PubMed Health⌕ Search

SEARCH · PubMed Health

Results for “metabolomics”

Explore indexed PubMed citations for clinical trials, systematic reviews and public health research. Read source abstracts and follow each citation to its original PubMed record.

Quote a phrase for an exact phrase match. Source license links do not imply unrestricted reuse.

At least 19 recordsLinked to original sources
PubMed2006

Metabolomics Standards Workshop and the development of international standards for reporting metabolomics experimental results.

Informatics standards and controlled vocabularies are essential for allowing information technology to help exchange, manage, interpret and compare large data collections. In a rapidly evolving field, the challenge is to work out how best to describe, but not prescribe, the use of these technologies and methods. A Metabolomics Standards Workshop was held by the US National Institutes of Health (NIH) to bring together multiple ongoing standards efforts in metabolomics with the NIH research community. The goals were to discuss metabolomics workflows (methods, technologies and data treatments) and the needs, challenges and potential approaches to developing a Metabolomics Standards Initiative that will help facilitate this rapidly growing field which has been a focus of the NIH roadmap effort. This report highlights specific aspects of what was presented and discussed at the 1st and 2nd August 2005 Metabolomics Standards Workshop.

Cell Physiological Phenomena↗
PubMed2026

Interconnected influences of diet, gut microbiome, and metabolome on cognition across three metabolomics platforms.

Cognitive impairment is increasing with global aging, yet mechanisms linking diet, the gut microbiome, and metabolism to cognitive function remain unclear. To investigate a diet-microbiome-metabolome axis associated with cognition, we integrated fecal metagenomics, diet, and multi-platform plasma metabolomics in 505 older adults from four ADRCs. Several microbes broadly associated with circulating metabolites were also linked to multiple measures of cognitive performance. These taxa exhibited coordinated metabolic signatures, with cognition-positive microbes associated with antioxidant, lipid, and microbial-host co-metabolites, and microbes negatively associated with cognition were linked to inflammatory and aromatic amino acid-derived metabolites. Dietary patterns, particularly the Healthy Eating Index Greens and Beans component, were associated with microbial composition and metabolomic structure. Mediation analyses supported a diet-microbe-metabolite-cognition pathway, while metabolites remained associated with cognition after accounting for microbial features. These findings highlight the metabolome as a central integrator of diet, microbial activity, and cognitive function.

Journal Article↗
PubMed2005

Metabolomics reviewed: a new "omics" platform technology for systems biology and implications for natural products research.

Metabolomics is the study of global metabolite profiles in a system (cell, tissue, or organism) under a given set of conditions. The analysis of the metabolome is particularly challenging due to the diverse chemical nature of metabolites. Metabolites are the result of the interaction of the system's genome with its environment and are not merely the end product of gene expression but also form part of the regulatory system in an integrated manner. Metabolomics has its roots in early metabolite profiling studies but is now a rapidly expanding area of scientific research in its own right. Metabolomics (or metabonomics) has been labeled one of the new "omics", joining genomics, transcriptomics, and proteomics as a science employed toward the understanding of global systems biology. Metabolomics is fast becoming one of the platform sciences of the "omics", with the majority of the papers in this field having been published only in the last two years. In this review metabolomic methodologies are discussed briefly followed by a more detailed review of the use of metabolomics in integrated applications where metabolomics information has been combined with other "omic" data sets (proteomics, transcriptomics) to enable greater understanding of a biological system. The potential of metabolomics for natural product drug discovery and functional food analysis, primarily as incorporated into broader "omic" data sets, is discussed.

Biological Products↗
PubMed2025

Untargeted metabolomics reveals anion and organ-specific metabolic responses of salinity tolerance in willow.

