Improving Visualization and Interpretation of Metabolome-Wide Association Studies: An Application in a Population-Based Cohort Using Untargeted 1H NMR Metabolic Profiling.

1H NMR spectroscopy of biofluids generates reproducible data allowing detection and quantification of small molecules in large population cohorts. Statistical models to analyze such data are now well-established, and the use of univariate metabolome wide association studies (MWAS) investigating the spectral features separately has emerged as a computationally efficient and interpretable alternative to multivariate models. The MWAS rely on the accurate estimation of a metabolome wide significance level (MWSL) to be applied to control the family wise error rate. Subsequent interpretation requires efficient visualization and formal feature annotation, which, in-turn, call for efficient prioritization of spectral variables of interest. Using human serum 1H NMR spectroscopic profiles from 3948 participants from the Multi-Ethnic Study of Atherosclerosis (MESA), we have performed a series of MWAS for serum levels of glucose. We first propose an extension of the conventional MWSL that yields stable estimates of the MWSL across the different model parameterizations and distributional features of the outcome. We propose both efficient visualization methods and a strategy based on subsampling and internal validation to prioritize the associations. Our work proposes and illustrates practical and scalable solutions to facilitate the implementation of the MWAS approach and improve interpretation in large cohort studies.

Workflow for Integrated Processing of Multicohort Untargeted 1H NMR Metabolomics Data in Large-Scale Metabolic Epidemiology.

Large-scale metabolomics studies involving thousands of samples present multiple challenges in data analysis, particularly when an untargeted platform is used. Studies with multiple cohorts and analysis platforms exacerbate existing problems such as peak alignment and normalization. Therefore, there is a need for robust processing pipelines that can ensure reliable data for statistical analysis. The COMBI-BIO project incorporates serum from ∼8000 individuals, in three cohorts, profiled by six assays in two phases using both 1H NMR and UPLC-MS. Here we present the COMBI-BIO NMR analysis pipeline and demonstrate its fitness for purpose using representative quality control (QC) samples. NMR spectra were first aligned and normalized. After eliminating interfering signals, outliers identified using Hotelling's T2 were removed and a cohort/phase adjustment was applied, resulting in two NMR data sets (CPMG and NOESY). Alignment of the NMR data was shown to increase the correlation-based alignment quality measure from 0.319 to 0.391 for CPMG and from 0.536 to 0.586 for NOESY, showing that the improvement was present across both large and small peaks. End-to-end quality assessment of the pipeline was achieved using Hotelling's T2 distributions. For CPMG spectra, the interquartile range decreased from 1.425 in raw QC data to 0.679 in processed spectra, while the corresponding change for NOESY spectra was from 0.795 to 0.636, indicating an improvement in precision following processing. PCA indicated that gross phase and cohort differences were no longer present. These results illustrate that the pipeline produces robust and reproducible data, successfully addressing the methodological challenges of this large multifaceted study.

Development and Application of Ultra-Performance Liquid Chromatography-TOF MS for Precision Large Scale Urinary Metabolic Phenotyping.

To better understand the molecular mechanisms underpinning physiological variation in human populations, metabolic phenotyping approaches are increasingly being applied to studies involving hundreds and thousands of biofluid samples. Hyphenated ultra-performance liquid chromatography-mass spectrometry (UPLC-MS) has become a fundamental tool for this purpose. However, the seemingly inevitable need to analyze large studies in multiple analytical batches for UPLC-MS analysis poses a challenge to data quality which has been recognized in the field. Herein, we describe in detail a fit-for-purpose UPLC-MS platform, method set, and sample analysis workflow, capable of sustained analysis on an industrial scale and allowing batch-free operation for large studies. Using complementary reversed-phase chromatography (RPC) and hydrophilic interaction liquid chromatography (HILIC) together with high resolution orthogonal acceleration time-of-flight mass spectrometry (oaTOF-MS), exceptional measurement precision is exemplified with independent epidemiological sample sets of approximately 650 and 1000 participant samples. Evaluation of molecular reference targets in repeated injections of pooled quality control (QC) samples distributed throughout each experiment demonstrates a mean retention time relative standard deviation (RSD) of <0.3% across all assays in both studies and a mean peak area RSD of <15% in the raw data. To more globally assess the quality of the profiling data, untargeted feature extraction was performed followed by data filtration according to feature intensity response to QC sample dilution. Analysis of the remaining features within the repeated QC sample measurements demonstrated median peak area RSD values of <20% for the RPC assays and <25% for the HILIC assays. These values represent the quality of the raw data, as no normalization or feature-specific intensity correction was applied. While the data in each experiment was acquired in a single continuous batch, instances of minor time-dependent intensity drift were observed, highlighting the utility of data correction techniques despite reducing the dependency on them for generating high quality data. These results demonstrate that the platform and methodology presented herein is fit-for-use in large scale metabolic phenotyping studies, challenging the assertion that such screening is inherently limited by batch effects. Details of the pipeline used to generate high quality raw data and mitigate the need for batch correction are provided.

