Explore the vast range of titles available.

IntroductionUrine is an ideal matrix for metabolomics investigation due to its non-invasive nature of collection and its rich metabolite content. Despite the advancements in mass spectrometry and 1H-NMR platforms in urine metabolomics, the statistical analysis of the generated data is challenged with the need to adjust for the hydration status of the person. Normalization to creatinine or osmolality values are the most adopted strategies, however, each technique has its challenges that can hinder its wider application. We have been developing targeted urine metabolomic methods to differentiate two important respiratory diseases, namely asthma and chronic obstructive pulmonary disease (COPD).ObjectiveTo assess whether the statistical model of separation of diseases using targeted metabolomic data would be improved by normalization to osmolality instead of creatinine.MethodsThe concentration of 32 metabolites was previously measured by two liquid chromatography-tandem mass spectrometry methods in 51 human urine samples with either asthma (n = 25) or COPD (n = 26). The data was normalized to creatinine or osmolality. Statistical analysis of the normalized values in each disease was performed using partial least square discriminant analysis (PLS-DA). Models of separation of diseases were compared.ResultsWe found that normalization to creatinine or osmolality did not significantly change the PLS-DA models of separation (R2Q2 = 0.919, 0.705 vs R2Q2 = 0.929, 0.671, respectively). The metabolites of importance in the models remained similar for both normalization methods.ConclusionOur findings suggest that targeted urine metabolomic data can be normalized for hydration using creatinine or osmolality with no significant impact on the diagnostic accuracy of the model.

Assays of urine biomarkers often use urine creatinine to account for urinary dilution, even though creatinine levels are influenced by underlying physiology and muscle catabolism. Urine osmolality—a measure of dissolved particles including ions, glucose, and urea—is thought to provide a more robust marker of urinary dilution but is seldom measured. The relationship between urine osmolality and creatinine is not well understood. We calculated correlation coefficients between urine creatinine and osmolality among 1375 members of a subcohort of the Danish Diet, Cancer, and Health Cohort, and within different subgroups. We used linear regression to relate creatinine with osmolality, and a lasso selection procedure to identify other variables that explain remaining variability in osmolality. Spearman correlation between urine creatinine and osmolality was strong overall (ρ = 0.90; 95% CI: 0.89–0.91) and in most subgroups. Linear regression showed that urine creatinine explained 60% of the variability in urine osmolality, with another 9% explained by urine thallium (Tl), cesium (Cs), and strontium (Sr). Urinary creatinine and osmolality are strongly correlated, although urine Tl, Cs, and Sr might help supplement urine creatinine for purposes of urine dilution adjustment when osmolality is not available.

Creatinine is an important biomarker for renal function diagnosis and normalizing variations in urinary drug/metabolites concentration. Quantification of creatinine in biological fluids such as urine and plasma is important for clinical diagnosis as well as in biomonitoring programs and urinary metabolomics/metabonomics research. Current methods for creatinine determination either are nonselective or involve the use of expensive mass spectrometers. In this paper, a novel reversed-phase high-performance liquid chromatographic (HPLC) method for the determination of creatinine of high hydrophilicity by pre-column derivatization with ethyl chloroformate is presented. N -Ethyloxycarbonylation of creatinine significantly enhanced the hydrophobicity of creatinine, facilitating its chromatographic retention as well as quantification by HPLC. Factors governing the derivatization reaction were studied and optimized. The developed method was validated and applied for the determination of creatinine in rat urine samples. Comparative studies with isotope-dilution mass spectrometric method revealed that the two methods do not yield systematic differences in creatinine concentrations, indicating the HPLC method is suitable for the determination of creatinine in urine samples. Figure ᅟ

Night shift work can have a serious impact on health. Here, we assess whether and how night shift work influences the metabolite profiles, specifically with respect to different chronotype classes. We have recruited 100 women including 68 nurses working both, day shift and night shifts for up to 5 consecutive days and collected 3640 spontaneous urine samples. About 424 waking-up urine samples were measured using a targeted metabolomics approach. To account for urine dilution, we applied three methods to normalize the metabolite values: creatinine-, osmolality- and regression-based normalization. Based on linear mixed effect models, we found 31 metabolites significantly (false discovery rate <0.05) affected in nurses working in night shifts. One metabolite, acylcarnitine C10:2, was consistently identified with all three normalization methods. We further observed 11 and 4 metabolites significantly associated with night shift in early and late chronotype classes, respectively. Increased levels of medium- and long chain acylcarnitines indicate a strong impairment of the fatty acid oxidation. Our results show that night shift work influences acylcarnitines and BCAAs, particularly in nurses in the early chronotype class. Women with intermediate and late chronotypes appear to be less affected by night shift work.

