Public metabolomics data repositories such as MetaboLights and Metabolomics Workbench host rapidly growing volumes of raw data, processed results, and metadata. As data deposition becomes a prerequisite for funding and publication, there is an increasing need for tools that enable integration and joint reanalysis of datasets across studies to maximise reuse and reproducibility. This study aims to enable large-scale integrative meta-analysis of public metabolomics data, exploiting harmonised metabolite annotations to identify robust multi-study metabolite and pathway signatures and to provide global visual overviews of repository content. We developed a network-based integration framework operating at both the study (dataset) level and the metabolite or pathway level. Metabolite-level meta-networks integrate studies with shared biological context using co-occurrences of differential metabolites represented as bipartite graphs. Study-level networks compare observed metabolites for overall repository exploration. Networks can be explored interactively using a dedicated Python Dash app available at https://github.com/EloisaRL/Metabolomic-data-analysis-app/tree/main . As an example, the approach was applied to six COVID-19 plasma datasets from MetaboLights generated using LC-MS and NMR. Ten metabolites were identified as differential in at least three studies, including consistently up-regulated pyroglutamic acid, in agreement with the literature. Pathway-level networks provided an overview of shared biological processes across studies. A global network of 1,181 studies in Metabolomics Workbench demonstrated clustering by assay coverage and associated metadata, as expected. Network-based integration of harmonised metabolomics data enables robust cross-study analyses and highlights the critical importance of standardised annotation pipelines. Such approaches enhance the reuse, reproducibility, and impact of public metabolomics datasets, accelerating biological discovery.

Metabolomics data are currently generated at scale thanks to the evolution of technologies that have led to marked improvements in the number of metabolites detected, spanning all chemical classes. These data are increasingly submitted to public repositories for data reuse, integration, and interpretation. Despite the availability of public resources and associated computational tools, the field still lacks a widely adopted, consistent data and analytics infrastructure capable of transforming this wealth of information into scientific insight. Indeed, the metabolomics field is just now scratching the surface of being able to harness the power of new computational technologies. In this review, we summarize discussions from the “Dagstuhl-Seminar 24181 Computational Metabolomics: Towards Molecules, Models, and their Meaning” with a focus on public data availability, open data standards, data and knowledge integration, and education. Our goal is to raise awareness and adoption of the latest open science resources while highlighting key areas needing further development.

Liquid chromatography-mass spectrometry (LC-MS) untargeted analysis enables comprehensive lipid profiling of biological samples. However, system-level interpretation is often limited by the large number of unannotated features. Assigning features to lipid classes provides a higher-level, yet informative, overview that complements detailed structural analysis and supports biological interpretation at the class level. Recent advances in the systematic prediction of chemical class using tandem mass spectrometry (MS2) help address this; however, a substantial proportion of features in untargeted LC-MS data sets are typically characterized only at the MS1 level. Here, we present a workflow to systematically predict the lipid class from MS1-only data in untargeted LC-MS, without requiring prior annotations or MS2. Motivated by previous research showing that Gaussian graphical models (GGMs) estimated from feature intensities can encode the lipid class structure, our method, GgmLipidClassifier (GLC), combines conventional accurate-mass database searching with a GGM-derived network structure in a unified scoring framework to predict lipid class according to the LIPID MAPS Structure Database (LMSD) ontology. Across three human serum and plasma data sets, GLC achieved overall accuracies of 82-90% at the LMSD main class-level and 72-86% at the lipid subclass level, with improved accuracy and reduced uncertainty compared to closest-m/z matching. GLC provides class predictions for most detected features and also generates prediction quality scores to support downstream interpretation. Applied to serum samples from an Alzheimer's disease study, lipid class enrichment based on GLC predictions was highly consistent with class enrichment derived from ground-truth lipid annotations. Importantly, GLC extended coverage to classes missing from the annotation set, revealing biologically plausible associations with Alzheimer's disease, including cholesterol and derivatives, vitamin D3 and derivatives, and plasmalogen glycerophosphoethanolamines. Overall, GLC provides robust lipid class predictions from MS1-only data, generating lipid class assignments for most detected features and complementing conventional analysis to support broader system-level interpretation.

