• Preventive medicine | Understanding risks before they manifest
• Predictive medicine | From patterns to forecasting
• Precision medicine | Individuality in context
• Population-based medicine | Power in numbers
• Participatory medicine | Empowered by omics
• The dynamic duo of the post-genomic era

When we speak about 5P medicine – preventive, predictive, precision, population-based, and participatory – the conversation often gravitates toward molecular measures of health. Yet, one essential influence on human biology that deserves a seat at the 5P table is the exposome.

Defined at the Banbury conference as “the integrated compilation of all physical, chemical, biological, and psychosocial influences that impact biology”, the exposome is becoming a necessary part of the omics and medical toolkits, and a particularly promising one when combined with metabolomics.

Metabolomists know that metabolic readouts integrate influences from both our genome and our environment. Exposomics allows us to map the upstream exposures that metabolomics reflects downstream, but it also contributes to the design of impactful metabolomic studies.

Exposomics is defined as “the field that studies the comprehensive and cumulative effects of the exposome on biological systems by integrating data from a variety of interdisciplinary methodologies and data streams” (Miller et al. 2025). These methodologies include mass spectrometry and NMR, as for metabolomics, but also dietary information, health monitoring records, medical questionnaires, geospatial data, meteorological data, and much more.

Because the effects of exogenous factors are known functions of time and intensity of exposure, exposomics is the only omic that emphasizes these parameters in the definition of its scope. There is much here to be learned for metabolomics enthusiasts.

I never tire of explaining how the flexibility and sensitivity of metabolomics is a strength rather than a weakness. But these are characteristics of exposomics too. For this reason, when combined, exposomics and metabolomics form a dynamic duo that leverages the strength of sensitive health measures in all its might.

I got confirmation of this once again recently, while recording an episode of The Metabolomist podcast where Léa Maitre from the Barcelona Institute of Global Health explains the unique strength of metabolomics in a multiomic study of early life exposures: “Metabolomics was the better omic to measure cross associations. [It was the strongest] when we measured the exposure and the omics at the same time in childhood.” You can listen to the full episode here.

This is just one example of the synergies that we unlock when we combine metabolomics and exposomics. In this blog, I will focus on the end applications of these technologies and how our dynamic duo ties to each of the 5Ps. Whether your focus is exclusively on precision medicine or you are looking for a truly holistic view of health, I hope these examples will encourage you to start integrating these two powerful omics in your research.

Preventive medicine | Understanding risks before they manifest

Preventive medicine aims to avoid disease altogether. Thus, prevention is only as strong as our ability to identify risks. Exposomics brings clarity by capturing environmental and behavioral factors such as air pollution, diet, stress, and chemical exposures that influence long-term health trajectories. Environmental and behavioral exposures strongly shape health, including drug response and chronic disease risk. Exposomics thus provides a critical foundation for anticipating and reducing exposure-derived health risks.

Metabolomics contributes here by identifying metabolic signatures linked to exposure-induced biological changes. For example, in a study of the composition of breast milk from mothers with apparently healthy infants versus stunted infants, even a small targeted metabolomic panel could identify signatures pointing to different nutrition levels (Hampel et al 2022). In the study I discuss with Léa Maitre on the podcast, metabolomics helped identify patterns linked to exposures in early childhood (Maitre, Bustamante et al. 2022) that can be followed in longitudinal studies or serve as a basis for mining the catalogue of exposome-related cohorts put together in the IHEN project.

Exposomics combined with metabolomics moves prevention from generic advice to evidence based, exposure and phenotype-specific interventions.

Predictive medicine | From patterns to forecasting

Predictive medicine hinges on data that can forecast health outcomes years before symptoms appear. Exposomics offers exactly that: the ability to quantify the cumulative external pressures shaping one’s biological trajectory. A review by Wan et al. (2025) highlights how exposomics supports diagnosis, disease prediction, early detection, and treatment prediction.

Metabolomics is also well-positioned to reflect the progressive drift of the metabolome from health towards disease outcomes. But one of its best known use is as a source of biomarkers predictive of patient drug response in pharmacometabolomics.

In non small cell lung cancer, quantitative metabolomics has shown that a patient’s baseline metabolic phenotype—shaped not just by genetics but also by diet, microbiome, inflammation and prior exposures—can predict response to immunotherapy, illustrating how the metabolome translates the cumulative exposome into actionable insight for predictive and personalized treatment (Lee et al. 2024).

In other words, exposomics tells us what happened, and metabolomics tells us how the phenotype changed; a powerful predictive duo when we want to leverage the impact of the environment on health.

Precision medicine | Individuality in context

The promise of precision medicine is the ability to tailor treatments to the individual. Genomics contributes the blueprint, but exposomics adds the context; the influences that shape how that blueprint is expressed. Metabolomics, in turn, contributes the resulting phenotype and some of the effectors of this impact on genome expression.

A type of exposure not always recognized by the public but highly relevant in medicine is the intentional exposure to chemicals such as pharmaceutical drugs. Not only do drugs influence our metabolome, but the levels of their downstream metabolic products when they pass through our organs are a powerful way to stratify patients. This is another powerful combination of exposomics and metabolomics.

In the ADNI cohort, metabolomics enabled stratification of individuals not only by disease stage, but also by medication exposure, revealing how drugs act as a critical and often overlooked dimension of the exposome (St John-Williams et al. 2017). By accounting for polypharmacy and treatment effects, this approach demonstrated how metabolomics can support more precise interpretation of molecular phenotypes and more informed patient stratification in clinical research.

In the field of nutrition research, stratification based on metabolomic profile, or “metabotyping” has become a popular tool, as it works well together with variables related to diet, another lesser-known source of deliberate exposures. In a 2023 randomized controlled trial, metabotypes were used to stratify individuals and deliver personalized dietary advice, demonstrating that people with different metabolic phenotypes respond differently to the same nutritional guidance. Leveraging metabolomics for stratification, this study demonstrated how to enable precision nutrition by translating dietary exposures into actionable, metabotype specific interventions rather than population level recommendations (Hillesheim & Brennan 2023). And in this case, the end result most likely will entail the modulation of the very exposures investigated (the diet), turning this knowledge into quickly actionable insights.

Population-based medicine | Power in numbers

The first population-based cohorts were built with genomics in mind, searching for the genetic determinants of disease. This approach opened the door for a new wave of knowledge, but it couldn’t answer all questions. Today, at the population level, exposomics reveals patterns that inform on non-genetic influencers of health especially relevant in the study of complex chronic disease.

Exposures vary dramatically between regions, occupations, socioeconomic backgrounds, and lifestyles, and the study of exposomics quickly takes us to investigate health disparities, environmental injustice, and geographically clustered risks, which are all likely to translate to metabolic differences too.

The HELIX cohort has been a pioneer in the integration of exposomics with other omics, notably combining over 200 measures of exposures with blood and urine metabolomics (Maitre et al. 2022). A follow up study investigated the links between the metabolome, health outcomes and chemical classes with known effects on health, namely endocrine disruptors. The study shows that childhood exposure to endocrine disrupting chemicals, including persistent pollutants, was associated with alterations in the metabolome, including differences in tryptophan derivatives. This work highlights the role of combined exposomics and metabolomics approaches in capturing early life biological responses to chronic environmental exposures at the population level (Fabbri et al. 2023).

Participatory medicine | Empowered by omics

When individuals engage in their own health decisions, this is one of the most direct applications of research that can be. The tenets of participatory medicine are easy-to-use sample collection, ideally performed at home to be extra accessible and reduce discriminations in access to health, and quantitative, robust measures of health that can be compared to reference values from the healthy population.

Today, measures of both exposures and health are already found in many homes, from wearables, to sensors, but also local environmental measures that lead to actionable big data. Tools that combine these measures of the exposome with reliable (metabol)omics measures will provide the solutions that will enable the application of omics-based knowledge in the home, at a scale of n=1.

Today, these offerings largely sit with private companies offering personalized fitness monitoring and advice. Tomorrow, the communities built around exposomics and metabolomics will be the cornerstone of the strategies implemented by healthcare systems providing regular checkups based on samples collected at home and sent in the mail, online questionnaires and exposure data collected by relevant home/health appliances and local exposome mapping.

The dynamic duo of the post-genomic era

To fully realize the goals of 5P medicine, we must integrate data from all layers of the biological and environmental ecosystem. Metabolomics provides the clearest snapshot of a phenotype influenced by both genetics and environment. Exposomics contributes the context in which drivers such as drugs, environmental pollutants, diet and socioeconomic factors influence this phenotype.

The intersection of these two rich omic layers hosts not only a sensitive measure of health outcomes but a wealth of information about determinants of health.
Increasingly used in population-based medicine, driving tailored approaches in preventive, predictive and precision medicine, and soon to enter the realm of participatory medicine, the combination of exposomics and metabolomics is about to revolutionize how we understand and modulate health.

The metabolic demands of cancer

Unlimited proliferation is a hallmark of cancer (Hanahan et al. 2000). To sustain this relentless growth, cancer cells require vast amounts of energy. They achieve this by hijacking the cell’s energy metabolism and pushing it into overdrive.

This article explores the ways cancer reprograms core metabolic pathways, such as glycolysis, the tricarboxylic acid (TCA) cycle, oxidative phosphorylation and lipid metabolism. It also looks at how metabolomics is helping us understand more about these changes, with implications for biomarker discovery and therapeutic interventions.

The Warburg effect – speed over efficiency

The main strategy cancer cells use to produce adenosine triphosphate (ATP) is aerobic fermentation. Remarkably, many cancer cells convert pyruvate from glycolysis into lactate, instead of the more efficient option of sending it into mitochondria to form acetyl-coenzyme A (acetyl-CoA) (Figure 1) (Finley 2023).

Why this counterintuitive choice? As Otto Warburg observed over a century ago, glycolysis produces ATP more quickly than oxidative phosphorylation, and as long as glucose is abundant, speed outweighs efficiency (Thompson et al. 2023).

Figure 1: Under normal oxygen conditions, pyruvate from glycolysis is transported to the mitochondria and converted to acetyl-CoA by pyruvate dehydrogenase (the Pasteur effect), suppressing fermentation that would usually occur in the absence of oxygen. The Warburg effect describes how cancer cells continue fermenting pyruvate to lactate even when oxygen is available. Pyruvate is also in balance with alanine via alanine transferase. Metabolites covered by biocrates kits are highlighted in blue.

Since the discovery of the ‘Warburg effect’, major advances have reshaped our understanding of cancer metabolism. Early theories that cancer cells rely on glucose fermentation because of mitochondrial damage have given way to evidence that mitochondrial function remains largely intact in many cancer types. What changes is the fuel for the TCA cycle: it shifts from glycolysis to glutaminolysis, allowing mitochondria to generate energy while glycolytic intermediates are diverted to biosynthetic pathways that support rapid cell growth (Mathew et al. 2024). Mitochondria are still crucial for cancer cells, not as energy bottlenecks, but as hubs that coordinate energy production with biosynthesis.

TCA cycle and oncometabolites

Because the conversion of pyruvate to acetyl-CoA is limited in many cancer cells, the TCA cycle is replenished primarily through glutaminolysis. Glutamine is converted to glutamate, which produces alpha-ketoglutarate (αKG) and downstream TCA cycle metabolites up to oxaloacetate (Figure 2). Limited acetyl-CoA reduces citrate synthesis from oxaloacetate, but citrate can instead be generated in reverse from αKG via reductive carboxylation and becomes a source for acetyl-CoA for biosynthesis.

A glutaminolysis-driven TCA cycle produces fewer reducing equivalents for oxidative phosphorylation but generates ample intermediates for proliferation, while glycolysis remains the primary energy source (Mathew et al. 2024). Oxaloacetate accumulation drives malate conversion to pyruvate and lactate, meaning lactate is produced from both glycolysis in the cytosol and mitochondrial glutaminolysis.
Once considered a waste product, lactate is now recognized as a key oncometabolite. It supplies carbon to cancer cells and helps maintain a low pH in the tumor microenvironment, which promotes tumor growth, angiogenesis and metastasis. It inhibits cytotoxic T cells and natural killer cells while supporting immunosuppressive populations such as regulatory T cells and myeloid-derived suppressor cells. Lactate also impairs dendritic cell function, limiting the presentation of tumor antigens (Chen et al. 2024).

Recent studies have revealed additional tumor-promoting effects. Through histone lactylation, lactate regulates gene expression, activating tumor-promoting pathways, including immune suppression. Lactate also acts as a signaling molecule, binding to G-protein-coupled receptors on immune and tumor cells to facilitate immune evasion (Chen et al. 2024).

Figure 2: In cancer cells, TCA cycle metabolites are replenished by glutaminolysis. Reactions that are reduced as a result are depicted in broken grey arrows. Metabolites covered by biocrates kits are highlighted in blue. Oncogenes are highlighted in ochre font. R = Reductive equivalents

The TCA cycle also gives rise to important oncometabolites like 2-hydroxyglutarate, succinate and fumarate:

• Accumulation of 2-hydroxyglutarate results from mutations in isocitrate dehydrogenase (IDH) 1 or 2, found in multiple cancer types. This produces D-2-hydroxyglutarate, which inhibits αKG-dependent dioxygenases, disrupting DNA and histone methylation, and promoting tumorigenesis and progression (Xu et al. 2011). αKG can also be converted to L-2-hydroxyglutarate. Normally, L-2-hydroxyglutarate is degraded by L-2-hydroxyglutarate dehydrogenase (L2HGDH), but in some cancers, L2HGDH activity is reduced, leading to 2-hydroxyglutarate accumulation without IDH mutation (Lanzetti 2024).
• Succinate accumulates when mutations disrupt the succinate dehydrogenase (SDH) complex. Excess succinate blocks αKG-dependent enzymes, which inhibits key regulators including the tumor suppressor phosphatase and tensin homolog (PTEN), DNA repair enzymes and antioxidant proteins. Succination of prolyl hydroxylases, essential for oxygen sensing, leads to pseudohypoxia, in turn driving tumor-promoting gene expression.
• Fumarate accumulates in cancers with mutations in fumarate hydratase (FH). Like succinate, it blocks αKG-dependent enzymes and induces pseudohypoxia, but it also inactivates glutathione, leading to reactive oxygen species (ROS) buildup and oxidative stress (Lanzetti 2024).

Redox balance and ROS

Energy metabolism is tightly linked to redox balance and reactive oxygen species (ROS) signaling. The TCA cycle and oxidative phosphorylation supply reducing equivalents to the electron transport chain. Imbalances in this flow produce ROS, which can act both as damaging agents and as signaling molecules.

