IntroductionInflammatory bowel disease (IBD) is a group of chronic inflammatory conditions of the gastrointestinal tract comprising two major phenotypes, Crohn’s disease (CD) and ulcerative colitis (UC). Up to 8% of patients with IBD also develop primary sclerosing cholangitis (PSC), characterised by cholestasis and progressive destruction of the biliary tree, resulting in cirrhosis, end-stage liver disease and cholangiocarcinoma. Clinical outcome can currently not be improved through medication, denoting the importance of diagnosis prior to irreversible damage, which requires biomarkers of (early) disease.ObjectivesWe employed direct infusion mass spectrometry (DI-MS)-based metabolomics on plasma to build predictive, potentially diagnostic models for PSC-IBC and other phenotypes including IBD subtype, stricture and fistula presence and more. We used this dataset to simultaneously investigate aetiology of these phenotypes.MethodsSamples of 348 IBD patients were included for analysis. The data was analysed using our previously reported tool, MetaboShiny. We built predictive models using Random Forest (RF), and subsequently combined with univariate statistics to rank m/z features connected to PSC-IBD. This ranking was used to perform mummichog enrichment analysis connected to metabolic and metagenomic changes.ResultsThe highest performing predictive model differentiated PSC-IBD from PSC. The metabolic signature was enriched in changes to amino acid and vitamin metabolism, alongside changes to the metagenome suggesting decreases in anti-inflammatory microbial species and increases in pro-inflammatory species.ConclusionThese results demonstrate the potential of DI-MS-based metabolomics with machine learning to create diagnostic models and generate hypotheses on the metabolomic-metagenomic level. Sharing our dataset of patients will enrich future human IBD metabolomics research possibilities.Supplementary InformationThe online version contains supplementary material available at 10.1007/s11306-026-02420-w.

BackgroundHeart failure with preserved ejection fraction (HFpEF) accounts for nearly half of heart failure cases, with obesity emerging as a key pathophysiologic driver. Oxylipins are lipid signaling molecules linked to adverse HFpEF outcomes, but their relationship with obesity remains unclear.ObjectivesTo evaluate association between obesity and arterialized and venous oxylipins in HFpEF patients.MethodsIn this prospective single-center cohort study, 90 patients with HFpEF underwent transthoracic echocardiography and right heart catheterization with arterialized pulmonary capillary and venous blood sampling. Serum oxylipins were quantified via liquid chromatography-mass spectrometry. Oxylipin levels were log-transformed and standardized. Oxylipin associations with obesity (BMI ≥ 30 kg/m²) were evaluated using logistic regression, and pulmonary hypertension (PH) subgroup analyses using a one-sample proportion test.ResultsOf the 90 patients, 61 (67.8%) were obese. Obese patients were younger, more frequently female and African American, had higher prevalence of diabetes (54.1% vs. 17.2%, p < 0.001) and exhibited greater interventricular septal and posterior wall thickness. Multivariable and volcano plot analyses demonstrated inverse associations between obesity and several arterialized oxylipins, including 12,13-DiHOME (OR:0.48; p = 0.03), 19,20-DiHDoPE (OR:0.53; p = 0.03), 11,12-DiHETrE (OR:0.31, p = 0.04), 9,10-DiHOME (OR;0.42; p = 0.02), and 5(S),6(S)-DiHETE (OR:0.51; p = 0.02). In contrast, arterialized 19R-hydroxy-prostaglandin E1 was positively associated with obesity (OR:1.91, p = 0.03). Among venous oxylipins, 15(R)-PGE1 (OR:2.23; p = 0.02) and 19R-hydroxy-prostaglandin E1 (OR:1.91; p = 0.02) showed positive associations with obesity. Obese patients constituted a higher proportion of combined pre- and post-capillary PH subgroup (p = 0.003).ConclusionsObesity in HFpEF is associated with reduced oxylipin diol levels, higher levels of prostaglandin E1 metabolites, and higher burden of pulmonary hypertension.Graphical abstract

IntroductionVanilla species represent a taxonomically complex and economically important group of orchids, yet species discrimination and chemotaxonomic characterization remain challenging due to high intrageneric metabolic variability and limited molecular insights.ObjectivesWe aimed to apply an untargeted metabolomic approach to characterize the metabolic signatures of three Brazilian Vanilla species (V. pompona, V. phaeantha, and V. calyculata) and to evaluate the relative contributions of species identity and biome origin to metabolic diversification.MethodsThe leaf metabolome of 102 Vanilla were profiled using high-resolution LC-HRMS/MS. Multivariate analyses including PCA and PLS-DA served to explore variance structures and build discriminant models. Discriminant features were selected using integrated criteria (VIP scores and volcano-plot significance). Annotation leveraged GNPS molecular networking and in silico databases to classify key metabolite classes.ResultsUnsupervised PCA revealed pronounced intraspecific metabolic heterogeneity, preventing spontaneous grouping by species or biome. Supervised PLS-DA models, however, provided robust species classification (Q² = 0.74–0.90), while biome-based models lacked predictive power, indicating limited environmental influence on leaf metabolomes. Consensus selection between models identified 17 core biomarkers, including phenolic acids, cinnamic acid derivatives, C-glycosylated flavonoids, lipids, terpenoids, and N-containing compounds, with ferulic acid (a key precursor of vanillin) emerging as a prominent discriminating metabolite.ConclusionsUntargeted LC-HRMS metabolomics coupled with chemometric modelling delineates species-specific metabolic fingerprints within Vanilla, offering a generalizable strategy for chemotaxonomy and species authentication. The conserved metabolic features also spotlight biologically meaningful pathways for biodiversity assessment and valorization of native Vanilla resources.Supplementary InformationThe online version contains supplementary material available at 10.1007/s11306-026-02422-8.

