Metabolomics and first-trimester prediction of early-onset preeclampsia

Bahado‐Singh, Ray O.; Akolekar, Ranjit; Mandal, Rupasri; Dong, Erbao; Xia, Jianguo; Krüger, Michael; Wishart, David S.; Nicolaides, Kypros H.

doi:10.3109/14767058.2012.680254

Cited by 108 publications

(87 citation statements)

References 33 publications

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“…For instance, the mother's blood composition during the third trimester of pregnancy showed that circulating levels of lipids (glycerophospholipids, sphingolipids, sterols), hormones (progesterone), and vitamins (vitamin D) were related to the rate of poor pregnancy outcomes (15). Similarly, blood plasma of mothers during their first trimester was associated with the risk of early onset of preeclampsia (16)(17)(18). Using different methodologies, the concentrations of specific carnitines, amino acids (alanine, phenylalanine glutamate, or methionine), and energy intermediates (citrate, glycerol, and hydroxyisovalerate) were highly predictive of preeclampsia.…”

Section: Metabolomics Technologies In Clinical Studiesmentioning

confidence: 99%

Metabolomics perspectives in pediatric research

et al. 2013

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Increasing evidence points toward the critical and long-term involvement of prenatal and early nutrition and lifestyle on later health and disease risk predisposition. Metabolomics is now a well-established top-down systems biology approach that explores the genetic-environment-health paradigm. The generalization of such approaches has opened new research areas to deepen our current understanding of many physiological processes, as well as foods and nutrient functionalities in target populations. It is envisioned that this will provide new avenues toward preventive medicine and prognostic strategies for tailored therapeutic and personalized nutrition management. The development of systems biology approaches and the new generation of biomarker patterns will provide the opportunity to associate complex metabolic regulations with the etiology of multifactorial pediatric diseases. This may subsequently lead to the development of system mechanistic hypotheses that could be targeted with new nutritional personalized concepts. Therefore, this review aims to describe recent applications of metabolomics in preclinical and clinical fields with insights into disease diagnostics/monitoring and improvement of homeostasis metabolic regulation that may be translatable to novel therapeutic and nutrition advances in pediatric research. Over the past decades, awareness about the role of nutrition and lifestyle in health and disease risk management has increased, with key emphasis on the prevention of metabolic disorders, including cardiometabolic diseases and type 2 diabetes (1-3). In parallel, increasing evidence points toward the critical and long-term involvement of early nutrition and lifestyle on later health and disease risk predisposition (4). It therefore becomes pertinent to look at newborn metabolism, development, and nutritional requirements to understand the onset of child and adult physiological conditions. Pregnancy, childbirth, lactation, and human milk composition are influenced by the intake of foods (macro-and micronutrients), which subsequently determine both the short-and long-term health and nutritional outcomes of an individual (5). Therefore, it is essential to build knowledge not only on the metabolic dysregulations but also on the effects of specific foods to comprehensively document the metabolic processes associated with individual health at the different stages of the life cycle (Figure 1). This will allow us to formulate nutritional solutions to improve or circumvent certain metabolic imbalances.The generalization of systems biology approaches has opened new research areas to further our current understanding of many physiological processes, as well as the functionalities of foods and nutrients. In particular, metabolomics is recognized as a powerful top-down systems biology approach to understand the genetic-environment-health paradigm and to identify clinically relevant biomarkers. Its application in clinical studies is envisioned to shed new light on the regulatory processes of complex mammalian sy...

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Section: Metabolomics Technologies In Clinical Studiesmentioning

confidence: 99%

Metabolomics perspectives in pediatric research

et al. 2013

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“…An NMR-based study on epithelial cancer detection (Odunsi et al, 2005) had a perfect-fitting model with an AUC=1.0 combining two signals. In this context, Bahado-Singh et al (Bahado-Singh et al, 2012) reported that with three metabolites cases can be distinguished from controls. Altogether, these findings demonstrate that there are several results on combining biomarkers in a model, although it is important to find the simplest combination of metabolites that can produce a proper effective outcome (Xia et al, 2013).…”

Section: Biomarkers Associated With Wine Exposure and Classification mentioning

confidence: 99%

An NMR metabolomics approach reveals a combined-biomarkers model in a wine interventional trial with validation in free-living individuals of the PREDIMED study

et al. 2014

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The development of robust biomarkers of consumption would improve the classification of participants with regard to their dietary exposure. In addition, validation of them in free-living individuals remains an important challenge. The aim of this study is to assess wine intake biomarkers using an NMR metabolomic approach to measure the utility of these biomarkers in a wine interventional study (WIS, n=56) and also to evaluate them in a free-living individuals (PREDIMED study, n=91). Nine metabolites showed a significantly higher presence in urinary excretion in the WIS after wine intake: five food metabolome metabolites (tartrate, ethyl glucuronide [EtG], 2,3-butanediol, mannitol, and ethanol); one related to the endogenous response to wine exposure (3-methyl-2-oxovalerate) and three unidentified compounds. Receiver operating characteristic (ROC) curve for each single metabolite were evaluated and exhibited areas under the curves (AUC) between 67.4% -86.3% when they were evaluated individually. Then, a logistic regression model was fitted to generate a combined-biomarkers model using these metabolites. The model generated which included tartrate-EtG, showed an AUC of 90.7% in WIS. Similarly, the AUC in the PREDIMED study, the biomarker model was 92.4%. Results showed that a model combining tartrate-EtG is more useful for evaluating exposure to wine than single biomarkers, both in interventional studies and epidemiological data. To our knowledge, this is the first time that a combined-biomarker model using an NMR platform in wine biomarkers' research has been generated and reproduced in a free-living population.2

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“…[12][13][14][15] Our group recently reported the feasibility of accurate first-trimester nuclear magnetic resonance (NMR)-based metabolomic prediction for both early and late PE. 16,17 It is important that the performance of the identified biomarkers be validated to reduce the risk of overfitting and overly optimistic estimates of diagnostic accuracy. 18 In this manuscript we report the results of a validation study to determine the diagnostic accuracy of the metabolomic biomarkers for the first-trimester prediction of early PE.…”

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confidence: 99%