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- 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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| 2. |
- Azmi, Jahanara, et al.
(författare)
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Metabolic trajectory characterisation of xenobiotic-induced hepatotoxic lesions using statistical batch processing of NMR data : Nicholson Jeremy K., Holmes Elaine
- 2002
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Ingår i: Analyst. - : Royal Society of Chemistry (RSC). ; 127, s. 271-6
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Tidskriftsartikel (refereegranskat)abstract
- Multivariate statistical batch processing (BP) analysis of 1H NMR urine spectra was employed to establish time-dependent metabolic variations in animals treated with the model hepatotoxin, -naphthylisothiocyanate (ANIT). ANIT (100 mg kg-1) was administered orally to rats (n = 5) and urine samples were collected from dosed and matching control rats at time-points up to 168 h post-dose. Urine samples were measured via1H NMR spectroscopy and partial least squares (PLS) based batch processing analysis was used to interpret the spectral data, treating each rat as an individual batch comprising a series of timed urine samples. A model defining the mean urine profile over the 7 day study period was established, together with model confidence limits (±3 standard deviation), for the control group. Samples obtained from ANIT treated animals were evaluated using the control model. Time-dependent deviations from the control model were evident in all ANIT treated animals consisting of glycosuria, bile aciduria, an initial decrease in taurine levels followed by taurinuria and a reduction of tricarboxylic acid cycle intermediate excretion. BP provided an efficient means of visualising the biochemical response to ANIT in terms of both inter-animal variation and net variation in metabolite excretion profiles. BP also allowed multivariate statistical limits for normality to be established and provided a template for defining the sequence of time-dependent metabolic consequences of toxicity in NMR based metabonomic studies.
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| 3. |
- Brindle, Joanne T, et al.
(författare)
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Rapid and Nonivasive Diagnosis of the Presence and Severity of Coronary Heart Disease Using 1H-NMR-Based Metabonomics
- 2002
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Ingår i: Nature Medicine. - : Springer Science and Business Media LLC. - 1078-8956 .- 1546-170X. ; 8, s. 1439-45
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Tidskriftsartikel (refereegranskat)abstract
- Although a wide range of risk factors for coronary heart disease have been identified from population studies, these measures, singly or in combination, are insufficiently powerful to provide a reliable, noninvasive diagnosis of the presence of coronary heart disease. Here we show that pattern-recognition techniques applied to proton nuclear magnetic resonance (1H-NMR) spectra of human serum can correctly diagnose not only the presence, but also the severity, of coronary heart disease. Application of supervised partial least squares-discriminant analysis to orthogonal signal-corrected data sets allows >90% of subjects with stenosis of all three major coronary vessels to be distinguished from subjects with angiographically normal coronary arteries, with a specificity of >90%. Our studies show for the first time a technique capable of providing an accurate, noninvasive and rapid diagnosis of coronary heart disease that can be used clinically, either in population screening or to allow effective targeting of treatments such as statins.
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