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BackgroundOvarian cancer remains the deadliest of all gynaecological cancers. Ultrasound-based models exist to support the classification of adnexal masses but are dependent on human assessment of features on ultrasound. Therefore, we aimed to develop an end-to-end machine learning (ML) model capable of automating the classification of adnexal masses.MethodsIn this retrospective study, transvaginal ultrasound scan images were extracted and segmented from Imperial College Healthcare, UK (ICH development dataset; n=577 masses; 1444 images) and Morgagni-Pierantoni Hospital, Italy (MPH external dataset; n=184 masses; 476 images). Clinical data including age, CA-125 and diagnosis (ultrasound subjective assessment, SA) or histology) were collected. A segmentation and classification model was developed by comparing several models using convolutional neural network-based models and traditional radiomics features. Dice surface coefficient was used to measure segmentation performance and area under the ROC curve (AUC), F1-score and recall for classification performance.FindingsThe ICH and MPH datasets had a median age of 45 (IQR 35-60) and 48 (IQR 38-57) and consisted of 23·1% and 31·5% malignant cases, respectively. The best segmentation model achieved a dice surface coefficient of 0·85 ±0·01, 0·88 ±0·01 and 0·85 ±0·01 in the ICH training, ICH validation and MPH test sets. The best classification model achieved a recall of 1·00 and F1-score of 0·88 (AUC 0·93), 0·94 (AUC 0·89) and 0·83 (AUC 0·90) in the ICH training, ICH validation and MPH test sets, respectively.InterpretationThe ML model provides an end-to-end method of adnexal mass segmentation and classification, with a comparable predictive performance (AUC 0·90) to the published performance of expert subjective assessment (SA, gold standard), and current risk models. Further prospective evaluation of the classification performance of the ML model against existing methods is required.FundingMedical Research Council, Imperial STRATiGRAD PhD programme and Imperial Health Charity.Research in ContextEvidence before this studyAdnexal masses are common, affecting up to 18% of postmenopausal women. Ultrasound is the primary imaging modality for the assessment of adnexal masses. Accurate classification of adnexal masses is fundamental to inform appropriate management. However, all existing classification methods are subjective and rely upon ultrasound expertise.Various models have been developed using ultrasound features and serological markers such as the Risk of malignancy index (RMI), International Ovarian Tumour Analysis (IOTA) Simple Rules (SR), the IOTA Assessment of Different NEoplasia’s in the AdneXa (ADNEX) model, and American College of Radiology (ACR) Ovarian-Adnexal Reporting and Data System Ultrasound (ORADS-US) to support the classification of adnexal masses. Despite modelling efforts, expert subjective assessment remains the gold standard method of classifying adnexal masses.The use of machine learning (ML) within clinical imaging is a rapidly evolving field due to its potential to overcome the subjectivity within image assessment and interpretation. Various studies (n=17) evaluating the use of ML within the classification of adnexal masses on ultrasound have been summarised within a recent meta-analysis by Xu et al, 2022. No studies used a radiomics-based approach to the classification of adnexal masses, and most have not been externally validated within a test set, questioning their generalisability. The largest study to date (Gao et al, 2022), used a deep learning (DL) based approach and was externally validated, yet its performance (F1 score 0·551) was not comparable to existing classification approaches.Added value of this studyWe have developed an end-to-end ML model (ODS) using DL and radiomics-based approaches, capable of identification (automated segmentation) and classification of adnexal masses with a high detection rate for malignancy. The ODS model had a performance comparable to the published performance of existing adnexal mass classification methods and does not rely upon ultrasound experience.Implications of all the available evidenceODS is a high performing, end-to-end model capable of classifying adnexal masses and requires limited ultrasound operator experience. The ODS model is potentially generalisable, having showed consistent performance in both validation (internal) and test (external) sets, highlighting the potential clinical value of a radiomics-based model within the classification of adnexal masses on ultrasound. The ODS model could function as a scalable triage tool, to identify high risk adnexal masses requiring further ultrasound assessment by an expert.