Although ROS accumulation might seem detrimental to tumors, given that many anti-cancer drugs rely on ROS to trigger apoptosis, moderate ROS levels actually support cancer progression. At controlled levels, ROS act as signaling molecules that activate pro-survival pathways, including phosphoinositide 3-kinase/protein kinase B (PI3K/AKT), nuclear factor kappa B (NF-κB), and mitogen-activated protein kinase (MAPK). They also induce tumor-promoting transcription factors such as hypoxia-inducible factor (HIF), supporting proliferation, angiogenesis and metabolic adaptation. Cancer cells carefully regulate ROS to avoid cytotoxicity while maintaining a level that favors growth and survival. For instance, fumarate not only inactivates glutathione but also covalently modifies reactive cysteine residues on Kelch-like ECH-associated protein 1 (KEAP1), leading to the release of nuclear factor erythroid 2–related factor 2 (NRF2), which drives an antioxidant gene program (Brunner et al. 2023).

The TCA cycle also gives rise to itaconic acid, derived from cis-aconitate via aconitate decarboxylase 1, an enzyme upregulated in activated macrophages. Structurally similar to succinate and fumarate, itaconate inhibits succinate dehydrogenase, which often has anti-inflammatory effects. In tumors, however, tumor-associated macrophages can accumulate itaconate, enhancing oxidative phosphorylation and mitochondrial ROS production, which promotes tumor growth (Weiss et al. 2018). Extracellular itaconate can be taken up by tumor cells, where it induces immune evasion and survival pathways (Fan et al. 2025).

Branching out from the TCA cycle, the porphyrin/heme pathway is critical for redox balance in cancer. Succinyl-CoA combines with glycine via aminolevulinic acid synthase (ALAS) to initiate heme synthesis. Some tumors increase ALAS activity, leading to excess production of 5-amino-4-oxovaleric acid (AOVA) (Huang et al. 2017; Zhao et al. 2020). In other cancers, transporters importing AOVA are overexpressed (Harada et al. 2022). The resulting accumulation of protoporphyrin IX downstream in heme synthesis promotes ROS generation, driving tumor proliferation (Owari et al. 2022). Accordingly, AOVA can be considered an oncometabolite-like molecule, despite its primary role as a metabolic intermediate.

Heme itself is pro-oxidant, but its catabolism via heme oxygenase produces biliverdin and bilirubin, which are potent antioxidants. Upregulation of this catabolic pathway helps tumor cells neutralize excess ROS and maintain survival under oxidative stress (Chiang et al. 2018).

Lipid metabolism in energy supply and signaling

In addition to glycolysis and amino acid catabolism, lipid metabolism is a key energy pathway feeding into oxidative phosphorylation that is frequently rewired in cancer cells. Lipids function not only as an energy source, but also as signaling molecules and membrane building blocks necessary for proliferation.

Although glycolysis often dominates cancer cell energy metabolism, beta-oxidation of fatty acids contributes significantly to ATP production. To meet this demand, cancer cells release exosomes containing pro-lipolytic factors that stimulate adipocytes in the tumor microenvironment. These break down stored lipids and release free fatty acids, which are then readily taken up by tumor cells. Beta-oxidation generates large amounts of acetyl-CoA, which enters the TCA cycle to produce reducing equivalents for ATP generation. Odd-chain fatty acids, though less frequent in the human diet, yield propionyl-CoA as well, which is converted into succinyl-CoA and integrated into the TCA cycle.

Even in the presence of external lipid sources, cancer cells frequently upregulate de novo fatty acid synthesis. Citrate, replenished through glutaminolysis-derived αKG, is converted to acetyl-CoA and then carboxylated to malonyl-CoA, the rate-limiting step in fatty acid synthesis. Sequential condensation of malonyl-CoA molecules produces palmitic acid, which serves as a precursor for longer and unsaturated fatty acids (Koundouros et al. 2020).

Sustained de novo lipogenesis offers cancer cells metabolic flexibility, allowing them to shunt fatty acids into various biosynthetic pathways and generate a diverse pool of lipid species with distinct cellular functions. Altered lipid metabolism modifies membrane fluidity and lipid raft formation, thereby influencing cell signaling. Because fatty acid synthesis contributes directly to growth-factor-dependent oncogenic signaling, it is a promising target in combination with different cancer therapies (Koundouros et al. 2020).

Metabolic plasticity and therapy resistance

Energy metabolism in cancer offers many avenues for therapeutic intervention, but most attempts to exploit them have failed in clinical translation due to the metabolic plasticity of cancer cells. Rapidly proliferating tumors encounter diverse microenvironments with varying oxygen levels, nutrient availability and surrounding cell types. This metabolic flexibility allows cancer cells to survive under fluctuating conditions and contributes to the transient effectiveness of drugs targeting energy metabolism.

For instance, tumors that rely mainly on glucose metabolism can often adapt to glycolysis-inhibiting treatments by switching to oxidative phosphorylation, and vice versa, depending on tumor type and microenvironment. Similarly, cancer cells that utilize ROS to promote proliferation can counteract drugs that induce excessive ROS by downregulating ROS production and increasing antioxidant synthesis, maintaining a balance that favors survival. The ability to draw on multiple nutrients and pathways makes it unlikely that cancer cells ever run out of energy, even under targeted metabolic interventions (Fendt et al. 2020).

Several large pharmaceutical companies have discontinued research on cancer metabolism after promising targets failed in late preclinical stages or early clinical trials. A key challenge is heterogeneity of metabolic enzymes in tumor tissue, particularly mitochondrial enzymes, which means that treatments targeting energy metabolism rarely affect all cancer cells uniformly.

One strategy that remains promising in some tumor types is autophagy inhibition. Many cancers with mitochondrial defects rely on autophagy-mediated organelle turnover. Blocking this process causes dysfunctional mitochondria to accumulate, which reduces metabolic plasticity and sensitizes cancer cells to energy-targeting therapies (Fendt et al. 2020). However, in tumors without mitochondrial defects and late-stage cancers, autophagy inhibition is associated with poor outcomes, and may even enhance tumor cell survival during stress (Carretero-Fernández et al. 2025).

Microenvironmental contributions to metabolic plasticity

The tumor microenvironment, including the extracellular matrix and adjacent non-cancerous cells, further contributes to metabolic plasticity and therapy resistance. A prominent example is the Reverse Warburg effect, where cancer cells induce aerobic glycolysis in cancer-associated fibroblasts (CAFs). These fibroblasts produce high-energy metabolites, such as pyruvate and lactate, which cancer cells use in mitochondrial oxidative phosphorylation, supporting tumor energy metabolism. In this scenario, glycolysis becomes less critical for cancer cells themselves, representing a reversal of the classical Warburg effect.

Cancer cells drive this metabolic reprogramming in fibroblasts via paracrine ROS signaling, mitochondrial stress induction and secretion of proinflammatory proteins. Fibroblasts respond by increasing lactate and pyruvate export, while cancer cells increase lactate importers to efficiently take up these metabolites (Wilde et al. 2017). The stimulation of tumor-adjacent adipocytes to release free fatty acids, as described earlier, follows the same principle of exploiting the microenvironment to sustain cancer cell metabolism. Figure 3 summarizes how the TCA cycle, ROS generation, lipid metabolism and tumor microenvironment interact to sustain tumor energy generation and proliferation.

Figure 3: Illustration of the links between TCA cycle, ROS generation, lipid metabolism and tumor microenvironment contributing to energy generation (symbolized by lightning) and proliferation (symbolized by dividing cell). The dashed line indicates several indirect means by which the TCA cycle metabolites increase ROS generation besides oxidative phosphorylation.

Energy metabolism biomarkers as research tools

While the metabolic plasticity of cancer cells complicates therapeutic targeting, metabolomics offers a window into the pathways that tumors rely on most. By profiling metabolite fluxes, metabolomics can pinpoint which metabolic programs are most active and which are disturbed, with the aim of identifying potential combination strategies for therapy. Metabolomics provides insights into early detection, disease aggressiveness and the efficacy of therapies. Pyruvate levels and the pyruvate-to-lactate ratio are particularly informative here.

A higher pyruvate-to-lactate ratio is generally associated with less malignant tumors, whereas a lower ratio, reflecting increased lactate production, is more typical of aggressive and metastatic cancers. The pyruvate-to-lactate ratio therefore holds promise as a prognostic biomarker and could, in some cases, aid in differential diagnosis (Sharma et al. 2023).

This ratio also provides a metabolic proxy for the redox state of cancer cells. The conversion of pyruvate to lactate consumes reducing equivalents. A low pyruvate-to-lactate ratio indicates a high abundance of reducing equivalents, while a high ratio indicates a low abundance. Radiotherapy and other treatments that generate ROS damage DNA through oxidative stress.

Tumor cells with high reducing potential (i.e., low pyruvate-to-lactate ratio) can better neutralize ROS via glutathione and other antioxidant systems, conferring radioresistance. Accordingly, this ratio has been suggested as a biomarker to predict the effectiveness of ROS-based therapies. Tumors with low pyruvate-to-lactate ratios could benefit from radiosensitizers or combination therapy to enhance treatment efficacy (Sharma et al. 2023).

Animal studies further indicate that in tumor models with a pronounced Warburg effect, decreased pyruvate-to-lactate conversion may be the first sign of treatment response, preceding measurable reductions in tumor growth (Sharma et al. 2023).
The plasma lactate-to-pyruvate ratio is already used as a clinical marker for mitochondrial respiratory chain disorders and inherited pyruvate metabolism defects (Gopan et al. 2021). In cancer, however, only a small subset of cells is affected, meaning systemic changes in plasma are typically observable only in large, highly glycolytic tumors or advanced cancers.

For reliable assessment of energy metabolism in cancer, metabolomic analyses should focus on tissue samples (from biopsies or in-vivo studies) or cells and their culture media (in an in-vitro setting), rather than plasma. These approaches are well-established and provide detailed insights into cancer metabolism by profiling key metabolites and uncovering metabolic vulnerabilities.

Spatial and in-vivo metabolomics for energy metabolism visualization

Spatial metabolomics adds another layer of detail, linking metabolic activity to tissue architecture. Matrix-assisted laser desorption/ionization mass spectrometry imaging (MALDI-MSI) provides high-resolution maps of energy metabolites within tumors, revealing metabolite gradients and regional specificity of glycolysis versus oxidative phosphorylation.

A complementary non-invasive approach is hyperpolarized ¹³C pyruvate (HP-pyruvate) magnetic resonance spectroscopic imaging (MRI), which enables dynamic real-time imaging of pyruvate conversion to lactate, alanine or bicarbonate. Hyperpolarization enhances the signal-to-noise ratio, allowing differentiation between cancer and normal cells based on upregulated glycolysis. Studies in breast and prostate cancer patients, as well as in animal models, show that HP-pyruvate MRI can detect treatment response earlier than conventional multiparametric MRI (Sharma et al. 2023). With technological advances making hyperpolarization more accessible and cost-effective, HP-pyruvate imaging has the potential to become a widespread translational biomarker, highlighting the clinical relevance of metabolism and expanding interest in metabolomics research.

biocrates kits for energy metabolism research

While emerging imaging techniques such as HP-pyruvate MRI provide dynamic, spatial insights, conventional metabolomics remains the cornerstone for mechanistic studies, biomarker discovery and preclinical validation.

The recently launched MxQuant kit by biocrates measures up to 327 small molecules, offering significantly broader coverage options for energy metabolism studies. The kit assesses almost all key energy metabolites discussed in this article, providing a comprehensive overview of cellular energy metabolism.

For an even more complete picture, the MxP Quant 1000 kit covers all metabolites of the MxQuant kit plus 906 lipids. This links fatty acid consumption with triglyceride stores and fatty acid synthesis with the phospholipid pool, both essential for proliferation and tumor growth. When combined with careful experimental design and emerging technologies, these products offer a robust, accessible approach to study cancer energy metabolism. They are well-positioned to drive discoveries that are both mechanistically illuminating and clinically relevant.

References

Brunner, J.S. et al.: Metabolic determinants of tumour initiation (2023) Nature reviews. Endocrinology | https://doi.org/10.1038/s41574-022-00773-5.

Carretero-Fernández, M. et al.: Autophagy and oxidative stress in solid tumors: Mechanisms and therapeutic opportunities (2025) Critical reviews in oncology/hematology | https://doi.org/10.1016/j.critrevonc.2025.104820.

Chen, S. et al.: The emerging role of lactate in tumor microenvironment and its clinical relevance (2024) Cancer letters | https://doi.org/10.1016/j.canlet.2024.216837.

Chiang, S.-K. et al.: A Dual Role of Heme Oxygenase-1 in Cancer Cells (2018) International journal of molecular sciences | https://doi.org/10.3390/ijms20010039.

Fan, Y. et al.: Itaconate transporter SLC13A3 confers immunotherapy resistance via alkylation-mediated stabilization of PD-L1 (2025) Cell metabolism | https://doi.org/10.1016/j.cmet.2024.11.012.

Fendt, S.-M. et al.: Targeting Metabolic Plasticity and Flexibility Dynamics for Cancer Therapy (2020) Cancer discovery | https://doi.org/10.1158/2159-8290.CD-20-0844.

Finley, L.W.S.: What is cancer metabolism? (2023) Cell | https://doi.org/10.1016/j.cell.2023.01.038.

Gopan, A. et al.: Mitochondrial hepatopathy: Respiratory chain disorders- ‘breathing in and out of the liver’ (2021) World journal of hepatology | https://doi.org/10.4254/wjh.v13.i11.1707.

Hanahan, D. et al.: The hallmarks of cancer (2000) Cell | https://doi.org/10.1016/S0092-8674(00)81683-9.

Harada, Y. et al.: 5-Aminolevulinic Acid-Induced Protoporphyrin IX Fluorescence Imaging for Tumor Detection: Recent Advances and Challenges (2022) International journal of molecular sciences | https://doi.org/10.3390/ijms23126478.

Huang, H. et al.: Over expression of 5-aminolevulinic acid synthase 2 increased protoporphyrin IX in nonerythroid cells (2017) Photodiagnosis and photodynamic therapy | https://doi.org/10.1016/j.pdpdt.2016.10.007.

Koundouros, N. et al.: Reprogramming of fatty acid metabolism in cancer (2020) British journal of cancer | https://doi.org/10.1038/s41416-019-0650-z.

