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- Antti, Henrik, et al.
(författare)
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Batch statistical processing of 1H NMR-derived urinary spectral data
- 2002
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Ingår i: Journal of Chemometrics: Special Issue: Proceedings of the 7th Scandinavian Symposium on Chemometrics. Issue Edited by Lars Nørgaard. - : Wiley. ; 16:8-10, s. 461-8
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Tidskriftsartikel (refereegranskat)abstract
- Multivariate statistical batch processing (BP) analysis of 1H nuclear magnetic resonance (NMR) urine spectra was employed to establish time-dependent metabolic variations in animals treated with the model hepatotoxin hydrazine. Hydrazine was administered orally to rats (at 90 mg kg-1), and urine samples were collected from dosed rats and matched control animals (n = 5 per group) at time points up to 168 h post-dose. Urine samples were analysed via 1H NMR spectroscopy and partial least squares-based batch processing analysis, treating each rat as an individual batch comprising a series of timed urine samples. A model defining the mean urine profile was established for the control group, and samples obtained from hydrazine-treated animals were assessed using this model. Time-dependent deviations from the control model were evident in all hydrazine-treated animals, and hepatotoxicity was manifested by increased urinary excretion of taurine, creatine, 2-aminoadipate, citrulline and -alanine together with depletion of urinary levels of citrate, succinate and hippurate. The experiment was repeated at six different pharmaceutical centres in order to assess the robustness of the technology and to establish the natural variability in the data. Results were consistent across the data for all centres. BP plots showed a characteristic pattern for each toxin, allowing the time points at which there were maximum metabolic differences to be determined and providing a means of visualizing the net toxin-induced metabolic movement of urinary metabolism. BP may prove to be a powerful metabonomic tool in defining time-dependent metabolic consequences of toxicity and is an efficient means of visualizing inter-animal variations in response as well as defining multivariate statistical limits of normality in terms of biofluid composition.
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| 2. |
- Antti, Henrik, et al.
(författare)
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Statistical experimental design and partial least squares regression analysis of biofluid metabonomic NMR and clinical chemistry data for screening of adverse drug effects
- 2004
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Ingår i: Chemometrics and Intelligent Laboratory Systems. - : Elsevier BV. - 0169-7439. ; 73:1, s. 139-49
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Tidskriftsartikel (refereegranskat)abstract
- Metabonomic analysis is increasingly recognised as a powerful approach for delineating the integrated metabolic changes in biofluids and tissues due to toxicity, disease processes or genetic modification in whole animal systems. When dealing with complex biological data sets, as generated within metabonomics, as well as related fields such as genomics and proteomics, reliability and significance of identified biomarkers associated with specific states related to toxicity or disease are crucial in order to gain detailed and relevant interpretations of the metabolic fluxes in the studied systems. Since various physiological factors, such as diet, state of health, age, diurnal cycles, stress, genetic drift, and strain differences, affect the metabolic composition of biological matrices, it is of great importance to create statistically reliable decision tools for distinguishing between physiological and pathological responses in animal models. In the screening for new biomarkers or patterns of pathological dysfunction, methods providing statistically valid measures of effect-related changes will become increasingly important as the data within areas such as genomics, proteomics and metabonomics continues to grow in size and complexity. 1H NMR spectroscopy and mass spectrometry are the principal analytical platforms used to derive the data and, because extensively large data sets are required, as much consideration has to be given to optimum design of experiments (DoE) as for subsequent data analysis. Thus, statistical experimental design combined with partial least squares (PLS) regression is proposed as an efficient approach for undertaking metabonomic studies and for analysis of the results. The method was applied to data from a liver toxicology study in the rat using hydrazine as a model toxin. 1D projections of 2D J-resolved (J-RES) 1H NMR spectra and the corresponding clinical chemistry parameters of blood serum samples from control and dosed rats (30 and 90 mg/kg) collected at 48 and 168 h post dose were analysed. Confidence intervals for the PLS regression coefficients were used to create a statistical means for screening of biomarkers in the two combined data blocks (NMR and clinical chemistry data). PLS analysis was also used to reveal the correlation pattern between the two blocks of data as well as the within the two blocks according to dose, time and the interaction dose×time.
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