To gain insight into the complex microbiome-gut-brain axis in irritable bowel syndrome (IBS), several modalities of biological and clinical data must be combined. We aimed to identify profiles of fecal microbiota and metabolites associated with IBS and to delineate specific phenotypes of IBS that represent potential pathophysiological mechanisms. Fecal metabolites were measured using proton nuclear magnetic resonance (1H-NMR) spectroscopy and gut microbiome using shotgun metagenomic sequencing (MGS) in a combined dataset of 142 IBS patients and 120 healthy controls (HCs) with extensive clinical, biological and phenotype information. Data were analyzed using support vector classification and regression and kernel t-SNE. Microbiome and metabolome profiles could distinguish IBS and HC with an area-under-the-receiver-operator-curve of 77.3% and 79.5%, respectively, but this could be improved by combining microbiota and metabolites to 83.6%. No significant differences in predictive ability of the microbiome–metabolome data were observed between the three classical, stool pattern-based, IBS subtypes. However, unsupervised clustering showed distinct subsets of IBS patients based on fecal microbiome–metabolome data. These clusters could be related plasma levels of serotonin and its metabolite 5-hydroxyindoleacetate, effects of psychological stress on gastrointestinal (GI) symptoms, onset of IBS after stressful events, medical history of previous abdominal surgery, dietary caloric intake and IBS symptom duration. Furthermore, pathways in metabolic reaction networks were integrated with microbiota data, that reflect the host-microbiome interactions in IBS. The identified microbiome–metabolome signatures for IBS, associated with altered serotonin metabolism and unfavorable stress response related to GI symptoms, support the microbiota-gut-brain link in the pathogenesis of IBS.

Habitual consumption of poor quality diets is linked directly to risk factors for many non-communicable diseases. This has resulted in the vast majority of countries and the World Health Organization developing policies for healthy eating to reduce the prevalence of non-communicable diseases in the population. However, there is mounting evidence of variability in individual metabolic responses to any dietary intervention. We have developed a method for applying a pipeline for understanding interindividual differences in response to diet, based on coupling data from highly controlled dietary studies with deep metabolic phenotyping. In this feasibility study, we create an individual Dietary Metabotype Score (DMS) that embodies interindividual variability in dietary response and captures consequent dynamic changes in concentrations of urinary metabolites. We find an inverse relationship between the DMS and blood glucose concentration. There is also a relationship between the DMS and urinary metabolic energy loss. Furthermore, we use a metabolic entropy approach to visualize individual and collective responses to dietary interventions. Potentially, the DMS offers a method to target and to enhance dietary response at the individual level, thereby reducing the burden of non-communicable diseases at the population level. Variability in the individual response to dietary interventions has been reported. Here a Dietary Metabotype Score is developed to embody those interindividual responses by coupling data from highly controlled dietary studies with urinary metabolic phenotypes. This score may offer a method to target and to enhance dietary response at the individual level, thereby reducing the burden of diet-related disease at the population level.

Background: Dietary advice remains the cornerstone of prevention and management of type 2 diabetes (T2D). However, understanding the efficacy of dietary interventions is confounded by the challenges inherent in assessing free living diet. Here we profiled dietary metabolites to investigate glycaemic deterioration and cardiometabolic risk in people at risk of or living with T2D. Methods: We analysed data from plasma collected at baseline and 18-month follow-up in individuals from the Innovative Medicines Initiative (IMI) Diabetes Research on Patient Stratification (DIRECT) cohort 1 n = 403 individuals with normal or impaired glucose regulation (prediabetic) and cohort 2 n = 458 individuals with new onset of T2D. A dietary metabolite profile model (Tpred) was constructed using multivariable regression of 113 plasma metabolites obtained from targeted metabolomics assays. The continuous Tpred score was used to explore the relationships between diet, glycaemic deterioration and cardio-metabolic risk via multiple linear regression models. Findings: A higher Tpred score was associated with healthier diets high in wholegrain (β=3.36 g, 95% CI 0.31, 6.40 and β=2.82 g, 95% CI 0.06, 5.57) and lower energy intake (β=-75.53 kcal, 95% CI -144.71, -2.35 and β=-122.51 kcal, 95% CI -186.56, -38.46), and saturated fat (β=-0.92 g, 95% CI -1.56, -0.28 and β=–0.98 g, 95% CI -1.53, -0.42 g), respectively for cohort 1 and 2. In both cohorts a higher Tpred score was also associated with lower total body adiposity and favourable lipid profiles HDL-cholesterol (β=0.07 mmol/L, 95% CI 0.03, 0.1), (β=0.08 mmol/L, 95% CI 0.04, 0.1), and triglycerides (β=-0.1 mmol/L, 95% CI -0.2, -0.03), (β=-0.2 mmol/L, 95% CI -0.3, -0.09), respectively for cohort 1 and 2. In cohort 2, the Tpred score was negatively associated with liver fat (β=-0.74%, 95% CI -0.67, -0.81), and lower fasting concentrations of HbA1c (β=-0.9 mmol/mol, 95% CI -1.5, -0.1), glucose (β=-0.2 mmol/L, 95% CI -0.4, -0.05) and insulin (β=-11.0 pmol/mol, 95% CI -19.5, -2.6). Longitudinal analysis showed at 18-month follow up a higher Tpred score was also associated lower total body adiposity in both cohorts and lower fasting glucose (β=-0.2 mmol/L, 95% CI -0.3, -0.01) and insulin (β=-9.2 pmol/mol, 95% CI -17.9, -0.4) concentrations in cohort 2. Interpretation: Plasma dietary metabolite profiling provides objective measures of diet intake, showing a relationship to glycaemic deterioration and cardiometabolic health. Funding: This work was supported by the Innovative Medicines Initiative Joint Undertaking under grant agreement no. 115,317 (DIRECT), resources of which are composed of financial contribution from the European Union's Seventh Framework Programme (FP7/2007–2013) and EFPIA companies.

