Chronic kidney disease (CKD) affects approximately 12% of the global population, posing a significant health threat. Inflammation plays a crucial role in the uremic phenotype of non-dialysis-dependent (NDD) stage 5 CKD, contributing to elevated cardiovascular and overall mortality in affected individuals. This study aimed to explore novel metabolic pathways in this population using semi-targeted metabolomics, which allowed us to quantify numerous metabolites with known identities before data acquisition through an in-house polar compound library. In a prospective observational design with 50 patients, blood samples collected before the initial hemodialysis session underwent liquid chromatography and high-resolution mass spectrometer analysis. Univariate (Mann–Whitney test) and multivariate (logistic regression with LASSO regularization) methods identified metabolomic variables associated with inflammation. Notably, adenosine-5′-phosphosulfate (APS), dimethylglycine, pyruvate, lactate, and 2-ketobutyric acid exhibited significant differences in the presence of inflammation. Cholic acid, homogentisic acid, and 2-phenylpropionic acid displayed opposing patterns. Multivariate analysis indicated increased inflammation risk with certain metabolites (N-Butyrylglycine, dimethylglycine, 2-Oxoisopentanoic acid, and pyruvate), while others (homogentisic acid, 2-Phenylpropionic acid, and 2-Methylglutaric acid) suggested decreased probability. These findings unveil potential inflammation-associated biomarkers related to defective mitochondrial fatty acid beta oxidation and branched-chain amino acid breakdown in NDD stage 5 CKD, shedding light on cellular energy production and offering insights for further clinical validation.

Worldwide, 3.9 million individuals rely on kidney replacement therapy. They experience heightened susceptibility to cardiovascular diseases and mortality, alongside an increased risk of infections and malignancies, with inflammation being key to explaining this intensified risk. This study utilized semi-targeted metabolomics to explore novel metabolic pathways related to inflammation in this population. We collected pre- and post-session blood samples of patients who had already undergone one year of chronic hemodialysis and used liquid chromatography and high-resolution mass spectrometry to perform a metabolomic analysis. Afterwards, we employed both univariate (Mann–Whitney test) and multivariate (logistic regression with LASSO regularization) to identify metabolites associated with inflammation. In the univariate analysis, indole-3-acetaldehyde, 2-ketobutyric acid, and urocanic acid showed statistically significant decreases in median concentrations in the presence of inflammation. In the multivariate analysis, metabolites positively associated with inflammation included allantoin, taurodeoxycholic acid, norepinephrine, pyroglutamic acid, and L-hydroorotic acid. Conversely, metabolites showing negative associations with inflammation included benzoic acid, indole-3-acetaldehyde, methionine, citrulline, alphaketoglutarate, n-acetyl-ornithine, and 3-4-dihydroxibenzeneacetic acid. Non-inflamed patients exhibit preserved autophagy and reduced mitochondrial dysfunction. Understanding inflammation in this group hinges on the metabolism of arginine and the urea cycle. Additionally, the microbiota, particularly uricase-producing bacteria and those metabolizing tryptophan, play critical roles.

Chronic kidney disease (CKD) affects approximately 12% of the global population posing a significant health threat. Inflammation plays a crucial role in the uremic phenotype of CKD contributing to elevated cardiovascular and overall mortality in affected individuals. This study aimed to explore novel metabolic pathways in non-dialysis dependent stage 5 CKD patients using semitargeted metabolomics. In a prospective observational design involving 50 patients, blood samples collected before the initial hemodialysis session underwent liquid chromatography and high resolution mass spectrometer analysis. Univariate (Mann-Whitney test) and multivariate (logistic regression with LASSO regularization) methods identified metabolomic variables associated with inflammation. Notably, adenosine-5’-phosphosulfate (APS), dimethylglycine, pyruvate, lactate, and 2-ketobutyric acid exhibited significant differences in the presence of inflammation. Cholic acid, homogentisic acid, and 2-phenylpropionic acid displayed opposing patterns. Multivariate analysis indicated increased inflammation risk with certain metabolites (N-Butyrylglycine, dimethylglycine, 2-Oxoisopentanoic acid, and pyruvate), while others (homogentisic acid, 2-Phenylpropionic acid, and 2-Methylglutaric acid) suggested decreased probability. These findings unveil potential inflammation-associated biomarkers related to defective mitochondrial fatty acid beta oxidation and branched-chain amino acid breakdown in CKD, shedding light on cellular energy production and offering insights for further clinical validation.

