High-throughput serum NMR metabonomics for cost-effective holistic studies on systemic metabolism

Soininen, Pasi; Kangas, Antti J.; Würtz, Peter; Tukiainen, Taru; Tynkkynen, Tuulia; Laatikainen, Reino; Järvelin, Marjo‐Riitta; Kähönen, Mika; Lehtimäki, Terho; Viikari, Jorma; Raitakari, Olli T.; Savolainen, Markku J.; Ala‐Korpela, Mika

doi:10.1039/b910205a

Cited by 498 publications

(536 citation statements)

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“…The sample preparation and NMR spectroscopy methods have been described in detail elsewhere [20,21]. The NMR metabolomics methodology provides quantitative information on lipoprotein subclass and particle concentrations, serum FAs including, e.g.…”

Section: The Nmr Metabolomics Platformmentioning

confidence: 99%

Abdominal obesity and circulating metabolites: A twin study approach

et al. 2016

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Objective. To investigate how obesity, insulin resistance and low-grade inflammation link to circulating metabolites, and whether the connections are due to genetic or environmental factors.Subjects and methods. Circulating serum metabolites were determined by proton NMR spectroscopy. Data from 1368 (531 monozygotic (MZ) and 837 dizygotic (DZ)) twins were used for bivariate twin modeling to derive the genetic (r g ) and environmental (r e ) correlations between waist circumference (WC) and serum metabolites. Detailed examination of the associations between fat distribution (DEXA) and metabolic health (HOMA-IR, CRP) was performed among 286 twins including 33 BMI-discordant MZ pairs. Results. Fat, especially in the abdominal area (i.e. WC, android fat % and android to gynoid fat ratio), together with HOMA-IR and CRP correlated significantly with an atherogenic lipoprotein profile, higher levels of branched-chain (BCAA) and aromatic amino acids, higher levels of glycoprotein, and a more saturated fatty acid profile. In contrast, a higher proportion of gynoid to total fat associated with a favorable metabolite profile. There was a significant genetic overlap between WC and several metabolites, most strongly with phenylalanine (r g = 0.40), glycoprotein (r g = 0.37), serum triglycerides (r g = 0.36), BCAAs (r g = 0.30-0.40), HDL Keywords:Twin study Bivariate twin model 2 0 1 6 ) 1 1 Conclusions. A wide range of unfavorable alterations in the serum metabolome was associated with abdominal obesity, insulin resistance and low-grade inflammation. Twin modeling and obesity-discordant twin analysis suggest that these associations are partly explained by shared genes but also reflect mechanisms independent of genetic liability. Genetic correlation Serum metabolites Obesity measures M E T A B O L I S M C L I N I C A L A N D E X P E R I M E N T A L 6 5 (

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Section: The Nmr Metabolomics Platformmentioning

confidence: 99%

Abdominal obesity and circulating metabolites: A twin study approach

et al. 2016

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“…The spectra were referenced to TSP (rCH 3 = 0 ppm). Total lipids in 12 lipoprotein subclasses were quantified using a calibrated regression model (Ala-Korpela, 2008;Kettunen et al, 2012;Soininen et al, 2009;van den Berg et al, 2009). The subclasses were defined via highperformance liquid chromatography (Okazaki et al, 2005) and are as follows: very large and large VLDL (average particle diameter 58.8 nm, (X)L-VLDL) medium VLDL (44.5 nm, M-VLDL), small VLDL (36.8 nm, S-VLDL), very small VLDL (31.3 nm, XS-VLDL), IDL (28.6 nm), large LDL (25.5 nm, L-LDL), medium LDL (23.0 nm, M-LDL), small LDL (18.7 nm, S-LDL), very large HDL (14.3 nm, VL-HDL), large HDL (12.1 nm, L-HDL), medium HDL (10.9 nm, M-HDL), and small HDL (8.7 nm, S-HDL).…”

Section: Nmr-based Metabolite Profilingmentioning

confidence: 99%

Gender-Dependent Associations of Metabolite Profiles and Body Fat Distribution in a Healthy Population with Central Obesity: Towards Metabolomics Diagnostics

