Assessment of 1 H NMR-based metabolomics analysis for normalization of urinary metals against creatinine

Cassiède, Marc; Nair, Sindhu; Dueck, Meghan; Mino, James; McKay, Roy T.; Mercier, Pascal; Quémerais, Bernadette; Lacy, Paige

doi:10.1016/j.cca.2016.10.037

Cited by 14 publications

(15 citation statements)

References 34 publications

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“…Supernatants were vortexed and had DSS containing internal standard added followed by pH adjustment to 7.0±0.25 to ensure uniformity in pH values across the samples [1].…”

Section: Experimental Design Materials and Methodsmentioning

confidence: 99%

“…Samples of urine were transferred into 3 mm (for 700 MHz magnet) or 5 mm (for 600 MHz) NMR tubes and spectra were collected as described [1].…”

Section: Experimental Design Materials and Methodsmentioning

confidence: 99%

“…Linear regression analysis of the resulting urinary metabolites from 600 and 700 MHz magnets were compared using GraphPad Prism 6 software (GraphPad Software, La Jolla, CA) [1].…”

Section: Experimental Design Materials and Methodsmentioning

confidence: 99%

“…The data in this article are related to the research entitled, “Assessment of 1 H NMR-based metabolomics analysis for normalization of urinary metals against creatinine” (M. Cassiède, S. Nair, M. Dueck, J. Mino, R. McKay, P. Mercier, B. Quémerais, P. Lacy, 2016) [1]. This article describes the analysis of urinary metabolites in normal, healthy individuals by 1 H NMR-based metabolomics.…”

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confidence: 99%

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Dataset of urinary metabolites measured by 1 H NMR analysis of normal human urine

et al. 2017

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The data in this article are related to the research entitled, “Assessment of 1H NMR-based metabolomics analysis for normalization of urinary metals against creatinine” (M. Cassiède, S. Nair, M. Dueck, J. Mino, R. McKay, P. Mercier, B. Quémerais, P. Lacy, 2016) [1]. This article describes the analysis of urinary metabolites in normal, healthy individuals by 1H NMR-based metabolomics. NMR spectra of urine samples typically contain hundreds of peaks that must be carefully screened for reproducibility and detectability. An important requirement in the screening of appropriate urinary metabolites is to ensure that they are reproducibly detected. In our study, we applied the peak profiles of 151 known urinary metabolites to 10 normal human urine samples and found that 50 metabolites were reproducibly measured between 600 and 700 MHz magnets in the same samples. The data set has been made publicly available to enable critical or extended analysis.

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“…Supernatants were vortexed and had DSS containing internal standard added followed by pH adjustment to 7.0±0.25 to ensure uniformity in pH values across the samples [1].…”

Section: Experimental Design Materials and Methodsmentioning

confidence: 99%

“…Samples of urine were transferred into 3 mm (for 700 MHz magnet) or 5 mm (for 600 MHz) NMR tubes and spectra were collected as described [1].…”

Section: Experimental Design Materials and Methodsmentioning

confidence: 99%

“…Linear regression analysis of the resulting urinary metabolites from 600 and 700 MHz magnets were compared using GraphPad Prism 6 software (GraphPad Software, La Jolla, CA) [1].…”

Section: Experimental Design Materials and Methodsmentioning

confidence: 99%

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confidence: 99%

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Dataset of urinary metabolites measured by 1 H NMR analysis of normal human urine

et al. 2017

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“…In a study performed at two different elds (600 and 700 MHz), and using a list of 151 metabolites potentially quanti able in urine, it was found that only 33% (50 metabolites) presented data that correlated strongly between the values obtained in the two experimental conditions 11 . This result sets a limit to the number of compounds that can be accurately quanti ed in urine using NMR and all studies in the literature use a dataset comprising a number similar to this [12][13][14][15][16][17] .…”

Section: Introductionmentioning

confidence: 98%

Personalized metabolic profile by synergic use of NMR and HRMS

Petrella

Montesano

Lentini

et al. 2021

Preprint

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A new strategy that takes advantage of the synergism between NMR and LC-HRMS (SYNHMET), and that allows to obtain a unique list of the absolute concentrations of 164 metabolites in urine, is presented. Metabolite identification and quantification by this method in one of the most difficult biofluids to characterize, due to complexity and variability, is more accurate than what can be obtained using the two techniques separately. This result is achieved without the need for chemical reactions to cross-check the data between the two types of spectra, nor the use of analytical standards and calibration curves. The fact that the absolute rather than relative concentration is obtained allows the final dataset to be used to determine a patient's personalized profile. The number of quantifiable metabolites by the application of this method can be expanded in the future with further analysis. We will illustrate the use of SYNHMET in the study of urine samples from healthy subjects, patients with chronic cystitis and bladder cancer.

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NMR‐based serum and muscle metabolomics for diagnosis and activity assessment in idiopathic inflammatory myopathies

Guleria

Kumar

et al. 2021

Analytical Science Advances

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Objectives Differentiating smoldering disease activity from weakness due to fatty replacement of atrophied muscle can often be a challenge in the idiopathic inflammatory myositis (IIM). We aimed to identify the metabolic disturbances associated with IIM and if these changes can aid in the assessment of disease activity. Methods Metabolic profiles of sera (N = 99) and muscle (N = 21) from patients with IIM (ACR‐EULAR criteria) were compared with healthy control (HC) samples (N = 75 for serum and N = 12 for muscle tissues) employing 800 MHz NMR (Nuclear Magnetic Resonance) spectroscopy. Metabolic disparity between IIM and HC was established based on Partial Least Squares Discriminant Analysis (PLS‐DA) and the discriminatory metabolites were identified based on variable importance in projection (VIP) statistics (P‐value < .05, corrected for false discovery rate (FDR)). Results Serum metabolomics profiles were distinctive in IIM as compared to HC, with a visible shift to anaerobic metabolism (increased lactate, low glucose), oxidative defect (high Phenylalanine/tyrosine), decreased muscle mass (low serum creatinine), increased muscle catabolism (increased branched‐chain amino acids), and dyslipidemia (higher lipids, higher very low‐density lipoprotein [VLDL]/low‐density lipoprotein [LDL] ratio, lower polyunsaturated fatty acid [PUFA]). The sera of active IIM patients were characterized by anaerobic metabolism (low glucose), loss of muscle mass (low creatinine, amino acids), and oxidative defect (high Phenylalanine/tyrosine). Three metabolites (isopropanol, succinate, and glycine) were distinctive in muscle tissue metabolomics. NMR‐based serum metabolic disparity was lacking between different clinical subsets of IIM. Conclusion Serum and muscle tissue metabolomics have the potential to distinguish (a) IIM from HC and (b) active IIM from inactive IIM irrespective of disease subtype.

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Assessment of 1 H NMR-based metabolomics analysis for normalization of urinary metals against creatinine

Cited by 14 publications

References 34 publications

Dataset of urinary metabolites measured by 1 H NMR analysis of normal human urine

Dataset of urinary metabolites measured by 1 H NMR analysis of normal human urine

Personalized metabolic profile by synergic use of NMR and HRMS

NMR‐based serum and muscle metabolomics for diagnosis and activity assessment in idiopathic inflammatory myopathies

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