• 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.

• Foreword
• How metabolomics is improving healthcare: 6 must-read studies from 2025
• Hepatology: functional detox capacity beyond fibrosis
• Cardiometabolic health: anticipating disease before symptoms
• Cohorts and mGWAS: from genetic signals to functional meaning
• Oncology: detecting cancer before its manifestation
• Neuropsychiatry and microbiome: modulating behavior through microbial metabolism
• Nanomedicine: designing safer therapies through metabolomics & lipidomics
• The next steps for metabolomics in 5P medicine

Foreword by Alice Limonciel, Chief Scientific Officer at biocrates

After decades of development, we are on the cusp of integrating metabolomics into medical practice. Numerous examples already exist in clinical settings, the result of the dedicated labor of passionate scientists and clinicians who recognized the potential of this omic and applied it across all areas of medicine. However, the broad adoption of metabolomics on a scale comparable to what we now see with genomics requires the development of robust, transferable, and scalable technology, which has been the mission of biocrates for the past 20 years.

In 2025, we chose to showcase the wide-ranging potential of metabolomics for all aspects of 5P medicine, from preventing chronic disease in a single individual through personalized strategies to enabling multiomic analyses in large cohort studies.

For this article, Franziska Hörburger selected six studies published in 2025 by biocrates’ community of users. These examples pave the way for the imminent implementation of metabolomics beyond the research lab and into clinical practice and our everyday lives. They span multiple regions, therapeutic areas, and dimensions of the future implementation of metabolomics in medicine and drug development.
These scientists are part of a community of early adopters of metabolomics, a technology that will transform how we understand and practice medicine. May this article inspire you and your team to join this community in 2026!

How metabolomics is improving healthcare: 6 must read studies from 2025

5P medicine – preventive, predictive, precision, population-based, and participatory – represents a paradigm shift in healthcare. It moves away from reactive treatment toward proactive, patient-centric strategies built on molecular insights. At its core, 5P medicine leverages high-quality standardized technologies to capture biology in unprecedented detail. It enables clinicians and researchers to predict disease risk, personalize interventions, while engaging patients in their individual health journey and at the population scale.

Among the molecular technologies shaping modern medicine, metabolomics stands out. While genomics can predict disease risk, it offers only a static view, like a snapshot of predisposition. Metabolomics, in contrast, captures the biochemical fingerprints of life, reflecting the dynamic interplay of genes, environment, lifestyle, microbiome, and pharmacological influences. This real-time perspective makes metabolomics indispensable for understanding complex chronic diseases, where genetic information alone cannot explain onset, progression, or therapeutic response.

Figure 1: Molecular health beyond genetic predisposition

When metabolomics is combined with other omics technologies, such as genomics or proteomics, the picture becomes even richer. Proteomics adds information about enzyme abundance and signaling networks, complementing metabolomics’ readout of pathway activity and flux. Together, these layers create a detailed system-level view of health and pathology, connecting genetic predisposition to molecular function and clinical phenotype. This integrated approach transforms omics from isolated data streams into actionable insights, connecting molecular complexity and medical decision-making based on the 5P concept.

Applying metabolomics within the 5P framework can be summarized in three steps. First, screen samples using broad metabolomics and lipidomics profiling. Second, leverage data to uncover biological meaning. This involves interpreting metabolite patterns, sums, and ratios, and linking them to pathways and literature. Third, translate insights into solutions: providing predictive biomarkers, metabotypes, risk scores, and decision-support tools that transform medicine from reactive to proactive.

Figure 2: Workflow for applying metabolomics in the 5P framework

This vision is not theoretical; it is already happening. Across biological matrices, continents and disciplines, researchers and clinicians are using biocrates’ technology to deliver actionable insights in fields as varied as hepatology, oncology, neuropsychiatry, cardiometabolic health, population studies, and nanomedicine.

Here we review six publications from 2025 in high-impact journals that illustrate how one standardized platform can drive breakthroughs aligned with the principles of 5P medicine.

Hepatology: functional detox capacity beyond fibrosis

Sugimoto et al.: Hepatic stellate cells control liver zonation, size and functions via R-spondin 3. Nature (2025), 640(8059):752–761 | https://doi.org/10.1038/s41586-025-08677-w Figure under creative commons license CC BY 4.0.

Sugimoto and colleagues uncovered how hepatic stellate cells orchestrate liver zonation and detoxification through the signaling molecule R-spondin 3 (RSPO3), a key regulator of the WNT pathway. When RSPO3 is lost, hepatocyte zonation collapses and regenerative capacity declines. Beyond structural changes, RSPO3 profoundly influences detoxification by modulating cytochrome P450 activity, which in turn alters circulating metabolite profiles. Liver tissue of RSPO3-deficient mice featured striking shifts in bile acid composition, particularly taurocholic, tauromuricholic, and taurochenodeoxycholic acids.

