Urine metabolomic analysis to detect metabolites associated with the development of contrast induced nephropathy

NMR-based metabolomic analysis of plasma from elderly patients with CVD before and after using contrast media

Contrast-induced acute kidney injury (CI-AKI) is a growingly common kidney problem caused by medical procedures involving contrast media (CM), especially in older patients with existing health issues. It is crucial to pinpoint potential biomarkers for the early detection of CI-AKI. Previously, we observed that iodixanol affects glucose, choline, and glutathione metabolism in endothelial cells under laboratory conditions. In this study, we used 1H NMR-based metabolomics to examine the metabolic changes in the blood plasma of elderly patients with cardiovascular disease (CVD) before and after receiving iodixanol. We identified altered metabolites in plasma 24 and 48 h after iodixanol injection compared to levels before injection. Notably, metabolites such as glucose, unsaturated fatty acids (UFA), low-density lipoprotein (LDL)/very low-density lipoprotein (VLDL), pyruvate, choline, and glycine showed potential as biomarkers at 24 h post-injection compared to levels before injection. Similarly, glucose, pyruvate, lactate, choline, and glycine in plasma could serve as potential biomarkers at 48 h post-injection. Iodixanol notably affected pathways related to glycolysis, fatty acid breakdown, and amino acid metabolism according to our metabolic pathway analysis. The altered levels of specific metabolites in plasma could be indicative of CM-induced kidney injury. Overall, this research aids in understanding the physiological mechanisms involved and in identifying early biomarkers and prevention strategies for CI-AKI.

The Effects of Single and Combined Stressors on Daphnids-Enzyme Markers of Physiology and Metabolomics Validate the Impact of Pollution

The continuous global increase in population and consumption of resources due to human activities has had a significant impact on the environment. Therefore, assessment of environmental exposure to toxic chemicals as well as their impact on biological systems is of significant importance. Freshwater systems are currently under threat and monitored; however, current methods for pollution assessment can neither provide mechanistic insight nor predict adverse effects from complex pollution. Using daphnids as a bioindicator, we assessed the impact in acute exposures of eight individual chemicals and specifically two metals, four pharmaceuticals, a pesticide and a stimulant, and their composite mixture combining phenotypic, biochemical and metabolic markers of physiology. Toxicity levels were in the same order of magnitude and significantly enhanced in the composite mixture. Results from individual chemicals showed distinct biochemical responses for key enzyme activities such as phosphatases, lipase, peptidase, β-galactosidase and glutathione-S-transferase. Following this, a more realistic mixture scenario was assessed with the aforementioned enzyme markers and a metabolomic approach. A clear dose-dependent effect for the composite mixture was validated with enzyme markers of physiology, and the metabolomic analysis verified the effects observed, thus providing a sensitive metrics in metabolite perturbations. Our study highlights that sensitive enzyme markers can be used in advance on the design of metabolic and holistic assays to guide the selection of chemicals and the trajectory of the study, while providing mechanistic insight. In the future this could prove to become a useful tool for understanding and predicting freshwater pollution.

Molecular signature of renal cell carcinoma by means of a multiplatform metabolomics analysis

Renal cell carcinoma (RCC) is a disease with no specific diagnostic method or treatment. Thus, the evaluation of novel diagnostic tools or treatment possibilities is essential. In this study, a multiplatform untargeted metabolomics analysis of urine was applied to search for a metabolic pattern specific for RCC, which could enable comprehensive assessment of its biochemical background. Thirty patients with diagnosed RCC and 29 healthy volunteers were involved in the first stage of the study. Initially, the utility of the application of the selected approach was checked for RCC with no differentiation for cancer subtypes. In the second stage, this approach was used to study clear cell renal cell carcinoma (ccRCC) in 38 ccRCC patients and 38 healthy volunteers. Three complementary analytical platforms were used: reversed-phase liquid chromatography coupled with time-of-flight mass spectrometry (RP-HPLC-TOF/MS), capillary electrophoresis coupled with time-of-flight mass spectrometry (CE-TOF/MS), and gas chromatography triple quadrupole mass spectrometry (GC-QqQ/MS). As a result of urine sample analyses, two panels of metabolites specific for RCC and ccRCC were selected. Disruptions in amino acid, lipid, purine, and pyrimidine metabolism, the TCA cycle and energetic processes were observed. The most interesting differences were observed for modified nucleosides. This is the first time that the levels of these compounds were found to be changed in RCC and ccRCC patients, providing a framework for further studies. Moreover, the application of the CE-MS technique enabled the determination of statistically significant changes in symmetric dimethylarginine (SDMA) in RCC.

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Multiplatform untargeted metabolomics analysis was applied for selection of tentative diagnostic indicators of RCC.

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LC-MS, GC-MS and CE-MS techniques were utilized for analysis of urine samples collected from RCC and ccRCC patients.

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Alterations in amino acid, purine, and pyrimidine metabolism, as well as TCA cycle and energy processes, were observed.

Urine and serum NMR-based metabolomics in pre-procedural prediction of contrast-induced nephropathy

Contrast induced nephropathy (CIN) has been reported to be the third foremost cause of acute renal failure. Metabolomics is a robust technique that has been used to identify potential biomarkers for the prediction of renal damage. We aim to analyze the serum and urine metabolites changes, before and after using contrast for coronary angiography, to determine if metabolomics can predict early development of CIN. 66 patients undergoing elective coronary angiography were eligible for enrollment. Urine and serum samples were collected prior to administration of CM and 72 h post procedure and analyzed by nuclear magnetic resonance. The significant differential metabolites between patients who develop CIN and patients who have stable renal function after angiography were identified using U test and receiver operating characteristic analysis was performed for each metabolite candidate. Potential susceptible pathways to cytotoxic effect of CM were investigated by pathway analysis. A predictive panel composed of six urinary metabolites had the best area under the curve. Glutamic acid, uridine diphosphate, glutamine and tyrosine were the most important serum predictive biomarkers. Several pathways related to amino acid and nicotinamide metabolism were suggested as impaired pathways in CIN prone patients. Changes exist in urine and serum metabolomics patterns in patients who do and do not develop CIN after coronary angiography hence metabolites may be potential predictive identifiers of CIN.

Elastic net regularized regression for time-series analysis of plasma metabolome stability under sub-optimal freezing condition

In this paper, the stability of the plasma metabolome at −20 °C for up to 30 days was evaluated using liquid chromatography-high resolution mass spectrometric metabolomics analysis. To follow the time-series deterioration of the plasma metabolome, the use of an elastic net regularized regression model for the prediction of storage time at −20 °C based on the plasma metabolomic profile, and the selection and ranking of metabolites with high temporal changes was demonstrated using the glmnet package in R. Out of 1229 (positive mode) and 1483 (negative mode) metabolite features, the elastic net model extracted 32 metabolites of interest in both positive and negative modes. L-gamma-glutamyl-L-(iso)leucine (tentative identification) was found to have the highest time-dependent change and significantly increased proportionally to the storage time of plasma at −20 °C (R² = 0.6378 [positive mode], R² = 0.7893 [negative mode], p-value < 0.00001). Based on the temporal profiles of the extracted metabolites by the model, results show only minimal deterioration of the plasma metabolome at −20 °C up to 1 month. However, majority of the changes appeared at around 12–15 days of storage. This allows scientists to better plan logistics and storage strategies for samples obtained from low-resource settings, where −80 °C storage is not guaranteed.