BackgroundMetabolomics is poised to significantly advance our ability to diagnose and characterize disease, identify diagnostic and prognostic biomarkers, and monitor a patient’s response to treatment. Despite these benefits, metabolomics-based diagnostic tests have not been adapted into clinical practice primarily because metabolomics technologies, which include high performance liquid chromatography and mass spectrometry, are not conducive to point-of-care. Expanding the reach of metabolomics-based testing requires centralized technology to be more accessible.MethodsOne way to achieve this is by utilizing dried blood spots (DBS) as a diagnostic testing sample type. Their easy collection, lack of reliance on cold chain, and cost-effective storage and shipment make them the ideal sample type for diagnostic testing that requires a centralized technology. To date, few studies have investigated the ability of DBS to capture the global metabolome and track biological changes in individual subjects.ResultsIn this feasibilty study, we tested these factors by performing untargeted metabolic profiling on DBS collected from study volunteers before and after exercising. We compared our results in DBS to results in serum and plasma that have been reported in the literature.ConclusionsOverall, our findings closely agreed with published studies, showing that DBS may be a viable sample type for metabolomics-based diagnostic and wellness testing.Supplementary InformationThe online version contains supplementary material available at 10.1007/s11306-025-02390-5.

BackgroundAutosomal dominant polycystic kidney disease (ADPKD) is the most common hereditary kidney disorder characterized by progressive renal cyst formation, often leading to end-stage kidney disease (ESKD). In contrast to the urinary metabolome in ADPKD, the composition of renal cyst fluid remains largely unexplored.MethodsWe conducted a comprehensive metabolomic analysis of renal cyst fluid from 26 ADPKD patients (20 on dialysis, six with kidney transplants) using ¹H-NMR spectroscopy and liquid chromatography-mass spectrometry (LC-MS). Cysts were clustered based on metabolite profiles, and differences were analyzed across groups defined by renal function status (dialysis vs. transplant), cyst volume, and cyst fluid sodium concentrations.ResultsDialysis patients and transplant recipients differed significantly in their renal cyst fluid metabolomes. The former exhibited higher concentrations of myoinositol, creatinine, sucrose, τ-methylhistidine, trigonelline, and sarcosine, while the latter showed increased levels of leucine, isoleucine, valine and alanine. Remarkably, metabolites of the immunosuppressive prodrug mycophenolate mofetil were detected in renal cyst fluids after kidney transplantation. Despite intra- and interindividual variability, cyst fluid from the same patient displayed greater homogeneity. Interestingly, metabolomic profiles were not altered by cyst size.ConclusionThis first systematic metabolomic analysis of renal cyst fluid in advanced ADPKD reveals distinct metabolic signatures linked to renal function status. The data provides novel insights into the pathophysiology of ADPKD and highlight the potentials of renal cyst fluid metabolomics for identifying biomarkers and therapeutic targets.

IntroductionThere are significant challenges remain in accurately categorizing the risk of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) patients.ObjectivesWe used an untargeted 1H NMR-based metabolomics to assess the metabolomic changes in serum samples from a Danish cohort of 106 COVID-19-infected patients with mild to fatal disease courses and from patients with fatal outcomes from other diseases.MethodsIn total, 240 serum samples were used for this study. We used the data for multiple analyses (1) to construct a predictive model for disease severity and outcome, (2) to identify prognostic markers for subsequent disease severity and outcome, and (3) to understand the disease consequences in the metabolome and how recovery or death is reflected in the altered biological pathways.ResultsOur results revealed distinct alterations in the serum metabolome that could differentiate patients with COVID-19 by severity (mild or severe) or outcome (death or survival). Using receiver operating characteristic (ROC) curve analysis and four machine learning algorithms (random forest, linear support vector machine, PLS-DA, and logistic regression), we identified two biomarker sets with relevant biological functions that predict subsequent disease severity and patient outcome. The range of these severity-associated biomarkers was equally broad and included inflammatory markers, amino acids, fluid balance, ketone bodies, glycolysis-related metabolites, lipoprotein particles, and fatty acid levels.ConclusionsOur data suggest the potential benefits of broader testing of these metabolites from newly diagnosed patients to predict which COVID-19 patients will progress to severe disease and which patients will manifest severe symptoms to minimize mortality.

