The Metabolomic Approach to Biomarker Research

Inside every organism, a continuous flux of chemical reactions keeps life humming along. Small molecules, or metabolites, involved in essential processes, such as breaking down nutrients, generating energy, and maintaining cellular balance, as a whole belong to the metabolome. And the metabolome is having a moment.

Unlike the genome and the proteome, the metabolome, downstream of these other -omes, offers a dynamic, real-time report of biochemical activity. And it's able to capture biochemical shifts underlying both health and disease. “While genomics tends to study an organism’s blueprint for life and transcriptomics and proteomics reflect how that blueprint is brought to life, metabolomics provides a read-out of how the biology encoded within a system interacts with the world around it,” explains Matt Lewis, Vice President of Metabolomics and Lipidomics at Bruker Daltonics.

Since metabolomics “captures the endpoints of physiological processes, [it] can therefore reveal subtle biological perturbations within a biological system,” adds Valerie Jones, Director of Marketing and Technical Support at RayBiotech. “This provides an additional layer of insight into the phenotype, which cannot be fully captured by a proteomic approach.”

Identifying metabolic pathway abnormalities can reveal disease mechanisms and new therapeutic targets. “An exciting facet is how these metabolomics assays and related biomarker discovery are now enabling personalized medicine,” says Dan Cuthbertson, Director of Life Science Research Market at Agilent. “Translational researchers can now leverage large data sets and AI tools to inform how biomarkers present at the individual level, which is helping tailor therapeutics based on individuals’ metabolic profiles, therefore improving efficacy and reducing side effects.”

Metabolomics can uncover individual or collective biomarker signatures that signal disease, predict treatment response, or reflect environmental influences like diet or stress. Many sample types, such as saliva or urine, are noninvasive to collect, facilitating routine monitoring. But the field faces significant hurdles—many of them practical. Here we’ll look at what’s on the horizon for metabolome-derived biomarkers, and what’s hindering progress.

Biomarkers as beacons (in a sea of data)

Metabolomics offers endless possibilities, if you can wade through all the data. Lewis refers to this as the “metabolite identification bottleneck.” There are far more metabolites than there are identified structures or known biochemical roles.

When using mass spectrometry, the most sensitive and high-throughput method available for detection, many mass-to-charge (m/z) peaks don’t correspond to known molecules, claims Jones. “You might be looking at a few familiar hits in a sea of unknown chemicals.” Human diversity adds another layer of complexity. To statistically validate a promising biomarker, researchers often need data from hundreds or even thousands of individuals, a costly endeavor.

Lewis believes if the goal is to drive discovery of new metabolic associations through the study of huge epidemiological populations, then “global measurement capability [must] exist to measure millions, if not billions of samples with high precision to ensure comparability across vast demographics.”

Integrating metabolomics with other omics data can help to address this complexity and offer insights into disease, says Cuthbertson. He stresses the need for tools for accurate analysis and interpretation to ensure consistent and reproducible results across labs. Here, AI may be of assistance with integrating multi-omics datasets. Cuthbertson highlights Agilent’s Triple Quadrupole LC/MS, which can help researchers by “providing high-quality data and supporting robust analytical workflows facilitating the training of next generation AI models leveraging multi-omic data.”

Lewis cites a concept proposed by Sir Archibald Garrod in 1902, that every person on earth is chemically unique. From the perspective of each individual’s metabolome, constantly changing across different tissues in the body, and over time, in response to factors like nutrition or environmental exposures, “the rate of sampling and metabolomics measurement required to facilitate true personalization of health, nutrition, or medicine in an actionable manner becomes a challenge in scalability itself.” And he adds, “Standardization and cooperation among metabolomics researchers remain completely surmountable challenges with no practical solution in sight.

“As someone once said, ‘Metabolomics methods are like toothbrushes—everyone has their own, and no one wants to use someone else’s.’” Figuring out how to ensure precise, and therefore comparable, metabolite measurements is one of the facets Lewis finds most compelling. And he says that is what Bruker is passionate about—developing high-precision, high-quality bioanalytical instruments to measure the metabolome.

From discovery to clinical utility

“Understanding individual variation in each metabolic pathway is a cornerstone of disease research and personalized medicine,” notes Jones. “After decades of metabolomic research, several metabolic biomarkers have demonstrated high diagnostic accuracy and are routinely used in the clinic, such as creatinine for kidney damage; others have diagnostic potential, but have not yet translated to the clinic, such as choline and phosphocholine derivatives in cancer, and non-esterified fatty acids in obesity.”

