Extended automated quantification algorithm (AQuA) for targeted 1H NMR metabolomics of highly complex samples: application to plant root exudates.

INTRODUCTION: The Automated Quantification Algorithm (AQuA) is a rapid and efficient method for targeted NMR-based metabolomics, currently optimised for blood plasma. AQuA quantifies metabolites from 1D-1H NMR spectra based on the height of only one signal per metabolite, which minimises the computational time and workload of the method without compromising the quantification accuracy. OBJECTIVES: To develop a fast and computationally efficient extension of AQuA for quantification of selected metabolites in highly complex samples, with minimal prior sample preparation. In particular, the method should be capable of handling interferences caused by broad background signals. METHODS: An automatic baseline correction function was combined with AQuA into an automated workflow, the extended AQuA, for quantification of metabolites in plant root exudate NMR spectra that contained broad background signals and baseline distortions. The approach was evaluated using simulations as well as a spike-in experiment in which known metabolite amounts were added to a complex sample matrix. RESULTS: The extended AQuA enables accurate quantification of metabolites in 1D-1H NMR spectra with varying complexity. The method is very fast (< 1 s per spectrum) and can be fully automated. CONCLUSIONS: The extended AQuA is an automated quantification method intended for 1D-1H NMR spectra containing broad background signals and baseline distortions. Although the method was developed for plant root exudates, it should be readily applicable to any NMR spectra displaying similar issues as it is purely computational and applied to NMR spectra post-acquisition.

Improved Automated Quantification Algorithm (AQuA) and Its Application to NMR-Based Metabolomics of EDTA-Containing Plasma.

We have recently presented an Automated Quantification Algorithm (AQuA) and demonstrated its utility for rapid and accurate absolute metabolite quantification in 1H NMR spectra in which positions and line widths of signals were predicted from a constant metabolite spectral library. The AQuA quantifies based on one preselected signal per metabolite and employs library spectra to model interferences from other metabolite signals. However, for some types of spectra, the interspectral deviations of signal positions and line widths can be pronounced; hence, interferences cannot be modeled using a constant spectral library. We here address this issue and present an improved AQuA that handles interspectral deviations. The improved AQuA monitors and characterizes the appearance of specific signals in each spectrum and automatically adjusts the spectral library to model interferences accordingly. The performance of the improved AQuA was tested on a large data set from plasma samples collected using ethylenediaminetetraacetic acid (EDTA) as an anticoagulant (n = 772). These spectra provided a suitable test system for the improved AQuA since EDTA signals (i) vary in intensity, position, and line width between spectra and (ii) interfere with many signals from plasma metabolites targeted for quantification (n = 54). Without the improvement, ca. 20 out of the 54 metabolites would have been overestimated. This included acetylcarnitine and ornithine, which are considered particularly difficult to quantify with 1H NMR in EDTA-containing plasma. Furthermore, the improved AQuA performed rapidly (<10 s for all spectra). We believe that the improved AQuA provides a basis for automated quantification in other data sets where specific signals show interspectral deviations.

Metabolomics analysis reveals altered metabolites in lean compared with obese adolescents and additional metabolic shifts associated with hyperinsulinaemia and insulin resistance in obese adolescents: a cross-sectional study.

INTRODUCTION: Hyperinsulinaemia and insulin resistance (IR) are strongly associated with obesity and are forerunners of type 2 diabetes. Little is known about metabolic alterations separately associated with obesity, hyperinsulinaemia/IR and impaired glucose tolerance (IGT) in adolescents. OBJECTIVES: To identify metabolic alterations associated with obesity, hyperinsulinaemia/IR and hyperinsulinaemia/IR combined with IGT in obese adolescents. METHODS: 81 adolescents were stratified into four groups based on body mass index (lean vs. obese), insulin responses (normal insulin (NI) vs. high insulin (HI)) and glucose responses (normal glucose tolerance (NGT) vs. IGT) after an oral glucose tolerance test (OGTT). The groups comprised: (1) healthy lean with NI and NGT, (2) obese with NI and NGT, (3) obese with HI and NGT, and (4) obese with HI and IGT. Targeted nuclear magnetic resonance-based metabolomics analysis was performed on fasting and seven post-OGTT plasma samples, followed by univariate and multivariate statistical analyses. RESULTS: Two groups of metabolites were identified: (1) Metabolites associated with insulin response level: adolescents with HI (groups 3-4) had higher concentrations of branched-chain amino acids and tyrosine, and lower concentrations of serine, glycine, myo-inositol and dimethylsulfone, than adolescents with NI (groups 1-2). (2) Metabolites associated with obesity status: obese adolescents (groups 2-4) had higher concentrations of acetylcarnitine, alanine, pyruvate and glutamate, and lower concentrations of acetate, than lean adolescents (group 1). CONCLUSIONS: Obesity is associated with shifts in fat and energy metabolism. Hyperinsulinaemia/IR in obese adolescents is also associated with increased branched-chain and aromatic amino acids.