(1)H NMR Spectroscopy of Fecal Extracts Enables Detection of Advanced Colorectal Neoplasia.

Colorectal cancer (CRC) is a growing cause of mortality in developing countries, warranting investigation into its etiopathogenesis and earlier diagnosis. Here, we investigated the fecal metabolic phenotype of patients with advanced colorectal neoplasia and controls using (1)H-nuclear magnetic resonance (NMR) spectroscopy and multivariate modeling. The fecal microbiota composition was assessed by quantitative real-time PCR as well as Wif-1 methylation levels in stools, serum, and urine and correlated to the metabolic profile of each patient. The predictivity of the model was 0.507 (Q(2)Y), and the explained variance was 0.755 (R(2)Y). Patients with advanced colorectal neoplasia demonstrated increased fecal concentrations of four short-chain fatty acids (valerate, acetate, propionate, and butyrate) and decreased signals relating to ß-glucose, glutamine, and glutamate. The predictive accuracy of the multivariate (1)H NMR model was higher than that of the guaiac-fecal occult blood test and the Wif-1 methylation test for predicting advanced colorectal neoplasia. Correlation analysis between fecal metabolites and bacterial profiles revealed strong associations between Faecalibacterium prausnitzii and Clostridium leptum species with short-chain fatty acids concentration and inverse correlation between Faecalibacterium prausnitzii and glucose. These preliminary results suggest that fecal metabonomics may potentially have a future role in a noninvasive colorectal screening program and may contribute to our understanding of the role of these dysregulated molecules in the cross-talk between the host and its bacterial microbiota.

Discovery and validation of urinary metabotypes for the diagnosis of hepatocellular carcinoma in West Africans.

UNLABELLED: There is no clinically applicable biomarker for surveillance of hepatocellular carcinoma (HCC), because the sensitivity of serum alpha-fetoprotein (AFP) is too low for this purpose. Here, we determined the diagnostic performance of a panel of urinary metabolites of HCC patients from West Africa. Urine samples were collected from Nigerian and Gambian patients recruited on the case-control platform of the Prevention of Liver Fibrosis and Cancer in Africa (PROLIFICA) program. Urinary proton nuclear magnetic resonance ((1) H-NMR) spectroscopy was used to metabolically phenotype 290 subjects: 63 with HCC; 32 with cirrhosis (Cir); 107 with noncirrhotic liver disease (DC); and 88 normal control (NC) healthy volunteers. Urine samples from a further cohort of 463 subjects (141 HCC, 56 Cir, 178 DC, and 88 NC) were analyzed, the results of which validated the initial cohort. The urinary metabotype of patients with HCC was distinct from those with Cir, DC, and NC with areas under the receiver operating characteristic (AUROC) curves of 0.86 (0.78-0.94), 0.93 (0.89-0.97), and 0.89 (0.80-0.98) in the training set and 0.81 (0.73-0.89), 0.96 (0.94-0.99), and 0.90 (0.85-0.96), respectively, in the validation cohort. A urinary metabolite panel, comprising inosine, indole-3-acetate, galactose, and an N-acetylated amino acid (NAA), showed a high sensitivity (86.9% [75.8-94.2]) and specificity (90.3% [74.2-98.0]) in the discrimination of HCC from cirrhosis, a finding that was corroborated in a validation cohort (AUROC: urinary panel = 0.72; AFP = 0.58). Metabolites that were significantly increased in urine of HCC patients, and which correlated with clinical stage of HCC, were NAA, dimethylglycine, 1-methylnicotinamide, methionine, acetylcarnitine, 2-oxoglutarate, choline, and creatine. CONCLUSION: The urinary metabotyping of this West African cohort identified and validated a metabolite panel that diagnostically outperforms serum AFP.