Anti‐programmed death‐ligand 1 (PD‐L1) Ab‐based therapies have demonstrated potential for treating metastatic urothelial cancer with high PD‐L1 expression. Urinary exosomes are promising biomarkers for liquid biopsy, but urine's high variability requires normalization for accurate analysis. This study proposes using the PD‐L1/Alix ratio to normalize exosomal PD‐L1 signal intensity with Alix, an internal exosomal protein less susceptible to heterogeneity concerns than surface protein markers. Extracellular vesicles were isolated using ExoDisc and characterized using various methods, including ExoView to analyze tetraspanins, PD‐L1, and Alix on individual exosomes. On‐disc ELISA was used to evaluate PD‐L1 and Alix‐normalized PD‐L1 in 15 urothelial cancer patients during the initial treatment cycle with Tecentriq. Results showed that Alix signal range was relatively uniform, whereas tetraspanin marker intensity varied for individual exosome particles. On‐disc ELISA was more reliable for detecting exosomal PD‐L1 expression than standard plate ELISA‐based measurement. Using exosomal Alix expression for normalization is a more reliable approach than conventional methods for monitoring patient status. Overall, the study provides a practical and reliable method for detecting exosomal PD‐L1 in urine samples from patients with urothelial cancer.

Urinary microRNAs (miRNAs) are promising noninvasive biomarkers for cancer detection, but their clinical utility is reduced by inconsistent normalization strategies, reducing reproducibility and comparability across studies. In this study, we assessed the stability of miR-20a as an endogenous normalizer for urinary miRNA profiling in clear cell renal cell carcinoma (ccRCC) while standardizing the pre-analytical phase using a urine stabilizing solution. Ninety-nine urine samples were analyzed: 47 from healthy individuals, 30 from ccRCC patients pre-surgery, and 22 post-operative patients. Six candidate miRNAs—miR-20a, miR-15b, miR-16, miR-15a, miR-210-3p, and miR-let-7b—were quantified via RT-qPCR. Stability analysis with RefFinder, integrating multiple algorithms (geNorm, normFinder, BestKeeper, and ΔCt methods), identified miR-20a as the most stable among the six candidates. Raw Ct values of miR-20a were normally distributed (Shapiro–Wilk test, p > 0.05), with no significant intergroup differences (one-way ANOVA, F(2.96) = 2.324, p = 0.103) and minimal intragroup variability (CV% 4.98–6.38). MiR-20a expression remained stable across different tumor staging, grading, and urine storage durations. These findings confirm miR-20a as a robust endogenous normalizer for urinary miRNA analyses and support the feasibility of developing reproducible urinary liquid biopsy workflows for ccRCC, even in settings where immediate sample processing is not feasible.

Urine is a popular biofluid for metabolomics studies due to its simple, non-invasive collection and its availability in large quantities, permitting frequent sampling, replicate analyses, and sample banking. The biggest disadvantage with using urine is that it exhibits significant variability in concentration and composition within an individual over relatively short periods of time (arising from various external factors and internal processes regulating the body’s water and solute content). In treating the data from urinary metabolomics studies, one must account for the natural variability of urine concentrations to avoid erroneous data interpretation. Amongst various proposed approaches to account for broadly varying urine sample concentrations, normalization to creatinine has been widely accepted and is most commonly used. MS total useful signal (MSTUS) is another normalization method that has been recently reported for mass spectrometry (MS)-based metabolomics studies. Herein, we explored total useful peak area (TUPA), a modification of MSTUS that is applicable to GC×GC-TOFMS (and data from other separations platforms), for sample normalization in urinary metabolomics studies. Performance of TUPA was compared to the two most common normalization approaches, creatinine adjustment and Total Peak Area (TPA) normalization. Each normalized dataset was evaluated using Principal Component Analysis (PCA). The results showed that TUPA outperformed alternative normalization methods to overcome urine concentration variability. Results also conclusively demonstrate the risks in normalizing data to creatinine.

