Analysis of NMR Metabolomics Data

2020 ◽  
pp. 61-97 ◽  
Author(s):  
Wimal Pathmasiri ◽  
Kristine Kay ◽  
Susan McRitchie ◽  
Susan Sumner
Keyword(s):  
Metabolomics Data ◽  
Nmr Metabolomics

scholarly journals Metabolomics: population epidemiology and concordance in Australian children aged 11–12 years and their parents

BMJ Open ◽  
2019 ◽  
Vol 9 (Suppl 3) ◽  
pp. 106-117 ◽  
Author(s):  
Susan Ellul ◽  
Melissa Wake ◽  
Susan A Clifford ◽  
Katherine Lange ◽  
Peter Würtz ◽  
...  
Keyword(s):  
Fatty Acids ◽  
Energy Homeostasis ◽  
Population Based ◽  
Lipoprotein Subclass ◽  
Cross Sectional ◽  
Metabolomics Data ◽  
Nmr Metabolomics ◽  
Markers Of Inflammation ◽  
Age And Sex ◽  
Parent Child

ObjectivesNuclear magnetic resonance (NMR) metabolomics is high throughput and cost-effective, with the potential to improve the understanding of disease and risk. We examine the circulating metabolic profile by quantitative NMR metabolomics of a sample of Australian 11–12 year olds children and their parents, describe differences by age and sex, and explore the correlation of metabolites in parent–child dyads.DesignThe population-based cross-sectional Child Health CheckPoint study nested within the Longitudinal Study of Australian Children.SettingBlood samples collected from CheckPoint participants at assessment centres in seven Australian cities and eight regional towns; February 2015–March 2016.Participants1180 children and 1325 parents provided a blood sample and had metabolomics data available. This included 1133 parent–child dyads (518 mother–daughter, 469 mother–son, 68 father–daughter and 78 father–son).Outcome measures228 metabolic measures were obtained for each participant. We focused on 74 biomarkers including amino acid species, lipoprotein subclass measures, lipids, fatty acids, measures related to fatty acid saturation, and composite markers of inflammation and energy homeostasis.ResultsWe identified differences in the concentration of specific metabolites between childhood and adulthood and in metabolic profiles in children and adults by sex. In general, metabolite concentrations were higher in adults than children and sex differences were larger in adults than in children. Positive correlations were observed for the majority of metabolites including isoleucine (CC 0.33, 95% CI 0.27 to 0.38), total cholesterol (CC 0.30, 95% CI 0.24 to 0.35) and omega 6 fatty acids (CC 0.28, 95% CI 0.23 to 0.34) in parent–child comparisons.ConclusionsWe describe the serum metabolite profiles from mid-childhood and adulthood in a population-based sample, together with a parent–child concordance. Differences in profiles by age and sex were observed. These data will be informative for investigation of the childhood origins of adult non-communicable diseases and for comparative studies in other populations.

scholarly journals Direct Implementation of Intestinal Permeability Test in NMR Metabolomics for Simultaneous Biomarker Discovery—A Feasibility Study in a Preterm Piglet Model

Metabolites ◽  
2020 ◽  
Vol 10 (1) ◽  
pp. 22 ◽  
Author(s):  
Masoumeh Alinaghi ◽  
Duc Ninh Nguyen ◽  
Per Torp Sangild ◽  
Hanne Christine Bertram
Keyword(s):  
Intestinal Permeability ◽  
1H Nmr ◽  
Biomarker Discovery ◽  
Urine Samples ◽  
Gastrointestinal Disorders ◽  
Urinary Recovery ◽  
Metabolomics Data ◽  
Enzymatic Assays ◽  
Nmr Metabolomics ◽  
Plasma And Urine Samples

Measurement of intestinal permeability (IP) is often used in the examination of inflammatory gastrointestinal disorders. IP can be assessed by measurement of urinary recovery of ingested non-metabolizable lactulose (L) and mannitol (M). The present study aimed to examine how measurements of IP can be integrated in a NMR-based metabolomics approach for a simultaneous quantification of L/M ratio and biomarker exploration. For this purpose, plasma and urine samples were collected from five-day-old preterm piglets (n = 20) with gastrointestinal disorders (subjected to intra-amniotic lipopolysaccharide (LPS, 1 mg/fetus)) after they had been administrated a 5% lactulose and 5% mannitol solution (15 mL/kg). The collected plasma and urine samples were analyzed by 1H NMR-based metabolomics. Urine L/M ratio measured by 1H NMR spectroscopy showed high correlation with the standard measurement of the urinary recoveries by enzymatic assays (r = 0.93, p < 0.05). Partial least squares (PLS) regressions and correlation analyses between L/M ratio and NMR metabolomics data revealed that L/M ratio was positively correlated with plasma lactate, acetate and succinate levels and negatively correlated with urinary hippuric acid and glycine. In conclusion, the present study demonstrated that NMR metabolomics enables simultaneous IP testing and discovery of biomarkers associated with an impaired intestinal permeability.

scholarly journals Proof of concept for quantitative urine NMR metabolomics pipeline for large-scale epidemiology and genetics

2019 ◽  
Vol 48 (3) ◽  
pp. 978-993 ◽  
Author(s):  
Tuulia Tynkkynen ◽  
Qin Wang ◽  
Jussi Ekholm ◽  
Olga Anufrieva ◽  
Pauli Ohukainen ◽  
...  
Keyword(s):  
Nmr Spectroscopy ◽  
Large Scale ◽  
Molecular Data ◽  
Population Based ◽  
Urine Samples ◽  
Metabolomics Data ◽  
Urine Metabolites ◽  
Nmr Metabolomics ◽  
Genome Wide ◽  
Metabolic Information