RayBiotech offers 96-well metabolic assays targeting key molecules involved in glycolysis, lipid metabolism, liver and renal function, and oxidative stress pathways. For instance, L-lactate, hydrogen peroxide, and lipid peroxidation, measured using these assays, can indicate tissue hypoxia and redox signaling. “These analytes are relevant in the inflammatory and oxidative stress aspects of septic shock, cardiovascular disease, Alzheimer’s, diabetes, and aging,” Jones says.

One recent study used the RayBio^® L-lactate Assay in engineered cardiac tissues to monitor metabolic changes during hypoxia. It found that “hypoxic preconditioning of the cells could partially rescue their function, leading to the potential improvement of cell-based therapies for heart regeneration.”¹ Another study used a multi-omic approach to explore how the airway microbiome influences chronic obstructive pulmonary disease (COPD). “Systematic analyses of omic data generated from both host and airway bacteria enabled the researchers to map microbiome–metabolite–host interactions that identified a key mechanism linked to altered tryptophan metabolism,"² Jones explains. “Possibly the most exciting future for metabolomics is in multi-omic integration.”

Metabolomics also plays a vital role throughout the drug development pipeline. “Metabolomics research has clear potential to go beyond development and into the realm of treatment personalization—for example, to tailor the dosage of a drug for maximum effectiveness while managing unwanted side effects,” says Lewis.

Finally, one of the most exciting aspects of this field is the ability to use non-invasive collection methods. Just about anything secreted by the body can be used, including breath. And Lewis points out, as bioanalytical technology becomes more sensitive, so too does the “ability to discover trace metabolic signatures of distant or inaccessible tissue-specific diseases (for example, neurodegenerative disease) from proxy biofluids, like blood plasma.” He also thinks of metabolomics as a way to “chemically define phenotypes” that are difficult to classify or sample. “Mental health disorders come to mind here as an important frontier of metabolomics research.”

New horizons

Metabolomics is poised to revolutionize medicine by offering real-time, personalized insights into health and disease, informing drug development, and reducing reliance on invasive diagnostics. But challenges remain. Data analysis and reproducibility and methodological standardization are still works in progress.

Despite these hurdles, metabolomics is delivering actionable biomarkers. Recent examples include the discovery of blood-based metabolomic signatures that may predict early-stage Parkinson’s disease (PD) before motor symptoms appear.^3,4 And, in another study, researchers skin-swabbed subjects for sebum and then used ultra-high-resolution mass spectrometry and machine learning to identify specific lipid metabolism patterns associated with PD.⁵ Using sebum as a diagnostic tool offers a promising, non-invasive, and cost-effective approach to early detection. As the field matures, metabolomics is not just uncovering biomarkers, it’s redefining the future of precision medicine.

References

1. Yan, Z., Chen, B., Yang, Y., Yi, X., Wei, M., Ecklu-Mensah, G., Buschmann, M. M., Liu, H., Gao, J., Liang, W., Liu, X., Yang, J., Ma, W., Liang, Z., Wang, F., Chen, D., Wang, L., Shi, W., Stampfli, M. R., Li, P., … Wang, Z. (2022). Multi-omics analyses of airway host-microbe interactions in chronic obstructive pulmonary disease identify potential therapeutic interventions. Nature microbiology, 7(9), 1361–1375. 

2. Snyder, C. A., Dwyer, K. D., & Coulombe, K. L. K. (2024). Advancing Human iPSC-Derived Cardiomyocyte Hypoxia Resistance for Cardiac Regenerative Therapies through a Systematic Assessment of In Vitro Conditioning. International journal of molecular sciences, 25(17), 9627. 

3. Mallet, D., Dufourd, T., Decourt, M., Carcenac, C., Bossù, P., Verlin, L., Fernagut, P. O., Benoit-Marand, M., Spalletta, G., Barbier, E. L., Carnicella, S., Sgambato, V., Fauvelle, F., & Boulet, S. (2022). A metabolic biomarker predicts Parkinson's disease at the early stages in patients and animal models. The Journal of clinical investigation, 132(4), e146400. 

4. de Lope, E. G., Loo, R. T. J., Rauschenberger, A., Ali, M., Pavelka, L., Marques, T. M., Gomes, C. P. C., Krüger, R., Glaab, E., & NCER-PD Consortium (2024). Comprehensive blood metabolomics profiling of Parkinson's disease reveals coordinated alterations in xanthine metabolism. NPJ Parkinson's disease, 10(1), 68. 

5. Sinclair, E., Trivedi, D. K., Sarkar, D., Walton-Doyle, C., Milne, J., Kunath, T., Rijs, A. M., de Bie, R. M. A., Goodacre, R., Silverdale, M., & Barran, P. (2021). Metabolomics of sebum reveals lipid dysregulation in Parkinson's disease. Nature communications, 12(1), 1592.