Mass spectrometry imaging (MSI) has enabled unprecedented advances in spatial mapping of metabolites and lipids within tissues. However, interpretation remains challenging when following common practice of visualizing one ion image at a time. To address this, we present a novel workflow for Spatial Pathway and Chemical Class Analysis (SPCCA), which applies single-sample pathway and chemical class scoring to MSI data. By integrating biological pathways from the Reactome database and chemical classes from the ChEBI resource, our Python-based framework transforms annotated MSI data into spatial maps based on biological function and chemical taxonomy. We illustrate the approach using MALDESI bladder cancer and DESI colorectal cancer datasets. Spatial Pathway Analysis (SPA) highlights known pathway connections despite sparse annotation coverage (~10%), while Spatial Chemical Class Analysis (SCCA) utilizes over 90% of filtered features, providing superior spatial congruence with H&E-defined histopathology. Our results demonstrate that SPCCA effectively recapitulates known cancer metabolic shifts and captures histological features in the original dataset, offering a robust and biologically grounded alternative to single-ion interpretation. This work highlights the potential of integrating biological and chemical knowledge into spatial metabolomics to enhance the depth and interpretability of MSI studies.

Metabolomic epidemiology has expanded rapidly, but publications often lack sufficient detail for readers to assess study design, analytical methods, sources of bias, and the robustness and reproducibility of findings. Existing reporting recommendations in epidemiology and metabolomics do not fully address the specific challenges that arise when these fields are combined. To improve the completeness and transparency of reporting, we developed the Strengthening the Reporting of Metabolomic Epidemiology (STROBE-MetEpi) statement, an extension of the original STROBE guidance for observational research. The STROBE-MetEpi checklist includes 31 items and subitems covering the Title, Abstract, Introduction, Methods, Results, Discussion, and Other Information sections of metabolomic epidemiology studies. This explanation and elaboration document is intended to complement the STROBE-MetEpi statement by explaining the rationale for each checklist item and providing published examples of transparent reporting. It applies to studies using metabolomic profiling to explore human health, but not to randomized trials, methodological studies, reviews, or multi-omics studies. As with previous STROBE explanation and elaboration documents, its purpose is to improve how studies are reported, not to prescribe how they should be conducted. The STROBE-MetEpi statement and this accompanying document should support authors, reviewers, editors, and readers in improving the reporting, appraisal, interpretation, and reproducibility of metabolomic epidemiology research.

BackgroundEvidence increasingly suggests a connection between cardiovascular disease and brain health in later life; however, the mechanistic pathways from human studies remain unclear. This study aimed to investigate whether urinary metabolites account for part of the association between cognition and cardiometabolic risk.MethodsData from 606 participants (aged 48-60; 55% female; 45.5% Black/African American) in the Year 30 follow-up of the Coronary Artery Risk Development in Young Adults Study were analyzed. Urinary metabolites were profiled using nuclear magnetic resonance spectroscopy and liquid chromatography-mass spectrometry; brain magnetic resonance imaging data were available for 281 participants. Structural equation models were used to assess pathways linking cardiometabolic factors to cognitive outcome, with urinary metabolites and brain MRI-derived parameters as mediators.ResultsFasting glucose showed a negative association with cognition. Valine, isoleucine, leucine, and phenylalanine were positively associated with fasting glucose. Valine and aminoadipic acid also showed positive associations between fasting glucose and cognition, while tryptophan was correlated with both fasting glucose and cognition. Indole-3-acetic acid showed negative associations with systolic blood pressure and fasting glucose. Brain MRI-derived parameters in memory-related medial temporal areas were associated with waist circumference.ConclusionsUrinary metabolites and brain imaging markers were linked with hyperglycemia, obesity, and cognitive performance, highlighting multimodal biomarkers relevant to global cognitive function in individuals with cardiometabolic risk.