Lanzetti, L.: Oncometabolites at the crossroads of genetic, epigenetic and ecological alterations in cancer (2024) Cell death and differentiation | https://doi.org/10.1038/s41418-024-01402-6.

Mathew, M. et al.: Metabolic Signature of Warburg Effect in Cancer: An Effective and Obligatory Interplay between Nutrient Transporters and Catabolic/Anabolic Pathways to Promote Tumor Growth (2024) Cancers | https://doi.org/10.3390/cancers16030504.

Owari, T. et al.: 5-Aminolevulinic acid overcomes hypoxia-induced radiation resistance by enhancing mitochondrial reactive oxygen species production in prostate cancer cells (2022) British journal of cancer | https://doi.org/10.1038/s41416-022-01789-4.

Sharma, G. et al.: Enhancing Cancer Diagnosis with Real-Time Feedback: Tumor Metabolism through Hyperpolarized 1-13C Pyruvate MRSI (2023) Metabolites | https://doi.org/10.3390/metabo13050606.

Thompson, C.B. et al.: A century of the Warburg effect (2023) Nature metabolism | https://doi.org/10.1038/s42255-023-00927-3.

Weiss, J.M. et al.: Itaconic acid mediates crosstalk between macrophage metabolism and peritoneal tumors (2018) The Journal of clinical investigation | https://doi.org/10.1172/JCI99169.

Wilde, L. et al.: Metabolic coupling and the Reverse Warburg Effect in cancer: Implications for novel biomarker and anticancer agent development (2017) Seminars in oncology | https://doi.org/10.1053/j.seminoncol.2017.10.004.

Xu, W. et al.: Oncometabolite 2-hydroxyglutarate is a competitive inhibitor of α-ketoglutarate-dependent dioxygenases (2011) Cancer cell | https://doi.org/10.1016/j.ccr.2010.12.014.

Zhao, Y. et al.: Inhibition of ALAS1 activity exerts anti-tumour effects on colorectal cancer in vitro (2020) Saudi journal of gastroenterology: official journal of the Saudi Gastroenterology Association | https://doi.org/10.4103/sjg.SJG_477_19.

If you are already familiar with the concept of 5P medicine and how omics contribute to the transformation of medicine, click to move forward to the participatory medicine section of this blog.

An introduction to 5P medicine

5P medicine is a concept developed to address the limitations of traditional Western medicine, which typically focuses on reacting to illness or injury. Making use of five components – preventive, predictive, precision, participatory and population-based medicine – 5P medicine aims to shift the focus towards a more proactive and patient-centric practice.

Personally, I first encountered this concept in a book by Leroy Hood and Nathan Price, The Age of Scientific Wellness. Hood and Price describe what they call P4 medicine. Rather than the familiar model that treats or manages disease after its occurrence, the authors offer an alternative that leverages the scientific tools at our disposal to understand health and disease. The result is a transition from what they call a “sickcare” system towards a genuine “healthcare” system. The 5P model expands this concept by adding population-based medicine to the original four and incorporating strategies that use the power of large cohort studies to find additional insights.

Omics and the future of research and health

Omics research has been around for over 30 years, and while genomics is gaining traction and beginning to be used in the clinics, other omics, and multiomics integration, are still lagging. Each omic addresses a distinct layer of biology, with its own codes, regulatory signals and sensitivity to external influences that offer unique insights.

Genomics is the layer least influenced by environment once a person is born. Of course, mutations can occur and change someone’s DNA, but these happen locally and are most often corrected. In contrast, metabolomics is the layer most sensitive to the environment. It responds to our diet, lifestyle, and exposures. For example, metabolomics profiles can shift in response to year after year of terrible food choices (Limonciel et al. 2013). Poor diet usually leads to chronic low-grade inflammation, which presents metabolic patterns closely associated with markers of inflammation at the granular level (Pietzner et al. 2017).

Genomics can tell you about your risk of developing an inflammatory disease, but it cannot track how this risk evolves throughout your life. Similarly, genomics can identify genotypes that will influence response to a specific drug, but it does not respond to influences from the environment that determine our drug response. Metabolomics can. That’s exactly why it’s such a great tool for population-based medicine where the need for measures of disease risk beyond genetic predisposition is high.

Learn more about the power of multiomics for 5P medicine in my webinar.

Participatory medicine with metabolomics

Participatory medicine puts the patient at the center of medical care. Often, patients begin collecting biological data even before clinical symptoms appear, enabling earlier detection of potential health issues, which aligns closely with the aims of preventive medicine. A growing number of companies offer services that allow individuals to get a detailed picture of their health at their own request. Starting with genomics in the 2000s, and soon followed by epigenomics, microbiomics, proteomics and now metabolomics, these “consumer health” companies made omics much more accessible to the general public, though usually still paid for by the individual.

Traditional healthcare systems are beginning to catch up, with genomics now included in routine investigations, for example in cancer prevention and treatment. Laboratories now also offer other omics-based tests, including metabolomics, expanding the range of biomarkers available in medical practice.

Sample collection to make healthcare more accessible

When you go to your doctor or to the hospital and a sample is needed, they will usually take blood. However, this collection method has several drawbacks when it comes to making medicine accessible: not only is it invasive, but it also requires trained medical staff to perform phlebotomy, which often means you need to travel to have your sample taken.

There are alternatives that allow individuals to collect their blood themselves, either on a filter paper card or on a more complex device. These at-home microsampling devices are less invasive and better suited to use with children and sensitive populations, supporting a more compassionate and patient-friendly model of care.

For metabolomics and lipidomics, analyte stability can be a concern. Because collection devices are often shipped at room temperature over several days, there’s a real risk that some analytes will degrade, compromising the interpretability of results. This must be mitigated to make the most of this convenient sampling method.

In a recent application note, we compared the stability of the metabolites covered by the MxP® Quant 500 kit and the reproducibility of quantification in samples collected with classical dried blood spots (DBS) and Neoteryx’s Mitra® tips. The results show that different analytes are affected to different degrees, depending on the device used. This information should be taken into account when planning experiments with these devices.

Many analytes are known to degrade quickly at room temperature, which can either reduce their measured concentration, or increase the concentration of their degradation products in a sample. Rather than being seen as a concern, this simply needs to be documented to inform interpretation of the results. Typically, one would remove the analytes with known instability from the analysis and focus on the ones that are more stable.

Blood is the most commonly used sample type, but other matrices bring added value when painting the picture of a patient’s health. Urine has been used for a long time in the clinics, sometimes after collection of large volumes at home. Today, devices exist to sample only a fraction of urine samples to be sent to the laboratory for analysis.

Similarly, feces are gaining momentum, especially for the combined analysis of the metabolome and the microbiome. For routine measurement, devices optimized to sample a fraction of feces samples were developed, with varying degrees of suitability for metabolomic analysis. In our recent application note, we compared the detectability and stability of metabolites measured with our SMartIDQ alpha kit in feces samples collected with two at-home sampling devices. Here again, differences exist and will determine the best device to be used for each study.

Overall, combining at-home collection using microsampling devices with mass spectrometry-based metabolomics and lipidomics is an effective way to assess a person’s metabolome from the comfort of their own home. Today, this may appeal primarily to people who can afford the services of private consumer health companies, but tomorrow these technologies will make it possible for any family doctor to request tests for patients living in remote locations or to support a virtual consultation.

The need for reference ranges

To interpret metabolomic profiles reliably, reference ranges are essential. Just as clinicians rely on known normal ranges for commonly measured metabolites like glucose, understanding what constitutes a “normal” value for each metabolite is key to drawing meaningful conclusions.

Establishing such reference ranges can be cumbersome, and laboratories that measure these values routinely will typically create their own set of reference measurements from “healthy controls,” which builds over time. The concentration range for glucose tends to be tightly regulated no matter which part of the (healthy) population one looks at. However, it can be useful to create more tailored ranges when looking at a broad range of metabolites that are influenced by exposures, diet and other factors.

Take creatine, for example – a metabolite involved in energy metabolism and widely used as a supplement by athletes. Blood concentration of creatine can vary greatly between women and men, across ethnicities and depending on an individual’s diet and supplement use. Having a reference range tailored to the person’s gender, age, ethnicity, diet and exercise level will enable a more accurate interpretation of their metabolomic profile.

Tools such as the quantitative metabolomics database (QMDB) support more meaningful interpretation by providing reference ranges that can be filtered and tailored to the specifics of each person’s biology and lifestyle. Currently, the database focuses on reference ranges in human plasma, but in future the same approach could be applicable for blood microsampling devices and other matrices, too.

Outlook

Metabolomics that comes in a quantitative and standardized shape is ideally suited to a participatory medicine approach. The small sample volumes collected with at-home sampling devices can be analyzed and interpreted in light of the known variations in analyte concentrations that occur due to sample degradation at room temperature.

Once measured, comparison to a relevant reference range enables the results measured in the laboratory to be translated into actionable health insights. These interpretations are made by medical doctors based on known “normal” concentration ranges and known causes of deviation. Creating appropriate reference ranges will be a vital step in extending this approach in remote areas and in populations that are not currently well represented in reference healthy populations. Tools like QMDB promise to facilitate access to such reference ranges, especially for scientists who do not yet have access to robust reference data.

While metabolomics and lipidomics are most often associated with blood plasma, we will likely have a broader range of collection devices to choose from in future, each with its own set of reference ranges. This will not only expand access to healthcare for patients, but it will also support the faster translation of omics into clinics and medical practice.

This blog concludes our five-part series on 5P medicine. I hope I have sparked your interest in this exciting new way of looking at medicine – putting the patient in the center, learning from multi-scale data, and focusing on real-life improvements in patient care, disease understanding and drug discovery.

To hear more about how (metabol)omics contributes to personalized, predictive and precision medicine, please sign up for our newsletter and listen to season 4 of The Metabolomist podcast.

Learn more about 5P medicine in our other articles:

References

Grant et al.: Variant of transcription factor 7-like 2 (TCF7L2) gene confers risk of type 2 diabetes (2006) Nature Genetics | https://doi.org/10.1038/ng1732

Helgadottir et al.: A common variant on chromosome 9p21 affects the risk of myocardial infarction (2007) Science | https://doi.org/10.1126/science.1142842

Hoffman et al.: Development of a metabolomic risk score for exposure to traffic-related air pollution: A multi-cohort study (2024) Environmental Research | https://doi.org/10.1016/j.envres.2024.120172

Kelly et al.: Metabo-endotypes of asthma reveal differences in lung function: Discovery and validation in two TOPMed cohorts (2021) ATS | http://doi.org/10.1164/rccm.202105-1268OC

Lacruz et al.: Instability of personal human metabotype is linked to all-cause mortality (2018) Nature | https://doi.org/10.1038/s41598-018-27958-1

Limonciel et al.: Complex chronic diseases have a common origin (2013) I https://biocrates.com/wp-content/uploads/2024/07/biocrates-Complex-chronic-diseases-have-a-common-origin.pdf

Ogishima et al.: dbTMM: an integrated database of large-scale cohort, genome and clinical data for the Tohoku Medical Megabank Project (2021) Human Genome Variation | https://doi.org/10.1038/s41439-021-00175-5

Pietzner et al.: Plasma proteome and metabolome characterization of an experimental human thyrotoxicosis model (2017) BMC Medicine | http://doi.org/10.1186/s12916-016-0770-8

Prince et al.: Phenotypically driven subgroups of ASD display distinct metabolomic profiles (2023) Brain, Behavior, and Immunity | https://doi.org/10.1016/j.bbi.2023.03.026

So et al.: Evaluating the heritability explained by known susceptibility variants: a survey of ten complex diseases (2011) Genetic Epidemiology | https://doi.org/10.1002/gepi.20579

If you are already familiar with the concept of 5P medicine and how omics contribute to the transformation of medicine, click to move forward to the population-based medicine section of this blog.

An introduction to 5P medicine

5P medicine is a concept developed to address the limitations of traditional Western medicine, which typically focuses on reacting to illness or injury. Making use of five components – preventive, predictive, precision, participatory and population-based medicine – 5P medicine aims to shift the focus towards a more proactive and patient-centric practice.

Personally, I first encountered this concept in a book by Leroy Hood and Nathan Price, The Age of Scientific Wellness. Hood and Price describe what they call P4 medicine. Rather than the familiar model that treats or manages disease after its occurrence, the authors offer an alternative that leverages the scientific tools at our disposal to understand health and disease. The result is a transition from what they call a “sickcare” system towards a genuine “healthcare” system. The 5P model expands this concept by adding population-based medicine to the original four and incorporating strategies that use the power of large cohort studies to find additional insights.

Omics and the future of research and health

Omics research has been around for over 30 years, and while genomics is gaining traction and beginning to be used in the clinics, other omics, and multiomics integration, are still lagging. Each omic addresses a distinct layer of biology, with its own codes, regulatory signals and sensitivity to external influences that offer unique insights.

Genomics is the layer least influenced by environment once a person is born. Of course, mutations can occur and change someone’s DNA, but these happen locally and are most often corrected. In contrast, metabolomics is the layer most sensitive to the environment. It responds to our diet, lifestyle, and exposures. For example, metabolomics profiles can shift in response to year after year of terrible food choices (Limonciel et al. 2013). Poor diet usually leads to chronic low-grade inflammation, which presents metabolic patterns closely associated with markers of inflammation at the granular level (Pietzner et al. 2017).

Genomics can tell you about your risk of developing an inflammatory disease, but it cannot track how this risk evolves throughout your life. Similarly, genomics can identify genotypes that will influence response to a specific drug, but it does not respond to influences from the environment that determine our drug response. Metabolomics can. That’s exactly why it’s such a great tool for population-based medicine where the need for measures of disease risk beyond genetic predisposition is high.

Learn more about the power of multiomics for 5P medicine in my webinar.

Population-based medicine with metabolomics

Population-based medicine takes advantage of the statistical power of large epidemiological cohorts to study health and disease at a different scale. At first glance, this seems like the opposite of precision medicine, but these large cohort studies can be a great source of biomarkers and complex risk scores to evaluate the health of a single individual.

Applying metabolomics in population-based cohort studies provides a wealth of information that enables:

• Metabolic risk scoring
• Metabotyping
• Multiomics integration

Metabolic risk scores

Population-based medicine was among the first disciplines to make use of genomics, designing genome-wide association studies (GWAS) that have identified numerous gene variants associated with the risk of developing disease, from type 2 diabetes (Grant et al. 2006) to coronary artery disease (Helgadottir et al. 2007). However, these gene variants rarely explain more than 10% of the risk for developing complex chronic diseases.