Willows can alleviate soil salinisation while generating sustainable feedstock for biorefinery, yet the metabolomic adaptations underlying their tolerance remain poorly understood. Salix miyabeana was treated with two environmentally abundant salts, NaCl and Na2SO4, in a 12-week pot trial. Willows tolerated salts across all treatments (up to 9.1 dS m-1 soil ECe), maintaining biomass while selectively partitioning ions, confining Na+ to roots and accumulating Cl- andin the canopy and adapting to osmotic stress via reduced stomatal conductance. Untargeted metabolomics captured >5000 putative compounds, including 278 core willow metabolome compounds constitutively produced across organs. Across all treatments, salinity drove widespread metabolic reprogramming, altering 28% of the overall metabolome, with organ-tailored strategies. Comparing salt forms at equimolar sodium, shared differentially abundant metabolites were limited to 3% of the metabolome, representing the generalised salinity response, predominantly in roots. Anion-specific metabolomic responses were extensive. NaCl reduced carbohydrates and tricarboxylic acid cycle intermediates, suggesting potential carbon and energy resource pressure, and accumulated root structuring compounds, antioxidant flavonoids, and fatty acids. Na2SO4 salinity triggered accumulation of sulphur-containing larger peptides, suggesting excess sulphate incorporation leverages ion toxicity to produce specialised salt-tolerance-associated metabolites. This high-depth picture of the willow metabolome underscores the importance of capturing plant adaptations to salt stress at organ scale and considering ion-specific contributions to soil salinity.

Salix↗
PubMed2006

Integrating metabolomics into a systems biology framework to exploit metabolic complexity: strategies and applications in microorganisms.

As an important functional genomic tool, metabolomics has been illustrated in detail in recent years, especially in plant science. However, the microbial category also has the potential to benefit from integration of metabolomics into system frameworks. In this article, we first examine the concepts and brief history of metabolomics. Next, we summarize metabolomic research processes and analytical platforms in strain improvements. The application cases of metabolomics in microorganisms answer what the metabolomics can do in strain improvements. The position of metabolomics in this systems biology framework and the real cases of integrating metabolomics into a system framework to explore the microbial metabolic complexity are also illustrated in this paper.

Bacteria↗
PubMed2026

Longitudinal development of infant oral ecosystem: salivary metabolomic, bacteriome, and virome dynamics in early infancy.

This prospective cohort study investigated the longitudinal development of the salivary bacteriome, virome, and metabolome during early infancy. We assessed the associations between oral bacteria, viruses, and metabolites from 10 mother-infant dyads, with oral samples collected at 1 and 2 years of age. Forty saliva and plaque samples underwent untargeted metabolomic analysis, and infant saliva samples underwent metagenomic sequencing. Maternal salivary and plaque metabolomic profiles remained largely stable, whereas infant profiles were clearly separated from maternal profiles and changed with age. Notably, infant dental plaque metabolism underwent more substantial changes from year 1 to year 2 than saliva, with age-dependent metabolite shifts mainly involving energy, amino acid, nucleotide, and lipid metabolic pathways. Our findings also revealed significant developmental shifts in salivary bacteriome, virome, and functional pathway profiles during early childhood. The most abundant oral bacteria in early life, comprising over 75% of total abundance, included Veillonella, Streptococcus, Rothia, Prevotella, Neisseria, and Actinomyces species. While human viruses like Roseolovirus were detected, bacteriophages constituted the majority of the virome. Comparing infants at year 1 and year 2, we identified differentially abundant bacteria, viruses, metabolic functional pathways, and specific metabolites. We observed associations between bacteria and viruses, noting that these cross-kingdom relationships attenuated as infants grew. The study results underscore the complex and dynamic development of the oral microbiome, virome, and metabolome during early childhood.IMPORTANCEThe human oral cavity undergoes substantial microbial and metabolic development during early childhood, yet the temporal changes in the infant oral ecosystem remain incompletely understood. In this study, we longitudinally profiled the salivary metabolome, bacteriome, and virome of infants at 1 and 2 years of age. We demonstrated that the infant oral metabolome undergoes substantial developmental shifts, particularly in pathways related to energy, amino acid, and lipid metabolism; whereas maternal metabolic profiles remained stable over the same period. Furthermore, our results revealed the dynamic assembly of infant salivary virome and bacteriome and their associations with the functional pathways and metabolites. These findings provide new insights into the complex and dynamic development of the oral microbiome, virome, and metabolome in early infancy.

bacteriome↗
PubMed2026

Metabolomic ageing across mental and behavioural disorders.

