions: Global metabolite profiling of serum yielded potentially valuable biomarkers of different stages of CKD. Additionally, these potential biomarkers might provide insight into the underlying pathophysiologic processes that contribute to the progression of CKD as well as improve GFR estimation.

Clinical assessment of nutritional status using the modified quantified subjective global assessment and anthropometric and biochemical parameters in patients undergoing hemodialysis in Macao.

OBJECTIVE: Malnutrition is widespread among patients undergoing hemodialysis and is linked to high morbidity and mortality rates. We evaluated the nutritional status and malnutrition markers in patients undergoing hemodialysis in Macao. METHODS: We performed a cross-sectional analysis of 360 patients in a hemodialysis center. The modified quantitative subjective global assessment (MQSGA), anthropometric indices and related biochemical test data were used to evaluate nutritional status. RESULTS: The sample's mean age was 63.47 ± 13.95 years. There were 210 well-nourished (58.3%), 139 mild-to-moderately malnourished (38.6%) and 11 severely malnourished (3.1%) patients. Older patients had a higher incidence of severe malnutrition, but there were no significant differences between diabetic and non-diabetic patients. Mid-arm circumference (MAC); mid-arm muscle circumference; body mass index; triceps skin fold thickness; serum albumin, creatinine and urea; and hemoglobin were all valid for assessing nutritional status. MAC and the serum albumin and creatinine concentrations significantly negatively correlated with MQSGA. CONCLUSIONS: Malnutrition is commonplace in patients undergoing hemodialysis in Macao, but their nutritional status is not affected by diabetes. Serum creatinine, serum albumin and MAC, and especially pre-dialysis creatinine concentration, represent effective, readily available, and easily remembered screening measures of nutritional status for patients undergoing maintenance dialysis.