In complex diseases such as Alzheimer’s disease, breast cancer or type 2 diabetes, environmental factors play a massive role, reflected in the low percentage of variance explained by genetic variants (So et al. 2011). This means that much of the remaining 90% is likely to be explained by metabolomics, an omic strongly influenced by the environment.

Understanding the external, environmental factors that associate with a disease or trait enables better risk assessment and prevention, more sensitive diagnosis, and ultimately enables precision medicine for the parts of the population exposed to the external factors identified.

For example, (Hoffman et al. 2024) created a metabolomic risk score that reflects the response of the organism to traffic-related air pollution. To strengthen their model, the authors combined metabolomic data from several cohorts together with simulated data. The power of the associations was relatively low, although the metabolites selected by the models made mechanistic sense, comforting the authors on their relevance to the risk score. The authors emphasized the need for large sample sizes and standardized metabolomics methods to support this approach.

Metabotyping

I’ve discussed this approach in previous blogs in this series, but metabotyping is also a great tool for population-based medicine. Metabotyping consists in forming sub-groups of a population based on their metabolomic profile. This type of clustering is a very good way to uncover sub-phenotypes of a disease that may benefit from distinct early diagnostic tools and different treatments.

On a recent episode of The Metabolomist podcast, Rachel Kelly explains how she and her group have applied this approach in epidemiological studies focused on asthma (Kelly et al. 2021) and autism spectrum disorder (Prince et al. 2023). In both cases, metabolomics was a powerful tool to better understand the condition and begin to think of novel approaches for precision medicine using these cohort-based results.

In 2018, Lacruz et al. showed that not only is metabolomics a great tool to stratify a population through metabotyping, but also that monitoring these metabotypes can be very informative. In the Cardiovascular Disease, Living and Ageing in Halle (CARLA) cohort (n=1,409), nearly 60% of the population had a stable metabotype after a four-year interval. Metabotype instability, however, was associated with a higher risk of all-cause mortality. The authors highlighted metabotype variability as a potential early indicator of pre-clinical disease. You can also listen to Gabi Kastenmüller, the senior author on this publication, discussing the use of metabolomics in this and other studies on large cohorts on the podcast.

Multiomics integration

As mentioned above, the large cohorts used in population-based medicine require robust omics methods to generate meaningful associations. Combining omics is also a way to highlight associations that wouldn’t be visible with a single omics layer.

A well-known example is the integration of metabolomics with GWAS, referred to as mGWAS. I’ve discussed this approach on the podcast with one of the pioneers of mGWAS, Karsten Suhre. In that episode, he explains how using metabolomics and ratios of metabolites helps us identify variants that were previously out of scope, and with excellent p values.

Read more about how to perform mGWAS here.

Large biobanks increasingly include the measurement of multiple omics to enable multiomics analysis for their users. For instance, the Tohoku Medical Megabank Project in Japan created the jMorp database, which compiles multiomics data including genomics, proteomics and metabolomics, as well as related clinical data (Ogishima et al. 2021). These initiatives help to democratize the use of omics in epidemiology and to the development of FAIR science.

Outlook

As the most comprehensive measure of phenotype and external influences on human biology, metabolomics is uniquely positioned to provide the missing pieces of the multiomics puzzle so greatly needed in epidemiology.

From the calculation of metabolic risk scores that can complement or even merge with polygenetic risk scores from genomic studies, to the enhanced understanding of disease provided by metabotyping, metabolomics has a lot to offer to population-base medicine.

We explored the application of this omic to cohort studies in our whitepaper on “FAIR compliant metabolomics profiling of population-based studies.” In particular, we discussed how the level of standardization required for the integration of metabolomics in large cohort studies is a critical point that requires dedicated solutions.

The increasing adoption of metabolomics in large cohort studies and biobanks holds great promise for uncovering the variance related to complex diseases and advancing population-based medicine.

Sign up for our newsletter to be notified when the next blog on 5P medicine comes out.

References

Grant et al.: Variant of transcription factor 7-like 2 (TCF7L2) gene confers risk of type 2 diabetes (2006) Nature Genetics | https://doi.org/10.1038/ng1732

Helgadottir et al.: A common variant on chromosome 9p21 affects the risk of myocardial infarction (2007) Science | https://doi.org/10.1126/science.1142842

Hoffman et al.: Development of a metabolomic risk score for exposure to traffic-related air pollution: A multi-cohort study (2024) Environmental Research | https://doi.org/10.1016/j.envres.2024.120172

Kelly et al.: Metabo-endotypes of asthma reveal differences in lung function: Discovery and validation in two TOPMed cohorts (2021) ATS | http://doi.org/10.1164/rccm.202105-1268OC

Lacruz et al.: Instability of personal human metabotype is linked to all-cause mortality (2018) Nature | https://doi.org/10.1038/s41598-018-27958-1

Limonciel et al.: Complex chronic diseases have a common origin (2013) I https://biocrates.com/wp-content/uploads/2024/07/biocrates-Complex-chronic-diseases-have-a-common-origin.pdf

Ogishima et al.: dbTMM: an integrated database of large-scale cohort, genome and clinical data for the Tohoku Medical Megabank Project (2021) Human Genome Variation | https://doi.org/10.1038/s41439-021-00175-5

Pietzner et al.: Plasma proteome and metabolome characterization of an experimental human thyrotoxicosis model (2017) BMC Medicine | http://doi.org/10.1186/s12916-016-0770-8

Prince et al.: Phenotypically driven subgroups of ASD display distinct metabolomic profiles (2023) Brain, Behavior, and Immunity | https://doi.org/10.1016/j.bbi.2023.03.026

So et al.: Evaluating the heritability explained by known susceptibility variants: a survey of ten complex diseases (2011) Genetic Epidemiology | https://doi.org/10.1002/gepi.20579

If you are already familiar with the concept of 5P medicine and how omics contribute to the transformation of medicine, click to move forward to the precision medicine section of this blog.

An introduction to 5P medicine

5P medicine is a concept developed to address the limitations of traditional Western medicine, which typically focuses on reacting to illness or injury. Making use of five components – preventive, predictive, precision, participatory and population-based medicine – 5P medicine aims to shift the focus towards a more proactive and patient-centric practice.

Personally, I first encountered this concept in a book by Leroy Hood and Nathan Price, The Age of Scientific Wellness. Hood and Price describe what they call P4 medicine. Rather than the familiar model that treats or manages disease after its occurrence, the authors offer an alternative that leverages the scientific tools at our disposal to understand health and disease. The result is a transition from what they call a “sickcare” system towards a genuine “healthcare” system. The 5P model expands this concept by adding population-based medicine to the original four and incorporating strategies that use the power of large cohort studies to find additional insights.

Omics and the future of research and health

Omics research has been around for over 30 years, and while genomics is gaining traction and beginning to be used in the clinics, other omics, and multiomics integration, are still lagging. Each omic addresses a distinct layer of biology, with its own codes, regulatory signals and sensitivity to external influences that offer unique insights.

Genomics is the layer least influenced by environment once a person is born. Of course, mutations can occur and change someone’s DNA, but these happen locally and are most often corrected. In contrast, metabolomics is the layer most sensitive to the environment. It responds to our diet, lifestyle, and exposures. For example, metabolomics profiles can shift in response to year after year of terrible food choices (Limonciel et al. 2013). Poor diet usually leads to chronic low-grade inflammation, which presents metabolic patterns closely associated with markers of inflammation at the granular level (Pietzner et al. 2017).

Genomics can tell you about your risk of developing an inflammatory disease, but it cannot track how this risk evolves throughout your life. Similarly, genomics can identify genotypes that will influence response to a specific drug, but it does not respond to influences from the environment that determine our drug response. Metabolomics can. That’s exactly why it’s such a great tool for precision medicine, from the identification of personalized therapeutics and nutrition to the stratification of groups of patients into phenotypically-relevant sub-groups (Wishart 2016).

Learn more about the power of multiomics for 5P medicine in my webinar.

Precision medicine with metabolomics

Precision medicine is a medical approach that tailors disease prevention, diagnosis and treatment to the individual characteristics of a patient or sub-group of patients. By focusing on factors such as their genetics, environment, lifestyle, and molecular profile, this approach aims to achieve the most effective and targeted healthcare outcomes.

I consider data disaggregation (or stratification) by sex or gender as ground zero of precision medicine. There are such marked differences between the metabolomes of women and men that it is essential to study them as two separate groups. In fact, I dedicated an entire chapter of my book on metabolomics data interpretation, The STORY principle, to this topic. Studying women and men independently helps ensure that meaningful differences in the data aren’t averaged out or overlooked when looking for diagnostic tools. It also enables the development more precisely tailored treatment regimens, or even entirely different drugs, that address disease through the specific biological mechanisms active in each group.

This level of detail becomes possible with metabolomics. A person’s metabolome provides a wealth of information that enables personalized diagnostics, treatment optimization, nutritional guidance.

Personalized prognostic and diagnostics

To refine our prognostic and diagnostic tools and to provide tailored support to patients, we need more precise measures of disease risk and disease state.

The 2020 paper by Arnold et al. “Sex and APOE ε4 genotype modify the Alzheimer’s disease serum metabolome” illustrates the differences between women and men in a disease that primarily affects women. Even when studying individuals with the strongest genetic risk factor for the disease, the differences between the sexes were striking, with women showing a stronger impact on mitochondrial energy production. I discuss this paper, and the added value of stratifying data by sex, in more detail in my conversation with Gabi Kastenmüller on The Metabolomist.

In 2021, a multiomics analysis of the blood of patients with Alzheimer’s disease uncovered molecular subtypes and regulatory mechanisms of the disease (Horgusluoglu et al. 2021). Combining genomics, transcriptomics, proteomics and metabolomics, the authors created a comprehensive molecular map of Alzheimer’s disease, identifying acylcarnitines and amino acids as deeply tied to disease progression. Specific target proteins related to acylcarnitines were identified, and the clusters or molecular subtypes identified correlated with disease severity and cognitive dysfunction.

Clustering patients based on their metabolic profile has become a popular approach, especially for complex diseases where a single diagnostic label can include numerous sub-pathologies. For example, I discuss the use of such grouping techniques to study asthma in the field of epidemiology with Rachel Kelly on another episode of The Metabolomist. In a 2021 paper, Kelly and her team identified clinically meaningful endotypes of asthma that not only helped identify more refined pathophysiologies than with clinical measures of the disease, but also provided a tool to group patients for further personalized treatments (Kelly et al. 2021).

Treatment optimization

Personalized treatment is of course a priority in precision medicine. More targeted treatments increase efficacy, often through lower doses, while reducing side effects. The one-size-fits-all approach that was once the holy grail of pharmaceutical development has become increasingly unworkable, especially in the context of chronic disease where multiple mechanisms may lead to the same diagnosis.

Returning to ground zero, incorporating sex as a biological variable in clinical trials and treatment planning has been used to optimize immunotherapy strategies for both women and men. A systematic review and meta-analysis of randomized controlled trials of immune checkpoint inhibitors (ICI) with sex-disaggregated results found that while ICI can improve survival for patients with advanced melanoma or non-small-cell lung cancer, the magnitude of benefit is sex-dependent (Conforti et al. 2018). The authors strongly recommend the inclusion of women in clinical trials – a practice still not consistently applied. In both the US and Europe, pre-menopausal women were actively excluded from most clinical trials until the 1990s. After that, the inclusion of women was not precluded, but was not mandatory either. Today, however, reporting sex distributions and sex-disaggregated results is increasingly seen as essential in clinical trials.

Nutritional guidance

The field of nutrition science was among the first to embrace metabolomics. What we eat is clearly a direct provider of metabolites that are absorbed through our digestive tract, but our food also has an impact on our metabolism and health (Limonciel et al. 2013). In addition, our metabolome reflects our metabolism, which deeply impacts how we process and respond to food.

Metabotyping, or classifying individuals into metabolic phenotypes, is a powerful tool in precision nutrition (Hillesheim et al. 2020). These “metabotypes” can enhance personalized nutrition by identifying subgroups that respond differently to dietary interventions, thereby improving health outcomes and informing targeted nutritional strategies. For example, in a 2023 study, metabotypes were used to tailor personalized nutrition advice, resulting in improved dietary quality and reduced plasma cholesterol levels compared to generic dietary recommendations (Hillesheim et al. 2023).

Outlook

Metabolomics offers a wealth of information to support a precision medicine approach. By analyzing broad panels of metabolites, we gain deep insights into an individual’s biochemical state, capturing real-time, systems-level information that reflects genetics, environment, lifestyle, nutrition and disease status. This comprehensive snapshot allows us to detect subtle shifts in interconnected pathways and identify molecular signatures unique to patient subgroups or even individuals.

To use this tool effectively, careful study design is crucial to control for confounding variables, and expert interpretation is essential to translate complex data into meaningful insights. Unsupervised analyses of large datasets are a wonderful source of original findings, but the final interpretation to leverage the data must be done considering our current knowledge of metabolism and medicine, as I explain in my book.

Tools like MetaboINDICATOR, which catalog disease-associated metabolic shifts and compute biologically relevant sums and ratios, provide valuable pre-interpretation layers that bridge data and decision-making. These insights form the foundation for developing personalized diagnostics, prognostics, therapeutic and nutritional strategies.

In the end, metabolomics doesn’t just deepen our understanding of disease, it enables us to tailor interventions to the individual and deliver truly personalized, precise healthcare. It’s an exciting time to work in science!

Sign up for our newsletter to be notified when the next blog on 5P medicine comes out.

References

Arnold et al.: Sex and APOE ε4 genotype modify the Alzheimer’s disease serum metabolome (2020) Nature | https://doi.org/10.1038/s41467-020-14959-w

Conforti et al.: Cancer immunotherapy efficacy and patients’ sex: a systematic review and meta-analysis (2018) Lancet | https://doi.org/10.1016/S1470-2045(18)30261-4

Hillesheim et al.: Metabotyping and its role in nutrition research (2020) Cambridge | https://doi.org/10.1017/S0954422419000179

Hillesheim et al.: Using a metabotype framework to deliver personalized nutrition improves dietary quality and metabolic health parameters: A 12-week randomized controlled trial (2023) Wiley | https://doi.org/10.1002/mnfr.202200620

Horgusluoglu et al.: Integrative metabolomics-genomics approach reveals key metabolic pathways and regulators of Alzheimer’s disease (2021) Alzheimer | http://doi.org/10.1002/alz.12468

Kelly et al.: Metabo-endotypes of asthma reveal differences in lung function: Discovery and validation in two TOPMed cohorts (2021) ATS | http://doi.org/10.1164/rccm.202105-1268OC

Pietzner et al.: Plasma proteome and metabolome characterization of an experimental human thyrotoxicosis model (2017) BMC Medicine | http://doi.org/10.1186/s12916-016-0770-8

Wishart et al.: Emerging applications of metabolomics in drug discovery and precision medicine (2016) Nature | https://doi.org/10.1038/nrd.2016.32

If you are already familiar with the concept of 5P medicine and how omics contribute to the transformation of medicine, click to move forward to the predictive medicine section of this blog.

