Metabolic profiles of placenta in preeclampsia using HR-MAS MRS metabolomics

Austdal, Marie; Thomsen, Liv Cecilie Vestrheim; Tangerås, Line Haugstad; Skei, Bente; Mathew, Seema; Bjørge, Line; Austgulen, Rigmor; Bathen, Tone Frost; Iversen, Ann‐Charlotte

doi:10.1016/j.placenta.2015.10.019

Cited by 56 publications

(58 citation statements)

References 97 publications

(103 reference statements)

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“…Blood plasma was the most commonly used biologic media (n = 10) (Bahado-Singh, Akolekar, Mandal, Dong, Xia, Kruger, Wishart et al 2012; De Oliveira, Câmara, Bonetti, Turco, Bertolla, Moron, Sass et al 2012; Kelly, Croteau-Chonka, Dahlin, Mirzakhani, Wu, Wan, McGeachie et al 2016; Kenny, Broadhurst, Dunn, Brown, North, McCowan, Roberts et al 2010; Kenny, Dunn, Ellis, Myers, Baker, Kell 2005; Odibo, Goetzinger, Odibo, Cahill, Macones, Nelson, Dietzen 2011; Pinto, Almeida, Martins, Duarte, Barros, Galhano, Pita et al 2015; Schott, Hahn, Kurbacher, Moka 2012; Turner, Brewster, Simpson, Walker, Fisher 2007; Turner, Brewster, Simpson, Walker, Fisher 2008), followed by serum (n = 7) (Bahado-Singh, Akolekar, Mandal, Dong, Xia, Kruger, Wishart et al 2013; Bahado-Singh, Syngelaki, Akolekar, Mandal, Bjondahl, Han, Dong et al 2015; Chen, He, Tan, Xu 2017; Kenny, Broadhurst, Brown, Dunn, Redman, Kell, Baker 2008; Koster, Vreeken, Harms, Dane, Kuc, Schielen, Hankemeier et al 2015; Kuc, Koster, Pennings, Hankemeier, Berger, Harms, Dane et al 2014), and placental or intrauterine tissue samples (n = 7) (Austdal, Thomsen, Tangerås, Skei, Mathew, Bjørge, Austgulen et al 2015; Baig, Lim, Fernandis, Wenk, Kale, Su, Biswas et al 2013; Dunn, Brown, Worton, Davies, Jones, Kell, Heazell 2012; Jain, Jayasimhulu, Clark 2004; Korkes, Sass, Moron, Câmara, Bonetti, Cerdeira, Da Silva et al 2014; Pearson, Zhang, Arya, Warren, Ortori, Fakis, Khan et al 2010; Sohlberg, Wikström, Olovsson, Lindgren, Axelsson, Mulic-Lutvica, Weis et al). The remaining studies used urine (n = 1) (Diaz, Barros, Goodfellow, Duarte, Galhano, Pita, Almeida et al 2013), urine and serum (n = 2) (Austdal, Skråstad, Gundersen, Austgulen, Iversen, Bathen 2014; Austdal, Tangerås, Skråstad, Salvesen, Austgulen, Iversen, Bathen 2015), or breast milk (n=1) (Dangat, Upadhyay, Kilari, Sharma, Kemse, Mehendale, Lalwani et al 2016).…”

Section: Resultsmentioning

confidence: 99%

“…The lowest accuracy was produced by a combination of triglycerides, 3-hydroxybutyrate, pyruvate, phosphatidylcholine, and lactose measured in serum, yielding an accuracy of 64.6%, 63.8% sensitivity, and 65.4% specificity (Austdal, Tangerås, Skråstad, Salvesen, Austgulen, Iversen, Bathen 2015). Austdal et al (2015) (Austdal, Thomsen, Tangerås, Skei, Mathew, Bjørge, Austgulen et al 2015) compared the ability of metabolomic biomarkers to improve upon traditionally used biomarkers of preeclampsia; a combination of mean arterial pressure (MAP), maternal age, and uterine artery pulsatility index (UtAPI) resulted in a model with an AUC of 0.738, which increased to 0.807 when including the ratio of hippurate to creatinine in urine. Similarly, Kelly et al (2016) (Kelly, Croteau-Chonka, Dahlin, Mirzakhani, Wu, Wan, McGeachie et al 2016) reported that the inclusion of a summary score based on 72 plasma metabolites significantly outperformed a baseline model including maternal age and race, study site, and gestational age (AUC: 0.573 versus AUC: 0.753).…”