MOTIVATION:Metabolomics extensively utilizes nuclear magnetic resonance (NMR) spectroscopy due to its excellent reproducibility and high throughput. Both 1D and 2D NMR spectra provide crucial information for metabolite annotation and quantification, yet present complex overlapping patterns which may require sophisticated machine learning algorithms to decipher. Unfortunately, the limited availability of labeled spectra can hamper application of machine learning, especially deep learning algorithms which require large amounts of labeled data. In this context, simulation of spectral data becomes a tractable solution for algorithm development. RESULTS:Here, we introduce MetAssimulo 2.0, a comprehensive upgrade of the MetAssimulo 1.b metabolomic 1H NMR simulation tool, reimplemented as a Python-based web application. Where MetAssimulo 1.0 only simulated 1D 1H spectra of human urine, MetAssimulo 2.0 expands functionality to urine, blood, and cerebral spinal fluid, enhancing the realism of blood spectra by incorporating a broad protein background. This enhancement enables a closer approximation to real blood spectra, achieving a Pearson correlation of approximately 0.82. Moreover, this tool now includes simulation capabilities for 2D J-resolved (J-Res) and Correlation Spectroscopy spectra, significantly broadening its utility in complex mixture analysis. MetAssimulo 2.0 simulates both single, and groups, of spectra with both discrete (case-control, e.g. heart transplant versus healthy) and continuous (e.g. body mass index) outcomes and includes inter-metabolite correlations. It thus supports a range of experimental designs and demonstrating associations between metabolite profiles and biomedical responses.By enhancing NMR spectral simulations, MetAssimulo 2.0 is well positioned to support and enhance research at the intersection of deep learning and metabolomics. AVAILABILITY AND IMPLEMENTATION:The code and the detailed instruction/tutorial for MetAssimulo 2.0 is available at https://github.com/yanyan5420/MetAssimulo_2.git. The relevant NMR spectra for metabolites are deposited in MetaboLights with accession number MTBLS12081.

Liquid chromatography-mass spectrometry (LC-MS) is a commonly used analytical technique in untargeted metabolomics. However, the diverse chemical and physical properties of metabolites often require the use of several different analytical assays for broad metabolome coverage. Conventionally, each assay is analyzed separately, but this fails to capture interassay relationships, making multiassay biomarker discovery and data interpretation difficult. Here we propose a workflow to integrate multiassay metabolomics data, designed to enable biomarker discovery and elucidation of unknown metabolites. We employ a multiblock-partial least-squares model (MB-PLS) coupled with multiblock variable importance in projection to estimate the importance of predictors to the outcome variable. Then we cluster the selected predictors and compare them to groups defined by their structural properties based on retention time and mass-to-charge ratio. To demonstrate and evaluate the approach, we used three multiassay data sets predicting biological sex, Alzheimer's disease status, and blood bilirubin levels as the outcomes of interest. The MB-PLS models outperformed single-assay models in both classification and regression tasks, indicating that modeling interblock relationships enabled an improved estimate of phenotypic outcome. Additionally, the MB-PLS models shed valuable insight into each data block's contribution to the predicted outcome. Our workflow enabled us to determine a set of potential cross-assay biomarkers. Following putative annotation, the majority of these and their signs of association agreed with results previously reported in the literature. Our workflow has the potential to benefit the metabolomics community and beyond as it offers interpretable integrative analysis of multiassay LC-MS data and facilitates discovery of potential biomarkers.

Metabolic regulation is a key feature of inflammasome activation and effector function. Using metabolomic approaches, we show that downregulation of taurine metabolism is crucial for NLRP3 inflammasome activation. Following NLRP3 activation stimuli, taurine rapidly egresses to the extracellular compartment. Taurine efflux is facilitated primarily by the volume-regulated anion channel (VRAC). Loss of intracellular taurine impairs sodium-potassium ATPase pump activity, promoting ionic dysregulation and disrupting ionic fluxes. Inhibiting VRAC, or supplementation of taurine, restores the ionic balance, abrogates IL-1β release, and reduces cellular cytotoxicity in macrophages. We further demonstrate that the protective effect of taurine is diminished when sodium-potassium ATPase is inhibited, highlighting the pump's role in taurine-mediated protection. Finally, taurine metabolism is significantly associated with the development of tuberculosis-associated immune reconstitution inflammatory syndrome, a systemic hyperinflammatory condition known to be mediated by inflammasome activation. Altogether, we identified a critical metabolic pathway that modulates inflammasome activation and drives disease pathogenesis.

