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Primary glomerular disease (PGD) is an idiopathic cause of renal glomerular lesions that is characterized by proteinuria or hematuria and is the leading cause of chronic kidney disease (CKD). The identification of circulating biomarkers for the diagnosis of PGD requires a thorough understanding of the metabolic defects involved. In this study, ultra-high performance liquid chromatography-tandem mass spectrometry was performed to characterize the amino acid (AA) profiles of patients with pathologically diagnosed PGD, including minimal change disease (MCD), focal segmental glomerular sclerosis (FSGS), membranous nephropathy, and immunoglobulin A nephropathy. The plasma concentrations of asparagine and ornithine were low, and that of aspartic acid was high, in patients with all the pathologic types of PGD, compared to healthy controls. Two distinct diagnostic models were generated using the differential plasma AA profiles using logistic regression and receiver operating characteristic analyses, with areas under the curves of 1.000 and accuracies up to 100.0% in patients with MCD and FSGS. In conclusion, the progression of PGD is associated with alterations in AA profiles, The present findings provide a theoretical basis for the use of AAs as a non-invasive, real-time, rapid, and simple biomarker for the diagnosis of various pathologic types of PGD., (© 2024. The Author(s).)

Background: The herbicide paraquat (1, 1'-dimethyl-4, 4'-bipyridylium dichloride; PQ) is a poison well-known to cause delayed mortality due to acute kidney injuries (AKI). This study examines the changes in serum amino acids (AAs) metabolite profiles as surrogate markers of renal cell metabolism and function after paraquat poisoning., Methods: To identify the metabolic profiling of free serum AAs and its metabolites, serum from 40 paraquat-poisoned patients with or without AKI is collected. LC-MS/GC-MS is performed to analyze AA molecules. A Cox proportional hazard model was used to assess for incidence of AKI. Receiver operating characteristic (ROC) curve is applied to evaluate AKI occurrence and prognosis., Results: A total of 102 serum AAs and its metabolites were identified. Compared with non-AKI patients, 37 varied significantly in AKI patients. The univariate Cox proportional hazard model analysis revealed that the estimated PQ amount, plasma PQ concentration, urine PQ concentration, APACHE, SOFA scores and 16 amino acids correlated with the incidence of AKI. Further analyses revealed that 3-methylglutarylcarnitine, 1-methylimidazoleacetate, and urea showed higher cumulative hazard ratios for the occurrence of AKI during follow-up (P < 0.05). The area under the curve (AUC) of 3-methylglutarylcarnitine, 1-methylimidazoleacetate and urea were 0.917, 0.857, 0.872, respectively., Conclusion: 3-methylglutarylcarnitine, 1-methylimidazoleacetate and urea were associated with AKI in patients with paraquat intoxication.

BACKGROUND Paraquat is a major cause of fatal poisoning after ingestion in many parts of Asia and the Pacific nations. However, optimal prognostic indicators to evaluate patient mortality have not been unequivocally established. Following acute paraquat poisoning, a number of amino acids (AA), are abnormally expressed in metabolic pathways. However, the alterations in AA metabolite levels after paraquat poisoning remain unknown in humans. MATERIAL AND METHODS In the present study, 40 patients were enrolled, of whom 16 survived and 24 died. A metabolomics approach was used to assess changes in AA metabolites in plasma and its potential prognostic value following paraquat poisoning. Mass spectrometry (MS) based on metabolite identification was conducted. RESULTS Twenty-five AA levels in plasma were abnormally expressed in non-survivor patients. Among them, creatinine, indolelactate, and 3-(4-hydroxyphenyl)lactate were found to be highly correlated with paraquat death prediction. It was noted that the intensity levels of these 3 AA metabolites in the non-survivor group were substantially higher than in the survivor group. Furthermore, we examined receiver operating characteristic (ROC) curves for clinical validation. ROC results showed that 3-(4-hydroxyphenyl)lactate had the highest AUC of 0.84, while indolelactate and creatinine had AUCs of 0.75 and 0.83, respectively, suggesting that they can be used to predict the clinical outcome (although this methodology is expensive to implement). CONCLUSIONS Metabolic profiling of AA levels could be a reliable tool to identify effective indicators for the early high precision prognosis of paraquat poisoning.