A scientist takes a small vial with a blue lid from a rack of similar vials, ready to place it in a liquid-chromatography mass spectrometer (LC-MS).
Researchers study metabolites in a range of sample types using high-throughput techniques such as liquid-chromatography mass spectrometry (LC-MS).
Credit: SlavkoSereda

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What Is Metabolomics?

Metabolomics is the high-throughput analysis of the metabolome—the collection of small molecules within a biological sample, such as cells, tissues, or biological fluids.1 These molecules, or metabolites, are the intermediate products of metabolic reactions and are required for cells to function.2 Metabolites are typically less than 1500 Da in size and include a range of different compounds such as amino acids, lipids, fatty acids, steroids, vitamins, minerals, drugs, and carbohydrates.1,2

How Is Metabolomics Data Generated and Analyzed?

Scientists generate metabolomics data using instruments called spectrometers. Prepared samples can be subjected to different mass spectrometry methods, such as gas chromatography mass spectrometry (GC-MS) or liquid chromatography mass spectrometry (LC-MS).3 Mass spectrometers work by detecting the relative intensity of ionized compounds via their mass-to-charge ratio.4 Scientists can also perform metabolomic analyses using nuclear magnetic resonance (NMR) spectroscopy,5 which measures the specific resonating frequency of samples when they are exposed to differences in the magnetic field.4

After these platforms detect metabolites in a sample, the resulting data are pre-processed and normalized.4 If the metabolites present in the sample are unknown, researchers can identify them based on publicly available metabolomics databases.6 Scientists can then determine if the presence of certain metabolites within the sample is significant, and they can integrate their results with other -omics data, such as genomics, transcriptomics, and proteomics, to examine the relationships between the datasets.4 

Scientists can perform metabolomic analyses in either a targeted manner, focusing on a specific metabolite or class of metabolites, or an untargeted manner, by assaying a sample’s metabolome globally.7 Quality control (QC) samples are used to filter out low quality features, such as background noise or co-eluting compounds, from the sample during pre-processing, assess analytical performance, and ensure that the metabolomics results are valid.8 In a targeted analysis, the QC sample is typically a reference metabolite of known value, while an untargeted analysis uses a pooled QC sample consisting of small study sample aliquots. 

Why Is the Metabolome Important?

Of all the different -omes in cells, the metabolome is considered to best reflect the biological phenotype of a sample because it shows what metabolic reactions have occurred.9 Targeted and untargeted metabolomics approaches can answer different questions about biological samples, including how they have been affected by environmental or dietary factors, genetic mutations, or disease states. 

For example, before a competition, a urine sample from an athlete may be subjected to a targeted metabolomics anti-doping analysis to determine if they have taken a prohibited drug, such as anabolic androgenic steroids.7 In contrast, an untargeted metabolomic analysis of human plasma can reveal metabolic pathways that differ between diseased and healthy individuals, providing useful data for disease diagnosis and modeling.10 

A graphic representation of the flow of biological information from genotype to phenotype through DNA (genomics), mRNA (transcriptomics), proteins (proteomics), and metabolites (metabolomics).
The instructions in DNA are transformed by mRNAs into proteins. Then, enzymatic reactions produce metabolites, which represent the downstream output of the genome.
Credit: The Scientist

Metabolomics Examples in Disease Research 

Metabolomics provides valuable information about human health and disease. It has a range of applications in health and medicine, spanning nutrition, biochemistry, pharmaceutical development and drug toxicity testing, diagnostics, microbiology, forensics, and more. To help researchers, physicians, and other individuals analyzing metabolomes, the human metabolome database (HMDB) was first published in 2007.1 Containing 114,100 metabolites, it is now the most comprehensive metabolomics database for human studies.6 This database contains information about metabolites, including their biological roles and disease associations, making it useful in metabolomics studies of human health and disease including the following.

Metabolic diseases

Researchers have used targeted metabolomics in etiological studies of metabolic diseases, such as diabetes, to identify serum metabolites that are associated with disease risk.11 They have also used this technique has to identify dietary patterns that associate with higher diabetes risk.9


Metabolomics has also transformed the field of cancer research: researchers have applied these methods for tumor biomarker discovery, drug discovery, toxicology testing, and nutritional studies.12 For example, researchers employed an untargeted metabolomics approach to identify novel biomarkers for gastroenterological cancer, which can be used in early tumor diagnosis.13 Scientists can also use known biomarkers of disease in a targeted manner to monitor disease progression over time.

Neurodegenerative diseases

Scientists applying metabolomics in Alzheimer’s disease (AD) research have identified differentially affected metabolic pathways in blood samples that link mild cognitive impairment to future risk of developing AD.10 This approach was able to identify those at high risk of developing AD up to two years earlier than current standard diagnostic methods.


Metabolomics is also critical for understanding the human microbiome. Through analysis of the metabolome, researchers detect, identify, and quantify the metabolites produced by the microbial communities that inhabit the human body and discern the relationships between these metabolites and human health.14 

As metabolomics approaches continue to evolve and additional novel metabolites are identified, there may be an increasing number of metabolomic applications in human disease research and biomedicine. 


  1. Wishart DS, et al. HMDB: the human metabolome database. Nucleic Acids Res. 2007;35.
  2.  Nambiar PR, et al. An “Omics” based survey of human colon cancer. Mutat Res Mol Mech Mutagen. 2010;693(1):3-18. 
  3. Ren J-L, et al. Advances in mass spectrometry-based metabolomics for investigation of metabolites. RSC Adv. 2018;8(40):22335-22350. 
  4. Chen Y, Li E-M, Xu L-Y. Guide to metabolomics analysis: a bioinformatics workflow. Metabolites. 2022;12(4). 
  5.  Soininen P, et al. Quantitative serum nuclear magnetic resonance metabolomics in cardiovascular epidemiology and genetics. Circ Cardiovasc Genet. 2015;8(1):192-206. 
  6. Wishart DS, et al. HMDB 4.0: the human metabolome database for 2018. Nucleic Acids Res. 2018;46(D1):D608-D617. 
  7. Keen B, et al. Metabolomics in clinical and forensic toxicology, sports anti-doping and veterinary residues. Drug Test Anal. 2022;14(5):794-807. 
  8. Godzien J, et al. Controlling the quality of metabolomics data: new strategies to get the best out of the QC sample. Metabolomics. 2015;11(3):518-528. 
  9. Gonzalez-Covarrubias V, et al. The potential of metabolomics in biomedical applications. Metabolites. 2022;12(2). 
  10. Graham SF, et al. Untargeted metabolomic analysis of human plasma indicates differentially affected polyamine and L-arginine metabolism in mild cognitive impairment subjects converting to Alzheimer’s disease. PLoS One. 2015;10(3):e0119452. 
  11. Floegel A, et al. Identification of serum metabolites associated with risk of type 2 diabetes using a targeted metabolomic approach. Diabetes. 2013;62(2):639-648. 
  12. Danzi F, et al. To metabolomics and beyond: a technological portfolio to investigate cancer metabolism. Signal Transduct Target Ther. 2023;8(1):137. 
  13. Nishiumi S, et al. Metabolomics for biomarker discovery in gastroenterological cancer. Metabolites. 2014;4(3):547-571. 
  14. Bauermeister A, et al. Mass spectrometry-based metabolomics in microbiome investigations. Nat Rev Microbiol. 2022;20(3):143-160. 
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