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.

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 ¼ 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.

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 (eGFR) 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 (UPLC–MS/MS). All participants were divided into four groups according to mGFR. The metabolite peak area data were uploaded to MetaboAnalyst5.0 for one-way ANOVA, principal component analysis (PCA) and partial least squares-discriminant analysis (PLS-DA) 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.

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.

Background: To identify the most important metabolic pathways by screening metabolites that improve the progression of CKD stage through metabolic profiling and thus to identify relevant targets that may improve renal function.Methods: Clinical data were collected in 145 chronic kidney disease (CKD) participants, and their measurements of GFR (mGFR) were measured by the iohexol method. Untargeted metabolomics analysis was performed by UPLC-MS /MSUPLC–MSMS/MS assays. Procession, extraction and peak identification of the raw mass spectrometry data were carried out, and metabolites were identified. All participants were divided into four groups according to mGFR. Metabolomic data were analyzed by MetaboAnalyst 5.0, and one-way ANOVA, PCA, and PLS-DA were carried out 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.Results: Four metabolic pathways were classified as important in the progression of CKD, and the most significant pathway was caffeine metabolism. A total of 12 metabolites were enriched in caffeine metabolism, four of which decreased with the deterioration of CKD stage, and two of them increased with the deterioration of CKD stage. Of the four decreased metabolites, the most important was caffeine, which may improve renal function.Conclusions: Caffeine metabolism appears to be the most important pathway in the progression of CKD. Caffeine may help to improve the progression of CKD.