Precision high-throughput proton NMR spectroscopy of human urine, serum, and plasma for large-scale metabolic phenotyping.

Proton nuclear magnetic resonance (NMR)-based metabolic phenotyping of urine and blood plasma/serum samples provides important prognostic and diagnostic information and permits monitoring of disease progression in an objective manner. Much effort has been made in recent years to develop NMR instrumentation and technology to allow the acquisition of data in an effective, reproducible, and high-throughput approach that allows the study of general population samples from epidemiological collections for biomarkers of disease risk. The challenge remains to develop highly reproducible methods and standardized protocols that minimize technical or experimental bias, allowing realistic interlaboratory comparisons of subtle biomarker information. Here we present a detailed set of updated protocols that carefully consider major experimental conditions, including sample preparation, spectrometer parameters, NMR pulse sequences, throughput, reproducibility, quality control, and resolution. These results provide an experimental platform that facilitates NMR spectroscopy usage across different large cohorts of biofluid samples, enabling integration of global metabolic profiling that is a prerequisite for personalized healthcare.

(1)H NMR spectroscopy for the in vitro understanding of the glycaemic index.

The glycaemic index (GI) characterises foods by using the incremental area under the glycaemic response curve relative to the same amount of oral glucose. Its ability to differentiate between curves of different shapes, the peak response and other aspects of the glycaemic response is contentious. The present pilot study aimed to explore the possibility of using 1H NMR spectroscopy to better understand in vivo digestion characteristics as reflected in the glycaemic response of carbohydrate-rich foods; such an approach might be an adjunct to the in vivo GI test. The glycaemic response of two types of raw wheat flour (2005 from Griffith NSW, Chara, Row 10, Plot 6:181 and store-bought Colese Plain Flour) and a cooked store-bought flour was tested and compared with results recorded during the in vitro enzymatic digestion of the wheat flour samples by glucoamylase from Aspergillus niger (EC 3.2.1.3) as monitored by 1H NMR spectroscopy. Comparing the digestion time courses of raw and cooked wheat starch recorded in vitro strongly suggests that the initial rate of glucose release in vitro correlates with the glycaemic spike in vivo. During the in vitro time courses, approximately four times as much glucose was released from cooked starch samples than from raw starch samples in 90 min. Monitoring enzymatic digestion of heterogeneous mixtures (food) by 1H NMR spectroscopy showcases the effectiveness of the technique in measuring glucose release and its potential use as the basis of an in vitro method for a better understanding of the GI.

Plasma metabolomics reveals major changes in carbohydrate, lipid, and protein metabolism of abruptly weaned beef calves.

1H NMR-based metabolomics was used to study the effect of abrupt weaning on the blood metabolome of beef calves. Twenty Angus calves (258 ± 5 kg BW; 5 to 6 months old) were randomly assigned to a non-weaned (NW) group that remained grazing with their dam or a weaned (W) group that underwent abrupt separation from their dam to a separate paddock on d 0 of the study. Body weight, behaviour, and blood samples for cortisol and metabolomics were measured at d 0, 1, 2, 7, and 14 of the study. On d 1 and 2, W calves spent less time grazing and ruminating, and more time vocalising and walking, had a greater concentration of cortisol, NEFA, 3-hydroxybutyrate, betaine, creatine, and phenylalanine, and lesser abundance of tyrosine (P < 0.05) compared to NW calves. Compared to NW calves at d 14, W calves had greater (P < 0.01) relative abundance of acetate, glucose, allantoin, creatinine, creatine, creatine phosphate, glutamate, 3-hydroxybutyrate, 3-hydroxyisobutyrate, and seven AA (alanine, glutamate, leucine, lysine, phenylalanine, threonine and valine) but lesser (P < 0.05) relative abundance of low density and very low-density lipids, and unsaturated lipids. Both PCA and OPLS-DA showed no clustering or discrimination between groups at d 0 and increasing divergence to d 14. Blood metabolomics is a useful tool to quantify the acute effects of stress in calves during the first 2 days after abrupt weaning, and longer-term changes in carbohydrate, lipid and protein metabolism due to nutritional changes from cessation of milk intake and greater reliance on forage intake.