Dietary assessment traditionally relies on self-reported data which are often inaccurate and may result in erroneous diet-disease risk associations. We illustrate how urinary metabolic phenotyping can be used as alternative approach for obtaining information on dietary patterns. We used two multi-pass 24-hr dietary recalls, obtained on two occasions on average three weeks apart, paired with two 24-hr urine collections from 1,848 U.S. individuals; 67 nutrients influenced the urinary metabotype measured with (1)H-NMR spectroscopy characterized by 46 structurally identified metabolites. We investigated the stability of each metabolite over time and showed that the urinary metabolic profile is more stable within individuals than reported dietary patterns. The 46 metabolites accurately predicted healthy and unhealthy dietary patterns in a free-living U.S. cohort and replicated in an independent U.K. cohort. We mapped these metabolites into a host-microbial metabolic network to identify key pathways and functions. These data can be used in future studies to evaluate how this set of diet-derived, stable, measurable bioanalytical markers are associated with disease risk. This knowledge may give new insights into biological pathways that characterize the shift from a healthy to unhealthy metabolic phenotype and hence give entry points for prevention and intervention strategies.

We applied a metabonomic strategy to identify host biomarkers in serum to diagnose paediatric tuberculosis (TB) disease. 112 symptomatic children with presumptive TB were recruited in The Gambia and classified as bacteriologically-confirmed TB, clinically diagnosed TB, or other diseases. Sera were analysed using 1H nuclear magnetic resonance (NMR) spectroscopy and mass spectrometry (MS). Multivariate data analysis was used to distinguish patients with TB from other diseases. Diagnostic accuracy was evaluated using Receiver Operating Characteristic (ROC) curves. Model performance was tested in a validation cohort of 36 children from the UK. Data acquired using 1H NMR demonstrated a sensitivity, specificity and Area Under the Curve (AUC) of 69% (95% confidence interval [CI], 56–73%), 83% (95% CI, 73–93%), and 0.78 respectively, and correctly classified 20% of the validation cohort from the UK. The most discriminatory MS data showed a sensitivity of 67% (95% CI, 60–71%), specificity of 86% (95% CI, 75–93%) and an AUC of 0.78, correctly classifying 83% of the validation cohort. Amongst children with presumptive TB, metabolic profiling of sera distinguished bacteriologically-confirmed and clinical TB from other diseases. This novel approach yielded a diagnostic performance for paediatric TB comparable to that of Xpert MTB/RIF and interferon gamma release assays.

Correction to: Nature Food https://doi.org/10.1038/s43016-020-0093-y, published online 22 June 2020.

Prior investigation has suggested a positive association between increased colonic propionate production and circulating odd-chain fatty acids [(OCFAs; pentadecanoic acid (C15:0), heptadecanoic acid (C17:0)]. As the major source of propionate in humans is the microbial fermentation of dietary fiber, OCFAs have been proposed as candidate biomarkers of dietary fiber. The objective of this study is to critically assess the plausibility, robustness, reliability, dose-response, time-response aspects of OCFAs as potential biomarkers of fermentable fibers in two independent studies using a validated analytical method. OCFAs were first assessed in a fiber supplementation study, where 21 participants received 10g dietary fiber supplementation for 7 days with blood samples collected on the final day at a 420 minute study visit. OCFAs were then assessed in a highly controlled inpatient setting, which 19 participants consumed a high fiber (45.1g/day) and a low fiber diet (13.6g/day) for 4 days. Collectively in both studies, dietary intakes of fiber as fiber supplementations or having consumed a high fiber diet did not increase circulating levels of OCFAs. The dose and temporal relations were not observed. Current study has generated new insight on the utility of OCFAs as fiber biomarkers and highlighted the importance of critical assessment of candidate dietary biomarkers before application.

(1)H nuclear magnetic resonance (NMR) spectroscopy-based metabolic phenotyping is now widely used for large-scale epidemiological applications. To minimize signal overlap present in 1D (1)H NMR spectra, we have investigated the use of 2D J-resolved (JRES) (1)H NMR spectroscopy for large-scale phenotyping studies. In particular, we have evaluated the use of the 1D projections of the 2D JRES spectra (pJRES), which provide single peaks for each of the J-coupled multiplets, using 705 human plasma samples from the FGENTCARD cohort. On the basis of the assessment of several objective analytical criteria (spectral dispersion, attenuation of macromolecular signals, cross-spectral correlation with GC-MS metabolites, analytical reproducibility and biomarker discovery potential), we concluded that the pJRES approach exhibits suitable properties for implementation in large-scale molecular epidemiology workflows.