An introduction to 5P medicine

5P medicine is a concept developed to address the limitations of traditional Western medicine, which typically focuses on reacting to illness or injury. Making use of five components – preventive, predictive, precision, participatory and population-based medicine – 5P medicine aims to shift the focus towards a more proactive and patient-centric practice.

Personally, I first encountered this concept in a book by Leroy Hood and Nathan Price, The Age of Scientific Wellness. Hood and Price describe what they call P4 medicine. Rather than the familiar model that treats or manages disease after its occurrence, the authors offer an alternative that leverages the scientific tools at our disposal to understand health and disease. The result is a transition from what they call a “sickcare” system towards a genuine “healthcare” system. The 5P model expands this concept by adding population-based medicine to the original four and incorporating strategies that use the power of large cohort studies to find additional insights.

Omics and the future of research and health

Omics research has been around for over 30 years, and while genomics is gaining traction and beginning to be used in the clinics, other omics, and multiomics integration, are still lagging. Each omic addresses a distinct layer of biology, with its own codes, regulatory signals and sensitivity to external influences that offer unique insights.

Genomics is the layer least influenced by environment once a person is born. Of course, mutations can occur and change someone’s DNA, but these happen locally and are most often corrected. In contrast, metabolomics is the layer most sensitive to the environment. It responds to our diet, lifestyle, and exposures. For example, metabolomics profiles can shift in response to year after year of terrible food choices. Poor diet usually leads to chronic low-grade inflammation, which presents metabolic patterns closely associated with markers of inflammation at the granular level (Pietzner et al. 2017).

Genomics can tell you about your risk of developing an inflammatory disease, but it cannot track changes throughout your life. Similarly, genomics can identify mutations that will influence response to a specific drug, but it does not respond to influences from the environment that determine our drug response. Metabolomics can. That’s exactly why it’s such a great tool for predictive medicine.

Learn more about the power of multiomics for 5P medicine in my webinar.

Predictive medicine with metabolomics

A person’s metabolome can provide a wealth of information regarding disease trajectory, disease stage, and their likelihood to respond to a specific therapy.

At the intersection of disease prognosis, personalized treatments, and metabolomics, some of the most interesting applications include:

• Prognosis – Predicting disease trajectory
• Pharmacometabolomics | Understanding patients’ drug response with the metabolome

Prognosis

Metabolites have long been used as biomarkers for prognosis. For example, the ratio of blood levels of the nitrogen-carrying metabolites urea and creatinine is a marker of renal function commonly used in the clinics. This ratio is also elevated in other related ailments, including heart disease and sepsis.

On the third season of The Metabolomist podcast, I discussed cancer prognosis and survival prediction with Robert Nagourney. In the episode, he highlighted a recent publication on pancreatic cancer, which showed that metabolomics signatures enabled earlier disease prognosis in pancreatic ductal adenocarcinoma (PDAC) than traditional tumor markers or radiographic measures (D’Amora et al. 2024). In addition, metabolomics profiling provided mechanistic insights into disease severity that enabled the stratification of patient into sub-groups based on their metabolome. Using metabolite concentrations in patient samples, predictive models were developed to improve prognosis accuracy. Specifically, metabolomics-based models achieved high predictive performance (AUC up to ~0.90), allowing better stratification of patients according to their survival risk and supporting more personalized and effective treatment decisions.

Pharmacometabolomics

Pharmacometabolomics studies interactions between the metabolome and pharmaceutical drugs, with applications in both clinical practice and drug development. We’ve already discussed an example of how metabolomics can predict patient response to treatment in last month’s article on preventive medicine. In brief, (Tintelnot et al. 2023) identified the tryptophan metabolite 3-indole acetic acid (3-IAA) as a modulating factor in chemotherapy response. Using a broad targeted panel of metabolites, the researchers were able to link 3-IAA synthesis in the gut microbiome and its influence on the patient’s immune system. They proposed a simple yet effective solution: supplementing 3-IAA to increase response rate. This worked in an animal model of PDAC, though it has still to be investigated in humans.

This example reflects a widely adopted and effective three-tiered approach in pharmacometabolomics:

• Screen samples with a broad metabolomics panel for biomarkers and mechanistic insights;
• Leverage data using a combination of bioinformatic tools and knowledge of biochemistry and physiology;
• Translate insights into innovative solutions that will support 5P medicine.

This strategy is replicated throughout the literature. For instance, broad metabolomics profiling identified a pattern of elevated polyamines and lysophospholipids (specifically lysophosphatidylcholines and lysophosphatidylethanolamines) that accurately predicts poor response to CAR-T cell therapy in patients with relapsed or refractory large B-cell lymphoma (Fahrmann et al. 2022). High levels of acetylated polyamines correlated with worse progression-free and overall survival. The findings align with previous evidence showing an effect on acetylated polyamines in several cancer types (Li et al. 2020).

Outlook

Metabolomics offers a wealth of information to support a predictive approach to disease. Broad panels of metabolites enable a deep screen of the metabolome that will “sense” changes, often in multiple pathways at once. This makes use of the interconnected nature of the metabolome, where changes in one metabolite can affect a vast number of pathways.

Careful study design is essential to limit the number of confounders that may bias the analysis. Expert analysis is equally important to extract meaningful insights from the data. Unsupervised analyses of large datasets are a wonderful source of original findings, however, the final interpretation to leverage the data must be done in light of our current knowledge of metabolism and medicine, as I explain in my book on metabolomics data interpretation The STORY principle. Databases such as MetaboINDICATOR, which catalogue known changes associated with disease and calculate sums and ratios of metabolites known to associate with different health states, can provide a useful ‘pre-interpretation’ of results.

Ultimately, these insights are only the starting point for developing innovative solutions, whether to predict a person’s prognosis, treatment response or disease progression. You’ll notice that the link between this predictive approach and precision medicine is never far. In next month’s blog, we’ll dive deeper into the ways that metabolomics is used in precision medicine.

Sign up for our newsletter to be notified when the next blog on 5P medicine comes out.

References

Pietzner et al.: Plasma proteome and metabolome characterization of an experimental human thyrotoxicosis model (2017) BMC Medicine | http://doi.org/10.1186/s12916-016-0770-8

Sakr et al.: The prognostic role of urea-to-creatinine ratio in patients with acute heart failure syndrome: a case–control study (2023) Egypt Heart J | https://doi.org/10.1186/s43044-023-00404-y

D’Amora et al.: Diagnostic and prognostic performance of metabolic signatures in pancreatic ductal adenocarcinoma: The clinical application of quantitative NextGen mass spectrometry (2024) Metabolites | https://doi.org/10.3390/metabo14030148

Tintelnot et al.: Microbiota-derived 3-IAA influences chemotherapy efficacy in pancreatic cancer (2023) Nature | https://doi.org/10.1038/s41586-023-05728-y

Fahrmann et al.: A polyamine-centric, blood-based metabolite panel predictive of poor response to CAR-T cell therapy in large B cell lymphoma (2022) Cell Rep Med.| https://doi.org/10.1016/j.xcrm.2022.100720

Li et al.: Polyamines and related signaling pathways in cancer (2020) Cancer Cell Int.| https://doi.org/10.1186/s12935-020-01545-9

An introduction to 5P medicine

5P medicine is a concept developed to address the limitations of traditional Western medicine, which typically focuses on reacting to illness or injury. Making use of five components – preventive, predictive, precision, participatory and population-based medicine – 5P medicine aims to shift the focus towards a more proactive and patient-centric practice.

Personally, I first encountered this concept in a book by Leroy Hood and Nathan Price, The Age of Scientific Wellness. Hood and Price describe what they call P4 medicine. Rather than the familiar model that treats or manages disease after its occurrence, the authors offer an alternative that leverages the scientific tools at our disposal to understand health and disease. The result is a transition from what they call a “sickcare” system towards a genuine “healthcare” system. The 5P model expands this concept by adding population-based medicine to the original four, and incorporating strategies that use the power of large cohort studies to find additional insights.

Omics and the future of research and health

Omics research has been around for over 30 years, and while genomics is gaining traction and beginning to be used in the clinics, other omics, and multiomics integration, are still lagging. Each omic addresses a distinct layer of biology, with its own codes, regulatory signals and sensitivity to external influences that offer unique insights.

Genomics is the layer least influenced by environment once a person is born. Of course, mutations can occur and change someone’s DNA, but these happen locally and are most often corrected. In contrast, metabolomics is the layer most sensitive to the environment. It responds to our diet, lifestyle, and exposures. For example, metabolomics profiles can shift in response to year after year of terrible food choices. Poor diet usually leads to chronic low-grade inflammation, which presents metabolic patterns closely associated with markers of inflammation at the granular level (Pietzner et al. 2017).

Genomics can tell you about your risk of developing an inflammatory disease, but it cannot track changes throughout your life. Metabolomics can. That’s exactly why it’s such a great tool for preventive medicine. Many chronic diseases have underlying inflammation, and by detecting changes in this “breeding ground” before the onset of disease, metabolomics offers a chance for early intervention. And for illnesses that can decrease in severity or disappear altogether, metabolomics is a unique omic tool to monitor patient status.

Preventive medicine with metabolomics

At the intersection of disease prevention and metabolomics, some of the most interesting applications include:

• changing what we eat (dietary intervention),
• changing our microbiome (probiotics),
• artificially increasing the levels of microbiome products (postbiotic supplements)
• identifying individuals at risk of developing disease using metabolomics.

Dietary intervention

With growing awareness of the impact of the gut microbiome on our health, much of today’s preventive work focuses on strategies to modulate either our microbiome or what we feed it through our diet.

In a study of type 2 diabetes (T2D) and low-grade inflammation, Tuomainen et al. identified an inverse association of the microbial metabolite indolepropionic acid (IPA) with T2D, suggesting a protective effect of IPA. Individuals with higher levels of this tryptophan derivative also reported greater dietary fiber consumption. This aligns with the understanding that dietary fiber influences gut microbiota composition and activity, potentially leading to increased IPA production. Elevated IPA levels were linked to reduced markers of low-grade inflammation, indicating potential anti-inflammatory effects of this metabolite (Tuomainen et al. 2018).
Building on these findings, interventions could explore:

• whether increasing the amount of fiber ingested reduces markers of inflammation and/or improves monitoring markers for patients with T2D,
• whether directly supplementation with IPA can lead to these effects.
  Understanding the mechanistic link between these significant metabolic changes and health outcomes is a very valuable tool to determine the best course of action.

In a 2023 study, Tintelnot et al. identified another derivative of tryptophan through the activity of the microbiome: 3-indole acetic acid (3-IAA). Here, the metabolite was predictive of which patient would respond to chemotherapy treatment, likely due to its activity on the immune system; a story we will dive into more deeply in a future blog focused on predictive medicine.
Tryptophan metabolites have become a huge focus in inflammation and immunology research. Since tryptophan is an essential amino acid, and many of its derivatives are only present in human blood through metabolic activity in the microbiome, studying tryptophan pathways is fascinating and highly relevant. Find out more about tryptophan metabolism.

Probiotics

The more I learn about the microbiome, the more I realize how much we still don’t know. However, well-structured studies are steadily helping us understand what makes a “good” microbiome. And part of the answer lies not in which microbes are present, but in which metabolites they generate. Indeed, far from being deleterious, many microbial metabolites constitute the message that is sent to us and can contribute to our health.

A striking example of this comes in a 2019 paper by Wilmanski et al., which showed that a signature of 11 blood metabolites could accurately predict gut alpha-diversity with an AUC of 0.88. However, a battery of clinical tests and blood proteome analysis did not yield such results, failing to predict microbiome diversity. This study was a milestone to establish the relevance of measuring the metabolome in blood in the context of microbiome research.

Given the association of the gut microbiome with almost every chronic disease that plagues our societies, from neurodegenerative to autoimmune disease and cancer, modulating the composition and impact of the gut microbiome using probiotics is naturally a method of choice.

For example, in a mouse model of colitis, the introduction of Lactobacilli increased the microbial synthesis of indole-3-lactic acid (ILA), a precursor of IPA described previously (Wang et al. 2024). In a mouse model of depression, it is the strain Bifidobacterium breve that caused the production of ILA, thereby replenishing ILA stocks in the hippocampus (Qian et al. 2024).

Postbiotics

Although a diverse and stable microbiome is a key to good health, a logical next step is simply to supplement with the relevant beneficial metabolite. Many such postbiotic supplements exist, and short-chain fatty acids (SCFAs) are among the most promising ones. For example, butyrate acts as an energy source for enterocytes (the cells lining the intestine) and has been shown to promote and preserve gut health (reviewed by Ji et al. 2023). In clinical trials, butyrate supplementation led to significant improvements in body mass index (BMI) and insulin sensitivity in a cohort of obese children (Coppola et al. 2022).

In patients with liver steatosis and metabolic syndrome, a butyrate-based formula improved fatty liver index and reduced circulating levels of total cholesterol and total triglycerides (Fogacci et al. 2024).

Metabolic risk scores

Metabolomics can be used to assess a person’s health and calculate a score for the risk of developing disease. For instance, in a cohort of 21,323 individuals, serum metabolites and lipoproteins could identify patients with metabolic syndrome with an AUROC of 0.94 (Gil-Redondo et al. 2024).

In 2019, a study on 44,168 individuals rendered model using 14 circulating metabolites that could predict 5- and 10-year mortality with a C-statistic of 0.77 and 0.79, respectively (Deelen et al. 2019).

In 2024, Liu et al. combined metabolomics and genomics data in a metabolomics-based genome-wide association (mGWAS) study that identified 14 metabolites related to Alzheimer’s disease and found associations with microbiome-related features.

