Identification of 4-deoxythreonic acid present in human urine using HPLC and NMR techniques

Appiah-Amponsah, Emmanuel; Shanaiah, Narasimhamurthy; Gowda, G. A. Nagana; Owusu-Sarfo, Kwadwo; Ye, Tao; Raftery, Daniel

doi:10.1016/j.jpba.2009.06.007

Cited by 33 publications

(28 citation statements)

References 36 publications

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“…The predictive metabolic profile for preeclampsia also included increased glycine and 4-deoxythreonic acid, as well as decreased lactate and creatine. 4-Deoxythreonic acid is a degradation product of 3-hydroxybutyrate [ 21 ], which is shown increased in maternal serum in our study. Both preeclampsia and gestational hypertension was associated with an increase in urinary dimethylamine.…”

Section: Discussionsupporting

confidence: 59%

First Trimester Urine and Serum Metabolomics for Prediction of Preeclampsia and Gestational Hypertension: A Prospective Screening Study

Austdal

Tangerås

Skråstad

et al. 2015

IJMS

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Hypertensive disorders of pregnancy, including preeclampsia, are major contributors to maternal morbidity. The goal of this study was to evaluate the potential of metabolomics to predict preeclampsia and gestational hypertension from urine and serum samples in early pregnancy, and elucidate the metabolic changes related to the diseases. Metabolic profiles were obtained by nuclear magnetic resonance spectroscopy of serum and urine samples from 599 women at medium to high risk of preeclampsia (nulliparous or previous preeclampsia/gestational hypertension). Preeclampsia developed in 26 (4.3%) and gestational hypertension in 21 (3.5%) women. Multivariate analyses of the metabolic profiles were performed to establish prediction models for the hypertensive disorders individually and combined. Urinary metabolomic profiles predicted preeclampsia and gestational hypertension at 51.3% and 40% sensitivity, respectively, at 10% false positive rate, with hippurate as the most important metabolite for the prediction. Serum metabolomic profiles predicted preeclampsia and gestational hypertension at 15% and 33% sensitivity, respectively, with increased lipid levels and an atherogenic lipid profile as most important for the prediction. Combining maternal characteristics with the urinary hippurate/creatinine level improved the prediction rates of preeclampsia in a logistic regression model. The study indicates a potential future role of clinical importance for metabolomic analysis of urine in prediction of preeclampsia.

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Section: Discussionsupporting

confidence: 59%

First Trimester Urine and Serum Metabolomics for Prediction of Preeclampsia and Gestational Hypertension: A Prospective Screening Study

Austdal

Tangerås

Skråstad

et al. 2015

IJMS

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“…The metabolite with the highest importance value for the prediction of sex from urine metabolite profiles, 4-deoxythreonic acid, has not been mentioned in literature in the context of sex differences before. It is known to be present in urine [ 57 , 58 ], and has been described as a metabolite of l -Threonine [ 59 ]. Its function, however, is yet unknown.…”

Section: Discussionmentioning

confidence: 99%

Metabolite patterns predicting sex and age in participants of the Karlsruhe Metabolomics and Nutrition (KarMeN) study

et al. 2017

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Physiological and functional parameters, such as body composition, or physical fitness are known to differ between men and women and to change with age. The goal of this study was to investigate how sex and age-related physiological conditions are reflected in the metabolome of healthy humans and whether sex and age can be predicted based on the plasma and urine metabolite profiles.In the cross-sectional KarMeN (Karlsruhe Metabolomics and Nutrition) study 301 healthy men and women aged 18–80 years were recruited. Participants were characterized in detail applying standard operating procedures for all measurements including anthropometric, clinical, and functional parameters. Fasting blood and 24 h urine samples were analyzed by targeted and untargeted metabolomics approaches, namely by mass spectrometry coupled to one- or comprehensive two-dimensional gas chromatography or liquid chromatography, and by nuclear magnetic resonance spectroscopy. This yielded in total more than 400 analytes in plasma and over 500 analytes in urine. Predictive modelling was applied on the metabolomics data set using different machine learning algorithms.Based on metabolite profiles from urine and plasma, it was possible to identify metabolite patterns which classify participants according to sex with > 90% accuracy. Plasma metabolites important for the correct classification included creatinine, branched-chain amino acids, and sarcosine. Prediction of age was also possible based on metabolite profiles for men and women, separately. Several metabolites important for this prediction could be identified including choline in plasma and sedoheptulose in urine. For women, classification according to their menopausal status was possible from metabolome data with > 80% accuracy.The metabolite profile of human urine and plasma allows the prediction of sex and age with high accuracy, which means that sex and age are associated with a discriminatory metabolite signature in healthy humans and therefore should always be considered in metabolomics studies.

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“…Metabolites were assigned using Chenomx NMR suite 7.7 (Chenomx Inc., Alberta, Canada), the Human Metabolome Database 20 , and/or literature values (4-deoxyerythronic acid (4-DEA) 21 22 ; 4-deoxythreonic acid (4-DTA) 22 ; P-cresol sulfate 23 24 ); and by 2D JRES spectroscopy. Statistical Total Correlation Spectroscopy (STOCSY) 25 was applied for some metabolites for identification of correlating peaks in the spectrum.…”

Section: Methodsmentioning

confidence: 99%

Diurnal rhythms in the human urine metabolome during sleep and total sleep deprivation

Giskeødegård

Davies

Revell

et al. 2015

Sci Rep

117

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Understanding how metabolite levels change over the 24 hour day is of crucial importance for clinical and epidemiological studies. Additionally, the association between sleep deprivation and metabolic disorders such as diabetes and obesity requires investigation into the links between sleep and metabolism. Here, we characterise time-of-day variation and the effects of sleep deprivation on urinary metabolite profiles. Healthy male participants (n = 15) completed an in-laboratory study comprising one 24 h sleep/wake cycle prior to 24 h of continual wakefulness under highly controlled environmental conditions. Urine samples were collected over set 2–8 h intervals and analysed by 1H NMR spectroscopy. Significant changes were observed with respect to both time of day and sleep deprivation. Of 32 identified metabolites, 7 (22%) exhibited cosine rhythmicity over at least one 24 h period; 5 exhibiting a cosine rhythm on both days. Eight metabolites significantly increased during sleep deprivation compared with sleep (taurine, formate, citrate, 3-indoxyl sulfate, carnitine, 3-hydroxyisobutyrate, TMAO and acetate) and 8 significantly decreased (dimethylamine, 4-DTA, creatinine, ascorbate, 2-hydroxyisobutyrate, allantoin, 4-DEA, 4-hydroxyphenylacetate). These data indicate that sampling time, the presence or absence of sleep and the response to sleep deprivation are highly relevant when identifying biomarkers in urinary metabolic profiling studies.

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Identification of 4-deoxythreonic acid present in human urine using HPLC and NMR techniques

Cited by 33 publications

References 36 publications

First Trimester Urine and Serum Metabolomics for Prediction of Preeclampsia and Gestational Hypertension: A Prospective Screening Study

First Trimester Urine and Serum Metabolomics for Prediction of Preeclampsia and Gestational Hypertension: A Prospective Screening Study

Metabolite patterns predicting sex and age in participants of the Karlsruhe Metabolomics and Nutrition (KarMeN) study

Diurnal rhythms in the human urine metabolome during sleep and total sleep deprivation

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