Section: Resultsmentioning

confidence: 99%

“…Seventeen studies compared metabolomic profiles between cases and controls either at or post diagnosis (Austdal, Skråstad, Gundersen, Austgulen, Iversen, Bathen 2014; Austdal, Thomsen, Tangerås, Skei, Mathew, Bjørge, Austgulen et al 2015; Braekke, Ueland, Harsem, Karlsen, Blomhoff, Staff 2007; Dunn, Brown, Worton, Davies, Jones, Kell, Heazell 2012; Kenny, Broadhurst, Brown, Dunn, Redman, Kell, Baker 2008; Kenny, Dunn, Ellis, Myers, Baker, Kell 2005; Schott, Hahn, Kurbacher, Moka 2012; Turner, Brewster, Simpson, Walker, Fisher 2007; Turner, Brewster, Simpson, Walker, Fisher 2008). Phospholipids, lipoproteins (n = 5) (Austdal, Skråstad, Gundersen, Austgulen, Iversen, Bathen 2014; Dunn, Brown, Worton, Davies, Jones, Kell, Heazell 2012; Korkes, Sass, Moron, Câmara, Bonetti, Cerdeira, Da Silva et al 2014; Schott, Hahn, Kurbacher, Moka 2012; Turner, Brewster, Simpson, Walker, Fisher 2007), creatinine (n = 2) (Braekke, Ueland, Harsem, Karlsen, Blomhoff, Staff 2007; Kenny, Broadhurst, Brown, Dunn, Redman, Kell, Baker 2008), histidine (n = 2,) (Austdal, Skråstad, Gundersen, Austgulen, Iversen, Bathen 2014; Turner, Brewster, Simpson, Walker, Fisher 2008), glutamate-related metabolites (n = 2) (Austdal, Thomsen, Tangerås, Skei, Mathew, Bjørge, Austgulen et al 2015; Kenny, Broadhurst, Brown, Dunn, Redman, Kell, Baker 2008), glycine-related metabolites (n = 2) (Austdal, Skråstad, Gundersen, Austgulen, Iversen, Bathen 2014; Dunn, Brown, Worton, Davies, Jones, Kell, Heazell 2012), and alanine-related metabolites (n = 3) (Dunn, Brown, Worton, Davies, Jones, Kell, Heazell 2012; Kenny, Broadhurst, Brown, Dunn, Redman, Kell, Baker 2008; Turner, Brewster, Simpson, Walker, Fisher 2008) were all significant (p < 0.05) in at least two studies.…”

Section: Resultsmentioning

confidence: 99%

“…Phospholipids, lipoproteins (n = 5) (Austdal, Skråstad, Gundersen, Austgulen, Iversen, Bathen 2014; Dunn, Brown, Worton, Davies, Jones, Kell, Heazell 2012; Korkes, Sass, Moron, Câmara, Bonetti, Cerdeira, Da Silva et al 2014; Schott, Hahn, Kurbacher, Moka 2012; Turner, Brewster, Simpson, Walker, Fisher 2007), creatinine (n = 2) (Braekke, Ueland, Harsem, Karlsen, Blomhoff, Staff 2007; Kenny, Broadhurst, Brown, Dunn, Redman, Kell, Baker 2008), histidine (n = 2,) (Austdal, Skråstad, Gundersen, Austgulen, Iversen, Bathen 2014; Turner, Brewster, Simpson, Walker, Fisher 2008), glutamate-related metabolites (n = 2) (Austdal, Thomsen, Tangerås, Skei, Mathew, Bjørge, Austgulen et al 2015; Kenny, Broadhurst, Brown, Dunn, Redman, Kell, Baker 2008), glycine-related metabolites (n = 2) (Austdal, Skråstad, Gundersen, Austgulen, Iversen, Bathen 2014; Dunn, Brown, Worton, Davies, Jones, Kell, Heazell 2012), and alanine-related metabolites (n = 3) (Dunn, Brown, Worton, Davies, Jones, Kell, Heazell 2012; Kenny, Broadhurst, Brown, Dunn, Redman, Kell, Baker 2008; Turner, Brewster, Simpson, Walker, Fisher 2008) were all significant (p < 0.05) in at least two studies. Increased creatinine, alanine, and phenylalanine levels were consistently associated with preeclampsia, while glycine, glutamate, and glutamine levels were inversely associated.…”

Section: Resultsmentioning

confidence: 99%

“…The most accurate diagnostic model was developed in placental tissue; it included glycerophosphocholine (GPC), phosphocholine (PCho), aspartate, ascorbate, ethanolamine (EtAm), taurine, glutamate, and glycine, and produced an AUC of 0.927, with a sensitivity of 87%, and a specificity of 98% (Austdal, Thomsen, Tangerås, Skei, Mathew, Bjørge, Austgulen et al 2015). While phosphatidylcholine (14:0/00) measured in serum had an AUC of 0.935 (Chen, He, Tan, Xu 2017).…”