Szymańska

Bouwman

Strassburg

et al. 2012

OMICS: A Journal of Integrative Biology

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Gender-dependent associations of metabolite profiles and body fat distribution in a healthy population with central obesity: towards metabolomics diagnostics Szymanska, E.; Bouwman, J.; Strassburg, K.; Vervoort, J.; Kangas, A.J.; Soininen, P.; AlaKorpela, M.; Westerhuis, J.A.; van Duynhoven, J.P.M.; Mela, D.J.; Macdonald, I.A.; Vreeken, R.J.; Smilde, A.K.; Jacobs, D.M. Published in: Omics DOI:10.1089/omi.2012.0062 Link to publication Citation for published version (APA):Szymaska, E., Bouwman, J., Strassburg, K., Vervoort, J., Kangas, A. J., Soininen, P., ... Jacobs, D. M. (2012). Gender-dependent associations of metabolite profiles and body fat distribution in a healthy population with central obesity: towards metabolomics diagnostics. Omics, 16(12), 652-667. DOI: 10.1089/omi.2012.0062 General rightsIt is not permitted to download or to forward/distribute the text or part of it without the consent of the author(s) and/or copyright holder(s), other than for strictly personal, individual use, unless the work is under an open content license (like Creative Commons). Disclaimer/Complaints regulationsIf you believe that digital publication of certain material infringes any of your rights or (privacy) interests, please let the Library know, stating your reasons. In case of a legitimate complaint, the Library will make the material inaccessible and/or remove it from the website. Please Ask the Library: http://uba.uva.nl/en/contact, or a letter to: Library of the University of Amsterdam, Secretariat, Singel 425, 1012 WP Amsterdam, The Netherlands. You will be contacted as soon as possible. AbstractObesity is a risk factor for cardiovascular diseases and type 2 diabetes especially when the fat is accumulated to central depots. Novel biomarkers are crucial to develop diagnostics for obesity and related metabolic disorders. We evaluated the associations between metabolite profiles (136 lipid components, 12 lipoprotein subclasses, 17 low-molecular-weight metabolites, 12 clinical markers) and 28 phenotype parameters (including different body fat distribution parameters such as android (A), gynoid (G), abdominal visceral (VAT), subcutaneous (SAT) fat) in 215 plasma/serum samples from healthy overweight men (n = 32) and women (n = 83) with central obesity.(Partial) correlation analysis and partial least squares (PLS) regression analysis showed that only specific metabolites were associated to A:G ratio, VAT, and SAT, respectively. These association patterns were gender dependent. For example, insulin, cholesterol, VLDL, and certain triacylglycerols (TG 54:1-3) correlated to VAT in women, while in men VAT was associated with TG 50:1-5, TG 55:1, phosphatidylcholine (PC 32:0), and VLDL ((X)L). Moreover, multiple regression analysis revealed that waist circumference and total fat were sufficient to predict VAT and SAT in women. In contrast, only VAT but not SAT could be predicted in men and only when plasma metabolites were included, with PC 32:0 being most strongly associated with VAT. These findings collectively highlight the p...

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“…Clearly, NMR-based metabolomics is an appropriate tool for high-throughput analysis of different body fluids as a broad range of metabolites can be quantified simultaneously and no complicated sample preparation protocol is required [23]. showed that the application of 1 H NMR spectroscopy to urine samples collected from T1DM children with different levels of glycated hemoglobin allowed for the monitoring of metabolic changes in the patient groups [31].…”

Section: Nuclear Magnetic Resonance Spectroscopymentioning

confidence: 99%

Section: Nuclear Magnetic Resonance Spectroscopymentioning

confidence: 99%

Metabolomics in critical care medicine: a new approach to biomarker discovery

Banoei¹,

Donnelly²,

Mickiewicz³

et al. 2014

CIM

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Metabolomics in critical care medicine: a new approach to biomarker discovery Abstract Purpose: To present an overview and comparison of the main metabolomics techniques ( 1 H NMR, GC-MS, and LC-MS) and their current and potential use in critical care medicine.Source: This is a focused review, not a systematic review, using the PubMed database as the predominant source of references to compare metabolomics techniques.Principal Findings: 1 H NMR, GC-MS, and LC-MS are complementary techniques that can be used on a variety of biofluids for metabolomics analysis of patients in the Intensive Care Unit (ICU). These techniques have been successfully used for diagnosis and prognosis in the ICU and other clinical settings; for example, in patients with septic shock and community-acquired pneumonia. Conclusion:Metabolomics is a powerful tool that has strong potential to impact diagnosis and prognosis and to examine responses to treatment in critical care medicine through diagnostic and prognostic biomarker and biopattern identification. REVIEW ARTICLE © 2014 CIMClin Invest Med • Vol 37, no 6, December 2014 E363 Metabolomics refers to the systems level analysis of the metabolism and metabolites in response to physiological stimuli, such as disease or drug administration [1]. The application of clinical metabolomics to the complex illnesses of critical care medicine is relatively new [1,2]. Metabolomics, which is most commonly based on nuclear magnetic resonance (NMR) spectroscopy, gas chromatography-mass spectrometry (GC-MS) and/or liquid chromatography-mass spectrometry (LC-MS) platforms, provides an accurate reflection of the metabolic processes and pathways at play within the human body at a particular moment in time [1]. History of metabolomicsWhile the terms metabolomics and metabonomics are now used interchangeably, their original definitions focused on the level of analysis and the type of analytical technology used. This paper uses the term metabolomics as the terms are often treated as interchangeable and it is more commonly used in the literature than the term metabonomics [1,2]. Nicholson, who first defined metabonomics in 1999, described it as a systemsbased strategy to "measure the global, dynamic metabolic response of living systems to biological stimuli or genetic manipulation" [2]. Conversely, metabolomics was defined as the identification, analysis, and quantification of each metabolite within a biosample [3]. Metabolomics and metabonomics can also be defined by their method of analysis: metabolomics was originally described as a GC-MS-based approach in the study of plant metabolomes [3] and metabonomics was originally defined as NMR-based study of mammalian systems [2]. Although metabolomics and metabonomics were not defined until the late 1990s, biofluid analysis by GC-MS was first reported in the 1960s and 1970s [4]. The basis of today's metabolomics field started in the early 1980s, when NMR technology became adequately sensitive for biofluid metabolite identification. Combined with the use of statisti...

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High-throughput serum NMR metabonomics for cost-effective holistic studies on systemic metabolism

Cited by 498 publications

References 21 publications

Abdominal obesity and circulating metabolites: A twin study approach

Abdominal obesity and circulating metabolites: A twin study approach

Gender-Dependent Associations of Metabolite Profiles and Body Fat Distribution in a Healthy Population with Central Obesity: Towards Metabolomics Diagnostics

Metabolomics in critical care medicine: a new approach to biomarker discovery

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