BACKGROUND: Alaska Native (AN) people have the world's highest recorded incidence of sporadic colorectal cancer (CRC) (∼91:100,000), whereas rural African (RA) people have the lowest risk (

MotivationIndoles have been shown to play a significant role in cardiometabolic disorders. While some individual bacterial species are known to produce indole-adducts, to our best knowledge no studies have made use of publicly available genome data to identify prokaryotes, specifically those associated with the human gut microbiota, contributing to the indole metabolic network.ResultsHere, we propose a computational strategy, comprising the integration of KEGG and BLAST, to identify prokaryote-specific metabolic reactions relevant for the production of indoles, as well as to predict new members of the human gut microbiota potentially involved in these reactions. By identifying relevant prokaryotic species for further validation studiesin vitro, this strategy represents a useful approach for those interrogating the metabolism of other gut-derived microbial metabolites relevant to human health.AvailabilityAll R scripts and files (gut microbial dataset, FASTA protein sequences, BLASTP output files) are available fromhttps://github.com/AndreaRMICL/Microbial_networks.ContactARM:andrea.rodriguez-martinez13@imperial.ac.uk; LH:lesley.hoyles@ntu.ac.uk.

Background Measurement of multiple food intake exposure biomarkers in urine may offer an objective method for monitoring diet. The potential of spot and cumulative urine samples that have reduced burden on participants as replacements for 24-h urine collections has not been evaluated. Objective The aim of this study was to determine the utility of spot and cumulative urine samples for classifying the metabolic profiles of people according to dietary intake when compared with 24-h urine collections in a controlled dietary intervention study. Methods Nineteen healthy individuals (10 male, 9 female, aged 21-65 y, BMI 20-35 kg/m2) each consumed 4 distinctly different diets, each for 1 wk. Spot urine samples were collected ∼2 h post meals on 3 intervention days/wk. Cumulative urine samples were collected daily over 3 separate temporal periods. A 24-h urine collection was created by combining the 3 cumulative urine samples. Urine samples were analyzed with metabolite fingerprinting by both high-resolution flow infusion electrospray mass spectrometry (FIE-HRMS) and proton nuclear magnetic resonance spectroscopy (1H-NMR). Concentrations of dietary intake biomarkers were measured with liquid chromatography triple quadrupole mass spectrometry and by integration of 1H-NMR data. Results Cross-validation modeling with 1H-NMR and FIE-HRMS data demonstrated the power of spot and cumulative urine samples in predicting dietary patterns in 24-h urine collections. Particularly, there was no significant loss of information when post-dinner (PD) spot or overnight cumulative samples were substituted for 24-h urine collections (classification accuracies of 0.891 and 0.938, respectively). Quantitative analysis of urine samples also demonstrated the relation between PD spot samples and 24-h urines for dietary exposure biomarkers. Conclusions We conclude that PD spot urine samples are suitable replacements for 24-h urine collections. Alternatively, cumulative samples collected overnight predict similarly to 24-h urine samples and have a lower collection burden for participants.

Background: Magnesium (Mg) and other mineral intakes are vital contributors to a healthy diet and appear to have a modest effect on the risk of hypertension. Studies suggest that supplemental Mg can significantly lower blood pressure (BP). Here, we investigate associations of total Mg consumption with the level of its urinary metabolomic biomarkers, and BP. Method: We used cross-sectional data from the International Study of Macro-/Micronutrients and Blood Pressure (INTERMAP) on 4269 men and women aged 40-59 years from Japan, China, United Kingdom, and United States (US). Regression coefficients with BP per 2 standard deviation (SD) higher dietary Mg from food plus supplement (obtained from 24-hr dietary recalls), urinary Mg (measured by atomic absorption flame photometry) and small molecules (analysed by direct injection mass spectrometry) were estimated using multivariable models, adjusted for age, gender, population sample, intake of total energy and other confounders (see Table). To estimate overall association, country-specific regression coefficients were pooled, weighted by inverse of their variance. Results: Average Mg intakes were 454 (SD=207) mg/day for 770 persons who reported Mg supplementation (89% from US), and 300 (SD=101) mg/day for those who did not. High correlation was found between Mg intake from food plus supplements with urinary Mg ( r =0.38, PP =6x10 -5 ). Urinary Mg excretion higher by 2.66 mmol/day was associated with a diastolic BP difference of -1.02 mm Hg (P=0.02). There was a non-significant inversed association between ascorbic acid and BP in some multivariable regression models. Conclusion: Higher intake of Mg, including supplements, was associated with lower BP levels.