Outlook

In conclusion, metabolomics offers a wealth of information for a preventive approach to disease. Because our metabolism is sensitive to external factors, metabolomics is the ideal tool to:

• search for early markers of disease,
• calculate risk scores and follow their evolution through life,
• combine with the more static measure of the genome.

Sign up for our newsletter to receive an email when the next blog on 5P medicine comes out.

References

Coppola, S., et al.: Butyrate supplementation improves body mass index and insulin sensitivity in obese children (2022) Clinical Nutrition | https://doi.org/10.1016/j.clnu.2022.03.010

Deelen, J., et al.: A model using 14 circulating metabolites predicts 5- and 10-year mortality (2019) Nature Communications | https://doi.org/10.1038/s41467-019-11311-9

Fogacci, F., et al.: Butyrate-based formula improves fatty liver index and reduces cholesterol and triglyceride levels (2024) Journal of Clinical Lipidology | https://doi.org/10.1016/j.jacl.2024.01.005

Gil-Redondo, R., et al.: Serum metabolites and lipoproteins identify metabolic syndrome with AUROC 0.94 in a cohort of 21,323 individuals (2024) Metabolomics | https://doi.org/10.1007/s11306-024-02047-3

Ji, Y., et al.: The role of butyrate in promoting gut health (2023) Gut Microbes | https://doi.org/10.1080/19490976.2023.2234568

Liu, Y., et al.: Metabolomics-based genome-wide association study identifies metabolites related to Alzheimer’s disease and microbiome features (2024) Genome Medicine | https://doi.org/10.1186/s13073-024-01235-9

Pietzner, M., et al.: Metabolomics profiles associated with markers of inflammation (2017) Nature Medicine | https://doi.org/10.1038/nm.4333

Qian, Y., et al.: Bifidobacterium breve replenishes indole-3-lactic acid stocks in the hippocampus in a mouse model of depression (2024) Neuropsychopharmacology | https://doi.org/10.1038/s41386-024-01825-9

Tintelnot, S., et al.: 3-indole acetic acid predicts chemotherapy response through immune system activity (2023) Cancer Research | https://doi.org/10.1158/0008-5472.CAN-23-0142

Tuomainen, M., et al.: Indolepropionic acid inversely associated with type 2 diabetes and inflammation markers (2018) Diabetes Care | https://doi.org/10.2337/dc18-0980

Wang, J., et al.: Lactobacilli increase microbial synthesis of indole-3-lactic acid in a mouse model of colitis (2024) Microbiome | https://doi.org/10.1186/s40168-024-01586-w

Wilmanski, T., et al.: A signature of 11 blood metabolites predicts gut alpha-diversity with AUC 0.88 (2019) Cell Metabolism | https://doi.org/10.1016/j.cmet.2019.06.012

Lifestyle and diet are widely recognized as significant determinants of health, driving demand for personalized health solutions (Rafiq et al. 2021). As individuals look for ways to actively manage their well-being, consumer tests such as microbiome sequencing for gut health or proteomics-based longevity assessments are gaining traction. These tests often combine biological measurements with self-reported dietary questionnaires to deliver tailored diet recommendations (Brennan 2017; Guasch-Ferré et al. 2018; Rafiq et al. 2021).

However, self-reported data is prone to inaccuracies, as consumers frequently misestimate their intake of vegetables, red meat, and other foods (Brennan 2017). Scientific research highlights the limitations of self-reported dietary data, with error rates for caloric intake and food portion size ranging from 30% to 88% (Rafiq et al. 2021). These misestimations stem from factors such as memory bias, cultural differences, and the inherent complexity of assessing habitual diets (Gibson et al. 2017).

Analyzing metabolites in biological samples, such as blood, urine, or saliva, can address these challenges. Metabolomics provides a snapshot of an individual’s current nutritional and physiological state, offering a robust, unbiased alternative to complement and validate traditional questionnaires (Gibson et al. 2017; Brennan 2017; Guasch-Ferré et al. 2018). By bridging the gap between subjective data and objective biomarkers, metabolomics enables more accurate diet assessments and personalized nutrition recommendations. This integrated approach deepens our understanding of the dietary impacts on health, facilitating more effective lifestyle interventions aimed at disease prevention.

How dietary patterns shape health through metabolic profiles

Metabolomics research shows that dietary intake is better reflected through food group biomarkers than isolated nutrients. Synergistic interactions between dietary components influence the metabolic response, and metabolomics captures these complex relationships. Studies consistently identify metabolite signatures linked to various food groups, including fruits, vegetables, high-fiber grains, meats, seafood, legumes, nuts, dairy, and caffeinated beverages (Rafiq et al. 2021).

For example, betaine and betaine-related metabolites are associated with fruits and vegetables, with proline betaine linked to citrus fruit consumption and tryptophan betaine to legume consumption (Pekkinen et al. 2015; Lever et al. 2010). High-fiber diets contribute to the production of short-chain fatty acids (SCFAs) by gut microbiota, essential for maintaining gut health and metabolic regulation (Nogal et al. 2021). Meats and seafood provide amino acids and carnitines, along with metabolites such as trimethylamine N-oxide (TMAO), a marker linked to cardiovascular risk (Gatarek et al. 2021; Wang et al. 2022). Fish intake is reflected by the concentrations of omega-3 fatty acids, with eicosapentaenoic acid (EPA) and docosahexaenoic acid (DHA) being the most reliable indicators. The vitamin B3-related trigonelline is considered a biomarker of coffee intake due to its high concentrations in coffee and related products (Mohamadi et al. 2018).

Metabolic profiling of these food groups helps identify dietary patterns that align with either protective or detrimental health outcomes:

• Risk-associated diets
  Diets high in processed meats, sugary foods, and refined grains – characteristic of a typical Western diet – are associated with obesity, metabolic disorders, cardiovascular disease, cancer and inflammation (Clemente-Suárez et al. 2023). 

• Protective diets
  Diets rich in vegetables, whole grains, and fish correlate with metabolomic profiles characterized by beneficial metabolites like betaines, omega-3 fatty acids, and short-chain fatty acids (SCFAs) (Emwas et al. 2021; Nogal et al. 2021; Pekkinen et al. 2015; Rafiq et al. 2021). Two typical examples are the Mediterranean and Nordic diets, which differ primarily in their preferred oils: extra virgin olive oil in the Mediterranean, and rapeseed oil in Nordic countries, which contains oleic acid, linoleic acid and alpha-linolenic acid. The Nordic diet emphasizes rye, barley, and oats as staple whole grains and favors berries over other fruit.

While specific metabolite markers can be used to provide insights into dietary habits, such as naringenin as a biomarker for grapefruit intake, they do not necessarily reflect overall dietary quality or health outcomes. In the context of disease prevention and nutritional research, it is more meaningful to assess whether an individual follows a healthy dietary pattern as a whole, rather than pinpointing the exact source of nutrients. Whether vitamins come from a grapefruit or a kiwi is ultimately less important than ensuring a diet is nutrient-rich, diverse, and aligned with established health guidelines.

To objectively assess diet quality, standardized scoring systems known as diet quality indices have been developed. These evaluate overall dietary patterns based on adherence to established nutritional guidelines, incorporating both nutrient density and food group composition. They are widely used in epidemiological studies, public health assessments, and clinical research toanalyze the impact of diet on chronic diseases, longevity, and overall health. A large cohort study by Kim et al. identified 17 metabolites that were significantly associated with better diet scores across four major healthy dietary indices (Healthy Eating Index, Alternative Healthy Eating Index, Dietary Approaches to Stop Hypertension and alternate Mediterranean diet). The study shows how metabolite concentrations directly reflect dietary habits because the molecules taken up with the diet feed into the universal core metabolic pathways (Kim et al. 2021). Furthermore, these metabolites might serve as biomarkers for healthy dietary patterns, providing an objective way to measure diet quality and its impact on health.

The science of well-being – how diet and stress influence metabolic health

In addition to reflecting dietary patterns, certain metabolites and their ratios serve as biomarkers for specific conditions and diseases, providing valuable insights into overall well-being and metabolic health.

Betaine and choline are obtained from the diet or synthesized de novo, with choline serving as a precursor for betaine. Both are vital methyl-donor nutrients involved in lipid metabolism, liver function, and the methylation cycle (Chang et al. 2022), and their uptake and balance are critical for metabolic health. While adequate choline intake supports lipid transport and hepatic function (Chang et al. 2022), excess serum choline is associated with metabolic syndrome risk factors such as dyslipidemia, hyperglycemia and cardiovascular disease. The increased risk for cardiovascular disease is linked to the microbiome: some gut bacteria convert choline (and sometimes betaine) to trimethylamine, which is taken up by the human host and further oxidized to TMAO. Trimethylamine and TMAO are both known risk factors for cardiovascular disease (Jang et al. 2021). In contrast, higher betaine levels correlate with favorable lipid and glycemic profiles (Gao et al. 2019). Consequently, optimal dietary intake of choline and betaine significantly reduces the risk of hepatic steatosis and improves indicators of metabolic syndrome (Gao et al. 2019; Chang et al. 2022).

A nutritional study with more than 10,000 participants in Spain found a correlation between eating habits and mood. A diet including fruit, nuts, legumes, and a high ratio of monounsaturated to saturated fats associated with better mood and lower risk of depression (Sánchez-Villegas et al. 2009).

Beyond diet, lifestyle factors — especially stress — are key factors affecting overall well-being. Sustained stress can elevate circulating cortisol levels, contributing to allostatic load, a state where chronic physiological strain disrupts the body’s regulatory networks. High cortisol levels have been linked to mood disorders, anxiety, sleep disturbances, and metabolic imbalances (Rodriquez et al. 2019).

Quality sleep is crucial for stress management, with γ-aminobutyric acid (GABA) playing a key role as a neurotransmitter known for its calming effects. Low GABA levels correlate with reduced sleep quality, though human trial data on the benefits of GABA supplementation remain inconclusive. The microbiome is also a major GABA producer. More research with standardized methodologies is needed to determine the interplay between stress, sleep, and metabolism, and the efficacy of nutritional interventions for stress resilience and metabolic health (Hepsomali et al. 2020).

Metabotyping – tailored health solutions

Metabotyping identifies metabolic phenotypes based on a wide range of factors, including diet, anthropometric measures, clinical parameters, metabolomics data, and the gut microbiota (Hillesheim et al. 2020). This comprehensive approach gains further depth by integrating metabolomics with other omics technologies – such as genomics, transcriptomics, and proteomics – as well as fitness tracking devices. Wearable technologies that monitor physical activity, heart rate variability, and sleep patterns provide valuable complementary insights (Kelly et al. 2020).
This comprehensive approach can be used to provide customized dietary guidance. For instance, individuals with similar metabotypes may share common metabolic responses to specific foods or nutrients, enabling interventions that are highly targeted and effective. Furthermore, metabotyping can identify individuals at higher risk for metabolic diseases like type 2 diabetes or cardiovascular disorders by analyzing biomarkers such as glucose tolerance, lipid profiles, and inflammatory markers (Palmnäs et al. 2020).

Figure 1: Personalized nutrition using metabolomics

Interestingly, research on personalized nutrition has uncovered significant individual variability in metabolic responses to identical foods. Studies have shown that, even when consuming the same meals, individuals display highly variable postprandial glucose responses, shaped by their distinct metabolic and microbiome profiles. While some experience sharp glucose spikes, others exhibit minimal increases, emphasizing the crucial role of gut microbiome composition and metabolic phenotypes in glycemic regulation (Zeevi et al. 2015).

Building on this, further research has shown that individuals in different metabotype subgroups exhibit varying glucose responses to an oral glucose tolerance test. Those classified in “intermediate” and “unfavorable” metabotypes tend to have significantly higher postprandial glucose concentrations, with the unfavorable subgroup displaying the highest glycemic response (Dahal et al. 2022).

Additionally, dietary fiber interventions reveal differential metabolic benefits depending on metabotype. While a 12-week fiber intervention led to modest reductions in metabolic risk factors overall, individuals with poorer baseline metabolic health experienced the greatest improvements in insulin levels, cholesterol, and blood pressure. These findings suggest that targeted dietary interventions may be particularly beneficial for those with higher metabolic risk (Dahal et al. 2022).

Dried blood spot sampling innovations – analytics with the donor in mind

One challenge of incorporating metabolomics into consumer health testing is the logistics of sample collection and transport. A scalable approach relies on accessible samples. Plasma samples, though widely used in research, require professional blood draws, rapid processing, and cold-chain logistics to preserve sample integrity. These requirements are impractical for at-home consumer tests.

Dried blood sampling addresses these limitations by offering a simple, reliable alternative for metabolite analysis. Dried blood sampling involves collecting small volumes of blood through a finger-prick, which are then dried on specialized devices. These samples are stable at ambient temperatures, eliminating the need for refrigeration or expedited transport.

Innovative dried blood sampling methods include volumetric absorptive microsampling (VAMS) devices such as the widely tested Mitra® tips, and newer technologies like the capillary-based qDBS Capitainer® and the TASSO-M20™ devices, which eliminate the need for a finger prick (Couacault et al. 2024). These technologies facilitate the straightforward collection of precise and predefined blood volumes, even by untrained individuals.

Figure 2: At home sampling with classical DBS cards vs. Mitra® devices as a volumetric alternative

Metabolomic studies confirm the reliability of dried blood in measuring key biomarkers of nutrition and wellbeing (Protti et al. 2022; Kok et al. 2019). Metabolites like vitamin D, SCFAs, and amino acids remain stable in dried blood samples, ensuring accurate quantification. A key advantage of VAMS is its ability to preserve the stability of small molecules, which are particularly informative for assessing an individual’s health (Qiu et al. 2023).

Dried blood sampling offers a scalable solution for integrating advanced metabolomics into precision nutrition, making personalized health insights more accessible to a broader audience. By combining user-friendly devices and robust analytical capabilities, this method has the potential to revolutionize precision health monitoring.

Companies offering consumer health tests have begun to implement these new technologies into their product line. For example, the medical diagnostic laboratory biovis, operating mainly in central Europe via medical practitioners, has recently launched the Prevent 360 test analyzing more than 70 metabolites from dried blood Mitra devices to provide comprehensive insight into the patient‘s health and well-being. Considering the advantages of combining these sampling devices with metabolomics, more companies are bound to follow their example.