BACKGROUND: Individuals with mental disorders face excess morbidity and premature mortality. Accelerated ageing has been proposed as a contributing mechanism but population-scale evidence across diverse diagnoses is limited. OBJECTIVE: To examine whether metabolomic ageing differs across mental disorders and whether associations vary by sex, age group and genetic liability. METHODS: Using plasma metabolomic profiles from UK Biobank participants, we applied a metabolomic ageing clock (MileAge) to estimate disorder-specific differences between metabolite-predicted and chronological age. Mental disorders were ascertained from health records and self-reported physician diagnoses. We analysed nine diagnostic groups and 45 individual disorders and assessed sex and age group differences and associations with polygenic scores. FINDINGS: Among 225&#x2009;212 participants (54% female; mean age 56.97), 38&#x2009;524 had a diagnosis preceding baseline. Substance use, psychotic, affective and neurotic disorders were associated with a metabolite-predicted age older than chronological age, largest for psychosis (&#x3b2;=0.556, 95% CI 0.250 to 0.861, p<0.001). Obsessive-compulsive and eating disorders were associated with a metabolite-predicted age younger than chronological age. Several associations were stronger in males and in individuals aged <65 years. Higher genetic liability to depression, autism and attention-deficit/hyperactivity disorder predicted an older metabolomic age (&#x3b2; range=0.020&#x2009;to 0.047), whereas polygenic scores for psychosis and tobacco use disorder predicted a younger metabolomic age (&#x3b2; range=-0.023&#x2009;to -0.040). For obsessive-compulsive disorder and anorexia nervosa, clinical and genetic associations indicated younger metabolomic ageing. CONCLUSIONS: Metabolomic ageing in mental disorders is heterogeneous. While many disorders are associated with an older biological age, some are linked to a younger biological age. Divergence between genetic liability and clinical phenotypes suggests that non-genetic factors shape biological ageing differences. CLINICAL IMPLICATIONS: Biological age should not be assumed to uniformly exceed chronological age across mental disorders. Sex and age-specific approaches could improve understanding of biological ageing processes in psychiatry.

Humans↗
PubMed2004

Development of biomarkers based on diet-dependent metabolic serotypes: concerns and approaches for cohort and gender issues in serum metabolome studies.

Mathematical models that reflect the effects of dietary restriction (DR) on the sera metabolome may have utility in understanding the mechanisms of DR and in applying this knowledge to human epidemiological studies. Previous studies demonstrated both the feasibility of identifying biomarkers through metabolome analysis and the validity of our approach in independent cohorts of 6-month-old male and female ad libitum fed or DR rats. Cross-cohort studies showed that cohort-specific effects distorted the dataset. The present study extends these observations across the entire sample set, thereby validating our markers independently of specific cohorts. Metabolites originally identified in males were examined in females and vice-versa. DR's effect on the metabolome is partially gender-specific and is modulated by environmental factors. DR reduces inter-gender differences in the metabolome. Univariate statistical methods showed that 56/93 metabolites in the female samples and 39/93 metabolites in the male samples were significantly altered (using our previous cut-off criteria of p < or = 0.2) by DR. The metabolites modulated by DR present a wide spectrum of concentration, redox reactivity and hydrophilicity, suggesting that our serotype is broadly representative of the metabolome and that DR has broad effects on the metabolome. These studies, coupled with those in the preceding and following reports, also highlight the utility for consideration of the metabolome as a network of metabolites using appropriate data analysis approaches. The inter-cohort and inter-gender differences addressed herein suggest potential cautions, and potential approaches, for identification of multivariate biomarker profiles that reflect changes in physiological status, such as a metabolism that predisposes to increased risk of neoplasia.

