Gas chromatographic profiling and pattern recognition analysis of urinary organic acids from uterine myoma patients and cervical cancer patients

Kim, Kyoung-Rae; Park, Hyoung-Geun; Paik, Man‐Jeong; Ryu, Hee Sug; Oh, Ki Keun; Myung, Seung-Woon; Liebich, H.M.

doi:10.1016/s0378-4347(98)00155-8

Cited by 37 publications

(22 citation statements)

References 19 publications

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“…In clinical routine, profiles of organic acids in urine samples could then be used to diagnose fifty different human diseases simultaneously (Jellum et al, 1988). Research in this area is still going on, aiming for a faster, more automated and more reliable categorization of human metabolic disorders by computational constraints (Kimura et al, 1999) or to detect patterns in metabolite profiles of cancer-related tissues by clustering algorithms (Kim et al, 1998). GC/MS analysis of patient tissues was later extended to the simultaneous identification and quantification of several compound classes such as organic acids, polyols, carbohydrates and amino acids (Ning et al, 1996).…”

Section: Metabolite Profilingmentioning

confidence: 99%

Metabolomics — the link between genotypes and phenotypes

Fiehn

2002

Functional Genomics

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Metabolites are the end products of cellular regulatory processes, and their levels can be regarded as the ultimate response of biological systems to genetic or environmental changes. In parallel to the terms 'transcriptome' and 'proteome', the set of metabolites synthesized by a biological system constitute its 'metabolome'. Yet, unlike other functional genomics approaches, the unbiased simultaneous identification and quantification of plant metabolomes has been largely neglected. Until recently, most analyses were restricted to profiling selected classes of compounds, or to fingerprinting metabolic changes without sufficient analytical resolution to determine metabolite levels and identities individually. As a prerequisite for metabolomic analysis, careful consideration of the methods employed for tissue extraction, sample preparation, data acquisition, and data mining must be taken. In this review, the differences among metabolite target analysis, metabolite profiling, and metabolic fingerprinting are clarified, and terms are defined. Current approaches are examined, and potential applications are summarized with a special emphasis on data mining and mathematical modelling of metabolism.

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Section: Metabolite Profilingmentioning

confidence: 99%

Metabolomics — the link between genotypes and phenotypes

Fiehn

2002

Functional Genomics

1,624

1,337

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“…In humans, metabolomics has been used mainly in studies focusing on diagnosis of disease [9,11,15,25,32,39,40,56,58], mode of drug/toxin action [4,31,36,42,47], and characterisation of novel foods [10,48]. However, several recent commentary articles have suggested that metabolomics will have great value for nutritional studies [12, 16-20, 38, 62, 65] and thus it is timely to exploit this technology platform to assess dietary exposure.…”

Section: Metabolomics Approaches Applied To Measurement Of Dietary Exmentioning

confidence: 99%

Measurement of dietary exposure: a challenging problem which may be overcome thanks to metabolomics?

et al. 2009

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The diet is an important environmental exposure, and its measurement is an essential component of much health-related research. However, conventional tools for measuring dietary exposure have significant limitations being subject to an unknown degree of misreporting and dependent upon food composition tables to allow estimation of intakes of energy, nutrients and non-nutrient food constituents. In addition, such tools may be inappropriate for use with certain groups of people. As an alternative approach, the recent techniques of metabolite profiling or fingerprinting, which allows simultaneous monitoring of multiple and dynamic components of biological fluids, may provide metabolic signals indicative of food intake. Samples can be analysed through numerous analytical platforms, followed by multivariate data analysis. In humans, metabolomics has been applied successfully in pharmacology, toxicology and medical screening, but nutritional metabolomics is still in its infancy. Biomarkers of a small number of specific foods and nutrients have been developed successfully but less targeted and more high-throughput methods, that do not need prior knowledge of which signals might be discriminatory, and which may allow a more global characterisation of dietary intake, remain to be tested. A proof a principle project (the MEDE Study) is currently underway in our laboratories to test the hypothesis that high-throughput, non-targeted metabolite fingerprinting using flow injection electrospray mass spectrometry can be applied to human biofluids (blood and urine) to characterise dietary exposure in humans.

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“…Our previous uterine cervical cancer study indicated that combined methoxime/tert-butyldimethylsilyl (MO/ TBDMS) reactions are suitable for profiling analysis of urinary carboxylic acids (CAs), including keto acids (KAs) (Kim et al, 1998). Our recent studies showed that a two-phase ethoxycarbonyl-MO/TBDMS reaction was useful for assaying clinically important amino acids and CAs, including KAs in a single run (Paik and Kim, 2004;Paik et al, 2005).…”

Section: Introductionmentioning

confidence: 99%

“…Our recent studies showed that a two-phase ethoxycarbonyl-MO/TBDMS reaction was useful for assaying clinically important amino acids and CAs, including KAs in a single run (Paik and Kim, 2004;Paik et al, 2005). The present study was performed to determine the levels of the targeted CAs (lactic acid, 3-hydroxybutyric acid) and KAs (pyruvic acid, acetoacetic acid) in plasma for clinical monitoring, using our previously developed GC method (Kim et al, 1998;Paik and Kim, 2004).…”

Section: Introductionmentioning

confidence: 99%

“…Therefore, we investigated the optimal conditions for accurate, rapid and sensitive GC-SIM-MS analysis, and then used this method to validate the conditions. Simultaneous clinical monitoring of lactic acid, pyruvic acid and ketone bodies in plasma 451 SHORT COMMUNICATION In this study, our MO/TBDMS reaction method (Kim et al, 1998;Paik and Kim, 2004) combined with the optimized GC-SIM-MS analysis was applied to accurately quantify targeted CAs in human plasma samples.…”

Section: Introductionmentioning

confidence: 99%

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Simultaneous clinical monitoring of lactic acid, pyruvic acid and ketone bodies in plasma as methoxime/tert‐butyldimethylsilyl derivatives by gas chromatography–mass spectrometry in selected ion monitoring mode

Paik

Cho

Kim

et al. 2008

Biomedical Chromatography

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Simultaneous determination of lactic acid, pyruvic acid, 3-hydroxybutyric acid and acetoacetic acid for clinical monitoring of lactic acidosis and ketone body formation in human plasma (20 μL) was performed by gas chromatography-mass spectrometry in selected ion monitoring (SIM) mode after generating methoxime/tert-butyldimethylsilyl derivatives. All of the targeted carboxylic acids were detected by characteristic fragment ions, which permitted sensitive and selective identification in the presence of co-extracted free fatty acids and other acidic metabolites at much higher levels. The method was linear (r ≥ 0.9991), reproducible (% relative standard deviation = 1.2-5.8), and accurate (% relative error = −7.2-7.6), with detection limits of 0.05-1.7 ng/mL. This rapid, accurate and selective method using minimal plasma samples (20 μL) is useful in the clinical monitoring of lactic acidosis and ketone body formation in plasma.

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Gas chromatographic profiling and pattern recognition analysis of urinary organic acids from uterine myoma patients and cervical cancer patients

Cited by 37 publications

References 19 publications

Metabolomics — the link between genotypes and phenotypes

Metabolomics — the link between genotypes and phenotypes

Measurement of dietary exposure: a challenging problem which may be overcome thanks to metabolomics?

Simultaneous clinical monitoring of lactic acid, pyruvic acid and ketone bodies in plasma as methoxime/tert‐butyldimethylsilyl derivatives by gas chromatography–mass spectrometry in selected ion monitoring mode

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