Advancing consumer testing options with metabolomics tools

Metabolomic analysis of dried blood samples requires robust absolute quantification of metabolites of different classes in a single run. Ideally, this should be standardized so local laboratories can perform measurements in different countries. The biocrates SMartIDQ alpha kit represents a major step forward in this regard. Specifically optimized for high throughput use with dried blood samples, this kit measures a comprehensive panel of health-related metabolites, enabling detailed nutritional and metabolic assessments.

For interpretation, it is important to consider not only the single metabolite concentrations, but also their sums and ratios. which help extract the biological significance of individual differences, linking metabolomics data to biological pathways and enzyme activities.

In conditions such as hepatic encephalopathy and liver cirrhosis, the Fischer ratio decreases due to impaired liver function. This occurs as AAAs accumulate since the liver is the only organ that catabolizes them, while BCAAs are preferentially used in the muscles for energy metabolism. The progressive decline reflects worsening liver function and metabolic imbalance (Fischer et al. 1975; Soeters et al. 1976).

The MetaboINDICATOR tool that accompanies biocrates kit software provides such advanced data interpretation by calculating more than 120 predefined sums and ratios of metabolite concentrations. The combination of cutting-edge technology and advanced tools enables laboratories and healthcare providers to deliver meaningful, personalized insights to consumers, combining state-of-the-art science and practical applications in precision health.

Empowering health monitoring through metabolomics

Predictive insights from metabolomics can identify early markers of disease risk, enabling timely interventions and fostering a proactive approach to health management. Personalized recommendations, based on robust metabolomic data, empower individuals to make informed dietary and lifestyle changes tailored to their unique health profiles. This preventive approach significantly reduces the burden of chronic disease, supporting public health objectives and aligning with integrative medicine’s focus on addressing the root causes of illness together with individualized treatment. The SMartIDQ alpha kit exemplifies how metabolomics can bring the integrative medicine approach into everyday life. Metabolomics is central to the future of 5P medicine – predictive, personalized, preventive, population-based, and participatory –advancing both nutrition science and consumer health solutions.

References

Advantages

Urine offers several advantages over other body fluids in medical and metabolic investigations (3):

• Sample collection is non-invasive and relatively easy. This makes urine sampling ideal for large-scale epidemiological and longitudinal studies as well as for pediatric and geriatric populations.

• Sample handling and storage is simple. Fresh samples can generally be kept on ice or at 4 °C for up to 8 hours before centrifugation. The supernatant should then be stored at -80 °C (4).

• Urine is typically available in larger quantities than plasma or serum, which can be especially advantageous for studies requiring larger amounts of sample material.

• Like plasma or serum (but unlike feces or tissue) samples, urine is a homogenous matrix, meaning there is no variation or gradient within a sample.

• Urine is mostly free from interfering proteins or lipids that may affect metabolite extraction, ionization of target metabolites, or mask the presence of low-abundance metabolites (5).

• Urine responds rapidly to dietary and lifestyle factors, disease, medications, and other influences, making it possible to monitor acute changes in metabolism and see how the body responds to different conditions or interventions.

• Because urine reflects both systemic and renal metabolism, it is useful for monitoring the effect of food, drugs, and chemical or pollutant exposure (6).

• Urine contains higher concentrations of substances such as organic acids, neurotransmitters, hormones, and gut microbial metabolites that are often undetectable in other biofluids (7).

Challenges

However, urine sampling is not without its challenges:

• Urine contains extremely high salt concentrations and high levels of urea. In addition, the waste products of foods and beverages, environmental pollutants, medicines and endogenous waste metabolites can be present at very high concentrations. This makes metabolite quantification in urine technically complex (5).

• Fluid intake has major effects on the concentration of urinary metabolites. Creatinine normalization goes some way to address this, but creatinine excretion itself varies depending on nutrition, kidney function, age and overall health.

• Metabolite concentrations in urine also vary depending on diurnal rhythm, fasting time and last meal content. Mitigation of these effects is most successful with the “second midstream morning urine” or “24-hour urine” collection methods. While it is not practical to standardize last meals, it is crucial to standardize collection methods and fasting time across the study (8).

• Many plasma or serum metabolites cannot be quantified in urine samples because their concentrations are too low to detect. Lipids in particular are almost completely absent. However, the metabolic picture provided by small molecules analysis in urine marries very well with measurements of broader panels (including lipids) in matching blood-derived samples.

Because of the differences between urine and blood-based matrices, the metabolomics solutions developed for plasma and serum cannot be simply applied to urine sample measurement. For this reason, biocrates has recently launched a urine extension for the MxP® Quant 500 and AbsoluteIDQ® p180 kits. This includes adapted calibration standards, urine quality control samples, urine-like zero samples, and optimized methods for a more accurate quantification of urinary metabolites.

Application of urine metabolomics

Some of the key application areas of urine metabolomics include:

Disease diagnosis and biomarker discovery

Urine samples are widely used for diagnosis of kidney diseases and metabolic diseases, and for diagnosis and biomarker discovery in several types of cancer (9, 10) and neurological disorders (11).

Drug efficacy and safety

Urine is used to monitor the effect of drugs on metabolism and identify potential adverse reactions or nephrotoxic effects.

Toxicology

Urine metabolomics can be used to detect and explore the mechanism of action in metabolite changes following exposure to toxins or environmental pollutants (12, 13).

Athletic and sports performance

Although urine metabolomics reveals the effects of training, performance and recovery on metabolism, the most common application is the detection of prohibited substances, i.e., doping control.

Nutrition and diet

Urine metabolomics can be used to investigate the impact of specific diets, nutrients, or dietary interventions on metabolism and monitor adherence to specific dietary regimens.

Urine metabolomics and precision nutrition

Urine metabolomics is attracting interest particularly in nutrition and diet research. Our white paper on complex diseases discusses the role of diet in many complex chronic diseases in the Western world, like type 2 diabetes, Alzheimer’s disease, inflammatory bowel disease (IBD) and cancer. Given the link between gut microbiota and disease metabolism, modifying the microbiome through dietary habits and consumption of pre- and probiotics may be an effective route to better overall health (14, 15).

Nutrient metabolism and dietary effects vary between individuals (16), which opens the door to personalized dietary recommendations based on an individual’s metabolism characteristics. Precision nutrition uses omics to analyze an individual’s response to different foods or dietary patterns and identify the most effective dietary or lifestyle changes to prevent or treat particular diseases (17).

Urine metabolomics aids precision nutrition research in the following ways:

• Assessing individual dietary intake and adherence: Specific urinary profiles were linked with adherence to the Alternative Healthy Eating Index (18). By analyzing the metabolite profiles in urine, researchers can identify markers associated with the consumption of specific foods or nutrients to monitor dietary habits and make necessary adjustments (19).

• Tracking response to dietary interventions: The ability to monitor how an individual’s metabolism responds to different diets or supplementation protocols supports the optimization of dietary strategies for weight management, blood sugar control or other health goals (19, 20).

• Identifying metabolic phenotypes (metabotypes): Metabolic phenotypes are specific patterns of metabolite concentrations associated with an individual’s response to dietary components. This information can be used to categorize individuals into different metabolic subgroups and tailor dietary recommendations.

• Monitoring nutrient status: Urine metabolomics can detect markers of nutrient deficiency or excess, which helps to assess malnutrition severity and inform individualized dietary modifications. Long-term longitudinal tracking of the nutrient status is particularly useful in chronic disease management.

• Identifying biomarkers: Urine metabolomics can help identify biomarkers associated with specific diet-related health conditions or disease risk factors. These biomarkers can be used to assess an individual’s risk for certain diseases and develop personalized dietary strategies to mitigate that risk (19, 21).

Overall, urine metabolomics provides a wealth of information about an individual’s metabolism and how it responds to dietary factors. This information can help individuals make more informed dietary choices and optimize their nutrition for better health outcomes.

Clinical relevance of urine metabolomics in precision nutrition

The National Institutes of Health promote precision nutrition as the best method to formulate clinically relevant diet plans for individuals and populations that share similar physiological, behavioral or sociocultural traits (22). Precision nutrition studies produce convincing results to demonstrate links between dietary change and several complex diseases. Below are a few examples that demonstrate the relevance of precision nutrition driven by urine metabolomics for a broad range of chronic diseases:

Cardiovascular disease

The North Karelia project found that dietary changes can significantly decrease coronary heart disease mortality in the general population (23). An elevated risk of atherosclerosis is linked to higher gut microbiota production of trimethylamine (TMA), which is readily detectable in urine but less so in plasma. Subjects who produce comparatively high amounts of TMA from red meat received the dietary advice to reduce their red meat intake, thus reducing their risk for cardiovascular events (20).

Epilepsy

Akiyama et al. investigated the effect of a ketogenic diet on epilepsy using plasma and urine metabolomics (24). Results showed a metabolic shift from glucose-based to fat-based energy generation associated with increased urine concentrations of 3-hydroxybutyric acid, a known fasting marker, and other organic acids. The altered energy source proved to be effective for a subset of epilepsy patients, indicating that energy metabolism and neuronal function are closely linked.

IBD

Another prospective study followed 20 ulcerative colitis patients for 12 months. Urine metabolomics revealed significant nutrition-related differences between patients with and without relapse that were sufficient to discriminate between these groups (25).

Obesity

A urine metabolomics-based study analyzed metabolomes and microbial features to understand the associated metabolic pathways and the effect of lifestyle interventions on pediatric obesity. After an eight-week weight-reduction lifestyle modification program, the responder group showed significantly decreased urinary myristic acid levels in correlation with an improved Bacteroidetes to Firmicutes gut bacteria ratio, indicating reduced fatty acid biosynthesis. Another finding was that high weight among the non-responders was associated with low urinary levels of hippuric acid, a metabolite resulting mainly from gut bacterial processing of polyphenols, suggesting that there are benefits of consuming polyphenol-rich plants on the weight that depend on the gut microbiome (26).

These examples show that the diet-microbiome-metabolome axis has an important role in linking macronutrient consumption and disease, and that urine metabolomics can help elucidate the metabolic pathways involved.

A major limitation of nutritional studies is that diet assessment often relies on self-reports, such as 24-hour dietary recalls or dietary diaries that are vulnerable to subjectivity, errors in estimated portion size and accidental omissions. Estimated prevalence of misreporting with these tools is around 30–88% (27). Urine metabolomics can help solve this problem.

Garcia-Perez et al. showed that urinary metabolic profiles developed in a controlled environment can be used to assess adherence to dietary patterns in free-living populations without the need to collect dietary data (23). Moreover, the Food Biomarker Alliance developed an objective biomarker system to define urine and blood biomarkers of food intake (BFIs) (28). This metabolite inventory aims to determine exactly what a person has eaten, how much they have eaten, and how it has been metabolized (17). Urinary BFIs have been identified for intake of meat (29), citrus fruit (30), fish (17) and coffee (31). In all cases, BFIs must be measured using targeted metabolomic methods (17).

Continued urine-based dietary pattern analysis in population-based programs and epidemiological studies will enhance understanding the relation between diet and disease and will improve the knowledge base for physicians to provide effective individualized precision nutrition recommendations.

Interested in incorporating urine metabolomics into your study? Find out more about the new urine extension for the biocrates MxP® Quant 500 and AbsoluteIDQ® p180 kits.

References

1. Bouatra S. et al.: The human urine metabolome. (2013) PLoS One | https://doi.org/10.1371/journal.pone.0073076

2. The Urine Metabolome Database [cited 2023 Sep 18]. Available from: URL: https://urinemetabolome.ca/statistics.

3. Ledinger S.: Which sample matrix should I use for my metabolomics study? (2022) biocrates life cciences ag 
[cited 2023 Sep 1].| Available from: URL: https://biocrates.com/metabolomics-study-sample-matrix/.

4. Stevens VL et al.: Pre-Analytical Factors that Affect Metabolite Stability in Human Urine, Plasma, and Serum: A Review. (2019) Metabolites | Available from: URL: https://www.mdpi.com/2218-1989/9/8/156.

5. Brezmes J. et al.: Urine NMR Metabolomics for Precision Oncology in Colorectal Cancer.(2022) Int J Mol Sci | https://doi.org/10.3390/ijms231911171.

6. Holmes E. et al.: Detection of urinary drug metabolite (xenometabolome) signatures in molecular epidemiology studies via statistical total correlation (NMR) spectroscopy. (2007) Anal Chem | https://doi.org/10.1021/ac800859x

7. Zhang Z. et al.: Urine Analysis has a Very Broad Prospect in the Future. (2022) Front. Anal. Sci. | https://doi.org/10.3389/frans.2021.812301

8. Lehmann R.: From bedside to bench-practical considerations to avoid pre-analytical pitfalls and assess sample quality for high-resolution metabolomics and lipidomics analyses of body fluids. (2021) Anal Bioanal Chem | https://doi.org/10.1007/s00216-021-03450-0.

9. Erben V. et al.: Comparing Metabolomics Profiles in Various Types of Liquid Biopsies among Screening Participants with and without Advanced Colorectal Neoplasms. (2021) Diagnostics (Basel) | https://doi.org/10.3390/diagnostics11030561.

10. Krossa S. et al.: Detectable biomarkers in urine for prostate cancer prognosis. (2019) European Urology Supplements | https://doi.org/10.1016/S1569-9056(19)33331-7

11. Yilmaz A. et al.: Targeted Metabolic Profiling of Urine Highlights a Potential Biomarker Panel for the Diagnosis of Alzheimer’s Disease and Mild Cognitive Impairment: A Pilot Study. (2020) Metabolites | https://doi.org/10.3390/metabo10090357.

12. Imam SZ et al.: Changes in the metabolome and microRNA levels in biological fluids might represent biomarkers of neurotoxicity: A trimethyltin study. (2018) Exp Biol Med (Maywood) | https://doi.org/10.1177/1535370217739859.

13. Li Y. et al.: Metabolomic insights into the lasting impacts of early-life exposure to BDE-47 in mice. (2020) Environ Pollut | https://doi.org/10.1016/j.envpol.2020.114524

14. Mills S. et al.: Precision Nutrition and the Microbiome Part II: Potential Opportunities and Pathways to Commercialisation. (2019) Nutrients | https://doi.org/10.3390/nu11071468.

15. Mills S. et al.: Precision Nutrition and the Microbiome, Part I: Current State of the Science. (2019) Nutrients | https://doi.org/10.3390/nu11040923.

16. Zeevi D. et al.: Personalized Nutrition by Prediction of Glycemic Responses. (2015) Cell | https://doi.org/10.1016/j.cell.2015.11.001.

