The Metabolomics Standards Initiative

Sansone, Susanna-Assunta; Fan, Teresa W.‐M.; Goodacre, Royston; Griffin, Julian L.; Hardy, Nigel; Kaddurah‐Daouk, Rima; Kristal, Bruce S.; Lindon, John C.; Mendes, Pedro; Morrison, Norman; Nikolau, Basil J.; Robertson, D. J.; Sumner, Lloyd W.; Taylor, Chris; Werf, Mariët Van Der; Ommen, Ben van; Fiehn, Oliver

doi:10.1038/nbt0807-846b

Cited by 300 publications

(68 citation statements)

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“…The compounds measured by LC-MS and NMR-COMP were identified according to the metabolomics standards initiative (MSI) level 1 using information coming from at least 2 different sources (Sansone et al 2007). The available ChEBI ID were shown in Supplementary Table 1.…”

Section: Methodsmentioning

confidence: 99%

Metabolomics based markers predict type 2 diabetes in a 14-year follow-up study

et al. 2017

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BackgroundThe growing field of metabolomics has opened up new opportunities for prediction of type 2 diabetes (T2D) going beyond the classical biochemistry assays.ObjectivesWe aimed to identify markers from different pathways which represent early metabolic changes and test their predictive performance for T2D, as compared to the performance of traditional risk factors (TRF).MethodsWe analyzed 2776 participants from the Erasmus Rucphen Family study from which 1571 disease free individuals were followed up to 14-years. The targeted metabolomics measurements at baseline were performed by three different platforms using either nuclear magnetic resonance spectroscopy or mass spectrometry. We selected 24 T2D markers by using Least Absolute Shrinkage and Selection operator (LASSO) regression and tested their association to incidence of disease during follow-up.ResultsThe 24 markers i.e. high-density, low-density and very low-density lipoprotein sub-fractions, certain triglycerides, amino acids, and small intermediate compounds predicted future T2D with an area under the curve (AUC) of 0.81. The performance of the metabolic markers compared to glucose was significantly higher among the young (age < 50 years) (0.86 vs. 0.77, p-value <0.0001), the female (0.88 vs. 0.84, p-value =0.009), and the lean (BMI < 25 kg/m2) (0.85 vs. 0.80, p-value =0.003). The full model with fasting glucose, TRFs, and metabolic markers yielded the best prediction model (AUC = 0.89).ConclusionsOur novel prediction model increases the long-term prediction performance in combination with classical measurements, brings a higher resolution over the complexity of the lipoprotein component, increasing the specificity for individuals in the low risk group.Electronic supplementary materialThe online version of this article (doi:10.1007/s11306-017-1239-2) contains supplementary material, which is available to authorized users.

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Section: Methodsmentioning

confidence: 99%

Metabolomics based markers predict type 2 diabetes in a 14-year follow-up study

et al. 2017

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“…According to Weckwerth et al the relative technical standard deviation for GC/TOF measurements is around 10 %, for Arabidopsis however, samples can show “up to several fold variances” in metabolite levels [16]. This high biological variability makes it hard to reveal differences with statistical reliability [17, 18]. …”

Section: Resultsmentioning

confidence: 99%

Quantitative HPLC-MS analysis of nucleotide sugars in plant cells following off-line SPE sample preparation

et al. 2014

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An analytical workflow was developed for the absolute quantification of uridine diphosphate (UDP)-sugars in plant material in order to compare their metabolism both in wild-type Arabidopsis thaliana and mutated plants (ugd2,3) possessing genetic alterations within the UDP-glucose dehydrogenase genes involved in UDP-sugar metabolism. UDP-sugars were extracted from fresh plant material by chloroform-methanol-water extraction and further purified by solid-phase extraction with a porous graphitic carbon adsorbent with extraction efficiencies between 80 ± 5 % and 90 ± 5 %. Quantitative determination of the UDP-sugars was accomplished through HPLC separation with a porous graphitic carbon column (HypercarbTM) which was interfaced to electrospray ionization Orbitrap mass spectrometry. The problem of instable retention times due to redox processes on the stationary phase were circumvented by grounding of the column effluent and incorporation of a column regeneration procedure using acetonitrile-water containing 0.10 % trifluoroacetic acid. The method was calibrated using external calibration and UDP as internal standard. Calibration functions were approximated by first- or second-order regression analysis for concentrations spanning three orders of magnitude. Upon injecting sample volumes of 2.65 μL, the limits of detection for the UDP-sugars were in the 70 nmol L−1 range. Six different UDP-sugars, including UDP-glucose, UDP-galactose, UDP-arabinose, UDP-xylose, UDP-glucuronic acid, and UDP-galacturonic acid were found in concentrations of 0.4 to 38 μg/g plant material. Data evaluation by analysis of variance (ANOVA) revealed statistically significant differences in UDP-sugar concentrations between wild-type and mutant plants, which were found to conclusively mirror the impaired metabolic pathways in the mutant plants.FigureᅟElectronic supplementary materialThe online version of this article (doi:10.1007/s00216-014-7746-3) contains supplementary material, which is available to authorized users.

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“…This in turn led to the establishment of the Metabolomics Standards Initiative (Sansone et al , 2007), an international programme co-ordinated by the Metabolomics Society. The remit of this initiative was to recommend standards covering all aspects of metabolomic studies from sample processing to data acquisition and reporting arrangements.…”

Section: Standards For Metabolomic Studiesmentioning

confidence: 99%