Discrimination of Type 2 diabetic patients from healthy controls by using metabonomics method based on their serum fatty acid profiles

Yang, Jun; Xu, Guowang; Hong, Qunfa; Liebich, H.M.; Lutz, Katja; Schmülling, R.-M.; Wahl, Hans Günther

doi:10.1016/j.jchromb.2004.09.023

Cited by 109 publications

(73 citation statements)

References 25 publications

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“…Environments such as Taverna [100,102,105] of the mouse model [77]. Other examples include nucleosides for liver cancer [78], various lipids for type II diabetes [79], and a series of biomarkers for caloric restriction [80]. An attraction of many of these methods is that they can begin to give mechanistic insight into the relevant aetiologies of often progressive and complex physiologies and pathologies.…”

Section: Figurementioning

confidence: 99%

Systems biology, metabolic modelling and metabolomics in drug discovery and development

Kell

2006

Drug Discovery Today

266

163

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Unlike signalling pathways, metabolic networks are subject to strict stoichiometric constraints. Metabolomics amplifies changes in the proteome, and represents more closely the phenotype of an organism. Recent advances enable the production (and computer-readable encoding as SBML) of metabolic network models reconstructed from genome sequences, as well as experimental measurements of much of the metabolome. There is increasing convergence between the number of human metabolites estimated via genomics ( 3000) and the number measured experimentally. It is thus both timely, and now possible, to bring these two approaches together as an integrated (if distributed) whole to help understand the genesis of metabolic biomarkers, the progress of disease, and the modes of action, efficacy, off-target effects and toxicity of pharmaceutical drugs. Systems biology and metabolic modelling in the 21st CenturyAlthough there are many individual definitions, most commentators (including this one [1,2]) take it that systems biology involves an iterative interplay between more or less high-throughput and high-content 'wet' experiments, technology development, theory and computational modelling, and that it is the involvement of computational modelling, in particular, in the process that sets systems biology apart from the more traditional and more reductionist molecular biology. Metabolomics illustrates this amply. There is also the view that the perceived decrease in the effectiveness of the target-based drug discovery process [3][4][5], including the still-high levels of attrition [6], means that we must move towards understanding organisms at something more akin to a whole-system level [7][8][9][10][11].The question then arises as to what part of a system one might first beneficially model? Although there has, unsurprisingly, been considerable interest in modelling the major signalling pathways (e.g. Refs [12,13]), there are several reasons why it is timely to turn our attention to the level of small molecule metabolism, which is the focus of this review. Metabolism is more discriminatingIt has long been known, and proven through the formalism of metabolic control analysis [7,10,[14][15][16], that whereas small changes in the concentrations of enzymes (and the transcripts that encode them) have only small effects on the fluxes through metabolic pathways, they have substantial effects on the concentrations of metabolic intermediates. Because the metabolome (nominally the concentrations of 'all' the metabolites measured in a system of interest [17]) is downstream of the proteome, it is thereby 'amplified' both in theory [18] and in practice [19,20] and represents a more sensitive level of organisation than do the macromolecular 'omes for understanding a complex biological system, and the changes in it that might be occasioned by disease or pharmaceutical intervention [21,22]. Metabolic reconstruction is now mature and timelyAn attractive feature for the purposes of modelling is that metabolism, in contrast to signalling pathways, ...

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Section: Figurementioning

confidence: 99%

Systems biology, metabolic modelling and metabolomics in drug discovery and development

Kell

2006

Drug Discovery Today

266

163

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“…In a preliminary study the metabonomics approach was applied in the diagnosis of type II diabetes. 65 It was found that a method based on serum lipid metabolite profiles obtained by GC-MS combined with pattern recognition analysis of data provided an effective approach to the discrimination of type II diabetic patients from healthy controls. Subsequently, the same research group carried out a more detailed metabonomics study by applying HPLC-MS followed by PLS-discriminant analysis to study phospholipids in plasma of type II diabetic patients and controls.…”

Section: Metabonomics In Diabetes Researchmentioning

confidence: 99%

Metabonomics in Diabetes Research

Faber

Malmodin

Toft

et al. 2007

J Diabetes Sci Technol

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“…Unsupervised pattern recognition methods such as cluster analysis and principal component analysis (PCA) have been used to examine the structure of these datasets [13,17,21,22]. In combination with PCA for dimensionality reduction, discriminant analysis (DA) methods can be used to classify samples according to their characteristic profiles [13,17,22,23]. Other methods used for the classification of CE data include neural networks [21] and support vector machines (SVMs) [17].…”

Section: Introductionmentioning

confidence: 99%

Data processing in metabolic fingerprinting by CE‐UV: Application to urine samples from autistic children

Soria

Wright²,

Goodall

et al. 2007

Electrophoresis

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Data processing in metabolic fingerprinting by CE-UV: Application to urine samples from autistic childrenMetabolic fingerprinting of biofluids such as urine can be used to detect and analyse differences between individuals. However, before pattern recognition methods can be utilised for classification, preprocessing techniques for the denoising, baseline removal, normalisation and alignment of electropherograms must be applied. Here a MEKC method using diode array detection has been used for high-resolution separation of both charged and neutral metabolites. Novel and generic algorithms have been developed for use prior to multivariate data analysis. Alignment is achieved by combining the use of reference peaks with a method that uses information from multiple wavelengths to align electropherograms to a reference signal. This metabolic fingerprinting approach by MEKC has been applied for the first time to urine samples from autistic and control children in a nontargeted and unbiased search for markers for autism. Although no biomarkers for autism could be determined using MEKC data here, the general approach presented could also be applied to the processing of other data collected by CE with UV-Vis detection. Keywords IntroductionMetabolic fingerprinting approaches [1] have recently emerged as a nonbiased alternative to the determination of individual levels of a specific metabolite or class of metabolites in large sets of samples, as is necessary for clinical diagnosis purposes. 1 H-NMR has been widely used in urinary fingerprinting and the search for disease biomarkers [2,3]. The approach has been recently complemented by chromatographic techniques hyphenated to MS (GC-MS and LC-MS), with the advantage over 1 H-NMR of a higher sensitivity [4][5][6][7]. Other spectroscopic techniques used less frequently for urinary fingerprinting include direct injection MS [8,9] and FTIR spectroscopy [10]. CE has recently been incorporated to the family of metabolic fingerprinting techniques used for clinical studies [11][12][13][14]. CE is an efficient and sensitive high-resolution separation technique that allows fast analysis of ionic species and the use of small samples.MEKC, applicable to both ionic and neutral species, has proved to be a useful tool in the study of different target metabolites in urine [15][16][17][18]. However, its application as a nontargeted approach has not yet been fully exploited. CD-modified MEKC allows the separation of over 80 UVabsorbing endogenous analytes of different charge types (neutral, anionic and cationic) below 25 min [18][19][20]. Since minimal sample pretreatment is required, this methodology has the potential for high-throughput analysis of the large number of samples required in screening applications.Urinary fingerprints produced by MEKC are highly complex datasets and require multivariate techniques for interpretation. Unsupervised pattern recognition methods such as cluster analysis and principal component analysis (PCA) have been used to examine the structure of these dataset...

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Discrimination of Type 2 diabetic patients from healthy controls by using metabonomics method based on their serum fatty acid profiles

Cited by 109 publications

References 25 publications

Systems biology, metabolic modelling and metabolomics in drug discovery and development

Systems biology, metabolic modelling and metabolomics in drug discovery and development

Metabonomics in Diabetes Research

Data processing in metabolic fingerprinting by CE‐UV: Application to urine samples from autistic children

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