17. LeVatte M. et al.: Applications of Metabolomics to Precision Nutrition. (2022) Lifestyle Genom 2022 | https://doi.org/10.1159/000518489

18. Brennan L. et al.: Role of metabolomics in the delivery of precision nutrition. (2023) Redox Biol | https://doi.org/10.1016/j.redox.2023.102808

19. Wang DD et al.: Precision nutrition for prevention and management of type 2 diabetes. (2018) The Lancet Diabetes & Endocrinology | https://doi.org/10.1016/S2213-8587(18)30037-8

20. Tebani A. et al.: Paving the Way to Precision Nutrition Through Metabolomics. (2019) Front Nutr | https://doi.org/10.3389/fnut.2019.00041

21. Cho K. et al.: Combined untargeted and targeted metabolomic profiling reveals urinary biomarkers for discriminating obese from normal-weight adolescents. (2017) Pediatric Obesity | https://doi.org/10.1111/ijpo.12114

22. Rodgers GP et al.: Precision Nutrition-the Answer to “What to Eat to Stay Healthy”. (2020) JAMA |  https://doi.org/10.1001/jama.2020.13601

23. Garcia-Perez I et al.: Objective assessment of dietary patterns by use of metabolic phenotyping: a randomised, controlled, crossover trial. (2017) The Lancet Diabetes & Endocrinology | https://www.thelancet.com/journals/landia/article/piis2213-8587(16)30419-3/fulltext

24. Akiyama M et al.: Comprehensive study of metabolic changes induced by a ketogenic diet therapy using GC/MS- and LC/MS-based metabolomics. (2023) Seizure | https://doi.org/10.1016/j.seizure.2023.03.014

25. Keshteli AH et al.: Dietary and metabolomic determinants of relapse in ulcerative colitis patients: A pilot prospective cohort study. (2017) World J Gastroenterol | https://doi.org/10.3748/wjg.v23.i21.3890

26. Lee Y et al.: Serum, Urine, and Fecal Metabolome Alterations in the Gut Microbiota in Response to Lifestyle Interventions in Pediatric Obesity: A Non-Randomized Clinical Trial. (2023) Nutrients | https://doi.org/10.3390/nu15092184

27. Poslusna K et al.: Misreporting of energy and micronutrient intake estimated by food records and 24 hour recalls, control and adjustment methods in practice. (2009) Br J Nutr | https://doi.org/10.1017/S0007114509990602

28. Brouwer-Brolsma EM et al.: Combining traditional dietary assessment methods with novel metabolomics techniques: present efforts by the Food Biomarker Alliance. (2017) Proc Nutr Soc | https://doi.org/10.1017/s0029665117003949

29. Cross AJ et al.: Urinary biomarkers of meat consumption. (2011) Cancer Epidemiol Biomarkers Prev | https://doi.org/10.1158/1055-9965.epi-11-0048

30. Lloyd AJ et al.: Proline betaine and its biotransformation products in fasting urine samples are potential biomarkers of habitual citrus fruit consumption. (2011) Br J Nutr | https://doi.org/10.1158/1055-9965.epi-11-0048

31. Heinzmann SS et al.: 2-Furoylglycine as a Candidate Biomarker of Coffee Consumption. (2015) J Agric Food Chem | https://doi.org/10.1158/1055-9965.epi-11-0048

History & Evolution

2000 BC: cinnamon is used to embalm mummies (Abdel-Maksoud et al. 2011) | 1834: isolation of cinnamaldehyde from cinnamon oil (Dumas et al. 1834) | 1854: first synthesis from unrelated compounds (Richmond 1947)

As the name suggests, cinnamaldehyde is a compound found in cinnamon, contributing to cinnamon’s flavor, aroma and potential health benefits. Cinnamaldehyde has antimicrobial, antioxidant and anti-inflammatory properties, and is also studied for its potential effects on cardiovascular and metabolic diseases.

Cinnamon is prepared from the inner bark of Asian evergreen trees, with Sri Lanka its primary producer. Tree bark is typically removed from the branches of mature trees and left to dry in the sun without additional treatment (Shang et al. 2021). Dried bark curls into cinnamon sticks and may be ground into powdered form. Different species of cinnamon tree contain different amounts of cinnamaldehyde and other metabolites. Cinnamomum verum (native to Sri Lanka and later introduced in other countries of the Indian subcontinent) is considered the original cinnamon tree for international trade.

Metabolomics has been used to find signatures of the different cinnamon tree species in cinnamon samples (Avula et al. 2015; Wang et al. 2020; Zhang et al. 2022). Cinnamon from C. verum is typically high in cinnamaldehyde and low in coumarin (Wang et al. 2020). Metabolic profiling can differentiate ‘true’ cinnamon from C. verum from other plants used to produce cinnamon such as C. cassia, simply by measuring the proportion of cinnamaldehyde, coumarin and other metabolites in the samples (Zhang et al. 2022). C. cassia and other species growing in China are prevalent in traditional Chinese medicine.

Cinnamaldehyde is also synthesized by a broad range of microorganisms that exploit its antibacterial and antifungal properties (Gan et al. 2020). In addition, bacteria (e.g., E. coli) can be engineered to synthesize cinnamaldehyde from phenylalanine (Bang et al. 2016).

Biosynthesis vs. dietary uptake

In plants, cinnamaldehyde is synthesized via the Shikimate pathway, a pathway that also yields aromatic amino acids and folates (Morrow 2013). Starting with phosphoenolpyruvate (PEP), this pathway generates aromatic amino acids that are precursors to cinnamaldehyde. Interestingly, bacteria and other microorganisms can also synthesize cinnamaldehyde through this pathway, for example, from phenylalanine.

In C. verum, phenylalanine ammonia-lyase catalyzes the conversion of phenylalanine into trans-cinnamic acid, a compound with antioxidant and anti-inflammatory properties also responsible for some of cinnamon’s biological activities.

It has also been suggested that cinnamic acid plays a role in improving insulin sensitivity (Stevens et al. 2022) and in protection of the cardiovascular system, making it potentially beneficial for people with diabetes and early-stage metabolic disease. Kinetic analysis in rat blood showed that cinnamaldehyde was quickly converted to cinnamic acid via a protein-driven mechanism (Yuan J. et al. 1992).

Cinnamaldehyde and infectious diseases

Cinnamaldehyde and its derivatives have attracted attention for their antimicrobial potential, for example in the development of tuberculosis treatment (Nordqvist et al. 2011). Metabolomics has shown that exposing cultures of Mycobacterium tuberculosis (the strain responsible for the disease) to cinnamon essential oil alters small molecules, including biotin levels and tetrahydrofolate biosynthesis, which is essential for optimal one-carbon metabolism (Sieniawska et al. 2020).

The same study revealed a significant effect on many lipid classes, with most changes seen in phospholipids (primarily phosphatidylethanolamines and phosphatidylglycerols) and glycerophospholipids (primarily triglycerides and monoglycerides).

Cinnamaldehyde is not the only antibacterial compound in cinnamon; other metabolites such as eugenol may contribute to the antimicrobial effects of cinnamon essential oil and extracts (Vasconcelos et al. 2018).

Cinnamaldehyde and metabolic disease

Cinnamon has been long considered a beneficial food for patients with type 2 diabetes. There is mounting evidence that cinnamon and its metabolites may improve glycemic and lipidemic indicators (Silva et al. 2022). For instance, a 2007 study in male rats with streptozotocin-induced diabetes showed that a 45-day treatment with 20 mg/kg bw of cinnamaldehyde reduced plasma glucose and glycosylated hemoglobin levels, serum total cholesterol and triglyceride levels while increasing insulin, high-density lipoprotein (HDL) cholesterol and liver glycogen levels (Subash Babu et al. 2007).

Randomized controlled clinical trials have investigated the effects of cinnamon and shown that 1 to 3 g of cinnamon per day could reduce glycosylated hemoglobin levels (Silva et al. 2022). Clinical trials also confirmed its anti-inflammatory effect in humans (Davari et al. 2020).

Of note, while C. verum is the plant of choice for culinary cinnamon, many studies focus on C. cassia, C. zeylanicum and others. Whether this is due to easier access, a higher prevalence of those species in traditional Chinese medicine, or a higher therapeutic potential in those species is unclear.

Nevertheless, there appear to be large differences in the effects and required doses depending on the tree of origin for the cinnamon used in these trials. This may explain why a recent meta-analysis of epidemiological studies found no associations between cinnamon intake and levels of low-density lipoprotein (LDL) cholesterol, HDL cholesterol or glycosylated hemoglobin (Krittanawong et al. 2022). Thus, more work is needed to fully understand this spice.

Cinnamaldehyde and chronic diseases

Finally, research into the health benefits of cinnamon points to potential to address multiple complex chronic diseases, even beyond its anti-inflammatory effect. For example, cinnamaldehyde may have applications in cancer, owing to its capacity to induce apoptosis in cancer cells (Sadeghi et al. 2019). Cinnamon’s anti-inflammatory properties and unique flavor have been hypothesized to help breast cancer survivors better adhere to a Mediterranean diet (Zuniga et al. 2019).

Cinnamon’s effects on the immune system have also made it a spice of interest in the field of autoimmune diseases (Pahan et al. 2020). There are also ongoing trials focusing on the effects of cinnamon in various chronic diseases, from Alzheimer’s disease to autoimmune diseases. These encouraging findings suggest that cinnamon and its key metabolites could play an important role in redressing the metabolic imbalance at the origin of many complex chronic diseases (Hariri et al. 2016).

References

Abdel-Maksoud et al. 2011: A review on the materials used during the mummification processes in ancient Egypt | A review on the materials used during mummification processes in Ancient Egypt

Avula et al. 2015: Authentication of true cinnamon (Cinnamon verum) utilising direct analysis in real time (DART)-QToF-MS. Food additives & contaminants. Part A, Chemistry, analysis, control, exposure & risk assessment | https://doi.org/10.1080/19440049.2014.981763

Bang et al. 2016: Metabolic engineering of Escherichia coli for the production of cinnamaldehyde. Microbial Cell Factories | https://doi.org/10.1186/s12934-016-0415-9

Davari et al. 2020: Effects of cinnamon supplementation on expression of systemic inflammation factors, NF-kB and Sirtuin-1 (SIRT1) in type 2 diabetes: a randomized, double blind, and controlled clinical trial. Nutrition Journal | https://doi.org/10.1186/s12937-019-0518-3

Dumas & Péligot 1834: Recherches de chimie organique – Sur l’huile de cannelle, l’acide hippurique et l’acide sébacique. Annales de chimie et de physique | https://gallica.bnf.fr/ark:/12148/bpt6k6568974z/f311.image.r

Gan et al. 2020: Synthesis and Antifungal Activities of Cinnamaldehyde Derivatives against Penicillium digitatum Causing Citrus Green Mold. Journal of Food Quality | https://doi.org/10.1155/2020/8898692

Hariri & Ghiasvand 2016: Cinnamon and Chronic Diseases. Advances in experimental medicine and biology | https://doi.org/10.1007/978-3-319-41342-6_1

Krittanawong et al. 2022: Association Between Cinnamon Consumption and Risk of Cardiovascular Health: A Systematic Review and Meta-Analysis. The American journal of medicine | https://doi.org/10.1016/j.amjmed.2021.07.019

Nordqvist et al. 2011: Synthesis of functionalized cinnamaldehyde derivatives by an oxidative Heck reaction and their use as starting materials for preparation of Mycobacterium tuberculosis 1-deoxy-D-xylulose-5-phosphate reductoisomerase inhibitors. The Journal of Organic Chemistry | https://doi.org/10.1021/jo201715x

Pahan & Prahan 2020 : Can cinnamon spice down autoimmune diseases? Journal of clinical & experimental immunology | https://doi.org/10.33140/jcei.05.06.01

Richmond 1947: Preparation of cinnamaldehyde (1947) Patent US2529186A | https://patents.google.com/patent/US2529186A/en

Sadeghi et al. 2019: Anti-cancer effects of cinnamon: Insights into its apoptosis effects. European journal of medicinal chemistry | https://doi.org/10.1016/j.ejmech.2019.05.067

Shang et al. 2021: Beneficial effects of cinnamon and its extracts in the management of cardiovascular diseases and diabetes. Food & Function | https://doi.org/10.1039/D1FO01935J

Sieniawska et al. 2020: Untargetted Metabolomic Exploration of the Mycobacterium tuberculosis Stress Response to Cinnamon Essential Oil. Biomolecules | https://doi.org/10.3390/biom10030357

Silva et al. 2022: Cinnamon as a Complementary Therapeutic Approach for Dysglycemia and Dyslipidemia Control in Type 2 Diabetes Mellitus and Its Molecular Mechanism of Action: A Review. Nutrients | https://doi.org/ 10.3390/nu14132773

Stevens et al. 2022: A Review and Exploration of Mechanisms Using In Silico Molecular Docking Simulations. Molecules | https://doi.org/10.3390/molecules27030853

Subash Babu et al. 2007: Cinnamaldehyde–a potential antidiabetic agent. Phytomedicine | https://doi.org/10.1016/j.phymed.2006.11.005

Morrow 2013: The Shikimate Pathway: Biosynthesis of phenolic products from shikimic acid. | https://doi.org/10.1093/oso/9780199860531.003.0009

Tohge et al. 2013: Shikimate and phenylalanine biosynthesis in the green lineage. Frontiers in Plant Science | https://doi.org/10.3389/fpls.2013.00062

Vasconcelos et al. 2018: Antibacterial mechanisms of cinnamon and its constituents: A review. Microbial pathogenesis | https://doi.org/10.1016/j.micpath.2018.04.036

Wang et al. 2020: Metabolomic profiling and comparison of major cinnamon species using UHPLC-HRMS. Analytical and bioanalytical chemistry | https://doi.org/10.1007/s00216-020-02904-1

Yuan et al. 1992: Quantitation of cinnamaldehyde and cinnamic acid in blood by HPLC. Journal of analytical toxicology | https://doi.org/10.1093/jat/16.6.359

Zhang et al. 2022: Development of a Metabolite Ratio Rule-Based Method for Automated Metabolite Profiling and Species Differentiation of Four Major Cinnamon Species. Journal of agricultural and food chemistry | https://doi.org/10.1021/acs.jafc.2c01245

Zuniga et al. 2019: Dietary intervention among breast cancer survivors increased adherence to a Mediterranean-style, anti-inflammatory dietary pattern: the Rx for Better Breast Health Randomized Controlled Trial (2019) Breast cancer research and treatment | https://doi.org/10.1007/s10549-018-4982-9