Oral pre-treatment with thiocyanate (SCN-) protects against myocardial ischaemia-reperfusion injury in rats.

Despite improvements in revascularization after a myocardial infarction, coronary disease remains a major contributor to global mortality. Neutrophil infiltration and activation contributes to tissue damage, via the release of myeloperoxidase (MPO) and formation of the damaging oxidant hypochlorous acid. We hypothesized that elevation of thiocyanate ions (SCN-), a competitive MPO substrate, would modulate tissue damage. Oral dosing of rats with SCN-, before acute ischemia-reperfusion injury (30 min occlusion, 24 h or 4 week recovery), significantly reduced the infarct size as a percentage of the total reperfused area (54% versus 74%), and increased the salvageable area (46% versus 26%) as determined by MRI imaging. No difference was observed in fractional shortening, but supplementation resulted in both left-ventricle end diastolic and left-ventricle end systolic areas returning to control levels, as determined by echocardiography. Supplementation also decreased antibody recognition of HOCl-damaged myocardial proteins. SCN- supplementation did not modulate serum markers of damage/inflammation (ANP, BNP, galectin-3, CRP), but returned metabolomic abnormalities (reductions in histidine, creatine and leucine by 0.83-, 0.84- and 0.89-fold, respectively), determined by NMR, to control levels. These data indicate that elevated levels of the MPO substrate SCN-, which can be readily modulated by dietary means, can protect against acute ischemia-reperfusion injury.

Kinetics of in vitro digestion of starches monitored by time-resolved (1)H Nuclear Magnetic Resonance.

A (1)H NMR method is presented that monitors the initial and later stages of in vitro enzymatic digestion of starch suspensions. It allows, for the first time to our knowledge, the accurate analysis of the initial 5% of the extent of hydrolysis. This is significant because rapidly digested starch produces glucose that determines the blood glucose concentration immediately after ingestion of food. The two key hydrolytic enzymes, alpha-amylase and amyloglucosidase, showed clear systematic deviation from Michaelis-Menten kinetics as the starch or wheat flour substrate that was used changed its character during the reaction. Estimates of Michaelis-Menten parameters for amyloglucosidase and alpha-amylase were successfully found by analyzing two stages of digestion separately. The Michaelis-Menten constants for purified starch were (6.4 +/- 0.8) and (1.1 +/- 0.3) g dL(-1) (% w/v), respectively; and the maximum velocities of glucose release by amyloglucosidase, and short oligoglucosides and glucose by alpha-amylase were (1.9 +/- 0.4) x 10(-2) and (1.6 +/- 0.2) x 10(-2) mmol L(-1) s(-1) for the first stage of digestion, and (9.0 +/- 1.0) x 10(-3) and (4.7 +/- 1.4) x 10(-3) mmol L(-1) s(-1) for the second stage, giving a ratio of the two V(max) values of 2.1 and 3.4, respectively.

Utility of metabolic profiling of serum in the diagnosis of pregnancy complications.

