ArticleMetabolites2020
Towards Standardization of Data Normalization Strategies to Improve Urinary Metabolomics Studies by GC×GC-TOFMS.
Article in Metabolites, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 31 papers.
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Who cites it
31 citing papers in PubMed.
- A design of experiments approach to production of medicinal bioactives inPharmaceutical biology · 2026Article
- Creatinine Normalization Over-Corrects Urinary Electrolytes but Not Protein: Implications for Hydration Monitoring in Athletes.Metabolites · 2026Article
- Wheat fertilization affects oviposition preference of wheat midge, Sitodiplosis mosellana (Géhin) (Diptera: Cecidomyiidae).Pest management science · 2026Article
- Protocol for high-throughput, automated, solid-phase extraction of sphingolipids from human urine.STAR protocols · 2026Article
- Quantification of a serum titin fragment reflects dystrophin restoration in mdx mice and disease severity in patients with dystrophinopathies.Journal of neuromuscular diseases · 2026Article
- Lipidomic profile of meningiomas harboring different NF2 mutation status.Metabolomics : Official journal of the Metabolomic Society · 2026Article
- Integrated metabolomic and cytokine profiling reveals biomarkers across the clinical spectrum of lupus nephritis.Frontiers in immunology · 2026Article
- Review
- Early changes in urineMetabolomics : Official journal of the Metabolomic Society · 2025Article
- Evaluation of solutions for stabilizing feces in metabolomics studies using GC × GC-TOFMS.Metabolomics : Official journal of the Metabolomic Society · 2025Article
- Animal models in tuberculosis metabolomics: a systematic review of current evidence and the road to translational relevance.Frontiers in molecular biosciences · 2025Review
- Diurnal Variation of 8-hydroxy-2'-deoxyguanosine in Continuous Time Series of Two Breast Cancer Survivors.Journal of circadian rhythms · 2025Article
- Proximity to a hazardous waste thermal treatment facility alters human physiology: a community-driven pilot study.Experimental biology and medicine (Maywood, N.J.) · 2025Article
- Detection and Validation of Organic Metabolites in Urine for Clear Cell Renal Cell Carcinoma Diagnosis.Metabolites · 2024Article
- The Alpine ibex (Capra ibex) gut microbiome, seasonal dynamics, and potential application in lignocellulose bioconversion.iScience · 2024Article
- Discovery of urinary biosignatures for tuberculosis and nontuberculous mycobacteria classification using metabolomics and machine learning.Scientific reports · 2024Article
- NMR Precision Metabolomics: Dynamic Peak Sum Thresholding and Navigators for Highly Standardized and Reproducible Metabolite Profiling of Clinical Urine Samples.Metabolites · 2024Article
- Neighbour-induced changes in root exudation patterns of buckwheat results in altered root architecture of redroot pigweed.Scientific reports · 2024Article
- Evaluation of normalization strategies for GC-based metabolomics.Metabolomics : Official journal of the Metabolomic Society · 2024Article
- Glyphosate-based herbicide metabolic profiles in human urine samples through proton nuclear magnetic resonance analysis.ADMET & DMPK · 2024Article
Corrections and comments
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Authors and funding
4 authors.
Funding
Abstract
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.
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