Metabolic profiling of biological samples provides important insights into multiple physiological and pathological processes but is hindered by a lack of automated annotation and standardized methods for structure elucidation of candidate disease biomarkers. Here we describe a system for identifying molecular species derived from nuclear magnetic resonance (NMR) spectroscopy-based metabolic phenotyping studies, with detailed information on sample preparation, data acquisition and data modeling. We provide eight different modular workflows to be followed in a recommended sequential order according to their level of difficulty. This multi-platform system involves the use of statistical spectroscopic tools such as Statistical Total Correlation Spectroscopy (STOCSY), Subset Optimization by Reference Matching (STORM) and Resolution-Enhanced (RED)-STORM to identify other signals in the NMR spectra relating to the same molecule. It also uses two-dimensional NMR spectroscopic analysis, separation and pre-concentration techniques, multiple hyphenated analytical platforms and data extraction from existing databases. The complete system, using all eight workflows, would take up to a month, as it includes multi-dimensional NMR experiments that require prolonged experiment times. However, easier identification cases using fewer steps would take 2 or 3 days. This approach to biomarker discovery is efficient and cost-effective and offers increased chemical space coverage of the metabolome, resulting in faster and more accurate assignment of NMR-generated biomarkers arising from metabolic phenotyping studies. It requires a basic understanding of MATLAB to use the statistical spectroscopic tools and analytical skills to perform solid phase extraction (SPE), liquid chromatography (LC) fraction collection, LC-NMR-mass spectroscopy and one-dimensional and two-dimensional NMR experiments.

Objective: Metabolic phenotyping (metabolomics) captures systems-level information on metabolic processes by simultaneously measuring hundreds of metabolites using spectroscopic techniques. Concentrations of these metabolites are affected by genetic (host, microbiome), environmental and dietary factors and may provide insights into biochemical pathways underlying raised blood pressure (BP) in populations. Design and method: Two separate, timed 24hr urine specimens were obtained from 2,031 women and men, aged 40–59, from 8 USA population samples in the INTERMAP Study. Proton Nuclear Magnetic Resonance (1H NMR) was used to characterize a urinary metabolic signature; this was unaffected by diurnal variability and sampling time as it captures end-products of metabolism over a 24hr period. Demographic, population, medical, lifestyle and anthropometric factors were accounted for in regression models to define a urinary metabolic phenotype associated with BP. Results: 29 structurally identified urinary metabolites covaried with systolic BP (SBP), after adjustment for demographic variables, and 18 metabolites with diastolic BP (DBP), with 16 metabolites overlapping between SBP and DBP. These included metabolites related to energy metabolism, renal function, diet and gut microbiota. After adjustment for medical and lifestyle covariates, 22/14 metabolites remained associated with SBP/DBP. Joint covariate-metabolite penalized regression models identified Body Mass Index, age and family history as most important contributors, with 14 metabolites, including gut microbial co-metabolites, also included in the model. Metabolites were mapped in a symbiotic metabolic reaction network, that includes reactions mediated by 3,344 commensal gut microbial species, to highlight affected pathways (Figure). Significant single nucleotide polymorphisms (SNPs) from genome-wide association studies on cardiometabolic risk factors were mapped to genes in this network. This revealed multiple subnetworks of gene-metabolite pairs related to BP and related cardiometabolic factors and includes 54 SNPs directly related to reactions in the network. These 54 SNPs were then used as instrumental variables to test for possible causative metabolite-BP associations in an external cohort (Airwave Study).

SCOPE: Iron deficiency (ID) compromises the health of infants worldwide. Although readily treated with iron, concerns remain about the persistence of some effects. Metabolic and gut microbial consequences of infantile ID were investigated in juvenile monkeys after natural recovery (pID) from iron deficiency or post-treatment with iron dextran and B vitamins (pID+Fe).METHODS AND RESULTS: Metabolomic profiling of urine and plasma is conducted with 1 H nuclear magnetic resonance (NMR) spectroscopy. Gut microbiota are characterized from rectal swabs by amplicon sequencing of the 16S rRNA gene. Urinary metabolic profiles of pID monkeys significantly differed from pID+Fe and continuously iron-sufficient controls (IS) with higher maltose and lower amounts of microbial-derived metabolites. Persistent differences in energy metabolism are apparent from the plasma metabolic phenotypes with greater reliance on anaerobic glycolysis in pID monkeys. Microbial profiling indicated higher abundances of Methanobrevibacter, Lachnobacterium, and Ruminococcus in pID monkeys and any history of ID resulted in a lower Prevotella abundance compared to the IS controls.CONCLUSIONS: Lingering metabolic and microbial effects are found after natural recovery from ID. These long-term biochemical derangements are not present in the pID+Fe animals emphasizing the importance of the early detection and treatment of early-life ID to ameliorate its chronic metabolic effects.