Identification of metabolites associated with prostate cancer risk: a nested case-control study with long follow-up in the Northern Sweden Health and Disease Study.

BACKGROUND: Prostate cancer is the second most frequently diagnosed cancer in men. Metabolomics can potentially provide new insights into the aetiology of prostate cancer by identifying new metabolic risk factors. This study investigated the prospective association between plasma metabolite concentrations and prostate cancer risk, both overall and by stratifying for disease aggressiveness and baseline age. METHODS: In a case-control study nested in the Northern Sweden Health and Disease Study, pre-diagnostic concentrations of 148 plasma metabolites were determined using targeted mass spectrometry- and nuclear magnetic resonance-based metabolomics in 777 prostate cancer cases (follow-up ≥ 5 years) and 777 matched controls. Associations between prostate cancer risk and metabolite concentrations were investigated using conditional logistic regression conditioned on matching factors (body mass index, age and sample storage time). Corrections for multiple testing were performed using false discovery rate (20%) and Bonferroni. Metabolomics analyses generated new hypotheses, which were investigated by leveraging food frequency questionnaires (FFQs) and oral glucose tolerance tests performed at baseline. RESULTS: After correcting for multiple testing, two lysophosphatidylcholines (LPCs) were positively associated with risk of overall prostate cancer (all ages and in older subjects). The strongest association was for LPC C17:0 in older subjects (OR = 2.08; 95% CI 1.45-2.98; p < 0.0001, significant also after the Bonferroni correction). Observed associations with risk of overall prostate cancer in younger subjects were positive for glycine and inverse for pyruvate. For aggressive prostate cancer, there were positive associations with six glycerophospholipids (LPC C17:0, LPC C20:3, LPC C20:4, PC ae C38:3, PC ae C38:4 and PC ae C40:2), while there was an inverse association with acylcarnitine C18:2. Moreover, plasma LPC C17:0 concentrations positively correlated with estimated dietary intake of fatty acid C17:0 from the FFQs. The associations between glycerophospholipids and prostate cancer were stronger in case-controls with normal glucose tolerance. CONCLUSIONS: Several glycerophospholipids were positively associated with risk of overall and aggressive prostate cancer. The strongest association was observed for LPC C17:0. The associations between glycerophospholipids and prostate cancer risk were stronger in case-controls with normal glucose tolerance, suggesting a link between the glucose metabolism status and risk of prostate cancer.

AQuA: An Automated Quantification Algorithm for High-Throughput NMR-Based Metabolomics and Its Application in Human Plasma.

A key limiting step for high-throughput NMR-based metabolomics is the lack of rapid and accurate tools for absolute quantification of many metabolites. We developed, implemented, and evaluated an algorithm, AQuA (Automated Quantification Algorithm), for targeted metabolite quantification from complex 1H NMR spectra. AQuA operates based on spectral data extracted from a library consisting of one standard calibration spectrum for each metabolite. It uses one preselected NMR signal per metabolite for determining absolute concentrations and does so by effectively accounting for interferences caused by other metabolites. AQuA was implemented and evaluated using experimental NMR spectra from human plasma. The accuracy of AQuA was tested and confirmed in comparison with a manual spectral fitting approach using the ChenomX software, in which 61 out of 67 metabolites quantified in 30 human plasma spectra showed a goodness-of-fit (r2) close to or exceeding 0.9 between the two approaches. In addition, three quality indicators generated by AQuA, namely, occurrence, interference, and positional deviation, were studied. These quality indicators permit evaluation of the results each time the algorithm is operated. The efficiency was tested and confirmed by implementing AQuA for quantification of 67 metabolites in a large data set comprising 1342 experimental spectra from human plasma, in which the whole computation took less than 1 s.