Animals↗
PubMed2026

NMR metabolomics and glycomics for cancer detection in patients with non-specific symptoms: a prospective observational cohort study.

BACKGROUND: Early cancer diagnosis in patients with non-specific symptoms is limited by the lack of discriminatory tests. Within the Oxfordshire Suspected CANcer (SCAN) pathway, exploratory biomarker work showed that serum 1H NMR-based metabolomics can identify cancer with high accuracy. SCAN2 evaluated whether integrating metabolomics with glycomics provides complementary molecular information and improves discrimination in a clinically complex, real-world population. METHODS: Serum from 369 SCAN patients (59 cancers) was analysed using AXINON&#xae; System-derived NMR metabolomics and HPLC-MS glycomics. Machine-learning models were trained to predict cancer status, with performance assessed by receiver operating characteristic (ROC) analysis of pooled cross-validated predictions. To place cancer risk in a broader clinical context, a second classifier modelling alternative non-cancer diagnosis was incorporated, and mean predicted probabilities from both models were jointly projected into a two-dimensional space, maintaining strict separation of training and test data. FINDINGS: In the full cohort, integration of glycomics with metabolomics achieved an AUC of 0.814 (95% CI 0.808-0.820). In a refined sub-cohort excluding major comorbidities and selected cancer types (32 cancers, 277 non-cancers), performance improved to an AUC of 0.884 (95% CI 0.879-0.890). Discriminatory features included cancer-associated biantennary fucosylated glycans alongside amino acid metabolites (glutamate, histidine) and lipoprotein-related measures. A classifier distinguishing metastatic from non-metastatic disease (n = 29 vs. 30) achieved an AUC of 0.80. Joint probability analysis in the full cohort preserved cancer-associated signatures across comorbidity burden, with projection-based classification achieving an accuracy of 89.2% (95% CI 85.7-92.6). INTERPRETATION: These findings validate the SCAN1 metabolomic signature in a more clinically complex cohort and indicate that integrating glycomics with metabolomics provides complementary biological information for cancer discrimination. Joint probability analysis provides an interpretable framework for cancer risk stratification within multimorbid diagnostic pathways, supporting the clinical potential of scalable multi-omics blood testing. FUNDING: EPSRC, EU Horizon 2020, Wellcome/MLSTF, Novo Nordisk Foundation.

Humans↗
PubMed2026

Network-based integration of metabolomics data from large-scale repositories.

INTRODUCTION: 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. OBJECTIVES: 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. METHODS: 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 . RESULTS: 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. CONCLUSION: 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↗
PubMed2018

High-Throughput Metabolomics by 1D NMR.

Metabolomics deals with the whole ensemble of metabolites (the metabolome). As one of the -omic sciences, it relates to biology, physiology, pathology and medicine; but metabolites are chemical entities, small organic molecules or inorganic ions. Therefore, their proper identification and quantitation in complex biological matrices requires a solid chemical ground. With respect to for example, DNA, metabolites are much more prone to oxidation or enzymatic degradation: we can reconstruct large parts of a mammoth's genome from a small specimen, but we are unable to do the same with its metabolome, which was probably largely degraded a few hours after the animal's death. Thus, we need standard operating procedures, good chemical skills in sample preparation for storage and subsequent analysis, accurate analytical procedures, a broad knowledge of chemometrics and advanced statistical tools, and a good knowledge of at least one of the two metabolomic techniques, MS or NMR. All these skills are traditionally cultivated by chemists. Here we focus on metabolomics from the chemical standpoint and restrict ourselves to NMR. From the analytical point of view, NMR has pros and cons but does provide a peculiar holistic perspective that may speak for its future adoption as a population-wide health screening technique.

Animals↗
PubMed2026

Untargeted-targeted metabolomics: energy metabolism characteristics in heart failure staging and discovery of novel biomarkers.

