ReviewMetabolites2019
From Samples to Insights into Metabolism: Uncovering Biologically Relevant Information in LC-HRMS Metabolomics Data.
Review in Metabolites, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 53 papers.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
53 citing papers in PubMed.
- Review
- Receptor-Mitochondria Crosstalk in the Kynurenine Metabolic Pathway: Integrating Metabolomics and Clinical Mass Spectrometry.Antioxidants (Basel, Switzerland) · 2026Review
- AI-enabled multi-omics integration in colorectal cancer: from molecular stratification to clinical translation.Frontiers in cell and developmental biology · 2026Review
- Untargeted Metabolomics for Diagnosis, Monitoring, and Understanding the Pathophysiology of Inherited Metabolic Disorders.Journal of inherited metabolic disease · 2026Review
- Targeted Metabolomic Methods forJournal of proteome research · 2025Article
- Bioactive compound identification without fractionation: an Ocimum spp. case study.Metabolomics : Official journal of the Metabolomic Society · 2025Article
- Nanoparticle-assisted strategies in mass spectrometry-based metabolite analysis: recent advances across analytical workflow.Mikrochimica acta · 2025Review
- The Utility of Metabolomics in Spinal Cord Injury: Opportunities for Biomarker Discovery and Neuroprotection.International journal of molecular sciences · 2025Review
- Metabolic Reprogramming in Respiratory Viral Infections: A Focus on SARS-CoV-2, Influenza, and Respiratory Syncytial Virus.Biomolecules · 2025Review
- Metabolomic analysis reveals key changes in amino acid metabolism in colorectal cancer patients.Amino acids · 2025Article
- Metabolomic Exploration of Colorectal Cancer Through Amino Acids and Acylcarnitines Profiling of Serum Samples.Cancers · 2025Article
- Statistical analysis of feature-based molecular networking results from non-targeted metabolomics data.Nature protocols · 2025Article
- Multiparametric Optimization of Data-Dependent Acquisition Towards More Holistic Bacterial Metabolite Coverage Through Molecular Networking.International journal of microbiology · 2025Article
- Metabolic fate of the natural anticancer agent cucurbitacin B: an LC-MS/MS-enabled profiling of its major phase I and II conjugates in vivo.Analytical and bioanalytical chemistry · 2024Article
- Multiplatform Metabolomics: Enhancing the Severity Risk Prognosis of SARS-CoV-2 Infection.ACS omega · 2024Article
- Metabolomics and Lipidomics for Studying Metabolic Syndrome: Insights into Cardiovascular Diseases, Type 1 & 2 Diabetes, and Metabolic Dysfunction-Associated Steatotic Liver Disease.Physiological research · 2024Review
- The Omics Revolution in Understanding Chicken Reproduction: A Comprehensive Review.Current issues in molecular biology · 2024Review
- Article
- Tackling new psychoactive substances through metabolomics: UHPLC-HRMS study on natural and synthetic opioids in male and female murine models.Scientific reports · 2024Article
- Selected Ion Monitoring for Orbitrap-Based Metabolomics.Metabolites · 2024Article
Corrections and comments
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Authors and funding
2 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Untargeted metabolomics (including lipidomics) is a holistic approach to biomarker discovery and mechanistic insights into disease onset and progression, and response to intervention. Each step of the analytical and statistical pipeline is crucial for the generation of high-quality, robust data. Metabolite identification remains the bottleneck in these studies; therefore, confidence in the data produced is paramount in order to maximize the biological output. Here, we outline the key steps of the metabolomics workflow and provide details on important parameters and considerations. Studies should be designed carefully to ensure appropriate statistical power and adequate controls. Subsequent sample handling and preparation should avoid the introduction of bias, which can significantly affect downstream data interpretation. It is not possible to cover the entire metabolome with a single platform; therefore, the analytical platform should reflect the biological sample under investigation and the question(s) under consideration. The large, complex datasets produced need to be pre-processed in order to extract meaningful information. Finally, the most time-consuming steps are metabolite identification, as well as metabolic pathway and network analysis. Here we discuss some widely used tools and the pitfalls of each step of the workflow, with the ultimate aim of guiding the reader towards the most efficient pipeline for their metabolomics studies.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.