Initially developed for transcriptomics data, pathway analysis (PA) methods can introduce biases when applied to metabolomics data, especially if input parameters are not chosen with care. This is particularly true for exometabolomics data, where there can be many metabolic steps between the measured exported metabolites in the profile and internal disruptions in the organism. However, evaluating PA methods experimentally is practically impossible when the sample’s “true" metabolic disruption is unknown. This study aims to show that PA can lead to non-specific enrichment, potentially resulting in false assumptions about the true cause of perturbed metabolic states. Using in silico metabolic modelling, we can create disruptions in metabolic networks. SAMBA, a constraint-based modelling approach, simulates metabolic profiles for entire pathway knockouts, providing both a known disruption site as well as a simulated metabolic profile for PA methods. PA should be able to detect the known disrupted pathway among the significantly enriched pathways for that profile. Through network-level statistics, visualisation, and graph-based metrics, we show that even when a given pathway is completely blocked, it may not be significantly enriched when using PA methods with its corresponding simulated metabolic profile. This can be due to various reasons such as the chosen PA method, the initial pathway set definition, or the network’s inherent structure. This work highlights how some metabolomics data may not be suited to typical PA methods, and serves as a benchmark for analysing, improving and potentially developing new PA tools.

MOTIVATION:Random forests (RFs) can deal with a large number of variables, achieve reasonable prediction scores, and yield highly interpretable feature importance values. As such, RFs are appropriate models for feature selection and further dimension reduction. However, RFs are often not appropriate for correlated datasets due to their mode of selecting individual features for splitting. Addressing correlation relationships in high-dimensional datasets is imperative for reducing the number of variables that are assigned high importance, hence making the dimension reduction most efficient. Here, we propose the LAtent VAriable Stochastic Ensemble of Trees (LAVASET) method that derives latent variables based on the distance characteristics of each feature and aims to incorporate the correlation factor in the splitting step. RESULTS:Without compromising on performance in the majority of examples, LAVASET outperforms RF by accurately determining feature importance across all correlated variables and ensuring proper distribution of importance values. LAVASET yields mostly non-inferior prediction accuracies to traditional RFs when tested in simulated and real 1D datasets, as well as more complex and high-dimensional 3D datatypes. Unlike traditional RFs, LAVASET is unaffected by single 'important' noisy features (false positives), as it considers the local neighbourhood. LAVASET, therefore, highlights neighbourhoods of features, reflecting real signals that collectively impact the model's predictive ability. AVAILABILITY AND IMPLEMENTATION:LAVASET is freely available as a standalone package from https://github.com/melkasapi/LAVASET.

BACKGROUND:Epidemiologic evidence linking blood pressure (BP) and body weight-lowering effects with fruit and vegetable consumption mostly relies on self-reported dietary assessment prone to misreport and under- or overestimation of relationships. OBJECTIVES:We aimed to characterize objective 24-h urinary metabolites and a derived metabolite score associated with fruit and vegetable intake and assessed their associations with BP and BMI, with validation across cohorts. METHODS:We used untargeted proton nuclear magnetic resonance spectroscopy (1H NMR) of 2 timed repeated 24-h urine collections from free-living participants from the US (n = 2032) and the UK (n = 449) of the cross-sectional International Study of Macro-/Micronutrients and Blood Pressure (INTERMAP). We evaluated correlations between fruit and vegetable intake assessed by 24-h dietary recalls with 7100 1H NMR features, adjusted for confounders and multiple testing. We related identified metabolites and a metabolite score with BP and BMI using extensively adjusted multiple linear regression models. RESULTS:We characterized 11 1H NMR-derived 24-h urinary metabolites related to fruit and vegetable intake, reproducible across multiple 24-h urine collections of both cohorts. Proline betaine, citrate, N-methylproline, scyllo-inositol, 2-hydroxy-2-(4-methyl cyclohex-3-en-1-yl) propoxyglucuronide, and proline were associated with fruit intake, specifically with Rutaceae intake, whereas S-methyl-L-cysteine sulfoxide and S-methyl-L-cysteine sulfoxide metabolite were associated with Brassicaceae intake. The metabolite score, explaining 39.8% of fruit and vegetable intake, was inversely associated with systolic BP [-1.65 mmHg; 95% confidence interval (CI): -2.68, -0.62; P < 0.002] and BMI (-1.21 kg/m2; 95% CI: -1.62, -0.78; P < 0.0001). These associations were, to a large extent, explained by urinary citrate excretion. CONCLUSIONS:We identified 1H NMR-derived urinary metabolites associated with fruit and vegetable consumption, consistent and reproducible between urine collections and across populations. A higher fruit and vegetable-related metabolite score showed associations with lower systolic BP and BMI, mainly mediated by citrate, but would need confirmation in further studies.