BACKGROUND: Heart Failure represents the severe stage of various heart diseases. Its global morbidity and mortality are on the rise, making it a serious public health issue that imposes a heavy burden on patients' families and society. Currently, there are relatively few systematic studies on the changes in specific metabolites and pathways in different stages of heart failure, such as Stage A, Stage B and Stage C. AIMS: Using untargeted-targeted metabolomics to explore the metabolic characteristics of Heart Failure, and screen out serum metabolic markers with potential diagnostic and prognostic value. METHODS: This study is a cross-sectional study. A total of 210 heart failure patients from Xiyuan Hospital of China Academy of Chinese Medical Sciences were enrolled between October 2023 and October 2024. Among them, 60 patients were selected for targeted metabolomics analysis via stratified sampling. Serum samples of the patients were collected and pretreated with methanol, then metabolites were detected using untargeted and targeted LC-MS respectively. After the raw data were processed with MSDIAL, pattern recognition was performed using principal component analysis (PCA) and partial least squares discriminant analysis (PLS-DA). Differential metabolites with variable importance in projection (VIP)&#x2009;>&#x2009;1 and P&#x2009;<&#x2009;0.05 were screened, and relevant pathways were analyzed via enrichment analysis using the Kyoto Encyclopedia of Genes and Genomes (KEGG) database. RESULTS: Untargeted metabolomics revealed that, compared with patients in Stages A and B, those with heart failure in Stage C had decreased serum levels of alanine, creatine, and branched-chain amino acids, along with increased levels of citric acid, fumaric acid, and malic acid. The differential metabolites were primarily enriched in pathways including the citric acid cycle, central carbon metabolism, and amino acid metabolism, indicating that energy metabolism plays a crucial role in the occurrence and progression of HF. Targeted metabolomics validated the findings from untargeted metabolomics: compared with Stage A, the level of phosphoenolpyruvate in Stage B was reduced; and in comparison with patients in Stage A or B, patients in Stage C showed decreased serum levels of multiple energy metabolites (e.g., glucose-6-phosphate, fructose-6-phosphate, 3-phosphoglyceric acid, AMP, ADP and ATP) as well as increased levels of malic acid, which is consistent with the characteristics of the "hypermetabolism-energy starvation" paradox. CONCLUSION: Stage C of heart failure is characterized by energy metabolism collapse (decreased ATP and TCA compensation), and differential metabolites (such as malic acid) may serve as potential candidate biomarkers pending longitudinal validation.

Humans↗
PubMed2003

A structure-based anatomy of the E.coli metabolome.

The Escherichia coli metabolome has been characterised using the two-dimensional structures of 745 metabolites, obtained from the EcoCyc and KEGG databases. Physicochemical properties of the metabolome have been calculated to provide an overview of this set of cognate ligands. A library of fragments commonly found among these molecules has been employed to reveal the main constituents of metabolites, and to assist a broad classification of the metabolome into biochemically relevant classes. Fragment-based fingerprints reveal the metabolome as a continuum in the two-dimensional structural space, where clusters of molecules sharing similar scaffolds can be identified, but are generally overlapping. Nucleotide, carbohydrate and amino acid-like molecules are the most prominent, but at high levels of similarity, a more detailed classification is possible. Classification schemes for the metabolome are a promising tool for understanding the chemical diversity of the metabolome. When used in conjunction with existing classifications of the proteome, they can help to elucidate the binding preferences and promiscuity of proteins and their cognate substrates.

Computational Biology↗
PubMed2005

The next wave in metabolome analysis.

The metabolome of a cell represents the amplification and integration of signals from other functional genomic levels, such as the transcriptome and the proteome. Although this makes metabolomics a useful tool for the high-throughput analysis of phenotypes, the lack of a direct connection to the genome makes it difficult to interpret metabolomic data. Nevertheless, functional genomics has produced examples of the use of metabolomics to elucidate the phenotypes of otherwise silent mutations. Despite several successes, we believe that future metabolomic studies must focus on the accurate measurement of the concentrations of unambiguously identified metabolites. The research community must develop databases of metabolite concentrations in cells that are grown in several well-defined conditions if metabolomic data are to be integrated meaningfully with data from the other levels of functional-genomic analysis and to make a significant contribution to systems biology.