INTRODUCTION: Currently there are no clinical screening tests available to identify pregnancies at risk of developing preeclampsia (PET) and/or intrauterine growth restriction (IUGR), both of which are associated with abnormal placentation. Metabolic profiling is now a stable analytical platform used in many laboratories and has successfully been used to identify biomarkers associated with various pathological states. METHODS: We used nuclear magnetic resonance spectroscopy (NMR) to metabolically profile serum samples collected from 143 pregnant women at 26-41 weeks gestation with pregnancy outcomes of PET, IUGR, PET IUGR or small for gestational age (SGA) that were age-matched to normal pre/term pregnancies. RESULTS: Spectral analysis found no difference in the measured metabolites from normal term, pre-term and SGA samples, and of 25 identified metabolites, only glutamate was marginally different between groups. Of the identified metabolites, 3-methylhistidine, creatinine, acetyl groups and acetate, were determined to be independent predictors of PET and produced area under the curves (AUC) = 0.938 and 0.936 for the discovery and validation sets. Only 3-hydroxybutyrate was determined to be an independent predictor of IUGR, however the model had low predictive power (AUC = 0.623 and 0.581 for the discovery and validation sets). CONCLUSIONS: A sub-panel of metabolites had strong predictive power for identifying PET samples in a validation dataset, however prediction of IUGR was more difficult using the identified metabolites. NMR based metabolomics can identify metabolites strongly associated with disease and has the potential to be useful in developing early clinical screening tests for at risk pregnancies.

Translational and emerging clinical applications of metabolomics in cardiovascular disease diagnosis and treatment.

Numerous molecular screening strategies have recently been developed to measure the chemical diversity of a population's biofluids with the ultimate aim to provide clinicians, medical scientists and epidemiologists with a clearer picture of the presence and severity of cardiovascular disease; prognosis; and response to treatment. Current cardiology practice integrates clinical history and examination with state-of-the-art imaging, invasive measures, and electrical interrogation. Biomarkers in common clinical use are relatively limited to troponin and brain natriuretic peptide, dependent on damage to heart muscle, or myocyte 'stretch' respectively. Although they have been recently applied to risk stratification in asymptomatic individuals at higher risk, the development of markers capable of detecting earlier phases of disease development would facilitate targeted strategies to prevent pathological complications in the general community. Metabolomics is the systematic study of small molecules in biological fluids. Profiling strategies aim to comprehensively measure and quantify such biomarkers in a fast, cost-effective and clinically informative manner. Techniques tend to be applied in an unbiased fashion, with advanced statistical methods allowing for identification of signature profiles in particular cohorts. In this manner, metabolomics has the potential to identify new pathophysiological pathways, and thus therapeutic targets, as well as assist in improved risk-stratification and personalized cardiovascular medicine. The latter has great potential in the primary and secondary cardiovascular disease prevention settings, integrating known and as yet unidentified host and environmental factors. The current review discusses applications of metabolomic techniques relevant to both the research and the clinical cardiologist.

A guide to the identification of metabolites in NMR-based metabonomics/metabolomics experiments.

Metabonomics/metabolomics is an important science for the understanding of biological systems and the prediction of their behaviour, through the profiling of metabolites. Two technologies are routinely used in order to analyse metabolite profiles in biological fluids: nuclear magnetic resonance (NMR) spectroscopy and mass spectrometry (MS), the latter typically with hyphenation to a chromatography system such as liquid chromatography (LC), in a configuration known as LC-MS. With both NMR and MS-based detection technologies, the identification of the metabolites in the biological sample remains a significant obstacle and bottleneck. This article provides guidance on methods for metabolite identification in biological fluids using NMR spectroscopy, and is illustrated with examples from recent studies on mice.

Assessment of the extent of starch dissolution in dimethyl sulfoxide by 1H NMR spectroscopy.

Complete dissolution is needed for the separation, characterization, or homogeneous labeling of whole starch molecules. A method is presented to quantify the extent of starch dissolution in DMSO for the first time; it is validated on a commercial rice starch. It is used directly on starch dispersions containing possible undissolved or co-dissolved species. High-amylose maize starches, known to be digested slowly in vivo, only quantitatively dissolve in the presence of high concentrations of an H-bond disrupter, LiBr, although they form clear dispersions at low LiBr concentrations. Starch quantitatively dissolves from waxy rice flours; non-starch components partially co-dissolve but do not interfere with the dissolution quantification.