IntroductionBased on distinct triggers, bacterial and allergen-induced inflammatory reactions have different pathophysiology. Metabolomic analysis is high-throughput technique that can provide potential biomarkers to distinguish between these responses.ObjectivesIn order to find out the metabolic profiles of two types of inflammation, metabolites were analysed in blood plasma and bronchoalveolar lavage fluid (BALF) of guinea pigs subjected to bacterial lipopolysaccharide (LPS) or allergen ovalbumin (OVA).MethodsHydrogen-1 nuclear magnetic resonance (1H NMR) spectroscopy for metabolite analysis was performed in samples of blood plasma and BALF of guinea pigs.ResultsRandom forest algorithm built on combination of levels of circulating and BALF metabolites resulted in almost ideal discrimination between acute allergic and bacterial inflammation. The differences between inflammation triggered by LPS and OVA were manifested in shift in energy metabolism, metabolism of branched-chain amino acids (BCAAs)/branched-chain keto acids (BCKAs) with alterations in alanine and glutamine, which are linked with both, ammonia homeostasis as well as gluconeogenesis.ConclusionDistinct molecule nutrients are to be utilized during acute bacterial and allergic inflammatory response.

IntroductionMushrooms contain besides proteins a diverse pallet of specialized metabolites bioactive in either beneficial or harmful manner. Therefore, mushrooms have been exploited by humans for centuries for dietary or medical purposes. For example, the edible and medicinal mushrooms Hericium erinaceus and Pleurotus eryngii are grown commercially around the world. In nature, H. erinaceus grows on old or dead tree trunks, and P. eryngii grows on Apiaceae plant roots, whereas in cultivation, they grow on substrates mainly consisting of dry wood chips, straw, and cereals. To make their farming more sustainable, supplements such as olive mill solid waste (OMSW) have been added to support mushroom development. However, so far, the impact of substrate additives on the edible mushroom metabolic content has not been assessed.MethodsHere, we examined the effect of different proportions of OMSW added to the substrate on the metabolic profiles of the fruiting body (FB) and mycelium of H. erinaceus and P. eryngii mushrooms. The study includes four groups for each mushroom species, mushrooms are grown on 0% OMSW, 33%, 60%, and 80% OMSW, with three biological repeats in each group. We used computational metabolomics strategies including GNPS molecular networking, MS2Query, and the FERMO dashboard, to organize, annotate, and prioritize metabolite features from the untargeted Q-Exactive Plus HR-LC-MS/MS metabolomics data. Following chromatography-based fractionation, the metabolite annotation of four metabolite features was further validated or fine-tuned using 1H-NMR, to resolve structural isomers.Results and discussionOur computational metabolomics strategies showed several annotated metabolite features to be affected by OSMW concentration. In general, the methanolic extracts of H. erinaceus FB and mycelium were more highly enriched with specialized metabolites than those of P. eryngii. Interestingly, OMSW increased several hericenone analogues in the H. erinaceus FB, as well as several erinacerin metabolites from the mycelium. In addition, high concentrations of OMSW decreased the enniatin metabolite abundance. In conclusion, we demonstrate how a change in substrate composition affects the mushroom’s specialized metabolome and can induce and alter mushroom metabolite content and diversity. These results highlight the importance of including computational metabolomic strategies to investigate new sustainable growth options for edible mushrooms and other natural foods.

IntroductionDementia can be prevented through early intervention; hence, there is an urgent need for biomarkers to help diagnose mild cognitive impairment (MCI).ObjectivesWe aimed to develop a multi-marker panel composed of plasma metabolites to aid in the diagnosis of MCI.MethodsWe performed an analysis of a multi-marker panel of MCI metabolites using a random forest algorithm with variable selection methods and a global surrogate with principal component analysis and partial least squares (PLS).ResultsBy incorporating variable selection methods, we constructed a predictive model that demonstrated robust performance, with an AUC of approximately 0.85 in both cross-validation and test evaluations, using only five metabolites (methionine, quinic acid, hypoxanthine, O-acetylcarnitine, and 2-oxoglutaric acid). However, owing to the limited number of selected metabolites, it was challenging to infer the biological meaning of this multi-marker panel. To interpret this multi-marker panel biologically, we constructed a global surrogate model using PLS. By examining the PLS loadings corresponding to the scores with intergroup differences, we identified a relationship between 14 metabolites involved in neuronal energy metabolism and neurotransmission. This suggests that the multi-marker panel constructed in this study is related to abnormalities in energy metabolism and neurotransmission in patients with MCI.ConclusionThe method used in this study may be broadly applicable for analyzing multi-marker panels of metabolites and their biological interpretation. This study included an independent validation, and further larger-scale studies using additional external cohorts are warranted to confirm the generalizability of this approach.Supplementary InformationThe online version contains supplementary material available at 10.1007/s11306-025-02372-7.