Why metabolomics offer us insights into human health

In a previous Nightingale feature, we discussed how “omics” sciences are revolutionizing public health research, heralding the advent of personalized medicine. In this article, we put metabolomics under the spotlight, discussing how we can use metabolic profiling to gain a more comprehensive picture of human health. 

Metabolomics is the field of research dedicated to deciphering the vast communications network that exists inside of our bodies. [1] Metabolites (biological molecules produced by metabolic reactions), provide us with direct measurements of the effects genetic and lifestyle factors (e.g. diet and exercise) can have on our biochemistry. This is important, as in many chronic diseases (e.g. diabetes) pathophysiological changes can be observed at the molecular level. [2] Metabolomics helps us to unravel these complex interactions and even identify the minute changes in molecular concentrations that occur, facilitating the development of new diagnostic techniques to monitoring disease progression and lifestyle-induced changes. 

A tale of two techniques

In the 1940s, Roger Williams and his team were the first to propose that each person has a “metabolic pattern” that could be identified through studying biological fluids. Researchers today employ a number of analytical techniques to detect and measure metabolic activity. Metabolomics studies can be targetedor untargeted in their approach, either focusing on measuring a “panel” of biologically relevant metabolites (biomarkers), or aiming to capture a wider range of biochemical intermediates and signaling molecules. [3]

Two primary technologies have emerged as being particularly well-suited to examining metabolites; Nuclear Magnetic Resonance (NMR) spectroscopy and Mass Spectrometry (MS). [4] Both techniques have clear strengths and weaknesses that inform their usage. For example, NMR provides highly-stable, repeatable measures, without batch effects and is non-destructive (allowing samples to be potentially reused). [5] 

Overall, the two techniques are highly complementary and can be used together to provide researchers with the opportunity to identify new metabolites, and routinely measure established biological pathways. For example, the recent integration of NMR and MS with pattern recognition models has helped to drive biomarker discovery. [6]  

Completing the picture of human health

Dubbed “the missing piece in the omics puzzle,” metabolomics studies fill a crucial gap in our knowledge, bridging the impact between gene expression with the effects of lifestyle factors. The central dogma of molecular biology (first established by Francis Crick in 1958), states that “information” flows between DNA and RNA, but only in one direction from RNA to proteins. In omics studies, researchers have focused on tracing this journey, first by examining genes (genomics), then transcribed mRNA (transcriptomics), and finally proteins (proteomics). Whilst gene expression undoubtedly has a major effect on protein-regulated metabolic reactions, these pathways alone can’t provide us with a fully-comprehensive picture of human metabolism.

In order to gain a more complete perspective on metabolic reactions, NMR and MS techniques can be used to generate a metabolic profile – a “fingerprint” of the different metabolites present in a sample (e.g. in blood or urine). 

Metabolomics has provided researchers with a number of noteworthy applications that have enhanced our understanding of the role lifestyle factors play in influencing human health. In toxicology for example, metabolic profiling has been used to screen people for the presence of toxins and supply physicians with an assessment of exposure effects (such as liver or kidney damage). Metabolic profiling volunteers in drug trial arms can be a valuable tool for drug makers, providing useful data that can identify side-effects at an earlier stage and establish the potential on/off drug target effects. In medical diagnostics for chronic diseases, profiling can be used to establish early-stage disease risk and more accurately classify patient groups.   

An area that has received a lot of current interest is the concept of mapping the “global metabolome,” a combination of all the metabolites produced by both the body and the microbes that live inside of it (i.e. the microbiota or microbiome). With the proposal of a gut-brain axis in the body, some researchers claim the changes in the microbiome have a significant impact on immunityrisk of bowel conditionsand even mental health. Metabolic profiling could potentially be used to test these claims and link differences in metabolic activity to the presence of specific microbial species. [7] 

Making disease risk prediction a reality  

If we consider metabolic reactions to be dynamic and sensitive to environmental changes, genetics represents the polar opposite. A genomics analysis can provide us with an impressively detailed, but largely static, picture of a person’s inherited health state. [8] Matching genetics with metabolite biomarkers then, enables us to assess the health state of patients; identifying and diagnosing genetic disorders such as inborn errors of metabolism, and observing pathophysiological changes caused by diseases. 

Fully combining genetic sequencing with metabolic profiling, provides us with powerful ways of evaluating the future risk of developing chronic diseases. This is because chronic conditions (e.g. cardiovascular diseases) are the result of a combination of genetic and environmental/lifestyle factors. Typically featuring a more multifaceted pathology than rare genetic diseases (such as single-gene disorders), chronic diseases can be triggered by any number of different factorial combinations, including the influence of inflammatory and immunological responses (such as the effects of viral infections). 