Abstract Background Quantitative molecular data from urine are rare in epidemiology and genetics. NMR spectroscopy could provide these data in high throughput, and it has already been applied in epidemiological settings to analyse urine samples. However, quantitative protocols for large-scale applications are not available. Methods We describe in detail how to prepare urine samples and perform NMR experiments to obtain quantitative metabolic information. Semi-automated quantitative line shape fitting analyses were set up for 43 metabolites and applied to data from various analytical test samples and from 1004 individuals from a population-based epidemiological cohort. Novel analyses on how urine metabolites associate with quantitative serum NMR metabolomics data (61 metabolic measures; n = 995) were performed. In addition, confirmatory genome-wide analyses of urine metabolites were conducted (n = 578). The fully automated quantitative regression-based spectral analysis is demonstrated for creatinine and glucose (n = 4548). Results Intra-assay metabolite variations were mostly <5%, indicating high robustness and accuracy of urine NMR spectroscopy methodology per se. Intra-individual metabolite variations were large, ranging from 6% to 194%. However, population-based inter-individual metabolite variations were even larger (from 14% to 1655%), providing a sound base for epidemiological applications. Metabolic associations between urine and serum were found to be clearly weaker than those within serum and within urine, indicating that urinary metabolomics data provide independent metabolic information. Two previous genome-wide hits for formate and 2-hydroxyisobutyrate were replicated at genome-wide significance. Conclusion Quantitative urine metabolomics data suggest broad novelty for systems epidemiology. A roadmap for an open access methodology is provided.

An integrative approach based on GC–qMS and NMR metabolomics data as a comprehensive strategy to search potential breast cancer biomarkers

Metabolomics ◽  
2021 ◽  
Vol 17 (8) ◽  
Author(s):  
Catarina Luís Silva ◽  
Rosa Perestrelo ◽  
Filipa Capelinha ◽  
Helena Tomás ◽  
José S. Câmara
Keyword(s):  
Breast Cancer ◽  
Cancer Biomarkers ◽  
Integrative Approach ◽  
Metabolomics Data ◽  
Comprehensive Strategy ◽  
Nmr Metabolomics ◽  
Breast Cancer Biomarkers

scholarly journals Evaluation of Multivariate Classification Models for Analyzing NMR Metabolomics Data

2019 ◽  
Vol 18 (9) ◽  
pp. 3282-3294 ◽  
Author(s):  
Thao Vu ◽  
Parker Siemek ◽  
Fatema Bhinderwala ◽  
Yuhang Xu ◽  
Robert Powers
Keyword(s):  
Classification Models ◽  
Multivariate Classification ◽  
Metabolomics Data ◽  
Nmr Metabolomics

scholarly journals Improved classification accuracy in 1- and 2-dimensional NMR metabolomics data using the variance stabilising generalised logarithm transformation

2007 ◽  
Vol 8 (1) ◽  
pp. 234 ◽  
Author(s):  
Helen M Parsons ◽  
Christian Ludwig ◽  
Ulrich L Günther ◽  
Mark R Viant
Keyword(s):  
Classification Accuracy ◽  
Metabolomics Data ◽  
Nmr Metabolomics

Targeted Profiling:  Quantitative Analysis of1H NMR Metabolomics Data

2006 ◽  
Vol 78 (13) ◽  
pp. 4430-4442 ◽  
Author(s):  
Aalim M. Weljie ◽  
Jack Newton ◽  
Pascal Mercier ◽  
Erin Carlson ◽  
Carolyn M. Slupsky
Keyword(s):  
Quantitative Analysis ◽  
Metabolomics Data ◽  
Nmr Metabolomics ◽  
Targeted Profiling

scholarly journals 1H-NMR metabolomics-based surrogates to impute common clinical risk factors and endpoints

2021 ◽  
Author(s):  
Daniele Bizzarri ◽  
Marcel J.T. Reinders ◽  
Marian Beekman ◽  
Pieternella Eline Slagboom ◽  
Erik B van den Akker ◽  
...  
Keyword(s):  
1H Nmr ◽  
Statistical Power ◽  
Association Studies ◽  
Epidemiological Studies ◽  
Medication Usage ◽  
Metabolomics Data ◽  
Data Resource ◽  
Nmr Metabolomics ◽  
Clinical Variables ◽  
Diabetes Status

Missing or incomplete phenotypic information can severely deteriorate the statistical power in epidemiological studies. High-throughput quantification of small-molecules in bio-samples, i.e. metabolomics, is steadily gaining popularity, as it is highly informative for various phenotypical characteristics. Here we aim to leverage metabolomics to impute missing data in clinical variables routinely assessed in large epidemiological and clinical studies. To this end, we have employed ~25,000 1H-NMR metabolomics samples from 28 Dutch cohorts collected within the BBMRI-NL consortium, to create 19 metabolomics-based predictors for clinical variables, including diabetes status (AUC5-Fold CV = 0.94) and lipid medication usage (AUC5-Fold CV = 0.90). Subsequent application in independent cohorts confirmed that our metabolomics-based predictors can indeed be used to impute a wide array of missing clinical variables from a single metabolomics data resource. In addition, application highlighted the potential use of our predictors to explore the effects of totally unobserved confounders in omics association studies. Finally, we show that our predictors can be used to explore risk factor profiles contributing to mortality in older participants. To conclude, we provide 1H-NMR metabolomics-based models to impute clinical variables routinely assessed in epidemiological studies and illustrate their merit in scenarios when phenotypic variables are partially incomplete or totally unobserved.

scholarly journals Variance stabilising transformations for NMR metabolomics data

2007 ◽  
Vol 1 (S1) ◽  
Author(s):  
Helen Parsons ◽  
Mark Viant
Keyword(s):  
Metabolomics Data ◽  
Nmr Metabolomics
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