Nephrotic syndrome with idiopathic membranous nephropathy as a major contributor, is characterized by proteinuria, hypoalbuminemia and oedema. Diagnosis is based on renal biopsy and the condition is treated using immunosuppressive drugs; however nephrotic syndrome treatment efficacy varies among patients. Multi-omic urine analyses can discover new markers of nephrotic syndrome that can be used to develop personalized treatments. For proteomics, a protease inhibitor (PI) is sometimes added at sample collection to conserve proteins but its impact on urine metabolic phenotyping needs to be evaluated. Urine from controls (n = 4) and idiopathic membranous nephropathy (iMN) patients (n = 6) were collected with and without PI addition and analysed using 1H NMR spectroscopy and UPLC-MS. PI-related data features were observed in the 1H NMR spectra but their removal followed by a median fold change normalisation, eliminated the PI contribution. PI-related metabolites in UPLC-MS data had limited effect on metabolic patterns specific to iMN. When using an appropriate data processing pipeline, PI-containing urine samples are appropriate for 1H NMR and MS metabolic profiling of patients with nephrotic syndrome.

MetaboSignal is an R/Bioconductor package designed to explore the relationships between genes and metabolites, using the Kyoto Encyclopedia of Genes and Genomes (KEGG) as its primary database. It is a network-based approach that allows overlaying metabolic and signaling pathways and exploring the topological relationship between genes (signaling or metabolic genes) and metabolites. MetaboSignal is ideally suited to identify candidate genes in metabolome genome-wide association studies (mGWAS), particularly in the case of trans-acting associations. It can also be used to provide mechanistic explanations of perturbed metabolic patterns observed in genetic models, as well as to identify novel target metabolic pathways of signaling genes. © 2018 by John Wiley & Sons, Inc.

Summary MWASTools is an R package designed to provide an integrated pipeline to analyse metabonomic data in large-scale epidemiological studies. Key functionalities of our package include: quality control analysis; metabolome-wide association analysis using various models (partial correlations, generalized linear models); visualization of statistical outcomes; metabolite assignment using statistical total correlation spectroscopy (STOCSY); and biological interpretation of metabolome-wide association studies results. Availability and implementation The MWASTools R package is implemented in R (version > =3.4) and is available from Bioconductor: https://bioconductor.org/packages/MWASTools/. Supplementary information Supplementary data are available at Bioinformatics online.

BACKGROUND: Accurate monitoring of changes in dietary patterns in response to food policy implementation is challenging. Metabolic profiling allows simultaneous measurement of hundreds of metabolites in urine, the concentrations of which can be affected by food intake. We hypothesised that metabolic profiles of urine samples developed under controlled feeding conditions reflect dietary intake and can be used to model and classify dietary patterns of free-living populations.METHODS: In this randomised, controlled, crossover trial, we recruited healthy volunteers (aged 21-65 years, BMI 20-35 kg/m(2)) from a database of a clinical research unit in the UK. We developed four dietary interventions with a stepwise variance in concordance with the WHO healthy eating guidelines that aim to prevent non-communicable diseases (increase fruits, vegetables, whole grains, and dietary fibre; decrease fats, sugars, and salt). Participants attended four inpatient stays (72 h each, separated by at least 5 days), during which they were given one dietary intervention. The order of diets was randomly assigned across study visits. Randomisation was done by an independent investigator, with the use of opaque, sealed, sequentially numbered envelopes that each contained one of the four dietary interventions in a random order. Participants and investigators were not masked from the dietary intervention, but investigators analysing the data were masked from the randomisation order. During each inpatient period, urine was collected daily over three timed periods: morning (0900-1300 h), afternoon (1300-1800 h), and evening and overnight (1800-0900 h); 24 h urine samples were obtained by pooling these samples. Urine samples were assessed by proton nuclear magnetic resonance ((1)H-NMR) spectroscopy, and diet-discriminatory metabolites were identified. We developed urinary metabolite models for each diet and identified the associated metabolic profiles, and then validated the models using data and samples from the INTERMAP UK cohort (n=225) and a healthy-eating Danish cohort (n=66). This study is registered with ISRCTN, number ISRCTN43087333.FINDINGS: Between Aug 13, 2013, and May 18, 2014, we contacted 300 people with a letter of invitation. 78 responded, of whom 26 were eligible and invited to attend a health screening. Of 20 eligible participants who were randomised, 19 completed all four 72 h study stays between Oct 2, 2013, and July 29, 2014, and consumed all the food provided. Analysis of (1)H-NMR spectroscopy data indicated that urinary metabolic profiles of the four diets were distinct. Significant stepwise differences in metabolite concentrations were seen between diets with the lowest and highest metabolic risks. Application of the derived metabolite models to the validation datasets confirmed the association between urinary metabolic and dietary profiles in the INTERMAP UK cohort (pINTERPRETATION: Urinary metabolite models developed in a highly controlled environment can classify groups of free-living people into consumers of diets associated with lower or higher non-communicable disease risk on the basis of multivariate metabolite patterns. This approach enables objective monitoring of dietary patterns in population settings and enhances the validity of dietary reporting.FUNDING: UK National Institute for Health Research and UK Medical Research Council.