Background: Kidney transplantation is the therapy of choice for patients with advanced chronic kidney disease; however, predicting graft outcomes remains a significant challenge. Early identification of reliable biomarkers could enhance post-transplant management and improve long-term outcomes. This study aimed to identify metabolomic biomarkers within the first week after kidney transplantation that predict renal function at six months. Methods: We conducted a prospective study involving 50 adult patients who received deceased donor kidney transplants. Plasma samples collected one week after transplant were analyzed using liquid chromatography–mass spectrometry in a semi-targeted metabolomic approach. A Partial Least Squares-Discriminant Analysis (PLS-DA) model identified metabolites associated with serum creatinine > 1.5 mg/dL at six months. Metabolites were selected based on a Variable Importance in Projection (VIP) score > 1.5, which was used to optimize model performance. Results: The PLS-DA model demonstrated strong predictive performance with an area under the curve (AUC) of 0.958. The metabolites negatively associated with serum creatinine > 1.5 mg/dL were 3-methylindole, guaiacol, histidine, 3-indolepropionic acid, and α-lipoic acid. Conversely, the metabolites positively associated with worse kidney graft outcomes included homocarnosine, 5-methylcytosine, xanthosine, choline, phenylalanine, kynurenic acid, and L-kynurenine. Conclusions: Early metabolomic profiling after transplantation shows promise in predicting renal function. Identifying metabolites with antioxidant and anti-inflammatory properties, as well as those that are harmful and could be targeted therapeutically, underscores their potential clinical significance. The link between several metabolites and the tryptophan pathway suggests that further specific evaluation of this pathway is warranted. These biomarkers can enhance patient management and graft survival.

Background: Chronic kidney disease (CKD) is a common complication of non-kidney solid organ transplant (NKSOT). Identifying predisposing factors is crucial for an early approach and correct referral to nephrology. Methods: This is a single-center retrospective observational study of a cohort of CKD patients under follow-up in Nephrology Department between 2010 to 2020. Statistical analysis was performed between all the risk factors and four dependent variables: end-stage renal disease (ESKD), increased serum creatinine ≥50%, renal replacement therapy (RRT), and death in pre-transplant, peri-transplant and post-transplant period. Results: 74 patients were studied (7 heart transplant, 34 liver transplants, and 33 lung transplant). Patients who were not followed-up by nephrology in the pre-transplant (p<0.027), peri-transplant (p<0.046), and the longest time until outpatient clinic follow-up (HR 1,032) were associated with a higher risk of creatinine increase ≥50%. Receiving a lung transplant conferred higher risk than liver or hear transplant for developing a creatinine increase ≥50% and ESKD. Peri-transplant mechanical ventilation, peri-transplant and post-transplant anticalcineurin overdose, nephrotoxic and the number of hospital admissions were significantly associated to present a creatinine increase ≥50% and develop ESKD. Conclusion: An early follow-up by the nephrology department was associated with a decrease in the worsening of renal function.

Background: Kidney transplantation is the therapy of choice for patients with advanced chronic kidney disease; however, predicting graft outcomes remains a significant challenge. Early identification of reliable biomarkers could enhance post-transplant management and improve long-term outcomes. This study aimed to identify metabolomic biomarkers within the first week post-kidney transplantation that predict renal function at six months. Methods: We conducted a prospective study involving 50 adult patients who received deceased donor kidney transplants. Plasma samples collected one-week post-transplant were analyzed using liquid chromatography-mass spectrometry in a semi-targeted metabolomic approach. A Partial Least Squares-Discriminant Analysis (PLS-DA) model identified metabolites associated with serum creatinine >1.5 mg/dL at six months. Metabolites were selected based on a Variable Importance in Projection (VIP) score >1.5, which was used to optimize model performance. Results: The PLS-DA model demonstrated strong predictive performance with an area under the curve (AUC) of 0.958. The metabolites negatively associated with serum creatinine >1.5 mg/dL were 3-methylindole, guaiacol, histidine, 3-indolepropionic acid, and α-lipoic acid. Conversely, metabolites positively associated with worse kidney graft outcomes included homocarnosine, 5-methylcytosine, xanthosine, choline, phenylalanine, kynurenic acid, and L-kynurenine. Conclusions: Early metabolomic profiling post-transplantation shows promise in predicting renal function. Identifying metabolites with antioxidant and anti-inflammatory properties, as well as those that are harmful and could be targeted therapeutically, underscores their potential clinical significance. The link between several metabolites and the tryptophan pathway suggests that further specific evaluation of this pathway is warranted. These biomarkers can enhance patient management and graft survival.