While grouping/read-across is widely used to fill data gaps, chemical registration dossiers are often rejected due to weak category justifications based on structural similarity only. Metabolomics provides a route to robust chemical categories via evidence of shared molecular effects across source and target substances. To gain international acceptance, this approach must demonstrate high reliability, and best-practice guidance is required. The MetAbolomics ring Trial for CHemical groupING (MATCHING), comprising six industrial, government and academic ring-trial partners, evaluated inter-laboratory reproducibility and worked towards best-practice. An independent team selected eight substances (WY-14643, 4-chloro-3-nitroaniline, 17α-methyl-testosterone, trenbolone, aniline, dichlorprop-p, 2-chloroaniline, fenofibrate); ring-trial partners were blinded to their identities and modes-of-action. Plasma samples were derived from 28-day rat tests (two doses per substance), aliquoted, and distributed to partners. Each partner applied their preferred liquid chromatography–mass spectrometry (LC–MS) metabolomics workflows to acquire, process, quality assess, statistically analyze and report their grouping results to the European Chemicals Agency, to ensure the blinding conditions of the ring trial. Five of six partners, whose metabolomics datasets passed quality control, correctly identified the grouping of eight test substances into three categories, for both male and female rats. Strikingly, this was achieved even though a range of metabolomics approaches were used. Through assessing intrastudy quality-control samples, the sixth partner observed high technical variation and was unable to group the substances. By comparing workflows, we conclude that some heterogeneity in metabolomics methods is not detrimental to consistent grouping, and that assessing data quality prior to grouping is essential. We recommend development of international guidance for quality-control acceptance criteria. This study demonstrates the reliability of metabolomics for chemical grouping and works towards best-practice.

Metabolic profiling (metabolomics) aims at measuring small molecules (metabolites) in complex samples like blood or urine for human health studies. While biomarker-based assessment often relies on a single molecule, metabolic profiling combines several metabolites to create a more complex and more specific fingerprint of the disease. However, in contrast to genomics, there is no unique metabolomics setup able to measure the entire metabolome. This challenge leads to tedious and resource consuming preliminary studies to be able to design the right metabolomics experiment. In that context, computer assisted metabolic profiling can be of strong added value to design metabolomics studies more quickly and efficiently. We propose a constraint-based modelling approach which predicts in silico profiles of metabolites that are more likely to be differentially abundant under a given metabolic perturbation (e.g. due to a genetic disease), using flux simulation. In genome-scale metabolic networks, the fluxes of exchange reactions, also known as the flow of metabolites through their external transport reactions, can be simulated and compared between control and disease conditions in order to calculate changes in metabolite import and export. These import/export flux differences would be expected to induce changes in circulating biofluid levels of those metabolites, which can then be interpreted as potential biomarkers or metabolites of interest. In this study, we present SAMBA (SAMpling Biomarker Analysis), an approach which simulates fluxes in exchange reactions following a metabolic perturbation using random sampling, compares the simulated flux distributions between the baseline and modulated conditions, and ranks predicted differentially exchanged metabolites as potential biomarkers for the perturbation. We show that there is a good fit between simulated metabolic exchange profiles and experimental differential metabolites detected in plasma, such as patient data from the disease database OMIM, and metabolic trait-SNP associations found in mGWAS studies. These biomarker recommendations can provide insight into the underlying mechanism or metabolic pathway perturbation lying behind observed metabolite differential abundances, and suggest new metabolites as potential avenues for further experimental analyses.