Contains fulltext : 168011.PDF (Publisher’s version ) (Open Access) BACKGROUND: In this large observational study population of 105 myotonic dystrophy type 1 (DM1) patients, we investigate whether bodyweight is a contributor of total lung capacity (TLC) independent of the impaired inspiratory muscle strength. METHODS: Body composition was assessed using the combination of body mass index (BMI) and fat-free mass index. Pulmonary function tests and respiratory muscle strength measurements were performed on the same day. Patients were stratified into normal (BMI < 25 kg/m2) and overweight (BMI >/= 25 kg/m2) groups. Multiple linear regression was used to find significant contributors for TLC. RESULTS: Overweight was present in 59% of patients, and body composition was abnormal in almost all patients. In overweight patients, TLC was significantly (p = 2.40x10-3) decreased, compared with normal-weight patients, while inspiratory muscle strength was similar in both groups. The decrease in TLC in overweight patients was mainly due to a decrease in expiratory reserve volume (ERV) further illustrated by a highly significant (p = 1.33x10-10) correlation between BMI and ERV. Multiple linear regression showed that TLC can be predicted using only BMI and the forced inspiratory volume in 1 second, as these were the only significant contributors. CONCLUSIONS: This study shows that, in DM1 patients, overweight further reduces lung volumes, as does impaired inspiratory muscle strength. Additionally, body composition is abnormal in almost all DM1 patients.

Lack of accurate dietary assessment in free-living populations requires discovery of new biomarkers reflecting food intake qualitatively and quantitatively to objectively evaluate effects of diet on health. We provide a proof-of-principle for an analytical pipeline to identify quantitative dietary biomarkers. Tartaric acid was identified by nuclear magnetic resonance spectroscopy as a dose-responsive urinary biomarker of grape intake and subsequently quantified in volunteers following a series of 4-day dietary interventions incorporating 0 g/day, 50 g/day, 100 g/day, and 150 g/day of grapes in standardized diets from a randomized controlled clinical trial. Most accurate quantitative predictions of grape intake were obtained in 24 h urine samples which have the strongest linear relationship between grape intake and tartaric acid excretion (r(2) = 0.90). This new methodological pipeline for estimating nutritional intake based on coupling dietary intake information and quantified nutritional biomarkers was developed and validated in a controlled dietary intervention study, showing that this approach can improve the accuracy of estimating nutritional intakes.

Obesity is a major public health problem worldwide. We used 24-hour urinary metabolic profiling by proton ((1)H) nuclear magnetic resonance (NMR) spectroscopy and ion exchange chromatography to characterize the metabolic signatures of adiposity in the U.S. (n = 1880) and UK (n = 444) cohorts of the INTERMAP (International Study of Macro- and Micronutrients and Blood Pressure) epidemiologic study. Metabolic profiling of urine samples collected over two 24-hour time periods 3 weeks apart showed reproducible patterns of metabolite excretion associated with adiposity. Exploratory analysis of the urinary metabolome using (1)H NMR spectroscopy of the U.S. samples identified 29 molecular species, clustered in interconnecting metabolic pathways, that were significantly associated (P = 1.5 × 10(-5) to 2.0 × 10(-36)) with body mass index (BMI); 25 of these species were also found in the UK validation cohort. We found multiple associations between urinary metabolites and BMI including urinary glycoproteins and N-acetyl neuraminate (related to renal function), trimethylamine, dimethylamine, 4-cresyl sulfate, phenylacetylglutamine and 2-hydroxyisobutyrate (gut microbial co-metabolites), succinate and citrate (tricarboxylic acid cycle intermediates), ketoleucine and the ketoleucine/leucine ratio (linked to skeletal muscle mitochondria and branched-chain amino acid metabolism), ethanolamine (skeletal muscle turnover), and 3-methylhistidine (skeletal muscle turnover and meat intake). We mapped the multiple BMI-metabolite relationships as part of an integrated systems network that describes the connectivities between the complex pathway and compartmental signatures of human adiposity.