Background Non-targeted metabolomics based on mass spectrometry enables high-throughput profiling of the metabolites in a biological sample. The large amount of data generated from mass spectrometry requires intensive computational processing for annotation of mass spectra and identification of metabolites. Computational analysis tools that are fully integrated with multiple functions and are easily operated by users who lack extensive knowledge in programing are needed in this research field. Results We herein developed an R package, metaX, that is capable of end-to-end metabolomics data analysis through a set of interchangeable modules. Specifically, metaX provides several functions, such as peak picking and annotation, data quality assessment, missing value imputation, data normalization, univariate and multivariate statistics, power analysis and sample size estimation, receiver operating characteristic analysis, biomarker selection, pathway annotation, correlation network analysis, and metabolite identification. In addition, metaX offers a web-based interface ( http://metax.genomics.cn ) for data quality assessment and normalization method evaluation, and it generates an HTML-based report with a visualized interface. The metaX utilities were demonstrated with a published metabolomics dataset on a large scale. The software is available for operation as either a web-based graphical user interface (GUI) or in the form of command line functions. The package and the example reports are available at http://metax.genomics.cn/ . Conclusions The pipeline of metaX is platform-independent and is easy to use for analysis of metabolomics data generated from mass spectrometry.

Urinary extracellular vesicles (uEVs) are emerging as non‐invasive biomarkers for various kidney diseases, but it is unknown how differences in nephron mass impact uEV excretion. To address this, uEV excretion was measured before and after human kidney donor nephrectomy and rat nephrectomy. In male and female donors, uEVs were quantified in cell‐free spot and 24‐h urine samples using nanoparticle tracking analysis (NTA), EVQuant, and CD9‐time‐resolved fluorescence immunoassay. Female donors had significantly lower total kidney volume (TKV) and excreted 49% fewer uEVs than male donors. uEV excretion correlated positively with estimated glomerular filtration rate (eGFR), creatinine clearance, and TKV (R's between 0.6 and 0.7). uEV excretion rate could also be predicted from spot urines after multiplying spot uEV/creatinine by 24‐h urine creatinine. Donor nephrectomy reduced eGFR by 36% ± 10%, but the excretion of uEVs by only 16% (CD9+ uEVs ‐37%, CD9‐ uEVs no decrease). Donor nephrectomy increased the podocyte marker WT‐1 and the proximal tubule markers NHE3, NaPi‐IIa, and cubilin in uEVs two‐ to four‐fold when correcting for the nephrectomy. In rats, the changes in GFR and kidney weight correlated with the changes in uEV excretion rate (R = 0.46 and 0.60, P < 0.01). Furthermore, the estimated degree of hypertrophy matched the change in uEV excretion rate (1.4‐ to 1.5‐fold after uninephrectomy and four‐fold after 5/6th nephrectomy). Taken together, our data show that uEV excretion depends on nephron mass, and that nephrectomy reduces uEV excretion less than expected based on nephron loss due to compensatory hypertrophy. The major implication of our findings is that a measure for nephron mass or uEV excretion rate should be included when comparing uEV biomarkers between individuals.

1H NMR spectra from urine can yield information-rich data sets that offer important insights into many biological and biochemical phenomena. However, the quality and utility of these insights can be profoundly affected by how the NMR spectra are processed and interpreted. For instance, if the NMR spectra are incorrectly referenced or inconsistently aligned, the identification of many compounds will be incorrect. If the NMR spectra are mis-phased or if the baseline correction is flawed, the estimated concentrations of many compounds will be systematically biased. Furthermore, because NMR permits the measurement of concentrations spanning up to five orders of magnitude, several problems can arise with data analysis. For instance, signals originating from the most abundant metabolites may prove to be the least biologically relevant while signals arising from the least abundant metabolites may prove to be the most important but hardest to accurately and precisely measure. As a result, a number of data processing techniques such as scaling, transformation and normalization are often required to address these issues. Therefore, proper processing of NMR data is a critical step to correctly extract useful information in any NMR-based metabolomic study. In this review we highlight the significance, advantages and disadvantages of different NMR spectral processing steps that are common to most NMR-based metabolomic studies of urine. These include: chemical shift referencing, phase and baseline correction, spectral alignment, spectral binning, scaling and normalization. We also provide a set of recommendations for best practices regarding spectral and data processing for NMR-based metabolomic studies of biofluids, with a particular focus on urine.