Furthermore, changes in steroid metabolism, lipid oxidation, and xenobiotic accumulation have been revealed. These metabolomic signatures predict functional liver capacity, drug metabolism potential, and ultimately toxicity risk. By identifying RSPO3 as both a prognostic and mechanistic marker, this work opens the door to early intervention, personalized risk stratification, and tailored therapeutic approaches for liver fibrosis, particularly in alcoholic liver disease and metabolic dysfunction associated liver disease.

Cardiometabolic health: anticipating disease before symptoms

Zieleniewska et al.: Preclinical Atherosclerosis and Prediabetes: A Cross-Sectional Metabolic Assessment In Apparently Healthy Population. Cardiovascular Diabetology (2025), 24(1), 280 | https://doi.org/10.1186/s12933-025-02841-2 Figure under creative commons license CC BY-NC-ND 4.0.

Cardiovascular disease and diabetes often develop silently over years, making early detection critical. The metabolic foundation of preclinical atherosclerosis compared to prediabetes was explored in 447 participants from the Bialystok PLUS cohort.

The analysis uncovered distinct and shared metabolic signatures in plasma for both conditions. Prediabetes exerted a broader impact on amino acid metabolism, lipid signaling and enzymatic activities than atherosclerosis. Glutamic acid, lactic acid, and alanine were strongly associated with prediabetes, indicating dysglycemia. Atherosclerosis was linked to lipid remodeling patterns captured by MetaboINDICATORs, including the ratio of polyunsaturated (PUFA)-lysophosphatidylcholines versus saturated fatty acids, the sum of steroid hormones, and cholesteryl ester (CE) classes such as monounsaturated CEs and long-chain fatty acids CEs. Trimethylamine N-oxide (TMAO) emerged as a unique link between prediabetes and its interaction with vascular pathology. At the same time, glutaminase activity, assessed through the glutamate/glutamine ratio, stood out as a robust shared predictor of both conditions. Metabolite set enrichment analysis observed converging disturbances in glutathione and folate metabolism, mitochondrial function, redox regulation and inflammation.

Cohorts and mGWAS: from genetic signals to functional meaning

Kodate et al.: Simulating metabolic pathways to enhance interpretations of metabolome genome-wide association studies. Scientific Reports (2025), 15(1), 17035 | https://doi.org/10.1038/s41598-025-01634-7 Figure under creative commons license CC BY-NC-ND 4.0.

Metabolome-genome-wide association studies (mGWAS) link genetic variation to metabolite concentrations in large cohorts. It provides great predictive power of risk models and enables rational intervention based on individual metabolic architecture. However, this powerful approach has some limitations: observed associations may reflect indirect effects through unmeasured metabolites, and the biological significance of many variants remains uncertain. To overcome these challenges, Kodate and colleagues combined mGWAS with mechanistic metabolic simulations, creating a framework that moves beyond correlation to causation.

By systematically adjusting enzyme reaction rates to mimic genetic variants, the team simulated their impact on plasma metabolite levels and validated most variant-metabolite pairs identified by mGWAS. For example, homocysteine was confirmed as a metabolite strongly influenced by methylenetetrahydrofolate reductase (MTHFR) activity. Both mGWAS and simulation agreed that reduced MTHFR activity increases homocysteine levels, reinforcing its role in folate and methionine metabolism. These simulations also revealed additional fluctuations that mGWAS had missed, suggesting that some associations could gain significance with larger sample sizes. Importantly, the study categorized enzymes into three tiers based on their influence on metabolite concentrations, highlighting variants with minimal biological impact and prioritizing those with strong functional relevance. This distinction is critical for guiding preventive strategies and therapeutic development.

Oncology: detecting cancer before its manifestation

Schulze et al.: Metabolomic liquid biopsy dynamics predict early-stage HCC and actionable candidates of human hepatocarcinogenesis. JHEP Reports (2025), 7(5):101340 | https://doi.org/10.1016/j.jhepr.2025.101340. Figure under creative commons license CC BY 4.0.

Hepatocellular carcinoma (HCC) develops through progressive metabolic reprogramming that begins long before tumors become radiologically or clinically detectable. In a global cohort of 654 patients, serum metabolome profiling captured these early, system-level alterations and predicted malignant transformation before overt tumor manifestation.