J-Resolved (J-Res) nuclear magnetic resonance (NMR) spectroscopy is pivotal in NMR-based metabolomics, but practitioners face a choice between time-consuming high-resolution (HR) experiments or shorter low-resolution (LR) experiments which exhibit significant peak overlap. Deep learning neural networks have been successfully used in many fields to enhance quality of natural images, especially with regard to resolution, and therefore offer the prospect of improving two-dimensional (2D) NMR data. Here, we introduce the J-RESRGAN, an adapted and modified generative adversarial network (GAN) for image super-resolution (SR), which we trained specifically for metabolomic J-Res spectra to enhance peak resolution. A novel symmetric loss function was introduced, exploiting the inherent vertical symmetry of J-Res NMR spectra. Model training used simulated high-resolution J-Res spectra of complex mixtures, with corresponding low-resolution spectra generated via blurring and down-sampling. Evaluation of peak pair resolvability on J-RESRGAN demonstrated remarkable improvement in resolution across a variety of samples. In simulated plasma data, 100% of peak pairs exhibited enhanced resolution in super-resolution spectra compared to their low-resolution counterparts. Similarly, enhanced resolution was observed in 80.8-100% of peak pairs in experimental plasma, 85.0-96.7% in urine, 94.4-98.9% in full fat milk, and 82.6-91.7% in orange juice. J-RESRGAN is not sample type, spectrometer or field strength dependent and improvements on previously acquired data can be seen in seconds on a standard desktop computer. We believe this demonstrates the promise of deep learning methods to enhance NMR metabolomic data, and in particular, the power of J-RESRGAN to elucidate overlapping peaks, advancing precision in a wide variety of NMR-based metabolomics studies. The model, J-RESRGAN, is openly accessible for download on GitHub at https://github.com/yanyan5420/J-RESRGAN.

As terabytes of multi-omics data are being generated, there is an ever-increasing need for methods facilitating the integration and interpretation of such data. Current multi-omics integration methods typically output lists, clusters, or subnetworks of molecules related to an outcome. Even with expert domain knowledge, discerning the biological processes involved is a time-consuming activity. Here we propose PathIntegrate, a method for integrating multi-omics datasets based on pathways, designed to exploit knowledge of biological systems and thus provide interpretable models for such studies. PathIntegrate employs single-sample pathway analysis to transform multi-omics datasets from the molecular to the pathway-level, and applies a predictive single-view or multi-view model to integrate the data. Model outputs include multi-omics pathways ranked by their contribution to the outcome prediction, the contribution of each omics layer, and the importance of each molecule in a pathway. Using semi-synthetic data we demonstrate the benefit of grouping molecules into pathways to detect signals in low signal-to-noise scenarios, as well as the ability of PathIntegrate to precisely identify important pathways at low effect sizes. Finally, using COPD and COVID-19 data we showcase how PathIntegrate enables convenient integration and interpretation of complex high-dimensional multi-omics datasets. PathIntegrate is available as an open-source Python package.

The computational metabolomics field brings together computer scientists, bioinformaticians, chemists, clinicians, and biologists to maximize the impact of metabolomics across a wide array of scientific and medical disciplines. The field continues to expand as modern instrumentation produces datasets with increasing complexity, resolution, and sensitivity. These datasets must be processed, annotated, modeled, and interpreted to enable biological insight. Techniques for visualization, integration (within or between omics), and interpretation of metabolomics data have evolved along with innovation in the databases and knowledge resources required to aid understanding. In this review, we highlight recent advances in the field and reflect on opportunities and innovations in response to the most pressing challenges. This review was compiled from discussions from the 2022 Dagstuhl seminar entitled “Computational Metabolomics: From Spectra to Knowledge”.