Section: Resultsmentioning

confidence: 99%

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Applications of metabolomics in the study and management of preeclampsia: a review of the literature

et al. 2017

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Introduction: Preeclampsia represents a major public health burden worldwide, but predictive and diagnostic biomarkers are lacking. Metabolomics is emerging as a valuable approach to generating novel biomarkers whilst increasing the mechanistic understanding of this complex condition. Objectives: To summarize the published literature on the use of metabolomics as a tool to study preeclampsia. Methods: PubMed and Web of Science were searched for articles that performed metabolomic profiling of human biosamples using either Mass-spectrometry or Nuclear Magnetic Resonance based approaches and which included preeclampsia as a primary endpoint. Results: Twenty-eight studies investigating the metabolome of preeclampsia in a variety of biospecimens were identified. Individual metabolite and metabolite profiles were reported to have discriminatory ability to distinguish preeclamptic from normal pregnancies, both prior to and post diagnosis. Lipids and carnitines were among the most commonly reported metabolites. Further work and validation studies are required to demonstrate the utility of such metabolites as preeclampsia biomarkers. Conclusion: Metabolomic-based biomarkers of preeclampsia have yet to be integrated into routine clinical practice. However, metabolomic profiling is becoming increasingly popular in the study of preeclampsia and is likely to be a valuable tool to better understand the pathophysiology of this disorder and to better classify its subtypes, particularly when integrated with other omic data.

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Section: Resultsmentioning

confidence: 99%

Section: Resultsmentioning

confidence: 99%

Section: Resultsmentioning

confidence: 99%

Section: Resultsmentioning

confidence: 99%

Section: Resultsmentioning

confidence: 99%

See 3 more Smart Citations

Applications of metabolomics in the study and management of preeclampsia: a review of the literature

et al. 2017

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Placental metabolic profiling in gestational diabetes mellitus: An important role of fatty acids

Yang

Pan

Guo

et al. 2021

Clinical Laboratory Analysis

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Aim Gestational diabetes mellitus (GDM) is the most common metabolic disorder during pregnancy. Accumulating studies have reported metabolites that are significantly associated with the development of GDM. However, studies on the metabolism of placenta, the most important organ of maternal‐fetal energy and material transport, are extremely rare. This study aimed to identify and discuss the relationship between differentially expressed metabolites (DEM) and clinical parameters of the mothers and newborns. Methods In this study, metabolites from 63 placenta tissues (32 GDM and 31 normal controls) were assayed by ultra‐performance liquid chromatography‐high resolution mass spectrometry (UPLC‐HRMS). Results A total of 1297 annotated metabolites were detected, of which 87 significantly different in GDM placenta. Lipids and lipid‐like molecules accounted for 62.1% of DEM as they were significantly enriched via the “biosynthesis of unsaturated fatty acids” and “fatty acid biosynthesis” pathways. Linoleic acid and α‐linolenic acid appeared to be good biomarkers for the prediction and diagnosis of GDM. In addition, the level of PC(14:0/18:0) was negatively correlated with neonatal weight. 14 metabolites significantly different in male and female offspring, with the most increase in female newborns. Conclusion Even if maternal blood glucose level is well controlled, there are still metabolic abnormalities in GDM. Lipids and lipid‐like molecules were the main differential metabolites, especially unsaturated fatty acids.

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Biomarker Discovery Using NMR-Based Metabolomics of Tissue

Grinde

Giskeødegård

Andreassen

et al. 2019

NMR-Based Metabolomics

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NMR-based metabolomics has shown promise in the diagnosis of diseases as it enables identification and quantification of metabolic biomarkers. Using high-resolution magic-anglespinning (HR-MAS) NMR spectroscopy, metabolic profiles from intact tissue specimens can be obtained with high spectral resolution. In addition, HR-MAS NMR requires minimal sample preparation and the sample is kept intact for subsequent analyses. In this chapter we describe a typical protocol for NMR based metabolomics of tissue samples. We cover all major steps ranging from tissue sample collection to determination of biomarkers, including experimental precautions taken to ensure reproducible and reliable reporting of data in the area of clinical application.

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Metabolic profiles of placenta in preeclampsia using HR-MAS MRS metabolomics

Cited by 56 publications

References 97 publications

Applications of metabolomics in the study and management of preeclampsia: a review of the literature

Applications of metabolomics in the study and management of preeclampsia: a review of the literature

Placental metabolic profiling in gestational diabetes mellitus: An important role of fatty acids

Biomarker Discovery Using NMR-Based Metabolomics of Tissue

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