Across chronic liver disease, cirrhosis, initial HCC, and advanced HCC, amino acid-, lipid-, and nucleotide-related pathways were systematically deregulated, with aspartic acid, glutamic acid, taurine, and hypoxanthine emerging as key markers. In a phase II biomarker case-control study, a blood-based metabolite signature achieved an area under the curve (AUC) of 94% for distinguishing early-stage HCC from cirrhotic controls, with independent validation in an external cohort. Multiomics integration links these circulating markers to enzymatic nodes such as RRM2, GMPS, and BCAT1 – targets for precision oncology. By providing a validated, minimal-invasive liquid biopsy that outperforms current surveillance tools, serum metabolomics enables predictive identification of cancer risk, chemoprevention strategies, and personalized monitoring.

Neuropsychiatry and microbiome: modulating behavior through microbial metabolism

Park et al.: Gut microbiota and brain-resident CD4+ T cells shape behavioral outcomes in autism spectrum disorder. Nature Communications (2025), 16(1), 1–17 | https://doi.org/10.1038/s41467-025-61544-0 Figure under creative commons license CC BY 4.0.

Autism spectrum disorder (ASD) emerges from complex interactions between neurodevelopment, immune regulation, and the gut microbiome. Metabolites serve as critical messengers of this gut-immune-brain axis, influencing neuroinflammation and neurotransmitter flux. In a recent study, the absence of gut microbiota in male mice ameliorated ASD-associated behaviors and reduced inflammatory brain-resident CD4⁺ T cells, while depletion of these T cells further mitigated neuroinflammation and behavioral abnormalities. Fecal metabolomics in a mouse model of ASD revealed several microbial and metabolic regulators of ASD, particularly those affecting the glutamate/gamma-amino-butyric acid (GABA) ratio and neurotoxic intermediates such as 3-hydroxyglutaric acid.

While GABA levels remained stable, the glutamate/GABA ratio was significantly elevated in ASD mice treated with a broad-spectrum antibiotic cocktail (vancomycin, neomycin, metronidazole), a group that also showed enrichment of Lactobacillus species compared to neurotypical controls. Strikingly, beneficial microbiota, derived from healthy mice or administered as probiotics, reversed this imbalance. These findings underscore how metabolites from live bacteria can drive or mitigate ASD-like behaviors by altering excitatory/inhibitory signaling and immune tone. Ultimately, the study demonstrates that gut microbiota can override genetic predisposition in ASD, highlighting a powerful opportunity for metabolomics-informed interventions that rebalance neuroactive metabolites, suppress neuroinflammation, and improve behavioral outcomes.

Nanomedicine: designing safer therapies through metabolomics & lipidomics

Shaw et al.: Inflammatory disease progression shapes nanoparticle biomolecular corona-mediated immune activation profiles. Nature Communications (2025),16(1), 924 | https://doi.org/10.1038/s41467-025-56210-4 Figure under creative commons license CC BY 4.0.

Polymeric nanoparticles (NPs) are engineered to carry, protect, and deliver bioactive molecules or modulate biological responses. Their biological identity, the biomolecular corona, is not fixed by formulation alone but is dynamically shaped by the host environment. Multiomics analysis showed that, during acute systemic inflammation, plasma proteins, lipids, and metabolites change profoundly.

As a result, nanoparticle coronas are reshaped. They feature elevated levels of clotting factors, inflammatory proteins, cytoskeletal components, and lipids such as phosphatidylcholines, sphingomyelins, lysophosphatidylcholines, and fatty acids. These molecular signatures reflect heightened inflammatory activity and trigger immune pathways like TLR4/MyD88/NF-κB. This activation leads to the release of pro-inflammatory cytokines, including TNFα and IL-6. Together, these findings show how metabolic variability determines nanoparticle-based therapeutic efficacy and toxicity risk. The concept of a “personalized biomolecular corona” underscores the need to design nanomedicines that account for patient-specific metabolic states. Incorporating metabolomic profiling into nanoparticle development helps anticipate immune responses, optimize timing, and improve safety.

The next steps for metabolomics in 5P medicine

What unites these global studies performed in various species and matrices beyond their drive to bring medicine to a higher level, is their use of the metabolomics kit technology developed by biocrates.
Across a wide range of applications, our standardized kits provide the reproducible and quality-controlled methods that enable multiomics integration, cohort comparability, and regulatory-compliant workflows.

While 2025 saw the broad application of our MxP® Quant 500 and MxP® Quant 500 XL kits, 2026 will be the year of the MxP® Quant 1000 kit. Our broadest panel to date, this kit expands quantitative analysis to up to 1,233 metabolites from 49 biochemical classes, showing coverage comparable to untargeted metabolomics approaches, yet with the reproducibility and sensitivity of a targeted workflow.

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For additional insights also explore a curated selection of 2025 publications from our Biognosys Group partners, Biognosys and PreOmics.