Background: Whether red meat consumption is associated with higher inflammation or confounded by increased adiposity remains unclear. Plasma metabolites capture the effects of diet after food is processed, digested, and absorbed, and correlate with markers of inflammation, so they can help clarify diet-health relationships.Objective: To identify whether any metabolites associated with red meat intake are also associated with inflammation.Methods: A cross-sectional analysis of observational data from older adults (52.84% women, mean age 63 +/- 0.3 y) participating in the Multi-Ethnic Study of Atherosclerosis (MESA). Dietary intake was assessed by food-frequency questionnaire, alongside C-reactive protein (CRP), interleukin-2, interleukin-6, fibrinogen, homocysteine, and tumor necrosis factor alpha, and untargeted proton nuclear magnetic resonance (H-1 NMR) metabolomic features. Associations between these variables were examined using linear regression models, adjusted for demographic factors, lifestyle behaviors, and body mass index (BMI).Results: In analyses that adjust for BMI, neither processed nor unprocessed forms of red meat were associated with any markers of inflammation (all P > 0.01). However, when adjusting for BMI, unprocessed red meat was inversely associated with spectral features representing the metabolite glutamine (sentinel hit: beta = -0.09 +/- 0.02, P = 2.0 x 10(-5)), an amino acid which was also inversely associated with CRP level (beta = -0.11 +/- 0.01, P = 3.3 x 10(-10)).Conclusions: Our analyses were unable to support a relationship between either processed or unprocessed red meat and inflammation, over and above any confounding by BMI. Glutamine, a plasma correlate of lower unprocessed red meat intake, was associated with lower CRP levels. The differences in diet-inflammation associations, compared with diet metabolite-inflammation associations, warrant further investigation to understand the extent that these arise from the following: 1) a reduction in measurement error with metabolite measures; 2) the extent that which factors other than unprocessed red meat intake contribute to glutamine levels; and 3) the ability of plasma metabolites to capture individual differences in how food intake is metabolized.

Background: Avocado consumption is linked to better glucose homeostasis, but small associations suggest potential population heterogeneity. Metabolomic data capture the effects of food intake after digestion and metabolism, thus accounting for individual differences in these processes.Objectives: To identify metabolomic biomarkers of avocado intake and to examine their associations with glycemia.Methods: Baseline data from 6224 multi-ethnic older adults (62% female) included self-reported avocado intake, fasting glucose and insulin, and untargeted plasma proton nuclear magnetic resonance metabolomic features (metabolomic data were available for a randomly selected subset; N = 3438). Subsequently, incident type 2 diabetes (T2D) was assessed over an similar to 18 y follow-up period. A metabolome-wide association study of avocado consumption status (consumer compared with nonconsumer) was conducted, and the relationship of these features with glycemia via cross-sectional associations with fasting insulin and glucose and longitudinal associations with incident T2D was examined.Results: Three highly-correlated spectral features were associated with avocado intake at metabolome-wide significance levels (P < 5.3 & lowast; 10(-7)) and combined into a single biomarker. We did not find evidence that these features were additionally associated with overall dietary quality, nor with any of 47 other food groups (all P > 0.001), supporting their suitability as a biomarker of avocado intake. Avocado intake showed a modest association only with lower fasting insulin (beta = -0.07 (+/-) 0.03, P = 0.03), an association that was attenuated to nonsignificance when additionally controlling for body mass index (kg/m(2)). However, our biomarker of avocado intake was strongly associated with lower fasting glucose (beta = -0.22 (+/-) 0.02, P < 2.0 & lowast; 10(-16)), lower fasting insulin (beta = -0.17 (+/-) 0.02, P < 2.0 & lowast; 10(-16)), and a lower incidence of T2D (hazard ratio: 0.68; 0.63-074, P < 2.0 & lowast; 10(-16)), even when adjusting for BMI.Conclusions: Highly significant associations between glycemia and avocado-related metabolomic features, which serve as biomarkers of the physiological impact of dietary intake after digestion and absorption, compared to modest relationships between glycemia and avocado consumption, highlights the importance of considering individual differences in metabolism when considering diet-health relationships.