Currently, we categorize patients into cardiovascular risk groups based on age-old diagnostic methods such as blood pressure readings and conventional cholesterol tests, but these methods cannot provide the level of detail required to effectively predict a patient’s risk of developing a chronic disease. For example, it’s been found that the blood levels of branched-chained amino acids can indicate an increased risk of diabetes onset. [9]

Assessing causality 

A growing number of experiments have employed Mendelian randomization as part of their study design. Mendelian randomization uses genetic variants as a proxy for a lifestyle risk factor and can be used to assess the causality of the different biomarkers measured using metabolic profiling. “If a biomarker is causally implicated in a disease, then genetic variants specifically associated with that biomarker should also be associated with the disease.” [10] For example, Mendelian randomization was used to assess the causality of circulating branched-chained amino acids (BCAAs) in type 2 diabetes risk and to clarify the causal role of insulin resistance on levels of BCAAs. 

An application of Mendelian randomization has been in the estimation of drug effects. By comparing the metabolic profiles of different groups of patients with and without these genetic variants, we can gain insights into the potential metabolic effects of proposed drug treatments. This can be taken one stage further by examining the impact genetic variants have on disease risk in longitudinal epidemiological studies, allowing for the evaluation of the effectiveness of drug and lifestyle interventions. As biochemical pathways are typically complex and feature multiple stages, understanding the “molecular intermediates” can help us to clarify the full impact of disease effects on a person’s metabolism. [11]     

With such far-reaching implications for public health research and clinical medicine, metabolic profiling will soon become commonplace in healthcare. Nightingale’s metabolomics platform has been applied in a wide range of public health research studies, demonstrating the powerful potential of metabolic profiling for disease risk prediction. 


Extra reading: Find out here how mendelian randomization and metabolic profiling was recently used to investigate drug target mechanism of action.  


1. Mike Milburn and Kirk Beebe. The Metabolome: The Missing Link that Binds the Microbiome with Human Health. Genetic Engineering § Biotechnology News. (2017) https://www.genengnews.com/gen-exclusives/the-metabolome-the-missing-link-that-binds-the-microbiome-with-human-health/77900826

2. Yolanda Smith. Applications of Metabolomics. News Medical Life Sciences. (2017) https://www.news-medical.net/life-sciences/Applications-of-Metabolomics.aspx

3. Svati Shah and Christopher Newgard. Integrated Metabolomics and Genomics: Systems Approaches to Biomarkers and Mechanisms of Cardiovascular Disease. Circ Cardiovasc Genet. 8(2): 410-419. (2015) https://dx.doi.org/10.1161%2FCIRCGENETICS.114.000223

4. Ashley Board. 6 Milestones in Metabolomics: Driving our understanding of the metabolome. Proteomics § Metabolomics. (2017) https://www.technologynetworks.com/proteomics/lists/6-milestones-in-metabolomics-driving-our-understanding-of-the-metabolome-288878

5. Timothy Veenstra. Metabolomics: the final frontier? Genome Medicine 4:40 (2012) https://doi.org/10.1186/gm339

6. Zhang, A. et al. Emerging role and recent applications of metabolomics biomarkers in obesity disease research. RSC Adv. 7, 14966-14973. (2017) http://pubs.rsc.org/-/content/articlehtml/2017/ra/c6ra28715h

7. Beger, R. et al. Metabolomics enables precision medicine: “A White Paper, Community Perspective.” Metabolomics. 12:149. (2016) https://link.springer.com/article/10.1007/s11306-016-1094-6

8. Alison Halliday. Metabolomics and Health – On the Cusp of a Revolution. Proteomics § Metabolomics. (2017) https://www.technologynetworks.com/proteomics/articles/metabolomics-and-health-on-the-cusp-of-a-revolution-289734

9. Bingbing, L. et al. Novel Applications of Metabolomics in Personalized Medicine: A Mini-Review. Molecules. 22(7), 1173. (2017) http://www.mdpi.com/1420-3049/22/7/1173/htm

10. Lotta, L. et al. Genetic Predisposition to an Impaired Metabolism of the Branched-Chain Amino Acids and Risk of Type 2 Diabetes: A Mendelian Randomisation Analysis. PLOS Med. (2016) https://doi.org/10.1371/journal.pmed.1002179

11. Würtz, P. et al. Quantitative Serum Nuclear Magnetic Resonance Metabolomics in Large-Scale Epidemiology: A Primer on -Omic Technologies. American Journal of Epidemiology. 186(9), 1084-1096. (2017) https://academic.oup.com/aje/article/186/9/1084/3813118