Rates of colon cancer are much higher in African Americans (65:100,000) than in rural South Africans (

Summary: MetaboNetworks is a tool to create custom sub-networks in Matlab using main reaction pairs as defined by the Kyoto Encyclopaedia of Genes and Genomes and can be used to explore transgenomic interactions, for example mammalian and bacterial associations. It calculates the shortest path between a set of metabolites (e.g. biomarkers from a metabonomic study) and plots the connectivity between metabolites as links in a network graph. The resulting graph can be edited and explored interactively. Furthermore, nodes and edges in the graph are linked to the Kyoto Encyclopaedia of Genes and Genomes compound and reaction pair web pages. Availability and implementation: MetaboNetworks is available from http://www.mathworks.com/matlabcentral/fileexchange/42684. Contact: jmp111@ic.ac.uk or j.nicholson@imperial.ac.uk Supplementary information: Supplementary data are available at Bioinformatics online.

Parasitic infections such as leishmaniasis induce a cascade of host physiological responses, including metabolic and immunological changes. Infection with Leishmania major parasites causes cutaneous leishmaniasis in humans, a neglected tropical disease that is difficult to manage. To understand the determinants of pathology, we studied L. major infection in two mouse models: the self-healing C57BL/6 strain and the nonhealing BALB/c strain. Metabolic profiling of urine, plasma, and feces via proton NMR spectroscopy was performed to discover parasite-specific imprints on global host metabolism. Plasma cytokine status and fecal microbiome were also characterized as additional metrics of the host response to infection. Results demonstrated differences in glucose and lipid metabolism, distinctive immunological phenotypes, and shifts in microbial composition between the two models. We present a novel approach to integrate such metrics using correlation network analyses, whereby self-healing mice demonstrated an orchestrated interaction between the biological measures shortly after infection. In contrast, the response observed in nonhealing mice was delayed and fragmented. Our study suggests that trans-system communication across host metabolism, the innate immune system, and gut microbiome is key for a successful host response to L. major and provides a new concept, potentially translatable to other diseases.

Breast milk (BM) is a biofluid that has a fundamental role in early life nutrition and has direct impact on growth, neurodevelopment, and health. Global metabolic profiling is increasingly being utilized to characterize complex metabolic changes in biological samples. However, in order to achieve broad metabolite coverage, it is necessary to employ more than one analytical platform, typically requiring multiple sample preparation protocols. In an effort to improve analytical efficiency and retain comprehensive coverage of the metabolome, a new extraction methodology was developed that successfully retains metabolites from BM in a single-phase using an optimized methyl-tert-butyl ether solvent system. We conducted this single-phase extraction procedure on a representative pool of BM, and characterized the metabolic composition using LC-QTOF-MS and GC-Q-MS for polar and lipidic metabolites. To ensure that the extraction method was reproducible and fit-for-purpose, the analytical procedure was evaluated on both platforms using 18 metabolites selected to cover a range of chromatographic retention times and biochemical classes. Having validated the method, the metabolic signature of BM composition was mapped as a metabolic reaction network highlighting interconnected biological pathways and showing that the LC-MS and GC-MS platforms targeted largely different domains of the network. Subsequently, the same protocol was applied to ascertain compositional differences between BM at week 1 (n = 10) and 4 weeks (n = 9) post-partum. This single-phase approach is more efficient in terms of time, simplicity, cost, and sample volume than the existing two-phase methods and will be suited to high-throughput metabolic profiling studies of BM.

African-Americans compared to non-Hispanic-White-Americans have higher systolic, diastolic blood pressure and rates of prehypertension/hypertension. Reasons for these adverse findings remain obscure. Analyses here focused on relations of foods/nutrients/urinary metabolites to higher African-American blood pressure for 369 African-Americans compared to 1,190 non-Hispanic-White-Americans ages 40-59 from 8 population samples. Standardized data were from four 24-hour dietary recalls/person, two 24-h urine collections, 8 blood pressure measurements; multiple linear regression quantitating role of foods, nutrients, metabolites in higher African-American blood pressure. Compared to non-Hispanic-White-Americans, African-Americans average systolic/diastolic pressure was higher by 4.7/3.4 mm Hg (men) and 9.0/4.8 mm Hg (women). Control for higher body mass index of African-American women reduced excess African-American systolic/diastolic pressure to 6.8/3.8 mm Hg. African American intake of multiple foods, nutrients related to blood pressure was less favorable - - less vegetables, fruits, grains, vegetable protein, glutamic acid, starch, fiber, minerals, potassium; more processed meats, pork, eggs, sugar-sweetened beverages, cholesterol, higher sodium to potassium ratio. Control for 11 nutrient and 10 non-nutrient correlates reduced higher African-American systolic/diastolic pressure to 2.3/2.3 mm Hg (52% and 33% reduction) (men) and to 5.3/2.8 mm Hg (21% and 27% reduction) (women). Control also for foods/urinary metabolites had little further influence on higher African-American blood pressure. Multiple nutrients with less favorable intakes by African-Americans than non-Hispanic-White-Americans account at least in part for higher African-American blood pressure. Improved dietary patterns can contribute to prevention/control of more adverse African-American blood pressure levels.