Animals↗
PubMed2005

Metabolomics in human nutrition: opportunities and challenges.

Metabolomics has been widely adopted in pharmacology and toxicology but is relatively new in human nutrition. The ultimate goal, to understand the effects of exogenous compounds on human metabolic regulation, is similar in all 3 fields. However, the application of metabolomics to nutritional research will be met with unique challenges. Little is known of the extent to which changes in the nutrient content of the human diet elicit changes in metabolic profiles. Moreover, the metabolomic signal from nutrients absorbed from the diet must compete with the myriad of nonnutrient signals that are absorbed, metabolized, and secreted in both urine and saliva. The large-bowel microflora also produces significant metabolic signals that can contribute to and alter the metabolome of biofluids in human nutrition. Notwithstanding these possible confounding effects, every reason exists to be optimistic about the potential of metabolomics for the assessment of various biofluids in nutrition research. This potential lies both in metabolic profiling through the use of pattern-recognition statistics on assigned and unassigned metabolite signals and in the collection of comprehensive data sets of identified metabolites; both objectives have the potential to distinguish between different dietary treatments, which would not have been targeted with conventional techniques. The latter objective sets out a well-recognized challenge to modern biology: the development of libraries of small molecules to aid in metabolite identification. The purpose of the present review was to highlight some early challenges that need to be addressed if metabolomics is to realize its great potential in human nutrition.

Diet↗
PubMed2005

Plant metabolomics: potential for practical operation.

In the postgenomic era, metabolomics is expected to be the newest useful omics science for functional genomics. However, in plant science, the present metabolomics technology cannot be considered a universal tool to perfectly elucidate perturbations imposed on sample plants although this is desired by plant physiologists. Despite it being an immature technology, metabolomics has already been used as a powerful tool for precise phenotyping, particularly for industrial application. Metabolomics is the best technology for the analysis of large mutant or transgenic libraries of model experimental plants, such as Arabidopsis, rice, etc. Here, we review the applications and technical problems of metabolomics. We also suggest the potential of metabolomics for plant post-genomic science.

Computational Biology↗
PubMed2026

Metabolomics in breast cancer: insights into treatment responses, disease progression, and prognostic assessment.

BACKGROUND: Alterations in metabolic pathways are a hallmark of cancer and play a pivotal role in breast cancer development and progression. The inherent metabolic heterogeneity of breast cancer contributes to differences in therapeutic response and patients' prognosis. Clinical metabolomics has emerged as a promising approach for identifying metabolic biomarkers that reflect tumor biology, treatment-related changes after diagnosis, and patients' outcomes. AIMS OF REVIEW: This review summarizes the metabolomic profiles of breast cancer patients, using various biological materials and analytical methods, to assess their potential role as biomarkers for monitoring therapeutic response, adverse treatment effects, tracking disease progression, and predicting prognosis. KEY SCIENTIFIC CONCEPT OF REVIEW: Metabolomic shifts generate unique signatures with promising potential as biomarkers for evaluating treatment response, monitoring therapeutic adverse effects, disease progression, and predicting clinical outcomes in breast cancer patients. Biological matrices, such as serum, plasma, and tumor tissue, were commonly used in both untargeted and targeted metabolomics approaches. Liquid chromatography-mass spectrometry is the most commonly used analytical method in clinical metabolomics studies. Altered metabolites were identified and linked to metabolic pathways, particularly amino acids, glucose, and fatty acids metabolism. When integrated with genomic and transcriptomic data, these metabolic fingerprints offer a multidimensional perspective on disease trajectory, thereby enhancing patient stratification and informing personalized therapeutic strategies.

Humans↗